METHODS AND SYSTEMS FOR ANALYSIS OF ORGAN TRANSPLANTATION

BACKGROUND

The current method for detecting organ rejection in a patient is a biopsy of the transplanted organ. However, organ biopsy results can be inaccurate, particularly if the area biopsied is not representative of the health of the organ as a whole (e.g., as a result of sampling error). There can be significant differences between individual observors when they read the same biopsies independently and these discrepancies are particularly an issue for complex histologies that can be challenging for clinicians. Biopsies, especially surgical biopsies, can also be costly and pose significant risks to a patient. In addition, the early detection of rejection of a transplant organ may require serial monitoring by obtaining multiple biopsies, thereby multiplying the risks to the patients, as well as the associated costs.

Transplant rejection is a marker of ineffective immunosuppression and ultimately if it cannot be resolved, a failure of the chosen therapy. The fact that 50% of kidney transplant patients will lose their grafts by ten years post transplant reveals the difficulty of maintaining adequate and effective longterm immunosuppression. There is a need to develop a minimally invasive, objective metric for detecting, identifying and tracking transplant rejection. In particular, there is a need to develop a minimally invasive metric for detecting, identifying and tracking transplant rejection in the setting of a confounding diagnosis, such as acute dysfunction with no rejection. This is especially true for identifying the rejection of a transplanted kidney. For example, elevated creatinine levels in a kidney transplant recipient may indicate either that the patient is undergoing an acute rejection or acute dysfunction without rejection. A minimally-invasive test that is capable of distinguishing between these two conditions would therefore be extremely valuable and would diminish or eliminate the need for costly, invasive biopsies.

SUMMARY

The methods and systems disclosed herein may be used for detecting or predicting a condition of a transplant recipient (e.g., acute transplant rejection, acute dysfunction without rejection, subclinical acute rejection, hepatitis C virus recurrence, etc.). In some aspects, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection or transplant dysfunction. In another embodiment, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant rejection. In another embodiment, a method for detecting or predicting a condition of a transplant recipient comprises a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a classifier capable of distinguishing between at least two conditions that are not normal conditions, and wherein one of the at least two conditions is transplant dysfunction. In some cases, the transplant recipient is a kidney transplant recipient. In some cases, the transplant recipient is a liver transplant recipient.

In some embodiments, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is capable of distinguishing between acute rejection and transplant dysfunction with no rejection. In some cases, the transplant dysfunction with no rejection is acute transplant dysfunction with no rejection. In some cases, the transplant recipient is a kidney transplant recipient. In some cases, the transplant recipient is a liver transplant recipient.

In an embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, or 1d, or any combination thereof; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In another embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In another embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, or 1d, in any combination.; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In another embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises five or more gene expression products from the transplant recipient; b) an assay to determine an expression level of the five or more gene expression products from the transplant recipient, wherein the five or more gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.; and c) detecting or predicting the condition of the transplant recipient based on the expression level determined in step (b). In some cases, the transplant recipient is a kidney transplant recipient.

In an embodiment, a method of detecting or predicting a condition of a transplant recipient comprises: a) obtaining a sample, wherein the sample comprises one or more gene expression products from the transplant recipient; b) performing an assay to determine an expression level of the one or more gene expression products from the transplant recipient; and c) detecting or predicting the condition of the transplant recipient by applying an algorithm to the expression level determined in step (b), wherein the algorithm is a three-way classifier capable of distinguishing between at least three conditions, and wherein one of the at least three conditions is transplant rejection. In some embodiments, one of the at least three conditions is normal transplant function. In some embodiments, one of the at least three conditions is transplant dysfunction. In some embodiments, the transplant dysfunction is transplant dysfunction with no rejection. In some cases, the transplant dysfunction with no rejection is acute transplant dysfunction with no rejection. In another embodiment, the method disclosed herein further comprises providing or terminating a treatment for the transplant recipient based on the detected or predicted condition of the transplant recipient.

In another aspect, a method of diagnosing, predicting or monitoring a status or outcome of a transplant in a transplant recipient comprises: a) determining a level of expression of one or more genes in a sample from a transplant recipient, wherein the level of expression is determined by RNA sequencing; and b) diagnosing, predicting or monitoring a status or outcome of a transplant in the transplant recipient.

In another aspect, a method disclosed herein comprises the steps of: a) determining a level of expression of one or more genes in a sample from a transplant recipient; b) normalizing the expression level data from step (a) using a frozen robust multichip average (fRMA) algorithm to produce normalized expression level data; c) producing one or more classifiers based on the normalized expression level data from step (b); and d) diagnosing, predicting or monitoring a status or outcome of a transplant in the transplant recipient based on the one or more classifiers from step (c). In another aspect, a method disclosed herein comprises the steps of: a) determining a level of expression of a plurality of genes in a sample from a transplant recipient; and b) classifying the sample by applying an algorithm to the expression level data from step (a), wherein the algorithm is validated by a combined analysis of a sample with an unknown phenotype and a subset of a cohort with known phenotypes.

In another aspect, the methods disclosed herein have an error rate of less than about 40%. In some embodiments, the method has an error rate of less than about 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, 3%, 2%, or 1%. For example, the method has an error rate of less than about 10%. In some embodiments, the methods disclosed herein have an accuracy of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example, the method has an accuracy of at least about 70%. In some embodiments, the methods disclosed herein have a sensitivity of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. For example, the method has a sensitivity of at least about 80%. In some embodiments, the methods disclosed herein have a positive predictive value of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%. In some embodiments, the methods disclosed herein have a negative predictive value of at least about 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, or 99%.

In some embodiments, the gene expression products described herein are RNA (e.g., mRNA). In some embodiments, the gene expression products are polypeptides. In some embodiments, the gene expression products are DNA complements of RNA expression products from the transplant recipient.

In an embodiment, the algorithm described herein is a trained algorithm. In another embodiment, the trained algorithm is trained with gene expression data from biological samples from at least three different cohorts. In another embodiment, the trained algorithm comprises a linear classifier. In another embodiment, the linear classifier comprises one or more linear discriminant analysis, Fisher's linear discriminant, Naïve Bayes classifier, Logistic regression, Perceptron, Support vector machine (SVM) or a combination thereof. In another embodiment, the algorithm comprises a Diagonal Linear Discriminant Analysis (DLDA) algorithm. In another embodiment, the algorithm comprises a Nearest Centroid algorithm. In another embodiment, the algorithm comprises a Random Forest algorithm or statistical bootstrapping. In another embodiment, the algorithm comprises a Prediction Analysis of Microarrays (PAM) algorithm. In another embodiment, the algorithm is not validated by a cohort-based analysis of an entire cohort. In another embodiment, the algorithm is validated by a combined analysis with an unknown phenotype and a subset of a cohort with known phenotypes.

In another aspect, the one or more gene expression products comprises five or more gene expression products with different sequences. In another embodiment, the five or more gene expression products correspond to 200 genes or less. In another embodiment, the five or more gene expression products correspond to less than (or at most) 200 genes listed in Table 1c. In another embodiment, the five or more gene expression products correspond to less than (or at most) 200 genes listed in Table 1a. In another embodiment, the five or more gene expression products correspond to less than (or at most) 200 genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In another embodiment, the five or more gene expression products correspond to less than about 200 genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In another embodiment, the five or more gene expression products correspond to less than or equal to 500 genes, to less than or equal to 400 genes, to less than or equal to 300 genes, to less than or equal to 250 genes, to less than or equal to 200 genes, to less than or equal to 150 genes, to less than or equal to 100 genes, to less than or equal to genes, to less than or equal to 80 genes, to less than or equal to 50 genes, to less than or equal to 40 genes, to less than or equal to genes, to less than or equal to 25 genes, to less than or equal to 20 genes, at most 15 genes, or to less than or equal to 10 genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

In one aspect, the biological samples are differentially classified based on one or more clinical features. For example, the one or more clinical features comprise status or outcome of a transplanted organ.

In another aspect, a three-way classifier is generated, in part, by comparing two or more gene expression profiles from two or more control samples. In another embodiment, the two or more control samples are differentially classified as acute rejection, acute dysfunction no rejection, or normal transplant function. In another embodiment, the two or more gene expression profiles from the two or more control samples are normalized. In another embodiment, the two or more gene expression profiles are not normalized by quantile normalization. In another embodiment, the two or more gene expression profiles from the two or more control samples are normalized by frozen multichip average (fRMA). In another embodiment, the three-way classifier is generated by creating multiple computational permutations and cross validations using a control sample set. In some cases, a four-way classifier is used instead or in addition to a three-way classifier.

In another aspect, the sample is a blood sample or is derived from a blood sample. In another embodiment, the blood sample is a peripheral blood sample. In another embodiment, the blood sample is a whole blood sample. In another embodiment, the sample does not comprise tissue from a biopsy of a transplanted organ of the transplant recipient. In another embodiment, the sample is not derived from tissue from a biopsy of a transplanted organ of the transplant recipient.

In another aspect, the assay is a microarray, SAGE, blotting, RT-PCR, sequencing and/or quantitative PCR assay. In another embodiment, the assay is a microarray assay. In another embodiment, the microarray assay comprises the use of an Affymetrix Human Genome U133 Plus 2.0 GeneChip. In another embodiment, the mircroarray uses the Hu133 Plus 2.0 cartridge arrays plates. In another embodiment, the microarray uses the HT HG-U133+ PM array plates. In another embodiment, determining the assay is a sequencing assay. In another embodiment, the assay is a RNA sequencing assay. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1c. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1a. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In another embodiment, the gene expression products correspond to five or more genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

In some embodiments, the transplant recipient has a serum creatinine level of at least 0.4 mg/dL, 0.6 mg/dL, 0.8 mg/dL, 1.0 mg/dL, 1.2 mg/dL, 1.4 mg/dL, 1.6 mg/dL, 1.8 mg/dL, 2.0 mg/dL, 2.2 mg/dL, 2.4 mg/dL, 2.6 mg/dL, 2.8 mg/dL, 3.0 mg/dL, 3.2 mg/dL, 3.4 mg/dL, 3.6 mg/dL, 3.8 mg/dL, or 4.0 mg/dL. For example, the transplant recipient has a serum creatinine level of at least 1.5 mg/dL. In another example, the transplant recipient has a serum creatinine level of at least 3 mg/dL.

In another aspect, the transplant recipient is a recipient of an organ or tissue. In some embodiments, the organ is an eye, lung, kidney, heart, liver, pancreas, intestines, or a combination thereof. In some embodiments, the transplant recipient is a recipient of tissue or cells comprising: stem cells, induced pluripotent stem cells, embryonic stem cells, amnion, skin, bone, blood, marrow, blood stem cells, platelets, umbilical cord blood, cornea, middle ear, heart valve, vein, cartilage, tendon, ligament, or a combination thereof. In preferred embodiments of any method described herein, the transplant recipient is a kidney transplant recipient. In other embodiments, the transplant recipient is a liver recipient.

In another aspect, this disclosure provides classifier probe sets for use in classifying a sample from a transplant recipient, wherein the classifier probe sets are specifically selected based on a classification system comprising two or more classes. In another embodiment, a classifier probe set for use in classifying a sample from a transplant recipient, wherein the classifier probe set is specifically selected based on a classification system comprising three or more classes. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, three of the three or more classes are transplant rejection, transplant dysfunction with no rejection and normal transplant function. In some cases, the transplant dysfunction with no rejection is acute transplant dysfunction with no rejection.

In another aspect, a non-transitory computer-readable storage media disclosed herein comprises: a) a database, in a computer memory, of one or more clinical features of two or more control samples, wherein i) the two or more control samples are from two or more transplant recipients; and ii) the two or more control samples are differentially classified based on a classification system comprising three or more classes; b) a first software module configured to compare the one or more clinical features of the two or more control samples; and c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, all three classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.

In another aspect, the storage media further comprising one or more additional software modules configured to classify a sample from a transplant recipient. In another embodiment, classifying the sample from the transplant recipient comprises a classification system comprising three or more classes. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, at least three of the classes are transplant rejection, transplant dysfunction with no rejection and normal transplant function.

In another aspect, a system comprising: a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; b) a computer program including instructions executable by the digital processing device to classify a sample from a transplant recipient comprising: i) a software module configured to receive a gene expression profile of one or more genes from the sample from the transplant recipient; ii) a software module configured to analyze the gene expression profile from the transplant recipient; and iii) a software module configured to classify the sample from the transplant recipient based on a classification system comprising three or more classes. In another embodiment, at least one of the classes is selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, at least two of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. In another embodiment, all three of the classes are selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.

In another aspect, analyzing the gene expression profile from the transplant recipient comprises applying an algorithm. In another embodiment, analyzing the gene expression profile comprises normalizing the gene expression profile from the transplant recipient. In another embodiment, normalizing the gene expression profile does not comprise quantile normalization.

INCORPORATION BY REFERENCE

All publications and patent applications mentioned in this specification are herein incorporated by reference in their entireties to the same extent as if each individual publication or patent application was specifically and individually incorporated by reference.

BRIEF DESCRIPTION OF THE DRAWINGS

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

FIG. 1 shows a schematic overview of certain methods in the disclosure.

FIG. 2 shows a schematic overview of certain methods of acquiring samples, analyzing results, transmitting reports over a computer network.

FIG. 3 shows a schematic of the workflows for cohort and bootstrapping strategies for biomarker discovery and validation.

FIG. 4 shows a graph of the Area Under the Curve (AUCs) for the normal transplant function (TX) versus acute rejection (AR), normal transplant function (TX) versus acute dysfunction no rejection (ADNR), and the acute rejection (AR) versus acute dysfunction no rejection (ADNR) comparisons for the locked nearest centroid (NC) classifier in the validation cohort.

FIG. 5 shows a graph of the Area Under the Curve (AUCs) for the normal transplant function (TX) versus acute rejection (AR), normal transplant function (TX) versus acute dysfunction no rejection (ADNR), and the acute rejection (AR) versus acute dysfunction no rejection (ADNR) using the locked nearest centroid (NC) classifier on 30 blinded validation acute rejection (AR), acute dysfunction no rejection (ADNR) and normal function (TX) samples using the one-by-one strategy.

FIG. 6 shows a system for implementing the methods of the disclosure.

FIG. 7 shows a graph of AUCs for the 200-classifier set obtained from the full study sample set of 148 samples. These results demonstrate that there is no over-fitting of the classifier.

DETAILED DESCRIPTION OF THE INVENTION

Overview

The present disclosure provides novel methods for characterizing and/or analyzing samples, and related kits, compositions and systems, particularly in a minimally invasive manner. Methods of classifying one or more samples from one or more subjects are provided, as well as methods of determining, predicting and/or monitoring an outcome or status of an organ transplant, and related kits, compositions and systems. The methods, kits, compositions, and systems provided herein are particularly useful for distinguishing between two or more conditions or disorders associated with a transplanted organ or tissue. For example, they may be used to distinguish between acute transplant rejection (AR), acute dysfunction with no rejection (ADNR), and normally functioning transplants (TX). Often, a three-way analysis or classifier is used in the methods provided herein.

This disclosure may be particularly useful for kidney transplant recipients with elevated serum creatinine levels, since elevated creatinine may be indicative of AR or ADNR. The methods provided herein may inform the treatment of such patiecants and assist with medical decisions such as whether to continue or change immunosuppressive therapies. In some cases, the methods provided herein may inform decisions as to whether to increase immunosuppression to treat immune-mediated rejection if detected or to decrease immunosuppression (e.g., to protect the patient from unintended toxicities of immunosuppressive drugs when the testing demonstrates more immunosuppression is not required). The methods disclosed herein (e.g., serial blood monitoring for rejection) may allow clinicians to make a change in an immunosuppression regimen (e.g., an increase, decrease or other modification in immunosuppression) and then follow the impact of the change on the blood profile for rejection as a function of time for each individual patient through serial monitoring of a bodily fluid, such as by additional blood drawings.

An overview of certain methods in the disclosure is provided in FIG. 1. In some instances, a method comprises obtaining a sample from a transplant recipient in a minimally invasive manner (110), such as via a blood draw, urine capture, saliva sample, throat culture, etc. The sample may comprise gene expression products (e.g., polypeptides, RNA, mRNA isolated from within cells or a cell-free source) associated with the status of the transplant (e.g., AR, ADNR, normal transplant function, etc.). In some instances, the method may involve reverse-transcribing RNA within the sample to obtain cDNA that can be analyzed using the methods described herein. The method may also comprise assaying the level of the gene expression products (or the corresponding DNA) using methods such as microarray or sequencing technology (120). The method may also comprise applying an algorithm to the assayed gene expression levels (130), wherein the algorithm is capable of distinguishing signatures for two or more transplantation conditions (e.g., AR, ADNR, TX, SCAR, CAN/IFTA, etc.) such as two or more non-normal transplant conditions (e.g., AR vs ADNR). Often, the algorithm is a trained algorithm obtained by the methods provided herein. In some instances, the algorithm is a three-way classifier and is capable of performing multi-class classification of the sample (140). The method may further comprise detecting, diagnosing, predicting, or monitoring the condition (e.g., AR, ADNR, TX, SCAR, CAN/IFTA etc.) of the transplant recipient. The methods may further comprise continuing, stopping or changing a therapeutic regimen based on the results of the assays described herein.

The methods, systems, kits and compositions provided herein may also be used to generate or validate an algorithm capable of distinguishing between at least two conditions of a transplant recipient (e.g., AR, ADNR, TX, SCAR, CAN/IFTA, etc.). The algorithm may be produced based on gene expression levels in various cohorts or sub-cohorts described herein.

The methods, systems, kits and compositions provided herein may also be capable of generating and transmitting results through a computer network. As shown in FIG. 2, a sample (220) is first collected from a subject (e.g. transplant recipient, 210). The sample is assayed (230) and gene expression products are generated. A computer system (240) is used in analyzing the data and making classification of the sample. The result is capable of being transmitted to different types of end users via a computer network (250). In some instances, the subject (e.g. patient) may be able to access the result by using a standalone software and/or a web-based application on a local computer capable of accessing the internet (260). In some instances, the result can be accessed via a mobile application (270) provided to a mobile digital processing device (e.g. mobile phone, tablet, etc.). In some instances, the result may be accessed by physicians and help them identify and track conditions of their patients (280). In some instances, the result may be used for other purposes (290) such as education and research.

Subjects

Often, the methods are used on a subject, preferably human, that is a transplant recipient. The methods may be used for detecting or predicting a condition of the transplant recipient such as acute rejection (AR), acute dysfunction with no rejection (ADNR), chronic allograft nephropathy (CAN), interstitial fibrosis and tubular atrophy (IF/TA), subclinical rejection acute rejection (SCAR), hepatitis C virus recurrence (HCV-R), etc. In some cases, the condition may be AR. In some cases, the condition may be ADNR. In some cases, the condition may be SCAR. In some cases, the condition may be transplant dysfunction. In some cases, the condition may be transplant dysfunction with no rejection. In some cases, the condition may be acute transplant dysfunction.

Typically, when the patient does not exhibit symptoms or test results of organ dysfunction or rejection, the transplant is considered a normal functioning transplant (TX: Transplant eXcellent). An unhealthy transplant recipient may exhibit signs of organ dysfunction and/or rejection (e.g., an increasing serum creatinine). However, a subject (e.g., kidney transplant recipient) with subclinical rejection may have normal and stable organ function (e.g. normal creatinine level and normal eGFR). In these subjects, at the present time, rejection may be diagnosed histologically through a biopsy. A failure to recognize, diagnose and treat subclinical AR before significant tissue injury has occurred and the transplant shows clinical signs of dysfunction could be a major cause of irreversible organ damage. Moreover, a failure to recognize a chronic, subclinical immune-mediated organ damage and a failure to make appropriate changes in immunosuppressive therapy to restore a state of effective immunosuppression in that patient could contribute to late organ transplant failure. The methods disclosed herein can reduce or eliminate these and other problems associated with transplant rejection or failure.

Acute rejection (AR) occurs when transplanted tissue is rejected by the recipient's immune system, which damages or destroys the transplanted tissue unless immunosuppression is achieved. T-cells, B-cells and other immune cells as well as possibly antibodies of the recipient may cause the graft cells to lyse or produce cytokines that recruit other inflammatory cells, eventually causing necrosis of allograft tissue. In some instances, AR may be diagnosed by a biopsy of the transplanted organ. In the case of kidney transplant recipients, AR may be associated with an increase in serum creatinine levels. The treatment of AR may include using immunosuppressive agents, corticosteroids, polyclonal and monoclonal antibodies, engineered and naturally occurring biological molecules, and antiproliferatives. AR more frequently occurs in the first three to 12 months after transplantation but there is a continued risk and incidence of AR for the first five years post transplant and whenever a patient's immunosuppression becomes inadequate for any reason for the life of the transplant.

Acute dysfunction with no rejection (ADNR) is an abrupt decrease or loss of organ function without histological evidence of rejection from a transplant biopsy. Kidney transplant recipients with ADNR will often exhibit elevated creatinine levels. Unfortunately, the levels of kidney dysfunction based on serum creatinines are usually not significantly different between AR and ADNR subjects.

Another condition that can be associated with a kidney transplant is chronic allograft nephropathy (CAN), which is characterized by a gradual decline in kidney function and, typically, accompanied by high blood pressure and hematuria. Histopathology of patients with CAN is characterized by interstitial fibrosis, tubular atrophy, fibrotic intimal thickening of arteries and glomerulosclerosis typically described as IFTA. CAN/IFTA usually happens months to years after the transplant though increased amounts of IFTA can be present early in the first year post transplant in patients that have received kidneys from older or diseased donors or when early severe ischemia perfusion injury or other transplant injury occurs. CAN is a clinical phenotype characterized by a progressive decrease in organ transplant function. In contrast, IFTA is a histological phenotype currently diagnosed by an organ biopsy. In kidney transplants, interstitial fibrosis (IF) is usually considered to be present when the supporting connective tissue in the renal parenchyma exceeds 5% of the cortical area. Tubular atrophy (TA) refers to the presence of tubules with thick redundant basement membranes, or a reduction of greater than 50% in tubular diameter compared to surrounding non-atrophic tubules. In certain instances, finding interstitial fibrosis and tubular atrophy (IFTA) on the biopsy may be early indicators that predict the later organ dysfunction associated with the clinical phenotype of CAN. Immunologically, CAN/IFTA usually represents a failure of effective longterm immunosuppression and mechanistically it is immune-mediated chronic rejection (CR) and can involve both cell and antibody-mediated mechanisms of tissue injury as well as activation of complement and other blood coagulation mechanisms and can also involve inflammatory cytokine-mediated tissue activation and injury.

Subclinical rejection (SCAR) is generally a condition that is histologically identified as acute rejection but without concurrent functional deterioration. For kidney transplant recipients, subclinical rejection (SCAR) is histologically defined acute rejection that is characterized by tubulointerstitial mononuclear infiltration identified from a biopsy specimen, but without concurrent functional deterioration (variably defined as a serum creatinine not exceeding about 10%, 20% or 25% of baseline values). A SCAR subject typically shows normal and/or stable serum creatinine levels. SCAR is usually diagnosed through biopsies that are taken at a fixed time after transplantation (e.g. protocol biopsies or serial monitoring biopsies) which are not driven by clinical indications but rather by standards of care. SCAR may be subclassified by some into acute SCAR (SCAR) or a milder form called borderline SCAR (suspicious for acute rejection) based on the biopsy histology.

A subject therefore may be a transplant recipient that has, or is at risk of having a condition such as AR, ADNR, TX, CAN, IFTA, or SCAR. In some instances, a normal serum creatinine level and/or a normal estimated glomerular filtration rate (eGFR) may indicate healthy transplant (TX) or subclinical rejection (SCAR). For example, typical reference ranges for serum creatinine are 0.5 to 1.0 mg/dL for women and 0.7 to 1.2 mg/dL for men, though typical kidney transplant patients have creatinines in the 0.8 to 1.5 mg/dL range for women and 1.0 to 1.9 mg/dL range for men. This may be due to the fact that most kidney transplant patients have a single kidney. In some instances, the trend of serum creatinine levels over time can be used to evaluate the recipient's organ function. This is why it may be important to consider both “normal” serum creatinine levels and “stable” serum creatinine levels in making clinical judgments, interpreting testing results, deciding to do a biopsy or making therapy change decisions including changing immunosuppressive drugs. For example, the transplant recipient may show signs of a transplant dysfunction or rejection as indicated by an elevated serum creatinine level and/or a decreased eGFR. In some instances, a transplant subject with a particular transplant condition (e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatinine level of at least 0.1 mg/dL, 0.2 mg/dL, 0.3 mg/dL, 0.4 mg/dL, 0.5 mg/dL, 0.6 mg/dL, 0.7 mg/dL 0.8 mg/dL, 0.9 mg/dL, 1.0 mg/dL, 1.1 mg/dL, 1.2 mg/dL, 1.3 mg/dL, 1.4 mg/dL, 1.5 mg/dL, 1.6 mg/dL, 1.7 mg/dL, 1.8 mg/dL, 1.9 mg/dL, 2.0 mg/dL, 2.1 mg/dL, 2.2 mg/dL, 2.3 mg/dL, 2.4 mg/dL, 2.5 mg/dL, 2.6 mg/dL, 2.7 mg/dL, 2.8 mg/dL, 2.9 mg/dL, 3.0 mg/dL, 3.1 mg/dL, 3.2 mg/dL, 3.3 mg/dL, 3.4 mg/dL, 3.5 mg/dL, 3.6 mg/dL, 3.7 mg/dL, 3.8 mg/dL, 3.9 mg/dL, or 4.0 mg/dL. In some instances, a transplant subject with a certain transplant condition (e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatinine level of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some instances, a transplant subject with a certain transplant condition (e.g., AR, ADNR, CAN, etc.) may have an increase of a serum creatinine level of at least 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, or 10-fold from baseline. In some cases, the increase in serum creatinine (e.g., any increase in the concentration of serum creatinine described herein) may occur over about 0.25 days, 0.5 days, 0.75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2.0 days, 3.0 days, 4.0 days, 5.0 days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10.0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, or more. In some instances, a transplant subject with a particular transplant condition (e.g., AR, ADNR, CAN, etc.) may have a decrease of a eGFR of at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% from baseline. In some cases, the decrease in eGFR may occur over 0.25 days, 0.5 days, 0.75 days, 1 day, 1.25 days, 1.5 days, 1.75 days, 2.0 days, 3.0 days, 4.0 days, 5.0 days, 6.0 days, 7.0 days, 8.0 days, 9.0 days, 10.0 days, 15 days, 30 days, 1 month, 2 months, 3 months, 4 months, 5 months, or 6 months, or more. In some instances, diagnosing, predicting, or monitoring the status or outcome of a transplant or condition comprises determining transplant recipient-specific baselines and/or thresholds.

In some cases, the methods provided herein are used on a subject who has not yet received a transplant, such as a subject who is awaiting a tissue or organ transplant. In other cases, the subject is a transplant donor. In some cases, the subject has not received a transplant and is not expected to receive such transplant. In some cases, the subject may be a subject who is suffering from diseases requiring monitoring of certain organs for potential failure or dysfunction. In some cases, the subject may be a healthy subject.

A transplant recipient may be a recipient of a solid organ or a fragment of a solid organ. The solid organ may be a lung, kidney, heart, liver, pancreas, large intestine, small intestine, gall bladder, reproductive organ or a combination thereof. Preferably, the transplant recipient is a kidney transplant or allograft recipient. In some instances, the transplant recipient may be a recipient of a tissue or cell. The tissue or cell may be amnion, skin, bone, blood, marrow, blood stem cells, platelets, umbilical cord blood, cornea, middle ear, heart valve, vein, cartilage, tendon, ligament, nerve tissue, embryonic stem (ES) cells, induced pluripotent stem cells (IPSCs), stem cells, adult stem cells, hematopoietic stem cells, or a combination thereof.

The donor organ, tissue, or cells may be derived from a subject who has certain similarities or compatibilities with the recipient subject. For example, the donor organ, tissue, or cells may be derived from a donor subject who is age-matched, ethnicity-matched, gender-matched, blood-type compatible, or HLA-type compatible with the recipient subject.

The transplant recipient may be a male or a female. The transplant recipient may be patients of any age. For example, the transplant recipient may be a patient of less than about 10 years old. For example, the transplant recipient may be a patient of at least about 0, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, or 100 years old. The transplant recipient may be in utero. Often, the subject is a patient or other individual undergoing a treatment regimen, or being evaluated for a treatment regimen (e.g., immunosuppressive therapy). However, in some instances, the subject is not undergoing a treatment regimen. A feature of the graft tolerant phenotype detected or identified by the subject methods is that it is a phenotype which occurs without immunosuppressive therapy, e.g., it is present in a host that is not undergoing immunosuppressive therapy such that immunosuppressive agents are not being administered to the host.

In various embodiments, the subjects suitable for methods of the invention are patients who have undergone an organ transplant within 6 hours, 12 hours, 1 day, 2 days, 3 days, 4 days, 5 days, 10 days, 15 days, 20 days, 25 days, 1 month, 2 months, 3 months, 4 months, 5 months, 7 months, 9 months, 11 months, 1 year, 2 years, 4 years, 5 years, 10 years, 15 years, 20 years or longer of prior to receiving a classification disclosed herein (e.g., a classification obtained by the methods disclosed herein). Some of the methods further comprise changing the treatment regime of the patient responsive to the detecting, prognosing, diagnosing or monitoring step. In some of these methods, the subject can be one who has received a drug before performing the methods, and the change in treatment comprises administering an additional drug, administering a higher or lower dose of the same drug, stopping administration of the drug, or replacing the drug with a different drug or therapeutic intervention.

The subjects can include transplant recipients or donors or healthy subjects. The methods can be useful on human subjects who have undergone a kidney transplant although can also be used on subjects who have gone other types of transplant (e.g., heart, liver, lung, stem cell, etc.). The subjects may be mammals or non-mammals. The methods can be useful on non-humans who have undergone kidney or other transplant. Preferably, the subjects are a mammal, such as, a human, non-human primate (e.g., apes, monkeys, chimpanzees), cat, dog, rabbit, goat, horse, cow, pig, rodent, mouse, SCID mouse, rat, guinea pig, or sheep. Even more preferably, the subject is a human. The subject may be male or female; the subject may be a fetus, infant, child, adolescent, teenager or adult.

In some methods, species variants or homologs of these genes can be used in a non-human animal model. Species variants may be the genes in different species having greatest sequence identity and similarity in functional properties to one another. Many of such species variants human genes may be listed in the Swiss-Prot database.

Samples

Methods for detecting molecules (e.g., nucleic acids, proteins, etc.) in a subject who has received a transplant (e.g., organ transplant, tissue transplant, stem cell transplant) in order to detect, diagnose, monitor, predict, or evaluate the status or outcome of the transplant are described in this disclosure. In some cases, the molecules are circulating molecules. In some cases, the molecules are expressed in blood cells. In some cases, the molecules are cell-free circulating nucleic acids.

The methods, kits, and systems disclosed herein may be used to classify one or more samples from one or more subjects. A sample may be any material containing tissues, cells, nucleic acids, genes, gene fragments, expression products, polypeptides, exosomes, gene expression products, or gene expression product fragments of a subject to be tested. Methods for determining sample suitability and/or adequacy are provided. A sample may include but is not limited to, tissue, cells, or biological material from cells or derived from cells of an individual. The sample may be a heterogeneous or homogeneous population of cells or tissues. In some cases, the sample is from a single patient. In some cases, the method comprises analyzing multiple samples at once, e.g., via massively parallel sequencing.

The sample is preferably a bodily fluid. The bodily fluid may be sweat, saliva, tears, urine, blood, menses, semen, and/or spinal fluid. In preferred embodiments, the sample is a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The sample may be a whole blood sample. The blood sample may be a peripheral blood sample. In some cases, the sample comprises peripheral blood mononuclear cells (PBMCs); in some cases, the sample comprises peripheral blood lymphocytes (PBLs). The sample may be a serum sample. In some instances, the sample is a tissue sample or an organ sample, such as a biopsy.

The methods, kits, and systems disclosed herein may comprise specifically detecting, profiling, or quantitating molecules (e.g., nucleic acids, DNA, RNA, polypeptides, etc.) that are within the biological samples. In some instances, genomic expression products, including RNA, or polypeptides, may be isolated from the biological samples. In some cases, nucleic acids, DNA, RNA, polypeptides may be isolated from a cell-free source. In some cases, nucleic acids, DNA, RNA, polypeptides may be isolated from cells derived from the transplant recipient.

The sample may be obtained using any method known to the art that can provide a sample suitable for the analytical methods described herein. The sample may be obtained by a non-invasive method such as a throat swab, buccal swab, bronchial lavage, urine collection, scraping of the skin or cervix, swabbing of the cheek, saliva collection, feces collection, menses collection, or semen collection. The sample may be obtained by a minimally-invasive method such as a blood draw. The sample may be obtained by venipuncture. In other instances, the sample is obtained by an invasive procedure including but not limited to: biopsy, alveolar or pulmonary lavage, or needle aspiration. The method of biopsy may include surgical biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, or skin biopsy. The sample may be formalin fixed sections. The method of needle aspiration may further include fine needle aspiration, core needle biopsy, vacuum assisted biopsy, or large core biopsy. In some embodiments, multiple samples may be obtained by the methods herein to ensure a sufficient amount of biological material. In some instances, the sample is not obtained by biopsy. In some instances, the sample is not a kidney biopsy.

Sample Data

The methods, kits, and systems disclosed herein may comprise data pertaining to one or more samples or uses thereof. The data may be expression level data. The expression level data may be determined by microarray, SAGE, sequencing, blotting, or PCR amplification (e.g. RT-PCR, quantitative PCR, etc.). In some cases, the expression level is determined by sequencing (e.g., RNA or DNA sequencing). The expression level data may be determined by microarray. Exemplary microarrays include but are not limited to the Affymetrix Human Genome U133 Plus 2.0 GeneChip or the HT HG-U133+ PM Array Plate.

In some cases, arrays (e.g., Illumina arrays) may use different probes attached to different particles or beads. In such arrays, the identity of which probe is attached to which particle or beads is usually determinable from an encoding system. The probes can be oligonucleotides. In some cases, the probes may comprise several match probes with perfect complementarity to a given target mRNA, optionally together with mismatch probes differing from the match probes. See, e.g., (Lockhart, et al., Nature Biotechnology 14:1675-1680 (1996); and Lipschutz, et al., Nature Genetics Supplement 21: 20-24, 1999). Such arrays may also include various control probes, such as a probe complementary to a housekeeping gene likely to be expressed in most samples. Regardless of the specifics of array design, an array generally contains one or more probes either perfectly complementary to a particular target mRNA or sufficiently complementary to the target mRNA to distinguish it from other mRNAs in the sample. The presence of such a target mRNA can be determined from the hybridization signal of such probes, optionally by comparison with mismatch or other control probes included in the array. Typically, the target bears a fluorescent label, in which case hybridization intensity can be determined by, for example, a scanning confocal microscope in photon counting mode. Appropriate scanning devices are described by e.g., U.S. Pat. No. 5,578,832, and U.S. Pat. No. 5,631,734. The intensity of labeling of probes hybridizing to a particular mRNA or its amplification product may provide a raw measure of expression level.

The data pertaining to the sample may be compared to data pertaining to one or more control samples, which may be samples from the same patient at different times. In some cases, the one or more control samples may comprise one or more samples from healthy subjects, unhealthy subjects, or a combination thereof. The one or more control samples may comprise one or more samples from healthy subjects, subjects suffering from transplant dysfunction with no rejection, subjects suffering from transplant rejection, or a combination thereof. The healthy subjects may be subjects with normal transplant function. The data pertaining to the sample may be sequentially compared to two or more classes of samples. The data pertaining to the sample may be sequentially compared to three or more classes of samples. The classes of samples may comprise control samples classified as being from subjects with normal transplant function, control samples classified as being from subjects suffering from transplant dysfunction with no rejection, control samples classified as being from subjects suffering from transplant rejection, or a combination thereof.

Biomarkers/Gene Expression Products

Biomarker refers to a measurable indicator of some biological state or condition. In some instances, a biomarker can be a substance found in a subject, a quantity of the substance, or some other indicator. For example, a biomarker may be the amount of RNA, mRNA, tRNA, miRNA, mitochondrial RNA, siRNA, polypeptides, proteins, DNA, cDNA and/or other gene expression products in a sample. In some instances, gene expression products may be proteins or RNA. In some instances, RNA may be an expression product of non-protein coding genes such as ribosomal RNA (rRNA), transfer RNA (tRNA), micro RNA (miRNA), or small nuclear RNA (snRNA) genes. In some instances, RNA may be messenger RNA (mRNA). In certain examples, a biomarker or gene expression product may be DNA complementary or corresponding to RNA expression products in a sample.

The methods, compositions and systems as described here also relate to the use of biomarker panels and/or gene expression products for purposes of identification, diagnosis, classification, treatment or to otherwise characterize various conditions of organ transplant comprising AR, ANDR, TX, IFTA, CAN, SCAR, hepatitis C virus recurrence (HCV-R). Sets of biomarkers and/or gene expression products useful for classifying biological samples are provided, as well as methods of obtaining such sets of biomarkers. Often, the pattern of levels of gene expression biomarkers in a panel (also known as a signature) is determined and then used to evaluate the signature of the same panel of biomarkers in a sample, such as by a measure of similarity between the sample signature and the reference signature. In some instances, biomarker panels or gene expression products may be chosen to distinguish acute rejection (AR) from transplant dysfunction with no acute rejection (ADNR) expression profiles. In some instances, biomarker panels or gene expression products may be chosen to distinguish acute rejection (AR) from normally functioning transplant (TX) expression profiles. In some instances, biomarker panels or gene expression products may be selected to distinguish acute dysfunction with no transplant rejection (ADNR) from normally functioning transplant (TX) expression profiles. In some instances, biomarker panels or gene expression products may be selected to distinguish transplant dysfunction from acute rejection (AR) expression profiles. In certain examples, this disclosure provides methods of reclassifying an indeterminate biological sample from subjects into a healthy, acute rejection or acute dysfunction no rejection categories, and related kits, compositions and systems.

The expression level may be normalized. In some instances, normalization may comprise quantile normalization. Normalization may comprise frozen robust multichip average (fRMA) normalization.

Determining the expression level may comprise normalization by frozen robust multichip average (fRMA). Determining the expression level may comprise reverse transcribing the RNA to produce cRNA.

The methods provided herein may comprise identifying a condition from one or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some cases, AR of a kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 10b, or 12b, in any combination. In some cases, ADNR of a kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 1a, 1b, 1c, 1d, 10b, or 12b, in any combination. In some cases, TX (or normal functioning) of a kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, or 14b, in any combination. In some cases, SCAR of kidney transplant (or other organ transplant) can be detected from one or more gene expression products from Table 8 or 9, in any combination. In some instances, AR of a liver transplant (or other organ transplant) can be detected from one or more gene expression products from Table 16b, 17b, or 18b, in any combination. In some instances, ADNR of liver can be detected from one or more gene expression products from Table 16b. In some cases, TX of liver can be detected from one or more gene expression products from Table 16b. In some cases, HCV of liver can be detected from one or more gene expression products from Table 17b or 18b, in any combination. In some cases, HCV+AR of liver can be detected from one or more gene expression products from Table 17b or 18b, in any combination.

The methods provided herein may also comprise identifying a condition from one or more gene expression products from a tissue biopsy sample. From example, AR of kidney biopsy can be detected from one or more gene expression products from Table 10b or 12b, in any combination. ADNR of kidney biopsy can be detected from one or more gene expression products from Table 10b or 12b, in any combination. CAN of kidney biopsy can be detected from one or more gene expression products from Table 12b or 14b, in any combination. TX of kidney biopsy can be detected from one or more gene expression products from Table 10b, 12b, or 14b, in any combination. AR of liver biopsy can be detected from one or more gene expression products from Table 18b. HCV of liver biopsy can be detected from one or more gene expression products from Table 18b. HCV+AR of liver biopsy can be detected from one or more gene expression products from Table 18b.

The gene expression product may be a peptide or RNA. At least one of the gene expression products may correspond to a gene found in Table 1a. The gene expression product may be a peptide or RNA. At least one of the gene expression products may correspond to a gene found in Table 1c. At least one of the gene expression products may correspond to a gene found in Table 1a, 1b, 1c or 1d, in any combination. At least one of the gene expression products may correspond to a gene found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1c. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1c. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1c. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or less genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1c. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 10 or more genes found in Table 1a. The gene expression products may correspond to 10 or more genes found in Table 1c. The gene expression products may correspond to 10 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 10 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 25 or more genes found in Table 1a. The gene expression products may correspond to 25 or more genes found in Table 1c. The gene expression products may correspond to 25 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 25 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 50 or more genes found in Table 1a. The gene expression products may correspond to 50 or more genes found in Table 1c. The gene expression products may correspond to 50 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 50 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 100 or more genes found in Table 1a. The gene expression products may correspond to 100 or more genes found in Table 1c. The gene expression products may correspond to 100 or more genes found in Table 1a, 1b, 1c, or 1d, in any combination. The gene expression products may correspond to 100 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The gene expression products may correspond to 200 or more genes found in Table 1a. The gene expression products may correspond to 200 or more genes found in Table 1c. The gene expression products may correspond to 200 or more genes found in Table 1a, 1b, 1c, or 1d in any combination. The gene expression products may correspond to 200 or more genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

At least a subset the gene expression products may correspond to the genes found in Table 1a. At least a subset the gene expression products may correspond to the genes found in Table 1c. At least a subset the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least a subset the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1a. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1c. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20% or more of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1a. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1c. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97%, or 100% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 5% of the gene expression products may correspond to the genes found in Table 1a. At least about 5% of the gene expression products may correspond to the genes found in Table 1c. At least about 5% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 5% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10% of the gene expression products may correspond to the genes found in Table 1a. At least about 10% of the gene expression products may correspond to the genes found in Table 1c. At least about 10% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 10% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 25% of the gene expression products may correspond to the genes found in Table 1a. At least about 25% of the gene expression products may correspond to the genes found in Table 1c. At least about 25% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 25% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 30% of the gene expression products may correspond to the genes found in Table 1a. At least about 30% of the gene expression products may correspond to the genes found in Table 1c. At least about 30% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, or 1d, in any combination. At least about 30% of the gene expression products may correspond to the genes found in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

In another aspect, the invention provides arrays, which contain a support or supports bearing a plurality of nucleic acid probes complementary to a plurality of mRNAs fewer than 5000 in number. Typically, the plurality of mRNAs includes mRNAs expressed by at least five genes selected from Table 1a. In another embodiment, the plurality of mRNAs includes mRNAs expressed by at least five genes selected from Table 1c. The plurality of mRNAs may also include mRNAs expressed by at least five genes selected from Table 1a, 1b, 1c, or 1d, in any combination. The plurality of mRNAs may also include mRNAs expressed by at least five genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, the plurality of mRNAs are fewer than 1000 or fewer than 100 in number. In some embodiments, the plurality of nucleic acid probes are attached to a planar support or to beads. In a related aspect, the invention provides arrays that contain a support or supports bearing a plurality of ligands that specifically bind to a plurality of proteins fewer than 5000 in number. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1a. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1c. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1a, 1b, 1c, or 1d, in any combination. The plurality of proteins typically includes at least five proteins encoded by genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, the plurality of proteins are fewer than 1000 or fewer than 100 in number. In some embodiments, the plurality of ligands are attached to a planar support or to beads. In some embodiments, the at least five proteins are encoded by genes selected from Table 1a. In some embodiments, the at least five proteins are encoded by genes selected from Table 1c. In some embodiments, the at least five proteins are encoded by genes selected from Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the at least five proteins are encoded by genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, the ligands are different antibodies that bind to different proteins of the plurality of proteins.

Methods, kits, and systems disclosed herein may have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. Methods, kits, and systems disclosed herein may have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. Methods, kits, and systems disclosed herein may also have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. Methods, kits, and systems disclosed herein may also have a plurality of genes associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some instances, there may be genes selected from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16 or more biomarker panels and can have from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more gene expression products from each biomarker panel, in any combination. In some instances, the biomarkers within each panel are interchangeable (modular). The plurality of biomarkers in all panels can be substituted, increased, reduced, or improved to accommodate the classification system described herein. In some embodiments, the set of genes combined give a specificity or sensitivity of greater than 70%, 75%, 80%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 99.5%, or a positive predictive value or negative predictive value of at least 95%, 95.5%, 96%, 96.5%, 97%, 97.5%, 98%, 98.5%, 99%, 99.5% or more.

Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a. Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1c. Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiers may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1c. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiers may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1a. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1c. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. Classifiers may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, or 20% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 22%, 25%, 27%, 30%, 32%, 35%, 37%, 40%, 42%, 45%, 47%, 50%, 52%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 3% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 5% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1c. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, or 1d, in any combination. At least about 10% of the biomarkers from the classifiers may be selected from biomarkers disclosed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

Classifier probe sets may comprise one or more oligonucleotides. The oligonucleotides may comprise at least a portion of a sequence that can hybridize to one or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to at least a portion of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to at least a portion of at least 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or fewer oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to fewer than 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more biomarkers from the panel of biomarkers. Classifier probe sets may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more oligonucleotides, wherein at least a portion of the oligonucleotide can hybridize to at least a portion of at least 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more biomarkers from the panel of biomarkers.

Training of multi-dimensional classifiers (e.g., algorithms) may be performed on numerous samples. For example, training of the multi-dimensional classifier may be performed on at least about 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more samples. Training of the multi-dimensional classifier may be performed on at least about 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 350, 400, 450, 500 or more samples. Training of the multi-dimensional classifier may be performed on at least about 525, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 2000 or more samples.

The total sample population may comprise samples obtained by venipuncture. Alternatively, the total sample population may comprise samples obtained by venipuncture, needle aspiration, fine needle aspiration, or a combination thereof. The total sample population may comprise samples obtained by venipuncture, needle aspiration, fine needle aspiration, core needle biopsy, vacuum assisted biopsy, large core biopsy, incisional biopsy, excisional biopsy, punch biopsy, shave biopsy, skin biopsy, or a combination thereof. In some embodiments, the samples are not obtained by biopsy. The percent of the total sample population that is obtained by venipuncture may be greater than about 1%, 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, or 95%. The percent of the total sample population that is obtained by venipuncture may be greater than about 1%. The percent of the total sample population that is obtained by venipuncture may be greater than about 5%. The percent of the total sample population that is obtained by venipuncture may be greater than about 10%

There may be a specific (or range of) difference in gene expression between subtypes or sets of samples being compared to one another. In some examples, the gene expression of some similar subtypes are merged to form a super-class that is then compared to another subtype, or another super-class, or the set of all other subtypes. In some embodiments, the difference in gene expression level is at least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, or 50% or more. In some embodiments, the difference in gene expression level is at least about 2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more.

The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1a. The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1c. The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. The present invention may initialize gene expression products corresponding to one or more biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1a, as well as any subset thereof, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1c, as well as any subset thereof, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination, as well as any subset thereof, in any combination. The methods, compositions and systems provided herein may include expression products corresponding to any or all of the biomarkers selected from gene expression products derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination, as well as any subset thereof, in any combination. For example, the methods may use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a. In another embodiment, the methods use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1c. In another example, the methods may use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a, 1b, 1c, or 1d, in any combination. In another example, the methods may use gene expression products corresponding to at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 of the markers provided Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1a. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1c. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. The methods may use gene expression products corresponding to at least about 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300 or more of the markers provided in gene expression products derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

Further disclosed herein are classifier sets and methods of producing one or more classifier sets. The classifier set may comprise one or more genes. The classifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more genes. The classifier set may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more genes. The classifier set may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more genes. The classifier set may comprise 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more genes. The classifier set may comprise 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or more genes. The classifier set may comprise 10 or more genes. The classifier set may comprise 30 or more genes. The classifier set may comprise 60 or more genes. The classifier set may comprise 100 or more genes. The classifier set may comprise 125 or more genes. The classifier set may comprise 150 or more genes. The classifier set may comprise 200 or more genes. The classifier set may comprise 250 or more genes. The classifier set may comprise 300 or more genes.

The classifier set may comprise one or more differentially expressed genes. The classifier set may comprise one or more differentially expressed genes. The classifier set may comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more differentially expressed genes. The classifier set may comprise 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more differentially expressed genes. The classifier set may comprise 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more differentially expressed genes. The classifier set may comprise 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more differentially expressed genes. The classifier set may comprise 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or more differentially expressed genes. The classifier set may comprise 10 or more differentially expressed genes. The classifier set may comprise 30 or more differentially expressed genes. The classifier set may comprise 60 or more differentially expressed genes. The classifier set may comprise 100 or more differentially expressed genes. The classifier set may comprise 125 or more differentially expressed genes. The classifier set may comprise 150 or more differentially expressed genes. The classifier set may comprise 200 or more differentially expressed genes. The classifier set may comprise 250 or more differentially expressed genes. The classifier set may comprise 300 or more differentially expressed genes.

In some instances, the method provides a number, or a range of numbers, of biomarkers or gene expression products that are used to characterize a sample. Examples of classification panels may be derived from genes listed in Table 1a. Examples of classification panels may be derived from genes listed in Table 1c. Examples of classification panels may be derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. Examples of classification panels may be derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. However, the present disclosure is not meant to be limited solely to the biomarkers disclosed herein. Rather, it is understood that any biomarker, gene, group of genes or group of biomarkers identified through methods described herein is encompassed by the present invention. In some embodiments, the method involves measuring (or obtaining) the levels of two or more gene expression products that are within a biomarker panel and/or within a classification panel. For example, in some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a. In some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1c. In some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, a biomarker panel or a gene expression product may contain at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1c. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, a biomarker panel or a gene expression product may contain no more than about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 or more genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1a. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1c. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1a, 1b, 1c, or 1d, in any combination. In other embodiments, a biomarker panel or a gene expression product may contain about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 33, 35, 38, 40, 43, 45, 47, 50, 52, 55, 57, 60, 62, 65, 67, 70, 72, 75, 77, 80, 85, 89, 92, 95, 97, 100, 103, 107, 110, 113, 117, 122, 128, 132, 138, 140, 142, 145, 147, 150, 155, 160, 165, 170, 175, 180, 183, 185, 187, 190, 192, 195, 197, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290 or 300 total genes chosen from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

Measuring Expression Levels

The methods, kits and systems disclosed herein may be used to obtain or to determine an expression level for one or more gene products in a subject. In some instances, the expression level is used to develop or train an algorithm or classifier provided herein. In some instances, where the subject is a patient, such as a transplant recipient; gene expression levels are measured in a sample from the transplant recipient and a classifier or algorithm (e.g., trained algorithm) is applied to the resulting data in order to detect, predict, monitor, or estimate the risk of a transplant condition (e.g., acute rejection).

The expression level of the gene products (e.g., RNA, cDNA, polypeptides) may be determined using any method known in the art. In some instances, the expression level of the gene products (e.g., nucleic acid gene products such as RNA) is measured by microarray, sequencing, electrophoresis, automatic electrophoresis, SAGE, blotting, polymerase chain reaction (PCR), digital PCR, RT-PCR, and/or quantitative PCR (qPCR). In certain preferred embodiments, the expression level is determined by microarray. For example, the microarray may be an Affymetrix Human Genome U133 Plus 2.0 GeneChip or a HT HG-U133+ PM Array Plate.

In certain preferred embodiments, the expression level of the gene products (e.g., RNA) is determined by sequencing, such as by RNA sequencing or by DNA sequencing (e.g., of cDNA generated from reverse-transcribing RNA (e.g., mRNA) from a sample). Sequencing may be performed by any available method or technique. Sequencing methods may include: high-throughput sequencing, pyrosequencing, classic Sangar sequencing methods, sequencing-by-ligation, sequencing by synthesis, sequencing-by-hybridization, RNA-Seq (Illumina), Digital Gene Expression (Helicos), next generation sequencing, single molecule sequencing by synthesis (SMSS) (Helicos), Ion Torrent Sequencing Machine (Life Technologies/Thermo-Fisher), massively-parallel sequencing, clonal single molecule Array (Solexa), shotgun sequencing, Maxim-Gilbert sequencing, primer walking, and any other sequencing methods known in the art.

Measuring gene expression levels may comprise reverse transcribing RNA (e.g., mRNA) within a sample in order to produce cDNA. The cDNA may then be measured using any of the methods described herein (e.g., PCR, digital PCR, qPCR, microarray, SAGE, blotting, sequencing, etc.). In some instances, the method may comprise reverse transcribing RNA originating from the subject (e.g., transplant recipient) to produce cDNA, which is then measured such as by microarray, sequencing, PCR, and/or any other method available in the art.

In some instances, the gene products may be polypeptides. In such instances, the methods may comprise measuring polypeptide gene products. Methods of measuring or detecting polypeptides may be accomplished using any method or technique known in the art. Examples of such methods include proteomics, expression proteomics, mass spectrometry, 2D PAGE, 3D PAGE, electrophoresis, proteomic chips, proteomic microarrays, and/or Edman degradation reactions.

The expression level may be normalized (e.g., signal normalization). In some instances, signal normalization (e.g., quantile normalization) is performed on an entire cohort. In general, quantile normalization is a technique for making two or more distributions identical in statistical properties. However, in settings where samples must be processed individually or in small batches, data sets that are normalized separately are generally not comparable. In some instances provided herein, the expression level of the gene products is normalized using frozen RMA (fRMA). fRMA is particularly useful because it overcomes these obstacles by normalization of individual arrays to large publicly available microarray databases allowing for estimates of probe-specific effects and variances to be pre-computed and “frozen” (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). In some instances, a method provided herein does not comprise performing a normalization step. In some instances, a method provided herein does not comprise performing quantile normalization. In some cases, the normalization does not comprise quantile normalization. In certain preferred embodiments, the methods comprise frozen robust multichip average (fRMA) normalization.

In some cases, analysis of expression levels initially provides a measurement of the expression level of each of several individual genes. The expression level can be absolute in terms of a concentration of an expression product, or relative in terms of a relative concentration of an expression product of interest to another expression product in the sample. For example, relative expression levels of genes can be expressed with respect to the expression level of a house-keeping gene in the sample. Relative expression levels can also be determined by simultaneously analyzing differentially labeled samples hybridized to the same array. Expression levels can also be expressed in arbitrary units, for example, related to signal intensity.

Biomarker Discovery and Validation

Exemplary workflows for cohort and bootstrapping strategies for biomarker discovery and validation are depicted in FIG. 3. As shown in FIG. 3, the cohort-based method of biomarker discovery and validation is outlined by the solid box and the bootstrapping method of biomarker discovery and validation is outlined in the dotted box. For the cohort-based method, samples for acute rejection (n=63) (310), acute dysfunction no rejection (n=39) (315), and normal transplant function (n=46) (320) are randomly split into a discovery cohort (n=75) (325) and a validation cohort (n=73) (345). The samples from the discovery cohort are analyzed using a 3-class univariate F-test (1000 random permutations, FDR <10%; BRB ArrayTools) (330). The 3-class univariate F-test analysis of the discovery cohort yielded 2977 differentially expressed probe sets (Table 1) (335). Algorithms such as the Nearest Centroid, Diagonal Linear Discriminant Analysis, and Support Vector Machines, are used to create a 3-way classifier for AR, ADNR and TX in the discovery cohort (340). The 25-200 classifiers are “locked” (350). The “locked” classifiers are validated by samples from the validation cohort (345). For the bootstrapping method, 3-class univariate F-test is performed on the whole data set of samples (n=148) (1000 random permutations, FDR <10%; BRB ArrayTools) (355). The significantly expressed genes are selected to produce a probe set (n=200, based on the nearest centroid (NC), diagonal linear discriminant analysis (DLDA), or support vector machines (SVM)). Optimism-corrected AUCs are obtained for the 200-probe set classifier discovered with the 2 cohort-based strategy (360). AUCs are obtained for the full data set (365). Optimism-corrected AUCs are obtained for the 200-probe set classifier by Bootstrapping from 1000 samplings of the full data set with replacement (370). Optimism-corrected AUCs are obtained for nearest centroid (NC), diagonal linear discriminant analysis (DLDA), or support vector machines (SVM) using the original 200 SVM classifier (375).

In some instances, the cohort-based method comprises biomarker discovery and validation. Transplant recipients with known conditions (e.g. AR, ADNR, CAN, SCAR, TX) are randomly split into a discovery cohort and a validation cohort. One or more gene expression products may be measured for all the subjects in both cohorts. In some instances, at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1500, 2000, 2500 or more gene expression products are measured for all the subjects. In some instances, the gene expression products with different conditions (e.g. AR, ADNR, CAN, SCAR, TX) in the discovery cohort are compared and differentially expressed probe sets are discovered as biomarkers. For example, the discovery cohort in FIG. 3 yielded 2977 differentially expressed probe sets (Table 1). In some instances, the difference in gene expression level is at least 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45% or 50% or more. In some instances, the difference in gene expression level is at least 2, 3, 4, 5, 6, 7, 8, 9, 10 fold or more. In some instances, the accuracy is calculated using a trained algorithm. For example, the present invention may provide gene expression products corresponding to genes selected from Table 1a. The present invention may also provide gene expression products corresponding to genes selected from Table 1c. In some instances, the accuracy is calculated using a trained algorithm. For example, the present invention may provide gene expression products corresponding to genes selected from Table 1a, 1b, 1c, or 1d, in any combination. In some instances, the accuracy is calculated using a trained algorithm. For example, the present invention may provide gene expression products corresponding to genes selected from Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. In some instances, the identified probe sets may be used to train an algorithm for purposes of identification, diagnosis, classification, treatment or to otherwise characterize various conditions (e.g. AR, ADNR, CAN, SCAR, TX) of organ transplant.

The differentially expressed probe sets and/or algorithm may be subject to validation. In some instances, classification of the transplant condition may be made by applying the probe sets and/or algorithm generated from the discovery cohort to the gene expression products in the validation cohort. In some instances, the classification may be validated by the known condition of the subject. For example, in some instances, the subject is identified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX) with an accuracy of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, the subject is identified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX) with a sensitivity of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, the subject is identified with a particular condition (e.g. AR, ADNR, CAN, SCAR, TX) with a specificity of greater than 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99% or more. In some instances, biomarkers and/or algorithms may be used in identification, diagnosis, classification and/or prediction of the transplant condition of a subject. For example, biomarkers and/or algorithms may be used in classification of transplant conditions for an organ transplant patient, whose condition may be unknown.

Biomarkers that have been validated and/or algorithms may be used in identification, diagnosis, classification and/or prediction of transplant conditions of subjects. In some instances, gene expression products of the organ transplant subjects may be compared with one or more different sets of biomarkers. The gene expression products for each set of biomarkers may comprise one or more reference gene expression levels. The reference gene expression levels may correlate with a condition (e.g. AR, ADNR, CAN, SCAR, TX) of an organ transplant.

The expression level may be compared to gene expression data for two or more biomarkers in a sequential fashion. Alternatively, the expression level is compared to gene expression data for two or more biomarkers simultaneously. Comparison of expression levels to gene expression data for sets of biomarkers may comprise the application of a classifier. For example, analysis of the gene expression levels may involve sequential application of different classifiers described herein to the gene expression data. Such sequential analysis may involve applying a classifier obtained from gene expression analysis of cohorts of transplant recipients with a first status or outcome (e.g., transplant rejection), followed by applying a classifier obtained from analysis of a mixture of different samples, some of such samples obtained from healthy transplant recipients, transplant recipients experiencing transplant rejection, and/or transplant recipients experiencing organ dysfunction with no transplant rejection. Alternatively, sequential analysis involves applying at least two different classifiers obtained from gene expression analysis of transplant recipients, wherein at least one of the classifiers correlates to transplant dysfunction with no rejection.

Classifiers and Classifier Probe Sets

Disclosed herein is the use of a classification system comprises one or more classifiers. In some instances, the classifier is a 2-, 3-, 4-, 5-, 6-, 7-, 8-, 9-, or 10-way classifier. In some instances, the classifier is a 15-, 20-, 25-, 30-, 35-, 40-, 45-, 50-, 55-, 60-, 65-, 70-, 75-, 80-, 85-, 90-, 95-, or 100-way classifier. In some preferred embodiments, the classifier is a three-way classifier. In some embodiments, the classifier is a four-way classifier.

A two-way classifier may classify a sample from a subject into one of two classes. In some instances, a two-way classifier may classify a sample from an organ transplant recipient into one of two classes comprising acute rejection (AR) and normal transplant function (TX). In some instances, a two-way classifier may classify a sample from an organ transplant recipient into one of two classes comprising acute rejection (AR) and acute dysfunction with no rejection (ADNR). In some instances, a two-way classifier may classify a sample from an organ transplant recipient into one of two classes comprising normal transplant function (TX) and acute dysfunction with no rejection (ADNR). In some instances, a three-way classifier may classify a sample from a subject into one of three classes. A three-way classifier may classify a sample from an organ transplant recipient into one of three classes comprising acute rejection (AR), acute dysfunction with no rejection (ADNR) and normal transplant function (TX). In some instances, a three-way classifier may a sample from an organ transplant recipient into one of three classes wherein the classes can include a combination of any one of acute rejection (AR), acute dysfunction with no rejection (ADNR), normal transplant function (TX), chronic allograft nephropathy (CAN), interstitial fibrosis and/or tubular atrophy (IF/TA), or Subclinical Acute Rejection (SCAR). In some cases, the three-way classifier may classify a sample as AR/HCV-R/Tx. In some cases, the classifier is a four-way classifier. In some cases, the four-way classifier may classify a sample as AR, HCV-R, AR+HCV, or TX.

Classifiers and/or classifier probe sets may be used to either rule-in or rule-out a sample as healthy. For example, a classifier may be used to classify a sample as being from a healthy subject. Alternatively, a classifier may be used to classify a sample as being from an unhealthy subject. Alternatively, or additionally, classifiers may be used to either rule-in or rule-out a sample as transplant rejection. For example, a classifier may be used to classify a sample as being from a subject suffering from a transplant rejection. In another example, a classifier may be used to classify a sample as being from a subject that is not suffering from a transplant rejection. Classifiers may be used to either rule-in or rule-out a sample as transplant dysfunction with no rejection. For example, a classifier may be used to classify a sample as being from a subject suffering from transplant dysfunction with no rejection. In another example, a classifier may be used to classify a sample as not being from a subject suffering from transplant dysfunction with no rejection.

Classifiers used in sequential analysis may be used to either rule-in or rule-out a sample as healthy, transplant rejection, or transplant dysfunction with no rejection. For example, a classifier may be used to classify a sample as being from an unhealthy subject. Sequential analysis with a classifier may further be used to classify the sample as being from a subject suffering from a transplant rejection. Sequential analysis may end with the application of a “main” classifier to data from samples that have not been ruled out by the preceding classifiers. For example, classifiers may be used in sequential analysis of ten samples. The classifier may classify 6 out of the 10 samples as being from healthy subjects and 4 out of the 10 samples as being from unhealthy subjects. The 4 samples that were classified as being from unhealthy subjects may be further analyzed with the classifiers. Analysis of the 4 samples may determine that 3 of the 4 samples are from subjects suffering from a transplant rejection. Further analysis may be performed on the remaining sample that was not classified as being from a subject suffering from a transplant rejection. The classifier may be obtained from data analysis of gene expression levels in multiple types of samples. The classifier may be capable of designating a sample as healthy, transplant rejection or transplant dysfunction with no rejection.

Classifier probe sets, classification systems and/or classifiers disclosed herein may be used to either classify (e.g., rule-in or rule-out) a sample as healthy or unhealthy. Sample classification may comprise the use of one or more additional classifier probe sets, classification systems and/or classifiers to further analyze the unhealthy samples. Further analysis of the unhealthy samples may comprise use of the one or more additional classifier probe sets, classification systems and/or classifiers to either classify (e.g., rule-in or rule-out) the unhealthy sample as transplant rejection or transplant dysfunction with no rejection. Sample classification may end with the application of a classifier probe set, classification system and/or classifier to data from samples that have not been ruled out by the preceding classifier probe sets, classification systems and/or classifiers. The classifier probe set, classification system and/or classifier may be obtained from data analysis of gene expression levels in multiple types of samples. The classifier probe set, classification system and/or classifier may be capable of designating a sample as healthy, transplant rejection or transplant dysfunction which may include transplant dysfunction with no rejection. Alternatively, the classifier probe set, classification system and/or classifier is capable of designating an unhealthy sample as transplant rejection or transplant dysfunction with no rejection.

The differentially expressed genes may be genes that may be differentially expressed in a plurality of control samples. For example, the plurality of control samples may comprise two or more samples that may be differentially classified as acute rejection, acute dysfunction no rejection or normal transplant function. The plurality of control samples may comprise three or more samples that may be differentially classified. The samples may be differentially classified based on one or more clinical features. The one or more clinical features may comprise status or outcome of a transplanted organ. The one or more clinical features may comprise diagnosis of transplant rejection. The one or more clinical features may comprise diagnosis of transplant dysfunction. The one or more clinical features may comprise one or more symptoms of the subject from which the sample is obtained from. The one or more clinical features may comprise age and/or gender of the subject from which the sample is obtained from. The one or more clinical features may comprise response to one or more immunosuppressive regimens. The one or more clinical features may comprise a number of immunosuppressive regimens.

The classifier set may comprise one or more genes that may be differentially expressed in two or more control samples. The two or more control samples may be differentially classified. The two or more control samples may be differentially classified as acute rejection, acute dysfunction no rejection or normal transplant function. The classifier set may comprise one or more genes that may be differentially expressed in three or more control samples. The three or more control samples may be differentially classified.

The method of producing a classifier set may comprise comparing two or more gene expression profiles from two or more control samples. The two or more gene expression profiles from the two or more control samples may be normalized. The two or more gene expression profiles may be normalized by different tools including use of frozen robust multichip average (fRMA). In some instances, the two or more gene expression profiles are not normalized by quantile normalization.

The method of producing a classifier set may comprise applying an algorithm to two or more expression profiles from two or more control samples. The classifier set may comprise one or more genes selected by application of the algorithm to the two or more expression profiles. The method of producing the classifier set may further comprise generating a shrunken centroid parameter for the one or more genes in the classifier set.

The classifier set may be generated by statistical bootstrapping. Statistical bootstrapping may comprise creating multiple computational permutations and cross validations using a control sample set.

Disclosed herein is the use of a classifier probe set for determining an expression level of one or more genes in preparation of a kit for classifying a sample from a subject, wherein the classifier probe set is based on a classification system comprising three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.

Further disclosed herein is a classifier probe set for use in classifying a sample from a subject, wherein the classifier probe set is based on a classification system comprising three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.

Further disclosed herein is the use of a classification system comprising three or more classes in preparation of a probe set for classifying a sample from a subject. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. At least three of the three or more classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. Often, the classes are different classes.

Further disclosed herein are classification systems for classifying one or more samples from one or more subjects. The classification system may comprise three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.

Classifiers may comprise panels of biomarkers. Expression profiling based on panels of biomarkers may be used to characterize a sample as healthy, transplant rejection and/or transplant dysfunction with no rejection. Panels may be derived from analysis of gene expression levels of cohorts containing healthy transplant recipients, transplant recipients experiencing transplant rejection and/or transplant recipients experiencing transplant dysfunction with no rejection. Panels may be derived from analysis of gene expression levels of cohorts containing transplant recipients experiencing transplant dysfunction with no rejection. Exemplary panels of biomarkers can be derived from genes listed in Table 1a. Exemplary panels of biomarkers can also be derived from genes listed in Table 1c. Exemplary panels of biomarkers can be derived from genes listed in Table 1a, 1b, 1c, or 1d, in any combination. Exemplary panels of biomarkers can be derived from genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination.

Sample Cohorts

In some embodiments, the methods, kits and systems of the present invention seek to improve upon the accuracy of current methods of classifying samples obtained from transplant recipients. In some embodiments, the methods provide improved accuracy of identifying samples as normal function (e.g., healthy), transplant rejection or transplant dysfunction with no rejection. In some embodiments, the methods provide improved accuracy of identifying samples as normal function (e.g., healthy), AR or ADNR. Improved accuracy may be obtained by using algorithms trained with specific sample cohorts, high numbers of samples, samples from individuals located in diverse geographical regions, samples from individuals with diverse ethnic backgrounds, samples from individuals with different genders, and/or samples from individuals from different age groups.

The sample cohorts may be from female, male or a combination thereof. In some cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, or 80 or more different geographical locations. The geographical locations may comprise sites spread out across a nation, a continent, or the world. Geographical locations include, but are not limited to, test centers, medical facilities, medical offices, hospitals, post office addresses, zip codes, cities, counties, states, nations, and continents. In some embodiments, a classifier that is trained using sample cohorts from the United States may need to be retrained for use on sample cohorts from other geographical regions (e.g., Japan, China, Europe, etc.). In some cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20 or more different ethnic groups. In some embodiments, a classifier that is trained using sample cohorts from a specific ethnic group may need to be retrained for use on sample cohorts from other ethnic groups. In some cases, the sample cohorts are from at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or more different age groups. The age groups may be grouped into 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 or more years, or a combination thereof. Age groups may include, but are not limited to, under 10 years old, 10-15 years old, 15-20 years old, 20-25 years old, 25-30 years old, 30-35 years old, 35-40 years old, 40-45 years old, 45-50 years old, 50-55 years old, 55-60 years old, 60-65 years old, 65-70 years old, 70-75 years old, 75-80 years old, and over 80 years old. In some embodiments, a classifier that is trained using sample cohorts from a specific age group (e.g., 30-40 years old) may need to be retrained for use on sample cohorts from other age groups (e.g., 20-30 years old, etc.).

Methods of Classifying Samples

The samples may be classified simultaneously. The samples may be classified sequentially. The two or more samples may be classified at two or more time points. The samples may be obtained at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more time points. The samples may be obtained at 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more time points. The samples may be obtained at 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more time points. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more minutes apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more hours apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more days apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more weeks apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more months apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more years apart. The two or more time points may be at least about 6 hours apart. The two or more time points may be at least about 12 hours apart. The two or more time points may be at least about 24 hours apart. The two or more time points may be at least about 2 days apart. The two or more time points may be at least about 1 week apart. The two or more time points may be at least about 1 month apart. The two or more time points may be at least about 3 months apart. The two or more time points may be at least about 6 months apart. The three or more time points may be at the same interval. For example, the first and second time points may be 1 month apart and the second and third time points may be 1 month apart. The three or more time points may be at different intervals. For example, the first and second time points may be 1 month apart and the second and third time points may be 3 months apart.

Methods of simultaneous classifier-based analysis of one or more samples may comprise applying one or more algorithm to data from one or more samples to simultaneously produce one or more lists, wherein the lists comprise one or more samples classified as being from healthy subjects (e.g. subjects with a normal functioning transplant (TX)), unhealthy subjects, subjects suffering from transplant rejection, subjects suffering from transplant dysfunction, subjects suffering from acute rejection (AR), subjects suffering from acute dysfunction with no rejection (ADNR), subjects suffering from chronic allograft nephropathy (CAN), subjects suffering from interstitial fibrosis and/or tubular atrophy (IF/TA), and/or subjects suffering from subclinical acute rejection (SCAR).

Methods of sequential classifier-based analysis of one or more samples may comprise (a) applying a first algorithm to data from one or more samples to produce a first list; and (b) applying a second algorithm to data from the one or more samples that were excluded from the first list to produce a second list. The first list or the second list may comprise one or more samples classified as being from healthy subjects (e.g. subjects with a normal functioning transplant (TX)). The first list or the second list may comprise one or more samples classified as being from unhealthy subjects. The first list or the second list may comprise one or more samples classified as being from subjects suffering from transplant rejection. The first list or the second list may comprise one or more samples classified as being from subjects suffering from transplant dysfunction. The first list or the second list may comprise one or more samples classified as being from subjects suffering from acute rejection (AR). The first list or the second list may comprise one or more samples classified as being from subjects suffering from acute dysfunction with no rejection (ADNR). The first list or the second list may comprise one or more samples classified as being from subjects suffering from chronic allograft nephropathy (CAN). The first list or the second list may comprise one or more samples classified as being from subjects suffering from interstitial fibrosis and/or tubular atrophy (IF/TA). The first list or the second list may comprise one or more samples classified as being from subjects suffering from subclinical acute rejection (SCAR). For example, a sequential classifier-based analysis may comprise (a) applying a first algorithm to data from one or more samples to produce a first list, wherein the first list comprises one or more samples classified as being from healthy subjects; and (b) applying a second algorithm to data from the one or more samples that were excluded from the first list to produce a second list, wherein the second list comprises one or more samples classified as being from subjects suffering from transplant rejection.

The methods may undergo further iteration. One or more additional lists may be produced by applying one or more additional algorithms. The first algorithm, second algorithm, and/or one or more additional algorithms may be the same. The first algorithm, second algorithm, and/or one or more additional algorithms may be different. In some instances, the one or more additional lists may be produced by applying one or more additional algorithms to data from one or more samples from one or more previous lists. The one or more additional lists may comprise one or more samples classified as being from healthy subjects (e.g. subjects with a normal functioning transplant (TX)). The one or more additional lists may comprise one or more samples classified as being from unhealthy subjects. The one or more additional lists may comprise one or more samples classified as being from subjects suffering from transplant rejection. The one or more additional lists may comprise one or more samples classified as being from subjects suffering from transplant dysfunction. The one or more additional lists may comprise one or more samples classified as being from subjects suffering from acute rejection (AR). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from acute dysfunction with no rejection (ADNR). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from chronic allograft nephropathy (CAN). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from interstitial fibrosis and/or tubular atrophy (IF/TA). The one or more additional lists may comprise one or more samples classified as being from subjects suffering from subclinical acute rejection (SCAR).

This disclosure also provides one or more steps or analyses that may be used in addition to applying a classifier or algorithm to expression level data from a sample, such as a clinical sample. Such series of steps may include, but are not limited to, initial cytology or histopathology study of the sample, followed by analysis of gene (or other biomarker) expression levels in the sample. In some embodiments, the one or more steps or analyses (e.g., cytology or histopathology study) occur prior to the step of applying any of the classifier probe sets or classification systems described herein. The one or more steps or analyses (e.g., cytology or histopathology study) may occur concurrently with the step of applying any of the classifier probe sets or classification systems described herein. Alternatively, the one or more steps or analyses (e.g., cytology or histopathology study) may occur after the step of applying any of the classifier probe sets or classification systems described herein.

Sequential classifier-based analysis of the samples may occur in various orders. For example, sequential classifier-based analysis of one or more samples may comprise classifying samples as healthy or unhealthy, followed by classification of unhealthy samples as transplant rejection or non-transplant rejection, followed by classification of non-transplant rejection samples as transplant dysfunction or transplant dysfunction with no rejection. In another example, sequential classifier-based analysis of one or more samples may comprise classifying samples as transplant dysfunction or no transplant dysfunction, followed by classification of transplant dysfunction samples as transplant rejection or no transplant rejection. The no transplant dysfunction samples may further be classified as healthy. In another example, sequential classifier-based analysis comprises classifying samples as transplant rejection or no transplant rejection, followed by classification of the no transplant rejection samples as healthy or unhealthy. The unhealthy samples may be further classified as transplant dysfunction or no transplant dysfunction. Sequential classifier-based analysis may comprise classifying samples as transplant rejection or no transplant rejection, followed by classification of the no transplant rejection samples as transplant dysfunction or no transplant dysfunction. The no transplant dysfunction samples may further be classified as healthy or unhealthy. The unhealthy samples may further be classified as transplant rejection or no transplant rejection. The unhealthy samples may further be classified as chronic allograft nephropathy/interstitial fibrosis and tubular atrophy (CAN/IFTA) or no CAN/IFTA. The unhealthy samples may further be classified as transplant dysfunction or no transplant dysfunction. The transplant dysfunction samples may be further classified as transplant dysfunction with no rejection or transplant dysfunction with rejection. The transplant dysfunction samples may be further classified as transplant rejection or no transplant rejection. The transplant rejection samples may further be classified as chronic allograft nephropathy/interstitial fibrosis and tubular atrophy (CAN/IFTA) or no CAN/IFTA.

Algorithms

The methods, kits, and systems disclosed herein may comprise one or more algorithms or uses thereof. The one or more algorithms may be used to classify one or more samples from one or more subjects. The one or more algorithms may be applied to data from one or more samples. The data may comprise gene expression data. The data may comprise sequencing data. The data may comprise array hybridization data.

The methods disclosed herein may comprise assigning a classification to one or more samples from one or more subjects. Assigning the classification to the sample may comprise applying an algorithm to the expression level. In some cases, the gene expression levels are inputted to a trained algorithm for classifying the sample as one of the conditions comprising AR, ADNR, or TX.

The algorithm may provide a record of its output including a classification of a sample and/or a confidence level. In some instances, the output of the algorithm can be the possibility of the subject of having a condition, such as AR, ADNR, or TX. In some instances, the output of the algorithm can be the risk of the subject of having a condition, such as AR, ADNR, or TX. In some instances, the output of the algorithm can be the possibility of the subject of developing into a condition in the future, such as AR, ADNR, or TX.

The algorithm may be a trained algorithm. The algorithm may comprise a linear classifier. The linear classifier may comprise one or more linear discriminant analysis, Fisher's linear discriminant, Naïve Bayes classifier, Logistic regression, Perceptron, Support vector machine, or a combination thereof. The linear classifier may be a Support vector machine (SVM) algorithm.

The algorithm may comprise one or more linear discriminant analysis (LDA), Basic perceptron, Elastic Net, logistic regression, (Kernel) Support Vector Machines (SVM), Diagonal Linear Discriminant Analysis (DLDA), Golub Classifier, Parzen-based, (kernel) Fisher Discriminant Classifier, k-nearest neighbor, Iterative RELIEF, Classification Tree, Maximum Likelihood Classifier, Random Forest, Nearest Centroid, Prediction Analysis of Microarrays (PAM), k-medians clustering, Fuzzy C-Means Clustering, Gaussian mixture models, or a combination thereof. The algorithm may comprise a Diagonal Linear Discriminant Analysis (DLDA) algorithm. The algorithm may comprise a Nearest Centroid algorithm. The algorithm may comprise a Random Forest algorithm. The algorithm may comprise a Prediction Analysis of Microarrays (PAM) algorithm.

The methods disclosed herein may comprise use of one or more classifier equations. Classifying the sample may comprise a classifier equation. The classifier equation may be Equation 1:

$δ_{k} (x^{*}) = \sum_{i = 1}^{p} \frac{{(x_{i}^{*} - {\overline{x}}_{ik}^{'})}^{2}}{{(s_{i} + s_{0})}^{2}} - 2 \log π_{k},$

wherein:

k is a number of possible classes;

δ_kmay be the discriminant score for class k;

x_i* represents the expression level of gene i;

x* represents a vector of expression levels for all p genes to be used for classification drawn from the sample to be classified;

x
_k′ may be a shrunken centroid calculated from a training data and a shrinkage factor;

x
_ik′: may be a component of x_k′ corresponding to gene i;

s_iis a pooled within-class standard deviation for gene i in the training data;

s₀is a specified positive constant; and

π_krepresents a prior probability of a sample belonging to class k.

Assigning the classification may comprise calculating a class probability. Calculating the class probability {circumflex over (p)}_k(x*) may be calculated by Equation 2:

${\hat{p}}_{k} (x^{*}) = \frac{e^{- \frac{1}{2} δ_{k} (x^{*})}}{\sum_{l = 1}^{K} e^{- \frac{1}{2} δ_{l} (x^{*})}} .$

Assigning the classification may comprise a classification rule. The classification rule C(x*) may be expressed by Equation 3:

$C (x^{*}) = \underset{k \in {1, K}}{\arg \max} {\hat{p}}_{k} (x^{*}) .$

Classification of Samples

The classifiers disclosed herein may be used to classify one or more samples. The classifiers disclosed herein may be used to classify 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more samples. The classifiers disclosed herein may be used to classify 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more samples. The classifiers disclosed herein may be used to classify 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more samples. The classifiers disclosed herein may be used to classify 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more samples. The classifiers disclosed herein may be used to classify 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000, 110000, 120000, 130000, 140000, 150000, 160000, 170000, 180000, 190000, 200000 or more samples. The classifiers disclosed herein may be used to classify at least about 5 samples. The classifiers disclosed herein may be used to classify at least about 10 samples. The classifiers disclosed herein may be used to classify at least about 20 samples. The classifiers disclosed herein may be used to classify at least about 30 samples. The classifiers disclosed herein may be used to classify at least about 50 samples. The classifiers disclosed herein may be used to classify at least about 100 samples. The classifiers disclosed herein may be used to classify at least about 200 samples.

Two or more samples may be from the same subject. The samples may be from two or more different subjects. The samples may be from 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more subjects. The samples may be from 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more subjects. The samples may be from 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more subjects. The samples may be from 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more subjects. The samples may be from 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 11000, 12000, 13000, 14000, 15000, 16000, 17000, 18000, 19000, 20000 or more subjects. The samples may be from 2 or more subjects. The samples may be from 5 or more subjects. The samples may be from 10 or more subjects. The samples may be from 20 or more subjects. The samples may be from 50 or more subjects. The samples may be from 70 or more subjects. The samples may be from 80 or more subjects. The samples may be from 100 or more subjects. The samples may be from 200 or more subjects. The samples may be from 300 or more subjects. The samples may be from 500 or more subjects.

The two or more samples may be obtained at the same time point. The two or more samples may be obtained at two or more different time points. The samples may be obtained at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more time points. The samples may be obtained at 10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200 or more time points. The samples may be obtained at 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, 1900, 2000 or more time points. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more minutes apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more hours apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more days apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more weeks apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more months apart. The two or more time points may be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20 or more years apart. The two or more time points may be at least about 6 hours apart. The two or more time points may be at least about 12 hours apart. The two or more time points may be at least about 24 hours apart. The two or more time points may be at least about 2 days apart. The two or more time points may be at least about 1 week apart. The two or more time points may be at least about 1 month apart. The two or more time points may be at least about 3 months apart. The two or more time points may be at least about 6 months apart. The three or more time points may be at the same interval. For example, the first and second time points may be 1 month apart and the second and third time points may be 1 month apart. The three or more time points may be at different intervals. For example, the first and second time points may be 1 month apart and the second and third time points may be 3 months apart.

Further disclosed herein are methods of classifying one or more samples from one or more subjects. The method of classifying one or more samples from one or more subjects may comprise (a) obtaining an expression level of one or more gene expression products of a sample from a subject; and (b) identifying the sample as normal transplant function if the gene expression level indicates a lack of transplant rejection and/or transplant dysfunction. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise identifying the sample as transplant dysfunction if the gene expression level indicates transplant rejection and/or transplant dysfunction. The method may further comprise identifying the sample as transplant dysfunction with no rejection if the gene expression level indicates transplant dysfunction and a lack transplant rejection. The method may further comprise identifying the sample as transplant rejection if the gene expression level indicates transplant rejection and/or transplant dysfunction. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+ PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Identifying the sample may comprise use of one or more algorithms. Identifying the sample may comprise use of one or more classification systems. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.

The method of classifying a sample may comprise (a) obtaining an expression level of one or more gene expression products of a sample from a subject; and (b) identifying the sample as transplant rejection if the gene expression level indicative of transplant rejection and/or transplant dysfunction. The one or more subjects may be transplant recipients. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise identifying the sample as transplant dysfunction if the gene expression level indicates transplant rejection and/or transplant dysfunction. The method may further comprise identifying the sample as transplant dysfunction with no rejection if the gene expression level indicates transplant dysfunction and a lack of transplant rejection. The method may further comprise identifying the sample as normal function if the gene expression level indicates a lacks of transplant rejection and transplant dysfunction. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+ PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Identifying the sample may comprise use of one or more algorithms. Identifying the sample may comprise use of one or more classification systems. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.

The method of classifying a sample may comprise (a) obtaining an expression level of one or more gene expression products of a sample from a subject; and (b) identifying the sample as transplant dysfunction with no rejection wherein the gene expression level indicative of transplant dysfunction and the gene expression level indicates a lack of transplant rejection. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise identifying the sample as normal transplant function if the gene expression level indicates a lack of transplant dysfunction. The method may further comprise identifying the sample as transplant rejection if the gene expression level indicates transplant rejection and/or transplant dysfunction. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+ PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Identifying the sample may comprise use of one or more algorithms. Identifying the sample may comprise use of one or more classification systems. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.

The method of classifying a sample may comprise (a) determining an expression level of one or more gene expression products in a sample from a subject; and (b) assigning a classification to the sample based on the level of expression of the one or more gene products, wherein the classification comprises transplant dysfunction with no rejection. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+ PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. The classification system may further comprise normal transplant function. The classification system may further comprise transplant rejection. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.

The method of classifying a sample may comprise (a) determining an expression level of one or more gene expression products in a sample from a subject; and (b) assigning a classification to the sample based on the level of expression of the one or more gene products, wherein the classification comprises transplant rejection, transplant dysfunction with no rejection and normal transplant function. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the gene expression products are associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The peg array may be a HT HG-U133+ PM Array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.

The method of classifying a sample may comprise (a) determining a level of expression of a plurality of genes in a sample from a subject; and (b) classifying the sample by applying an algorithm to the expression level data from step (a), wherein the algorithm is not validated by a cohort-based analysis of an entire cohort. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. Classifying the sample may comprise use of a classification system. The classification system may further comprise normal transplant function. The classification system may further comprise transplant rejection. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject. The algorithm may be validated by analysis of less than or equal to about 97%, 95%, 93%, 90%, 87%, 85%, 83%, 80%, 77%, 75%, 73%, 70%, 67%, 65%, 53%, 60%, 57%, 55%, 53%, 50%, 47%, 45%, 43%, 40%, 37%, 35%, 33%, 30%, 27%, 25%, 23%, 20%, 17%, 15%, 13%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, or 3% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 70% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 60% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 50% of the entire cohort. The algorithm may be validated by analysis of less than or equal to about 40% of the entire cohort.

The method of classifying a sample may comprise (a) determining a level of expression of a plurality of genes in a sample from a subject; and (b) classifying the sample by applying an algorithm to the expression level data from step (a), wherein the algorithm is validated by a combined analysis of expression level data from a plurality of samples, wherein the plurality of samples comprises at least one sample with an unknown phenotype and at least one sample with a known phenotype. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1c. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, or 1d, in any combination. In some embodiments, the plurality of genes is associated with one or more biomarkers selected from gene expression products corresponding to genes listed in Table 1a, 1b, 1c, 1d, 8, 9, 10b, 12b, 14b, 16b, 17b, or 18b, in any combination. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Classifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. Classifying the sample may comprise use of a classification system. The classification system may further comprise normal transplant function. The classification system may further comprise transplant rejection. The classification system may further comprise CAN. The classification system may further comprise IF/TA. The classification system may comprise a three-way classification. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, transplant rejection, or a combination thereof. The three-way classification may comprise normal transplant function, transplant dysfunction with no rejection, and transplant rejection. The method may further comprise generating one or more reports based on the identification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 12%, 15%, 17%, 20%, 23%, 25%, 27%, 30% or more of the samples from the plurality of samples may have an unknown phenotype. At least about 35%, 40%, 45%, 50%, 55%, 57%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97% or more of the samples from the plurality of samples may have an unknown phenotype. At least about 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 12%, 15%, 17%, 20%, 23%, 25%, 27%, 30% or more of the samples from the plurality of samples may have a known phenotype. At least about 35%, 40%, 45%, 50%, 55%, 57%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 97% or more of the samples from the plurality of samples may have a known phenotype.

The method of classifying one or more samples from one or more subjects may comprise (a) determining an expression level of one or more gene expression products in a sample from a subject; and (b) assigning a classification to the sample based on the level of expression of the one or more gene products, wherein the classification comprises transplant rejection, transplant dysfunction with no rejection and normal transplant function. The subject may be a transplant recipient. The subject may be a transplant donor. The subject may be a healthy subject. The subject may be an unhealthy subject. The method may comprise determining an expression level of one or more gene expression products in one or more samples from one or more subjects. The one or more subjects may be transplant recipients, transplant donors, or combination thereof. The one or more subjects may be healthy subjects, unhealthy subjects, or a combination thereof. The method may further comprise classifying the sample as transplant dysfunction. The method may further comprise classifying the sample as transplant dysfunction with no rejection. The method may further comprise classifying the sample as normal function. The method may further comprise classifying the sample as transplant rejection. The expression level may be obtained by sequencing. The expression level may be obtained by RNA-sequencing. The expression level may be obtained by array. The array may be a microarray. The microarray may be a peg array. The peg array may be a Gene 1.1^STpeg array. The peg array may be a Hu133 Plus 2.0PM peg array. The sample may be a blood sample. The sample may comprise one or more peripheral blood lymphocytes. The blood sample may be a peripheral blood sample. The sample may be a serum sample. The sample may be a plasma sample. The expression level may be based on detecting and/or measuring one or more RNA. Identifying the sample may comprise use of one or more classifier probe sets. Classifying the sample may comprise use of one or more algorithms. The classification may further comprise CAN. The classification may further comprise IF/TA. The method may further comprise generating one or more reports based on the classification of the sample. The method may further comprise transmitting one or more reports comprising information pertaining to the identification of the sample to the subject or a medical representative of the subject.

Classifying the sample may be based on the expression level of 10, 20, 30, 40, 50, 60, 70, 80, 90, 100 or more gene products. Classifying the sample may be based on the expression level of 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000 or more gene products. Classifying the sample may be based on the expression level of 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000 or more gene products. Classifying the sample may be based on the expression level of 10000, 20000, 30000, 40000, 50000, 60000, 70000, 80000, 90000, 100000 or more gene products. Classifying the sample may be based on the expression level of 25 or more gene products. Classifying the sample may be based on the expression level of 50 or more gene products. Classifying the sample may be based on the expression level of 100 or more gene products. Classifying the sample may be based on the expression level of 200 or more gene products. Classifying the sample may be based on the expression level of 300 or more gene products.

Classifying the sample may comprise statistical bootstrapping.

Clinical Applications

The methods, compositions, systems and kits provided herein can be used to detect, diagnose, predict or monitor a condition of a transplant recipient. In some instances, the methods, compositions, systems and kits described herein provide information to a medical practitioner that can be useful in making a therapeutic decision. Therapeutic decisions may include decisions to: continue with a particular therapy, modify a particular therapy, alter the dosage of a particular therapy, stop or terminate a particular therapy, altering the frequency of a therapy, introduce a new therapy, introduce a new therapy to be used in combination with a current therapy, or any combination of the above. In some cases, the methods provided herein can be applied in an experimental setting, e.g., clinical trial. In some instances, the methods provided herein can be used to monitor a transplant recipient who is being treated with an experimental agent such as an immunosuppressive drug or compound. In some instances, the methods provided herein can be useful to determine whether a subject can be administered an experimental agent (e.g., an agonist, antagonist, peptidomimetic, protein, peptide, nucleic acid, small molecule, or other drug candidate) to reduce the risk of rejection. Thus, the methods described herein can be useful in determining if a subject can be effectively treated with an experimental agent and for monitoring the subject for risk of rejection or continued rejection of the transplant.

Additionally or alternatively, the physician can change the treatment regime being administered to the patient. A change in treatment regime can include administering an additional or different drug, or administering a higher dosage or frequency of a drug already being administered to the patient. Many different drugs are available for treating rejection, such as immunosuppressive drugs used to treat transplant rejection calcineurin inhibitors (e.g., cyclosporine, tacrolimus), mTOR inhibitors (e.g., sirolimus and everolimus), anti-proliferatives (e.g., azathioprine, mycophenolic acid), corticosteroids (e.g., prednisolone and hydrocortisone) and antibodies (e.g., basiliximab, daclizumab, Orthoclone, anti-thymocyte globulin and anti-lymphocyte globulin). Conversely, if the value or other designation of aggregate expression levels of a patient indicates the patient does not have or is at reduced risk of transplant rejection, the physician need not order further diagnostic procedures, particularly not invasive ones such as biopsy. Further, the physician can continue an existing treatment regime, or even decrease the dose or frequency of an administered drug.

In some cases, a clinical trial can be performed on a drug in similar fashion to the monitoring of an individual patient described above, except that drug is administered in parallel to a population of transplant patients, usually in comparison with a control population administered a placebo.

Detecting/Diagnosing a Condition of a Transplant Recipient

The methods, compositions, systems and kits provided herein are particularly useful for detecting or diagnosing a condition of a transplant recipient such as a condition the transplant recipient has at the time of testing. Exemplary conditions that can be detected or diagnosed with the present methods include organ transplant rejection, acute rejection (AR), chronic rejection, Acute Dysfunction with No Rejection (ADNR), normal transplant function (TX) and/or Sub-Clinical Acute Rejection (SCAR). The methods provided herein are particularly useful for transplant recipients who have received a kidney transplant. Exemplary conditions that can be detected or diagnosed in such kidney transplant recipients include: AR, chronic allograft nephropathy (CAN), ADNR, SCAR, IF/TA, and TX.

The diagnosis or detection of condition of a transplant recipient may be particularly useful in limiting the number of invasive diagnostic interventions that are administered to the patient. For example, the methods provided herein may limit or eliminate the need for a transplant recipient (e.g., kidney transplant recipient) to receive a biopsy (e.g., kidney biopsies) or to receive multiple biopsies. In some instances, the methods provided herein may also help interpreting a biopsy result, especially when the biopsy result is inconclusive.

In a further embodiment, the methods provided herein can be used alone or in combination with other standard diagnosis methods currently used to detect or diagnose a condition of a transplant recipient, such as but not limited to results of biopsy analysis for kidney allograft rejection, results of histopathology of the biopsy sample, serum creatinine level, creatinine clearance, ultrasound, radiological imaging results for the kidney, urinalysis results, elevated levels of inflammatory molecules such as neopterin, and lymphokines, elevated plasma interleukin (IL)-1 in azathioprine-treated patients, elevated IL-2 in cyclosporine-treated patients, elevated IL-6 in serum and urine, intrarenal expression of cytotoxic molecules (granzyme B and perforin) and immunoregulatory cytokines (IL-2, -4, -10, interferon gamma and transforming growth factor-b1).

The methods provided herein are useful for distinguishing between two or more conditions or disorders (e.g., AR vs ADNR, SCAR vs ADNR, etc.). In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR or TX. In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR, SCAR and/or TX, or any subset or combination thereof. In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR, SCAR, TX, HCV, or any subset or combination thereof. As previously described, elevated serum creatinine levels from baseline levels in kidney transplant recipients may be indicative of AR or ADNR. In preferred embodiments, the methods provided herein are used to distinguish AR from ADNR in a kidney transplant recipient. In some preferred embodiments, the methods provided herein are used to distinguish AR from ADNR in a liver transplant recipient. In some instances, the methods are used to determine whether a transplant recipient has AR, ADNR, SCAR, TX, acute transplant dysfunction, transplant dysfunction, transplant dysfunction with no rejection, or any subset or combination thereof. In some instances, the methods provided herein are used to distinguish AR from HCV from HCV+AR in a liver transplant recipient. In some instances, the methods provided herein are used to distinguish AR from HCV-R from HCV-R+AR in a liver transplant recipient. In some instances, the methods provided herein are used to distinguish HCV-R from HCV-R+AR in a liver transplant recipient. In some instances, the methods provided herein are used to distinguish AR from ADNR from CAN a kidney transplant recipient.

In some instances, the methods are used to distinguish between AR and ADNR in a kidney transplant recipient. In some instances, the methods are used to distinguish between AR and SCAR in a kidney transplant recipient. In some instances, the methods are used to distinguish between AR, TX, and SCAR in a kidney transplant recipient. In some instances, the methods are used to determine whether a kidney transplant recipient has AR, ADNR or TX. In some instances, the methods are used to determine whether a kidney transplant recipient has AR, ADNR, SCAR, CAN or TX, or any combination thereof. In some instances, the methods are used to distinguish between AR, ADNR, and CAN in a kidney transplant recipient.

In some instances, the methods provided herein are used to detect or diagnose AR in a transplant recipient (e.g., kidney transplant recipient) in the early stages of AR, in the middle stages of AR, or the end stages of AR. In some instances, the methods provided herein are used to detect or diagnose ADNR in a transplant recipient (e.g., kidney transplant recipient) in the early stages of ADNR, in the middle stages of ADNR, or the end stages of ADNR. In some instances, the methods are used to diagnose or detect AR, ADNR, IFTA, CAN, TX, SCAR, or other disorders in a transplant recipient with an accuracy, error rate, sensitivity, positive predictive value, or negative predictive value provided herein.

Predicting a Condition of a Transplant Recipient

In some embodiments, the methods provided herein can predict AR, CAN, ADNR, and/or SCAR prior to actual onset of the conditions. In some instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SCAR or other disorders in a transplant recipient at least 1 day, 5 days, 10 days, 30 days, 50 days or 100 days prior to onset. In other instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SCAR or other disorders in a transplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days prior to onset. In other instances, the methods provided herein can predict AR, IFTA, CAN, ADNR, SCAR or other disorders in a transplant recipient at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 months prior to onset.

Monitoring a Condition of a Transplant Recipient

Provided herein are methods, systems, kits and compositions for monitoring a condition of a transplant recipient. Often, the monitoring is conducted by serial testing, such as serial non-invasive tests, serial minimally-invasive tests (e.g., blood draws), serial invasive tests (biopsies), or some combination thereof. Preferably, the monitoring is conducted by administering serial non-invasive tests or serial minimally-invasive tests (e.g., blood draws).

In some instances, the transplant recipient is monitored as needed using the methods described herein. Alternatively the transplant recipient may be monitored hourly, daily, weekly, monthly, yearly or at any pre-specified intervals. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23 or 24 hours. In some instances the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30 or 31 days. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 months. In some instances, the transplant recipient is monitored at least once every 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 years or longer, for the lifetime of the patient and the graft.

In some instances, gene expression levels in the patients can be measured, for example, within, one month, three months, six months, one year, two years, five years or ten years after a transplant. In some methods, gene expression levels are determined at regular intervals, e.g., every 3 months, 6 months or every year post-transplant, either indefinitely, or until evidence of a condition is observed, in which case the frequency of monitoring is sometimes increased. In some methods, baseline values of expression levels are determined in a subject before a transplant in combination with determining expression levels at one or more time points thereafter.

The results of diagnosing, predicting, or monitoring a condition of a transplant recipient may be useful for informing a therapeutic decision such as determining or monitoring a therapeutic regimen. In some instances, determining a therapeutic regimen may comprise administering a therapeutic drug. In some instances, determining a therapeutic regimen comprises modifying, continuing, initiating or stopping a therapeutic regimen. In some instances, determining a therapeutic regimen comprises treating the disease or condition. In some instances, the therapy is an immunosuppressive therapy. In some instances, the therapy is an antimicrobial therapy. In other instances, diagnosing, predicting, or monitoring a disease or condition comprises determining the efficacy of a therapeutic regimen or determining drug resistance to the therapeutic regimen.

Modifying the therapeutic regimen may comprise terminating a therapy. Modifying the therapeutic regimen may comprise altering a dosage of a therapy. Modifying the therapeutic regimen may comprise altering a frequency of a therapy. Modifying the therapeutic regimen may comprise administering a different therapy. In some instances, the results of diagnosing, predicting, or monitoring a condition of a transplant recipient may be useful for informing a therapeutic decision such as removal of the transplant. In some instances, the removal of the transplant can be an immediate removal. In other instances, the therapeutic decision can be a retransplant. Other examples of therapeutic regimen can include a blood transfusion in instances where the transplant recipient is refractory to immunosuppressive or antibody therapy.

Examples of therapeutic regimen can include administering compounds or agents that are e.g., compounds or agents having immunosuppressive properties (e.g., a calcineurin inhibitor, cyclosporine A or FK 506); a mTOR inhibitor (e.g., rapamycin, 40-O-(2-hydroxyethyl)-rapamycin, CCI779, ABT578, AP23573, biolimus-7 or biolimus-9); an ascomycin having immuno-suppressive properties (e.g., ABT-281, ASM981, etc.); corticosteroids; cyclophosphamide; azathioprene; methotrexate; leflunomide; mizoribine; mycophenolic acid or salt; mycophenolate mofetil; 15-deoxyspergualine or an immunosuppressive homologue, analogue or derivative thereof; a PKC inhibitor (e.g., as disclosed in WO 02/38561 or WO 03/82859); a JAK3 kinase inhibitor (e.g., N-benzyl-3,4-dihydroxy-benzylidene-cyanoacetamide a-cyano-(3,4-dihydroxy)-]N-benzylcinnamamide (Tyrphostin AG 490), prodigiosin 25-C(PNU156804), [4-(4′-hydroxyphenyl)-amino-6,7-dimethoxyquinazoline] (WHI-P131), [4-(3′-bromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline] (WHI-P154), [4-(3′,5′-dibromo-4′-hydroxylphenyl)-amino-6,7-dimethoxyquinazoline] WHI-P97, KRX-211, 3-{(3R,4R)-4-methyl-3-[methyl-(7H-pyrrolo[2,3-d]pyrimidin-4-yl)-amino]-piperidin-1-yl}-3-oxo-propionitrile, in free form or in a pharmaceutically acceptable salt form, e.g., mono-citrate (also called CP-690,550), or a compound as disclosed in WO 04/052359 or WO 05/066156); a SIP receptor agonist or modulator (e.g., FTY720 optionally phosphorylated or an analog thereof, e.g., 2-amino-2-[4-(3-benzyloxyphenylthio)-2-chlorophenyl]ethyl-1,3-propanediol optionally phosphorylated or 1-{4-[1-(4-cyclohexyl-3-trifluoromethyl-benzyloxyimino)-ethyl]-2-ethyl-benzyl}-azetidine-3-carboxylic acid or its pharmaceutically acceptable salts); immunosuppressive monoclonal antibodies (e.g., monoclonal antibodies to leukocyte receptors, e.g., MHC, CD2, CD3, CD4, CD7, CD8, CD25, CD28, CD40, CD45, CD52, CD58, CD80, CD86 or their ligands); other immunomodulatory compounds (e.g., a recombinant binding molecule having at least a portion of the extracellular domain of CTLA4 or a mutant thereof, e.g., an at least extracellular portion of CTLA4 or a mutant thereof joined to a non-CTLA4 protein sequence, e.g., CTLA4Ig (for ex. designated ATCC 68629) or a mutant thereof, e.g., LEA29Y); adhesion molecule inhibitors (e.g., LFA-1 antagonists, ICAM-1 or -3 antagonists, VCAM-4 antagonists or VLA-4 antagonists). These compounds or agents may also be used alone or in combination. Immunosuppressive protocols can differ in different clinical settings. In some instances, in AR, the first-line treatment is pulse methylprednisolone, 500 to 1000 mg, given intravenously daily for 3 to 5 days. In some instances, if this treatment fails, than OKT3 or polyclonal anti-T cell antibodies will be considered. In other instances, if the transplant recipient is still experiencing AR, antithymocyte globulin (ATG) may be used.

Kidney Transplants

The methods, compositions, systems and kits provided herein are particularly useful for detecting or diagnosing a condition of a kidney transplant. Kidney transplantation may be needed when a subject is suffering from kidney failure, wherein the kidney failure may be caused by hypertension, diabetes melitus, kidney stone, inherited kidney disease, inflammatory disease of the nephrons and glomeruli, side effects of drug therapy for other diseases, etc. Kidney transplantation may also be needed by a subject suffering from dysfunction or rejection of a transplanted kidney.

Kidney function may be assessed by one or more clinical and/or laboratory tests such as complete blood count (CBC), serum electrolytes tests (including sodium, potassium, chloride, bicarbonate, calcium, and phosphorus), blood urea test, blood nitrogen test, serum creatinine test, urine electrolytes tests, urine creatinine test, urine protein test, urine fractional excretion of sodium (FENA) test, glomerular filtration rate (GFR) test. Kidney function may also be assessed by a renal biopsy. Kidney function may also be assessed by one or more gene expression tests. The methods, compositions, systems and kits provided herein may be used in combination with one or more of the kidney tests mentioned herein. The methods, compositions, systems and kits provided herein may be used before or after a kidney transplant. In some instances, the method may be used in combination with complete blood count. In some instances, the method may be used in combination with serum electrolytes (including sodium, potassium, chloride, bicarbonate, calcium, and phosphorus). In some instances, the method may be used in combination with blood urea test. In some instances, the method may be used in combination with blood nitrogen test. In some instances, the method may be used in combination with a serum creatinine test. In some instances, the method may be used in combination with urine electrolytes tests. In some instances, the method may be used in combination with urine creatinine test. In some instances, the method may be used in combination with urine protein test. In some instances, the method may be used in combination with urine fractional excretion of sodium (FENA) test. In some instances, the method may be used in combination with glomerular filtration rate (GFR) test. In some instances, the method may be used in combination with a renal biopsy. In some instances, the method may be used in combination with one or more other gene expression tests. In some instances, the method may be used when the result of the serum creatinine test indicates kidney dysfunction and/or transplant rejection. In some instances, the method may be used when the result of the glomerular filtration rate (GFR) test indicates kidney dysfunction and/or transplant rejection. In some instances, the method may be used when the result of the renal biopsy indicates kidney dysfunction and/or transplant rejection. In some instances, the method may be used when the result of one or more other gene expression tests indicates kidney dysfunction and/or transplant rejection.

Sensitivity, Specificity, and Accuracy

The methods, kits, and systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a specificity of at least about 50%. The specificity of the method may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The specificity of the method may be at least about 63%. The specificity of the method may be at least about 68%. The specificity of the method may be at least about 72%. The specificity of the method may be at least about 77%. The specificity of the method may be at least about 80%. The specificity of the method may be at least about 83%. The specificity of the method may be at least about 87%. The specificity of the method may be at least about 90%. The specificity of the method may be at least about 92%.

In some embodiments, the present invention provides a method of identifying, classifying or characterizing a sample that gives a sensitivity of at least about 50% using the methods disclosed herein. The sensitivity of the method may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The sensitivity of the method may be at least about 63%. The sensitivity of the method may be at least about 68%. The sensitivity of the method may be at least about 72%. The sensitivity of the method may be at least about 77%. The sensitivity of the method may be at least about 80%. The sensitivity of the method may be at least about 83%. The sensitivity of the method may be at least about 87%. The sensitivity of the method may be at least about 90%. The sensitivity of the method may be at least about 92%.

The methods, kits and systems disclosed herein may improve upon the accuracy of current methods of monitoring or predicting a status or outcome of an organ transplant. The methods, kits, and systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having an accuracy of at least about 50%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 50%, 53%, 55%, 57%, 60%, 63%, 65%, 67%, 70%, 72%, 75%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 63%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 68%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 72%. The accuracy of the method may be at least about 77%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 80%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 83%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 87%. The accuracy of the methods, kits, and systems disclosed herein may be at least about 90%. The accuracy of the method may be at least about 92%.

The methods, kits, and/or systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a specificity of at least about 50% and/or a sensitivity of at least about 50%. The specificity may be at least about 50% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 50%. The specificity may be at least about 60% and/or the sensitivity may be at least about 70%. The specificity may be at least about 70% and/or the sensitivity may be at least about 60%. The specificity may be at least about 75% and/or the sensitivity may be at least about 75%.

The methods, kits, and systems for use in identifying, classifying or characterizing a sample may be characterized by having a negative predictive value (NPV) greater than or equal to 90%. The NPV may be at least about 90%, 91%, 92%, 93%, 94%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 100%. The NPV may be greater than or equal to 95%. The NPV may be greater than or equal to 96%. The NPV may be greater than or equal to 97%. The NPV may be greater than or equal to 98%.

The methods, kits, and/or systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a positive predictive value (PPV) of at least about 30%. The PPV may be at least about 32%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 95.2%, 95.5%, 95.7%, 96%, 96.2%, 96.5%, 96.7%, 97%, 97.2%, 97.5%, 97.7%, 98%, 98.2%, 98.5%, 98.7%, 99%, 99.2%, 99.5%, 99.7%, or 100%. The PPV may be greater than or equal to 95%. The PPV may be greater than or equal to 96%. The PPV may be greater than or equal to 97%. The PPV may be greater than or equal to 98%.

The methods, kits, and/or systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having a NPV may be at least about 90% and/or a PPV may be at least about 30%. The NPV may be at least about 90% and/or the PPV may be at least about 50%. The NPV may be at least about 90% and/or the PPV may be at least about 70%. The NPV may be at least about 95% and/or the PPV may be at least about 30%. The NPV may be at least about 95% and/or the PPV may be at least about 50%. The NPV may be at least about 95% and/or the PPV may be at least about 70%.

The methods, kits, and systems disclosed herein for use in identifying, classifying or characterizing a sample may be characterized by having an error rate of less than about 30%, 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9.5%, 9%, 8.5%, 8%, 7.5%, 7%, 6.5%, 6%, 5.5%, 5%, 4.5%, 4%, 3.5%, 3%, 2.5%, 2%, 1.5%, or 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1% or 0.005%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 10%. The method may be characterized by having an error rate of less than about 5%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 3%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 0.5%.

The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having an accuracy of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 70%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 80%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 85%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 90%. The methods, kits, and systems disclosed herein may be characterized by having an accuracy of at least about 95%.

The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having a specificity of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 70%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 80%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 85%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 90%. The methods, kits, and systems disclosed herein may be characterized by having a specificity of at least about 95%.

The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having a sensitivity of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 70%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 80%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 85%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 90%. The methods, kits, and systems disclosed herein may be characterized by having a sensitivity of at least about 95%.

The methods, kits, and systems disclosed herein for use in diagnosing, prognosing, and/or monitoring a status or outcome of a transplant in a subject in need thereof may be characterized by having an error rate of less than about 30%, 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9.5%, 9%, 8.5%, 8%, 7.5%, 7%, 6.5%, 6%, 5.5%, 5%, 4.5%, 4%, 3.5%, 3%, 2.5%, 2%, 1.5%, or 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%, 0.9%, 0.8%, 0.7%, 0.6%, 0.5%, 0.4%, 0.3%, 0.2%, 0.1% or 0.005%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 10%. The method may be characterized by having an error rate of less than about 5%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 3%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 1%. The methods, kits, and systems disclosed herein may be characterized by having an error rate of less than about 0.5%.

The classifier, classifier set, classifier probe set, classification system may be characterized by having a accuracy for distinguishing two or more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The classifier, classifier set, classifier probe set, classification system may be characterized by having a sensitivity for distinguishing two or more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%. The classifier, classifier set, classifier probe set, classification system may be characterized by having a selectivity for distinguishing two or more conditions (AR, ANDR, TX, CAN) of at least about 50%, 55%, 57%, 60%, 62%, 65%, 67%, 70%, 72%, 75%, 77%, 80%, 82%, 85%, 87%, 90%, 92%, 95%, or 97%.

Computer Program

The methods, kits, and systems disclosed herein may include at least one computer program, or use of the same. A computer program may include a sequence of instructions, executable in the digital processing device's CPU, written to perform a specified task. Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those of skill in the art will recognize that a computer program may be written in various versions of various languages.

The functionality of the computer readable instructions may be combined or distributed as desired in various environments. The computer program will normally provide a sequence of instructions from one location or a plurality of locations. In various embodiments, a computer program includes, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add-ins, or add-ons, or combinations thereof.

Further disclosed herein are systems for classifying one or more samples and uses thereof. The system may comprise (a) a digital processing device comprising an operating system configured to perform executable instructions and a memory device; (b) a computer program including instructions executable by the digital processing device to classify a sample from a subject comprising: (i) a first software module configured to receive a gene expression profile of one or more genes from the sample from the subject; (ii) a second software module configured to analyze the gene expression profile from the subject; and (iii) a third software module configured to classify the sample from the subject based on a classification system comprising three or more classes. At least one of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. Analyzing the gene expression profile from the subject may comprise applying an algorithm. Analyzing the gene expression profile may comprise normalizing the gene expression profile from the subject. In some instances, normalizing the gene expression profile does not comprise quantile normalization.

FIG. 4 shows a computer system (also “system” herein) 401 programmed or otherwise configured for implementing the methods of the disclosure, such as producing a selector set and/or for data analysis. The system 401 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 405, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The system 401 also includes memory 410 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 415 (e.g., hard disk), communications interface 420 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 425, such as cache, other memory, data storage and/or electronic display adapters. The memory 410, storage unit 415, interface 420 and peripheral devices 425 are in communication with the CPU 405 through a communications bus (solid lines), such as a motherboard. The storage unit 415 can be a data storage unit (or data repository) for storing data. The system 401 is operatively coupled to a computer network (“network”) 430 with the aid of the communications interface 420. The network 430 can be the Internet, an internet and/or extranet, or an intranet and/or extranet that is in communication with the Internet. The network 430 in some instances is a telecommunication and/or data network. The network 430 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 430 in some instances, with the aid of the system 401, can implement a peer-to-peer network, which may enable devices coupled to the system 401 to behave as a client or a server.

The system 401 is in communication with a processing system 435. The processing system 435 can be configured to implement the methods disclosed herein. In some examples, the processing system 435 is a nucleic acid sequencing system, such as, for example, a next generation sequencing system (e.g., Illumina sequencer, Ion Torrent sequencer, Pacific Biosciences sequencer). The processing system 435 can be in communication with the system 401 through the network 430, or by direct (e.g., wired, wireless) connection. The processing system 435 can be configured for analysis, such as nucleic acid sequence analysis.

Methods as described herein can be implemented by way of machine (or computer processor) executable code (or software) stored on an electronic storage location of the system 401, such as, for example, on the memory 410 or electronic storage unit 415. During use, the code can be executed by the processor 405. In some examples, the code can be retrieved from the storage unit 415 and stored on the memory 410 for ready access by the processor 405. In some situations, the electronic storage unit 415 can be precluded, and machine-executable instructions are stored on memory 410.

Digital Processing Device

The methods, kits, and systems disclosed herein may include a digital processing device, or use of the same. In further embodiments, the digital processing device includes one or more hardware central processing units (CPU) that carry out the device's functions. In still further embodiments, the digital processing device further comprises an operating system configured to perform executable instructions. In some embodiments, the digital processing device is optionally connected a computer network. In further embodiments, the digital processing device is optionally connected to the Internet such that it accesses the World Wide Web. In still further embodiments, the digital processing device is optionally connected to a cloud computing infrastructure. In other embodiments, the digital processing device is optionally connected to an intranet. In other embodiments, the digital processing device is optionally connected to a data storage device.

In accordance with the description herein, suitable digital processing devices include, by way of non-limiting examples, server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. Those of skill in the art will recognize that many smartphones are suitable for use in the system described herein. Those of skill in the art will also recognize that select televisions, video players, and digital music players with optional computer network connectivity are suitable for use in the system described herein. Suitable tablet computers include those with booklet, slate, and convertible configurations, known to those of skill in the art.

The digital processing device will normally include an operating system configured to perform executable instructions. The operating system is, for example, software, including programs and data, which manages the device's hardware and provides services for execution of applications. Those of skill in the art will recognize that suitable server operating systems include, by way of non-limiting examples, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Those of skill in the art will recognize that suitable personal computer operating systems include, by way of non-limiting examples, Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU/Linux®. In some embodiments, the operating system is provided by cloud computing. Those of skill in the art will also recognize that suitable mobile smart phone operating systems include, by way of non-limiting examples, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.

The device generally includes a storage and/or memory device. The storage and/or memory device is one or more physical apparatuses used to store data or programs on a temporary or permanent basis. In some embodiments, the device is volatile memory and requires power to maintain stored information. In some embodiments, the device is non-volatile memory and retains stored information when the digital processing device is not powered. In further embodiments, the non-volatile memory comprises flash memory. In some embodiments, the non-volatile memory comprises dynamic random-access memory (DRAM). In some embodiments, the non-volatile memory comprises ferroelectric random access memory (FRAM). In some embodiments, the non-volatile memory comprises phase-change random access memory (PRAM). In other embodiments, the device is a storage device including, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing based storage. In further embodiments, the storage and/or memory device is a combination of devices such as those disclosed herein.

A display to send visual information to a user will normally be initialized. Examples of displays include a cathode ray tube (CRT, a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD, an organic light emitting diode (OLED) display. In various further embodiments, on OLED display is a passive-matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments, the display may be a plasma display, a video projector or a combination of devices such as those disclosed herein.

The digital processing device would normally include an input device to receive information from a user. The input device may be, for example, a keyboard, a pointing device including, by way of non-limiting examples, a mouse, trackball, track pad, joystick, game controller, or stylus; a touch screen, or a multi-touch screen, a microphone to capture voice or other sound input, a video camera to capture motion or visual input or a combination of devices such as those disclosed herein.

Non-Transitory Computer Readable Storage Medium

The methods, kits, and systems disclosed herein may include one or more non-transitory computer readable storage media encoded with a program including instructions executable by the operating system to perform and analyze the test described herein; preferably connected to a networked digital processing device. The computer readable storage medium is a tangible component of a digital that is optionally removable from the digital processing device. The computer readable storage medium includes, by way of non-limiting examples, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. In some instances, the program and instructions are permanently, substantially permanently, semi-permanently, or non-transitorily encoded on the media.

A non-transitory computer-readable storage media may be encoded with a computer program including instructions executable by a processor to create or use a classification system. The storage media may comprise (a) a database, in a computer memory, of one or more clinical features of two or more control samples, wherein (i) the two or more control samples may be from two or more subjects; and (ii) the two or more control samples may be differentially classified based on a classification system comprising three or more classes; (b) a first software module configured to compare the one or more clinical features of the two or more control samples; and (c) a second software module configured to produce a classifier set based on the comparison of the one or more clinical features.

At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. The storage media may further comprise one or more additional software modules configured to classify a sample from a subject. Classifying the sample from the subject may comprise a classification system comprising three or more classes. At least two of the classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function. All three classes may be selected from transplant rejection, transplant dysfunction with no rejection and normal transplant function.

Web Application

In some embodiments, a computer program includes a web application. In light of the disclosure provided herein, those of skill in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, a web application is created upon a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, a web application utilizes one or more database systems including, by way of non-limiting examples, relational, non-relational, object oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL™, and Oracle®. Those of skill in the art will also recognize that a web application, in various embodiments, is written in one or more versions of one or more languages. A web application may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web application is written to some extent in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or eXtensible Markup Language (XML). In some embodiments, a web application is written to some extent in a presentation definition language such as Cascading Style Sheets (CSS). In some embodiments, a web application is written to some extent in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. In some embodiments, a web application is written to some extent in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In some embodiments, a web application is written to some extent in a database query language such as Structured Query Language (SQL). In some embodiments, a web application integrates enterprise server products such as IBM® Lotus Domino®. In some embodiments, a web application includes a media player element. In various further embodiments, a media player element utilizes one or more of many suitable multimedia technologies including, by way of non-limiting examples, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

Mobile Application

In some embodiments, a computer program includes a mobile application provided to a mobile digital processing device. In some embodiments, the mobile application is provided to a mobile digital processing device at the time it is manufactured. In other embodiments, the mobile application is provided to a mobile digital processing device via the computer network described herein.

In view of the disclosure provided herein, a mobile application is created by techniques known to those of skill in the art using hardware, languages, and development environments known to the art. Those of skill in the art will recognize that mobile applications are written in several languages. Suitable programming languages include, by way of non-limiting examples, C, C++, C#, Objective-C, Java™, Javascript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML, and XHTML/HTML with or without CSS, or combinations thereof.

Suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting examples, AirplaySDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available without cost including, by way of non-limiting examples, Lazarus, MobiFlex, MoSync, and Phonegap. Also, mobile device manufacturers distribute software developer kits including, by way of non-limiting examples, iPhone and iPad (iOS) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

Those of skill in the art will recognize that several commercial forums are available for distribution of mobile applications including, by way of non-limiting examples, Apple® App Store, Android™ Market, BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® Apps, and Nintendo® DSi Shop.

Standalone Application

In some embodiments, a computer program includes a standalone application, which is a program that is run as an independent computer process, not an add-on to an existing process, e.g., not a plug-in. Those of skill in the art will recognize that standalone applications are often compiled. A compiler is a computer program(s) that transforms source code written in a programming language into binary object code such as assembly language or machine code. Suitable compiled programming languages include, by way of non-limiting examples, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB .NET, or combinations thereof. Compilation is often performed, at least in part, to create an executable program. In some embodiments, a computer program includes one or more executable complied applications.

Web Browser Plug-in

In some embodiments, the computer program includes a web browser plug-in. In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Makers of software applications support plug-ins to enable third-party developers to create abilities which extend an application, to support easily adding new features, and to reduce the size of an application. When supported, plug-ins enable customizing the functionality of a software application. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display particular file types. Those of skill in the art will be familiar with several web browser plug-ins including, Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbar comprises one or more web browser extensions, add-ins, or add-ons. In some embodiments, the toolbar comprises one or more explorer bars, tool bands, or desk bands.

In view of the disclosure provided herein, those of skill in the art will recognize that several plug-in frameworks are available that enable development of plug-ins in various programming languages, including, by way of non-limiting examples, C++, Delphi, Java™, PHP, Python™, and VB .NET, or combinations thereof.

Web browsers (also called Internet browsers) are software applications, designed for use with network-connected digital processing devices, for retrieving, presenting, and traversing information resources on the World Wide Web. Suitable web browsers include, by way of non-limiting examples, Microsoft® Internet Explorer®, Mozilla® Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called mircrobrowsers, mini-browsers, and wireless browsers) are designed for use on mobile digital processing devices including, by way of non-limiting examples, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting examples, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

Software Modules

The methods, kits, and systems disclosed herein may include software, server, and/or database modules, or use of the same. In view of the disclosure provided herein, software modules are created by techniques known to those of skill in the art using machines, software, and languages known to the art. The software modules disclosed herein are implemented in a multitude of ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or combinations thereof. In further various embodiments, a software module comprises a plurality of files, a plurality of sections of code, a plurality of programming objects, a plurality of programming structures, or combinations thereof. In various embodiments, the one or more software modules comprise, by way of non-limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, software modules are in one computer program or application. In other embodiments, software modules are in more than one computer program or application. In some embodiments, software modules are hosted on one machine. In other embodiments, software modules are hosted on more than one machine. In further embodiments, software modules are hosted on cloud computing platforms. In some embodiments, software modules are hosted on one or more machines in one location. In other embodiments, software modules are hosted on one or more machines in more than one location.

Databases

The methods, kits, and systems disclosed herein may comprise one or more databases, or use of the same. In view of the disclosure provided herein, those of skill in the art will recognize that many databases are suitable for storage and retrieval of information pertaining to gene expression profiles, sequencing data, classifiers, classification systems, therapeutic regimens, or a combination thereof. In various embodiments, suitable databases include, by way of non-limiting examples, relational databases, non-relational databases, object oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases. In some embodiments, a database is internet-based. In further embodiments, a database is web-based. In still further embodiments, a database is cloud computing-based. In other embodiments, a database is based on one or more local computer storage devices.

Data Transmission

The methods, kits, and systems disclosed herein may be used to transmit one or more reports. The one or more reports may comprise information pertaining to the classification and/or identification of one or more samples from one or more subjects. The one or more reports may comprise information pertaining to a status or outcome of a transplant in a subject. The one or more reports may comprise information pertaining to therapeutic regimens for use in treating transplant rejection in a subject in need thereof. The one or more reports may comprise information pertaining to therapeutic regimens for use in treating transplant dysfunction in a subject in need thereof. The one or more reports may comprise information pertaining to therapeutic regimens for use in suppressing an immune response in a subject in need thereof.

The one or more reports may be transmitted to a subject or a medical representative of the subject. The medical representative of the subject may be a physician, physician's assistant, nurse, or other medical personnel. The medical representative of the subject may be a family member of the subject. A family member of the subject may be a parent, guardian, child, sibling, aunt, uncle, cousin, or spouse. The medical representative of the subject may be a legal representative of the subject.

The term “about,” as used herein and throughout the disclosure, generally refers to a range that may be 15% greater than or 15% less than the stated numerical value within the context of the particular usage. For example, “about 10” would include a range from 8.5 to 11.5.

The term “or” as used herein and throughout the disclosure, generally means “and/or”.

EXAMPLES

The following illustrative examples are representative of embodiments of the software applications, systems, and methods described herein and are not meant to be limiting in any way.

Example 1

Introduction

Improvements in kidney transplantation have resulted in significant reductions in clinical acute rejection (AR) (8-14%) (Meier-Kriesche et al. 2004, Am J Transplant, 4(3): 378-383). However, histological AR without evidence of kidney dysfunction (i.e. subclinical AR) occurs in >15% of protocol biopsies done within the first year. Without a protocol biopsy, patients with subclinical AR would be treated as excellent functioning transplants (TX). Biopsy studies also document significant rates of progressive interstitial fibrosis and tubular atrophy in >50% of protocol biopsies starting as early as one year post transplant.

Two factors contribute to AR: the failure to optimize immunosuppression and individual patient non-adherence. Currently, there is no validated test to measure or monitor the adequacy of immunosuppression; the failure of which is often first manifested directly as an AR episode. Subsequently, inadequate immunosuppression results in chronic rejection and allograft failure. The current standards for monitoring kidney transplant function are serum creatinine and estimated glomerular filtration rates (eGFR). Unfortunately, serum creatinine and eGFR are relatively insensitive markers requiring significant global injury before changing and are influenced by multiple non-immunological factors.

Performing routine protocol biopsies is one strategy to diagnose and treat AR prior to extensive injury. A study of 28 patients one week post-transplant with stable creatinines showed that 21% had unsuspected “borderline” AR and 25% had inflammatory tubulitis (Shapiro et al. 2001, Am J Transplant, 1(1): 47-50). Other studies reveal a 29% prevalence of subclinical rejection (Hymes et al. 2009, Pediatric transplantation, 13(7): 823-826) and that subclinical rejection with chronic allograft nephropathy was a risk factor for late graft loss (Moreso et al. 2006, Am J Transplant, 6(4): 747-752). A study of 517 renal transplants followed after protocol biopsies showed that finding subclinical rejection significantly increased the risk of chronic rejection (Moreso et al. 2012, Transplantation 93(1): 41-46).

We originally reported a peripheral blood gene expression signature by DNA microarrays to diagnose AR (Flechner et al. 2004, Am J Transplant, 4(9): 1475-1489). Subsequently, others have reported qPCR signatures of AR in peripheral blood based on genes selected from the literature or using microarrays (Gibbs et al. 2005, Transpl Immunol, 14(2): 99-108; Li et al. 2012, Am J Transplant, 12(10): 2710-2718; Sabek et al. 2002, Transplantation, 74(5): 701-707; Sarwal et al. 2003, N Engl J Med, 349(2): 125-138; Simon et al. 2003, Am J Transplant, 3(9): 1121-1127; Vasconcellos et al. 1998, Transplantation, 66(5): 562-566). As the biomarker field has evolved, validation requires independently collected sample cohorts and avoidance of over-training during classifier discovery (Lee et al. 2006, Pharm Res, 23(2): 312-328; Chau et at 2008, Clin Cancer Res, 14(19): 5967-5976). Another limitation is that the currently published biomarkers are designed for 2-way classifications, AR vs. TX, when many biopsies reveal additional ADNR.

We prospectively followed over 1000 kidney transplants from 5 different clinical centers (Transplant Genomics Collaborative Group) to identify 148 instances of unequivocal biopsy-proven AR (n=63), ADNR (n=39), and TX (n=45). Global gene expression profiling was done on peripheral blood using DNA microarrays and robust 3-way class prediction tools (Dabney et al. 2005, Bioinformatics, 21(22): 4148-4154; Shen et al. 2006, Bioinformatics, 22(21): 2635-2642; Zhu et al. 2009, BMC bioinformatics, 10 Suppl 1:S21). Classifiers were comprised of the 200 highest value probe sets ranked by the prediction accuracies with each tool were created with three different classifier tools to insure that our results were not subject to bias introduced by a single statistical method. Importantly, even using three different tools, the 200 highest value probe set classifiers identified were essentially the same. These 200 classifiers had sensitivity, specificity, positive predictive accuracy (PPV), negative predictive accuracy (NPV) and Area Under the Curve (AUC) for the Validation cohort depending on the three different prediction tools used ranging from 82-100%, 76-95%, 76-95%, 79-100%, 84-100% and 0.817-0.968, respectively. Next, the Harrell bootstrapping method (Miao et al. 2013, SAS Global Forum, San Francisco; 2013) based on sampling with replacement was used to demonstrate that these results, regardless of the tool used, were not the consequence of statistical over-fitting. Finally, to model the use of our test in real clinical practice, we developed a novel one-by-one prediction strategy in which we created a large reference set of 118 samples and then randomly took 10 samples each from the AR, ADNR and TX cohorts in the Validation set. These were then blinded to phenotype and each sample was tested by itself against the entire reference set to model practice in a real clinical situation where there is only a single new patient sample obtained at any given time.

Materials and Methods

Patient Populations:

We studied 46 kidney transplant patients with well-functioning grafts and biopsy-proven normal histology (TX; controls), 63 patients with biopsy-proven acute kidney rejection (AR) and 39 patients with acute kidney dysfunction without histological evidence of rejection (ADNR). Inclusion/exclusion criteria are in Table 2. Subjects were enrolled serially as biopsies were performed by 5 different clinical centers (Scripps Clinic, Cleveland Clinic, St. Vincent Medical Center, University of Colorado and Mayo Clinic Arizona). Human Subjects Research Protocols approved at each Center and by the Institutional Review Board of The Scripps Research Institute covered all studies.

Pathology:

All subjects had kidney biopsies (either protocol or “for cause”) graded for evidence of acute rejection by the Banff 2007 criteria (Solez et al. 2008, Am J Transplant, 8(4): 753-760). All biopsies were read by local pathologists and then reviewed and graded in a blinded fashion by a single pathologist at an independent center (LG). The local and single pathologist readings were then reviewed by DRS to standardize and finalize the phenotypes prior to cohort construction and any diagnostic classification analysis. C4d staining was done per the judgment of the local clinicians and pathologists on 69 of the 148 samples (47%; Table 3). Positive was defined as linear, diffuse staining of peritubular capillaries. Donor specific antibodies were not measured on these patients and thus, we cannot exclude the new concept of C4d negative antibody-mediated rejection (Sis et at 2009, Am J Transplant, 9(10): 2312-2323; Wiebe et al. 2012, Am J Transplant, 12(5): 1157-1167).

Gene Expression Profiling and Statistical Analysis:

RNA was extracted from Paxgene tubes using the Paxgene Blood RNA system (PreAnalytix) and GlobinClear (Ambion). Biotinylated cRNA was prepared with Ambion MessageAmp Biotin H kit (Ambion) and hybridized to Affymetrix Human Genome U133 Plus 2.0 GeneChips. Normalized Signals were generated using frozen RMA (fRMA) in R (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). The complete strategy used to discover, refine and validate the biomarker panels is shown in FIG. 1. Class predictions were performed with multiple tools: Nearest Centroids, Support Vector Machines (SVM) and Diagonal Linear Discriminant Analysis (DLDA). Predictive accuracy is calculated as true positives+true negatives/true positives+false positives+false negatives+true negatives. Other diagnostic metrics given are sensitivity, specificity, Postive Predictive Value (PPV), Negative Predictive Value (NPV) and Area Under the Curve (AUC). Receiver Operating Characteristic (ROC) curves were generated using pROC in R (Robin et al. 2011, BMC bioinformatics, 12:77). Clinical study parameters were tested by multivariate logistic regression with an adjusted (Wald test) p-value and a local false discovery rate calculation (q-value). Chi Square analysis was done using GraphPad. CEL files and normalized signal intensities are posted in NIH Gene Expression Omnibus (GEO) (accession number GSE15296).

Results

Patient Population

Subjects were consented and biopsied in a random and prospective fashion at five Centers (n=148; Table 3). Blood was collected at the time of biopsy. TX represented protocol biopsies of transplants with excellent, stable graft function and normal histology (n=45). AR patients were biopsied “for cause” based on elevated serum creatinine (n=63). We excluded subjects with recurrent kidney disease, BKV or other infections. ADNRs were biopsied “for cause” based on suspicion of AR but had no AR by histology (n=39). Differences in steroid use (less in TX) reflect more protocol biopsies done at a steroid-free center. As expected, creatinines were higher in AR and ADNR than TX. Creatinine was the only significant variable by multivariable logistic regression by either phenotype or cohort. C4d staining, when done, was negative in TX and ADNR. C4d staining was done in 56% of AR subjects by the judgment of the pathologists and was positive in 1⅔ 6 (33%) of this selected group.

Three-Way Predictions

We randomly split the data from 148 samples into two cohorts, Discovery and Validationas shown in FIG. 1. Discovery was 32 AR, 20 ADNR, 23 TX and Validation was 32 AR, 19 ADNR, 22 TX. Normalization used Frozen Robust Multichip Average (fRMA) (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). Probe sets with median Log₂signals less than 5.20 in >70% of samples were eliminated. A 3-class univariate F-test was done on the Discovery cohort (1000 random permutations, FDR <10%; BRB ArrayTools) yielding 2977 differentially expressed probe sets using the Hu133 Plus 2.0 cartridge arrays plates (Table 1b). In another experiment, 4132 differentially expressed probe sets were yield using the HT HG-U133+ PM array plates (Table 1d). The Nearest Centroid algorithm (Dabney et al. 2005, Bioinformatics, 21(22): 4148-4154) was used to create a 3-way classifier for AR, ADNR and TX in the Discovery cohort revealing 200 high-value probe sets (Table 1a: using the Hu133 Plus 2.0 cartridge arrays plates; Table 1c: using the HT HG-U133+ PM array plates) defined by having the lowest class predictive error rates (Table 4; see also Supplemental Statistical Methods).

Thustesting our locked classifier in the validation cohort demonstrated predictive accuracies of 83%, 82% and 90% for the TX vs. AR, TX vs. ADNR and AR vs. ADNR respectively (Table 4). The AUCs for the TX vs. AR, TX vs. ADNR and the AR vs. ADNR comparisons were 0.837, 0.817 and 0.893, respectively as shown in FIG. 5. The sensitivity, specificity, PPV, NPV for the three comparisons were in similar ranges and are shown in Table 4. To determine a possible minimum classifier set, we ranked the 200 probe sets by p values and tested the top 25, 50, 100 and 200 (Table 4). The conclusion is that given the highest value classifiers discovered using unbiased whole genome profiling, the total number of classifiers necessary for testing may be 25. However, below that number the performance of our 3-way classifier falls off to about 50% AUC at 10 or lower (data not shown).

Alternative Prediction Tools

Robust molecular diagnostic strategies should work using multiple tools. Therefore, we repeated the entire 3-way locked discovery and validation process using DLDA and Support Vector Machines (Table 5). All the tools perform nearly equally well with 100-200 classifiers though small differences were observed.

It is also important to test whether a new classifier is subject to statistical over-fitting that would inflate the claimed predictive results. This testing can be done with the method of Harrell et al. using bootstrapping where the original data set is sampled 1000 times with replacement and the AUCs calculated for each (Miao et at 2013, SAS Global Forum, San Francisco; 2013). The original AUCs minus the calculated AUCs for each tool create the corrections in the AUCs for “optimism” in the original predictions that adjust for potential over-fitting (Table 6). Therefore we combined the Discovery and Validation cohorts and performed a 3-class univariate F-test on the whole data set of 148 samples (1000 random permutations, FDR <10%; BRB ArrayTools). This yielded 2666 significantly expressed genes from which we selected the top 200 by p-values. Results using NC, SVM and DLDA with these 200 probe sets are shown in Table 6. Optimism-corrected AUCs from 0.823-0.843 were obtained for the 200-probe set classifier discovered with the 2 cohort-based strategy. Results for the 200-classifier set obtained from the full study sample set of 148 were 0.851-0.866. These results demonstrate that over-fitting is not a major problem as would be expected from a robust set of classifiers (FIG. 7). These results translate to sensitivity, specificity, PPV and NPV of 81%, 93%, 92% and 84% for AR vs. TX; 90%, 85%, 86% and 90% for ADNR vs. TX and 85%, 96%, 95% and 87% for AR vs. ADNR.

Validation in One-by-One Predictions

In clinical practice the diagnostic value of a biomarker is challenged each time a single patient sample is acquired and analyzed. Thus, prediction strategies based on large cohorts of known clinical classifications do not address the performance of biomarkers in their intended application. Two problems exist with cohort-based analysis. First, signal normalization is typically done on the entire cohort, which is not the case in a clinical setting for one patient. Quantile normalization is a robust method but has 2 drawbacks; it cannot be used in clinical settings where samples must be processed individually or in small batches and data sets normalized separately are not comparable. Frozen RMA (fRMA) overcomes these limitations by normalization of individual arrays to large publicly available microarray databases allowing for estimates of probe-specific effects and variances to be pre-computed and “frozen” (McCall et al. 2010, Biostatistics, 11(2): 242-253; McCall et al. 2011, BMC bioinformatics, 12:369). The second problem with cohort analysis is that all the clinical phenotypes are already known and classification is done on the entire cohort. To address these challenges, we removed 30 random samples from the Validation cohort (10 AR, 10 ADNR, 10 TX), blinded their classifications and left a Reference cohort of 118 samples with known phenotypes. Classification was done by adding one blinded sample at a time to the Reference cohort. Using the 200-gene, 3-way classifier derived in NC, we demonstrated an overall predictive accuracy of 80% and individual accuracies of 80% AR, 90% ADNR and 70% TX and AUCs of 0.885, 0.754 and 0.949 for the AR vs. TX, ADNR vs. TX and the AR vs. ADNR comparisons, respectively as shown in FIG. 6.

Discussion

Ideally, molecular markers will serve as early warnings for immune-mediated injury, before renal function deteriorates, and also permit optimization of immunosuppression. We studied a total of 148 subjects with biopsy-proven phenotypes identified in 5 different clinical centers by following over 1000 transplant patients. Global RNA expression of peripheral blood was used to profile 63 patients with biopsy-proven AR, 39 patients with ADNR and 46 patients with excellent function and normal histology (TX).

We addressed several important and often overlooked aspects of biomarker discovery. To avoid over training, we used a discovery cohort to establish the predictive equation and its corresponding classifiers, then locked these down and allowed no further modification. We then tested the diagnostic on our validation cohort. To demonstrate the robustness of our approach, we used multiple, publically available prediction tools to establish that our results are not simply tool-dependent artifacts. We used the bootstrapping method of Harrell to calculate optimism-corrected AUCs and demonstrated that our predictive accuracies are not inflated by over-fitting. We also modeled the actual clinical application of this diagnostic, with a new strategy optimized to normalizing individual samples by fRMA. We then used 30 blinded samples from the validation cohort and tested them one-by-one. Finally, we calculated the statistical power of our analysis and determined that we have greater than 90% power at a significance level of p<0.001. We concluded that peripheral blood gene expression profiling can be used to diagnose AR and ADNR in patients with acute kidney transplant dysfunction. An interesting finding is that we got the same results using the classic two-cohort strategy (discovery vs. validation) as we did using the entire sample set and creating our classifiers with the same tools but using the Harrell bootstrapping method to control for over-fitting. Thus, the current thinking that all biomarker signatures require independent validation cohorts may need to be reconsidered.

In the setting of acute kidney transplant dysfunction, we are the first to address the common clinical challenge of distinguishing AR from ADNR by using 3-way instead of 2-way classification algorithms.

Additional methods may comprise a prospective, blinded study. The biomarkers may be further validated using a prospective, blinded study. Methods may comprise additional samples. The additional samples may be used to classify the different subtypes of T cell-mediated, histologically-defined AR. The methods may further comprise use of one or more biopsies. The one or more biopsies may be used to develop detailed histological phenotyping. The methods may comprise samples obtained from subjects of different ethnic backgrounds. The methods may comprise samples obtained from subjects treated with various therapies (e.g., calcineurin inhibitors, mycophenolic acid derivatives, and steroids. The methods may comprise samples obtained from one or more clinical centers. The use of samples obtained from two or more clinical centers may be used to identify any differences in the sensitivity and/or specificity of the methods to classify and/or characterize one or more samples. The use of samples obtained from two or more clinical centers may be used to determine the effect of race and/or therapy on the sensitivity and/or specificity of the methods disclosed herein. The use of multiple samples may be used to determine the impact of bacterial and/or viral infections on the sensitivity and/or specificity of the methods disclosed herein.

The samples may comprise pure ABMR (antibody mediated rejection). The samples may comprise mixed ABMR/TCMR (T-cell mediated rejection). In this example, we had 12 mixed ABMR/TCMR instances but only 1 of the 12 was misclassified for AR. About 30% of our AR subjects had biopsies with positive C4d staining. However, supervised clustering to detect outliers did not indicate that our signatures were influenced by C4d status. At the time this study was done it was not common practice to measure donor-specific antibodies. However, we note the lack of correlation with C4d status for our data.

The methods disclosed herein may be used to determine a mechanism of ADNR since these patients were biopsied based on clinical judgments of suspected AR after efforts to exclude common causes of acute transplant dysfunction. While our results from this example do not address this question, it is evident that renal transplant dysfunction is common to both AR and ADNR. The levels of kidney dysfunction based on serum creatinines were not significantly different between AR and ADNR subjects. Thus, these gene expression differences are not based simply on renal function or renal injury. Also, the biopsy histology for the ADNR patients revealed nonspecific and only focal tubular necrosis, interstitial edema, scattered foci of inflammatory cells that did not rise to even borderline AR and nonspecific arteriolar changes consistent but not diagnostic of CNI toxicity.

Biopsy-based diagnosis may be subject to the challenge of sampling errors and differences between the interpretations of individual pathologists (Mengel et at 2007, Am J Transplant, 7(10): 2221-2226). To mitigate this limitation, we used the Banff schema classification and an independent central biopsy review of all samples to establish the phenotypes. Another question is how these signatures would reflect known causes of acute kidney transplant dysfunction (e.g. urinary tract infection, CMV and BK nephropathy). Our view is that there are already well-established, clinically validated and highly sensitive tests available to diagnose each of these. Thus, for implementation and interpretation of our molecular diagnostic for AR and ADNR clinicians would often do this kind of laboratory testing in parallel. In complicated instances a biopsy will still be required, though we note that a biopsy is also not definitive for sorting out AR vs. BK nephropathy.

The methods may be used for molecular diagnostics to predict outcomes like AR, especially diagnose subclinical AR, prior to enough tissue injury to result in kidney transplant dysfunction. The methods may be used to measure and ultimately optimize the adequacy of long term immunosuppression by serial monitoring of blood gene expression. The design of the present study involved blood samples collected at the time of biopsies. The methods may be used to predict AR or ADNR. The absence of an AR gene profile in a patient sample may be a first measure of adequate immunosuppression and may be integrated into a serial blood monitoring protocol. Demonstrating the diagnosis of subclinical AR and the predictive capability of our classifiers may create the first objective measures of adequate immunosuppression. One potential value of our approach using global gene expression signatures developed by DNA microarrays rather than highly reduced qPCR signatures is that these more complicated predictive and immunosuppression adequacy signatures can be derived later from prospective studies like CTOT08. In turn, an objective metric for the real-time efficacy of immunosuppression may allow the individualization of drug therapy and enable the long term serial monitoring necessary to optimize graft survival and minimize drug toxicity.

While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention.

Supplemental Statistical Methods

All model selection was done in Partek Genomics Suite v6.6 using the Partek user guide model selection, 2010: Nearest Centroid

The Nearest Centroid classification method was based on [Tibshirani, R., Hastie, T., Narasimham, B., and Chu, G (2003): Class Prediction by Nearest Shrunken Centroids, with Applications to DNA Microarrays. Statist. Sci. Vol. 18 (1):104-117] and [Tou, J. T., and Gonzalez, R. C. (1974): Pattern Recognition Principals, Addison-Wesley, Reading, Massachusetts]. The centroid classifications were done by assigning equal prior probabilities.

Support Vector Machines

Support Vector Machines (SVMs) attempt to find a set of hyperplanes (one for each pair of classes) that best classify the data. It does this by maximizing the distance of the hyperplanes to the closest data points on both sides. Partek uses the one-against-one method as described in “A comparison of methods for multi-class support vector machines” (C. W. Hsu and C. J. Lin. IEEE Transactions on Neural Networks, 13(2002), 415-425).

To run model selection with SVM cost with shrinking was used. Cost of 1 to 1000 with Step 100 was chosen to run several models. The radial basis kernel (gamma) was used. The kernel parameters were 1/number of columns.

Diagonal Linear Discriminant Analysis

The Discriminant Analysis method can do predictions based on the class variable.

The linear with equal prior probability method was chosen.

Linear Discriminant Analysis is performed in Partek using these steps:

- Calculation of a common (pooled) covariance matrix and within-group means
- Calculation of the set of linear discriminant functions from the common covariance and the within-group means
- Classification using the linear discriminant functions

The common covariance matrix is a pooled estimate of the within-group covariance matrices:

ΣSWi

S=i

Σni−Ci

Thus, for linear discriminant analysis, the linear discriminant function for class i is defined as: d(x)=−1 (x −m)t S −1 (x −m)+In P(w).

Optimism-Corrected AUC's

The steps for estimating the optimism-corrected AUCs are based on the work of F. Harrell published in [Regression Modeling Strategies: With applications to linear models, logistic regression, and survival analysis. Springer, New York (2001)].

The basic approach is described in [Miao Y M, Cenzer I S, Kirby K A, Boscardin J W. Estimating Harrell's Optimism on Predictive Indices Using Bootstrap Samples. SAS Global Forum 2013; San Francisco]:

- 1. Select the predictors and fit a model using the full dataset and a particular variable selection method. From that model, calculate the apparent discrimination (capp).
- 2. Generate M=100 to 200 datasets of the same sample size (n) using bootstrap samples with replacement.
- 3. For each one of the new datasets m . . . M, select predictors and fit the model using the exact same algorithmic approach as in step 1 and calculate the discrimination (cboot (m)).
- 4. For each one of the new models, calculate its discrimination back on the original data set (corig(m)). For this step, the regression coefficients can either be fixed to their values from step 3 to determine the joint degree of over-fitting from both selection and estimation or can be re-estimated to determine the degree of over-fitting from selection only.
- 5. For each one of the bootstrap samples, the optimism in the fit is o(m)=corig(m)−cboot(m). The average of these values is the optimism of the original model.
- 6. The optimism-corrected performance of the original model is then cadj=capp−o. This value is a nearly unbiased estimate of the expected values of the optimism that would be obtained in external validation.

We adapted this model in Partek Genomics Suite using 1000 samplings with replacement of our dataset (n=148). An original AUC was calculated on the full dataset, and then the average of the M=1000 samplings was also estimated. The difference between the original and the estimated AUC's was designated as the optimism and this was subtracted from the original AUC to arrive at the “optimism-corrected AUC”. In the text, we specifically compared the AUC's that we reported by testing our locked 200-probe set classifiers on only our Validation cohort (see Table 4) to the optimism-corrected AUC's (see Table 5). The results demonstrate little difference consistent with the conclusion that our high predictive accuracies are not the result of over-fitting.

TABLE 1a

The top 200 gene probeset used in the 3-Way AR, ADNR TX ANOVA Analysis (using the Hu133

Plus 2.0 cartridge arrays plates)

Geom
Geom
Geom

mean
mean
mean

of in-
of in-
of in-

tensities
tensities
tensities

in
in
in

Parametric
class
class
class

Pairwise

p-value
1
2
3
ProbeSet
Symbol
Name
EntrezID
DefinedGenelist
significant

1
<1e−07
6.48
7.08
7.13
212167_
SMARCB1
SWI/SNF
6598
Chromatin
(1, 2), (1, 3)

s_at

related, matrix

Remodeling by

associated, actin

hSWI/SNF ATP-

dependent

dependent

regulator of

Complexes

chromatin,

subfamily b,

member 1

2
<1e−07
9.92
9.42
10.14
201444_
ATP6AP2
ATPase, H+
10159

(2, 1), (2, 3)

s_at

transporting,

lysosomal

accessory

protein 2

3
<1e−07
5.21
5.01
5.68
227658_
PLEKHA3
pleckstrin
65977

(1, 3), (2, 3)

s_at

homology

domain

containing,

family A

(phosphoinositide

binding

specific)

member 3

4
1.00E−07
6.73
7.62
7.73
201746_at
TP53
tumor protein
7157
Apoptotic Signaling
(1, 2), (1, 3)

p53

in Response to DNA

Damage, ATM

Signaling Pathway,

BTG family proteins

and cell cycle

regulation, Cell Cycle:

G1/S Check Point,

Cell Cycle: G2/M

Checkpoint,

Chaperones modulate

interferon Signaling

Pathway, CTCF: First

Multivalent Nuclear

Factor, Double

Stranded RNA

Induced Gene

Expression, Estrogen-

responsive protein Efp

controls cell cycle and

breast tumors growth,

Hypoxia and p53 in

the Cardiovascular

system, Overview of

telomerase protein

component gene hTert

Transcriptional

Regulation, p53

Signaling Pathway,

RB Tumor

Suppressor/Checkpoint

Signaling in

response to DNA

damage, Regulation of

cell cycle progression

by Plk3, Regulation of

transcriptional activity

by PML, Role of

BRCA1, BRCA2 and

ATR in Cancer

Susceptibility,

Telomeres,

Telomerase, Cellular

Aging, and

Immortality, Tumor

Suppressor Arf

Inhibits Ribosomal

Biogenesis,

Amyotrophic lateral

sclerosis (ALS),

Apoptosis, Cell cycle,

Colorectal cancer,

Huntington\'s disease,

MAPK signaling

pathway, Wnt sign . . .

5
1.00E−07
6.64
6.07
6.87
218292_
PRKAG2
protein kinase,
51422
ChREBP regulation
(2, 1), (2, 3)

s_at

AMP-activated,

by carbohydrates and

gamma 2 non-

cAMP, Reversal of

catalytic subunit

Insulin Resistance by

Leptin, Adipocytokine

signaling pathway,

Insulin signaling

pathway

6
1.00E−07
11.08
11.9
12.02
1553551_
ND2
MTND2
4536

(1, 2), (1, 3)

s_at

7
2.00E−07
8.6
9.41
9.21
210996_s_
YWHAE
tyrosine 3-
7531
Cell cycle
(1, 2), (1, 3)

at

monooxygenase/

tryptophan 5-

monooxygenase

activation

protein, epsilon

polypeptide

8
2.00E−07
5.89
6.92
6.23
243037_at

(1, 2), (3, 2)

9
2.00E−07
7.61
7
7.81
200890_s_
SSR1
signal sequence
6745

(2, 1), (2, 3)

at

receptor, alpha

10
2.00E−07
5.18
6.71
6.12
1570571_
CCDC91
coiled-coil
55297

(1, 2), (1, 3)

at

domain

containing 91

11
3.00E−07
7.01
6.55
7.21
233748_
PRKAG2
protein kinase,
51422
ChREBP regulation
(2, 1), (2, 3)

x_at

AMP-activated,

by carbohydrates and

gamma 2 non-

cAMP, Reversal of

catalytic subunit

Insulin Resistance by

Leptin, Adipocytokine

signaling pathway,

Insulin signaling

pathway

12
3.00E−07
7.96
7.29
7.86
224455_s_
ADPGK
ADP-dependent
83440

(2, 1), (2, 3)

at

glucokinase

13
3.00E−07
8.42
7.93
8.41
223931_s_
CHFR
checkpoint with
55743
Tryptophan
(2, 1), (2, 3)

at

forkhead and

metabolism

ring finger

domains, E3

ubiquitin protein

ligase

14
3.00E−07
4.84
5.44
5.11
236766_at

(1, 2), (1, 3),

(3, 2)

15
3.00E−07
7.95
8.72
7.73
242068_at

(1, 2), (3, 2)

16
3.00E−07
6.96
6.58
7.31
215707_s_
PRNP
prion protein
5621
Prion Pathway,
(2, 1), (2, 3)

at

Neurodegenerative

Disorders, Prion

disease

17
3.00E−07
6.68
7.73
7.4
1558220_at

(1, 2), (1, 3)

18
3.00E−07
6.53
6.19
6.87
203100_s_
CDYL
chromodomain
9425

(2, 1), (2, 3)

at

protein, Y-like

19
3.00E−07
6.19
5.66
6.28
202278_s_
SPTLC1
serine
10558
Sphingolipid
(2, 1), (2, 3)

at

palmitoyl-

metabolism

transferase,

long chain

base subunit 1

20
4.00E−07
7.73
7.83
6.71
232726_at

(3, 1), (3, 2)

21
4.00E−07
9.78
9.22
9.81
218178_s_
CHMP1B
charged
57132

(2, 1), (2, 3)

at

multivesicular

body protein 1B

22
4.00E−07
7.08
6.35
7.25
223585_x_
KBTBD2
kelch repeat and
25948

(2, 1), (2, 3)

at

BTB (POZ)

domain

containing 2

23
4.00E−07
4.85
4.53
5.49
224407_s_
MST4
serine/threonine
51765

(1, 3), (2, 3)

at

protein kinase

MST4

24
4.00E−07
9.49
9.74
8.95
239597_at

(3, 1), (3, 2)

25
4.00E−07
4.3
4.76
4.42
239987_at

(1, 2), (3, 2)

26
5.00E−07
5.49
6.06
5.84
243667_at

(1, 2), (1, 3)

27
6.00E−07
8.08
7.32
7.95
209287_s_
CDC42EP3
CDC42 effector
10602

(2, 1), (2, 3)

at

protein (Rho

GTPase binding)

3

28
6.00E−07
7.81
7.17
8
212008_at
UBXN4
UBX domain
23190

(2, 1), (2, 3)

protein 4

29
6.00E−07
4.88
4.57
5.27
206288_at
PGGT1B
protein
5229

(1, 3), (2, 3)

geranylgeranyl-

transferase type I,

beta subunit

30
6.00E−07
9.75
9.98
9.26
238883_at

(3, 1), (3, 2)

31
7.00E−07
6.19
5.42
6.79
207794_at
CCR2
chemokine (C-C
729230

(2, 1), (2, 3)

motif) receptor 2

32
7.00E−07
8.17
8.58
7.98
242143_at

(1, 2), (3, 2)

33
7.00E−07
4.52
5.01
5.07
205964_at
ZNF426
zinc finger
79088

(1, 2), (1, 3)

protein 426

34
8.00E−07
6.68
5.68
6.75
1553685_
SP1
Sp1
6667
Agrin in Postsynaptic
(2, 1), (2, 3)

s_at

transcription

Differentiation,

factor

Effects of calcineurin

in Keratinocyte

Differentiation,

Human

Cytomegalovirus and

Map Kinase

Pathways,

Keratinocyte

Differentiation,

MAPKinase Signaling

Pathway, Mechanism

of Gene Regulation by

Peroxisome

Proliferators via

PPARa(alpha),

Overview of

telomerase protein

component gene hTert

Transcriptional

Regulation, Overview

of telomerase RNA

component gene

hTerc Transcriptional

Regulation, TGF-beta

signaling pathway

35
8.00E−07
5.08
5.8
6.03
219730_at
MED18
mediator
54797

(1, 2), (1, 3)

complex subunit

18

36
9.00E−07
5.74
6.07
5.74
233004_x_

(1, 2), (3, 2)

at

37
9.00E−07
5.5
6.4
6.06
242797_x_

(1, 2), (1, 3)

at

38
9.00E−07
8.4
8.03
8.65
200778_s_
2-Sep
septin 2
4735

(2, 1), (2, 3)

at

39
1.00E−06
7.64
6.65
7.91
211559_s_
CCNG2
cyclin G2
901

(2, 1), (2, 3)

at

40
1.00E−06
6.71
7.3
7.23
221090_s_
OGFOD1
2-oxoglutarate
55239

(1, 2), (1, 3)

at

and iron-

dependent

oxygenase

domain

containing 1

41
1.00E−06
4.36
5.14
4.87
240232_at

(1, 2), (1, 3)

42
1.10E−06
6.55
6.86
7.12
221650_s_
MED18
mediator
54797

(1, 2), (1, 3),

at

complex subunit

(2, 3)

18

43
1.10E−06
8
8.48
8.26
214670_at
ZKSCAN1
zinc finger with
7586

(1, 2), (1, 3),

KRAB and

(3, 2)

SCAN domains

1

44
1.20E−06
6.55
6.17
7.04
202089_s_
SLC39A6
solute carrier
25800

(1, 3), (2, 3)

at

family 39 (zinc

transporter),

member 6

45
1.20E−06
7.05
6.31
7.45
211825_s_
FLI1
Friend leukemia
2313

(2, 1), (2, 3)

at

virus integration

1

46
1.20E−06
6.05
6.85
6.71
243852_at
LUC7L2
LUC7-like 2 (S.
51631

(1, 2), (1, 3)

cerevisiae)

47
1.20E−06
8.27
7.44
8.6
207549_x_
CD46
CD46 molecule,
4179
Complement and
(2, 1), (2, 3)

at

complement

coagulation cascades

regulatory

protein

48
1.30E−06
4.7
5.52
4.65
242737_at

(1, 2), (3, 2)

49
1.30E−06
4.93
4.67
4.57
239189_at
CASKIN1
CASK
57524

(2, 1), (3, 1)

interacting

protein 1

50
1.30E−06
7.66
8.08
7.47
232180_at
UGP2
UDP-glucose
7360
Galactose
(1, 2), (3, 2)

pyrophosphorylase

metabolism,

2

Nucleotide sugars

metabolism, Pentose

and glucuronate

interconversions,

Starch and sucrose

metabolism

51
1.40E−06
7.47
6.64
7.31
210971_s_
ARNTL
aryl
406
Circadian Rhythms
(2, 1), (2, 3)

at

hydrocarbon

receptor nuclear

translocator-like

52
1.40E−06
8.66
8.99
8.1
232307_at

(3, 1), (3, 2)

53
1.40E−06
7.06
6.48
7.56
222699_s_
PLEKHF2
pleckstrin
79666

(2, 1), (2, 3)

at

homology

domain

containing,

family F (with

FYVE domain)

member 2

54
1.60E−06
6.59
6.62
6.13
234435_at

(3, 1), (3, 2)

55
1.60E−06
3.94
3.51
4.17
207117_at
ZNF117
zinc finger
51351

(2, 1), (2, 3)

protein 117

56
1.60E−06
7.57
7.25
8.26
1553530_
ITGB1
integrin, beta 1
3688
Adhesion and
(1, 3), (2, 3)

a_at

(fibronectin

Diapedesis of

receptor, beta

Lymphocytes,

polypeptide,

Adhesion Molecules

antigen CD29

on Lymphocyte,

includes MDF2,

Agrin in Postsynaptic

MSK12)

Differentiation,

Aspirin Blocks

Signaling Pathway

Involved in Platelet

Activation, B Cell

Survival Pathway,

Cells and Molecules

involved in local acute

inflammatory

response, Eph Kinases

and ephrins support

platelet aggregation,

Erk and PI-3 Kinase

Are Necessary for

Collagen Binding in

Corneal Epithelia,

Erk1/Erk2 Mapk

Signaling pathway,

Integrin Signaling

Pathway, mCalpain

and friends in Cell

motility, Monocyte

and its Surface

Molecules, PTEN

dependent cell cycle

arrest and apoptosis,

Ras-Independent

pathway in NK cell-

mediated cytotoxicity,

Signaling of

Hepatocyte Growth

Factor Receptor,

Trefoil Factors Initiate

Mucosal Healing,

uCalpain and friends

in Cell spread, Axon

guidance, Cell

adhesion molecules

(CAMs), ECM-

receptor interaction,

Focal adhesion,

Leukocyte

transendothelial

migration, Regulation

of actin cytoskeleton

57
1.60E−06
5.67
5.02
6.16
214786_at
MAP3K1
mitogen-
4214
Angiotensin II
(2, 1), (2, 3)

activated protein

mediated activation of

kinase kinase

JNK Pathway via

kinase 1, E3

Pyk2 dependent

ubiquitin protein

signaling, BCR

ligase

Signaling Pathway,

CD40L Signaling

Pathway, Ceramide

Signaling Pathway,

EGF Signaling

Pathway, FAS

signaling pathway

(CD95), Fc Epsilon

Receptor I Signaling

in Mast Cells, fMLP

induced chemokine

gene expression in

HMC-1 cells, HIV-I

Nef: negative effector

of Fas and TNF,

Human

Cytomegalovirus and

Map Kinase

Pathways, Inhibition

of Cellular

Proliferation by

Gleevec, Keratinocyte

Differentiation, Links

between Pyk2 and

Map Kinases, Map

Kinase Inactivation of

SMRT Corepressor,

MAPKinase Signaling

Pathway,

Neuropeptides VIP

and PACAP inhibit

the apoptosis of

activated T cells, NF-

kB Signaling

Pathway, p38 MAPK

Signaling Pathway,

PDGF Signaling

Pathway, Rac 1 cell

motility signaling

pathway, Role of

MAL in Rho-

Mediated Activation

of SRF, Signal

transduction through

IL1R, T Cell Receptor

Signaling Pathway,

The 4-1BB-dependent

immune response,

TNF/Stress Related

Signaling, TNFR1

Signaling Pathway,

TNFR2 Sig . . .

58
1.70E−06
7.49
7.27
7.73
222729_at
FBXW7
F-box and WD
55294
Cyclin E Destruction
(2, 1), (2, 3)

repeat domain

Pathway,

containing 7, E3

Neurodegenerative

ubiquitin protein

Disorders, Ubiquitin

ligase

mediated proteolysis

59
1.80E−06
7.73
7.41
8.08
208310_s_

(2, 1), (2, 3)

at

60
1.80E−06
9.06
9.28
8.54
242471_at
SRGAP2B
SLIT-ROBO
647135

(3, 1), (3, 2)

Rho GTPase

activating

protein 2B

61
1.80E−06
7.84
8.15
7.5
238812_at

(3, 1), (3, 2)

62
1.80E−06
6.64
7.28
7.08
206240_s_
ZNF136
zinc finger
7695

(1, 2), (1, 3)

at

protein 136

63
1.80E−06
10.29
9.86
10.35
1555797_
ARPC5
actin related
10092
Regulation of actin
(2, 1), (2, 3)

a_at

protein ⅔

cytoskeleton

complex,

subunit 5,

16 kDa

64
1.90E−06
5.05
5.49
5.24
215068_s_
FBXL18
F-box and
80028

(1, 2), (3, 2)

at

leucine-rich

repeat protein 18

65
2.00E−06
6.84
6.08
7.16
204426_at
TMED2
transmembrane
10959

(2, 1), (2, 3)

emp24 domain

trafficking

protein 2

66
2.00E−06
5.6
5.19
5.13
234125_at

(2, 1), (3, 1)

67
2.10E−06
9.87
9.38
10.27
200641_s_
YWHAZ
tyrosine 3-
7534
Cell cycle
(2, 1), (2, 3)

at

monooxygenase/

tryptophan 5-

monooxygenase

activation

protein, zeta

polypeptide

68
2.10E−06
7.6
6.83
7.89
214544_s_
SNAP23
synaptosomal-
8773
SNARE interactions
(2, 1), (2, 3)

at

associated

in vesicular transport

protein, 23 kDa

69
2.10E−06
9.57
10.14
9.29
238558_at

(1, 2), (3, 2)

70
2.20E−06
6.79
7.12
6.72
221071_at

(1, 2), (3, 2)

71
2.40E−06
6.98
6.38
7.29
232591_s_
TMEM30A
transmembrane
55754

(2, 1), (2, 3)

at

protein 30A

72
2.40E−06
7.22
7.49
6.35
1569477_

(3, 1), (3, 2)

at

73
2.60E−06
7.97
7.26
8.22
211574_s_
CD46
CD46 molecule,
4179
Complement and
(2, 1), (2, 3)

at

complement

coagulation cascades

regulatory

protein

74
2.60E−06
7.63
7.37
8.18
201627_s_
INSIG1
insulin induced
3638

(1, 3), (2, 3)

at

gene 1

75
2.60E−06
5.27
5.97
5.53
215866_at

(1, 2), (3, 2)

76
2.80E−06
9.6
9.77
9.42
201986_at
MED13
mediator
9969

(3, 2)

complex subunit

13

77
2.80E−06
9.21
8.85
9.44
200753_x_
SRSF2
serine/arginine-
6427
Spliceosomal
(2, 1), (2, 3)

at

rich splicing

Assembly

factor 2

78
3.00E−06
4.65
4.75
5.36
214959_s_
API5
apoptosis
8539

(1, 3), (2, 3)

at

inhibitor 5

79
3.10E−06
7.39
8.28
7.81
217704_x_
SUZ12P1
suppressor of
440423

(1, 2), (3, 2)

at

zeste 12

homolog

pseudogene 1

80
3.30E−06
7.38
7.93
7
244535_at

(1, 2), (3, 2)

81
3.40E−06
7.17
6.66
7.86
210786_s_
FLI1
Friend leukemia
2313

(1, 3), (2, 3)

at

virus integration

1

82
3.40E−06
7.33
7.87
7.47
235035_at
SLC35E1
solute carrier
79939

(1, 2), (3, 2)

family 35,

member E1

83
3.40E−06
10.42
10.84
10.08
241681_at

(1, 2), (3, 2)

84
3.40E−06
7.13
6.16
7.1
212720_at
PAPOLA
poly(A)
10914
Polyadenylation of
(2, 1), (2, 3)

polymerase

mRNA

alpha

85
3.50E−06
5.81
5.47
6.03
205408_at
MLLT10
myeloid/lympho
8028

(2, 1), (2, 3)

id or mixed-

lineage leukemia

(trithorax

homolog,

Drosophila);

translocated to,

10

86
3.50E−06
5.51
6.12
5.83
238418_at
SLC35B4
solute carrier
84912

(1, 2), (1, 3)

family 35,

member B4

87
3.50E−06
6.03
7.01
6.37
1564424_

(1, 2), (3, 2)

at

88
3.60E−06
8.65
9.02
8.39
243030_at

(1, 2), (3, 2)

89
3.60E−06
5.52
5.25
5.78
215207_x_

(2, 1), (2, 3)

at

90
3.90E−06
6.77
7.36
7.21
235058_at

(1, 2), (1, 3)

91
4.20E−06
8.15
7.94
8.48
202092_s_
ARL2BP
ADP-
23568

(1, 3), (2, 3)

at

ribosylation

factor-like 2

binding protein

92
4.40E−06
8.55
8.24
8.6
202162_s_
CNOT8
CCR4-NOT
9337

(2, 1), (2, 3)

at

transcription

complex,

subunit 8

93
4.40E−06
8.21
8.1
8.68
201259_s_
SYPL1
synaptophysin-
6856

(1, 3), (2, 3)

at

like 1

94
4.40E−06
7.68
7.79
7.2
236168_at

(3, 1), (3, 2)

95
4.40E−06
6.72
7.58
6.89
1553252_
BRWD3
bromodomain
254065

(1, 2), (3, 2)

a_at

and WD repeat

domain

containing 3

96
4.50E−06
6.71
7.67
7.19
244872_at
RBBP4
retinoblastoma
5928
The PRC2 Complex
(1, 2), (3, 2)

binding protein

Sets Long-term Gene

4

Silencing Through

Modification of

Histone Tails

97
4.50E−06
5.58
6.53
6.36
215390_at

(1, 2), (1, 3)

98
4.60E−06
4.93
6.29
5.36
1566966_

(1, 2), (3, 2)

at

99
4.90E−06
5.46
5.07
5.68
225700_at
GLCCI1
glucocorticoid
113263

(2, 1), (2, 3)

induced

transcript 1

100
5.00E−06
4.96
5.17
4.79
236324_at
MBP
myelin basic
4155

(1, 2), (3, 2)

protein

101
5.10E−06
8.08
7.26
8.33
222846_at
RAB8B
RAB8B,
51762

(2, 1), (2, 3)

member RAS

oncogene family

102
5.10E−06
6.24
5.75
6.58
1564053_
YTHDF3
YTH domain
253943

(2, 1), (2,3)

a_at

family, member

3

103
5.20E−06
7
6.36
7.35
216100_s_
TOR1AIP1
torsin A
26092

(2, 1), (2, 3)

at

interacting

protein 1

104
5.20E−06
6.15
5.97
6.63
1565269_
ATF1
activating
466
TNF/Stress Related
(1, 3), (2, 3)

s_at

transcription

Signaling

factor 1

105
5.30E−06
8.13
7.73
8.57
220477_s_
TMEM230
transmembrane
29058

(2, 3)

at

protein 230

106
5.30E−06
7.45
8.09
7.72
1559490_
LRCH3
leucine-rich
84859

(1, 2), (3, 2)

at

repeats and

calponin

homology (CH)

domain

containing 3

107
5.30E−06
7.44
8.05
7.44
225490_at
ARID2
AT rich
196528

(1, 2), (3, 2)

interactive

domain 2

(ARID, RFX-

like)

108
5.50E−06
7.49
8.18
7.83
244766_at

(1, 2), (3, 2)

109
5.50E−06
7.71
8.41
8
242673_at

(1, 2), (3, 2)

110
5.60E−06
8.97
8.59
9.24
202164_s_
CNOT8
CCR4-NOT
9337

(2, 1), (2, 3)

at

transcription

complex,

subunit 8

111
5.70E−06
7.75
8.26
7.52
222357_at
ZBTB20
zinc finger and
26137

(1, 2), (3, 2)

BTB domain

containing 20

112
5.90E−06
5.07
5.52
4.71
240594_at

(1, 2), (3, 2)

113
6.00E−06
7.78
7.45
7.96
1554577_
PSMD10
proteasome
5716

(2, 1), (2, 3)

a_at

(prosome,

macropain) 26S

subunit, non-

ATPase, 10

114
6.00E−06
6.55
7.03
6.58
215137_at

(1, 2), (3, 2)

115
6.10E−06
9.46
9.66
9.05
243527_at

(3, 1), (3, 2)

116
6.30E−06
7.8
7.27
8.15
214449_s_
RHOQ
ras homolog
23433
Insulin signaling
(2, 1), (2,3)

at

family member

pathway

Q

117
6.30E−06
7.3
7.92
7.44
216197_at
ATF7IP
activating
5572

(1, 2), (3, 2)

transcription

factor 7

interacting

protein

118
6.40E−06
7.38
8.17
7.51
1558569_
LOC100131541
uncharacterized
100131541

(1, 2), (3, 2)

at

LOC100131541

119
6.50E−06
4.79
5.23
4.55
244030_at
STYX
serine/threonine/
6815

(1, 3), (2, 3)

tyrosine

interacting

protein

120
6.70E−06
7.2
7.95
7.27
244010_at

(1, 2), (3, 2)

121
7.20E−06
6.36
6.78
6.05
232002_at

(1, 2), (3, 2)

122
7.20E−06
6.11
6.95
6.18
243051_at
CNIH4
cornichon
29097

(1, 2), (3, 2)

homolog 4

(Drosophila)

123
7.20E−06
5.89
6.55
6.32
212394_at
EMC1
ER membrane
23065

(1, 2), (1, 3)

protein complex

subunit 1

124
7.30E−06
5.6
6.37
5.86
1553407_
MACF1
microtubule-
23499

(1, 2), (3, 2)

at

actin

crosslinking

factor 1

125
7.50E−06
5.08
5.84
5.74
214123_s_
NOP14-
NOP14
317648

(1, 2), (1, 3)

at
AS1
antisense RNA 1

126
7.50E−06
4.89
5.65
5.06
1564438_

(1, 2), (3, 2)

at

127
7.60E−06
8.54
8.88
8.22
229858_at

(3, 2)

128
7.60E−06
9.26
8.77
9.49
215933_s_
HHEX
hematopoietically
3087
Maturity onset
(2, 1), (2, 3)

at

expressed

diabetes of the young

homeobox

129
7.60E−06
7.97
8.14
7.59
239234_at

(3, 1), (3, 2)

130
7.70E−06
9.71
9.93
9.17
238619_at

(3, 1), (3, 2)

131
7.70E−06
5.46
6.05
5.61
1559039_
DHX36
DEAH (Asp-
170506

(1, 2), (3, 2)

at

Glu-Ala-His)

box polypeptide

36

132
7.70E−06
9.19
8.57
9.36
222859_s_
DAPP1
dual adaptor of
27071

(2, 1), (2, 3)

at

phosphotyrosine

and 3-

phosphoinositides

133
7.80E−06
7.76
7.35
8.07
210285_x_
WTAP
Wilms tumor 1
9589

(2, 1), (2, 3)

at

associated

protein

134
7.90E−06
5.64
5.2
5.67
238816_at
PSEN1
presenilin 1
5663
Generation of amyloid
(2, 1), (2, 3)

b-peptide by PS1, g-

Secretase mediated

ErbB4 Signaling

Pathway, HIV-I Nef:

negative effector of

Fas and TNF,

Presenilin action in

Notch and Wnt

signaling, Proteolysis

and Signaling

Pathway of Notch,

Alzheimer\'s disease,

Neurodegenerative

Disorders, Notch

signaling pathway,

Wnt signaling

pathway

135
7.90E−06
5.6
5.42
5.26
239112_at

(2, 1), (3, 1),

(3, 2)

136
8.40E−06
6.99
6.66
7.22
211536_x_
MAP3K7
mitogen-
6885
ALK in cardiac
(2, 1), (2, 3)

at

activated protein

myocytes, FAS

kinase kinase

signaling pathway

kinase 7

(CD95), MAPKinase

Signaling Pathway,

NFkB activation by

Nontypeable

Hemophilus

influenzae, NF-kB

Signaling Pathway,

p38 MAPK Signaling

Pathway, Signal

transduction through

IL1R, TGF beta

signaling pathway,

Thrombin signaling

and protease-activated

receptors, TNFR1

Signaling Pathway,

Toll-Like Receptor

Pathway, WNT

Signaling Pathway,

Adherens junction,

MAPK signaling

pathway, Toll-like

receptor signaling

pathway, Wnt

signaling pathway

137
8.40E−06
7.82
8.34
7.98
228070_at
PPP2R5E
protein
5529

(1, 2), (3, 2)

phosphatase 2,

regulatory

subunit B′,

epsilon isoform

138
8.60E−06
5.38
5.07
5.54
220285_at
FAM108B1
family with
51104

(2, 1), (2,3)

sequence

similarity 108,

member B1

139
8.60E−06
8.07
7.56
8.2
210284_s_
TAB2
TGF-beta
2311
MAPK signaling
(2, 1), (2, 3)

at

activated kinase

pathway, Toll-like

1/MAP3K7

receptor signaling

binding protein

pathway

2

140
8.60E−06
5.22
4.5
5.59
1558014_
FAR1
fatty acyl CoA
84188

(2, 1), (2, 3)

s_at

reductase 1

141
8.60E−06
6.25
6.53
6.04
240247_at

(1, 2), (3, 2)

142
8.80E−06
6.64
6.6
7.21
235177_at
METTL21A
methyltransferase
151194

(1, 3), (2, 3)

like 21A

143
8.90E−06
6.46
7.47
6.7
1569540_

(1, 2), (3, 2)

at

144
8.90E−06
6.8
6.34
7.16
224642_at
FYTTD1
forty-two-three
84248

(2, 1), (2, 3)

domain

containing 1

145
8.90E−06
7.94
7.19
8.19
204427_s_
TMED2
transmembrane
10959

(2, 1), (2, 3)

at

emp24 domain

trafficking

protein 2

146
8.90E−06
9.75
9.99
9.23
233867_at

(3, 1), (3, 2)

147
9.00E−06
10.08
10.61
10.12
212852_s_
TROVE2
TROVE domain
6738

(1, 2), (3, 2)

at

family, member

2

148
9.20E−06
7.39
7.76
7.12
215221_at

(1, 2), (3, 2)

149
9.30E−06
9.17
9.71
9.05
231866_at
LNPEP
leucyl/cystinyl
4012

(1, 2), (3, 2)

aminopeptidase

150
9.50E−06
5.34
5.61
5.22
217293_at

(1, 2), (3, 2)

151
9.50E−06
7.2
6.59
7.35
224311_s_
CAB39
calcium binding
51719
mTOR signaling
(2, 1), (2, 3)

at

protein 39

pathway

152
9.60E−06
8.5
9
8.52
231716_at
RC3H2
ring finger and
54542

(1, 2), (3, 2)

CCCH-type

domains 2

153
9.70E−06
6.99
7.48
6.92
1565692_

(1, 2), (3, 2)

at

154
9.70E−06
8.45
8.64
7.76
232174_at

(3, 1), (3, 2)

155
9.70E−06
6.72
7.22
6.23
243827_at

(3, 2)

156
9.90E−06
5.13
6.2
5.51
217536_x_

(1, 2), (3, 2)

at

157
1.00E−05
9
8.88
9.33
206052_s_
SLBP
stem-loop
7884

(1, 3), (2, 3)

at

binding protein

158
1.00E−05
7.26
6.61
7.55
209131_s_
SNAP23
synaptosomal-
8773
SNARE interactions
(2, 1), (2, 3)

at

associated

in vesicular transport

protein, 23 kDa

159
1.00E−05
4.46
5.13
4.73
1568801_
VWA9
von Willebrand
81556

(1, 2), (3, 2)

at

factor A domain

containing 9

160
1.00E−05
8.01
7.85
8.32
211061_s_
MGAT2
mannosyl
4247
Glycan structures-
(1, 3), (2, 3)

at

(alpha-1,6-)-

biosynthesis 1, N-

glycoprotein

Glycan biosynthesis

beta-1,2-N-

acetylglucos-

aminyltransferase

161
1.01E−05
8.55
8.23
8.9
223010_s_
OCIAD1
OCIA domain
54940

(2, 3)

at

containing 1

162
1.01E−05
6.75
7.5
7.6
207460_at
GZMM
granzyme M
3004

(1, 2), (1, 3)

(lymphocyte

met-ase 1)

163
1.02E−05
4.77
4.59
5.46
1553176_
SH2D1B
SH2 domain
117157
Natural killer cell
(1, 3), (2, 3)

at

containing 1B

mediated cytotoxicity

164
1.02E−05
6.36
6.24
6.62
211033_s_
PEX7
peroxisomal
5191

(1, 3), (2, 3)

at

biogenesis factor

7

165
1.04E−05
7.01
7.75
7.77
203547_at
CD4
CD4 molecule
920
Activation of Csk by
(1, 2), (1, 3)

cAMP-dependent

Protein Kinase

Inhibits Signaling

through the T Cell

Receptor, Antigen

Dependent B Cell

Activation, Bystander

B Cell Activation,

Cytokines and

Inflammatory

Response, HIV

Induced T Cell

Apoptosis, HIV-1

defeats host-mediated

resistance by CEM15,

IL 17 Signaling

Pathway, IL 5

Signaling Pathway,

Lck and Fyn tyrosine

kinases in initiation of

TCR Activation,

NO2-dependent IL 12

Pathway in NK cells,

Regulation of

hematopoiesis by

cytokines, Selective

expression of

chemokine receptors

during T-cell

polarization, T Helper

Cell Surface

Molecules, Antigen

processing and

presentation, Cell

adhesion molecules

(CAMs),

Hematopoietic cell

lineage, T cell

receptor signaling

pathway

166
1.04E−05
8.82
8.4
9.05
200776_s_
BZW1
basic leucine
9689

(2, 1), (2, 3)

at

zipper and W2

domains 1

167
1.07E−05
6.71
7.95
7.51
207735_at
RNF125
ring finger
54941

(1, 2), (1, 3)

protein 125, E3

ubiquitin protein

ligase

168
1.08E−05
6.5
7.08
6.99
46947_at
GNL3L
guanine
54552

(1, 2), (1, 3)

nucleotide

binding protein-

like 3

(nucleolar)-like

169
1.08E−05
7.92
8.54
8.12
240166_x_
TRMT10B
tRNA
158234

(1, 2), (3, 2)

at

methyltransferase

10 homolog B

(S. cerevisiae)

170
1.13E−05
8.39
7.96
8.54
1555780_
RHEB
Ras homolog
6009
mTOR Signaling
(2, 1), (2, 3)

a_at

enriched in brain

Pathway, Insulin

signaling pathway,

mTOR signaling

pathway

171
1.14E−05
8.37
8.82
8.56
214948_s_
TMF1
TATA element
7110

(1, 2), (3,2)

at

modulatory

factor 1

172
1.15E−05
6.77
7.43
7.07
221191_at
STAG3L1
stromal antigen
54441

(1, 2), (3, 2)

3-like 1

173
1.16E−05
5.47
4.68
5.89
201295_s_
WSB1
WD repeat and
26118

(2, 1), (2, 3)

at

SOCS box

containing 1

174
1.18E−05
7.31
6.73
7.62
211302_s_
PDE4B
phosphodiesterase
5142
Purine metabolism
(2, 1), (2, 3)

at

4B, cAMP-

specific

175
1.19E−05
9.02
9.36
8.68
227576_at

(3, 2)

176
1.23E−05
7.34
7.82
7.28
1553349_
ARID2
AT rich
196528

(1, 2), (3, 2)

at

interactive

domain 2

(ARID, RFX-

like)

177
1.23E−05
8.56
9.02
8.26
242405_at

(1, 2), (3, 2)

178
1.24E−05
5.32
6.33
5.71
238723_at
ATXN3
ataxin 3
4287

(1, 2), (3, 2)

179
1.25E−05
6.97
7.45
6.59
241508_at

(1, 2), (3, 2)

180
1.27E−05
7.73
7.49
7.74
225374_at

(2, 1), (2, 3)

181
1.29E−05
8.64
9.09
8.43
244414_at

(1, 2), (3, 2)

182
1.29E−05
7.52
6.99
7.57
202213_s_
CUL4B
cullin 4B
8450

(2, 1), (2, 3)

at

183
1.29E−05
5.63
5.52
5.19
243002_at

(3, 1), (3, 2)

184
1.34E−05
4.51
5.32
4.8
210384_at
PRMT2
protein arginine
3275
Aminophosphonate
(1, 2), (3, 2)

methyltransferase

metabolism,

2

Androgen and

estrogen metabolism,

Histidine metabolism,

Nitrobenzene

degradation,

Selenoamino acid

metabolism,

Tryptophan

metabolism, Tyrosine

metabolism

185
1.35E−05
4.65
4.25
4.81
1569952_

(2, 1), (2, 3)

x_at

186
1.36E−05
12.44
12.13
12.55
202902_s_
CTSS
cathepsin S
1520
Antigen processing
(2, 1), (2, 3)

at

and presentation

187
1.37E−05
7.56
7.92
7.09
239561_at

(3, 2)

188
1.37E−05
6.8
7.38
7.18
218555_at
ANAPC2
anaphase
29882
Cell cycle, Ubiquitin
(1, 2), (1, 3)

promoting

mediated proteolysis

complex subunit

2

189
1.38E−05
8.03
7.89
8.57
200946_x_
GLUD1
glutamate
2746
Arginine and proline
(1, 3), (2, 3)

at

dehydrogenase 1

metabolism, D-

Glutamine and D-

glutamate

metabolism,

Glutamate

metabolism, Nitrogen

metabolism, Urea

cycle and metabolism

of amino groups

190
1.39E−05
5.15
4.77
5.96
221268_s
SGPP1
sphingosine-1-
81537
Sphingolipid
(1, 3), (2, 3)

_at

phosphate

metabolism

phosphatase 1

191
1.40E−05
5.36
6.5
5.8
216166_at

(1, 2), (3, 2)

192
1.41E−05
7.07
7.64
7.09
1553909_
FAM178A
family with
55719

(1, 2), (3, 2)

x_at

sequence

similarity 178,

member A

193
1.42E−05
7.23
6.95
7.5
1554747_
2-Sep
septin 2
4735

(2, 3)

a_at

194
1.45E−05
6.04
6.69
6.38
242751_at

(1, 2)

195
1.46E−05
7.74
8.22
7.79
239363_at

(1, 2), (3, 2)

196
1.47E−05
5.57
5.19
5.76
222645_s_
KCTD5
potassium
54442

(2, 1), (2, 3)

at

channel

tetramerisation

domain

containing 5

197
1.53E−05
3.93
3.73
4.26
210875_s_
ZEB1
zinc finger E-
6935
SUMOylation as a
(1, 3), (2, 3)

at

box binding

mechanism to

homeobox 1

modulate CtBP-

dependent gene

responses

198
1.55E−05
8.42
8
8.63
1567458_
RAC1
ras-related C3
5879
Agrin in Postsynaptic
(2, 1), (2, 3)

s_at

botulinum toxin

Differentiation,

substrate 1 (rho

Angiotensin II

family, small

mediated activation of

GTP binding

JNK Pathway via

protein Rac1)

Pyk2 dependent

signaling, BCR

Signaling Pathway,

fMLP induced

chemokine gene

expression in HMC-1

cells, How does

salmonella hijack a

cell, Influence of Ras

and Rho proteins on

G1 to S Transition,

Links between Pyk2

and Map Kinases,

MAPKinase Signaling

Pathway, p38 MAPK

Signaling Pathway,

Phosphoinositides and

their downstream

targets., Phospholipids

as signalling

intermediaries, Rac 1

cell motility signaling

pathway, Ras

Signaling Pathway,

Ras-Independent

pathway in NK cell-

mediated cytotoxicity,

Role of MAL in Rho-

Mediated Activation

of SRF, Role of PI3K

subunit p85 in

regulation of Actin

Organization and Cell

Migration, T Cell

Receptor Signaling

Pathway,

Transcription factor

CREB and its

extracellular signals,

Tumor Suppressor Arf

Inhibits Ribosomal

Biogenesis, uCalpain

and friends in Cell

spread, Y branching

of actin filaments,

Adherens junction,

Axon guidance, B cell

receptor signaling pat

. . .

199
1.56E−05
9
9.37
8.89
233893_s_
UVSSA
UV-stimulated
57654

(1, 2), (3, 2)

at

scaffold protein

A

200
1.59E−05
5.95
6.42
6.55
226539_s_

(1, 2), (1, 3)

at

TABLE 1b

The 2977 gene probeset used in the 3-Way AR, ADNR TX ANOVA

Analysis (using the Hu133 Plus 2.0 cartridge arrays plates)

SMARCB1
233900_at
RNPC3
FLJ44342
1563715_at
244088_at

ATP6AP2
KCNC4
COQ9
CNIH
ZBTB20
OXTR

PLEKHA3
GLRX3
HSPD1
HMGXB4
BLOC1S3
220687_at

TP53
BCL2L11
NFYA
GJC2
GMCL1
TLE4

PRKAG2
HNRPDL
TNRC18
236370_at
SRSF3
1556655_s_at

ND2
XRN2
238745_at
SH2B3
227730_at
TMEM161B

YWHAE
EXOC3
215595_x_at
232834_at
CLIP1
FTSJD1

243037_at
RNGTT
228799_at
OTUB2
236742_at
AAGAB

SSR1
ENY2
1555485_s_at
LINC00028
MAT2B
1570335_at

CCDC91
KLHL36
SEMA7A
INTS1
217185_s_at
CIZ1

PRKAG2
UBR4
ZNF160
DIS3
MTUS2
ZNF45

ADPGK
RALB
G6PD
ATP2A3
MOB3B
215650_at

CHFR
UBE2D3
1556205_at
LOC100128822
1558922_at
UBTF

236766_at
DR1
DSTN
PRKAR2A
233376_at
ZNF555

242068_at
CLEC7A
HSF1
214027_x_at
SLC16A7
MED28

PRNP
SDHD
NPHP4
TCF4
241106_at
SIGLEC10

1558220_at
1565701_at
242126_at
CCDC115
IRAK3
TM6SF2

CDYL
216813_at
SRSF11
244677_at
242362_at
PACSIN2

SPTLC1
C2orf72
231576_at
SRP72
ADAMTS16
DLX3

232726_at
236109_at
RBM4
221770_at
KIAA1715
PKM

CHMP1B
240262_at
FAM175B
1556003_a_at
202648_at
SUMO3

KBTBD2
1563364_at
ZNF592
1559691_at
PTPRO
GATM

MST4
235263_at
RAPGEF2
KANK2
ADAM12
NECAP1

239597_at
PRR12
MALAT1
PEX7
XBP1
LPPR3

239987_at
CBFB
216621_at
PTGS2
222180_at
CAMK2D

243667_at
JAK1
KIAA1683
RDH11
MGAT2
CRH

CDC42EP3
240527_at
1559391_s_at
PHF7
CUBN
243310_at

UBXN4
6-Sep
DR1
236446_at
ZNF626
DYNC1H1

PGGT1B
ITGB1
ARF4
242027_at
CD84
C17orf80

238883_at
1562948_at
RAB39B
1565877_at
LRRC8C
233931_at

CCR2
YRDC
GPR27
ACBD3
AMBP
C20orf78

242143_at
1566001_at
HPS3
SNAI3-AS1
C3orf38
MED13L

ZNF426
SETD5
215900_at
215630_at
ATG4C
226587_at

SP1
RNGTT
220458_at
POLR1B
217701_x_at
226543_at

MED18
240789_at
ARFGAP3
PON2
LOC100996246
LPP

233004_x_at
239227_at
1566428_at
MED27
244473_at
LOC100131043

242797_x_at
236802_at
EID2B
LOC100289058
FOLR1
AGA

2-Sep
TM2D1
1559347_at
1565976_at
231682_at
STX11

CCNG2
TBX4
ARPP19
YEATS4
NRP2
215586_at

OGFOD1
232528_at
IFRD1
VIPR2
PTPRD
244177_at

240232_at
TSEN15
1564227_at
233761_at
RBBP6
240892_at

MED18
MAP3K8
1561058_at
SLC25A16
SMARCD2
GPBP1L1

ZKSCAN1
TMEM206
SFT2D3
OSTC
CLEC4A
242872_at

SLC39A6
239600_at
LOC646482
ATP11B
RBM3
240550_at

FLI1
TMOD3
TMEM43
C1orf43
POLE3
KIAA0754

LUC7L2
GLS
CSNK1A1
CASK
MAPK9
213704_at

CD46
RPS10
233816_at
PAXBP1
ACTR10
GABPA

242737_at
TSN
C3orf17
ADSSL1
1557551_at
240347_at

CASKIN1
C7orf53
239901_at
C11orf58
ZNF439
G3BP1

UGP2
MTMR1
ABCC6
1569312_at
RTFDC1
PIK3AP1

ARNTL
TNPO1
7-Mar
STRN
ND6
KRIT1

232307_at
232882_at
NRIP2
243634_at
LIN7C
CGNL1

PLEKHF2
SSR1
1569930_at
240392_at
CNOT7
C11orf30

234435_at
233223_at
DLAT
SDK2
237846_at
1562059_at

ZNF117
VNN3
USP14
WNK1
GAPVD1
SPG20

ITGB1
ARFGEF1
UACA
AKAP10
CDAN1
1561749_at

MAP3K1
244100_at
PGK1
UNK
TECR
C16orf87

FBXW7
TMEM245
CHRNB2
GUCA1B
LINC00476
232234_at

208310_s_at
CDC42EP3
SUMF2
PTPRH
229448_at
MEGF9

SRGAP2B
244433_at
NEDD1
216756_at
ZBTB43
RAD18

238812_at
MYCBP2
MFN1
1557993_at
230659_at
ZCCHC3

ZNF136
ATF5
PIGY
MAEA
1560199_x_at
TOR2A

ARPC5
RBBP4
1564248_at
215628_x_at
BET1L
SMIM14

FBXL18
232929_at
APH1A
SPSB1
PAFAH1B1
SLMO2

TMED2
PCGF1
DCTN1
APC
243350_at
NAA15

234125_at
239567_at
KLHL42
TIMM50
TAF1B
ZNF80

YWHAZ
233799_at
OGFRL1
240080_at
MRPL42
GKAP1

SNAP23
FBXO9
FBXL14
SLC38A9
232867_at
FBXO8

238558_at
SS18
240013_at
MON1A
ASIC4
233027_at

221071_at
ARMC10
SREK1IP1
239809_at
WNT7B
237051_at

TMEM30A
232700_at
CDH16
243088_at
ZNF652
YJEFN3

1569477_at
UNKL
234278_at
233940_at
GMEB1
213048_s_at

CD46
1561389_at
LIX1L
KIAA1468
BCAS4
PPP1R12A

INSIG1
SLC35B3
NBR2
1563320_at
GFM1
TCEB3

215866_at
235912_at
SEC31A
APOL2
IGHMBP2
201380_at

MED13
234759_at
MLX
FLJ12334
BEX4
LOC283788

SRSF2
SLC15A4
ATP6V1H
FANCF
SNX24
RNA45S5

API5
TMEM70
USP36
242403_at
AKIRIN1
RBBP4

SUZ12P1
RANBP9
C2orf68
ERLIN2
ZNF264
SNTB2

244535_at
NMD3
LOC149373
241965_at
LYSMD2
234046_at

FLI1
LARP1
TMEM38B
SLC30A5
RUFY2
231346_s_at

SLC35E1
240765_at
TNPO1
238024_at
239925_at
AGFG2

241681_at
SCFD1
RSU1
1562283_at
SPSB4
NONO

PAPOLA
RAB28
FXR1
216902_s_at
ZNF879
THEMIS

MLLT10
242144_at
TET3
SMAP1
XIAP
NDUFS1

SLC35B4
PAFAH2
MGC57346
C6orf89
SNX19
RAB18

1564424_at
ZNF43
231107_at
FBXO33
RER1
NAP1L1

243030_at
CYP4V2
PHF20
ANKRD10
1566472_s_at
235701_at

215207_x_at
MREG
1560332_at
INSIG1
QSOX2
1557353_at

235058_at
BECN1
PAIP2
239106_at
DDR2
HOPX

ARL2BP
LOXL2
235805_at
KIAA1161
1566491_at
CCT6A

CNOT8
TPD52
KRBA2
1563590_at
PRNP
VGLL4

SYPL1
API5
BCKDHB
LOC100129175
HNMT
EXOSC6

236168_at
222282_at
VAV3
DLGAP4
GGA1
TM2D3

BRWD3
HHEX
MC1R
ARID5A
AQR
BAZ2A

RBBP4
237048_at
HMGCR
SMAD6
KIF3B
HIF1AN

215390_at
1562853_x_at
MBD4
ZNF45
SMO
MBD4

1566966_at
CCNL1
PPP2R2A
SLC38A10
241303_x_at
DET1

GLCCI1
TIGD1
242480_at
GFM1
MTPAP
227383_at

MBP
241391_at
INTS9
221579_s_at
230998_at
239005_at

RAB8B
DDX3X
ABHD6
CCIN
241159_x_at
PIGF

YTHDF3
FTX
CEP350
ASAP1-IT1
TGDS
SREBF2

TOR1AIP1
HIBADH
RHEB
FAM126B
CRLS1
TLE4

ATF1
PXK
1559598_at
232906_at
REXO1
PLCB1

TMEM230
231644_at
ING4
RALGAPA2
1557699_x_at
DNAAF2

LRCH3
FPR2
RHNO1
SPCS1
FAM208B
PALLD

ARID2
CPSF2
1552867_at
MAGT1
ZNF721
USP28

244766_at
G2E3
212117_at
TMEM19
PRDM11
217572_at

242673_at
237600_at
TLR8
ZNF24
243869_at
C1orf86

CNOT8
244357_at
231324_at
NAIP
G2E3
DAAM2

ZBTB20
215577_at
DDA1
232134_at
233270_x_at
GNB3

240594_at
CLEC7A
243207_at
B2M
DMPK
RNF145

PSMD10
GAREML
9-Sep
NPR3
NLK
RANBP9

215137_at
1566965_at
SRP72
ATP6AP2
TRIM28
1557543_at

243527_at
TLK1
233832_at
1563104_at
238785_at
ZNF250

RHOQ
231281_at
GABBR1
ERCC3
243691_at
PPP1R15B

ATF7IP
EXOC5
TRIM50
STEAP4
LOC283482
FLI1

LOC100131541
GHITM
KIAA1704
232937_at
LOC285300
SMURF1

STYX
ZCCHC9
LRRFIP2
238892_at
242310_at
EAF1

244010_at
ZNF330
LIG4
NDUFS4
239449_at
SUMO2

232002_at
TMEM230
HECA
ERLEC1
SOCS5
MED21

CNIH4
ZNF207
243561_at
UBL3
233121_at
PIGR

EMC1
LOC440993
215961_at
BBS12
NUMBL
240154_at

MACF1
PAPOLA
BRAP
242637_at
PCNP
UPF1

NOP14-AS1
CXorf36
SURF4
231125_at
PRG3
217703_x_at

1564438_at
AKAP13
PANK2
SMCHD1
SRSF2
SKAP2

229858_at
ASXL2
236338_at
PDE3B
MOAP1
ARL6IP5

HHEX
RAB14
FNBP1
TRPM7
SPG11
STIM2

239234_at
DIP2A
G2E3
RPL18
ZFP41
WBP11

238619_at
243391_x_at
FLT3LG
PTOV1
CARD11
ADAM10

DHX36
PBXIP1
233800_at
HAVCR2
MFSD8
PHF20L1

DAPP1
MFN1
SMIM7
242890_at
EEF1D
CR1

WTAP
DENND4C
ADRA2B
BFAR
NAPEPLD
STEAP4

PSEN1
SLC25A40
PTPN23
ARHGAP21
GM2A
MAML2

239112_at
242490_at
SNX2
SRPK2
EBAG9
236450_at

MAP3K7
LMBR1
P2RY10
PRKCA
242749_at
RTN4IP1

PPP2R5E
CDK6
ABHD15
TRAPPC2L
PRKCA
221454_at

FAM108B1
TMEM19
1565804_at
LRRFIP1
SUV39H1
RMDN2

TAB2
237310_at
236966_at
CCDC6
LOC100507281
UFM1

FAR1
PIK3C3
SRPK2
232344_at
PLXDC1
228911_at

240247_at
ABI1
242865_at
TRAK1
ZP1
BEST1

METTL21A
CHD8
AGMAT
SRGN
HR
239086_at

1569540_at
SLC30A5
GPHN
PAPOLA
RYBP
216380_x_at

FYTTD1
216056_at
ZNF75D
ATXN7L1
POPDC2
KCTD5

TMED2
MAPKAPK5
SON
TBATA
SNX2
SFTPC

233867_at
CSNK1A1
AP1AR
TTC17
BMP7
KLF7

TROVE2
243149_at
TXNDC12
ELP6
226532_at
ERBB2IP

215221_at
1560349_at
1569527_at
CACHD1
MAN2A2
232622_at

LNPEP
SERBP1
237377_at
ELOVL5
LOC100129726
234882_at

217293_at
MDM4
NPTN
1563173_at
PDE1B
225494_at

CAB39
217702_at
239431_at
CHRNA6
SCRN3
OSGIN1

RC3H2
RPAP3
242132_x_at
SLC9A5
FAM3C
SLC26A6

1565692_at
NR3C1
AP1S2
UBE4B
GPHA2
MALT1

232174_at
SMAD4
TMEM38B
233674_at
USP38
216593_s_at

243827_at
FBXO11
CDC42SE1
CHRNE
IDH3A
237176_at

217536_x_at
SNAPC3
TLR4
TIMM23
FGFR1OP2
1570087_at

SLBP
SVIL
C14orf169
FCGR2C
DST
ENTPD2

SNAP23
TSPAN14
BTF3L4
232583_at
NSMF
232744_x_at

VWA9
SERBP1
AUH
KIAA2026
241786_at
NUDT6

MGAT2
238544_at
RDX
242551_at
WHSC1L1
AGPAT1

OCIAD1
LRRFIP2
224175_s_at
MALAT1
234033_at
242926_at

GZMM
SNAP29
240813_at
229434_at
244086_at
CLASP2

SH2D1B
215648_at
FGL1
YWHAE
C14orf142
239241_at

PEX7
PPTC7
235028_at
COPS8
ME2
MIR143HG

CD4
241932_at
SENP7
215599_at
NFYB
232472_at

BZW1
CNBP
215386_at
KDM2A
AIF1L
FCAR

RNF125
239463_at
MGC34796
TFAP2D
MALT1
XPNPEP3

GNL3L
POGZ
ME2
PKHD1
226250_at
ACAD8

TRMT10B
215083_at
PPP6R1
OGFOD1
233099_at
PARP15

RHEB
TMEM64
211180_x_at
GPATCH2L
237655_at
TMEM128

TMF1
ERP44
GFM1
DSTN
ZBED3
PTPN7

STAG3L1
LOC100272216
CEP120
ZSCAN9
BAZ1B
215474_at

WSB1
1562062_at
MAN1A2
MFSD11
CDC42SE2
215908_at

PDE4B
1559154_at
STX3
SERPINI1
ISG20
AASDHPPT

227576_at
CDC40
243469_at
XRCC3
KLHDC8A
NCBP1

ARID2
PIGM
NDUFS1
ADNP
DLAT
233272_at

242405_at
238000_at
CAAP1
LOC100506651
DIABLO
240870_at

ATXN3
RAP2B
CHD4
242532_at
PDXDC1
LPIN1

241508_at
236685_at
ZNF644
CD200R1
PTGDR
AFG3L1P

225374_at
GLIPR1
FLJ13197
ZMYND11
231992_x_at
CNEP1R1

244414_at
OGT
HSPA14
PACRGL
221381_s_at
PMS2P5

CUL4B
CREB1
CNNM2
RNFT1
ZNF277
C14orf1

243002_at
YAF2
SENP2
CD58
RBM47
THOP1

PRMT2
215385_at
KLF4
USP42
SYN1
CLASP2

1569952_x_at
PPM1K
1558410_s_at
216745_x_at
SCP2
241477_at

CTSS
1562324_a_at
241837_at
235493_at
234159_at
233733_at

239561_at
SH2D1B
LOC100130654
CLASP1
FLJ10038
SNX19

ANAPC2
GNAI3
SNTB2
HACL1
FAM84B
ZNF554

GLUD1
AKAP11
233417_at
ANAPC7
BRAP
G2E3

SGPP1
1569578_at
236149_at
LOC286437
FOXJ1
SLC30A1

216166_at
ATF7
237404_at
RPS12
244845_at
ATF7IP

FAM178A
234260_at
DHRS4-AS1
PALLD
244550_at
228746_s_at

2-Sep
KLHDC10
KHSRP
MTO1
244422_at
232779_at

242751_at
RFWD3
SLC25A43
241114_s_at
RAB30
227052_at

239363_at
STX16
MESP1
UBXN7
TTLL5
240405_at

KCTD5
CTBP2
PSMD12
215376_at
NUCKS1
ZNF408

ZEB1
FOXN3
HMP19
241843_at
PRMT8
PIP5K1A

RAC1
239861_at
240020_at
TSR1
239659_at
CCPG1

UVSSA
LOC100505876
LRRFIP1
CD164
CENPL
ASCC1

226539_s_at
MLLT10
TNPO1
MRPS10
THADA
LINC00527

MS4A6A
243874_at
GALNT7
MACF1
MRPS11
UBQLN4

CNOT4
MDM4
VPS37A
213833_x_at
DBH
MAST4

1559491_at
TAOK1
PPP2CA
244665_at
LRRFIP1
RAP1GAP

NOP16
PIK3R5
TFEC
HPN
239445_at
SRSF4

HIRIP3
LINC00094
ENTPD6
PAK2
234753_x_at
LOC149401

PTPN11
242369_x_at
M6PR
MOB1A
TSHZ2
11-Sep

GFM2
242357_x_at
TAF9B
MTMR9LP
GTSE1
NIT1

SMCR8
243035_at
1564077_at
IGLJ3
243674_at
PTGS2

ZNF688
TMEM50B
PMAIP1
ADNP2
237201_at
ACTG1

KIAA0485
XAGE3
234596_at
PLA2G4F
HSP90AB1
ARHGEF1

ABHD10
FAS
1558748_at
NRBF2
216285_at
TRIM8

LOC729013
FBXO9
TMEM41A
SYTL3
ACTR3
TTC27

233440_at
239655_at
MIER3
C1orf174
KRCC1
GABPA

224173_s_at
VAMP3
UBAP2L
C16orf72
RHOA
243736_at

TAOK1
230918_at
NXT2
ZNF836
TRMT2B
TLR2

SLC39A6
ZNF615
PRRT3
KCNK3
ADK
VPS35

NAMPT
DLST
BRD3
UBXN8
PNRC1
MED28

CNBP
231042_s_at
243414_at
TIA1
216607_s_at
242542_at

MBP
TNPO3
MTMR2
NCOA2
GRM2
241893_at

PRKAR2A
CS
243178_at
CEP135
EIF3L
ZNF92

GNL3L
CLTC-IT1
TCF3
BCL7B
236704_at
GPC2

YWHAZ
MEF2C
SFTPB
HELB
MYO9B
TSPAN16

PPP6R2
PACSIN3
UBE2N
LOC642236
NGFRAP1
LOC100507602

RSPRY1
TROVE2
238836_at
PAK2
PSPC1
1561155_at

MBNL1
PPIF
RPRD1A
FPR2
239560_at
218458_at

DENR
RBM15B
RDH11
ITGA4
ERCC8
ZNF548

DNAJB9
242793_at
WWP2
MSI2
VPS13D
ADAM17

216766_at
ARHGAP32
CLYBL
SMPD1
MTM1
1561067_at

CLINT1
SHOX2
230240_at
TMEM92
ANKRD13D
207186_s_at

FBXO9
208811_s_at
VNN3
ARHGAP26
AKAP17A
MED29

ATXN1
217055_x_at
PECR
ZBTB1
LOC100506748
DTD2

1570021_at
LMNA
HLA-DPA1
GALNT9
RCN2
242695_at

ARFGEF1
PSEN1
TPCN2
CTNNB1
NTN5
238040_at

232333_at
CLCN7
LARS
227608_at
OAT
EIF1B-AS1

GOSR1
C15orf37
ZDHHC8
TMEM106C
ZNF562
1561181_at

FAM73A
TUG1
BCL6
PSD4
1560049_at
SERTAD2

244358_at
WDYHV1
IMPACT
DYNLRB1
215861_at
1561733_at

SPPL3
SELT
1558236_at
237868_x_at
A2M
FGFR1

ARFIP1
BBIP1
235295_at
PADI2
CADM3
PSMD10

SF3B3
TMCO3
APOBEC2
RALGPS1
CAMK2D
CCND3

TMEM185A
FBXO22
PIK3CG
ZNF790-AS1
TRIM4
DPYSL4

LARP1
PICALM
ZNF706
214194_at
CRYZL1
ANGPTL4

SETX
KLHL7
239396_at
FTCD
IL1R2
CAPS

POLR3A
233431_x_at
GATAD2B
1570299_at
WNT10A
238159_at

RSBN1
TNPO3
ZDHHC21
WDR20
SEH1L
225239_at

SNX13
SMAD4
MED4
208638_at
216789_at
STOX2

ZNF542
FAS
PRKACB
9-Sep
JAK3
207759_s_at

228623_at
RPL27A
HERPUD1
KLHL20
TFDP2
LOC100996349

242688_at
222295_x_at
PPFIA3
SOS2
C4orf29
ZNF805

237881_at
GHITM
222358_x_at
PTPN22
TACC2
LOC100131825

239414_at
IER5
TUBGCP4
CYB5R4
FBXO33
ZFP36L2

RASSF5
HSPD1
210338_s_at
232601_at
GOPC
KIF1B

222319_at
UEVLD
RABL5
IMPAD1
C1QBP
ZNF117

PPP1R3B
LPXN
242859_at
LOC100128751
NXPH3
SLC35D2

TAOK1
DENND2A
1554948_at
CXCR4
PLAUR
235441_at

MAP2K4
SPRTN
ZNF551
PODNL1
ZNF175
214996_at

NMT2
SF3A2
GPSM3
ZNF567
POLR2L
LOC100506127

STX16
DICER1
1556339_a_at
242839_at
ZAN
BTBD7

PDE7A
ANKRD17
236545_at
MAPRE2
239296_at
PNRC2

RALGAPA2
GOLGA7
MKRN2
PGM2
TBX2
232991_at

MED1
UBE2J1
AGAP2
PHF20L1
TUBB
PRKCSH

PICALM
215212_at
CCNK
TTF1
244847_at
PRM3

BTG2
236944_at
233103_at
ETV4
MEA1
GNAI3

KIAA2018
CREB1
239408_at
FAM159A
MLL
243839_s_at

230590_at
232835_at
NXPE3
EIF4E
SECISBP2L
PHTF2

AP5M1
LOC100631377
222306_at
232535_at
MCRS1
TSPYL5

217615_at
ZNF880
ANGPTL1
UBA1
219422_at
DCAF17

238519_at
TMEM63A
RYK
ZNF493
USP7
IDS

TBL1XR1
AKAP7
SCARB2
224105_x_at
TRDMT1
242413_at

MAT2A
LMBRD1
1557224_at
GPR137
EFS
213601_at

CDC42SE2
235138_at
DCUN1D4
ZKSCAN5
RAB30
INSIG2

228105_at
POLK
STX7
PRKAR2A
1562468_at
AP1G2

CHAMP1
TBCC
CCT2
SATB1
236592_at
SLC5A9

ASAH1
AK2
233107_at
ZNF346
MNX1
215462_at

TAF8
216094_at
ZNF765
ZFP90
236908_at
240046_at

PAPOLG
TRAPPC11
239933_x_at
SPG11
E2F5
PTPLAD1

240500_at
CSNK1A1
LOC283867
ALPK1
TRO
1559117_at

200041_s_at
PPP1R17
TNKS2
GOLGA2
MAN2A1
WARS2

233727_at
244383_at
ZNF70
PRRC2A
TPM2
SMAD4

DPP8
CNOT2
239646_at
216704_at
SH3BGRL
ZNF500

MAP3K7
1569041_at
NUFIP1
SMARCA2
FAM81A
KCTD12

SBNO1
MBIP
FOXK2
233626_at
MAST4
SRSF10

VPS13D
1555392_at
PRPF18
236404_at
FARSB
SAT1

1556657_at
RBM25
DARS
LOC100507918
HEATR3
1569538_at

TMEM170A
PRDX3
TLK2
NAP1L1
200653_s_at
REV1

ZFAND5
C1orf170
244219_at
FOPNL
SLC23A3
1558877_at

UBE2G1
FOXP1-IT1
LOC145474
LINC00526
LIN52
212929_s_at

233664_at
ERO1L
235422_at
ZMYND8
PDIA6
GRAMD4

1565743_at
TMCO1
244341_at
240118_at
242616_at
CNST

RAB5A
SORL1
HERC4
242380_at
ZNF419
NPY5R

243524_at
BRI3BP
MAST4
1565975_at
SETDB2
STK38

ATG5
PAFAH1B2
TMED10
MAP1A
XYLT2
ZFAND1

DSERG1
TRIM32
TRIM2
TMEM50B
PDXDC1
240216_at

ZNF440
1567101_at
236114_at
RABGAP1
1561128_at
P2RX6

IL18BP
PDK4
BAZ2A
GFM1
244035_at
239876_at

F2RL1
GOLGB1
FAM206A
GLOD4
GRIK2
UBE2D2

CRLS1
PRRC2A
ZC2HC1A
NAPB
HNMT
UBE2W

238277_at
ATM
239923_at
HYMAI
SP6
PLEKHB1

COL7A1
209084_s_at
SWAP70
PRKAB1
220719_at
232400_at

MAP3K2
240252_at
222197_s_at
1559362_at
244156_at
GJB1

TMX1
1557520_a_at
LRBA
EXOC5
227777_at
UBA6

MCFD2
216729_at
203742_s_at
C12orf43
PEX3
FCF1

SLMAP
ZCWPW1
235071_at
HAX1
HDAC2
240241_at

CNOT1
PRDX3
240520_at
CRISPLD2
229679_at
242343_x_at

242407_at
CWC25
NDNL2
TNPO1
RCOR2
ULK4

CDC5L
KIF5B
RPS23
ARF6
EIF3M
GATC

DCTN4
ETV5
235053_at
239661_at
SNRPA1
BET1

TM6SF1
232264_at
ZNF565
1562600_at
RBM8A
HIPK2

ZFP3
ARL8B
CCDC126
MAF
EMX1
GNB4

SRSF1
1562033_at
VEZF1
ETNK1
USP46
ERO1L

GDI2
NIN
COPS8
PSMD11
KCNE3
220728_at

TERF2
1558093_s_at
NSUN4
CNOT11
EDC3
MYOD1

ATM
236060_at
LOC283887
MEGF9
ALG13
1561318_at

1558425_x_at
243895_x_at
FBXO28
NNT
NUDT7
FAM213A

234148_at
244548_at
MRPL30
LUZP1
RAD1
1557688_at

ITGA4
VTA1
215278_at
MFGE8
EIF3B
208810_at

1556658_a_at
BRD2
243003_at
ZNF12
RPL35A
RNF207

POLR3E
UBXN2B
FNTA
KRAS
UBE2B
BHLHE40

BRD2
SLC33A1
ZKSCAN4
1555522_s_at
MBD4
S1PR1

RECQL4
SUZ12
HSPH1
PEX2
BIRC3
233690_at

PTPRC
QRSL1
OR4D1
FOXP1
240665_at
ZNF93

AURKAIP1
ARNTL
219112_at
238769_at
235123_at
BCR

TLR1
LTBP4
FAM170A
NIP7
242194_at
1559663_at

243203_at
CENPT
DDX51
STAM2
ARHGAP42
234942_s_at

PRPF4B
1556352_at
CANX
PSMB4
PQBP1
CHN2

239603_x_at
TTYH2
208750_s_at
NUDT13
221079_s_at
237013_at

ZBTB18
RAB40B
230350_at
232338_at
GTF2H2
SERPINB9

SLC35A3
TBL1XR1
234345_at
SSR3
229469_at
244597_at

239166_at
SPOP
SNX6
RABEP1
244123_at
230386_at

TM9SF2
NKTR
DCAF8
FCGR2C
MED28
SH2B1

237185_at
ATP2A2
ALG2
242691_at
ALDH3A2
234369_at

233824_at
RNF130
OSGIN2
230761_at
PLEKHG5
CALU

COG8
234113_at
CCDC85C
225642_at
1569519_at
DNM2

215528_at
NEK4
CNOT6L
ND4
RASA1
SRSF4

CDV3
ARPC5
ERBB4
DHDH
AKAP13
TMEM134

GINS4
ZBTB44
DNAJC16
FABP6
NT5DC1
TTF2

ATF1
YTHDF2
SPOPL
ZNF316
ATP5F1
E2F3

IBTK
1562412_at
FGD5-AS1
G3BP1
PMAIP1
239585_at

PRKAR1A
242995_at
ZNF44
KRCC1
212241_at
VANGL1

PSME3
TPD52
1568795_at
FOXK2
HTR7
CYP21A2

PRKCB
EML4
1555014_x_at
MORF4L1
TRIM65
214223_at

FAM27E3
PLA2G12A
HADH
FAM105B
244791_at
MTRF1L

244579_at
CANX
DPT
TLN1
CXorf56
STARD4

XYLT2
240666_at
GPSM3
220582_at
TAGLN
207488_at

5-Mar
PPP2R1B
TP73-AS1
241692_at
MRM1
ZNF451

DAZAP2
232919_at
234488_s_at
SLC35F5
ZNF503-AS2
LOC728537

CILP2
SYNPO2
240529_at
AKAP10
1569234_at
MPZL2

TMEM168
ORAI2
ZNF566
USP34
MAOB
ABCA11P

PDGFC
SETD5-AS1
GTF3C5
R3HDM1
MTM1
CSNK1G3

MYCN
BARD1
GSPT2
SMG6
CASC4
CDRT15L2

ZNF747
ND4
SAMD4B
PPP6R2
TMEM214
FAM204A

TTF1
SLC5A8
TFR2
MRPS10
LRCH3
IGSF9B

1566426_at
R3HDM1
236615_at
CREB1
TBC1D16
232759_at

ARV1
LYZ
1554771_at
NUS1
IQSEC2
HSF2BP

PRKD3
241769_at
P2RY10
MOB1A
MROH7
REST

1557538_at
TRIM15
JKAMP
C16orf55
CDK12
GLYR1

CACNA2D4
232290_at
DCAF16
ANKFY1
230868_at
GPCPD1

C22orf43
AKAP13
CCDC50
FBXL5
USP7
WDR45B

SMCR8
GDE1
UBE3A
PLA2G7
RFC3
231351_at

236931_at
SLC30A5
236322_at
CNOT6L
FAM22F
217679_x_at

RAB1A
1565677_at
EIF4G2
CUX1
MDM4
PARP10

RAB9A
PKN1
IPO13
1564378_a_at
242527_at
RBBP5

YWHAE
ACTR2
PON2
215123_at
TRIP6
233570_at

DIS3
GRB10
1561834_a_at
AKT2
HAUS6
1570229_at

RAB1A
NADKD1
COG6
SLC5A5
MRPL50
CRYL1

213740_s_at
LRCH3
GLB1L3
XPO7
240399_at
KATNBL1

244019_at
LOC100132874
TMEM64
AP1S2
231934_at
PRSS3P2

1570439_at
RAB7L1
MARS2
NFKBIB
TRAK1
1-Sep

CACNA2D4
ORC5
RP2
MLL2
240238_at
1561195_at

GLUD2
RNF170
TGIF1
NOC4L
230630_at
LIN7C

BLOC1S6
MS4A6A
PPARA
RPRD1A
RBM22
240319_at

LINC00667
SLC35D2
233313_at
RBM7
PIGM
237575_at

SCAMP1
TUBD1
SMARCA4
TTLL9
TSHZ2
S100A14

MAP3K2
SPOPL
ADO
ATPBD4
SPAG9
ARL6IP6

IBA57
SWAP70
ADH5
VASN
SPPL3
ARF4

ZCCHC10
HIBADH
243673_at
KLC4
ABCB8
P4HA2

1560741_at
TIMM17A
MOSPD2
ABHD5
COL17A1
1559702_at

FAM43A
DOCK4
TAF9B
214740_at
230617_at
TMEM106B

ALS2CR8
C9orf53
YY1
PTP4A2
HADH
236125_at

233296_x_at
1569311_at
MESDC1
EPC1
ATF6
WTAP

INF2
231005_at
GON4L
MZT2B
1554089_s_at
IGDCC3

PRMT6
C1orf228
LARP4
RASGRF1
PLEKHA4
ENDOV

IL13RA1
NLGN3
217446_x_at
SETD5-AS1
200624_s_at
1556931_at

SF3A2
1558670_at
RPP14
220691_at
TMEM239
TP53AIP1

FBXL12
PAGR1
RNF113A
FBXL20
237171_at
242233_at

233554_at
RRAS2
1558237_x_at
SQSTM1
MANEA
SPEN

241867_at
PDE8A
PTGER1
TRAPPC10
CLDN16
C16orf52

OGFRL1
IFNGR1
1561871_at
1565762_at
LOC100506369
SYMPK

231191_at
UHRF1BP1L
LCMT2
STX17
C6orf120
ITSN2

1560082_at
240446_at
MED23
PRKRA
ANKRD13C
RFC1

RHEB
GBAS
215874_at
MED30
1556373_a_at
ZC3H7A

RHOA
MRPS10
TRAM1
RHAG
214658_at
TRIM23

RRN3P3
ANKS1B
ATPAF1
PPID
1561167_at
240505_at

ELMSAN1
202374_s_at
NR2C2
APP
235804_at
DYRK1B

ADAT1
244382_at
ZNF451
RERE
ASPHD1
TOR1AIP1

242320_at
MCL1
DENND6B
1560862_at
PTEN
TMEM203

1555194_at
ZNF430
FAM83G
239453_at
CCNT2
ASAH1

238108_at
SFRP1
NAA40
ERN2
237264_at
CUL4B

240315_at
ARNT
TSPAN3
240279_at
ERICH1
BZRAP1-AS1

SCOC
ZNF318
MLLT10
222371_at
TTC9C
MTSS1

C15orf57
ZFR
WDR4
PEBP1
IGIP
DNM1

1562067_at
MEF2A
234609_at
AP1G1
MGC70870
ZNF777

235862_at
COL7A1
NIPSNAP3A
TRIOBP
JAK2
RALGAPA1

UBE2D4
ZNRF2
ANKRD52
RNF213
221242_at
IMPAD1

240478_at
RFC3
TMEM251
HNRNPM
LUC7L
222315_at

239409_at
FAM110A
234590_x_at
EBPL
217549_at
CDKN2AIP

ZNF146
CRYBB3
NAP1L1
LOC142937
LOC283174
SCAMP1

RASA2
WDR19
242920_at
CEP57
234788_x_at
216584_at

CSNK1A1
RPS2
FAM103A1
239121_at
UBE2D1
238656_at

B3GNT2
ETNK1
STK38L
C6orf106
PTP4A1
CAT

LOC100131067
227505_at
DNAJC10
PPP1R12C
SMG5
LOC100505876

FDX1
C2orf43
ARFIP2
ETNK1
CEP350
EMC4

TADA3
NCK1
DEFB1
IRF8
233473_x_at
GNAQ

IKZF1
222378_at
TMEM55A
TMEM88
LMBRD2
228694_at

242827_x_at
ST3GAL6
SSBP2
ENOPH1
LOC100505555
RICTOR

DNAJC14
222375_at
MAU2
CEP68
FAM45A
NDFIP2

NEDD9
ENTHD2
OSBPL11
WDR48
HBP1
243473_at

CD47
1557270_at
PAFAH1B2
PRKCH
PSME3
ACYP2

GSDMB
TPGS1
NOL9
ZNF224
HNRNPUL1
MTFR1

FABP3
PHLPP2
POU5F1P4
EPN1
244732_at
TRIM4

DR1
CYP2A6
CHD9
TET3
LILRA2
ZAN

FLT1
ADAM10
HEATR5B
PPP1R8
ANTXR1
215986_at

PCBD2
202397_at
RPRD1A
USP9X
PATL1
SRP72

1562194_at
MAVS
CAMSAP1
CCL22
1562056_at
KLF4

TRAPPC1
PDIA6
1570408_at
WDR77
236438_at
DRAXIN

PRKCB
234201_x_at
239171_at
FAM175A
C9orf84
NUP188

APCDD1L-AS1
VAMP2
CCDC108
INPP4A
OTOR
VAV3

243249_at
239264_at
NLRP1
HIGD1A
MRPL19
YWHAZ

CHMP2B
IFNGR2
240165_at
MAVS
TGFB1
CHD9

ABCD3
HNRNPC
1554413_s_at
LYPLA1
ARHGAP27
233960_s_at

DSTYK
ARHGAP26
1566959_at
FAM99B
FAM131C
228812_at

CASP8
243568_at
GGT7
CAT
SETBP1
DLD

CUL4A
ATXN10
RPS15
RPS10P7
ELP2
SMIM15

233648_at
FOXP3
1565915_at
ETNK1
1557512_at
1557724_a_at

242558_at
217164_at
232584_at
SUMO2
TRMT11
NSMCE4A

1557562_at
222436_s_at
233783_at
TCOF1
227556_at
ENDOG

MAGI1
DNAJB11
SNX5
POGZ
ST8SIA4
EVI5L

UBTD2
237311_at
241460_at
RHOQ
PDLIM5
FAM63A

EIF4E3
SMAD7
MUC3B
DKFZP547L112
BTBD7
IL1R2

CFDP1
204347_at
1556336_at
EDIL3
239613_at
MCTP2

COG7
UCHL5
MOGAT2
GHDC
UFL1
238807_at

234645_at
MGA
242476_at
COMMD10
POLH
CADM1

243992_at
CSGALNACT1
NICN1
HAP1
237456_at
OBSCN

VAMP3
NR5A1
DDX42
TMEM48
229717_at
HNRNPH1

243280_at
SEC23A
DNAJC10
ANKZF1
C17orf70
NUCKS1

NAA50
C15orf57
1555996_s_at
MUC20
1566823_a_at
USP31

AVL9
SRSF5
241445_at
DDR1-AS1
CTSC
HDGFRP2

TOB2
CTDSPL2
240008_at
RCN2
244674_at
XRCC6

C5orf22
6-Mar
PRKAR1B
240538_at
242058_at
226252_at

236612_at
PCBP3
LOC374443
CHD2
INTS10
OXR1

1558710_at
1556055_at
231604_at
FASTKD3
SARDH
ALPI

RAB3IP
SPTLC2
B4GALT1
243286_at
SCLT1
CASP8

KLHL28
PCMT1
EXOC6
CPNE6
229575_at
6-Sep

CCNG2
TMTC4
LYRM4
ZDHHC13
NKTR
232626_at

ZNF207
SGK494
ERVK13-1
CD44
SEZ6L2
211910_at

PEBP1
243860_at
NECAP2
RECQL5
ACHE
DNASE1L3

ROCK1
244332_at
CCDC40
SULT4A1
TTC9B
NF1

242927_at
MBD2
237330_at
SEC23IP
SUPT6H
TTC5

241387_at
235613_at
KIAA0141
ID2
GPR65
222156_x_at

ZNF304
LOC100507283
FAM174A
LINC00661
FLNB
FAM181A

227384_s_at
RAB22A
241376_at
DRD2
ATP9A
ATF1

HOXB2
PDCD6IP
TOR3A
FYTTD1
PLAGL1
RHOQ

AGGF1
9-Sep
243454_at
MAPRE1
LRRC37A3
IL13RA1

HIPK3
ZBTB43
SLC20A2
POT1
LINS
CCNY

SERP1
235847_at
ZNF655
242768_at
1566166_at
CEP57

TOR1AIP1
VAMP4
KCP
243078_at
RICTOR
LOC729852

242824_at
1556865_at
ZNF398
RSBN1L
DCAF13
PEX3

IKBIP
GOLPH3
BCLAF1
UPRT
GRB10
ARID4B

CHD7
PEX12
243396_at
C19orf43
1560982_at
DCAF8

SETD5
KBTBD4
216567_at
PTER
PTP4A2
1557422_at

REPS1
MLLT11
COX2
239780_at
1562051_at
SNAP23

233323_at
FLJ31813
ATP11A
239451_at
CCNL1
OGFRL1

TTBK2
RAB6A
216448_at
CADM3
C17orf58
239164_at

237895_at
PTPN11
PDIA6
243512_x_at
GRIPAP1
207756_at

IL13RA1
CHMP1B
UBE2D3
ZBTB20
SIAH1
242732_at

DIO3
242457_at
APPBP2
239555_at
232788_at
NOL9

234091_at
CIAPIN1
MCTP2
236610_at
RTCA
TCF3

233228_at
PRRC2A
ZNF708
MIER3
NENF
SAE1

B4GALNT3
240775_at
MIA
SLC32A1
233922_at
ZNF280D

MED30
238712_at
XRCC1
215197_at
216850_at
C12orf5

243826_at
216465_at
RBM15
ADD1
SNX10
1556327_a_at

TANK
ZFYVE21
PDP1
230980_x_at
BTBD18
MMP28

232909_s_at
RQCD1
CD9
FAM184B
BRCC3
ACSS1

TNFRSF19
SCAF11
COMMD10
FGF18
FAHD2CP
ULK2

1569999_at
1562063_x_at
KCTD18
234449_at
243509_at
AGO1

1558418_at
SMARCC2
HEATR5A
LACTB2
TFB2M
TPI1

UBE4B
XAB2
NCOA2
1556778_at
PALM3
TPSAB1

239557_at
SLC25A40
TRAPPC13
KDELR2
242386_x_at
CCDC28A

232890_at
TSPAN12
CELF2
238559_at
ATP5S
CEP152

ITPR1
KLHL9
206088_at
ZNF235
DCTD
CDK9

MEAF6
217653_x_at
PRSS53
239619_at
REL
RAE1

239331_at
U2SURP
SLC22A31
GNL3L
TOMM22
PROK2

243808_at
RALGAPA2
POLK
216656_at
SSTR5-AS1
CCNY

ZNF580
TPM3
SEC24A
USP2
LRMP
233790_at

KLF9
244474_at
MFI2
CGGBP1
TTL
PLEKHG3

241788_x_at
LOC150381
RUNX1-IT1
SOAT1
ALG13
G3BP2

204006_s_at

3-class univariate F-test was done on the Discovery cohort (1000 random permutations and FDR <10%; BRB ArrayTools)

Number of significant genes by controlling the proportion of false positive genes: 2977

Sorted by p-value of the univariate test.

Class 1: ADNR; Class 2: AR; Class 3: TX.

With probability of 80% the first 2977 genes contain no more than 10% of false discoveries. Further extension of the list was halted because the list would contain more than 100 false discoveries

The ‘Pairwise significant’ column shows pairs of classes with significantly different gene expression at alpha =0.01. Class labels in a pair are ordered (ascending) by their averaged gene expression.

TABLE 1c

The top 200 gene probeset used in the 3-Way AR, ADNR TX

ANOVA Analysis (using the HT HG-U133 + PM Array Plates)

3-Way AR, ADNR TX ANOVA Analysis

p-value -

#
Probeset ID
Gene Symbol
Gene Title
phenotype

1
213718_PM_at
RBM4
RNA binding motif protein 4
5.39E−10

2
227878_PM_s_at
ALKBH7
alkB, alkylation repair homolog 7 (E.
3.15E−08

coli)

3
214405_PM_at
214405_PM_at_EST1
EST1
3.83E−08

4
210792_PM_x_at
SIVA1
SIVA1, apoptosis-inducing factor
4.64E−08

5
214182_PM_at
214182_PM_at_EST2
EST2
6.38E−08

6
1554015_PM_a_at
CHD2
chromodomain helicase DNA binding
6.50E−08

protein 2

7
225839_PM_at
RBM33
RNA binding motif protein 33
7.41E−08

8
1554014_PM_at
CHD2
chromodomain helicase DNA binding
7.41E−08

protein 2

9
214263_PM_x_at
POLR2C
polymerase (RNA) II (DNA directed)
7.47E−08

polypeptide C, 33 kDa

10
1556865_PM_at
1556865_PM_at_EST3
EST3
9.17E−08

11
203577_PM_at
GTF2H4
general transcription factor IIH,
9.44E−08

polypeptide 4, 52 kDa

12
218861_PM_at
RNF25
ring finger protein 25
1.45E−07

13
206061_PM_s_at
DICER1
dicer 1, ribonuclease type III
1.53E−07

14
225377_PM_at
C9orf86
chromosome 9 open reading frame 86
1.58E−07

15
1553107_PM_s_at
C5orf24
chromosome 5 open reading frame 24
2.15E−07

16
1557246_PM_at
KIDINS220
kinase D-interacting substrate, 220 kDa
2.43E−07

17
224455_PM_s_at
ADPGK
ADP-dependent glucokinase
3.02E−07

18
201055_PM_s_at
HNRNPA0
heterogeneous nuclear ribonucleoprotein
3.07E−07

A0

19
236237_PM_at
236237_PM_at_EST4
EST4
3.30E−07

20
211833_PM_s_at
BAX
BCL2-associated X protein
3.92E−07

21
1558111_PM_at
MBNL1
muscleblind-like (Drosophila)
3.93E−07

22
206113_PM_s_at
RAB5A
RAB5A, member RAS oncogene family
3.95E−07

23
202306_PM_at
POLR2G
polymerase (RNA) II (DNA directed)
4.83E−07

polypeptide G

24
242268_PM_at
CELF2
CUGBP, Elav-like family member 2
5.87E−07

25
223332_PM_x_at
RNF126
ring finger protein 126
6.24E−07

26
1561909_PM_at
1561909_PM_at_EST5
EST5
6.36E−07

27
213940_PM_s_at
FNBP1
formin binding protein 1
6.60E−07

28
210655_PM_s_at
FOXO3 /// FOXO3B
forkhead box O3 /// forkhead box O3B
6.82E−07

pseudogene

29
233303_PM_at
233303_PM_at_EST6
EST6
8.00E−07

30
244219_PM_at
244219_PM_at_EST7
EST7
9.55E−07

31
35156_PM_at
R3HCC1
R3H domain and coiled-coil containing
1.02E−06

1

32
215210_PM_s_at
DLST
dihydrolipoamide S-succinyltransferase
1.08E−06

(E2 component of 2-oxo-glutarate

complex)

33
1563431_PM_x_at
CALM3
Calmodulin 3 (phosphorylase kinase,
1.15E−06

delta)

34
202858_PM_at
U2AF1
U2 small nuclear RNA auxiliary factor 1
1.18E−06

35
1555536_PM_at
ANTXR2
anthrax toxin receptor 2
1.21E−06

36
210313_PM_at
LILRA4
leukocyte immunoglobulin-like receptor,
1.22E−06

subfamily A (with TM domain), member

4

37
216997_PM_x_at
TLE4
transducin-like enhancer of split 4
1.26E−06

(E(sp1) homolog, Drosophila)

38
201072_PM_s_at
SMARCC1
SWI/SNF related, matrix associated,
1.28E−06

actin dependent regulator of chromatin,

subfamily c

39
223023_PM_at
BET1L
blocked early in transport 1 homolog (S.
1.36E−06

cerevisiae)-like

40
201556_PM_s_at
VAMP2
vesicle-associated membrane protein 2
1.39E−06

(synaptobrevin 2)

41
218385_PM_at
MRPS18A
mitochondrial ribosomal protein S18A
1.44E−06

42
1555420_PM_a_at
KLF7
Kruppel-like factor 7 (ubiquitous)
1.50E−06

43
242726_PM_at
242726_PM_at_EST8
EST8
1.72E−06

44
233595_PM_at
USP34
ubiquitin specific peptidase 34
1.79E−06

45
218218_PM_at
APPL2
adaptor protein, phosphotyrosine
1.80E−06

interaction, PH domain and leucine

zipper containing 2

46
240991_PM_at
240991_PM_at_EST9
EST9
1.95E−06

47
210763_PM_x_at
NCR3
natural cytotoxicity triggering receptor 3
2.05E−06

48
201009_PM_s_at
TXNIP
thioredoxin interacting protein
2.10E−06

49
221855_PM_at
SDHAF1
succinate dehydrogenase complex
2.19E−06

assembly factor 1

50
241955_PM_at
HECTD1
HECT domain containing 1
2.37E−06

51
213872_PM_at
C6orf62
Chromosome 6 open reading frame 62
2.57E−06

52
243751_PM_at
243751_PM_at_EST10
EST10
2.60E−06

53
232908_PM_at
ATAD2B
ATPase family, AAA domain containing
2.64E−06

2B

54
222413_PM_s_at
MLL3
myeloid/lymphoid or mixed-lineage
2.74E−06

leukemia 3

55
217550_PM_at
ATF6
activating transcription factor 6
2.86E−06

56
223123_PM_s_at
C1orf128
chromosome 1 open reading frame 128
2.87E−06

57
202283_PM_at
SERPINF1
serpin peptidase inhibitor, clade F
2.87E−06

(alpha-2 antiplasmin, pigment epithelium

derived fa

58
200813_PM_s_at
PAFAH1B1
platelet-activating factor acetylhydrolase
3.10E−06

1b, regulatory subunit 1 (45 kDa)

59
223312_PM_at
C2orf7
chromosome 2 open reading frame 7
3.30E−06

60
217399_PM_s_at
FOXO3 /// FOXO3B
forkhead box O3 /// forkhead box O3B
3.31E−06

pseudogene

61
218571_PM_s_at
CHMP4A
chromatin modifying protein 4A
3.47E−06

62
228727_PM_at
ANXA11
annexin A11
3.73E−06

63
200055_PM_at
TAF10
TAF10 RNA polymerase II, TATA box
3.76E−06

binding protein (TBP)-associated factor,

30 kDa

64
242854_PM_x_at
DLEU2
deleted in lymphocytic leukemia 2 (non-
3.84E−06

protein coding)

65
1562250_PM_at
1562250_PM_at_EST11
EST11
3.99E−06

66
208657_PM_s_at
SEPT9
septin 9
4.12E−06

67
201394_PM_s_at
RBM5
RNA binding motif protein 5
4.33E−06

68
200898_PM_s_at
MGEA5
meningioma expressed antigen 5
4.55E−06

(hyaluronidase)

69
202871_PM_at
TRAF4
TNF receptor-associated factor 4
4.83E−06

70
1558527_PM_at
LOC100132707
hypothetical LOC100132707
4.85E−06

71
203479_PM_s_at
OTUD4
OTU domain containing 4
4.86E−06

72
219931_PM_s_at
KLHL12
kelch-like 12 (Drosophila)
4.88E−06

73
203496_PM_s_at
MED1
mediator complex subunit 1
4.99E−06

74
216112_PM_at
216112_PM_at_EST12
EST12
5.22E−06

75
1557418_PM_at
ACSL4
Acyl-CoA synthetase long-chain family
5.43E−06

member 4

76
212113_PM_at
ATXN7L3B
ataxin 7-like 3B
5.67E−06

77
204246_PM_s_at
DCTN3
dynactin 3 (p22)
5.68E−06

78
235868_PM_at
MGEA5
Meningioma expressed antigen 5
5.70E−06

(hyaluronidase)

79
232725_PM_s_at
MS4A6A
membrane-spanning 4-domains,
5.73E−06

subfamily A, member 6A

80
212886_PM_at
CCDC69
coiled-coil domain containing 69
5.84E−06

81
226840_PM_at
H2AFY
H2A histone family, member Y
5.86E−06

82
226825_PM_s_at
TMEM165
transmembrane protein 165
5.96E−06

83
227924_PM_at
INO80D
INO80 complex subunit D
6.18E−06

84
238816_PM_at
PSEN1
presenilin 1
6.18E−06

85
224798_PM_s_at
C15orf17
chromosome 15 open reading frame 17
6.31E−06

86
243295_PM_at
RBM27
RNA binding motif protein 27
6.34E−06

87
207460_PM_at
GZMM
granzyme M (lymphocyte met-ase 1)
6.46E−06

88
242131_PM_at
ATP6
ATP synthase F0 subunit 6
6.56E−06

89
228637_PM_at
ZDHHC1
zinc finger, DHHC-type containing 1
6.80E−06

90
233575_PM_s_at
TLE4
transducin-like enhancer of split 4
7.08E−06

(E(sp1) homolog, Drosophila)

91
215088_PM_s_at
SDHC
succinate dehydrogenase complex,
7.18E−06

subunit C, integral membrane protein,

15 kDa

92
209675_PM_s_at
HNRNPUL1
heterogeneous nuclear ribonucleoprotein
7.35E−06

U-like 1

93
37462_PM_i_at
SF3A2
splicing factor 3a, subunit 2, 66 kDa
7.38E−06

94
236545_PM_at
236545_PM_at_EST13
EST13
7.42E−06

95
232846_PM_s_at
CDH23
cadherin-related 23
7.42E−06

96
242679_PM_at
LOC100506866
hypothetical LOC100506866
7.52E−06

97
229860_PM_x_at
C4orf48
chromosome 4 open reading frame 48
7.60E−06

98
243557_PM_at
243557_PM_at_EST14
EST14
7.62E−06

99
222638_PM_s_at
C6orf35
chromosome 6 open reading frame 35
7.67E−06

100
209477_PM_at
EMD
emerin
7.70E−06

101
213328_PM_at
NEK1
NIMA (never in mitosis gene a)-related
7.72E−06

kinase 1

102
1555843_PM_at
HNRNPM
Heterogeneous nuclear ribonucleoprotein
7.72E−06

M

103
241240_PM_at
241240_PM_at_EST15
EST15
7.74E−06

104
218600_PM_at
LIMD2
LIM domain containing 2
7.81E−06

105
212994_PM_at
THOC2
THO complex 2
7.84E−06

106
243046_PM_at
243046_PM_at_EST16
EST16
8.03E−06

107
211947_PM_s_at
BAT2L2
HLA-B associated transcript 2-like 2
8.04E−06

108
238800_PM_s_at
ZCCHC6
Zinc finger, CCHC domain containing 6
8.09E−06

109
228723_PM_at
228723_PM_at_EST17
EST17
8.11E−06

110
242695_PM_at
242695_PM_at_EST18
EST18
8.30E−06

111
216971_PM_s_at
PLEC
plectin
8.39E−06

112
220746_PM_s_at
UIMC1
ubiquitin interaction motif containing 1
8.44E−06

113
238840_PM_at
LRRFIP1
leucine rich repeat (in FLU) interacting
8.59E−06

protein 1

114
1556055_PM_at
1556055_PM_at_EST19
EST19
8.74E−06

115
AFFX-
AFFX-
EST20
9.08E−06

M27830_5_at
M27830_5_at_EST20

116
215248_PM_at
GRB10
growth factor receptor-bound protein 10
9.43E−06

117
211192_PM_s_at
CD84
CD84 molecule
1.01E−05

118
214383_PM_x_at
KLHDC3
kelch domain containing 3
1.04E−05

119
208478_PM_s_at
BAX
BCL2-associated X protein
1.08E−05

120
229422_PM_at
NRD1
nardilysin (N-arginine dibasic
1.12E−05

convertase)

121
206636_PM_at
RASA2
RAS p21 protein activator 2
1.14E−05

122
1559589_PM_a_at
1559589_PM_a_at_EST21
EST21
1.16E−05

123
229676_PM_at
MTPAP
Mitochondrial poly(A) polymerase
1.18E−05

124
201369_PM_s_at
ZFP36L2
zinc finger protein 36, C3H type-like 2
1.19E−05

125
215535_PM_s_at
AGPAT1
1-acylglycerol-3-phosphate O-
1.25E−05

acyltransferase 1 (lysophosphatidic acid

acyltransferase,

126
212162_PM_at
KIDINS220
kinase D-interacting substrate, 220 kDa
1.26E−05

127
218893_PM_at
ISOC2
isochorismatase domain containing 2
1.26E−05

128
204334_PM_at
KLF7
Kruppel-like factor 7 (ubiquitous)
1.26E−05

129
221598_PM_s_at
MED27
mediator complex subunit 27
1.31E−05

130
221060_PM_s_at
TLR4
toll-like receptor 4
1.32E−05

131
224821_PM_at
ABHD14B
abhydrolase domain containing 14B
1.35E−05

132
244349_PM_at
244349_PM_at_EST22
EST22
1.38E−05

133
244418_PM_at
244418_PM_at_EST23
EST23
1.41E−05

134
225157_PM_at
MLXIP
MLX interacting protein
1.42E−05

135
228469_PM_at
PPID
Peptidylprolyl isomerase D
1.44E−05

136
224332_PM_s_at
MRPL43
mitochondrial ribosomal protein L43
1.47E−05

137
1553588_PM_at
ND3 /// SH3KBP1
NADH dehydrogenase, subunit 3
1.48E−05

(complex I) /// SH3-domain kinase

binding protein 1

138
238468_PM_at
TNRC6B
trinucleotide repeat containing 6B
1.49E−05

139
235727_PM_at
KLHL28
kelch-like 28 (Drosophila)
1.53E−05

140
218978_PM_s_at
SLC25A37
solute carrier family 25, member 37
1.59E−05

141
221214_PM_s_at
NELF
nasal embryonic LHRH factor
1.62E−05

142
204282_PM_s_at
FARS2
phenylalanyl-tRNA synthetase 2,
1.64E−05

mitochondrial

143
236155_PM_at
ZCCHC6
Zinc finger, CCHC domain containing 6
1.65E−05

144
224806_PM_at
TRIM25
tripartite motif-containing 25
1.66E−05

145
202840_PM_at
TAF15
TAF15 RNA polymerase II, TATA box
1.67E−05

binding protein (TBP)-associated factor,

68 kDa

146
207339_PM_s_at
LTB
lymphotoxin beta (TNF superfamily,
1.68E−05

member 3)

147
221995_PM_s_at
221995_PM_s_at_EST24
EST24
1.69E−05

148
242903_PM_at
IFNGR1
interferon gamma receptor 1
1.70E−05

149
228826_PM_at
228826_PM_at_EST25
EST25
1.70E−05

150
220231_PM_at
C7orf16
chromosome 7 open reading frame 16
1.71E−05

151
242861_PM_at
NEDD9
neural precursor cell expressed,
1.72E−05

developmentally down-regulated 9

152
202785_PM_at
NDUFA7
NADH dehydrogenase (ubiquinone) 1
1.74E−05

alpha subcomplex, 7, 14.5 kDa

153
205787_PM_x_at
ZC3H11A
zinc finger CCCH-type containing 11A
1.76E−05

154
1554333_PM_at
DNAJA4
DnaJ (Hsp40) homolog, subfamily A,
1.77E−05

member 4

155
1563315_PM_s_at
ERICH1
glutamate-rich 1
1.82E−05

156
202101_PM_s_at
RALB
v-ral simian leukemia viral oncogene
1.82E−05

homolog B (ras related; GTP binding

protein)

157
210210_PM_at
MPZL1
myelin protein zero-like 1
1.84E−05

158
217234_PM_s_at
EZR
ezrin
1.85E−05

159
219222_PM_at
RBKS
ribokinase
1.86E−05

160
213161_PM_at
TMOD1 /// TSTD2
tropomodulin 1 /// thiosulfate
1.88E−05

sulfurtransferase (rhodanese)-like

domain containing 2

161
236497_PM_at
LOC729683
hypothetical protein LOC729683
1.91E−05

162
203111_PM_s_at
PTK2B
PTK2B protein tyrosine kinase 2 beta
1.93E−05

163
1554571_PM_at
APBB1IP
amyloid beta (A4) precursor protein-
1.97E−05

binding, family B, member 1 interacting

protein

164
212007_PM_at
UBXN4
UBX domain protein 4
1.98E−05

165
1569106_PM_s_at
SETD5
SET domain containing 5
1.98E−05

166
243032_PM_at
243032_PM_at_EST26
EST26
2.00E−05

167
216380_PM_x_at
216380_PM_x_at_EST27
EST27
2.00E−05

168
217958_PM_at
TRAPPC4
trafficking protein particle complex 4
2.10E−05

169
200884_PM_at
CKB
creatine kinase, brain
2.11E−05

170
208852_PM_s_at
CANX
calnexin
2.12E−05

171
1558624_PM_at
1558624_PM_at_EST28
EST28
2.19E−05

172
203489_PM_at
SIVA1
SIVA1, apoptosis-inducing factor
2.23E−05

173
240652_PM_at
240652_PM_at_EST29
EST29
2.25E−05

174
214639_PM_s_at
HOXA1
homeobox A1
2.37E−05

175
203257_PM_s_at
C11orf49
chromosome 11 open reading frame 49
2.45E−05

176
217507_PM_at
SLC11A1
solute carrier family 11 (proton-coupled
2.56E−05

divalent metal ion transporters), member

1

177
223166_PM_x_at
C9orf86
chromosome 9 open reading frame 86
2.57E−05

178
206245_PM_s_at
IVNS1ABP
influenza virus NS1A binding protein
2.62E−05

179
223290_PM_at
PDXP /// SH3BP1
pyridoxal (pyridoxine, vitamin B6)
2.64E−05

phosphatase /// SH3-domain binding

protein 1

180
224732_PM_at
CHTF8
CTF8, chromosome transmission fidelity
2.69E−05

factor 8 homolog (S. cerevisiae)

181
204560_PM_at
FKBP5
FK506 binding protein 5
2.75E−05

182
1556283_PM_s_at
FGFR1OP2
FGFR1 oncogene partner 2
2.75E−05

183
212451_PM_at
SECISBP2L
SECIS binding protein 2-like
2.76E−05

184
208750_PM_s_at
ARF1
ADP-ribosylation factor 1
2.81E−05

185
238987_PM_at
B4GALT1
UDP-Gal:betaGlcNAc beta 1,4-
2.82E−05

galactosyltransferase, polypeptide 1

186
227211_PM_at
PHF19
PHD finger protein 19
2.84E−05

187
223960_PM_s_at
C16orf5
chromosome 16 open reading frame 5
2.86E−05

188
223009_PM_at
C11orf59
chromosome 11 open reading frame 59
2.88E−05

189
229713_PM_at
PIP4K2A
Phosphatidylinositol-5-phosphate 4-
2.96E−05

kinase, type II, alpha

190
1555330_PM_at
GCLC
glutamate-cysteine ligase, catalytic
2.96E−05

subunit

191
242288_PM_s_at
EMILIN2
elastin microfibril interfacer 2
2.97E−05

192
207492_PM_at
NGLY1
N-glycanase 1
2.98E−05

193
233292_PM_s_at
ANKHD1 /// ANKHD1-
ankyrin repeat and KH domain
3.00E−05

EIF4EBP3
containing 1 /// ANKHD1-EIF4EBP3

readthrough

194
1569600_PM_at
DLEU2
Deleted in lymphocytic leukemia 2 (non-
3.00E−05

protein coding)

195
218387_PM_s_at
PGLS
6-phosphogluconolactonase
3.03E−05

196
239660_PM_at
RALGAPA2
Ral GTPase activating protein, alpha
3.07E−05

subunit 2 (catalytic)

197
230733_PM_at
230733_PM_at_EST30
EST30
3.07E−05

198
1557804_PM_at
1557804_PM_at_EST31
EST31
3.11E−05

199
210969_PM_at
PKN2
protein kinase N2
3.12E−05

200
233937_PM_at
GGNBP2
gametogenetin binding protein 2
3.13E−05

TABLE 1d

The gene list of all 4132 genes analyzed in the 3-Way AR, ADNR

TX ANOVA Analysis (using the HT HG-U133 + PM Array Plates)

RBM4
IFT52
TMSB4Y
SLAMF6

ALKBH7
240759_PM_at
FLT3LG
LOC100287401

214405_PM_at_EST1
EZR
MTMR2
CFLAR

SIVA1
DEXI
C8orf82
PIGV

214182_PM_at_EST2
SMARCC2
ELP4
RB1CC1

CHD2
ADPGK
TEX264
SLC25A37

RBM33
TACR1
STX3
CES2

CHD2
FLJ10661
208278_PM_s_at
CLYBL

POLR2C
ACSL4
VPS25
SDHD

1556865_PM_at_EST3
STAC3
ISCA1
TRAF3IP3

GTF2H4
PHF20
SNCA
MRPL2

RNF25
UBXN7
CCDC17
CISH

DICER1
SCFD1
CCDC51
FBXO9

C9orf86
1559391_PM_s_at
MTPAP
LOC284454

C5orf24
1570151_PM_at
1566958_PM_at
CUL4B

KIDINS220
MED13L
STRADB
PHTF1

ADPGK
XCL1
HEY1
TACC1

HNRNPA0
1563833_PM_at
LOC100129361
RBM8A

236237_PM_at_EST4
CPEB4
EHBP1
PCMT1

BAX
PFKFB2
TMEM223
LOC100507315 ///

PPP2R5C

MBNL1
244357_PM_at
OGT
TNPO1

RAB5A
DAAM2
231005_PM_at
FXR2

POLR2G
CCDC57
C16orf5
SLC25A19

CELF2
CDKN1C
GNGT2
RAB34

RNF126
CTAGE5
IKZF1
IFT52

1561909_PM_at_EST5
PTEN /// PTENP1
242008_PM_at
LCP1

FNBP1
DENND1C
STON2
ZNF677

FOXO3 /// FOXO3B
TAGAP
CALM1
HCG22

233303_PM_at_EST6
ANXA3
217347_PM_at
1564155_PM_x_at

244219_PM_at_EST7
UBE2I
ID3
OBFC2B

R3HCC1
LOC728723
TGIF2
SRSF2

DLST
243695_PM_at
CELF2
SORBS1

CALM3
C15orf57
MAP3K13
WDR82

U2AF1
ALG8
SLC4A7
ALOX5

ANTXR2
PSMD7
CCDC97
TES

LILRA4
C9orf84
UBE2J2
CD46

TLE4
TSPAN18
PATL1
CALU

SMARCC1
PIAS2
DTX3
ARHGDIA

BET1L
RNF145
ZNF304
SDCCAG8

VAMP2
C19orf43
GDAP2
C7orf26

MRPS18A
208324_PM_at
227729_PM_at
SEMA4F

KLF7
FCAR
TMED2
TLR1

242726_PM_at_EST8
LTN1
C6orf125
222300_PM_at

USP34
PKN2
PGAP3
NUDT4

APPL2
TTLL3
ZMYND11
MLL3

240991_PM_at_EST9
NAA15
CYB5B
SVIL

NCR3
GNAQ
MBD6
IFT46

TXNIP
KLF6
241434_PM_at
ARIH2

SDHAF1
TOR1A
GHITM
NFYC

HECTD1
SLC27A5
244555_PM_at
RABL2A /// RABL2B

C6orf62
SLC2A11
CTSB
235661_PM_at

243751_PM_at_EST10
240939_PM_x_at
LYRM7
PTPRC

ATAD2B
LSMD1
DNASE1
243031_PM_at

MLL3
WAC
FECH
ESD

ATF6
LOC100128590
UBE2W
244642_PM_at

C1orf128
NSMAF
TLK1
EDF1

SERPINF1
VAV3
229483_PM_at
C1orf159

PAFAH1B1
239166_PM_at
MKLN1
CHP

C2orf7
ST8SIA4
DERL2
1558385_PM_at

FOXO3 /// FOXO3B
ACTR10
C11orf31
1557667_PM_at

CHMP4A
PXK
PHC3
PDE6B

ANXA11
DGUOK
PPM1L
MFAP1

TAF10
MTG1
LOC100505764
TMED4

DLEU2
1557538_PM_at
ZNF281
PPARA

1562250_PM_at_EST11
TXNL4B
JAK3
ARHGAP24

SEPT9
239723_PM_at
NME6
PSMD9

RBM5
MAPKAPK2
ZMAT3
LOC100507006

MGEA5
CXXC5
MADD
GLUD1

TRAF4
CHST2
SLC2A8
CCDC19

LOC100132707
GSTM1
PALLD
AKR7A3

OTUD4
210824_PM_at
LOC283392
SCLY

KLHL12
ADAM17
243105_PM_at
GCLC

MED1
244267_PM_at
FCAR
USP32

216112_PM_at_EST12
LOC339862
CNOT8
GSPT1

ACSL4
1561733_PM_at
1555373_PM_at
RUFY2

ATXN7L3B
PEX14
HNRNPL
RHEBL1

DCTN3
EXOSC1
LOC646014
FAF1

MGEA5
230354_PM_at
VAV3
WSB1

MS4A6A
ZBTB43
LRBA
DNAJC7

CCDC69
C8orf60
HGD
MGC16275

H2AFY
C15orf54
TADA2B
1556508_PM_s_at

TMEM165
CELF2
227223_PM_at
VAPA

INO80D
241722_PM_x_at
SLC25A33
RRAGC

PSEN1
LIMD1
LMBR1L
TDG

C15orf17
OTUB1
SPRED1
ENO1

RBM27
KCTD10
SEC24D
SIDT2

GZMM
1560443_PM_at
239848_PM_at
UMPS

ATP6
FBXO41
232867_PM_at
FLJ35816

ZDHHC1
DCAF6 /// ND4
SLC35C2
RPRD1A

TLE4
PEPD
ANGEL1
PPIB

SDHC
CERK
242320_PM_at
ALAD

HNRNPUL1
PTPMT1
DSTYK
C10orf54

SF3A2
FAM189B
ERGIC2
C16orf88

236545_PM_at_EST13
TMED1
QRSL1
EPB41

CDH23
UBL4A
KDM2A
PRDX2

LOC100506866
SLC25A37
MED6
MMAB

C4orf48
SUGT1
ATP5D
PRDX3

243557_PM_at_EST14
MPZL1
RNF130
FOXO3

C6orf35
GSTM2
TCOF1
1560798_PM_at

EMD
MLL5
ARHGEF2
ENY2

NEK1
C19orf25
REPIN1
PTEN

HNRNPM
C6orf108
MRPS27
ZNF879

241240_PM_at_EST15
FPR2
RNASET2
233223_PM_at

LIMD2
EXOSC9
C9orf70
AGAP10 /// AGAP4 ///

AGAP9 /// BMS1P1 ///

BMS1P5 /// LOC399753

THOC2
BMS1P1 ///BMS1P5
243671_PM_at
NUBPL

243046_PM_at_EST16
CCDC92
GFM2
C20orf4

BAT2L2
HIF1AN
ZNHIT1
BBS2

ZCCHC6
IDS
ZNF554
BTBD19

228723_PM_at_EST17
240108_PM_at
KIAA1609
230987_PM_at

242695_PM_at_EST18
BMP6
ZNF333
C1QBP

PLEC
AKR1B1
CD59
HIST1H3B

UIMC1
NDUFB8
UBXN1
C16orf88

LRRFIP1
CCDC69
LZTFL1
RANGRF

1556055_PM_at_EST19
LRRFIP2
C17orf79
DSTN

AFFX-
OXR1
PSMG2
ADCY3

M27830_5_at_EST20

GRB10
MED20
LOC151146
SSH2

CD84
ADNP2
C15orf26
ATP5A1

KLHDC3
ADORA2A /// SPECC1L
SPRED1
C20orf30

BAX
HERC4
ARAF
1557410_PM_at

NRD1
MAT2A
241865_PM_at
237517_PM_at

RASA2
C20orf196
244249_PM_at
MYEOV2

1559589_PM_a_at_EST21
ABTB1
243233_PM_at
226347_PM_at

MTPAP
ZBTB3
NAP1L4
YTHDF3

ZFP36L2
COX5B
ANKRD55
JMJD1C

AGPATI
SELM
DAD1
ZNRD1

KIDINS220
DKC1
MCTP2
CAPNS2

ISOC2
PPP1R11
242134_PM_at
229635_PM_at

KLF7
SLC35B2
NDUFA3
1569362_PM_at

MED27
FAM159A
243013_PM_at
241838_PM_at

TLR4
LOC100505501
CDV3
VAV2

ABHD14B
213574_PM_s_at
SRD5A1
CYP2W1

244349_PM_at_EST22
COX5B
229327_PM_s_at
TBC1D14

244418_PM_at_EST23
SRGN
ERICH1
244781_PM_x_at

MLXIP
SF3A2
230154_PM_at
NUDC

PPID
CLPP
APOB48R
C12orf45

MRPL43
ATP6V1C1
ACSL1
PTEN

ND3 /// SH3KBP1
CARD16 /// CASP1
C5orf56
CAMTA1

TNRC6B
CNPY3
232784_PM_at
240798_PM_at

KLHL28
HIRA
CD44
TOMM22

SLC25A37
MBP
SNPH
237165_PM_at

NELF
SLC41A3
LPP
MRPL46

FARS2
NDUFA2
ARPC5L
CSRNP2

ZCCHC6
GPN2
238183_PM_at
JAK1

TRIM25
233094_PM_at
ZNF83
ZNF2

TAF15
IL32
10-Sep
HLA-C

LTB
241774_PM_at
BMPR2
RPL10L

221995_PM_s_at_EST24
1558371_PM_a_at
TMEM101
TUG1

IFNGR1
SMEK1
TRUB2
227121_PM_at

228826_PM_at_EST25
UBE4B
216568_PM_x_at
LOC100287911

C7orf16
LOC100287482
244860_PM_at
KBTBD4 /// PTPMT1

NEDD9
PRPF18
GNB1
USP37

NDUFA7
1556942_PM_at
H6PD
CTNNA1

ZC3H11A
COX8A
LARP7
LEPREL4

DNAJA4
NFAT5
1560102_PM_at
DNMT3A

ERICH1
CDKN1C
PHF20L1
CD81

RALB
236962_PM_at
CFLAR
1557987_PM_at

MPZL1
CCL28
NIPAL3
ZBTB20

EZR
1560026_PM_at
TSEN15
FGD2

RBKS
SURF2
MAEA
DTX1

TMOD1 /// TSTD2
COG2
EPRS
ST7

LOC729683
PYGM
SBNO1
RBM42

PTK2B
LOC401320
SNTB2
242556_PM_at

APBB1IP
ZNF341
TNPO2
TOR3A

UBXN4
235107_PM_at
WDR77
FHL1

SETD5
MDM4
HBA1 /// HBA2
MRPS22

243032_PM_at_EST26
ADK
MAST4
231205_PM_at

216380_PM_x_at_EST27
RBPJ
RPP25
1557810_PM_at

TRAPPC4
FAM162A
GKAP1
ZNF569

CKB
WDR11
C19orf42
UBE2F

CANX
240695_PM_at
INVS
LOC283485

1558624_PM_at_EST28
BIN2
CYP3A43
SCP2

SIVA1
242279_PM_at
NHP2L1
CSH2

240652_PM_at_EST29
USP4
PCCA
DVL2

HOXA1
C19orf20
SAMM50
CD164

C11orf49
240220_PM_at
ENTPD1
CFL1

SLC11A1
ZNF689
MED13L
INHBB

C9orf86
235999_PM_at
HLA-E
PAPOLA

IVNS1ABP
235743_PM_at
NT5C2
GSPT1

PDXP /// SH3BP1
238420_PM_at
SNX5
TUBGCP5

CHTF8
NUBP2
FBXL3
SPPL3

FKBP5
MSH6
PSMD4
PFDN6

FGFR1OP2
CFLAR
PSMF1
NASP

SECISBP2L
ACAD9
LOC100127983
HAVCR2

ARF1
LOC284454
DRAM1
CDKN2A

B4GALT1
PPM1K
CARD16
SLC40A1

PHF19
243149_PM_at
STAG3L4
PRKDC

C16orf5
SKP1
GTDC1
RRN3P2

C11orf59
MED13
RINT1
SLC8A1

PIP4K2A
SNORD89
CSTB
ILVBL

GCLC
235288_PM_at
244025_PM_at
241184_PM_x_at

EMILIN2
PCK2
COL1A1
GPR44

NGLY1
TXN2
TTC1
FAM195A

ANKHD1 /// ANKHD1-
KHSRP
JAK2
DENND3

EIF4EBP3

DLEU2
ENTPD1
UTRN
NOSIP

PGLS
C19orf60
TMEM126B
241466_PM_at

RALGAPA2
TOX4
240538_PM_at
NCRNA00182

230733_PM_at_EST30
CCDC13
ZNF638
CAB39

1557804_PM_at_EST31
DGUOK
FAM178A
215029_PM_at

PKN2
SLC22A15
ABTB1
PGBD4

GGNBP2
SDHC
KIAA1704 ///
FXN

LOC100507773

SLC25A43
PIGW
ELF1
HEMGN

CHMP6
LOC100287911
G6PC3
1566473_PM_a_at

CSTF2T
216589_PM_at
GYPB
FXYD2

PELI3
C1orf93
CFLAR
C9orf30

DNASE1L3
PLXNA2
SURF4
ZNF532

ARPC5L
232354_PM_at
DUSP8
GPR97

ITSN2
MPZL1
ECHDC1
ISCA2

PURB
PKP4
POLH
DHRS3

240870_PM_at
APOBEC3G
KRTAP1-3
FBXW9

ORAI1
239238_PM_at
237655_PM_at
IKZF4

SYNCRIP
ALPK1
DYNLRB1
ARHGEF10L

NUTF2
NUMB
RPRD1A
SHPK

238918_PM_at
DNAJB12
C17orf104
237071_PM_at

CD58
UCK1
RAD50
237337_PM_at

DLEU2
ASPH
STX8
FCGR3A /// FCGR3B

SPTLC2
FUS
AARSD1
HIVEP3

B3GALT4
PRDM4
CRCP
LOC728153

239405_PM_at
MBD3
FBXO31
SYTL3

TSPAN14
GGCX
GRAP2
NUS1 /// NUS1P3

AVEN
UBA7
C18orf55
HBA1 /// HBA2

213879_PM_at
NDUFA13
OS9
KPNB1

DPH1 /// OVCA2
CFLAR
1565918_PM_a_at
RTN4IP1

NACC1
JAK1
SF3B5
IKZF1

MTMR14
SSR4
215212_PM_at
ZNF570

SLC9A8
SLC35B4
244341_PM_at
ACIN1

MRPL52
LARP4B
PECI
C12orf52

BRD2
AP2B1
CORO1B
216143_PM_at

ARF1
232744_PM_x_at
BACE2
LOC646470

RAD1
MGC16142
TMED8
ALAS2

234278_PM_at
TFB1M
TCOF1
CHMP4B

TECPR1
C12orf39
TBC1D12
SEC24D

9-Sep
STS
SYTL3
PTGDS

NUDT18
SUCLG1
239046_PM_at
DDX46

HBD
PLEKHA9
230350_PM_at
ARHGAP24

NARFL
ATXN7
ZNF493
CSF2RA

ARMCX6
TMEM55B
RPL36AL
SMARCE1

PPFIA1
MAN2C1
C15orf41
GOLGA2P2Y ///

GOLGA2P3Y

RALGAPA2
MRPS17 /// ZNF713
ANKRD19
TPI1

MED13L
237185_PM_at
LOC144438
CD83

CELF2
DPH1 /// OVCA2
222330_PM_at
RALGAPA1

PELI1
235190_PM_at
CHMP7
PGAM1

MLXIP
KLHL8
CPAMD8
C7orf68

244638_PM_at
ABCG1
244454_PM_at
238544_PM_at

HIPK1
MTMR3
LOC100130175
C1orf135

FNDC3B
DCAF7
239850_PM_at
TMOD1

CCR9
ARF4
HDAC3
DLD

HIPK2
RUNX3
SLC15A4
PPP4R1L

TLR4
242106_PM_at
C12orf43
FAM105A

HIPK1
C11orf73
LOC100288939
244648_PM_at

SMG7
EIF4E2
AP1S1
FAM45A

LLGL1
DDX6
239379_PM_at
1562468_PM_at

APH1A
RABL3
FOXP2
PARP2

241458_PM_at
ILKAP
GRHPR
ESRRA

DICER1
RBM47
HAS3
CNTNAP3

C17orf49
MDP1
ZDHHC19
STYXL1

VNN3
NDUFAF3
ZFP3
ANKRD54

CDKN1C
TRAF3
B3GNT6
230395_PM_at

ZNF593
IRF8
ALG1
LOC100288618

SMARCA2
217572_PM_at
CYP4V2
GANAB

241388_PM_at
TMEM93
UBR5
228390_PM_at

IL1R2
EZR
GFM1
C22orf29

BRD2
GDF15
1565597_PM_at
DCAF6 /// ND4

TPST1
243158_PM_at
BAGE2 /// MLL3
BRCC3

GAR1
KIAA0141
OGT
SRSF3

PTPN1
FHL3
PLIN4
227762_PM_at

RNF126
SRSF9
231695_PM_at
KIAA1109

COQ6
BCL6
CBR3
ROPN1L

SDHAF1
232081_PM_at
CALR
C17orf44

ERLIN1
ARL6IP4
1561893_PM_at
LAGE3

SMAD4
LOC151438
NFKBIE
ALG2

ARHGAP26
VPS39
TMEM189
1569854_PM_at

241091_PM_at
C17orf90
SSX2IP
LOC729683

YY1AP1
CD7
SRP72
231652_PM_at

1562265_PM_at
TLR8
NDUFB10
STK24

MYLIP
SLC2A4RG
CDC42EP3
IPO4

HINT2
LFNG
MYST3
C9orf16

PHF20L1
SSBP4
MED7
LOC100507602

MCTP2
ECHDC1
1570639_PM_at
RNF115

1554948_PM_at
243837_PM_x_at
TMEM189-UBE2V1 ///
1556657_PM_at

UBE2V1

TBC1D5
NUDT22
HNRNPH2
MLC1

CR1
CUEDC2
KLRC3
TBC1D25

EDF1
UBIAD1
PHKA2
FBLIM1

GSTA4
FAM134C
240803_PM_at
217152_PM_at

ZXDC
DCUN1D1
CFLAR
ALOX5

MPI
E2F2
USP48
VAMP3

PKP4
SIPA1L2
WDR8
DEFB122

PATZ1
ZNF561
H1FX
231934_PM_at

CCL5
FAM113B
KLHL36
214731_PM_at

GTF2H2
CDC42
GRPEL1
SUPT7L

PMM1
TNPO3
APITD1
TOX

AHNAK
DUSP7
NEK9
PTEN

SH3BGRL
1569528_PM_at
ZNF524
SAMM50

1560926_PM_at
RPL18A /// RPL18AP3
LPP
241508_PM_at

ASH1L
NDUFC2
MARCKSL1
TPI1

RBM3
SHISA6
NSFL1C
ZNF580

NPM3
ADAM10
GLYR1
1569727_PM_at

1561872_PM_at
ATL1
ATP11B
235959_PM_at

DGCR6L
NSFL1C
ZNF37BP
ASPH

TSC22D3
NVL
TBL1Y
RPRD2

POLR2C
COX2
YWHAZ
USP28

MIPEP
1560868_PM_s_at
PRPF19
239571_PM_at

FKBP5
C1orf128
1565894_PM_at
LAT /// SPNS1

POLR3K
MED19
RPS19
EIF4E3

TSC22D3
MXD4
1564107_PM_at
CDS2

CALM3
TMEM179B
RASSF4
CXorf40B

NUFIP2
243659_PM_at
ECHDC3
TMEM161B

NCR3
PREPL
N4BP2L1
MRPL42

UBE4B
PSEN1
OLAH
C22orf32

KCNK17
HIRIP3
HDAC4
PIK3CA

TMEM69
PRO2852
KCNJ15
HEY1

1557852_PM_at
MRPS24
PRKAR2A
TTBK2

RNASEH2C
NAF1
CYSLTR1
NAMPT

AGPAT1
241219_PM_at
1559020_PM_a_at
AHDC1

1565614_PM_at
241993_PM_x_at
TMEM48
LYST

MED27
TOM1L2
PHC1
1559119_PM_at

PRPF6
LYST
CABIN1
242611_PM_at

NDUFB6
242407_PM_at
OCIAD1
PATL1

SORL1
LIMK2
CCR2
FAM82A2

PATL1
IL2RB
WDR48
HSPA4

242480_PM_at
ADI1
UBE2G1
RHOF

221205_PM_at
C12orf76 ///
MLL4
240240_PM_at

LOC100510175

UBE2D3
PNPLA4
ETV6
GABRR2

MBOAT2
HIST1H1T
CENPB
AFG3L2

ZNF576
GOSR2
EP400
RGS14

U2AF1
VSIG4
1560625_PM_s_at
SKP2

U2AF1
RPL23AP32
KDM5A
SH3BGRL

TKTL1
APC
CIAPIN1
BEX4

240960_PM_at
MRPL12
CEP170 /// CEP170P1
PNPO

243546_PM_at
SGSM3
CASP2
239759_PM_at

IL6ST
LRP10
ZNF696
TMEM229B

PON2
MMP8
CFLAR
1556043_PM_a_at

213945_PM_s_at
ICMT
PLSCR3
241441_PM_at

LOC339290
PACSIN2
PDE7B
FPGS

CCL5
DSTNP2
C5orf4
NR1I3

C11orf83
NFATC2IP
ELAC1
DR1

243072_PM_at
ATP5D
SAMSN1
POLA2

1559154_PM_at
FASTK
ZNF23
ENSA

215109_PM_at
ZBTB11
SUMO3
RGS10

C5orf41
PPP1R3B
MRPS12
PDE6D

EGLN2
239124_PM_at
ZDHHC24
PSMB5

1568852_PM_x_at
PLD4
GLTPD1
NDNL2

UGP2
NSUN4
RBMX2
CHD6

TMEM141
ARPC5L
ACRBP
RUVBL2

KLRAQ1
242926_PM_at
NHP2
MAP3K7

MGEA5
PEX7
GNAQ
242142_PM_at

C14orf118
226146_PM_at
C10orf46
240780_PM_at

KLHL8
HIC1
CRKL
LOC729082

RERE
ANKRD11
C6orf26 /// MSH5
241786_PM_at

CIAO1
TNFRSF21
FAM104A
ROBLD3

SEC61B
ZMYM6
TWISTNB
GSTO1

INPP5D
239901_PM_at
FAM50B
PDZD7

SRI
IL12RB1
TMEM201
EIF3F

PATZ1
KPNB1
SNX13
PSEN1

241590_PM_at
NCOA2
PTPN9
DUSP14

ADAM17
238988_PM_at
MMACHC
GPBP1L1

LAT2
THOC4
SAV1
CASP4

MUDENG
F2R
CUL4B
PTENP1

ANAPC11
FGD4
RBBP6
CLCN7

ASB8
ABCF2
232478_PM_at
DOCK11

TPCN2
DDX28
SFXN3
PECAM1

SNX27
ATG2B
GATAD2A
FLOT1

238733_PM_at
PTPMT1
MLYCD
243787_PM_at

ALDH9A1
PSME3
BAT4
TPM4

TBL1XR1
RASSF4
PLA2G12A
OLAH

FAM126B
ZNF598
C7orf28B /// CCZ1
233270_PM_x_at

ATF6B
NEDD9
TK2
CHAF1A

CHCHD10
230123_PM_at
UHMK1
MRFAP1

MYLIP
ARIH2
NCRNA00094
GATM

NENF
NDUFB9
WNK2
TXNIP

UQCR10
SMAD4
TAOK3
IL28RA

TGIF2
PIK3AP1
ALMS1
AVIL

BCL7B
MRPL14
HBA1 /// HBA2
ACTG1

EP400
GFER
CASP2
LOC645166

RFX3
TELO2
PFKFB2
MRPS10

NCR3
C9orf23
ENTPD1
ANKZF1

CHCHD8
RFTN1
SRSF5
PDGFA

RAB5C
215392_PM_at
SMARCA4
217521_PM_at

244772_PM_at
GUCY1A3
LOC401320
PHAX

222303_PM_at
CCDC123
238619_PM_at
TTLL3

SART3
PFDN1
PHTF2
PPIL4 /// ZC3H12D

WWC3
235716_PM_at
MKLN1
URM1

240231_PM_at
FAM173A
DHX40
TSFM

AKAP17A
PTGDS
GADD45GIP1
ST7L

ZFP91
229249_PM_at
233808_PM_at
235917_PM_at

239809_PM_at
TET3
KIF3B
SAAL1

235841_PM_at
PON2
1562669_PM_at
PDE6D

ATP5G3
242362_PM_at
STX7
HNRNPD

CCDC107
COX5A
LYRM4
ASCL2

GPX1
C20orf30
NUDT4 /// NUDT4P1
KREMEN1

RHOC
C19orf53
P2RY10
CD163

VPS13B
GTF3C5
TES
TMEM43

TNPO1
DEF8
PRDX1
TRGV5

IL1R2
SPTLC2
ITCH
C11orf31

DNMT3A
KLF12
CX3CR1
243229_PM_at

C17orf90
TROVE2
MOBKL2A
KIAA0748

C19orf52
RABGAP1L
NUCKS1
C8orf33

CYTH2
EMB /// EMBP1
C8orf42
FBXO9

MYLIP
CCNK
FBXL12
CMTM6

CORO1B
237330_PM_at
PID1
KLRB1

JMJD4
UBTF
TNFSF8
ANXA4

UGGT1
239474_PM_at
NOL7
TMEM64

YIF1A
ELF2
SLC29A3
HNRNPUL2

233010_PM_at
LOC550643
PIGP
241145_PM_at

CHD7
PDE4A
C5orf41
238672_PM_at

MBNL1
CUTA
HLA-E
PHF21A

TXNIP
NSFL1C
MDM2
232205_PM_at

DPP3
1569238_PM_a_at
IRAK3
MDM4

GLE1
DNAJC30
ELK1
TFAP2E

NCRNA00094
PLA2G12A
IDH3A
243236_PM_at

232307_PM_at
RPS15A
GMPPB
DUSP5

ELOVL5
CTSC
TFCP2
CD36

CNO
229548_PM_at
1557238_PM_s_at
DGCR14

CDV3
ABHD6
ZDHHC19
INF2

MDH2
RBMS1
232726_PM_at
ITGB2

RHOF
PSMB6
244356_PM_at
RIOK3

STK17B
SCFD2
HECW2
RALBP1

240271_PM_at
SMARCC2
1557633_PM_at
TBRG1

KLF9
CISH
243578_PM_at
CYHR1

237881_PM_at
ADAM10
DLEU2
BMP1

ANKHD1
GPBP1L1
LOC285830
PLBD1

DENND1B
UBE2D2
RPAIN
NOL3

NSFL1C
HIATL2
SP1
SFRS18

RAB6A
232963_PM_at
SLC2A4RG
BID

N4BP2L2-IT
233867_PM_at
242865_PM_at
MRI1

240867_PM_at
PRR14
DDX51
STS

COMMD5
1568903_PM_at
242232_PM_at
234578_PM_at

PSMD6
PPP1R7
233264_PM_at
SNX3

AKAP10
ARL2BP
ECE1
ARNTL

NCKAP1L
IKBKB
SF3A3
UTP20

IQGAP1
CYC1
NUP50
GPCPD1

BTF3
ZNF644
RSRC1
PRKAB2

C5orf22
NUDT4
CR1
1556518_PM_at

MAPRE2
AES
ALG14
233406_PM_at

236772_PM_s_at
ZC3H7B
TRBC1
PITRM1

240326_PM_at
LHFPL5
MAP3K2
TMEM64

LOC100128439
REPS1
RNF24
TRIOBP

7-Mar
EWSR1 /// FLI1
238902_PM_at
LFNG

RSBN1L
LOC100271836 ///
AGAP2
PAM

LOC641298

ARRB1
DDX28
BAZ1A
MON1B

PAOX
N4BP2L2
236901_PM_at
NDUFB8 /// SEC31B

ZDHHC17
RASSF7
TCTN3
HIST1H4C

ZNF394
242461_PM_at
ACRBP
STX11

C22orf32
YWHAB
WDR61
235466_PM_s_at

PELI1
KIAA1267
UBA7
MRPL9

236558_PM_at
PABPC1 /// RLIM
SH2D2A
1562982_PM_at

TMED9
233315_PM_at
CSF2RA
ATF2

238320_PM_at
SMAP1
PRPF31
TIMM17A

NLRP1
LOC100190939
NF1
ZNF641

GPR137B
MED13
229668_PM_at
DNAJC4

NDUFA8
LOC100127983
CHCHD1
ASXL2

POLR2C
PREX1
TAOK2
FBXO16 /// ZNF395

FAM118B
PAIP2
MGMT
DPY19L4

ASAH1
232952_PM_at
1566201_PM_at
NBR1

MRPL10
STS
ATPBD4
237588_PM_at

TTYH2
UBE2D3
MFNG
NUDT16

HECA
MRPS25
HIPK3
FAM65B

COX3
LOC100133321
SPAG9
XIRP1

REST
PUSL1
243888_PM_at
BECN1

242126_PM_at
ZDHHC24
C10orf58
RNF126

ARRB1
234326_PM_at
RSRC2
ZNF207

RBBP9
WWC3
FAM86B2 /// FAM86C ///
DNAH1

LOC645332 ///

LOC729375

STX16
216756_PM_at
CD160
C10orf78

232095_PM_at
229670_PM_at
AK2
YY1

C19orf33
RAB30
LOC100128590
223860_PM_at

CISD3
CNOT2
PABPC3
CRADD

PFAS
MAPK7
UPF1
229264_PM_at

FLJ31306
WDR48
LAMA3
1568449_PM_at

TBC1D7
241692_PM_at
RASSF5
LARP1B

MDM2
PSMC2
1570165_PM_at
LSG1

GSTZ1
243178_PM_at
SAP30L
1563092_PM_at

222378_PM_at
C6orf129
FDX1
ADO

KLHDC10
1562289_PM_at
UBE2J1
NFATC1

PPFIA1
CALM1
PRSS33
PHPT1

C1orf122
MYO1C
MBD3
POP4

244026_PM_at
HSBP1
COX4NB
244536_PM_at

RANBP9
EFEMP2
DYRK1B
STOML1

241688_PM_at
UBE2E2
TUFM
SSH2

NDUFB10
233810_PM_x_at
PDLIM2
ZDHHC16

LIMS1
PDIA3
PSMD9
OAT

C17orf106
241595_PM_at
BCL7A
RAB34

242403_PM_at
229841_PM_at
PSMA5
SEC61G

GSK3B
RWDD2B
RAD52
LOC221442

KBTBD2
NDST2
VAV3
GNL2

ARF5
TFAM
TAAR8
PACS1

LCOR
235592_PM_at
227501_PM_at
CACNB1

238954_PM_at
C1orf50
ADAM22
MAP4K4

1565975_PM_at
MIA3
MCL1
LRRC14

WIBG
RPS4X /// RPS4XP6
BCL2L11
C11orf31

OPRL1
ZBTB7A
TRPM6
BCKDHB

C11orf31
237396_PM_at
215147_PM_at
LOC728903 ///

MGC21881

241658_PM_at
USP8
NANP
NUMB

ZC3HAV1
E2F3
ONECUT2
PPP1R15A

SLC9A3R1
CYTH3
RHOH
MALAT1

YLPM1
EXOC6
PIK3R2
INO80D

LRRFIP1
MDM2
R3HDM1
SNX24

PRDX6
239775_PM_at
TMEM107
RBMX2

FOXO3 /// FOXO3B
C15orf63 /// SERF2
ZNF395
244022_PM_at

230970_PM_at
LCP2
ITM2A
TOMM22

ACAA2
CRY2
PHYH
FLJ12334

FXR2
GGA2
XPA
232113_PM_at

ILF3
CCL5
DDR2
VPS13B

ANKLE2
HOPX
SEC11A
CSHL1

1560271_PM_at
CPEB4
DVL1
MLL3

GNS
AGER
PREB
PSMA1

SRSF9
C1orf151
SUB1
MED21

AP1S1
FRYL
TSEN15
C20orf11

SPCS1
CMTM5
ECHDC1
CRYZL1

PADI4
C7orf30
227384_PM_s_at
ORC5

MEF2A
HIST1H4J
KCNE3
SLC16A3

IL21R
LOC100129195
ACTG1
MRAS

EDEM3
NAT6
SNORA28
N4BP2L2

C7orf30
216683_PM_at
WTAP
TSN

CHMP4A
SERPINB5
MED31
NUDC

MRPL52
TRD@
COMTD1
OCEL1

SRSF1
RBM43
JMJD4
LOC441461

ARFGEF1
FLOT2
SFRP1
223409_PM_at

MAN1A1
BOLA3
PPP3R1 /// WDR92
1557796_PM_at

VAPA
237442_PM_at
QDPR
LOC284009

TGFBR2
HLA-E
RPL23
MLANA

232472_PM_at
LOC648987
HMOX1
PTP4A3

GNB5
RICTOR
NINJ1
CX3CR1

MYLIP
ARID4B
C9orf119
TMEM53

GTF2A2
ANKRD57
PSMF1
PWP1

ZNF688
DNAJB6 /// TMEM135
RNF17
MRPL18

ATP6
PPTC7
CATSPERG
PTAR1

RNF14
MRPL16
222315_PM_at
DPY30 /// MEMO1

NCRNA00116
LSM2
KCNE3
SPATA2L

PIK3R5
COX5B
FNIP1
FAM82B

ARFGEF1
COX5B
ZNF80
BTF3

C1orf63
HSD17B10
ANKS1A
ZNF638

ATP2A3
COX19
ATP5SL
216782_PM_at

EIF4A2
CRK
239574_PM_at
SUGT1

ND2
NOP16
1555977_PM_at
MBP

PITPNC1
ZBTB40
RNF5
LOC100507192

SFSWAP
HIGD2A ///
236679_PM_x_at
KCTD20

LOC100506614

FKBP15
CRIPAK
BLVRB
SCAMP1

ZNF238
C1orf128
232406_PM_at
B3GALT6

MAZ
QRSL1
LOC100134822 ///
TRAPPC4

LOC100288069

NUPL1
TMEM107
RTN4IP1
SBF2

220691_PM_at
MTMR11
HIBADH
FANCF

CDKN1C
GLUL
C14orf167
GRPEL1

C9orf69
COPB1
CYP19A1
ATP8B1

SMAP2
RASA4 /// RASA4B ///
239570_PM_at
ZNF24

RASA4P

ACSL1
FBXL21
VAPB
SPECC1

MBD4
RALGDS
SDHAF2
CHD1L

FCER1A
PTPRCAP
1556462_PM_a_at
POP5

241413_PM_at
NSUN4
242995_PM_at
1564154_PM_at

240094_PM_at
UROD
ACVR1B
LOC728392 /// NLRP1

DPM3
1560230_PM_at
C12orf57
SSNA1

236572_PM_at
FCAR
231111_PM_at
FRG1

ATP11A
1564733_PM_at
C1orf58
DCAF7

SASH3
NRD1
TPM4
237586_PM_at

MRPS18B
FAM110A
CPEB3
240123_PM_at

231258_PM_at
NUDT16
241936_PM_x_at
NUP37

PIAS2
MTMR10
QDPR
AIF1

LGALS3
XRCC5
CPD
PRKAR1A

2-Sep
DAB2
RSRC1
238064_PM_at

239557_PM_at
GTF3C6
CHCHD2
TEX264

IVD
DHX30
HBB
TMEM41A

236495_PM_at
SENP5
LPAR1
RAB4A

RBKS
SAMHD1
SHMT2
TAF9B

DPP3
SSR2
NID1
RBMS1

ABHD6
1556007_PM_s_at
239331_PM_at
239721_PM_at

SLC12A9
DYNLRB1
241106_PM_at
MBD4

1565889_PM_at
CCDC93
WDR61
RAB6A

OTUB1
C1orf77
ACACB
KIAA0319L

243663_PM_at
ZNF70
MRP63
MKKS

FAM96B
TNFAIP8L1
TNFRSF10A
PMVK

LOC100130175
EML2
SERBP1
STK36

LOC401320
MCTP2
CYB5R3
VNN2

CHD4
227505_PM_at
ZNF384
RBBP4

242612_PM_at
TSPAN32
1563075_PM_s_at
FAM192A

RPS26
214964_PM_at
EVL
IKBKE

PDCD2
236139_PM_at
LPIN2
216813_PM_at

NCRNA00275
QKI
ZNRD1
MRPL24

SLC2A8
WBP11
FLJ38717
ZNF75D

RLIM
EXOSC6
DND1
1566680_PM_at

MRPL20
DNAJC10
ACLY
242306_PM_at

STK38
PLCL2
CSN1S2AP
CSGALNACT1

242542_PM_at
BOLA2 /// BOLA2B
236528_PM_at
242457_PM_at

PIK3C3
236338_PM_at
ARHGAP26
SNW1

1560342_PM_at
MAPK1
SPG20
PPARA

TRA2A
243423_PM_at
C10orf93
RUFY3

UBE2B
240137_PM_at
236005_PM_at
SEC61A1

UBE2D3
244433_PM_at
COQ4
SUMO2

SRSF4
FBXO9
GTPBP8
242857_PM_at

PPP6R3
IL13RA1
LOC148413
NCOA2

TIGIT
FBXO38
1565913_PM_at
CANX

ASB2
IMMP2L
RIN3
ZNF561

RHOT1
242125_PM_at
C15orf28
ISYNA1

LAMP1
SEMA4D
239859_PM_x_at
VHL

ATP2A3
RTN4
242958_PM_x_at
KRI1

FAM13AOS
GDI2
FLJ44342
PDCD6IP

ZBTB4
ATXN7L1
240547_PM_at
DNAJC17

SELPLG
STK35
C9orf16
GDAP1L1

233369_PM_at
SRGAP2
GAS7
EIF4EBP1

FLJ39582
1557637_PM_at
ZNF148
215369_PM_at

233876_PM_at
FAM174B
TMEM5
GPRIN3

NSMAF
238888_PM_at
FAM126B
C17orf63

PPP1R2
ALG13
CSNK2B /// LY6G5B
STAU1

BTF3
USF2
ASAP2
ZNF439

1560706_PM_at
ADAMTS2
ZNF407
CSTB

236883_PM_at
CABIN1
GLI4
RBM47

FAM98B
TCP11L2
AKAP13
240497_PM_at

HP1BP3
RBM25
ZBTB16
TADA2B

CELF2
233473_PM_x_at
1557993_PM_at
MRPS6

COPG2
HIST1H1T
TTF1
HEATR6

ZNF148
SNCA
NUDT10
MLL3

DNAJC3
229879_PM_at
COQ4
MDH1

C17orf59
SLC24A6
GLT25D1
SELENBP1

POLR2I
PXK
IKZF2
SLC7A6OS

UBE2H
TAGAP
GCNT7
MTMR11

LPAR2
ATP5G1
GPATCH2
NFATC2

TNPO1
AKT2
NMT2
244373_PM_at

CCDC97
SLC8A1
ADHFE1
NOLC1

KCTD5
DIP2B
ZNF784
C3orf21

238563_PM_at
MKRN1
CYBASC3
SIPA1L2

RNF4
MAL
BAT2
HDDC3

HBB
ZKSCAN1
APP
SBK1

TMCO3
ATF7IP
241630_PM_at
PFDN2

239245_PM_at
C1orf174
MRPL45
DPY19L1

CDK5RAP1
TAGAP
TMEM161A
MYL6

FAM43A
LSS
240008_PM_at
ZNF703

ATP5S
1565701_PM_at
AGTRAP
RBM47

LRCH4
1560332_PM_at
234164_PM_at
CD226

ANKFY1
CAPZA2
241152_PM_at
ORM1

STK32C
COX6A1
HBA1 /// HBA2
1560246_PM_at

TMCO3
SNORA37
TMX4
242233_PM_at

IL21R
CSTF1
TNFSF4
ZNF398

LOC100506902 ///
EXPH5
243395_PM_at
PIKFYVE

ZNF717

NDUFB11
CD74
NOL7
LRP6

FAM193B
NPY2R
236683_PM_at
C9orf86

BCL6
ATP13A3
TNRC6B
SHISA5

C6orf106
GIGYF2
UFM1
MCTP2

C14orf138
MAP4K4
FH
LOC100128071

FAM65B
THAP7
ESYT1
PRSS23

1569237_PM_at
ACP1
LGALS1
C1orf212

231351_PM_at
SLC22A17
238652_PM_at
NDN

GATAD2B
DLEU2 /// DLEU2L
DIMT1L
TBC1D22A

NDUFS6
BBX
TRIM66
242471_PM_at

GRAP
ESD
MTPN
LOC146880

KLF6
FAM49B
SNRNP27
MYOC

SLC6A6
CARS
FAIM3
SMOX

GHITM
PLEC
TTC39C
MBD2

FLT3
ENTPD1
RBL2
JMJD6

1566257_PM_at
LOC285370
CHMP1A
ACTR2

FKBP2
PRDX6
UQCRB
1562383_PM_at

HPS1
CBX6
ERCC8
GPM6A

229673_PM_at
C1orf21
CCNB1IP1
LOC284751

SGCB
ORMDL3
CC2D1B
239045_PM_at

HGD
RHBDD2
241444_PM_at
TFCP2

229571_PM_at
LSG1
NRG1
237377_PM_at

KIF5B
DNAJC30
JAGN1
COPZ1

CSAD
ENTPD4
RAF1
ATP8B4

MBP
PCMT1
FAM181B
AMZ2P1

HBB
COX2
1561915_PM_at
PARL

CTSZ
TIMM13
SH3GLB1
PROSC

AFF4
CAPRIN1
TMF1
C1orf85

RBM6
TASP1
SSRP1
CISD1

PGLS
1558588_PM_at
IGF2R
COPA

FKBP5
ZCRB1
EPRS
ZBTB4

244048_PM_x_at
SNAP23
IGLON5
1558299_PM_at

2-Sep
FLJ33630
UQCRB
ZNF333

P4HB
ACPP
HUS1
SPOPL

PFDN6
CNST
STX16
DUSP28

ARL16
SLC8A1
LOC100287227
PDZD8

ABHD5
CDK3
237710_PM_at
GPR27

SCFD2
A1BG
RBM22
TMEM30A

ITPA
243217_PM_at
LOC100286909
RPL23A

236889_PM_at
1557688_PM_at
UBIAD1
C20orf196

236168_PM_at
SLC8A1
SNTB2
238243_PM_at

TMED3
ARHGEF40
CTBP1
NOP16

RARS2
TMEM134
EIF2B1
SNAP23

PALLD
PLA2G7
C12orf75
ZNF542

RALGAPA2
216614_PM_at
243222_PM_at
SSR1

RP2
SERINC3
CASC4
5-Mar

FAM162A
PRPF18
CTSO
C1orf151

PTGER4
RNF144B
ETFB
DDX10

ASPH
ZCCHC6
SOCS2
RBM3

SPTLC1
1564568_PM_at
220711_PM_at
NUDT3

MAP3K3
SAFB
TTC12
234227_PM_at

234151_PM_at
CD163
TRAPPC2 ///
UBL5

TRAPPC2P1

PSMB10
CCNL2
ALS2
GUSBP1

INSIG1
DLEU2
COX17
1559452_PM_a_at

ASPH
FLJ14107
ABHD15
MSH6

1565598_PM_at
EML2
ZFYVE27
BAGE2 /// BAGE4

PEBP1
244474_PM_at
TNRC6B
MIDN

CXorf40A
NOP16
ADPRH
LONP2

239383_PM_at
CLPTM1
ANKRD36
PPM1L

AKIRIN2
LOC285949
IDH2
ZNF608

240146_PM_at
1563076_PM_x_at
TP53TG1
HAX1

BMP2K
CXorf40A /// CXorf40B
FCGR2C
GIMAP1

240417_PM_at
THAP11
FAM13A
ISCA1

SH2D1B
1565888_PM_at
1565852_PM_at
215204_PM_at

FBXO25
BNC2
JMJD8
C1orf25

GRB10
RNF214
TRIM46
RNASEH2B

1558877_PM_at
235493_PM_at
PDE8A
TCP1

KIAA1609
MUSTN1
MTMR3
RAP1GDS1

C2orf18
SRPK2
DNAJC8
GP5

CMTM8
COMMD7
CCNL1
228734_PM_at

RNPEPL1
CLCN3
TMEM204
UBB

TMEM160
C11orf48
WDR1
SMARCB1

GRB10
RBM15B
GPER
236592_PM_at

LOC100129907
227479_PM_at
C11orf31
NCRNA00202

DGCR6 /// DGCR6L
BMPR1B
TMEM191A
C5orf30

MRPS18A
CD163
APOOL
ARF1

PCGF3
CHN2
242235_PM_x_at
SLC41A3

DCTPP1
243338_PM_at
SBK1
FGFBP2

244732_PM_at
EPS15L1
240839_PM_at
ITCH

2-Sep
CHCHD5
231513_PM_at
PACSIN1

SERBP1
ZNF689
C1orf144
DUSP1

C19orf56
ARL6IP4
C22orf30
HAVCR1

ING4
ETFA
HMGN3
242527_PM_at

LSMD1
WDR61
TEX10
ATF6B

ADPRH
MRO
C1orf27
TRAF3IP3

C7orf68
ZNF169
FKBP1A
TGFBR3

CACNA2D4
MYST3
ATP6V1C1
ICAM4

EXOSC7
GATAD1
DLEU2
242075_PM_at

CCND3
RAB9A
1564077_PM_at
C5orf4

TLR2
FAM120C
PRNP
AMBRA1

TRABD
C7orf53
RPL15
AHCY

AKR7A2
RGS10
HCFC1R1
RNF14

231644_PM_at
RYBP
SERPINE2
UEVLD

ACP5
CD247
NFKBIA
RGS10

239845_PM_at
TRADD
232580_PM_x_at
ENO1

ARHGAP26
239274_PM_at
ME2
SLC16A6

MCAT
CBX5
TNF
GTPBP6 ///

LOC100508214 ///

LOC100510565

NUCKS1
STAT6
H2AFY
APOOL

LRCH4 /// SAP25
HEATR2
PSME1
KLF4

LOC100131015
GUSBP3
WHAMML2
MS4A7

CFLAR
243992_PM_at
FLJ38109
DRAP1

MIF4GD
UCKL1
TAP2
244607_PM_at

RBCK1
DYRK4
C6orf89
232615_PM_at

PTPRO
NGLRAP1
PSMA3
236961_PM_at

227082_PM_at
PPP2R5E
1557456_PM_a_at
1563487_PM_at

241974_PM_at
SNCA
1559133_PM_at
EGLN3

TMEM102
1555261_PM_at
IFNGR1
239793_PM_at

IL13RA1
PPP1R16B
ZNF397
RBBP7

LOC100506295
SPATA7
FAHD2A
PLDN

LOC100510649
HEXB
MPZL1
PSMG4

MRPS34
240145_PM_at
MOBKL2C
WIPF2

RPP21 /// TRIM39 ///
ABCB7
DPY19L1
ALG3

TRIM39R

KIL22
NAT8B
SRD5A1
ZSWIM1

241114_PM_s_at
AKAP10
SEMA4C
SSX2IP

STAT6
243625_PM_at
DCTN6
BSG

RNF113A
CRYL1
E2F2
242968_PM_at

RPP38
CDADC1
CDC42SE1
DNAJB4

1559362_PM_at
PBX2
240019_PM_at
PWP2

INO80B /// WBP1
SERINC3
244580_PM_at
243350_PM_at

NLRP1
TP53
227333_PM_at
SCP2

239804_PM_at
240984_PM_at
MEAF6
TOX2

CDKN1C
1556769_PM_a_at
FCF1 /// LOC100507758 ///
QKI

MAPK1IP1L

C19orf70
ADCK2
PNN
AIF1L

INSIG1
IMPACT
ZNL395
242901_PM_at

COG1
GLTP
243107_PM_at
240990_PM_at

EIF5
CMTM4
242117_PM_at
RPL14

XCL1 /// XCL2
1563303_PM_at
GNL1
GTF3C1

ARHGAP27
LOC100509088
C2orf88
NCKAP1

SNHG7
TMEM189-UBE2V1 ///
HIST1H4J /// HIST1H4K
PPAPDC1B

UBE2V1

LIMK1
HMGB3
TDRD3
ATXN10

BOLA1
ZFAND2B
TRIP12
SORT1

235028_PM_at
PTPRS
ANXA4
NCAPH2

SH3BP1
CLCN5
SNHG11
TANK

PSMB4
CHCHD3
C17orf58
SNRPA1

PHTF1
UQCRFS1
PTEN
OSBPL3

REM2
MED8
HRASLS5
BPGM

EXOC6
RPP30
KRT10
TNS1

CELF2
1558048_PM_x_at
SLC46A2
MRAS

FBXO38
CXXC5
1562314_PM_at
IVD

TIMM23 /// TIMM23B
SETBP1
SAC3D1
PRTG

DNAJA4
DNAJC10
TRD@
KLHL6

240638_PM_at
BZW1
SRP72
TTLL3

HIGD2A
ZDHHC2
215252_PM_at
RHOBTB2

228151_PM_at
237733_PM_at
ARHGDIA
MARK3

FBXO33
MBOAT2
224254_PM_x_at
C19orf60

1560386_PM_at
ETV3
215981_PM_at
CTSB

TMEM59
FASLG
SLC43A3
PRDM4

MADD
SEPT7P2
1570281_PM_at
1563210_PM_at

MDH2
CHAF1A
DCAF7
HNRNPUL2

1566959_PM_at
GSTK1
237341_PM_at
ARHGEF2

NBR1
RAB27B
ATP5O
DCPS

PHB
CXXC5
TSPYL1
DNAJA4

RC3H2
HCFC1R1
DIS3L
ADCK2

GTF2H2B
ZCCHC6
239780_PM_at
RPA1

PIGU
KCTD6
PPP1R15A
216342_PM_x_at

C16orf58
CSRP1
NAALADL1
DGKZ

IL21R
234218_PM_at
ITGAM
244688_PM_at

MRPL55
JTB
CYTH4
C1orf21

NPFF
FARS2
POLG
FASTKD3

6-Sep
237018_PM_at
DCAF12
241762_PM_at

237118_PM_at
PRKAR1A
RNF34
TWIST2

CXCL5
HLA-DPA1
C14orf128
TPI1

SLC25A26
HBA1 /// HBA2
A2LD1
239842_PM_x_at

1555303_PM_at
1556107_PM_at
LOC100505935
FCHSD1

234604_PM_at
SYF2
JMJD1C
243064_PM_at

SLC2A6
238552_PM_at
SH3GLB1
239358_PM_at

TRA2A
NFIC
NDUFB2
LANCL2

SSSCA1
215846_PM_at
DNAJC8
MDM2

IFNGR1
LEPROTL1
ZNL792
CD3D

HBG1 /// HBG2
COX1
DCAKD
TMPO

FAM162A
ZDHHC4
RPL23A
SDHC

SORL1
UXS1
ALPK1
SCD5

HERC4
NDUFB5
MLAP3L
SLC4A11

LPAR5
1558802_PM_at
RBM14
YWHAB

POP4
CCDC147
ZNL330
RPL28

C14orf179
6-Sep
238883_PM_at
DCXR

232527_PM_at
CLC
RAB6A /// RAB6C
6-Mar

PHF3
227571_PM_at
RAB8B
MAP3K7

COX5A
CFLAR
RQCD1
KPNA6

243858_PM_at
SDF2L1
ABR
RFC3

CLEC10A
CD3E
PLEK2
SNRPD3

234807_PM_x_at
UBN2
UFC1
242997_PM_at

CTBP1
MRPS9
TMX1
PPIE

9-Sep
216871_PM_at
241913_PM_at
HLA-DPA1

GZMB
MPZL1
233931_PM_at
GLT8D1

VPS24
MTMR4
232700_PM_at
GPR56

C1orf43
NUP98
ZNL638
C12orf29

IRS2
TRRAP
ZBTB20
HLA-G

PEX26
USE1
GUCY1A3
ITPR2

TP53RK
FKBP1A
VAMP3
METTL6

ZZEF1
238842_PM_at
MBP
MCL1

XPNPEP1
MAN2A2
WWOX
229569_PM_at

ATP6AP1
PTMA
AFFX-M27830_M_at
GTF2H5

TMED9
CDK10
1557878_PM_at
DNAJC3

PHF19
SAMSN1
NUB1
C3orf78

EIF5
SNX5
CBX3
HLA-A /// HLA-F ///

HLA-J

TRAPPC3
AKAP8
233219_PM_at
ILF2

244633_PM_at
SERGEF
SLBP
TMED2

RIPK1
IGFL2
COX15
EPS8L2

243904_PM_at
NOB1
PSPC1
PLA2G6

1566166_PM_at
NME7
LRRC8C
ERLIN1

C2orf28
CELF1
1566001_PM_at
JTB

CD84
MRPL9
PXK
CCND2

MTA2
CD3G
1558401_PM_at
C11orf73

NEAT1
DHX35
CHRAC1
SSBP1

TSTD1
PDS5B
MGC16275
LRRFIP1

236752_PM_at
FBXL17
1557224_PM_at
241597_PM_at

MRPL49
SLC39A4 /// SLC39A7
242059_PM_at
LUC7L

PLAGL2
223964_PM_x_at
MTIF2
MRPL34

VIPAR
NDUFB7
PDHB
GUF1

GSTK1
LIMD1
ACSL3
217379_PM_at

241191_PM_at
THUMPD3
ICAM2
PRPSAP2

THRAP3
GNLY
EBLN2
COPS6

PTGDS
C12orf66
LSS
YSK4

SLC38A7
HSD17B1
1563629_PM_a_at
BCL2L1

RHOBTB3
JTB
TARSL2
CIRBP

LOC100507255
240217_PM_s_at
MBD6
1561644_PM_x_at

SRRM2
PDLIM4
QARS
SIPA1L3

TANK
237239_PM_at
240665_PM_at
1556492_PM_a_at

ZNF511
FASTK
LOC400099
239023_PM_at

REPS2
SRD5A1
OPA1
TMEM147

ND6
1558695_PM_at
IFT81
DKFZp667F0711

237456_PM_at
RICTOR
GATAD2A
ZNPHT2

PI4KB
IFI27L2
OLAH
CMTM3

KLF6
1559156_PM_at
RAI1
ELOVL5

SNX3
RASA2
MAGED2
GPSM3

FXC1
CYTH1
C12orf5
MOGS

244010_PM_at
RBP4
ARHGAP24
MGC16384

1562505_PM_at
215397_PM_x_at
RAB4A /// SPHAR
1558783_PM_at

237544_PM_at
232047_PM_at
ICT1
CDK11A /// CDK11B

C7orf26
1563277_PM_at
MED29
C14orf64

AP1S1
MTCH2
SLC25A38
CPT1A

SRPK1
TP53TG1
SPTLC2
COPS8

RNF216
HEXIM2
CCNDBP1
EPB41L5

CFLAR
SSH2
USP48
KPNA2

HBG1 /// HBG2
C12orf62
242380_PM_at
APLP2

233239_PM_at
KRT81
MRPL54
ARHGEF40

RAP2B
BAT4
225906_PM_at
PABPC1

1563958_PM_at
C19orf40
HELQ
RALGPS1

BCL6
GNLY
NOTCH4
DNAJB14

NAA10
CEP152
HBA1 /// HBA2
PSMA1

LSM12
242875_PM_at
IL1RAP
COTL1

240636_PM_at
239716_PM_at
SLC6A6
RPS19

NSMCE1
1559037_PM_a_at
CPT1A
TRIM41

HADH
PHF21A
USP25
243469_PM_at

PPTC7
PSMC5
216890_PM_at
INSR

242167_PM_at
IL13RA1
236417_PM_at
TCEB2

PHB2
GPA33
239923_PM_at
NCOA2

LPP
TMEM203
PODN
ETFDH

PEMT
BTBD6
N6AMT2
GAS7

MRPS12
PDE1C
242374_PM_at
227897_PM_at

NECAP2
UBN2
NDUFS3
222626_PM_at

GRB10
BAK1
FAM120A
FRMD8

ATP5G3
PTK7
LOC441454 ///
PPP1R3B

LOC728026 /// PTMA ///

PTMAP5

KPNB1
C12orf65
COX4I1
1565862_PM_a_at

CTNNB1
238712_PM_at
GGCX
243682_PM_at

ALKBH3
SPNS1
TTC39C
RECK

STAG2
DHX37
ZNF625
TCTN3

PNKD
236370_PM_at
C20orf103
CAST

FAM113A
240038_PM_at
VDAC3
TAB2

RAMP1
ZNF638
SLC25A12
METRN

236322_PM_at
IRS2
MRP63
TCEAL8

UBE2H
ANAPC5
BAT2L2
239479_PM_x_at

236944_PM_at
PDSS2
230868_PM_at
GLS2

CHD2
240103_PM_at
PPP2R2B
238812_PM_at

SPTLC2
LMO7
NEDD8
ZNF33A

PRRG3
MYST3
RANGRF
MPP5

FAR1
C7orf53
1561202_PM_at
FOXP1

MRPS11
ZNF207
TOMM40L
CDC14A

237072_PM_at
NOC4L
CBX6
MAPKAPK5

PAPD4
PTPLB
MVP
LOC100506245

CC2D1A
1556332_PM_at
LASS5
EIF2AK3

GIPC1
MYCL1
229255_PM_x_at
ACSL3

SELPLG
EXOSC7
LYPLAL1
HSCB

PICALM
ATP5F1
CDV3
MLKL

ZNF581
LOC100272228
LOC100505592
TBC1D9

NDUFA11
PRKCB
LDOC1L
LOC728825 /// SUMO2

ATXN10
TAX1BP3
GBF1
LOC100130522

SIT1
LSM12
240176_PM_at
RSU1

GSTM1
STXBP3
TRADD
RNF24

ZNF207
RPS19BP1
220912_PM_at
242868_PM_at

RASGRP2
PLEKHJ1
RPL36
1558154_PM_at

ST20
SELK
HSD17B8
SUGP1

1557551_PM_at
243305_PM_at
SEL1L
220809_PM_at

CANX
MXD1
FCGR2C
PSMA1

PRPF4B
CDYL
PHF15
ZNRF1

NCRNA00152
ASAP1
234150_PM_at
RRS1

LRCH4 /// SAP25
RABGGTB
COBLL1
PDE5A

IL2RA
ESR2
STYXL1
GZF1

SUDS3
C14orf109
SRSF6
TIMM9

TFB1M
EPCI
1556944_PM_at
213979_PM_s_at

MIAT
PDCD7
DDX24
TTC27

STX16
SSTR2
AVIL
BHLHE40

PRKAB1
1569477_PM_at
SEC14L2
FGD4

UFSP2
CNPY2
PRO0471
RPL15

SAMD4B
DDX42
LOC553103
NLRP1

1566825_PM_at
PDHB
CHD4
24076 l_PM_at

APC
COX4I1
KCNJ2
BZW2

GDPD3
EEF1D
C7orf41
1556195_PM_a_at

HBG1 /// HBG2
LOC339352
PTPN2
BCAS2

CD300A
226252_PM_at
239048_PM_at
SOS2

SCUBE3
PARK7
232595_PM_at
NCRNA00107

PALLD
CPT2
RFK
GAS7

SIPA1L2
USP40
ACAP2
DTHD1

ZNF615
SNRNP25
PGGT1B
CDK2AP1

PICALM
TSC22D1
IAH1
ARPP19

HBB
GGNBP2
TTC12
ABCA5

241613_PM_at
LONP2
ABHD5
TROVE2

LOC729013
WDYHV1
ROD1
PSMB4

PRKCD
TCP11L1
CHFR
PGS1

UGCG
IL13RA1
1556646_PM_at
C16orf13

213048_PM_s_at
243931_PM_at
230599_PM_at
C4orf45

1560474_PM_at
FLJ38717
PEX11A
NUDT19

NOTCH2
229370_PM_at
TRBC2
241445_PM_at

LOC100128439
KIAA0141
SEMA4F
LOC115110

SRGAP2P1
SLC6A13
ARPP21
1560738_PM_at

ROCK1
ACP1
NFATC2
CDC16

GDE1
SYNGR2
RNPC3
RHOB

DOCK8
ZNF207
POC1B
ERGIC1

CEP72
CUL2
GORASP2
OR2W3

WHAMML1 ///
RASGRP2
239603_PM_x_at
C1orf220

WHAMML2

TMSB4X /// TMSL3
ATG13
DHX37
235680_PM_at

NFYA
C3orf37
TWF1
UBE3C

239709_PM_at
PHAX
232685_PM_at
NACA

237626_PM_at
KDELR1
LOC100507596
SLC6A6

SH3BP2
243089_PM_at
239597_PM_at
RIOK2

235894_PM_at
243482_PM_at
BCL10
241860_PM_at

SSBP4
CCDC50
WIPF2
C10orf76

PRELID1
CD68
230324_PM_at
1561128_PM_at

MDM2
HYLS1
232330_PM_at
LIG3

RNF181
CACNA2D3
TBC1D8
MRPS31

CUL4B
ZNF43
MGAT2
243249_PM_at

DOLK
LOC91548
NXT1
ZFAND5

TOR1AIP2
1566501_PM_at
TNRC6B
DERA

CNIH4
SNRPA1
PER1
TMEM159

MAX
PNPO
TMEM43
SCGB1C1

244592_PM_at
IL6ST
224989_PM_at
ANO6

SMARCAL1
C15orf63
TM6SF2
233506_PM_at

237554_PM_at
DNAJB11
FNTA
UNKL

NDUFB4
MESDC1
AP4M1
GAB2

ACBD6
PPP1R14B
MRC2
1570621_PM_at

238743_PM_at
WTAP
FNDC3B
SRGAP2

RQCD1
HSPC157
ADAMTS1
LOC151657

RBBP6
TRA2A
DSC2
PTPRE

C17orf77
C14orf119
234753_PM_x_at
MPV17

TRIB3
PPP1R16B
1558418_PM_at
C17orf108

236781_PM_at
SLC25A5
YY1
240154_PM_at

C15orf63 /// SERF2
MEX3D
VEGFB
PRMT1

C6orf226
DDOST
AMBRA1
216490_PM_x_at

COX6B1
KLHL22
CCT6A
CSNK1A1

ARID2
232344_PM_at
C4orf21
HDDC2

CD97
VNN3
MGA
MRPL33

LRCH4
CLEC4E
SAP30
ORMDL2

APBA3
ANKRD57
C20orf72
TNFSF12-TNFSF13 ///

TNFSF13

FBXO41
ERICH1 /// FLJ00290
IQCK
RFX7

242772_PM_x_at
SRSF2IP
VIL1
ZNF397

C10orf76
DPYSL5
244502_PM_at
PTPRC

IK /// TMCO6
ANPEP
ATF6B /// TNXB
PARP8

ATP6AP2
ADAM17
214848_PM_at
C18orf21

LOC727820
SRD5A3
PREX1
ZEB2

PIK3CD
CCDC12
LSM14B
STK35

AIP
SRC
NIP7
244548_PM_at

UBAC2
242440_PM_at
PPA2
ASAH1

MED23
USP42
PLIN5
ZNF552

EI24
MIB2
NAMPT
CAND1

NME3
GDF10
RAPGEF2
LOC647979

1559663_PM_at
RNPC3
SNX2
KIAA2026

237264_PM_at
CCDC154
WIBG
SLC12A6

WAC
DPH2
ZBED1
C11orf10

C17orf37
P4HB
TRO
GRIN2B

1560622_PM_at
CSNK1G2
TPSAB1
HNRNPA3 ///

HNRNPA3P1

232876_PM_at
ATXN7
SNX20
BAZ2A

1566966_PM_at
PITPNM1
KRTAP9-2
CISH

ZNF362
PDLIM1
C17orf63
NSMAF

239555_PM_at
KIAA1143
SUPT4H1
LETM1

RASSF7
KLF6
PRKAA1
235596_PM_at

C17orf81
235685_PM_at
TRIM23
ADORA3

224082_PM_at
SORD
ATP11B
RSBN1

PALB2
MALAT1
ACTG1
233354_PM_at

239819_PM_at
TMEM107
PIGC
STAG2

LOC200772
PER1
GRAMD1A
CD300LB

PCBP2
NBR1
SNUPN
1564996_PM_at

BTF3
RIBC1
FLJ44342
PHF1

LOC221442
234255_PM_at
SMU1
SEMA7A

239893_PM_at
IFT27
LOC100134822
1557772_PM_at

WDR1
IMPAD1
229206_PM_at
WDFY3

SMYD3
IL17RA /// LOC150166
IDH3A
242016_PM_at

242384_PM_at
LOC100190986
FCHO2
231039_PM_at

MGEA5
SNRK
GHITM
RNF213

CTDNEP1
ASTE1
ELP3
EML4

210598_PM_at
KIAA1659
SUSD1
HIPK3

1562898_PM_at
231471_PM_at
STYXL1
C14orf1

ALKBH7
RAC2
URB1
242688_PM_at

XAB2
C9orf89
1556645_PM_s_at
CD44

DUSP23
ZNF416
EXOSC3
WBSCR16

PRKAR1A
ZNF599
TRD@
237683_PM_s_at

PDSS1
CCNH
CDYL
SLC14A1

SAP18
FAM190B
ITPKC
GNAS

CIRBP
SLC16A3
NUDT2
C4orf23

MED19
229968_PM_at
XRCC6BP1
DUSP10

LOC644613
RPRD1A
241501_PM_at
1564886_PM_at

PDK3
WSB1
C5orf20
MBD1

RBM5
USP15
RNMTL1
KLHDC7B

TABLE 2

Inclusion/Exclusion criteria

General Inclusion Criteria
1) Adult kidney transplant (age >18 years): first or multiple transplants, high or

low risk, cadaver or living donor organ recipients.

2) Any cause of end-stage renal disease except as described in Exclusions.

3) Consent to allow gene expression and proteomic studies to be done on samples.

4) Meeting clinical and biopsy criteria specified below for Groups 1-3.

General Exclusion Criteria
1) Combined organ recipients: kidney/pancreas, kidney/islet, heart/kidney and

liver/kidney.

2) A recipient of two kidneys simultaneously unless the organs are both adult

and considered normal organs (rationale is to avoid inclusion of pediatric en bloc

or dual adult transplants with borderline organs).

3) Any technical situation or medical problem such as a known bleeding disorder in

which protocol biopsies would not be acceptable for safety reasons in the best

judgment of the clinical investigators.

4) Patients with active immune-related disorders such as rheumatoid arthritis, SLE,

scleroderma and multiple sclerosis.

5) Patients with acute viral or bacterial infections at the time of biopsy.

6) Patients with chronic active hepatitis or HIV.

7) CAN, that at the time of identification are in the best judgment of the clinicians

too far along in the process or progressing to rapidly to make it likely that they will still

have a functioning transplant a year later.

8) Patients enrolled in another research study that in the best judgment of the

clinical center investigator involves such a radical departure from standard therapy

that the patient would not be representative of the groups under study in the

Program Project.

Acute Rejection (AR) Specific
1) Clinical presentation with acute kidney transplant dysfunction at any time

Inclusion Criteria
post transplant

a. Biopsy-proven AR with tubulointerstitial cellular rejection with or

without acute vascular rejection

Acute Rejection (AR) Specific
1) Evidence of concomitant acute infection

Exclusion Criteria
a. CMV

b. BK nephritis

c. Bacterial pyelonephritis

d. Other

2) Evidence of anatomical obstruction or vascular compromise

3) If the best judgment of the clinical team prior to the biopsy is that the acute

decrease in kidney function is due to dehydration, drug effect (i.e. ACE inhibitor) or

calcineurin inhibitor excess

4) If the biopsy is read as drug hypersensitivity (i.e. sulfa-mediated interstitial

nephritis)

5) Evidence of hemolytic uremic syndrome

Well-functioning Transplant/No
1) Patient between 12 and 24 months post transplant

Rejection (TX) Specific Inclusion
2) Stable renal function defined as at least three creatinine levels over a three

Criteria
month period that do not change more than 20% and without any pattern of a

gradual increasing creatinine.

3) No history of rejection or acute transplant dysfunction by clinical criteria or

previous biopsy

4) Serum creatinines <1.5 mg/dL for women, <1.6 mg/dL for men

5) They must also have a calculated or measured creatinine clearance >45 ml/

minute

6) They must have well controlled blood pressure defined according to the JNC

7 guidelines of <140/90 (JNC7 Express, The Seventh Report of the Noint National

Committee on Prevention, Detection, Evaluation and Treatment of High Blood

Pressure, NIH Publication No. 03-5233, December 2003)

Well-functioning Transplant/No
1) Patient less than one month after steroid withdrawal

Rejection (TX) Specific Exclusion
2) Patients with diabetes (Type I or II, poorly controlled)

Criteria
3) Evidence of concomitant acute infection

a. CMV

b. BK nephritis

c. Bacterial pyelonephritis

*A special note regarding why noncompliance is not an exclusion criterion is important to emphasize. Noncompliance is not a primary issue in determining gene expression and proteomics profiles associated with molecular pathways of transplant immunity and tissue injury/repair.

TABLE 3

Clinical characteristics for the 148 study samples.

Multivariate

Multivariate
Analysis^¶

Analysis^¶
Significance

All Study Samples
Significance
(Phenotypes/

TX
AR
ADNR
Significance*
(Phenotypes)
Cohorts)

Subject Numbers
45
64
39
—
—
—

Recipient Age ± SD^§
50.1 ± 14.5
44.9 ± 14.3
49.7 ± 14.6
NS{circumflex over ( )}
NS
NS

(Years)

% Female Recipients
34.8
23.8
20.5
NS
NS
NS

% Recipient African
6.8
12.7
12.8
NS
NS
NS

American

% Pre-tx Type II
25.0
17.5
21.6
NS
NS
NS

Diabetes

% PRA >20
29.4
11.3
11.5
NS
NS
NS

HU Mismatch ± SD
4.2 ± 2.1
4.3 ± 1.6
3.7 ± 2.1
NS
NS
NS

% Deceased Donor
43.5
65.1
53.8
NS
NS
NS

Donor Age ± SD
40.3 ± 14.5
38.0 ± 14.3
46.5 ± 14.6
NS
NS
NS

(Years)

% Female Donors
37.0
50.8
46.2
NS
NS
NS

% Donor African
3.2
4.9
13.3
NS
NS
NS

American

% Delayed Graft
19.0
34.4
29.0
NS
NS
NS

Function

% Induction
63.0
84.1
82.1
NS
NS
NS

Serum Creatinine ± SD
1.5 ± 0.5
3.2 ± 2.8
2.7 ± 1.8
TX vs. AR = 0.00001
TX vs. AR = 0.04
TX vs. AR vs.

(mg/dL)

TX vs. ADNR = 0.0002
TX vs. ADNR = 0.01
ADNR = 0.00002

AR vs. ADNR = NS

AR vs. ADNR = NS

Time to Biopsy ± SD
512 ± 1359
751 ± 1127
760 ± 972
NS
NS
NS

(Days)

Biopsy ≤365 days (%)
27
38
23
NS
NS
NS

(54.2%)
(49.0%)
(52.4%)

Biopsy >366 days (%)
19
32
18
NS
NS
NS

(45.8%)
(51.0%)
(47.6%)

% Calcineurin
89.7
94.0
81.1
NS
NS
NS

Inhibitors

% Mycophenolic Acid
78.3
85.7
84.6
NS
NS
NS

Derivatives

% Oral Steroids
26.1
65.1
74A
TX vs. AR = 0.001
TX vs. ADNR = 0.04
NS

TX vs. ADNR = 0.001

C4d Positive Staining
0/13
12/36
1/20
NS
NS
NS

(%)^§
(0%)
(33.3%)
(5%)

*Significance for at comparisons were determined with paired Students t-test for pair-wise comparisons of data with Standard Deviations and for dichotomous data comparisons by Chi-Square.

^¶A multivariate logistic regression model was used with a Wald test correction. In the first analysis (Phenotypes) we used all 148 samples and in the second analysis (Phenotypes/Cohorts) we did the analysis for each randomized set of 2 cohorts (Discovery and Validation).

{circumflex over ( )}NS = not significant (p ≥ 0.05)

^§Subjects with biopsy-positive staining for C4d and total number of subjects whose biopsies were stained for C4d with (%).

TABLE 4

Diagnostic metrics for the 3-way Nearest Centroid classifiers for AR, ADNR and TX in Discovery and Validation Cohorts

%
%

Predictive
Predictive

Positive
Negative

Positive
Negative

Accuracy
Accuracy
Sensi-
Speci-
Predictive
Predictive

Sensi-
Speci-
Predictive
Predictive

(Discovery
(Validation
tivity
ficity
Value
Value

tivity
ficity
Value
Value

Method
Classifies
Cohort)
Cohort)
(%)
(X)
(%)
(%)
AUC
(%)
(%)
(%)
(%)
AUC

200
TX vs. AR
92%
83%
87%
96%
95%
89%
0.917
73%
92%
89%
79%
0.837

Classifiers

TX vs.
91%
82%
95%
90%
91%
95%
0.913
89%
76%
76%
89%
0.817

ADNR

AR vs.
92%
90%
87%
100%
100%
86%
0.933
89%
92%
89%
92%
0.893

ADNR

100
TX vs. AR
91%
83%
87%
93%
91%
90%
0.903
76%
88%
84%
82%
0.825

Classifiers

TX vs.
98%
81%
95%
100%
100%
95%
0.975
84%
79%
80%
83%
0.814

ADNR

AR vs.
98%
90%
95%
100%
100%
97%
0.980
88%
92%
88%
92%
0.900

ADNR

50
TX vs. AR
92%
94%
88%
96%
95%
90%
0.923
88%
91%
89%
89%
0.891

Classifiers

TX vs.
94%
95%
92%
98%
98%
90%
0.944
92%
90%
88%
89%
0.897

ADNR

AR vs.
97%
93%
95%
97%
100%
97%
0.969
89%
91%
89%
89%
0.893

ADNR

25
TX vs. AR
89%
92%
81%
96%
95%
84%
0.890
88%
90%
90%
89%
0.894

Classifiers

TX vs.
95%
95%
95%
95%
95%
95%
0.948
92%
92%
89%
89%
0.898

ADNR

AR vs.
96%
91%
95%
96%
95%
96%
0.955
85%
90%
89%
88%
0.882

ADNR

TABLE 5

Diagnostic metrics for the 3-way DLDA and SVM classifiers for AR, ADNR and TX in Discovery and Validation Cohorts

%
%

Predictive
Predictive

Positive
Negative

Positive
Negative

Accuracy
Accuracy
Sensi-
Speci-
Predictive
Predictive

Sensi-
Speci-
Predictive
Predictive

(Discovery
(Validation
tivity
ficity
Value
Value

tivity
ficity
Value
Value

Method
Classifies
Cohort)
Cohort)
(%)
(%)
(%)
(%)
AUC
(%)
(%)
(%)
(%)
AUC

200

Classifiers

DLDA
TX vs. AR
90%
84%
87%
93%
91%
89%
0.896
76%
92%
89%
81%
0.845

TX vs.
95%
82%
95%
94%
95%
95%
0.945
89%
76%
76%
89%
0.825

ADNR

AR vs.
92%
84%
83%
100%
100%
86%
0.923
84%
92%
89%
88%
0.880

ADNR

SVM
TX vs. AR
100%
83%
100%
100%
100%
100%
1.000
82%
86%
81%
86%
0.833

TX vs.
100%
96%
100%
100%
100%
100%
1.000
100%
95%
95%
100%
0.954

ADNR

AR vs.
100%
95%
100%
100%
100%
100%
1.000
95%
96%
95%
96%
0.954

ADNR

100

84%
82%
0.825

Classifiers

DLDA
TX vs. AR
91%
83%
88%
93%
91%
90%
0.905
76%
88%
80%
83%
0.815

TX vs.
97%
82%
95%
100%
100%
95%
0.970
84%
79%

ADNR

AR vs.
98%
90%
95%
100%
100%
97%
0.980
88%
92%
88%
92%
0.900

ADNR

SVM
TX vs. AR
97%
87%
88%
100%
100%
91%
0.971
83%
92%
90%
85%
0.874

TX vs.
98%
86%
96%
100%
100%
95%
0.988
86%
88%
90%
83%
0.867

ADNR

AR vs.
100%
87%
100%
100%
100%
100%
1.000
83%
92%
88%
88%
0.875

ADNR

50

Classifiers

DLDA
TX vs. AR
93%
83%
88%
97%
96%
90%
0.927
78%
88%
86%
81%
0.832

TX vs.
96%
84%
92%
100%
100%
90%
0.955
82%
87%
90%
76%
0.836

ADNR

AR vs.
98%
85%
95%
100%
100%
97%
0.979
81%
88%
81%
88%
0.845

ADNR

SVM
TX vs. AR
95%
83%
88%
100%
100%
91%
0.946
77%
88%
87%
79%
0.827

TX vs.
98%
92%
96%
100%
100%
95%
0.976
87%
100%
100%
81%
0.921

ADNR

AR vs.
100%
85%
100%
100%
100%
100%
1.000
87%
85%
77%
92%
0.852

ADNR

25

Classifiers

DLDA
TX vs. AR
88%
92%
83%
93%
90%
88%
0.884
88%
85%
78%
90%
0.852

TX vs.
92%
93%
95%
90%
90%
95%
0.924
92%
88%
85%
81%
0.864

ADNR

AR vs.
100%
91%
100%
100%
100%
100%
1.000
85%
87%
88%
76%
0.841

ADNR

SVM
TX vs. AR
95%
92%
92%
97%
96%
94%
0.945
84%
87%
88%
84%
0.857

TX vs.
96%
100%
96%
95%
96%
95%
0.955
100%
85%
83%
81%
0.874

ADNR

AR vs.
100%
100%
100%
100%
100%
100%
1.000
100%
86%
82%
81%
0.873

ADNR

DLDA—Diagonal linear Discriminant Analysis

SVM—Support Vector Machines

TABLE 6

Optimism-corrected Area Under the Curves (AUC's) comparing two

methods for creating and validating 3-Way classifiers for AR vs. ADNR vs.

TX that demonstrates they provide equivalent results.

Discovery Cohort-based 200 probeset classifier*

Optimism

Corrected

Method
Classifies
Original AUC
Optimism
AUC

Nearest Centroid
AR, TX, ADNR
0.8500
0.0262
0.8238

Diagonal Linear Discriminant Analysis
AR, TX, ADNR
0.8441
0.0110
0.8331

Support Vector Machines
AR, TX, ADNR
0.8603
0.0172
0.8431

Full study sample-based 200 probeset classifier*

Optimism

Original AUC

Corrected

Method
Classifies
(Bootstrapping)
Optimism
AUC

Nearest Centroid
AR, TX, ADNR
0.8641
0.0122
0.8519

Diagonal Linear Discriminant Analysis
AR, TX, ADNR
0.8590
0.0036
0.8554

Support Vector Machines
AR, TX, ADNR
0.8669
0.0005
0.8664

*153/200 (77%) of the discovery cohort-based classifier probesets were in the Top 500 of the full study sample-based 200 probeset classifier. Similarly, 141/200 (71%) of the full study sample-based 200 probeset classifier was in the top 500 probesets of the discovery cohort-based classifier.

Example 2

Materials and Methods

This Example describes some of the materials and methods employed in identification of differentially expressed genes in SCAR.

The discovery set of samples consisted of the following biopsy-documented peripheral blood samples. 69 PAXgene whole blood samples were collected from kidney transplant patients. The samples that were analyzed comprised 3 different phenotypes: (1) Acute Rejection (AR; n=21); (2) Sub-Clinical Acute Rejection (SCAR; n=23); and (3) Transplant Excellent (TX; n=25). Specifically, SCAR was defined by a protocol biopsy done on a patient with totally stable kidney function and the light histology revealed unexpected evidence of acute rejection (16 “Borderline”, 7 Banff 1A). The SCAR samples consisted of 3 month and 1 year protocol biopsies, whereas the TXs were predominantly 3 month protocol biopsies. All the AR biopsies were “for cause” where clinical indications like a rise in serum creatinine prompted the need for a biopsy. All patients were induced with Thymoglobulin.

All samples were processed on the Affymetrix HG-U133 PM only peg microarrays. To eliminate low expressed signals we used a signal filter cut-off that was data dependent, and therefore expression signals <Log₂3.74 (median signals on all arrays) in all samples were eliminated leaving us with 48734 probe sets from a total of 54721 probe sets. We performed a 3-way ANOVA analysis of AR vs. ADNR vs. TX. This yielded over 6000 differentially expressed probesets at a p-value <0.001. Even when a False Discovery rate cut-off of (FDR <10%), was used it gave us over 2700 probesets. Therefore for the purpose of a diagnostic signature we used the top 200 differentially expressed probe sets (Table 8) to build predictive models that could differentiate the three classes. We used three different predictive algorithms, namely Diagonal Linear Discriminant Analysis (DLDA), Nearest Centroid (NC) and Support Vector Machines (SVM) to build the predictive models. We ran the predictive models using two different methodologies and calculated the Area Under the Curve (AUC). SVM, DLDA and NC picked classifier sets of 200, 192 and 188 probesets as the best classifiers. Since there was very little difference in the AUC's we decided to use all 200 probesets as classifiers for all methods. We also demonstrated that these results were not the consequence of statistical over-fitting by using the replacement method of Harrell to perform a version of 1000-test cross-validation. Table 7 shows the performance of these classifier sets using both one-level cross validation as well as the Optimism Corrected Bootstrapping (1000 data sets).

An important point here is that in real clinical practice the challenge is actually not to distinguish SCAR from AR because by definition only AR presents with a significant increase in baseline serum creatinine. The real challenge is to take a patient with normal and stable creatinine and diagnose the hidden SCAR without having to depend on invasive and expensive protocol biopsies that cannot be done frequently in any case. Though we have already successfully done this using our 3-way analysis, we also tested a 2-way prediction of SCAR vs. TX. The point was to further validate that a phenotype as potentially subtle clinically as SCAR can be truly distinguished from TX. At a p-value <0.001, there were 33 probesets whose expression signals highly differentiated SCAR and TX, a result in marked contrast with the >2500 probesets differentially expressed between AR vs. TX at that same p-value. However, when these 33 probesets (Table 9) were used in NC to predict SCAR and TX creating a 2-way classifier, the predictive accuracies with a one-level cross-validation was 96% and with the Harrell 1000 test optimism correction it was 94%. Thus, we are confident that we can distinguish SCAR, TX and AR by peripheral blood gene expression profiling using this proof of principle data set.

TABLE 7

Blood Expression Profiling of Kidney Transplants: 3-Way Classifier AR vs. SCAR vs. TX.

Postive
Negative

Predictive

Predictive
Predictive

AUC after
Accuracy
Sensitivity
Specificity
Value
Value

Algorithm
Predictors
Comparison
Thresholding
(%)
(%)
(%)
(%)
(%)

Nearest Centroid
200
SCAR vs. TX
1.000
100
100
100
100
100

Nearest Centroid
200
SCAR vs. AR
0.953
95
92
100
100
90

Nearest Centroid
200
AR vs. TX
0.932
93
96
90
92
95

It is understood that the examples and embodiments described herein are for illustrative purposes only and that various modifications or changes in light thereof will be suggested to persons skilled in the art and are to be included within the spirit and purview of this application and scope of the appended claims. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, the preferred methods and materials are described.

All publications, GenBank sequences, ATCC deposits, patents and patent applications cited herein are hereby expressly incorporated by reference in their entirety and for all purposes as if each is individually so denoted. Improvements in kidney transplantation have resulted in significant reductions in clinical acute rejection (AR) (8-14%) (Meier-Kriesche et al. 2004, Am J Transplant, 4(3): 378-383). However, histological AR without evidence of kidney dysfunction (i.e. subclinical AR) occurs in >15% of protocol biopsies done within the first year. Without a protocol biopsy, patients with subclinical AR would be treated as excellent functioning transplants (TX). Biopsy studies also document significant rates of progressive interstitial fibrosis and tubular atrophy in >50% of protocol biopsies starting as early as one year post transplant.

TABLE 8

200 Probeset classifer for distinguishing AR, SCAR and TX based on a 3-way ANOVA

stepup
AR-
SCAR-
TX-

p-value
p-value
Mean
Mean
Mean

#
Probeset ID
Gene Symbol
Gene Title
(Phenotype)
(Phenotype)
Signal
Signal
Signal

1
238108_PM_at
—
—
1.70E−10
8.27E−06
73.3
45.4
44.4

2
243524_PM_at
—
—
3.98E−10
9.70E−06
72.3
41.3
37.7

3
1558831_PM_x_at
—
—
5.11E−09
8.30E−05
48.1
30.8
31.4

4
229858_PM_at
—
—
7.49E−09
8.31E−05
576.2
359.3
348.4

5
236685_PM_at
—
—
8.53E−09
8.31E−05
409.1
213.3
211.0

6
213546_PM_at
DKFZP586l1420
hypothetical protein
3.52E−08
2.60E−04
619.2
453.7
446.0

DKFZp586l1420

7
231958_PM_at
C3orf31
Chromosome 3 open
4.35E−08
2.60E−04
22.8
20.1
16.4

reading frame 31

8
210275_PM_s_at
ZFAND5
zinc finger, AN1-type
4.96E−08
2.60E−04
1045.9
1513.6
1553.8

domain 5

9
244341_PM_at
—
—
5.75E−08
2.60E−04
398.3
270.7
262.8

10
1558822_PM_at
—
—
5.84E−08
2.60E−04
108.6
62.9
56.8

11
242175_PM_at
—
—
5.87E−08
2.60E−04
69.1
37.2
40.0

12
222357_PM_at
ZBTB20
zinc finger and BTB
6.97E−08
2.83E−04
237.4
127.4
109.8

domain containing 20

13
206288_PM_at
PGGT1B
protein
9.42E−08
3.53E−04
20.8
34.7
34.2

geranylgeranyltransferase

type I, beta

subunit

14
222306_PM_at
—
—
1.03E−07
3.59E−04
23.3
15.8
16.0

15
1569601_PM_at
—
—
1.67E−07
4.80E−04
49.5
34.1
29.7

16
235138_PM_at
—
—
1.69E−07
4.80E−04
1169.9
780.0
829.7

17
240452_PM_at
GSPT1
G1 to S phase
1.74E−07
4.80E−04
97.7
54.4
48.6

transition 1

18
243003_PM_at
—
—
1.77E−07
4.80E−04
92.8
52.5
51.3

19
218109_PM_s_at
MFSD1
major facilitator
1.90E−07
4.87E−04
1464.0
1881.0
1886.4

superfamily domain

containing 1

20
241681_PM_at
—
—
2.00E−07
4.87E−04
1565.7
845.7
794.6

21
243878_PM_at
—
—
2.19E−07
5.08E−04
76.1
39.7
39.5

22
233296_PM_x_at
—
—
2.33E−07
5.17E−04
347.7
251.5
244.7

23
243318_PM_at
DCAF8
DDB1 and CUL4
2.52E−07
5.34E−04
326.2
229.5
230.2

associated factor 8

24
236354_PM_at
—
—
3.23E−07
6.39E−04
47.1
31.2
27.8

25
243768_PM_at
—
—
3.35E−07
6.39E−04
1142.0
730.6
768.5

26
238558_PM_at
—
—
3.65E−07
6.39E−04
728.5
409.4
358.4

27
237825_PM_x_at
—
—
3.66E−07
6.39E−04
34.2
20.9
19.9

28
244414_PM_at
—
—
3.67E−07
6.39E−04
548.7
275.2
284.0

29
215221_PM_at
—
—
4.06E−07
6.83E−04
327.2
176.7
171.9

30
235912_PM_at
—
—
4.46E−07
7.25E−04
114.1
71.4
59.5

31
239348_PM_at
—
—
4.87E−07
7.54E−04
20.1
14.5
13.4

32
240499_PM_at
—
—
5.06E−07
7.54E−04
271.4
180.1
150.2

33
208054_PM_at
HERC4
hect domain and RLD 4
5.11E−07
7.54E−04
114.9
57.6
60.0

34
240263_PM_at
—
—
5.46E−07
7.81E−04
120.9
78.7
66.6

35
241303_PM_x_at
—
—
5.78E−07
7.81E−04
334.5
250.3
261.5

36
233692_PM_at
—
—
5.92E−07
7.81E−04
22.4
15.5
15.0

37
243561_PM_at
—
—
5.93E−07
7.81E−04
341.1
215.1
207.3

38
232778_PM_at
—
—
6.91E−07
8.86E−04
46.5
31.0
28.5

39
237632_PM_at
—
—
7.09E−07
8.86E−04
108.8
61.0
57.6

40
233690_PM_at
—
—
7.30E−07
8.89E−04
351.1
222.7
178.1

41
220221_PM_at
VPS13D
vacuolar protein
7.50E−07
8.89E−04
93.5
60.0
59.9

sorting 13 homolog D

(S. cerevisiae)

42
242877_PM_at
—
—
7.72E−07
8.89E−04
173.8
108.1
104.0

43
218155_PM_x_at
TSR1
TSR1, 20S rRNA
7.86E−07
8.89E−04
217.2
165.6
164.7

accumulation,

homolog (S. cerevisiae)

44
239603_PM_x_at
—
—
8.24E−07
8.89E−04
120.9
75.5
81.1

45
242859_PM_at
—
—
8.48E−07
8.89E−04
221.1
135.4
138.3

46
240866_PM_at
—
—
8.54E−07
8.89E−04
65.7
33.8
35.2

47
239661_PM_at
—
—
8.72E−07
8.89E−04
100.5
48.3
45.2

48
224493_PM_x_at
C18orf45
chromosome 18
8.77E−07
8.89E−04
101.8
78.0
89.7

open reading frame 45

49
1569202_PM_x_at
—
—
8.98E−07
8.89E−04
23.3
18.5
16.6

50
1560474_PM_at
—
—
9.12E−07
8.89E−04
25.2
17.8
18.5

51
232511_PM_at
—
—
9.48E−07
9.06E−04
77.2
46.1
49.9

52
228119_PM_at
LRCH3
leucine-rich repeats
1.01E−06
9.51E−04
117.2
84.2
76.1

and calponin

homology (CH)

domain containing 3

53
228545_PM_at
ZNF148
zinc finger protein 148
1.17E−06
9.99E−04
789.9
571.1
579.7

54
232779_PM_at
—
—
1.17E−06
9.99E−04
36.7
26.0
20.7

55
239005_PM_at
FLJ39739
Hypothetical
1.18E−06
9.99E−04
339.1
203.7
177.7

FLJ39739

56
244478_PM_at
LRRC37A3
leucine rich repeat
1.20E−06
9.99E−04
15.7
12.6
12.7

containing 37,

member A3

57
244535_PM_at
—
—
1.28E−06
9.99E−04
261.5
139.5
137.8

58
1562673_PM_at
—
—
1.28E−06
9.99E−04
77.4
46.5
51.8

59
240601_PM_at
—
—
1.29E−06
9.99E−04
212.6
107.7
97.7

60
239533_PM_at
GPR155
G protein-coupled
1.30E−06
9.99E−04
656.3
396.7
500.1

receptor 155

61
222358_PM_x_at
—
—
1.32E−06
9.99E−04
355.2
263.1
273.7

62
214707_PM_x_at
ALMS1
Alstrom syndrome 1
1.32E−06
9.99E−04
340.2
255.9
266.0

63
236435_PM_at
—
—
1.32E−06
9.99E−04
144.0
92.6
91.1

64
232333_PM_at
—
—
1.33E−06
9.99E−04
487.7
243.7
244.3

65
222366_PM_at
—
—
1.33E−06
9.99E−04
289.1
186.1
192.8

66
215611_PM_at
TCF12
transcription factor 12
1.38E−06
1.02E−03
45.5
32.4
30.8

67
1558002_PM_at
STRAP
Serine/threonine
1.40E−06
1.02E−03
199.6
146.7
139.7

kinase receptor

associated protein

68
239716_PM_at
—
—
1.43E−06
1.02E−03
77.6
49.5
45.5

69
239091_PM_at
—
—
1.45E−06
1.02E−03
76.9
44.0
45.0

70
238883_PM_at
—
—
1.68E−06
1.15E−03
857.1
475.5
495.1

71
235615_PM_at
PGGT1B
protein
1.72E−06
1.15E−03
127.0
235.0
245.6

geranylgeranyltransferase

type I, beta

subunit

72
204055_PM_s_at
CTAGE5
CTAGE family,
1.77E−06
1.15E−03
178.8
115.2
105.9

member 5

73
239757_PM_at
ZFAND6
Zinc finger, AN1-type
1.81E−06
1.15E−03
769.6
483.3
481.9

domain 6

74
1558409_PM_at
—
—
1.82E−06
1.15E−03
14.8
10.9
11.8

75
242688_PM_at
—
—
1.85E−06
1.15E−03
610.5
338.4
363.4

76
242377_PM_x_at
THUMPD3
THUMP domain
1.87E−06
1.15E−03
95.5
79.0
81.3

containing 3

77
242650_PM_at
—
—
1.88E−06
1.15E−03
86.0
55.5
47.4

78
243589_PM_at
KIAA1267 ///
KIAA1267 ///
1.89E−06
1.15E−03
377.8
220.3
210.4

LOC100294337
hypothetical

LOC100294337

79
227384_PM_s_at
—
—
1.90E−06
1.15E−03
3257.0
2255.5
2139.7

80
237864_PM_at
—
—
1.91E−06
1.15E−03
121.0
69.2
73.4

81
243490_PM_at
—
—
1.92E−06
1.15E−03
24.6
17.5
16.5

82
244383_PM_at
—
—
1.96E−06
1.17E−03
141.7
93.0
77.5

83
215908_PM_at
—
—
2.06E−06
1.19E−03
98.5
67.9
67.5

84
230651_PM_at
—
—
2.09E−06
1.19E−03
125.9
74.3
71.5

85
1561195_PM_at
—
—
2.14E−06
1.19E−03
86.6
45.1
43.9

86
239268_PM_at
NDUFS1
NADH
2.14E−06
1.19E−03
14.0
12.0
11.3

dehydrogenase

(ubiquinone) Fe—S

protein 1, 75 kDa

(NADH-coenzyme Q

reductase)

87
236431_PM_at
SR140
U2-associated SR140
2.16E−06
1.19E−03
69.4
47.9
43.9

protein

88
236978_PM_at
—
—
2.19E−06
1.19E−03
142.4
88.6
88.1

89
1562957_PM_at
—
—
2.21E−06
1.19E−03
268.3
181.8
165.4

90
238913_PM_at
—
—
2.21E−06
1.19E−03
30.9
20.2
20.1

91
239646_PM_at
—
—
2.23E−06
1.19E−03
100.3
63.1
60.8

92
235701_PM_at
—
—
2.34E−06
1.24E−03
133.2
66.1
60.0

93
235601_PM_at
—
—
2.37E−06
1.24E−03
121.9
75.5
79.0

94
230918_PM_at
—
—
2.42E−06
1.25E−03
170.4
114.5
94.4

95
219112_PM_at
FNIP1 ///
folliculin interacting
2.49E−06
1.28E−03
568.2
400.2
393.4

RAPGEF6
protein 1 /// Rap

guanine nucleotide

exchange factor

(GEF) 6

96
202228_PM_s_at
NPTN
neuroplastin
2.52E−06
1.28E−03
1017.7
1331.5
1366.4

97
242839_PM_at
—
—
2.78E−06
1.39E−03
17.9
14.0
13.6

98
244778_PM_x_at
—
—
2.85E−06
1.42E−03
105.1
68.0
65.9

99
237388_PM_at
—
—
2.91E−06
1.42E−03
59.3
38.0
33.0

100
202770_PM_s_at
CCNG2
cyclin G2
2.92E−06
1.42E−03
142.2
269.0
270.0

101
240008_PM_at
—
—
2.96E−06
1.42E−03
96.2
65.6
56.2

102
1557718_PM_at
PPP2R5C
protein phosphatase
2.97E−06
1.42E−03
615.2
399.8
399.7

2, regulatory subunit

B′, gamma

103
215528_PM_at
—
—
3.01E−06
1.42E−03
126.8
62.6
69.0

104
204689_PM_at
HHEX
hematopoietically
3.08E−06
1.44E−03
381.0
499.9
567.9

105
213718_PM_at
RBM4
expressed homeobox
3.21E−06
1.46E−03
199.3
140.6
132.2

RNA binding motif

protein 4

106
243233_PM_at
—
—
3.22E−06
1.46E−03
582.3
343.0
337.1

107
239597_PM_at
—
—
3.23E−06
1.46E−03
1142.9
706.6
720.8

108
232890_PM_at
—
—
3.24E−06
1.46E−03
218.0
148.7
139.9

109
232883_PM_at
—
—
3.42E−06
1.53E−03
127.5
79.0
73.1

110
241391_PM_at
—
—
3.67E−06
1.62E−03
103.8
51.9
48.3

111
244197_PM_x_at
—
—
3.71E−06
1.62E−03
558.0
397.3
418.8

112
205434_PM_s_at
AAK1
AP2 associated
3.75E−06
1.62E−03
495.2
339.9
301.2

kinase 1

113
235725_PM_at
SMAD4
SMAD family
3.75E−06
1.62E−03
147.1
102.1
112.0

member 4

114
203137_PM_at
WTAP
Wilms tumor 1
3.89E−06
1.66E−03
424.1
609.4
555.8

associated protein

115
231075_PM_x_at
RAPH1
Ras association
3.91E−06
1.66E−03
30.4
19.3
18.2

(RalGDS/AF-6) and

pleckstrin homology

domains 1

116
236043_PM_at
LOC100130175
hypothetical protein
3.98E−06
1.67E−03
220.6
146.2
146.5

LOC100130175

117
238299_PM_at
—
—
4.09E−06
1.70E−03
217.1
130.4
130.3

118
243667_PM_at
—
—
4.12E−06
1.70E−03
314.5
225.3
232.8

119
223937_PM_at
FOXP1
forkhead box P1
4.20E−06
1.72E−03
147.7
85.5
90.9

120
238666_PM_at
—
—
4.25E−06
1.72E−03
219.1
148.3
145.5

121
1554771_PM_at
—
—
4.28E−06
1.72E−03
67.2
41.5
40.8

122
202379_PM_s_at
NKTR
natural killer-tumor
4.34E−06
1.73E−03
1498.2
1170.6
1042.6

recognition sequence

123
244695_PM_at
GHRLOS
ghrelin opposite
4.56E−06
1.79E−03
78.0
53.0
52.5

strand (non-protein

coding)

124
239393_PM_at
—
—
4.58E−06
1.79E−03
852.0
554.2
591.7

125
242920_PM_at
—
—
4.60E−06
1.79E−03
392.8
220.9
251.8

126
242405_PM_at
—
—
4.66E−06
1.80E−03
415.8
193.8
207.4

127
1556432_PM_at
—
—
4.69E−06
1.80E−03
61.5
43.1
38.1

128
1570299_PM_at
—
—
4.77E−06
1.81E−03
27.0
18.0
19.8

129
225198_PM_at
VAPA
VAMP (vesicle-
4.85E−06
1.83E−03
192.0
258.3
273.9

associated

membrane protein)-

associated protein A,

33 kDa

130
230702_PM_at
—
—
4.94E−06
1.85E−03
28.2
18.4
17.5

131
240262_PM_at
—
—
5.07E−06
1.88E−03
46.9
22.8
28.0

132
232216_PM_at
YME1L1
YME1-like 1 (S.
5.14E−06
1.89E−03
208.6
146.6
130.1

cerevisiae)

133
225171_PM_at
ARHGAP18
Rho GTPase
5.16E−06
1.89E−03
65.9
109.1
121.5

activating protein 18

134
243992_PM_at
—
—
5.28E−06
1.92E−03
187.1
116.0
125.6

135
227082_PM_at
—
—
5.45E−06
1.96E−03
203.8
140.4
123.0

136
239948_PM_at
NUP153
nucleoporin 153 kDa
5.50E−06
1.96E−03
39.6
26.5
27.8

137
221905_PM_at
CYLD
cylindromatosis
5.51E−06
1.96E−03
433.0
316.8
315.1

(turban tumor

syndrome)

138
242578_PM_x_at
SLC22A3
Solute carrier family
5.56E−06
1.96E−03
148.4
109.2
120.1

22 (extraneuronal

monoamine

transporter),

member 3

139
1569238_PM_a_at
—
—
5.73E−06
1.99E−03
71.0
33.0
36.1

140
201453_PM_x_at
RHEB
Ras homolog
5.76E−06
1.99E−03
453.3
600.0
599.0

enriched in brain

141
236802_PM_at
—
—
5.76E−06
1.99E−03
47.9
29.1
29.6

142
232615_PM_at
—
—
5.82E−06
1.99E−03
4068.5
3073.4
2907.4

143
237179_PM_at
PCMTD2
protein-L-
5.84E−06
1.99E−03
48.7
30.2
26.8

isoaspartate (D-

aspartate) O—

methyltransferase

domain containing 2

144
203255_PM_at
FBX011
F-box protein 11
5.98E−06
2.02E−03
748.3
529.4
539.6

145
212989_PM_at
SGMS1
sphingomyelin
6.04E−06
2.03E−03
57.2
93.1
107.9

synthase 1

146
236754_PM_at
PPP1R2
protein phosphatase
6.17E−06
2.05E−03
505.3
380.7
370.1

1, regulatory

(inhibitor) subunit 2

147
1559496_PM_at
PPA2
pyrophosphatase
6.24E−06
2.05E−03
68.8
39.7
39.3

(inorganic) 2

148
236494_PM_x_at
—
—
6.26E−06
2.05E−03
135.0
91.1
82.9

149
237554_PM_at
—
—
6.30E−06
2.05E−03
53.4
31.5
30.1

150
243469_PM_at
—
—
6.37E−06
2.05E−03
635.2
308.1
341.5

151
240155_PM_x_at
ZNF493 ///
zinc finger protein
6.45E−06
2.05E−03
483.9
299.9
316.6

ZNF738
493 /// zinc finger

protein 738

152
222442_PM_s_at
ARL8B
ADP-ribosylation
6.47E−06
2.05E−03
201.5
292.6
268.3

factor-like 8B

153
240307_PM_at
—
—
6.48E−06
2.05E−03
55.4
36.8
33.1

154
200864_PM_s_at
RAB11A
RAB11A, member
6.50E−06
2.05E−03
142.1
210.9
233.0

RAS oncogene family

155
235757_PM_at
—
—
6.53E−06
2.05E−03
261.4
185.2
158.9

156
222351_PM_at
PPP2R1B
protein phosphatase
6.58E−06
2.06E−03
75.8
51.1
45.4

2, regulatory subunit

A, beta

157
222788_PM_s_at
RSBN1
round spermatid
6.63E−06
2.06E−03
389.9
302.7
288.2

basic protein 1

158
239815_PM_at
—
—
6.70E−06
2.06E−03
216.9
171.4
159.5

159
219392_PM_x_at
PRR11
proline rich 11
6.77E−06
2.07E−03
1065.3
827.5
913.2

160
240458_PM_at
—
—
6.80E−06
2.07E−03
414.3
244.6
242.0

161
235879_PM_at
MBNL1
Muscleblind-like
6.88E−06
2.08E−03
1709.2
1165.5
1098.0

(Drosophila)

162
230529_PM_at
HECA
headcase homolog
7.08E−06
2.13E−03
585.1
364.3
418.4

(Drosophila)

163
1562063_PM_x_at
KIAA1245 ///
KIAA1245 ///
7.35E−06
2.20E−03
350.4
238.8
260.8

NBPF1 ///
neuroblastoma

NBPF10 ///
breakpoint family,

NBPF11 ///
member 1 ///

NBPF12 ///
neuroblastoma

NBPF24 ///
breakpoint fam

NBPF8 ///

NBPF9

164
202769_PM_at
CCNG2
cyclin G2
7.42E−06
2.20E−03
697.1
1164.0
1264.6

165
1556493_PM_a_at
KDM4C
lysine (K)-specific
7.64E−06
2.24E−03
81.4
49.0
44.5

166
216509_PM_x_at
MLLT10
demethylase 4C
7.64E−06
2.24E−03
22.4
17.9
19.3

myeloid/lymphoid or

mixed-lineage

leukemia (trithorax

homolog,

Drosophila);

translocate

167
223697_PM_x_at
C9orf64
chromosome 9 open
7.70E−06
2.25E−03
1013.6
771.2
836.8

reading frame 64

168
235999_PM_at
—
—
7.77E−06
2.25E−03
227.6
174.1
182.1

169
244766_PM_at
LOC100271836 ///
SMG1 homolog,
8.03E−06
2.31E−03
133.4
99.4
87.5

LOC440354 ///
phosphatidylinositol

LOC595101 ///
3-kinase-related

LOC641298 ///
kinase pseudogene ///

SMG1
PI-3-kinase-r

170
230332_PM_at
ZCCHC7
Zinc finger, CCHC
8.07E−06
2.31E−03
467.4
265.1
263.2

domain containing 7

171
235308_PM_at
ZBTB20
zinc finger and BTB
8.17E−06
2.32E−03
256.7
184.2
167.3

domain containing 20

172
242492_PM_at
CLNS1A
Chloride channel,
8.19E−06
2.32E−03
128.5
82.8
79.2

nucleotide-sensitive, 1A

173
215898_PM_at
TTLL5
tubulin tyrosine
8.24E−06
2.32E−03
20.9
14.0
13.8

ligase-like family,

member 5

174
244840_PM_x_at
DOCK4
dedicator of
8.65E−06
2.42E−03
43.1
16.5
21.5

cytokinesis 4

175
220235_PM_s_at
C1orf103
chromosome 1 open
8.72E−06
2.43E−03
88.4
130.5
143.3

reading frame 103

176
229467_PM_at
PCBP2
Poly(rC) binding
8.80E−06
2.44E−03
186.5
125.4
135.8

protein 2

177
232527_PM_at
—
—
8.99E−06
2.48E−03
667.4
453.9
461.3

178
243286_PM_at
—
—
9.24E−06
2.53E−03
142.6
98.2
87.2

179
215628_PM_x_at
—
—
9.28E−06
2.53E−03
49.6
36.3
39.4

180
1556412_PM_at
—
—
9.45E−06
2.56E−03
34.9
24.7
23.8

181
204786_PM_s_at
IFNAR2
interferon (alpha,
9.64E−06
2.59E−03
795.6
573.0
639.2

beta and omega)

receptor 2

182
234258_PM_at
—
—
9.73E−06
2.60E−03
27.4
17.8
20.3

183
233274_PM_at
—
—
9.76E−06
2.60E−03
109.9
77.5
79.4

184
239784_PM_at
—
—
9.82E−06
2.60E−03
137.0
80.1
70.1

185
242498_PM_x_at
—
—
1.01E−05
2.65E−03
59.2
40.4
38.9

186
231351_PM_at
—
—
1.02E−05
2.67E−03
124.8
70.8
60.6

187
222368_PM_at
—
—
1.03E−05
2.67E−03
89.9
54.5
44.3

188
236524_PM_at
—
—
1.03E−05
2.67E−03
313.2
234.7
214.2

189
243834_PM_at
TNRC6A
trinucleotide repeat
1.04E−05
2.67E−03
211.8
145.1
146.9

containing 6A

190
239167_PM_at
—
—
1.04E−05
2.67E−03
287.4
150.2
160.3

191
239238_PM_at
—
—
1.05E−05
2.67E−03
136.0
81.6
92.0

192
237194_PM_at
—
—
1.05E−05
2.67E−03
57.2
34.4
27.9

193
242772_PM_x_at
—
—
1.06E−05
2.67E−03
299.2
185.2
189.4

194
243827_PM_at
—
—
1.06E−05
2.67E−03
115.9
50.1
56.4

195
1552536_PM_at
VTI1A
vesicle transport
1.10E−05
2.75E−03
61.7
35.1
34.6

through interaction

with t-SNAREs

homolog 1A (yeast)

196
243696_PM_at
KIAA0562
KIAA0562
1.12E−05
2.77E−03
19.0
14.8
15.0

197
233648_PM_at
—
—
1.12E−05
2.77E−03
33.9
21.0
24.1

198
225858_PM_s_at
XIAP
X-linked inhibitor of
1.16E−05
2.85E−03
1020.7
760.3
772.6

apoptosis

199
238736_PM_at
REV3L
REV3-like, catalytic
1.19E−05
2.91E−03
214.2
135.8
151.6

subunit of DNA

polymerase zeta

(yeast)

200
221192_PM_x_at
MFSD11
major facilitator
1.20E−05
2.92E−03
100.4
74.5
81.2

superfamily domain

containing 11

TABLE 9

33 probesets that differentiate SCAR and TX at p-value <0.001 in PAXGene blood tubes

FFold-

Change

Gene

p-value
(SCAR

SCAR-
TX-

Probeset ID
Symbol
Gene Title
(Phenotype)
vs. TX)
ID
Mean
Mean

1553094_PM_at
TAC4
tachykinin 4
0.000375027
−1.1
1553094_PM_at
8.7
9.6

(hemokinin)

1553352_PM_x_at
ERVWE1
endogenous retroviral
0.000494742
−1.26
1553352_PM_x_at
15.5
19.6

family W, env(C7),

member 1

1553644_PM_at
C14orf49
chromosome 14 open
0.000868817
−1.16
1553644_PM_at
10.1
11.7

reading frame 49

1556178_PM_x_at
TAF8
TAF8 RNA
0.000431074
1.24
1556178_PM_x_at
39.2
31.7

polymerase II, TATA

box binding protein

(TBP)-associated

factor, 43 kDa

1559687_PM_at
TMEM221
transmembrane
8.09E−05
−1.16
1559687_PM_at
13.0
15.1

protein 221

1562492_PM_at
LOC340090
hypothetical
0.00081096
−1.1
1562492_PM_at
8.8
9.7

LOC340090

1563204_PM_at
ZNF627
Zinc finger protein
0.000784254
−1.15
1563204_PM_at
10.6
12.2

627

1570124_PM_at
—
—
0.000824814
−1.14
1570124_PM_at
10.6
12.2

204681_PM_s_at
RAPGEF5
Rap guanine
0.000717727
−1.18
204681_PM_s_at
9.6
11.3

nucleotide exchange

factor (GEF) 5

206154_PM_at
RLBP1
retinaldehyde binding
0.000211941
−1.13
206154_PM_at
11.0
12.4

protein 1

209053_PM_s_at
WHSC1
Wolf-Hirschhorn
0.000772412
1.23
209053_PM_s_at
15.1
12.3

syndrome candidate 1

209228_PM_x_at
TUSC3
tumor suppressor
0.000954529
−1.13
209228_PM_x_at
8.9
10.1

candidate 3

211701_PM_s_at
TRO
trophinin
0.000684486
−1.13
211701_PM_s_at
10.0
11.3

213369_PM_at
CDHR1
cadherin-related
0.000556648
−1.14
213369_PM_at
10.8
12.3

family member 1

215110_PM_at
MBL1P
mannose-binding
0.000989176
−1.13
215110_PM_at
9.2
10.4

lectin (protein A) 1,

pseudogene

215232_PM_at
ARHGAP44
Rho GTPase
0.000332776
−1.18
215232_PM_at
11.1
13.1

activating protein 44

217158_PM_at
LOC442421
hypothetical
2.98E−05
1.18
217158_PM_at
14.2
12.0

LOC442421 ///

prostaglandin E2

receptor EP4 subtype-

like

218365_PM_s_at
DARS2
aspartyl-tRNA
0.000716035
1.18
218365_PM_s_at
17.2
14.5

synthetase 2,

mitochondrial

219695_PM_at
SMPD3
sphingomyelin
0.000377151
−1.47
219695_PM_at
12.0
17.6

phosphodiesterase 3,

neutral membrane

(neutral

sphingomyelinase II)

220603_PM_s_at
MCTP2
multiple C2 domains,
0.000933412
−1.38
220603_PM_s_at
338.5
465.8

transmembrane 2

224963_PM_at
SLC26A2
solute carrier family
0.000961242
1.47
224963_PM_at
94.3
64.0

26 (sulfate transporter),

member 2

226729_PM_at
USP37
ubiquitin specific
0.000891038
1.24
226729_PM_at
32.9
26.6

peptidase 37

228226_PM_s_at
ZNF775
zinc finger protein 775
0.000589512
1.2
228226_PM_s_at
20.5
17.1

230608_PM_at
C1orf182
chromosome 1 open
0.000153478
−1.18
230608_PM_at
15.9
18.8

reading frame 182

230756_PM_at
ZNF683
zinc finger protein 683
0.00044751
1.52
230756_PM_at
26.7
17.6

231757_PM_at
TAS2R5
taste receptor, type 2,
0.000869775
−1.12
231757_PM_at
9.3
10.4

member 5

231958_PM_at
C3orf31
Chromosome 3 open
4.09E−05
1.22
231958_PM_at
20.1
16.4

reading frame 31

237290_PM_at
—
—
0.000948318
−1.22
237290_PM_at
10.3
12.5

237806_PM_s_at
LOC729296
hypothetical
0.00092234
−1.18
237806_PM_s_at
10.2
12.0

LOC729296

238459_PM_x_at
SPATA6
spermatogenesis
0.000116525
−1.15
238459_PM_x_at
9.2
10.5

associated 6

241331_PM_at
SKAP2
Src kinase associated
0.000821476
−1.39
241331_PM_at
16.4
22.9

phosphoprotein 2

241368_PM_at
PLIN5
perilipin 5
0.000406066
−1.61
241368_PM_at
84.5
136.3

241543_PM_at
—
—
0.000478221
−1.17
241543_PM_at
9.4
11.0

Example 3

Differentially Expressed Genes Associated with Kidney Transplant Rejections

This Example describes global analysis of gene expressions in kidney transplant patients with different types of rejections or injuries.

A total of biopsy-documented 274 kidney biopsy samples from the Transplant Genomics Collaborative Group (TGCG) were processed on the Affymetrix HG-U133 PM only peg microarrays. The 274 samples that were analyzed comprised of 4 different phenotypes: Acute Rejection (AR; n=75); Acute Dysfunction No Rejection (ADNR; n=39); Chronic Allograft Nephropathy (CAN; n=61); and Transplant Excellent (TX; n=99).

Signal Filters: To eliminate low expressed signals we used a signal filter cut-off that was data driven, and expression signals <Log 2 4.23 in all samples were eliminated leaving us with 48882 probe sets from a total of 54721 probe sets.

4-Way AR/ADNR/CAN/TX Classifier:

We first did a 4 way comparison of the AR, ADNR, CAN and TX samples. The samples comprised of four different classes a 4-way ANOVA analysis yielded more than 10,000 differentially expressed genes even at a stringent p value cut-off of <0.001. Since we were trying to discover a signature that could differentiate these four classes we used only the top 200 differentially expressed probe sets to build predictive models. We ran the Nearest Centroid (NC) algorithm to build the predictive models. When we used the top 200 differentially expressed probe sets between all four phenotypes, the best predictor model was based on 199 probe sets.

Nearest Centroid (NC) classification takes the gene expression profile of a new sample, and compares it to each of the existing class centroids. The class whose centroid that it is closest to, in squared distance, is the predicted class for that new sample. It also provides the centroid distances for each sample to each of the possible phenotypes being tested. In other words, in a 2-way classifier like AR vs. TX, the tool provides the “best” classification and provides the centroid distances to the two possible outcomes: TX and AR.

We observed in multiple datasets that there are 4 classes of predictions made. First, are correctly classified as TX by both biopsy and NC. Second, are correctly classified as AR by both biopsy and NC. Third, are truly misclassified samples. In other words, the biopsy says one thing and the molecular profile another. In these cases, the centroid distances for the given classifications are dramatically different, making the molecular classification very straightforward and simply not consistent with the biopsy phenotype assigned. Whether this is because the gold standard biopsy classification is wrong or the molecular classification is wrong is impossible to know at this point.

However, there is a fourth class that we call “mixed” classifications. In these cases supposedly “misclassified” samples by molecular profile show a nearest centroid distance that is not very different when compared to that of the “correct” classification based on the biopsy. In other words, the nearest centroid distances of most of these misclassified “mixed” samples are actually very close to the correct biopsy classification. However, because NC has no rules set to deal with the mixed situation it simply calls the sample by the nominally higher centroid distance.

The fact is that most standard implementations of class prediction algorithms currently available treat all classes as dichotomous variables (yes/no diagnostically). They are not designed to deal with the reality of medicine that molecular phenotypes of clinical samples can actually represent a continuous range of molecular scores based on the expression signal intensities with complex implications for the diagnoses. Thus, “mixed” cases where the centroid distances are only slightly higher for TX than AR is still classified as a TX, even if the AR distances are only slightly less. In this case, where there is a mixture of TX and AR by expression, it is obvious that the case is actually an AR for a transplant clinician, not a TX. Perhaps just a milder form of AR and this is the reason for using thresholding.

Thus, we set a threshold for the centroid distances. The threshold is driven by the data. The threshold equals the mean difference NC provides in centroid distances for the two possible classifications (i.e. AR vs. TX) for all correctly classified samples in the data set (e.g. classes 1 and 2 of the 4 possible outcomes of classification). This means that for the “mixed” class of samples, if a biopsy-documented sample was misclassified by molecular profiling, but the misclassification was within the range of the mean calculated centroid distances of the true classifications in the rest of the data, then that sample would not be considered as a misclassified sample.

Table 10a shows the performance of the 4 way AR, ADNR, CAN, TX NC classifier using such a data driven threshold. Table 10b shows the top 200 probeset used for the 4 way AR, ADNR, CAN, TX NC classifier. So, using the top 200 differentially expressed probesets from a 4-way AR, ADNR, CAN and TX ANOVA with a Nearest Centroid classifier, we are able to molecularly classify the 4 phenotypes at 97% accuracy. Smaller classifier sets did not afford any significant increase in the predictive accuracies. To validate this data we applied this classification to an externally collected data set. These were samples collected at the University of Sao Paolo in Brazil. A total of 80 biopsy-documented kidney biopsy samples were processed on the same Affymetrix HG-U133 PM only peg microarrays. These 80 samples that were analyzed comprised of the same 4 different phenotypes: AR (n=23); ADNR (n=11); CAN (n=29); and TX (n=17).

We performed the classification based on the “locked” NC predictor (meaning that none of the thresholding parameters were changed. Table 11 shows the performance of our locked 4 way AR, ADNR, CAN, TX NC classifier in the Brazilian cohort. So, using the top 200 differentially expressed probesets from a 4-way AR, ADNR, CAN and TX ANOVA with a “locked” Nearest Centroid classifier we are able to molecularly classify the 4 phenotypes with similar accuracy in an independently and externally collected validation set. This validates our molecular classifier of the biopsy on an independent external data set. It also demonstrates that the classifier is not subject to influence based on significant racial differences represented in the Brazilian population.

3-Way AR/ADNR/TX Classifier:

Similarly, we did a 3 way comparison of the AR, ADNR and TX samples since these are the most common phenotypes encountered during the early post-transplant period with CAN usually being a late manifestation of graft injury which is progressive. The samples comprised of these 3 different classes, and a 4-way ANOVA analysis again yielded more than 10,000 differentially expressed genes, so we used only the top 200 differentially expressed probe sets to build predictive models. We ran the Nearest Centroid (NC) algorithm to build the predictive models. When we used the top 200 differentially expressed probe sets between all four phenotypes the best predictor model was based on 197 probe sets.

Table 12a shows the performance of the 3 way AR, ADNR, TX NC classifier with which we are able to molecularly classify the 3 phenotypes at 98% accuracy in the TGCG cohort. Table 12b shows the top 200 probeset used for the 3 way AR, ADNR, TX NC classifier in the TGCG cohort. Similarly the locked 3 way classifier performs equally well on the Brazilian cohort with 98% accuracy (Table 13). Therefore, our 3 way classifier also validates on the external data set.

2-Way CAN/TX Classifier:

Finally we also did a 2 way comparison of the CAN and TX samples. The samples comprised of these 2 classes with an ANOVA analysis again yielded ˜11,000 differentially expressed genes, so we used only the top 200 differentially expressed probe sets to build predictive models. We ran the Nearest Centroid (NC) algorithm to build the predictive models. When we used the top 200 differentially expressed probe sets the best predictor model was based on all 200 probe sets. Table 14a shows the performance of the 2 way CAN, TX NC classifier with which we are able to molecularly classify the 4 phenotypes at 97% accuracy in the TGCG cohort. Table 14b shows the top 200 probeset used for the 2 way CAN, TX NC classifier in the TGCG cohort. This locked classifier performs equally well on the Brazilian cohort with 95% accuracy (Table 15). Again we show that our 2 way CAN, TX classifier also validates on the external data set.

TABLE 10a

Biopsy Expression Profiling of Kidney Transplants: 4-Way Classifier AR vs. ADNR vs. CAN vs. TX

(TGCG Samples)

Validation Cohort

Postive
Negative

Predictive

Predictive
Predictive

Accuracy
Sensitivity
Specificity
Value
Value

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
(%)
(%)

Nearest Centroid
199
AR vs. TX
0.957
95
96
96
94
97

Nearest Centroid
199
ADNR vs. TX
0.977
97
94
100
100
97

Nearest Centroid
199
CAN vs. TX
0.992
99
98
100
100
99

TABLE 10b

Biopsy Expression Profiling of Kidney Transplants: 4-Way Classifier AR vs. ADNR vs. CAN vs. TX (TGCG Samples)

p-value

Probeset
Entrez
Gene

(Final
ADNR-
AR-
CAN-
TX-

#
ID
Gene
Symbol
Gene Title
Phenotype)
Mean
Mean
Mean
Mean

1
204446_PM_s_at
240
ALOX5
arachidonate 5-lipoxygenase
2.82E−34
91.9
323.9
216.7
54.7

2
202207_PM_at
10123
ARL4C
ADP-ribosylation factor-
1.31E−32
106.9
258.6
190.4
57.2

like 4C

3
204698_PM_at
3669
ISG20
interferon stimulated
1.50E−31
41.5
165.1
96.1
27.6

exonuclease gene 20 kDa

4
225701_PM_at
80709
AKNA
AT-hook transcription
1.75E−31
37.7
102.8
73.2
29.0

factor

5
207651_PM_at
29909
GPR171
G protein-coupled receptor
6.30E−31
25.8
89.9
57.0
20.9

171

6
204205_PM_at
60489
APOBE
apolipoprotein B mRNA
1.27E−30
95.4
289.4
192.0
78.7

C3G
editing enzyme, catalytic

polypeptide-like 3G

7
208948_PM_s_at
6780
STAU1
staufen, RNA binding
1.37E−30
1807.9
1531.8
1766.0
2467.4

protein, homolog 1

(Drosophila)

8
217733_PM_s_at
9168
TMSB10
thymosin beta 10
2.38E−30
4414.7
6331.3
5555.2
3529.0

9
205831_PM_at
914
CD2
CD2 molecule
2.73E−30
40.4
162.5
100.9
33.9

10
209083_PM_at
11151
CORO1A
coronin, actin binding
5.57E−30
46.9
163.8
107.1
34.3

protein, 1A

11
210915_PM_x_at
28638
TRBC2
T cell receptor beta constant 2
5.60E−30
39.7
230.7
129.7
37.5

12
211368_PM_s_at
834
CASP1
caspase 1, apoptosis-related
6.21E−30
102.6
274.3
191.4
81.8

cysteine peptidase

(interleukin 1, beta,

convertase)

13
201042_PM_at
7052
TGM2
transglutaminase 2 (C
6.28E−30
131.8
236.5
172.6
80.1

polypeptide, protein-

glutamine-gamma-

glutamyltransferase)

14
227353_PM_at
147138
TMC8
transmembrane channel-like 8
7.76E−30
19.8
64.2
42.7
16.6

15
1555852_PM_at
100507463
LOC100507463
hypothetical
8.29E−30
78.9
202.6
154.2
70.9

LOC100507463

16
226878_PM_at
3111
HLA-DOA
major histocompatibility
1.63E−29
102.0
288.9
201.4
94.3

complex, class II, DO alpha

17
238327_PM_at
440836
ODF3B
outer dense fiber of sperm
1.74E−29
32.8
81.4
58.5
26.1

tails 3B

18
229437_PM_at
114614
MIR155HG
MIR155 host gene (non-
1.78E−29
15.4
50.4
28.5
12.9

protein coding)

19
33304_PM_at
3669
ISG20
interferon stimulated
2.40E−29
33.2
101.3
63.4
22.1

exonuclease gene 20 kDa

20
226621_PM_at
9180
OSMR
oncostatin M receptor
2.42E−29
545.6
804.5
682.9
312.1

21
1553906_PM_s_at
221472
FGD2
FYVE, RhoGEF and PH
2.43E−29
104.6
321.0
219.3
71.9

domain containing 2

22
1405_PM_i_at
6352
CCL5
chemokine (C—C motif)
2.54E−29
68.0
295.7
195.6
54.6

ligand 5

23
226219_PM_at
257106
ARHG
Rho GTPase activating
2.92E−29
46.4
127.9
91.8
37.5

AP30
protein 30

24
204891_PM_s_at
3932
LCK
lymphocyte-specific protein
3.79E−29
19.3
74.2
43.4
17.8

tyrosine kinase

25
210538_PM_s_at
330
BIRC3
baculoviral IAP repeat-
5.06E−29
106.7
276.7
199.1
84.6

containing 3

26
202644_PM_s_at
7128
TNFAI
tumor necrosis factor, alpha-
5.47E−29
169.8
380.4
278.2
136.6

P3
induced protein 3

27
227346_PM_at
10320
IKZF1
IKAROS family zinc finger 1
7.07E−29
24.9
79.7
53.3
19.8

(Ikaros)

28
202957_PM_at
3059
HCLS1
hematopoietic cell-specific
8.26E−29
119.2
299.5
229.9
82.2

Lyn substrate 1

29
202307_PM_s_at
6890
TAP1
transporter 1, ATP-binding
1.01E−28
172.4
420.6
280.0
141.0

cassette, sub-family B

(MDR/TAP)

30
202748_PM_at
2634
GBP2
guanylate binding protein 2,
1.10E−28
196.7
473.0
306.7
141.0

interferon-inducible

31
211796_PM_s_at
28638 ///
TRBC1 ///
T cell receptor beta constant
1.31E−28
69.2
431.5
250.2
63.6

28639
TRBC2
1 /// T cell receptor beta

constant 2

32
213160_PM_at
1794
DOCK2
dedicator of cytokinesis 2
1.36E−28
33.7
92.6
66.0
27.8

33
211656_PM_x_at
100133583 ///
HLA-DQB1 ///
major histocompatibility
1.63E−28
211.3
630.2
459.8
208.2

3119
LOC100133583
complex, class II, DQ beta 1 ///

HLA class II

histocompatibili

34
223322_PM_at
83593
RASSF5
Ras association
1.68E−28
41.5
114.4
79.3
39.2

(RalGDS/AF-6) domain

family member 5

35
205488_PM_at
3001
GZMA
granzyme A (granzyme 1,
1.72E−28
37.3
164.8
102.3
33.4

cytotoxic T-lymphocyte-

associated serine esterase 3)

36
213603_PM_s_at
5880
RAC2
ras-related C3 botulinum
1.87E−28
113.9
366.5
250.3
86.5

toxin substrate 2 (rho

family, small GTP binding

protein Rac2)

37
229390_PM_at
441168
FAM26F
family with sequence
1.94E−28
103.8
520.0
272.4
75.9

38
206804_PM_at
917
CD3G
CD3g molecule, gamma
1.99E−28
19.7
60.6
36.4
17.3

(CD3-TCR complex)

39
209795_PM_at
969
CD69
CD69 molecule
2.06E−28
17.6
57.6
40.6
15.2

40
219574_PM_at
55016
1-Mar
membrane-associated ring
2.07E−28
51.5
126.0
87.5
36.2

finger (C3HC4) 1

41
207320_PM_x_at
6780
STAU1
staufen, RNA binding
2.21E−28
1425.1
1194.6
1383.2
1945.3

protein, homolog 1

(Drosophila)

42
218983_PM_at
51279
C1RL
complement component 1, r
2.97E−28
167.1
244.5
206.9
99.4

subcomponent-like

43
206011_PM_at
834
CASP1
caspase 1, apoptosis-related
3.23E−28
74.5
198.0
146.2
60.7

cysteine peptidase

(interleukin 1, beta,

convertase)

44
213539_PM_at
915
CD3D
CD3d molecule, delta
5.42E−28
70.8
335.1
168.1
60.0

(CD3-TCR complex)

45
213193_PM_x_at
28639
TRBC1
T cell receptor beta constant 1
5.69E−28
95.3
490.9
286.5
92.1

46
232543_PM_x_at
64333
ARHGAP9
Rho GTPase activating
6.79E−28
31.7
99.1
62.3
25.8

protein 9

47
200986_PM_at
710
SERPING1
serpin peptidase inhibitor,
7.42E−28
442.7
731.5
590.2
305.3

clade G (Cl inhibitor),

member 1

48
213037_PM_x_at
6780
STAU1
staufen, RNA binding
9.35E−28
1699.0
1466.8
1670.1
2264.9

protein, homolog 1

(Drosophila)

49
204670_PM_x_at
3123 /// 3126
HLA-DRB1 ///
major histocompatibility
1.03E−27
2461.
4344.
3694.
2262.

HLA-DRB4
complex, class II, DR beta 1 ///

major histocompatibility

comp

50
217028_PM_at
7852
CXCR4
chemokine (C—X—C motif)
1.52E−27
100.8
304.4
208.4
75.3

receptor 4

51
203761_PM_at
6503
SLA
Src-like-adaptor
1.61E−27
69.6
179.2
138.4
51.4

52
201137_PM_s_at
3115
HLA-DPB1
major histocompatibility
1.95E−27
1579.1
3863.7
3151.7
1475.9

complex, class II, DP beta 1

53
205269_PM_at
3937
LCP2
lymphocyte cytosolic
2.16E−27
30.2
92.2
58.9
22.9

protein 2 (SH2 domain

containing leukocyte protein

of 76 kDa)

54
205821_PM_at
22914
KLRK1
killer cell lectin-like
2.56E−27
30.3
111.5
72.5
31.3

receptor subfamily K,

member 1

55
204655_PM_at
6352
CCL5
chemokine (C—C motif)
3.28E−27
77.5
339.4
223.4
66.8

ligand 5

56
226474_PM_at
84166
NLRC5
NLR family, CARD domain
3.54E−27
64.4
173.5
129.8
55.1

containing 5

57
212503_PM_s_at
22982
DIP2C
DIP2 disco-interacting
3.69E−27
559.7
389.2
502.0
755.0

protein 2 homolog C

(Drosophila)

58
213857_PM_s_at
961
CD47
CD47 molecule
4.33E−27
589.6
858.0
703.2
481.2

59
206118_PM_at
6775
STAT4
signal transducer and
4.58E−27
21.0
49.5
37.7
18.1

activator of transcription 4

60
227344_PM_at
10320
IKZF1
IKAROS family zinc finger 1
5.87E−27
17.8
40.0
28.5
14.9

(Ikaros)

61
230550_PM_at
64231
MS4A6A
membrane-spanning 4-
5.98E−27
44.8
124.3
88.0
30.9

domains, subfamily A,

member 6A

62
235529_PM_x_at
25939
SAMHD1
SAM domain and HID
6.56E−27
189.3
379.9
289.1
128.0

domain 1

63
205758_PM_at
925
CD8A
CD8a molecule
7.28E−27
24.2
105.8
60.3
22.2

64
211366_PM_x_at
834
CASP1
caspase 1, apoptosis-related
7.37E−27
115.3
261.0
186.0
87.1

cysteine peptidase

(interleukin 1, beta,

convertase)

65
209606_PM_at
9595
CYTIP
cytohesin 1 interacting
7.48E−27
41.4
114.3
79.0
32.9

protein

66
201721_PM_s_at
7805
LAPTM5
lysosomal protein
8.04E−27
396.5
934.6
661.3
249.4

transmembrane 5

67
204774_PM_at
2123
EVI2A
ecotropic viral integration
8.14E−27
63.6
168.5
114.7
44.9

site 2A

68
215005_PM_at
54550
NECAB2
N-terminal EF-hand calcium
8.32E−27
36.7
23.4
30.9
65.7

binding protein 2

69
229937_PM_x_at
10859
LILRB1
Leukocyte immunoglobulin-
8.33E−27
23.5
79.9
50.0
18.5

like receptor, subfamily B

(with TM and ITIM

domains), member

70
209515_PM_s_at
5873
RAB27A
RAB27A, member RAS
8.93E−27
127.3
192.2
160.5
85.2

oncogene family

71
242916_PM_at
11064
CEP110
centrosomal protein 110 kDa
8.98E−27
30.8
68.1
51.2
26.2

72
205270_PM_s_at
3937
LCP2
lymphocyte cytosolic
9.04E−27
56.8
162.6
104.4
44.6

protein 2 (SH2 domain

containing leukocyte protein

of 76 kDa)

73
214022_PM_s_at
8519
IFITM1
interferon induced
9.31E−27
799.1
1514.7
1236.6
683.3

transmembrane protein 1 (9-27)

74
1552703_PM_s_at
114769 /// 834
CARD16 ///
caspase recruitment domain
1.01E−26
64.6
167.8
120.6
54.9

CASP1
family, member 16 ///

caspase 1, apoptosis-related

cysteine

75
202720_PM_at
26136
TES
testis derived transcript
1.05E−26
285.4
379.0
357.9
204.2

(3 LIM doma ins)

76
202659_PM_at
5699
PSMB10
proteasome (prosome,
1.10E−26
180.7
355.6
250.3
151.5

macropain) subunit, beta

type, 10

77
236295_PM_s_at
197358
NLRC3
NLR family, CARD domain
1.19E−26
19.0
52.5
37.0
18.6

containing 3

78
229041_PM_s_at
—
—
—
1.31E−26
36.5
132.1
84.7
32.4

79
205798_PM_at
3575
IL7R
interleukin 7 receptor
1.32E−26
44.1
136.8
106.3
33.4

80
209970_PM_x_at
834
CASP1
caspase 1, apoptosis-related
1.36E−26
116.0
266.6
181.6
88.7

cysteine peptidase

(interleukin 1, beta,

convertase)

81
204336_PM_s_at
10287
RGS19
regulator of G-protein
1.54E−26
95.2
187.7
135.7
67.0

signaling 19

82
204912_PM_at
3587
IL10RA
interleukin 10 receptor,
1.61E−26
57.0
178.7
117.2
46.1

alpha

83
227184_PM_at
5724
PTAFR
platelet-activating factor
1.70E−26
89.8
191.4
134.4
62.7

receptor

84
209969_PM_s_at
6772
STAT1
signal transducer and
1.82E−26
395.8
1114.5
664.6
320.8

activator of transcription 1,

91 kDa

85
232617_PM_at
1520
CTSS
cathepsin S
1.88E−26
209.6
537.9
392.2
154.8

86
224451_PM_x_at
64333
ARHGAP9
Rho GTPase activating
1.94E−26
34.2
103.4
71.8
29.4

protein 9

87
209670_PM_at
28755

T cell receptor alpha
2.06E−26
37.9
149.9
96.2
38.5

TRAC
constant

88
1559584_PM_a_at
283897
C16orf54
chromosome 16 open
2.22E−26
31.3
95.8
71.5
26.1

reading frame 54

89
208306_PM_x_at
3123
HLA-DRB1
Major histocompatibility
2.29E−26
2417.5
4278.5
3695.8
2255.0

complex, class II, DR beta 1

90
229383_PM_at
55016
1-Mar
membrane-associated ring
2.36E−26
33.8
88.0
52.1
22.9

finger (C3HC4) 1

91
235735_PM_at
—
—
—
2.46E−26
13.0
34.9
24.5
11.2

92
203416_PM_at
963
CD53
CD53 molecule
2.56E−26
215.9
603.0
422.7
157.8

93
212504_PM_at
22982
DIP2C
DIP2 disco-interacting
3.21E−26
334.5
227.2
289.4
452.5

protein 2 homolog C

(Drosophila)

94
204279_PM_at
5698
PSMB9
proteasome (prosome,
3.45E−26
241.6
637.4
419.4
211.3

macropain) subunit, beta

type, 9 (large

multifunctional peptidase

95
235964_PM_x_at
25939
SAMHD1
SAM domain and HID
3.60E−26
172.9
345.9
270.1
117.5

domain 1

96
213566_PM_at
6039
RNASE6
ribonuclease, RNase A
3.84E−26
180.9
482.0
341.1
134.3

family, k6

97
221698_PM_s_at
64581
CLEC7A
C-type lectin domain family
4.00E−26
61.5
164.6
112.8
49.7

7, member A

98
227125_PM_at
3455
IFNAR2
interferon (alpha, beta and
4.03E−26
70.0
126.2
96.7
55.8

omega) receptor 2

99
226525_PM_at
9262
STK17B
serine/threonine kinase 17b
4.14E−26
146.8
338.7
259.1
107.6

100
221666_PM_s_at
29108
PYCARD
PYD and CARD domain
4.95E−26
60.7
132.8
95.5
44.7

containing

101
209774_PM_x_at
2920
CXCL2
chemokine (C—X—C motif)
5.73E−26
24.9
52.9
38.2
15.5

ligand 2

102
206082_PM_at
10866
HCP5
HLA complex P5
5.98E−26
76.2
185.1
129.0
66.6

103
229391_PM_s_at
441168
FAM26F
family with sequence
6.03E−26
98.6
379.7
212.1
73.7

similarity 26, member F

104
229295_PM_at
150166 /// 23765
IL17RA ///
interleukin 17 receptor A ///
6.13E−26
76.4
131.8
98.4
50.0

LOC150166
hypothetical protein

LOC150166

105
202901_PM_x_at
1520
CTSS
cathepsin S
6.32E−26
67.8
180.5
130.9
45.1

106
226991_PM_at
4773
NFATC2
nuclear factor of activated
6.49E−26
37.9
87.0
66.7
30.3

T-cells, cytoplasmic,

calcineurin-dependent 2

107
223280_PM_x_at
64231
M54A6A
membrane-spanning 4-
6.72E−26
269.0
711.8
451.2
199.5

domans, subfamily A,

member 6A

108
201601_PM_x_at
8519
IFITM1
interferon induced
7.27E−26
1471.3
2543.5
2202.5
1251.1

transmembrane protein 1 (9-27)

109
1552701_PM_a_at
114769
CARD16
caspase recruitment domain
7.33E−26
143.8
413.6
273.3
119.8

family, member 16

110
229625_PM_at
115362
GBPS
guanylate binding protein 5
7.80E−26
29.3
133.3
68.6
24.0

111
38149_PM_at
9938
ARHGAP25
Rho GTPase activating
9.83E−26
51.3
108.9
83.2
43.2

protein 25

112
203932_PM_at
3109
HLA-
major histocompatibility
1.03E−25
422.4
853.9
633.5
376.0

DMB
complex, class II, DM beta

113
228964_PM_at
639
PRDM1
PR domain containing 1,
1.15E−25
21.2
52.1
41.5
17.0

with ZNF domain

114
225799_PM_at
112597 ///
LOC541471 ///
hypothetical LOC541471 ///
1.23E−25
230.5
444.3
339.2
172.0

541471
NCRNA00152
non-protein coding RNA 152

115
204118_PM_at
962
CD48
CD48 molecule
1.34E−25
82.9
341.5
212.4
65.2

116
211742_PM_s_at
2124
EVI2B
ecotropic viral integration
1.36E−25
73.9
236.5
166.2
53.2

site 2B

117
213416_PM_at
3676
ITGA4
integrin, alpha 4 (antigen
1.47E−25
26.3
78.1
50.8
22.8

CD49D, alpha 4 subunit of

VLA-4 receptor)

118
211991_PM_s_at
3113
HLA-DPA1
major histocompatibility
1.50E−25
1455.0
3605.4
2837.2
1462.9

complex, class II, DP alpha 1

119
232024_PM_at
26157
GIMAP2
GTPase, IMAP family
1.57E−25
90.2
197.7
146.7
72.5

member 2

120
205159_PM_at
1439
CSF2RB
colony stimulating factor 2
1.73E−25
33.7
107.5
70.4
26.3

receptor, beta, low-affinity

(granulocyte-macrophage)

121
228471_PM_at
91526
ANKRD44
ankyrin repeat domain 44
1.79E−25
106.1
230.3
184.6
86.5

122
203332_PM_s_at
3635
INPP5D
inositol polyphosphate-5-
1.88E−25
27.9
60.5
42.6
24.0

phosphatase, 145 kDa

123
223502_PM_s_at
10673
TNFSF13B
tumor necrosis factor
2.02E−25
73.0
244.3
145.5
60.0

(ligand) superfamily,

member 13b

124
229723_PM_at
117289
TAGAP
T-cell activation
2.07E−25
29.2
82.9
55.9
26.2

RhoGTPase activating

protein

125
206978_PM_at
729230
CCR2
chemokine (C—C motif)
2.17E−25
32.1
100.7
68.6
27.3

receptor 2

126
1555832_PM_s_at
1316
KLF6
Kruppel-like factor 6
2.31E−25
899.4
1076.8
1003.3
575.1

127
211990_PM_at
3113
HLA-DPA1
major histocompatibility
2.53E−25
2990.7
5949.1
5139.9
3176.3

complex, class II, DP alpha

128
202018_PM_s_at
4057
LTF
lactotransferrin
2.90E−25
392.3
1332.4
624.5
117.7

129
210644_PM_s_at
3903
LAIR1
leukocyte-associated
2.90E−25
29.7
74.6
45.3
21.2

immunoglobulin-like

receptor 1

130
222294_PM_s_at
5873
RAB27A
RAB27A, member RAS
3.13E−25
198.7
309.1
263.0
146.4

oncogene family

131
238668_PM_at
—
—
—
3.29E−25
18.2
49.2
33.6
14.5

132
213975_PM_s_at
4069
LYZ
lysozyme
3.31E−25
458.4
1626.0
1089.7
338.1

133
204220_PM_at
9535
GMFG
glia maturation factor,
3.46E−25
147.0
339.3
241.4
128.9

gamma

134
243366_PM_s_at
—
—
—
3.46E−25
24.7
72.1
52.5
22.0

135
221932_PM_s_at
51218
GLRX5
glutaredoxin 5
3.64E−25
1351.5
1145.8
1218.1
1599.3

136
225415_PM_at
151636
DTX3L
deltex 3-like (Drosophila)
3.77E−25
230.2
376.4
290.8
166.9

137
205466_PM_s_at
9957
HS3ST1
heparan sulfate
4.15E−25
73.6
123.8
96.0
42.1

(glucosamine) 3-O—

sulfotransferase 1

138
200904_PM_at
3133
HLA-E
major histocompatibility
4.20E−25
1142.5
1795.2
1607.7
994.7

complex, class I, E

139
228442_PM_at
4773
NFATC2
nuclear factor of activated
4.48E−25
39.0
84.8
62.8
32.0

T-cells, cytoplasmic,

calcineurin-dependent 2

140
204923_PM_at
54440
SASH3
SAM and SH3 domain
4.49E−25
25.4
68.2
47.6
21.7

containing 3

141
223640_PM_at
10870
HCST
hematopoietic cell signal
4.52E−25
91.0
234.0
158.3
72.7

transducer

142
211582_PM_x_at
7940
LST1
leukocyte specific transcript 1
4.53E−25
57.5
183.8
121.2
49.4

143
219014_PM_at
51316
PLAC8
placenta-specific 8
5.94E−25
38.8
164.1
88.6
30.7

144
210895_PM_s_at
942
CD86
CD86 molecule
6.21E−25
32.3
85.0
52.6
21.6

145
AFFX-
6772
STAT1
signal transducer and
6.81E−25
642.1
1295.1
907.8
539.6

HUMISGF3A/

activator of transcription 1,

M97935_3_at

91 kDa

146
201315_PM_x_at
10581
IFITM2
interferon induced
6.87E−25
2690.9
3712.1
3303.7
2175.3

transmembrane protein 2 (1-8D)

147
228532_PM_at
128346
C1orf162
chromosome 1 open reading
7.07E−25
82.6
217.7
140.2
60.0

frame 162

148
202376_PM_at
12
SERPINA3
serpin peptidase inhibitor,
7.13E−25
186.2
387.1
210.2
51.7

clade A (alpha-1

antiproteinase, antitrypsin),

member 3

149
212587_PM_s_at
5788
PTPRC
protein tyrosine
7.18E−25
114.8
398.6
265.7
90.3

phosphatase, receptor type, C

150
223218_PM_s_at
64332
NFKBIZ
nuclear factor of kappa light
7.26E−25
222.6
497.9
399.9
159.1

polypeptide gene enhancer

in B-cells inhibitor, zeta

151
224356_PM_x_at
64231
MS4A6A
membrane-spanning 4-
7.33E−25
150.6
399.6
249.6
111.2

domains, subfamily A,

member 6A

152
206420_PM_at
10261
IGSF6
immunoglobulin
7.58E−25
45.1
131.5
74.3
32.7

superfamily, member 6

153
225764_PM_at
2120
ETV6
ets variant 6
7.66E−25
92.6
133.0
112.8
77.0

154
1555756_PM_a_at_
64581
CLEC7A
C-type lectin domain family
7.74E−25
16.6
45.4
28.9
13.2

7, member A

155
226218_PM_at
3575
IL7R
interleukin 7 receptor
8.14E−25
55.6
197.0
147.1
41.4

156
209198_PM_s_at
23208
SYT11
synaptotagmin XI
8.28E−25
30.0
45.3
41.8
22.8

157
202803_PM_s_at
3689
ITGB2
integrin, beta 2
9.57E−25
100.5
253.0
182.6
65.2

(complement component 3

receptor 3 and 4 subunit)

158
215049_PM_x_at
9332
CD163
CD163 molecule
9.85E−25
232.8
481.3
344.5
112.9

159
202953_PM_at
713
C1QB
complement component 1, q
9.99E−25
215.8
638.4
401.1
142.5

subcomponent, B chain

160
208091_PM_s_at
81552
VOPP1
vesicular, overexpressed in
1.02E−24
495.5
713.9
578.1
409.7

cancer, prosurvival protein 1

161
201288_PM_at
397
ARHGDIB
Rho GDP dissociation
1.13E−24
354.9
686.8
542.2
308.1

inhibitor (GDI) beta

162
213733_PM_at
4542
MYO1F
myosin IF
1.27E−24
26.8
52.7
39.4
20.9

163
212588_PM_at
5788
PTPRC
protein tyrosine
1.41E−24
94.4
321.0
217.7
76.4

phosphatase, receptor type, C

164
242907_PM_at
—
—
—
1.49E−24
59.3
165.1
99.7
39.8

165
209619_PM_at
972
CD74
CD74 molecule, major
1.55E−24
989.0
1864.7
1502.3
864.9

histocompatibility complex,

class II invariant chain

166
239237_PM_at
—
—
—
1.75E−24
15.9
34.9
25.3
14.5

167
217022_PM_s_at
100126583 ///
IGHA1 ///
immunoglobulin heavy
1.80E−24
77.7
592.5
494.6
49.4

3493 /// 3494
IGHA2 ///
constant alpha 1 ///

LOC100126583
immunoglobulin heavy

constant alpha 2 (A2m ma

168
201859_PM_at
5552
SRGN
serglycin
1.82E−24
1237.9
2171.99
1747.0
981.8

169
243418_PM_at
—
—
—
1.88E−24
56.3
31.1
49.8
104.8

170
202531_PM_at
3659
IRF1
interferon regulatory factor 1
1.93E−24
92.9
226.0
154.5
77.0

171
208966_PM_x_at
3428
IFI16
interferon, gamma-inducible
1.98E−24
406.7
760.4
644.9
312.6

protein 16

172
1555759_PM_a_at
6352
CCL5
chemokine (C—C motif)
2.02E−24
81.4
350.8
233.2
68.3

ligand 5

173
202643_PM_s_at
7128
TNFAIP3
tumor necrosis factor, alpha-
2.11E−24
43.7
92.8
68.1
34.8

induced protein 3

174
223922_PM_x_at
64231
MS4A6A
membrane-spanning 4-
2.22E−24
289.2
656.8
424.1
214.5

domains, subfamily A,

member 6A

175
209374_PM_s_at
3507
IGHM
immunoglobulin heavy
2.26E−24
61.8
437.0
301.0
45.0

constant mu

176
227677_PM_at
3718
JAK3
Janus kinase 3
2.29E−24
18.6
51.7
32.0
15.5

177
221840_PM_at
5791
PTPRE
protein tyrosine
2.38E−24
71.0
133.2
102.5
51.8

phosphatase, receptor type, E

178
200887_PM_s_at
6772
STAT1
signal transducer and
2.47E−24
1141.7
2278.6
1602.9
972.9

activator of transcription 1,

91 kDa

179
221875_PM_x_at
3134
HLA-F
major histocompatibility
2.72E−24
1365.3
2400.6
1971.8
1213.0

complex, class I, F

180
206513_PM_at
9447
AIM2
absent in melanoma 2
2.87E−24
17.2
50.7
30.5
13.9

181
214574_PM_x_at
7940
LST1
leukocyte specific transcript 1
2.95E−24
74.1
222.8
142.0
61.7

182
231776_PM_at
8320
EOMES
eomesodermin
3.07E−24
24.0
63.9
43.5
22.4

183
205639_PM_at
313
AOAH
acyloxyacyl hydrolase
4.03E−24
30.3
72.6
45.3
25.2

(neutrophil)

184
201762_PM_s_at
5721
PSME2
proteasome (prosome,
4.45E−24
1251.6
1825.1
1423.4
1091.0

macropain) activator subunit

2 (PA28 beta)

185
217986_PM_s_at
11177
BAZ1A
bromodom ainadjacent to
4.79E−24
87.5
145.2
116.4
62.5

zinc finger domain, 1A

186
235229_PM_at
—
—
—
4.84E−24
50.9
210.6
135.0
41.9

187
204924_PM_at
7097
TLR2
toll-like receptor 2
4.84E−24
96.8
162.0
116.6
66.6

188
202208_PM_s_at
10123
ARL4C
ADP-ribosylation factor-
4.89E−24
54.0
99.6
77.0
42.2

like 4C

189
227072_PM_at
25914
RTTN
rotatin
5.01E−24
101.1
74.5
83.6
132.9

190
202206_PM_at
10123
ARL4C
ADP-ribosylation factor-
5.08E−24
60.8
128.5
96.1
36.0

like 4C

191
204563_PM_at
6402
SELL
selectin L
5.11E−24
40.7
134.7
76.1
31.7

192
219386_PM_s_at
56833
SLAMF8
SLAM family member 8
5.17E−24
28.2
92.1
52.2
19.4

193
218232_PM_at
712
C1QA
complement component 1, q
5.88E−24
128.8
287.1
197.0
85.8

subcomponent, A chain

194
232311_PM_at
567
B2M
Beta-2-microglobulin
6.06E−24
42.3
118.6
83.7
35.2

195
219684_PM_at
64108
RTP4
receptor (chemosensory)
6.09E−24
63.1
129.3
93.7
50.4

transporter protein 4

196
204057_PM_at
3394
IRF8
interferon regulatory factor 8
6.59E−24
89.8
184.8
134.9
71.4

197
208296_PM_x_at
25816
TNFAIP8
tumor necrosis factor, alpha-
6.65E−24
136.9
242.5
195.1
109.6

induced protein 8

198
204122_PM_at
7305
TYROBP
TYRO protein tyrosine
6.73E−24
190.5
473.4
332.8
143.3

kinase binding protein

199
224927_PM_at
170954
KIAA1949
KIAA1949
6.87E−24
98.8
213.6
160.2
74.7

TABLE 11

Biopsy Expression Profiling of Kidney Transplants: 4-Way

Classifier AR vs. ADNR vs. CAN vs. TX (Brazilian Samples)

Validation Cohort

Predictive

Postive
Negative

Accuracy
Sensitivity
Specificity
Predictive
Predictive

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
Value (%)
Value (%)

Nearest Centroid
199
AR vs. TX
0.976
98
100
95
95
100

Nearest Centroid
199
ADNR vs. TX
1.000
100
100
100
100
100

Nearest Centroid
199
CAN vs. TX
1.000
100
100
100
100
100

TABLE 12a

Biopsy Expression Profiling of Kidney Transplants: 3-Way

Classifier AR vs. ADNR vs. TX (TGCG Samples)

Validation Cohort

Predictive

Postive
Negative

Accuracy
Sensitivity
Specificity
Predictive
Predictive

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
Value (%)
Value (%)

Nearest Centroid
197
AR vs. TX
0.979
98
96
100
100
96

Nearest Centroid
197
ADNR vs. TX
0.987
99
97
100
100
98

Nearest Centroid
197
AR vs. ADNR
0.968
97
100
93
95
100

TABLE 12b

Biopsy Expression Profiling of Kidney Transplants: 3-Way

Classifier AR vs. ADNR vs. TX (TGCG Samples)

p-value

Entrez
Gene

(Final
ADNR-
AR-
TX-

#
Probeset ID
Gene
Symbol
Gene Title
Phenotype)
Mean
Mean
Mean

1
242956_PM_at
3417
IDH1
Isocitrate dehydrogenase
2.95E−22
32.7
29.9
53.6

1 (NADP+), soluble

2
208948_PM_s_at
6780
STAU1
staufen, RNA binding
1.56E−29
1807.9
1531.8
2467.4

protein, homolog 1

(Drosophila)

3
213037_PM_x_at
6780
STAU1
staufen, RNA binding
4.77E−27
1699.0
1466.8
2264.9

protein, homolog 1

(Drosophila)

4
207320_PM_x_at
6780
STAU1
staufen, RNA binding
6.17E−28
1425.1
1194.6
1945.3

protein, homolog 1

(Drosophila)

5
1555832_PM_s_at
1316
KLF6
Kruppel-like factor 6
5.82E−23
899.4
1076.8
575.1

6
202376_PM_at
12
SERPINA3
serpin peptidase inhibitor,
1.05E−25
186.2
387.1
51.7

clade A (alpha-1

antiproteinase,

antitrypsin), member 3

7
226621_PM_at
9180
OSMR
oncostatin M receptor
1.28E−27
545.6
804.5
312.1

8
218983_PM_at
51279
C1RL
complement component
9.46E−25
167.1
244.5
99.4

1, r subcomponent-like

9
215005_PM_at
54550
NECAB2
N-terminal EF-hand
1.95E−25
36.7
23.4
65.7

calcium binding protein 2

10
202720_PM_at
26136
TES
testis derived transcript
4.32E−24
285.4
379.0
204.2

(3 LIM domains)

11
240320_PM_at
100131781
C14orf164
chromosome 14 open
1.64E−23
204.9
84.2
550.0

reading frame 164

12
243418_PM_at
—
—
—
1.81E−24
56.3
31.1
104.8

13
205466_PM_s_at
9957
HS3ST1
heparan sulfate
5.64E−24
73.6
123.8
42.1

(glucosamine) 3-O-

sulfotransferase 1

14
201042_PM_at
7052
TGM2
transglutaminase 2 (C
3.87E−28
131.8
236.5
80.1

polypeptide, protein-

glutamine-gamma-

glutamyltransferase)

15
202018_PM_s_at
4057
LTF
lactotransferrin
4.00E−25
392.3
1332.4
117.7

16
212503_PM_s_at
22982
DIP2C
DIP2 disco-interacting
6.62E−27
559.7
389.2
755.0

protein 2 homolog C

(Drosophila)

17
215049_PM_x_at
9332
CD163
CD163 molecule
4.65E−23
232.8
481.3
112.9

18
209515_PM_s_at
5873
RAB27A
RAB27A, member RAS
2.11E−23
127.3
192.2
85.2

oncogene family

19
221932_PM_s_at
51218
GLRX5
glutaredoxin 5
2.08E−22
1351.5
1145.8
1599.3

20
202207_PM_at
10123
ARL4C
ADP-ribosylation factor-
1.83E−29
106.9
258.6
57.2

like 4 C

21
227697_PM_at
9021
SOCS3
suppressor of cytokine
2.93E−23
35.9
69.1
19.9

signaling 3

22
227072_PM_at
25914
RTTN
rotatin
4.26E−23
101.1
74.5
132.9

23
201136_PM_at
5355
PLP2
proteolipid protein 2
2.71E−22
187.7
274.0
131.7

(colonic epithelium-

enriched)

24
212504_PM_at
22982
DIP2C
DIP2 disco-interacting
2.57E−25
334.5
227.2
452.5

protein 2 homolog C

(Drosophila)

25
200986_PM_at
710
SERPING1
serpin peptidase inhibitor,
2.88E−26
442.7
731.5
305.3

clade G (C1 inhibitor),

member 1

26
203233_PM_at
3566
IL4R
interleukin 4 receptor
6.30E−23
97.6
138.5
72.0

27
229295_PM_at
150166
IL17RA ///
interleukin 17 receptor A
6.40E−25
76.4
131.8
50.0

///
LOC150166
/// hypothetical protein

23765

LOC150166

28
231358_PM_at
83876
MRO
maestro
1.11E−22
199.7
81.2
422.1

29
201666_PM_at
7076
TIMP1
TIMP metallopeptidase
1.54E−22
1035.3
1879.4
648.0

inhibitor 1

30
209774_PM_x_at
2920
CXCL2
chemokine (C-X-C motif)
1.50E−26
24.9
52.9
15.5

ligand 2

31
217733_PM_s_at
9168
TMSB10
thymosin beta 10
9.34E−27
4414.7
6331.3
3529.0

32
222939_PM_s_at
117247
SLC16A10
solute carrier family 16,
2.55E−22
156.6
93.7
229.6

member 10 (aromatic

amino acid transporter)

33
204924_PM_at
7097
TLR2
toll-like receptor 2
2.11E−22
96.8
162.0
66.6

34
225415_PM_at
151636
DTX3L
deltex 3-like (Drosophila)
3.04E−24
230.2
376.4
166.9

35
202206_PM_at
10123
ARL4C
ADP-ribosylation factor-
1.16E−22
60.8
128.5
36.0

like 4 C

36
213857_PM_s_at
961
CD47
CD47molecule
2.56E−27
589.6
858.0
481.2

37
235529_PM_x_at
25939
SAMHD1
SAM domain and HD
4.49E−24
189.3
379.9
128.0

domain 1

38
206693_PM_at
3574
IL7
interleukin 7
3.12E−22
37.3
57.0
28.9

39
219033_PM_at
79668
PARP8
poly (ADP-ribose)
1.88E−22
47.9
79.2
35.7

polymerase family,

member 8

40
201721_PM_s_at
7805
LAPTM5
lysosomal protein
3.72E−24
396.5
934.6
249.4

transmembrane 5

41
204336_PM_s_at
10287
RGS19
regulator of G-protein
3.52E−25
95.2
187.7
67.0

signaling 19

42
235964_PM_x_at
25939
SAMHD1
SAM domain and HD
2.45E−23
172.9
345.9
117.5

domain 1

43
208091_PM_s_at
81552
VOPP1
vesicular, overexpressed
9.47E−25
495.5
713.9
409.7

in cancer, prosurvival

protein 1

44
204446_PM_s_at
240
ALOX5
arachidonate 5-
6.25E−31
91.9
323.9
54.7

lipoxygenase

45
212703_PM_at
83660
TLN2
talin 2
6.13E−23
270.8
159.7
357.5

46
213414_PM_s_at
6223
RPS19
ribosomal protein S19
1.57E−22
4508.2
5432.1
4081.7

47
1565681_PM_s_at
22982
DIP2C
DIP2 disco-interacting
2.57E−22
66.4
35.7
92.5

protein 2 homolog C

(Drosophila)

48
225764_PM_at
2120
ETV6
ets variant 6
2.63E−23
92.6
133.0
77.0

49
227184_PM_at
5724
PTAFR
platelet-activating factor
1.73E−24
89.8
191.4
62.7

receptor

50
221840_PM_at
5791
PTPRE
protein tyrosine
8.52E−23
71.0
133.2
51.8

phosphatase, receptor

type, E

51
225799_PM_at
112597
LOC541471
hypothetical LOC541471
1.18E−23
230.5
444.3
172.0

///
///
/// non-protein coding

541471
NCRNA00152
RNA 152

52
202957_PM_at
3059
HCLS1
hematopoietic cell-
1.94E−25
119.2
299.5
82.2

specific Lyn substrate 1

53
229383_PM_at
55016
1-Mar
membrane-associated ring
8.09E−25
33.8
88.0
22.9

finger (C3HC4) 1

54
33304_PM_at
3669
ISG20
interferon stimulated
2.83E−27
33.2
101.3
22.1

exonuclease gene 20 kDa

55
222062_PM_at
9466
IL27RA
interleukin 27 receptor,
1.79E−22
34.9
65.8
26.4

alpha

56
219574_PM_at
55016
1-Mar
membrane-associated ring
6.65E−25
51.5
126.0
36.2

finger (C3HC4) 1

57
202748_PM_at
2634
GBP2
guanylate binding protein
7.51E−26
196.7
473.0
141.0

2, interferon-inducible

58
210895_PM_s_at
942
CD86
CD86 molecule
3.80E−23
32.3
85.0
21.6

59
202208_PM_s_at
10123
ARL4C
ADP-ribosylation factor-
1.24E−23
54.0
99.6
42.2

like 4 C

60
221666_PM_s_at
29108
PYCARD
PYD and CARD domain
5.55E−24
60.7
132.8
44.7

containing

61
227125_PM_at
3455
IFNAR2
interferon (alpha, beta and
3.57E−24
70.0
126.2
55.8

omega) receptor 2

62
226525_PM_at
9262
STK17B
serine/threonine kinase
3.43E−24
146.8
338.7
107.6

17 b

63
210644_PM_s_at
3903
LAIR1
leukocyte-associated
2.96E−24
29.7
74.6
21.2

immunoglobulin-like

receptor 1

64
230391_PM_at
8832
CD84
CD84 molecule
1.89E−22
47.9
130.1
32.5

65
242907_PM_at
—
—
—
2.54E−22
59.3
165.1
39.8

66
1553906_PM_s_at
221472
FGD2
FYVE, RhoGEF and PH
1.67E−26
104.6
321.0
71.9

domain containing 2

67
223922_PM_x_at
64231
MS4A6A
membrane-spanning 4-
8.68E−24
289.2
656.8
214.5

domains, subfamily A,

member 6 A

68
230550_PM_at
64231
MS4A6A
membrane-spanning 4-
7.63E−24
44.8
124.3
30.9

domains, subfamily A,

member 6 A

69
202953_PM_at
713
C1QB
complement component
2.79E−22
215.8
638.4
142.5

1, q subcomponent, B

chain

70
213733_PM_at
4542
MYO1F
myosin IF
2.25E−23
26.8
52.7
20.9

71
204774_PM_at
2123
EVI2A
ecotropic viral integration
5.10E−24
63.6
168.5
44.9

site 2 A

72
211366_PM_x_at
834
CASP1
caspase 1, apoptosis-
3.40E−24
115.3
261.0
87.1

related cysteine peptidase

(interleukin 1, beta,

convertase)

73
204698_PM_at
3669
ISG20
interferon stimulated
1.27E−28
41.5
165.1
27.6

exonuclease gene 20 kDa

74
201762_PM_s_at
5721
PSME2
proteasome (prosome,
2.31E−22
1251.6
1825.1
1091.0

macropain) activator

subunit 2 (PA28 beta)

75
204470_PM_at
2919
CXCL1
chemokine (C-X-C motif)
2.81E−24
22.9
63.7
16.2

ligand 1 (melanoma

growth stimulating

activity, alpha)

76
242827_PM_x_at
—
—
—
9.00E−23
22.1
52.3
16.3

77
209970_PM_x_at
834
CASP1
caspase 1, apoptosis-
4.40E−25
116.0
266.6
88.7

related cysteine peptidase

(interleukin 1, beta,

convertase)

78
228532_PM_at
128346
C1orf162
chromosome 1 open
1.57E−22
82.6
217.7
60.0

reading frame 162

79
232617_PM_at
1520
CTSS
cathepsin S
2.83E−23
209.6
537.9
154.8

80
203761_PM_at
6503
SLA
Src-like-adaptor
3.80E−23
69.6
179.2
51.4

81
219666_PM_at
64231
MS4A6A
membrane-spanning 4-
4.29E−23
159.2
397.6
118.7

domains, subfamily A,

member 6 A

82
223280_PM_x_at
64231
MS4A6A
membrane-spanning 4-
2.53E−24
269.0
711.8
199.5

domains, subfamily A,

member 6 A

83
225701_PM_at
80709
AKNA
AT-hook transcription
2.87E−29
37.7
102.8
29.0

factor

84
224356_PM_x_at
64231
MS4A6A
membrane-spanning 4-
1.56E−23
150.6
399.6
111.2

domains, subfamily A,

member 6 A

85
202643_PM_s_at
7128
TNFAIP3
tumor necrosis factor,
9.92E−24
43.7
92.8
34.8

alpha-induced protein 3

86
202644_PM_s_at
7128
TNFAIP3
tumor necrosis factor,
2.12E−27
169.8
380.4
136.6

alpha-induced protein 3

87
213566_PM_at
6039
RNASE6
ribonuclease, RNase A
1.55E−23
180.9
482.0
134.3

family, k6

88
219386_PM_s_at
56833
SLAMF8
SLAM family member 8
1.22E−22
28.2
92.1
19.4

89
203416_PM_at
963
CD53
CD53 molecule
3.13E−23
215.9
603.0
157.8

90
200003_PM_s_at
6158
RPL28
ribosomal protein L28
1.73E−22
4375.2
5531.2
4069.9

91
206420_PM_at
10261
IGSF6
immunoglobulin
5.65E−23
45.1
131.5
32.7

superfamily, member 6

92
217028_PM_at
7852
CXCR4
chemokine (C-X-C motif)
6.84E−27
100.8
304.4
75.3

receptor 4

93
232024_PM_at
26157
GIMAP2
GTPase, IMAP family
2.94E−24
90.2
197.7
72.5

member 2

94
238327_PM_at
440836
ODF3B
outer dense fiber of sperm
1.75E−27
32.8
81.4
26.1

tails 3 B

95
209083_PM_at
11151
CORO1A
coronin, actin binding
2.78E−27
46.9
163.8
34.3

protein, 1 A

96
232724_PM_at
64231
MS4A6A
membrane-spanning 4-
6.28E−23
24.8
47.6
20.7

domains, subfamily A,

member 6 A

97
211742_PM_s_at
2124
EVI2B
ecotropic viral integration
1.56E−22
73.9
236.5
53.2

site 2 B

98
202659_PM_at
5699
PSMB10
proteasome (prosome,
2.29E−24
180.7
355.6
151.5

macropain) subunit, beta

type, 10

99
226991_PM_at
4773
NFATC2
nuclear factor of activated
2.42E−23
37.9
87.0
30.3

T-cells, cytoplasmic,

calcineurin-dependent 2

100
210538_PM_s_at
330
BIRC3
baculoviral IAP repeat-
5.40E−26
106.7
276.7
84.6

containing 3

101
205269_PM_at
3937
LCP2
lymphocyte cytosolic
7.35E−25
30.2
92.2
22.9

protein 2 (SH2 domain

containing leukocyte

protein of 76 kDa)

102
211368_PM_s_at
834
CASP1
caspase 1, apoptosis-
9.43E−27
102.6
274.3
81.8

related cysteine peptidase

(interleukin 1, beta,

convertase)

103
205798_PM_at
3575
IL7R
interleukin 7 receptor
1.57E−24
44.1
136.8
33.4

104
228442_PM_at
4773
NFATC2
nuclear factor of activated
5.58E−23
39.0
84.8
32.0

T-cells, cytoplasmic,

calcineurin-dependent 2

105
213603_PM_s_at
5880
RAC2
ras-related C3 botulinum
3.38E−25
113.9
366.5
86.5

toxin substrate 2 (rho

family, small GTP

binding protein Rac2)

106
228964_PM_at
639
PRDM1
PR domain containing 1,
1.42E−23
21.2
52.1
17.0

with ZNF domain

107
209606_PM_at
9595
CYTIP
cytohesin 1 interacting
1.66E−26
41.4
114.3
32.9

protein

108
214022_PM_s_at
8519
IFITM1
interferon induced
1.61E−23
799.1
1514.7
683.3

transmembrane protein 1

(9-27)

109
202307_PM_s_at
6890
TAP1
transporter 1, ATP-
8.61E−26
172.4
420.6
141.0

binding cassette, sub-

family B (MDR/TAP)

110
204882_PM_at
9938
ARHGAP25
Rho GTPase activating
8.14E−23
51.5
107.3
43.0

protein 25

111
227344_PM_at
10320
IKZF1
IKAROS family zinc
5.30E−26
17.8
40.0
14.9

finger 1 (Ikaros)

112
205270_PM_s_at
3937
LCP2
lymphocyte cytosolic
3.75E−24
56.8
162.6
44.6

protein 2 (SH2 domain

containing leukocyte

protein of 76 kDa)

113
223640_PM_at
10870
HCST
hematopoietic cell signal
5.55E−23
91.0
234.0
72.7

transducer

114
226218_PM_at
3575
IL7R
interleukin 7 receptor
2.15E−23
55.6
197.0
41.4

115
226219_PM_at
257106
ARHGAP30
Rho GTPase activating
1.87E−26
46.4
127.9
37.5

protein 30

116
38149_PM_at
9938
ARHGAP25
Rho GTPase activating
1.20E−23
51.3
108.9
43.2

protein 25

117
213975_PM_s_at
4069
LYZ
lysozyme
2.70E−22
458.4
1626.0
338.1

118
238668_PM_at
—
—
—
1.35E−23
18.2
49.2
14.5

119
200887_PM_s_at
6772
STAT1
signal transducer and
1.27E−22
1141.7
2278.6
972.9

activator of transcription

1.91 kDa

120
1555756_PM_a_at
64581
CLEC7A
C-type lectin domain
2.11E−22
16.6
45.4
13.2

family 7, member A

121
205039_PM_s_at
10320
IKZF1
IKAROS family zinc
6.73E−23
29.1
65.9
24.2

finger 1 (Ikaros)

122
206011_PM_at
834
CASP1
caspase 1, apoptosis-
6.34E−25
74.5
198.0
60.7

related cysteine peptidase

(interleukin 1, beta,

convertase)

123
221698_PM_s_at
64581
CLEC7A
C-type lectin domain
9.79E−24
61.5
164.6
49.7

family 7, memberA

124
227346_PM_at
10320
IKZF1
IKAROS family zinc
1.51E−26
24.9
79.7
19.8

finger 1 (Ikaros)

125
230499_PM_at
—
—
—
8.55E−23
29.7
68.7
24.7

126
229391_PM_s_at
441168
FAM26F
family with sequence
2.74E−23
98.6
379.7
73.7

similarity 26, member F

127
205159_PM_at
1439
CSF2RB
colony stimulating factor
1.74E−23
33.7
107.5
26.3

2 receptor, beta, low-

affinity (granulocyte-

macrophage)

128
205639_PM_at
313
AOAH
acyloxyacyl hydrolase
4.43E−23
30.3
72.6
25.2

(neutrophil)

129
204563_PM_at
6402
SELL
selectin L
1.00E−23
40.7
134.7
31.7

130
201288_PM_at
397
ARHGDIB
Rho GDP dissociation
1.92E−22
354.9
686.8
308.1

inhibitor (GDI) beta

131
209969_PM_s_at
6772
STAT1
signal transducer and
5.49E−24
395.8
1114.5
320.8

activator of transcription

1.91 kDa

132
229390_PM_at
441168
FAM26F
family with sequence
3.91E−25
103.8
520.0
75.9

similarity 26, member F

133
242916_PM_at
11064
CEP110
centrosomal protein
1.99E−23
30.8
68.1
26.2

110 kDa

134
207651_PM_at
29909
GPR171
G protein-coupled
1.90E−29
25.8
89.9
20.9

receptor 171

135
229937_PM_x_at
10859
LILRB1
Leukocyte
1.67E−24
23.5
79.9
18.5

immunoglobulin-like

receptor, subfamily B

(with TM and ITIM

domains), member

136
232543_PM_x_at
64333
ARHGAP9
RhoGTPase activating
3.66E−26
31.7
99.1
25.8

protein 9

137
203332_PM_s_at
3635
INPP5D
inositol polyphosphate-5-
3.66E−24
27.9
60.5
24.0

phosphatase, 145 kDa

138
213160_PM_at
1794
DOCK2
dedicator of cytokinesis 2
1.03E−24
33.7
92.6
27.8

139
204912_PM_at
3587
IL10RA
interleukin 10 receptor,
1.28E−24
57.0
178.7
46.1

alpha

140
204205_PM_at
60489
APOBEC3G
apolipoprotein B mRNA
6.40E−27
95.4
289.4
78.7

editing enzyme, catalytic

polypeptide-like 3 G

141
206513_PM_at
9447
AIM2
absent in melanoma 2
9.67E−23
17.2
50.7
13.9

142
203741_PM_s_at
113
ADCY7
adenylate cyclase 7
2.34E−22
23.6
59.3
19.7

143
206118_PM_at
6775
STAT4
signal transducer and
8.28E−26
21.0
49.5
18.1

activator of transcription

4

144
227677_PM_at
3718
JAK3
Janus kinase 3
6.48E−24
18.6
51.7
15.5

145
227353_PM_at
147138
TMC8
transmembrane channel-
6.49E−29
19.8
64.2
16.6

like 8

146
1552701_PM_a_at
114769
CARD16
caspase recruitment
2.52E−23
143.8
413.6
119.8

domain family, member

16

147
1552703_PM_s_at
114769
CARD16
caspase recruitment
7.57E−24
64.6
167.8
54.9

/// 834
/// CASP1
domain family, member

16 /// caspase 1,

apoptosis-related cysteine

148
229437_PM_at
114614
MIR155HG
MIR155 host gene (non-
3.90E−27
15.4
50.4
12.9

protein coding)

149
204319_PM_s_at
6001
RGS10
regulator of G-protein
6.54E−24
159.7
360.4
139.4

signaling 10

150
204118_PM_at
962
CD48
CD48 molecule
3.85E−23
82.9
341.5
65.2

151
1559584_PM_a_at
283897
C16orf54
chromosome 16 open
2.86E−24
31.3
95.8
26.1

reading frame 54

152
212588_PM_at
5788
PTPRC
protein tyrosine
2.46E−22
94.4
321.0
76.4

phosphatase, receptor

type, C

153
219014_PM_at
51316
PLAC8
placenta-specific 8
2.03E−23
38.8
164.1
30.7

154
235735_PM_at
—
—
—
1.39E−25
13.0
34.9
11.2

155
203932_PM_at
3109
HLA-DMB
major histocompatibility
6.25E−23
422.4
853.9
376.0

complex, class II, DM

beta

156
223502_PM_s_at
10673
TNFSF13B
tumor necrosis factor
2.62E−23
73.0
244.3
60.0

(ligand) superfamily,

member 13 b

157
1405_PM_i_at
6352
CCL5
chemokine (C-C motif)
2.22E−25
68.0
295.7
54.6

ligand 5

158
226474_PM_at
84166
NLRC5
NLR family, CARD
1.33E−23
64.4
173.5
55.1

domain containing 5

159
204220_PM_at
9535
GMFG
glia maturation factor,
1.56E−23
147.0
339.3
128.9

gamma

160
204923_PM_at
54440
SASH3
SAM and SH3 domain
4.56E−23
25.4
68.2
21.7

containing 3

161
206082_PM_at
10866
HCP5
HLA complex P5
1.23E−23
76.2
185.1
66.6

162
204670_PM_x_at
3123 ///
HLA-
major histocompatibility
1.31E−23
2461.9
4344.6
2262.0

3126
DRB1 ///
complex, class II, DR beta

HLA-
1 /// major

DRB4
histocompatibility comp

163
228869_PM_at
124460
SNX20
sorting nexin 20
2.59E−22
25.7
67.3
22.2

164
205831_PM_at
914
CD2
CD2 molecule
2.30E−27
40.4
162.5
33.9

165
206978_PM_at
729230
CCR2
chemokine (C-C motif)
4.46E−23
32.1
100.7
27.3

receptor 2

166
224451_PM_x_at
64333
ARHGAP9
Rho GTPase activating
4.23E−24
34.2
103.4
29.4

protein 9

167
204279_PM_at
5698
PSMB9
proteasome (prosome,
2.81E−23
241.6
637.4
211.3

macropain) subunit, beta

type, 9 (large

multifunctional peptidase

168
209795_PM_at
969
CD69
CD69 molecule
5.81E−27
17.6
57.6
15.2

169
229625_PM_at
115362
GBP5
guanylate binding
5.85E−24
29.3
133.3
24.0

protein 5

170
213416_PM_at
3676
ITGA4
integrin, alpha 4 (antigen
1.19E−23
26.3
78.1
22.8

CD49D, alpha 4 subunit

of VLA-4 receptor)

171
206804_PM_at
917
CD3G
CD3g molecule, gamma
1.50E−27
19.7
60.6
17.3

(CD3-TCR complex)

172
222895_PM_s_at
64919
BCL11B
B-cell CLL/lymphoma
7.01E−23
22.0
64.5
19.1

11 B (zinc finger protein)

173
211582_PM_x_at
7940
LST1
leukocyte specific
2.13E−22
57.5
183.8
49.4

transcript 1

174
1555852_PM_at
100507463
LOC100507463
hypothetical
8.92E−26
78.9
202.6
70.9

LOC100507463

175
213539_PM_at
915
CD3D
CD3d molecule, delta
9.01E−27
70.8
335.1
60.0

(CD3-TCR complex)

176
239237_PM_at
—
—
—
9.82E−24
15.9
34.9
14.5

177
229723_PM_at
117289
TAGAP
T-cell activation
5.21E−24
29.2
82.9
26.2

RhoGTPase activating

protein

178
204655_PM_at
6352
CCL5
chemokine (C-C motif)
7.63E−24
77.5
339.4
66.8

ligand 5

179
229041_PM_s_at
—
—
—
1.64E−24
36.5
132.1
32.4

180
205267_PM_at
5450
POU2AF1
POU class 2 associating
4.47E−23
17.9
86.8
15.7

factor 1

181
226878_PM_at
3111
HLA-DOA
major histocompatibility
2.59E−25
102.0
288.9
94.3

complex, class II, DO

alpha

182
205488_PM_at
3001
GZMA
granzyme A (granzyme 1,
4.14E−25
37.3
164.8
33.4

cytotoxic T-lymphocyte-

associated serine

esterase 3)

183
201137_PM_s_at
3115
HLA-
major histocompatibility
2.17E−22
1579.1
3863.7
1475.9

DPB1
complex, class II, DP

beta 1

184
204891_PM_s_at
3932
LCK
lymphocyte-specific
7.95E−28
19.3
74.2
17.8

protein tyrosine kinase

185
231776_PM_at
8320
EOMES
eomesodermin
1.71E−23
24.0
63.9
22.4

186
211339_PM_s_at
3702
ITK
IL2-inducible T-cell
7.40E−23
16.7
44.4
15.7

kinase

187
223322_PM_at
83593
RASSF5
Ras association
5.21E−26
41.5
114.4
39.2

(RalGDS/AF-6) domain

family member 5

188
205758_PM_at
925
CD8A
CD8a molecule
2.80E−25
24.2
105.8
22.2

189
231124_PM_x_at
4063
LY9
lymphocyte antigen 9
2.81E−23
16.7
45.8
15.7

190
211796_PM_s_at
28638
TRBC1 ///
T cell receptor beta
4.38E−25
69.2
431.5
63.6

///
TRBC2
constant 1 /// T cell

28639

receptor beta constant 2

191
210915_PM_x_at
28638
TRBC2
T cell receptor beta
1.91E−27
39.7
230.7
37.5

constant 2

192
205821_PM_at
22914
KLRK1
killer cell lectin-like
1.18E−25
30.3
111.5
31.3

receptor subfamily K,

member 1

193
236295_PM_s_at
197358
NLRC3
NLR family, CARD
6.22E−26
19.0
52.5
18.6

domain containing 3

194
213193_PM_x_at
28639
TRBC1
T cell receptor beta
4.22E−25
95.3
490.9
92.1

constant 1

195
211656_PM_x_at
100133
HLA-
major histocompatibility
5.98E−25
211.3
630.2
208.2

583 ///
DQB1 ///
complex, class II, DQ

3119
LOC100133583
beta 1 /// HLA class II

histocompatibili

196
209670_PM_at
28755
TRAC
T cell receptor alpha
1.44E−24
37.9
149.9
38.5

constant

197
213888_PM_s_at
80342
TRAF3IP3
TRAF3 interacting
1.66E−22
30.8
101.9
30.5

protein 3

TABLE 13

Biopsy Expression Profiling of Kidney Transplants: 3-Way Classifier AR vs. ADNR vs. TX (Brazilian Samples)

Validation Cohort

Predictive

Postive
Negative

Accuracy
Sensitivity
Specificity
Predictive
Predictive

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
Value (%)
Value (%)

Nearest Centroid
197
AR vs. TX
0.976
98
100
95
95
100

Nearest Centroid
197
ADNR vs. TX
1.000
100
100
100
100
100

Nearest Centroid
197
AR vs. ADNR
0.962
97
100
91
95
100

TABLE 14a

Biopsy Expression Profiling of Kidney Transplants: 2-Way Classifier CAN vs. TX (TGCG Samples)

Validation Cohort

Predictive

Postive
Negative

Accuracy
Sensitivity
Specificity
Predictive
Predictive

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
Value (%)
Value (%)

Nearest Centroid
200
AR vs. TX
0.965
97
96
97
96
97

TABLE 14b

Biopsy Expression Profiling of Kidney Transplants. 2-Way Classifier CAN vs. TX

p-value

(Final

Entrez
Gene

Pheno-
CAN-
TX-

#
Probeset ID
Gene
Symbol
Gene Title
type)
Mean
Mean

1
204698_PM_at
3669
ISG20
interferon stimulated
1.93E−19
96.1
27.6

exonuclease gene 20 kDa

2
33304_PM_at
3669
ISG20
interferon stimulated
2.02E−19
63.4
22.1

exonuclease gene 20 kDa

3
217022_PM_s_at
100126
IGHA1 ///
immunoglobulin heavy
4.31E−19
494.6
49.4

583 ///
IGHA2 ///
constant alpha 1 ///

3493 ///
LOC100126583
immunoglobulin heavy

3494

constant alpha 2 (A2m ma

4
202957_PM_at
3059
HCLS1
hematopoietic cell-specific
5.13E−19
229.9
82.2

Lyn substrate 1

5
203761_PM_at
6503
SLA
Src-like-adaptor
1.10E−18
138.4
51.4

6
204446_PM_s_at
240
ALOX5
arachidonate 5-lipoxygen ase
1.36E−18
216.7
54.7

7
209198_PM_s_at
23208
SYT11
synaptotagmin XI
1.93E−18
41.8
22.8

8
228964_PM_at
639
PRDM1
PR domain containing 1,
2.37E−18
41.5
17.0

with ZNF domain

9
201042_PM_at
7052
TGM2
transglutaminase 2 (C
3.13E−18
172.6
80.1

polypeptide, protein-

glutamine-gamma-

glutamyltransferase)

10
226219_PM_at
257106
ARHGAP30
Rho GTPase activating
7.21E−18
91.8
37.5

protein 30

11
225701_PM_at
80709
AKNA
AT-hook transcription
7.27E−18
73.2
29.0

factor

12
202207_PM_at
10123
ARL4C
ADP-ribosylation factor-
7.98E−18
190.4
57.2

like 4 C

13
219574_PM_at
55016
MAR1
membrane-associated ring
8.98E−18
87.5
36.2

finger (C3HC4) 1

14
209083_PM_at
12-Jul
CORO1A
coronin, actin binding
1.06E−17
107.1
34.3

protein, 1 A

15
226621_PM_at
9180
OSMR
oncostatin M receptor
1.85E−17
682.9
312.1

16
1405_PM_i_at
6352
CCL5
chemokine (C-C motif)
2.19E−17
195.6
54.6

ligand 5

17
213160_PM_at
1794
DOCK2
dedicator of cytokinesis 2
2.75E−17
66.0
27.8

18
227346_PM_at
10320
IKZF1
IKAROS family zinc finger
2.92E−17
53.3
19.8

1 (Ikaros)

19
204205_PM_at
60489
APOBEC3G
apolipoprotein B mRNA
2.92E−17
192.0
78.7

editing enzyme, catalytic

polypeptide-like 3 G

20
218322_PM_s_at
51703
ACSL5
acyl-CoA synthetase long-
3.15E−17
84.5
48.1

chain family member

21
238327_PM_at
440836
ODF3B
outer dense fiber of sperm
3.42E−17
58.5
26.1

tails 3 B

22
218983_PM_at
51279
C1RL
complement component 1, r
4.33E−17
206.9
99.4

subcomponent-like

23
210538_PM_s_at
330
BIRC3
baculoviral IAP repeat-
4.49E−17
199.1
84.6

containing 3

24
207651_PM_at
29909
GPR171
G protein-coupled receptor
5.48E−17
57.0
20.9

171

25
201601_PM_x_at
8519
IFITM1
interferon induced
6.07E−17
2202.5
1251.1

transmembrane protein 1

(9-27)

26
226878_PM_at
3111
HLA-DOA
major histocompatibility
6.12E−17
201.4
94.3

complex, class II, DO alpha

27
1555756_PM_a_at
64581
CLEC7A
C-type lectin domain family
6.22E−17
28.9
13.2

7, member A

28
1559584_PM_a_at
283897
C16orf54
chromosome 16 open
6.73E−17
71.5
26.1

reading frame 54

29
209795_PM_at
969
CD69
CD69 molecule
9.46E−17
40.6
15.2

30
230550_PM_at
64231
MS4A6A
membrane-spanning 4-
1.20E−16
88.0
30.9

domains, subfamily A,

member 6 A

31
1553906_PM_s_at
221472
FGD2
FYVE, RhoGEF and PH
1.34E−16
219.3
71.9

domain containing 2

32
205798_PM_at
3575
IL7R
interleukin 7 receptor
1.55E−16
106.3
33.4

33
1555852_PM_at
100507463
LOC100507463
hypothetical
1.81E−16
154.2
70.9

LOC100507463

34
224916_PM_at
340061
TMEM173
transmembrane protein 173
1.84E−16
67.8
40.0

35
211368_PM_s_at
834
CASP1
caspase 1, apoptosis-related
1.85E−16
191.4
81.8

cysteine peptidase

(interleukin 1, beta,

convertase)

36
226474_PM_at
84166
NLRC5
NLRC5 family, CARD
1.85E−16
129.8
55.1

domain containing 5

37
201137_PM_s_at
3115
HLA-
major histocompatibility
1.90E−16
3151.7
1475.9

DPB1
complex, class II, DP beta 1

38
210785_PM_s_at
9473
C1orf38
chromosome 1 open reading
2.07E−16
39.9
16.4

frame 38

39
215121_PM_x_at
100290
IGLC7 ///
immunoglobulin lambda
2.13E−16
1546.8
250.4

481 ///
IGLV1-44
constant 7 ///

28823
///
immunoglobulin lambda

///
LOC100290481
variable 1-44 ///

28834

immunoglob

40
1555832_PM_s_at
1316
KLF6
Kruppel-like factor 6
2.35E−16
1003.3
575.1

41
221932_PM_s_at
51218
GLRX5
glutaredoxin 5
2.49E−16
1218.1
1599.3

42
207677_PM_s_at
4689
NCF4
neutrophil cytosolic factor
2.65E−16
39.5
19.2

4.40 kDa

43
202720_PM_at
26136
TES
testis derived transcript (3
2.68E−16
357.9
204.2

LIM domains)

44
220005_PM_at
53829
P2RY13
purinergic receptor P2Y, G-
2.72E−16
29.6
14.8

protein coupled, 13

45
200904_PM_at
3133
HLA-E
major histocompatibility
2.73E−16
1607.7
994.7

complex, class I, E

46
222294_PM_s_at
5873
RAB27A
RAB27A, member RAS
2.91E−16
263.0
146.4

oncogene family

47
205831_PM_at
914
CD2
CD2 molecule
3.32E−16
100.9
33.9

48
227344_PM_at
10320
IKZF1
IKAROS family zinc finger
3.39E−16
28.5
14.9

1 (Ikaros)

49
209374_PM_s_at
3507
IGHM
immunoglobulin heavy
3.73E−16
301.0
45.0

constant mu

50
202307_PM_s_at
6890
TAP1
transporter 1, ATP-binding
4.84E−16
280.0
141.0

cassette, sub-family B

(MDR/TAP)

51
223218_PM_s_at
64332
NFKBIZ
nuclear factor of kappa light
5.05E−16
399.9
159.1

polypeptide gene enhancer

in B-cells inhibitor, zeta

52
229437_PM_at
114614
MIR155HG
MIR155 host gene (non-
5.85E−16
28.5
12.9

protein coding)

53
213603_PM_s_at
5880
RAC2
ras-related C3 botulinum
5.98E−16
250.3
86.5

toxin substrate 2 (rho

family, small GTP binding

protein Rac2)

54
214669_PM_x_at
3514 ///
IGK@ ///
immunoglobulin kappa
6.32E−16
3449.1
587.1

50802
IGKC
locus /// immunoglobulin

kappa constant

55
211430_PM_s_at
28396
IGHG1 ///
immunoglobulin heavy
6.39E−16
2177.7
266.9

/// 3500
IGHM ///
constant gamma 1 (G1m

/// 3507
IGHV4-
marker) /// immunoglobulin

31
heavy constant mu

56
228471_PM_at
91526
ANKRD4
ankyrin repeat domain 44
6.42E−16
184.6
86.5

57
209138_PM_x_at
3535
IGL@
Immunoglobulin lambda
7.54E−16
2387.0
343.2

locus

58
227353_PM_at
147138
TMC8
transmembrane channel-
8.01E−16
42.7
16.6

like 8

59
200986_PM_at
710
SERPING1
serpin peptidase inhibitor,
8.10E−16
590.2
305.3

clade G (C1 inhibitor),

member 1

60
212203_PM_x_at
10410
IFITM3
interferon induced
8.17E−16
4050.1
2773.8

transmembrane protein 3

(1-8U)

61
221651_PM_x_at
3514 ////
IGK@ ///
immunoglobulin kappa
9.60E−16
3750.2
621.2

50802
IGKC
locus /// immunoglobulin

kappa constant

62
214836_PM_x_at
28299
IGK@ ///
immunoglobulin kappa
9.72E−16
544.2
109.1

/// 3514
IGKC ///
locus /// immunoglobulin

///
IGKV1-5
kappa constant ///

50802

immunoglobulin kappa v

63
1552703_PM_s_at
114769
CARD16
caspase recruitment domain
1.12E−15
120.6
54.9

/// 834
/// CASP1
family, member 16 ///

caspase 1, apoptosis-related

cysteine

64
202901_PM_x_at
1520
CTSS
cathepsin S
1.13E−15
130.9
45.1

65
215379_PM_x_at
28823
IGLC7 ///
immunoglobulin lambda
1.16E−15
1453.0
248.1

///
IGLV1-44
constant 7 ///

28834

immunoglobulin lambda

variable 1-44

66
222939_PM_s_at
117247
SLC16A10
solute carrier family 16,
1.22E−15
115.1
229.6

member 10 (aromatic amino

acid transporter)

67
232617_PM_at
1520
CTSS
cathepsin S
1.22E−15
392.2
154.8

68
235964_PM_x_at
25939
SAMHD1
SAM domain and HD
1.26E−15
270.1
117.5

domain 1

69
205159_PM_at
1439
CSF2RB
colony
1.28E−15
70.4
26.3

stimulating factor 2

receptor, beta, low-affinity

(granulocyte-macrophage)

70
224451_PM_x_at
64333
ARHGAP9
Rho GTPase activating
1.34E−15
71.8
29.4

protein 9

71
214677_PM_x_at
100287
IGL@ ///
Immunoglobulin lambda
1.35E−15
2903.1
433.7

927 ///
LOC100287927
locus /// Hypothetical

3535

protein LOC100287927

72
217733_PM_s_at
9168
TMSB10
thymosin beta 10
1.37E−15
5555.2
3529.0

73
38149_PM_at
9938
ARHGAP25
Rho GTPase activating
1.46E−15
83.2
43.2

protein 25

74
221671_PM_x_at
3514 ///
IGK@ ///
immunoglobulin kappa
1.57E−15
3722.5
642.9

50802
IGKC
locus /// immunoglobulin

kappa constant

75
214022_PM_s_at
8519
IFITM1
interferon induced
1.59E−15
1236.6
683.3

transmembrane protein 1

(9-27)

76
223217_PM_s_at
64332
NFKBIZ
nuclear factor of kappa light
1.61E−15
196.6
79.7

polypeptide gene enhancer

in B-cells inhibitor, zeta

77
206118_PM_at
6775
STAT4
signal transducer and
1.67E−15
37.7
18.1

activator of transcription 4

78
221666_PM_s_at
29108
PYCARD
PYD and CARD domain
1.82E−15
95.5
44.7

containing

79
207375_PM_s_at
3601
IL15RA
interleukin 15 receptor,
1.94E−15
51.2
28.2

alpha

80
209197_PM_at
23208
SYT11
synaptotagmin XI
2.02E−15
38.2
24.9

81
243366_PM_s_at
—
—
—
2.05E−15
52.5
22.0

82
224795_PM_x_at
3514 ///
IGK@ ///
immunoglobulin kappa
2.18E−15
3866.2
670.5

50802
IGKC
locus /// immunoglobulin

kappa constant

83
36711_PM_at
23764
MAFF
v-maf musculoaponeurotic
2.26E−15
113.0
40.7

fibrosarcoma oncogene

homolog F (avian)

84
227125_PM_at
3455
IFNAR2
interferon (alpha, beta and
2.27E−15
96.7
55.8

omega) receptor 2

85
235735_PM_at
—
—
—
2.58E−15
24.5
11.2

86
209515_PM_s_at
5873
RAB27A
RAB27A, member RAS
2.61E−15
160.5
85.2

oncogene family

87
204670_PM_x_at
3123 ///
HLA-
major
2.61E−15
3694.9
2262.0

3126
DRB1 ///
histocompatibility

HLA-
complex, class II, DR beta 1

DRB4
/// major histocompatibility

comp

88
205269_PM_at
3937
LCP2
lymphocyte cytosolic
2.85E−15
58.9
22.9

protein 2 (SH2 domain

containing leukocyte protein

of 76 kDa)

89
226525_PM_at
9262
STK17B
serine/threonine kinase 17 b
3.00E−15
259.1
107.6

90
229295_PM_at
150166
IL17RA
interleukin 17 receptor A ///
3.02E−15
98.4
50.0

///
///
hypothetical protein

23765
LOC150166
LOC150166

91
206513_PM_at
9447
AIM2
absent in melanoma 2
3.18E−15
30.5
13.9

92
209774_PM_x_at
2920
CXCL2
chemokine (C-X-C motif)
3.45E−15
38.2
15.5

ligand 2

93
211656_PM_x_at
100133
HLA-
major histocompatibility
3.51E−15
459.8
208.2

583 ///
DQB1 ///
complex, class II, DQ beta 1

3119
LOC100133583
/// HLA class II

histocompatibili

94
206011_PM_at
834
CASP1
caspase 1, apoptosis-related
3.56E−15
146.2
60.7

cysteine peptidase

(interleukin 1, beta,

convertase)

95
202803_PM_s_at
3689
ITGB2
integrin, beta 2
3.68E−15
182.6
65.2

(complement component 3

receptor 3 and 4 subunit)

96
221698_PM_s_at
64581
CLEC7A
C-type
3.69E−15
112.8
49.7

lectin domain family

7, member A

97
229937_PM_x_at
10859
LILRB1
Leukocyte immunoglobulin-
3.75E−15
50.0
18.5

like receptor, subfamily B

(with TM and ITIM

domains), member

98
235529_PM_x_at
25939
SAMHD1
SAM domain and HD
3.99E−15
289.1
128.0

domain 1

99
223322_PM_at
83593
RASSF5
Ras association
4.03E−15
79.3
39.2

(RalGDS/AF-6) domain

family member 5

100
211980_PM_at
1282
COL4A1
collagen, type IV, alpha 1
4.75E−15
1295.8
774.7

101
201721_PM_s_at
7805
LAPTM5
lysosomal protein
4.83E−15
661.3
249.4

transmembrane 5

102
242916_PM_at
11064
CEP110
centrosomal protein 110 kDa
4.89E−15
51.2
26.2

103
206978_PM_at
729230
CCR2
chemokine (C-C motif)
5.01E−15
68.6
27.3

receptor 2

104
244353_PM_s_at
154091
SLC2A12
solute carrier family 2
5.72E−15
51.6
100.5

(facilitated glucose

transporter), member 12

105
215049_PM_x_at
9332
CD163
CD163 molecule
6.21E−15
344.5
112.9

106
1552510_PM_at
142680
SLC34A3
solute carrier family 34
6.40E−15
95.6
206.6

(sodium phosphate),

member 3

107
225636_PM_at
6773
STAT2
signal transducer and
6.63E−15
711.5
485.3

activator of transcription 2,

113 kDa

108
229390_PM_at
441168
FAM26F
family with sequence
6.73E−15
272.4
75.9

similarity 26, member F

109
235229_PM_at
—
—
—
6.90E−15
135.0
41.9

110
226218_PM_at
3575
IL7R
interleukin 7 receptor
7.22E−15
147.1
41.4

111
217028_PM_at
7852
CXCR4
chemokine (C-X-C motif)
7.40E−15
208.4
75.3

receptor 4

112
204655_PM_at
6352
CCL5
chemokine (C-C motif)
8.57E−15
223.4
66.8

ligand 5

113
227184_PM_at
5724
PTAFR
platelet-activating factor
8.78E−15
134.4
62.7

receptor

114
202748_PM_at
2634
GBP2
guanylate binding protein 2,
8.91E−15
306.7
141.0

interferon-inducible

115
226991_PM_at
4773
NFATC2
nuclear factor of activated
9.05E−15
66.7
30.3

T-cells, cytoplasmic,

calcineurin-dependent 2

116
216565_PM_x_at
—
—
—
9.49E−15
1224.6
779.1

117
203104_PM_at
1436
CSF1R
colony stimulating factor 1
9.57E−15
42.7
22.1

receptor

118
238668_PM_at
—
—
—
9.84E−15
33.6
14.5

119
204923_PM_at
54440
SASH3
SAM and SH3 domain
9.93E−15
47.6
21.7

containing 3

120
230036_PM_at
219285
SAMD9L
sterile alpha motif domain
1.02E−14
128.0
72.7

containing 9-like

121
211742_PM_s_at
2124
EVI2B
ecotropic viral integration
1.03E−14
166.2
53.2

site 2 B

122
236782_PM_at
154075
SAMD3
sterile alpha motif domain
1.11E−14
23.3
13.3

containing 3

123
232543_PM_x_at
64333
ARHGAP9
Rho GTPase activating
1.13E−14
62.3
25.8

protein 9

124
231124_PM_x_at
4063
LY9
lymphocyte antigen 9
1.18E−14
33.7
15.7

125
215946_PM_x_at
3543 ///
IGLL1 ///
immunoglobulin lambda-
1.22E−14
187.5
52.1

91316
IGLL3P
like polypeptide 1 ///

///
///
immunoglobulin lambda-

91353
LOC91316
like polypeptide 3,

126
208306_PM_x_at
3123
HLA-
Major histocompatibility
1.25E−14
3695.8
2255.0

DRB1
complex, class II, DR beta 1

127
217235_PM_x_at
28816
IGLV2-11
immunoglobulin lambda
1.29E−14
196.8
37.8

variable 2-11

128
209546_PM_s_at
8542
APOL1
apolipoprotein L, 1
1.33E−14
206.8
114.1

129
203416_PM_at
963
CD53
CD53 molecule
1.34E−14
422.7
157.8

130
211366_PM_x_at
834
CASP1
caspase 1, apoptosis-related
1.35E−14
186.0
87.1

cysteine peptidase

(interleukin 1, beta,

convertase)

131
200797_PM_s_at
4170
MCL1
myeloid cell leukemia
1.38E−14
793.7
575.9

sequence 1 (BCL2-related)

132
31845_PM_at
2000
ELF4
E74-like factor 4 (ets
1.40E−14
60.7
34.3

domain transcription factor)

133
221841_PM_s_at
9314
KLF4
Kruppel-like factor 4 (gut)
1.48E−14
132.3
65.2

134
229391_PM_s_at
441168
FAM26F
family with sequence
1.49E−14
212.1
73.7

similarity 26, member F

135
203645_PM_s_at
9332
CD163
CD163 molecule
1.51E−14
274.9
85.0

136
211643_PM_x_at
100510
IGK@ ///
immunoglobulin kappa
1.61E−14
131.1
32.6

044 ///
IGKC ///
locus /// immunoglobulin

28875
IGKV3D-
kappa constant ///

/// 3514
15 ///
immunoglobulin kappa v

///
LOC100510044

50802

137
205488_PM_at
3001
GZMA
granzyme A (granzyme 1,
1.82E−14
102.3
33.4

cytotoxic T-lymphocyte-

associated serine esterase 3)

138
201464_PM_x_at
3725
JUN
jun proto-oncogene
1.90E−14
424.7
244.5

139
204774_PM_at
2123
EVI2A
ecotropic viral integration
1.95E−14
114.7
44.9

site 2 A

140
204336_PM_s_at
10287
RGS19
regulator of G-protein
2.01E−14
135.7
67.0

signaling 19

141
244654_PM_at
64005
MYO1G
myosin IG
2.03E−14
26.8
14.9

142
228442_PM_at
4773
NFATC2
nuclear factor of activated
2.06E−14
62.8
32.0

T-cells, cytoplasmic,

calcineurin-dependent 2

143
206804_PM_at
917
CD3G
CD3g molecule, gamma
2.18E−14
36.4
17.3

(CD3-TCR complex)

144
201315_PM_x_at
10581
IFITM2
interferon induced
2.21E−14
3303.7
2175.3

transmembrane protein 2

(1-8 D)

145
203561_PM_at
2212
FCGR2A
Fc fragment of IgG, low
2.22E−14
66.4
29.2

affinity IIa, receptor (CD32)

146
219117_PM_s_at
51303
FKBP11
FK506 binding protein 11,
2.31E−14
341.3
192.9

19 kDa

147
242827_PM_x_at
—
—
—
2.37E−14
38.9
16.3

148
214768_PM_x_at
28299
IGK@ ///
immunoglobulin kappa
2.38E−14
116.7
21.1

/// 3514
IGKC ///
locus /// immunoglobulin

///
IGKV1-5
kappa constant ///

50802

immunoglobulin kappa v

149
227253_PM_at
1356
CP
ceruloplasmin (ferroxidase)
2.49E−14
44.7
22.0

150
209619_PM_at
972
CD74
CD74 molecule, major
2.51E−14
1502.3
864.9

histocompatibility complex,

class II invariant chain

151
208966_PM_x_at
3428
IFI16
interferon, gamma-inducible
2.65E−14
644.9
312.6

protein 16

152
239237_PM_at
—
—
—
2.79E−14
25.3
14.5

153
213566_PM_at
6039
RNASE6
ribonuclease, RNase A
2.82E−14
341.1
134.3

family, k6

154
201288_PM_at
397
ARHGDIB
Rho GDP dissociation
2.86E−14
542.2
308.1

inhibitor (GDI) beta

155
209606_PM_at
9595
CYTIP
cytohesin 1 interacting
2.90E−14
79.0
32.9

protein

156
205758_PM_at
925
CD8A
CD8a molecule
2.91E−14
60.3
22.2

157
202953_PM_at
713
C1QB
complement component 1, q
3.00E−14
401.1
142.5

subcomponent, B chain

158
203233_PM_at
3566
IL4R
interleukin 4 receptor
3.06E−14
116.7
72.0

159
205270_PM_s_at
3937
LCP2
lymphocyte cytosolic
3.12E−14
104.4
44.6

protein 2 (SH2 domain

containing leukocyte protein

of 76 kDa)

160
223658_PM_at
9424
KCNK6
potassium channel,
3.18E−14
35.9
22.0

subfamily K, member 6

161
202637_PM_s_at
3383
ICAM1
intercellular adhesion
3.18E−14
89.1
45.7

molecule 1

162
202935_PM_s_at
6662
SOX9
SRY (sex determining
3.18E−14
117.0
46.1

region Y)-box 9

163
217986_PM_s_at
11177
BAZ1A
bromodomain adjacent to
3.21E−14
116.4
62.5

zinc finger domain, 1 A

164
210915_PM_x_at
28638
TRBC2
T cell receptor beta constant
3.27E−14
129.7
37.5

2

165
223343_PM_at
58475
MS4A7
membrane-spanning 4-
3.38E−14
346.0
128.3

domains, subfamily A,

member 7

166
1552701_PM_a_at
114769
CARD16
caspase recruitment domain
3.60E−14
273.3
119.8

family, member 16

167
226659_PM_at
50619
DEF6
differentially expressed in
3.63E−14
35.2
22.2

FDCP 6 homolog (mouse)

168
213502_PM_x_at
91316
LOC91316
glucuronidase,
3.63E−14
1214.7
419.3

beta/immunoglobulin

lambda-like polypeptide 1

pseudogene

169
219332_PM_at
79778
MICALL2
MICAL-like 2
3.71E−14
68.7
44.4

170
204891_PM_s_at
3932
LCK
lymphocyte-specific protein
3.74E−14
43.4
17.8

tyrosine kinase

171
224252_PM_s_at
53827
FXYD5
FXYD domain containing
3.76E−14
73.8
32.5

ion transport regulator 5

172
242878_PM_at
—
—
—
3.90E−14
53.2
30.1

173
224709_PM_s_at
56990
CDC42SE2
CDC42 small effector 2
4.07E−14
1266.2
935.7

174
40420_PM_at
6793
STK10
serine/threonine kinase 10
4.32E−14
42.0
24.4

175
218084_PM_x_at
53827
FXYD5
FXYD domain containing
4.52E−14
89.2
39.1

ion transport regulator 5

176
218232_PM_at
712
C1QA
complement component 1, q
4.63E−14
197.0
85.8

subcomponent, A chain

177
202208_PM_s_at
10123
ARL4C
ADP-ribosylation factor-
4.63E−14
77.0
42.2

like 4 C

178
220146_PM_at
51284
TLR7
toll-like receptor 7
4.93E−14
31.6
17.8

179
228752_PM_at
84766
EFCAB4B
EF-hand calcium binding
5.05E−14
20.6
12.1

domain 4 B

180
208948_PM_s_at
6780
STAU1
staufen, RNA binding
5.23E−14
1766.0
2467.4

protein, homolog 1

(Drosophila)

181
211645_PM_x_at
—
—
—
5.24E−14
166.7
27.4

182
236295_PM_s_at
197358
NLRC3
NLR family, CARD domain
5.28E−14
37.0
18.6

containing 3

183
224927_PM_at
170954
KIAA1949
KIAA1949
5.44E−14
160.2
74.7

184
225258_PM_at
54751
FBLIM1
filamin binding LIM
6.03E−14
228.7
125.4

protein 1

185
202898_PM_at
9672
SDC3
syndecan 3
6.07E−14
64.8
32.0

186
218789_PM_s_at
54494
C11orf71
chromosome 11 open
6.12E−14
175.8
280.8

reading frame 71

187
204912_PM_at
3587
IL10RA
interleukin 10 receptor,
6.25E−14
117.2
46.1

alpha

188
211582_PM_x_at
7940
LST1
leukocyte specific
6.48E−14
121.2
49.4

transcript 1

189
214617_PM_at
5551
PRF1
perforin 1 (pore forming
6.77E−14
85.6
40.8

protein)

190
231887_PM_s_at
27143
KIAA1274
KIAA1274
7.00E−14
45.6
30.0

191
223773_PM_s_at
85028
SNHG12
small nucleolar RNA host
7.00E−14
174.8
93.2

gene 12 (non-protein

coding)

192
202644_PM_s_at
7128
TNFAIP3
tumor necrosis factor, alpha-
7.11E−14
278.2
136.6

induced protein 3

193
211796_PM_s_at
28638
TRBC1 ///
T cell receptor beta constant
7.13E−14
250.2
63.6

///
TRBC2
1 /// T cell receptor beta

28639

constant 2

194
206254_PM_at
1950
EGF
epidermal growth factor
7.38E−14
176.6
551.3

195
216207_PM_x_at
28299
IGKC ///
immunoglobulin kappa
7.51E−14
266.3
50.9

///
IGKV1-5
constant /// immunoglobulin

28904
///
kappa variable 1-5 ///

/// 3514
IGKV1D-8
immunoglobulin

///
////

652493
LOC652493

///
///

652694
LOC652694

196
232311_PM_at
567
B2M
Beta-2-microglobulin
7.73E−14
83.7
35.2

197
205466_PM_s_at
9957
HS3ST1
heparan sulfate
7.84E−14
96.0
42.1

(glucosamine) 3-O-

sulfotransferase 1

198
203332_PM_s_at
3635
INPP5D
inositol polyphosphate-5-
7.89E−14
42.6
24.0

phosphatase, 145 kDa

199
64064_PM_at
55340
GIMAP5
GTPase, IMAP family
7.98E−14
170.6
111.4

member 5

200
211644_PM_x_at
3514 ///
IGK@ ///
immunoglobulin kappa
8.04E−14
246.9
47.5

50802
IGKC
locus /// immunoglobulin

kappa constant

TABLE 15

Biopsy Expression Profiling of Kidney Transplants:

2-Way Classifier CAN vs. TX (Brazilian Samples)

Validation Cohort

Postive
Negative

Predictive

Predictive
Predictive

Accuracy
Sensitivity
Specificity
Value
Value

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
(%)
(%)

Nearest
200
AR vs. TX
0.954
95
95
96
95
96

Centroid

Example 4

Expression Signatures to Distinguish Liver Transplant Injuries

Biomarker profiles diagnostic of specific types of graft injury post-liver transplantation (LT), such as acute rejection (AR), hepatitis C virus recurrence (HCV-R), and other causes (acute dysfunction no rejection/recurrence; ADNR) could enhance the diagnosis and management of recipients. Our aim was to identify diagnostic genomic (mRNA) signatures of these clinical phenotypes in the peripheral blood and allograft tissue.

Patient Populations: The study population consisted of 114 biopsy-documented Liver PAXgene whole blood samples comprised of 5 different phenotypes: AR (n=25), ADNR (n=16), HCV(n=36), HCV+AR (n=13), and TX (n=24).

Gene Expression Profiling and Analysis: All samples were processed on the Affymetrix HG-U133 PM only peg microarrays. To eliminate low expressed signals we used a signal filter cut-off that was data dependent, and therefore expression signals <Log 2 4.23 (median signals on all arrays) in all samples were eliminated leaving us with 48882 probe sets from a total of 54721 probe sets. The first comparison performed was a 3-way ANOVA analysis of AR vs. ADNR vs. TX. This yielded 263 differentially expressed probesets at a False Discovery rate (FDR <10%). We used these 263 probesets to build predictive models that could differentiate the three classes. We used the Nearest Centroid (NC) algorithm to build the predictive models. We ran the predictive models using two different methodologies and calculated the Area Under the Curve (AUC). First we did a one-level cross validation, where the data is first divided into 10 random partitions. At each iteration, 1/10 of the data is held out for testing while the remaining 9/10 of the data is used to fit the parameters of the model. This can be used to obtain an estimate of prediction accuracy for a single model. Then we modeled an algorithm for estimating the optimism, or over-fitting, in predictive models based on using bootstrapped datasets to repeatedly quantify the degree of over-fitting in the model building process using sampling with replacement. This optimism corrected AUC value is a nearly unbiased estimate of the expected values of the optimism that would be obtained in external validation (we used 1000 randomly created data sets). Table 16a shows the optimism corrected AUCs for the 263 probesets that were used to predict the accuracies for distinguishing between AR, ADNR and TX in Liver PAXgene samples. Table 16b shows the 263 probesets used for distinguishing between AR, ADNR and TX in Liver PAXgene samples.

It is clear from the above Table 16a that the 263 probeset classifier was able to distinguish the three phenotypes with very high predictive accuracy. The NC classifier had a sensitivity of 83%, specificity of 93%, and positive predictive value of 95% and a negative predictive value of 78% for the AR vs. ADNR comparison. It is important to note that these values did not change after the optimism correction where we simulated 1000 data sets showing that these are really robust signatures.

The next comparison we performed was a 3-way ANOVA of AR vs. HCV vs. HCV+AR which yielded 147 differentially expressed probesets at a p value <0.001. We chose to use this set of predictors because at an FDR <10% we had only 18 predictors, which could possibly be due to the smaller sample size of the HCV+AR (n=13) or a smaller set of differentially expressed genes in one of the phenotypes. However, since this was a discovery set to test the proof of principle whether there were signatures that could distinguish samples that had an admixture of HCV and AR from the pure AR and the pure HCV populations, we ran the predictive algorithms on the 147 predictors. Table 17a shows the AUCs for the 147 probesets that were used to predict the accuracies for distinguishing between AR, HCV and HCV+AR in Liver PAXgene samples. Table 17b shows the 147 probesets used for distinguishing between AR, HCV and HCV+AR in Liver PAXgene samples.

The NC classifier had a sensitivity of 87%, specificity of 97%, and positive predictive value of 95% and a negative predictive value of 92% for the AR vs HCV comparison using the optimism correction where we simulated 1000 data sets giving us confidence that the simulations that were done to mimic a real clinical situation did not alter the robustness of this set of predictors.

For the biopsies, again, we performed a 3-way ANOVA of AR vs. HCV vs. HCV+AR that yielded 320 differentially expressed probesets at an FDR <10%. We specifically did this because at a p-value <0.001 there were over 950 probesets. We ran the predictive models on this set of classifiers in the same way mentioned for the PAXgene samples. Table 18a shows the AUCs for the one-level cross validation and the optimism correction for the classifier set comprised of 320 probesets that were used to predict the accuracies for distinguishing between AR, HCV and HCV+AR in Liver biopsies. Table 18b shows the 320 probesets that used for distinguishing AR vs. HCV vs. HCV+AR in Liver biopsies.

In summary, for both the blood and the biopsy samples from liver transplant subjects we have classifier sets that can distinguish AR, HCV and HCV+AR with AUCs between 0.79-0.83 in blood and 0.69-0.83 in the biopsies. We also have a signature from whole blood that can distinguish AR, ADNR and TX samples with AUC's ranging from 0.87-0.92.

TABLE 16a

AUCs for the 263 probes to predict AR, ADNR and TX in Liver whole blood samples.

Postive
Negative

Predictive

Predictive
Predictive

Accuracy
Sensitivity
Specificity
Value
Value

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
(%)
(%)

Nearest
263
AR vs. ADNR
0.882
88
83
93
95
78

Centroid

Nearest
263
AR vs. TX
0.943
95
95
95
95
95

Centroid

Nearest
263
ADNR vs. TX
0.883
88
93
83
78
95

Centroid

TABLE 16b

The 263 probesets for distinguishing between AR, ADNR and TX in Liver PAXgene samples

p-value
ADNR -
AR -
TX -

#
Probeset ID
Gene Symbol
Gene Title
(Phenotype)
Mean
Mean
Mean

1
215415_PM_s_at
LYST
lysosomal trafficking
3.79E−07
32.3
25.8
43.6

regulator

2
241038_PM_at
—
—
4.79E−07
16.1
21.0
16.4

3
230776_PM_at
—
—
2.10E−06
10.4
13.7
10.2

4
212805_PM_at
PRUNE2
prune homolog 2
4.09E−06
15.8
15.2
33.9

(Drosophila)

5
215090_PM_x_at
LOC440434
aminopeptidase puromycin
7.28E−06
164.6
141.0
208.0

sensitive pseudogene

6
243625_PM_at
—
—
7.64E−06
31.2
20.8
29.9

7
232222_PM_at
C18orf49
chromosome 18 open
8.85E−06
33.7
35.7
42.4

reading frame 49

8
235341_PM_at
DNAJC3
DnaJ (Hsp40) homolog,
1.06E−05
21.8
22.1
35.0

subfamily C, member 3

9
1557733_PM_a_at
—
—
1.21E−05
83.8
116.0
81.2

10
212906_PM_at
GRAMD1B
GRAM domain containing
1.26E−05
52.7
51.0
45.7

1B

11
1555874_PM_x_at
MGC21881
hypothetical locus
1.53E−05
20.5
20.0
19.3

MGC21881

12
227645_PM_at
PIK3R5
phosphoinositide-3-kinase,
1.66E−05
948.4
824.5
1013.0

regulatory subunit 5

13
235744_PM_at
PPTC7
PTC7 protein phosphatase
1.73E−05
21.3
18.0
25.7

homolog (S. cerevisiae)

14
1553873_PM_at
KLHL34
kelch-like 34 (Drosophila)
1.89E−05
11.1
12.1
9.9

15
218408_PM_at
TIMM10
translocase of inner
2.16E−05
125.9
137.7
99.4

mitochondrial membrane 10

homolog (yeast)

16
227486_PM_at
NT5E
5′-nucleotidase, ecto (CD73)
2.46E−05
14.7
18.6
15.6

17
231798_PM_at
NOG
noggin
2.49E−05
17.0
25.9
15.1

18
205920_PM_at
SLC6A6
solute carrier family 6
2.53E−05
25.9
25.0
39.3

(neurotransmitter

transporter, taurine),

member 6

19
222435_PM_s_at
UBE2J1
ubiquitin-conjugating
2.63E−05
212.6
292.4
324.0

enzyme E2, J1 (UBC6

homolog, yeast)

20
207737_PM_at
—
—
2.89E−05
8.2
8.5
8.6

21
209644_PM_x_at
CDKN2A
cyclin-dependent kinase
2.91E−05
13.7
13.9
11.5

inhibitor 2A (melanoma,

p16, inhibits CDK4)

22
241661_PM_at
JMJD1C
jumonji domain containing
2.99E−05
18.4
21.9
34.8

1C

23
202086_PM_at
MX1
myxovirus (influenza virus)
3.04E−05
562.6
496.4
643.9

resistance 1, interferon-

inducible protein p78

(mouse)

24
243819_PM_at
—
—
3.11E−05
766.7
495.1
661.8

25
210524_PM_x_at
—
—
3.12E−05
154.5
209.2
138.6

26
217714_PM_x_at
STMN1
stathmin 1
3.39E−05
22.3
28.5
20.4

27
219659_PM_at
ATP8A2
ATPase, aminophospholipid
3.65E−05
10.4
10.8
9.8

transporter, class I, type 8A,

member 2

28
219915_PM_s_at
SLC16A10
solute carrier family 16,
3.70E−05
19.4
21.8
15.8

member 10 (aromatic amino

acid transporter)

29
214039_PM_s_at
LAPTM4B
lysosomal protein
3.81E−05
70.4
104.0
74.2

transmembrane 4 beta

30
214107_PM_x_at
LOC440434
aminopeptidase puromycin
4.27E−05
182.8
155.0
224.7

sensitive pseudogene

31
225408_PM_at
MBP
myelin basic protein
4.54E−05
34.1
32.6
47.9

32
1552623_PM_at
HSH2D
hematopoietic SH2 domain
4.93E−05
373.7
323.9
401.3

containing

33
206974_PM_at
CXCR6
chemokine (C-X-C motif)
5.33E−05
24.6
31.0
22.9

receptor 6

34
203764_PM_at
DLGAP5
discs, large (Drosophila)
5.41E−05
9.3
10.9
8.6

homolog-associated protein

5

35
213915_PM_at
NKG7
natural killer cell group 7
5.73E−05
2603.1
1807.7
1663.1

sequence

36
1570597_PM_at
—
—
5.86E−05
8.3
7.8
7.5

37
228290_PM_at
PLK1S1
Polo-like kinase 1 substrate
6.00E−05
47.2
35.6
45.8

1

38
230753_PM_at
PATL2
protein associated with
6.11E−05
169.0
123.0
131.6

topoisomerase II homolog 2

(yeast)

39
202016_PM_at
MEST
mesoderm specific
6.25E−05
18.3
27.5
17.3

transcript homolog (mouse)

40
212730_PM_at
SYNM
synemin, intermediate
6.30E−05
16.7
19.5
14.4

filament protein

41
209203_PM_s_at
BICD2
bicaudal D homolog 2
6.50E−05
197.8
177.0
256.6

(Drosophila)

42
1554397_PM_s_at
UEVLD
UEV and lactate/malate
6.59E−05
20.8
17.7
25.2

dehyrogenase domains

43
217963_PM_s_at
NGFRAP1
nerve growth factor receptor
7.61E−05
505.9
713.1
555.7

(TNFRSF16) associated

protein 1

44
201656_PM_at
ITGA6
integrin, alpha 6
7.75E−05
87.4
112.6
84.1

45
1553685_PM_s_at
SP1
Sp1 transcription factor
7.83E−05
27.4
27.3
41.3

46
236717_PM_at
FAM179A
family with sequence
8.00E−05
55.1
39.8
42.1

similarity 179, member A

47
240913_PM_at
FGFR2
fibroblast growth factor
8.33E−05
9.2
9.6
10.2

receptor 2

48
243756_PM_at
—
—
8.47E−05
7.9
8.5
7.4

49
222036_PM_s_at
MCM4
minichromosome
8.52E−05
29.5
35.1
25.4

maintenance complex

component 4

50
202644_PM_s_at
TNFAIP3
tumor necrosis factor, alpha-
8.57E−05
516.0
564.5
475.8

induced protein 3

51
229625_PM_at
GBP5
guanylate binding protein 5
9.23E−05
801.9
1014.7
680.8

52
235545_PM_at
DEPDC1
DEP domain containing 1
9.83E−05
8.0
8.7
8.3

53
204641_PM_at
NEK2
NIMA (never in mitosis
0.000100269
10.2
12.5
10.0

gene a)-related kinase 2

54
213931_PM_at
ID2 /// ID2B
inhibitor of DNA binding 2,
0.000101645
562.9
504.9
384.6

dominant negative helix-

loop-helix protein ///

inhibitor of

55
216125_PM_s_at
RANBP9
RAN binding protein 9
0.000102366
35.4
37.0
50.3

56
205660_PM_at
OASL
2′-5′-oligoadenylate
0.000102776
470.5
394.6
493.4

synthetase-like

57
222816_PM_s_at
ZCCHC2
zinc finger, CCHC domain
0.000105861
301.3
308.7
320.8

containing 2

58
1554696_PM_s_at
TYMS
thymidylate synthetase
0.000110478
11.1
16.2
11.2

59
232229_PM_at
SETX
senataxin
0.000113076
44.2
34.5
48.7

60
204929_PM_s_at
VAMP5
vesicle-associated
0.000113182
152.8
197.8
153.6

membrane protein 5

(myobrevin)

61
203819_PM_s_at
IGF2BP3
insulin-like growth factor 2
0.000113349
45.4
75.4
51.1

mRNA binding protein 3

62
210164_PM_at
GZMB
granzyme B (granzyme 2,
0.000113466
955.2
749.5
797.1

cytotoxic T-lymphocyte-

associated serine esterase 1)

63
202589_PM_at
TYMS
thymidylate synthetase
0.000113758
50.0
85.8
44.4

64
240507_PM_at
—
—
0.000116854
8.8
8.4
8.2

65
204475_PM_at
MMP1
matrix metallopeptidase 1
0.000116902
9.2
15.4
9.6

(interstitial collagenase)

66
222625_PM_s_at
NDE1
nudE nuclear distribution
0.000119388
60.6
55.3
72.2

gene E homolog 1

(A. nidulans)

67
1562697_PM_at
LOC339988
hypothetical LOC339988
0.000125343
145.2
97.8
105.4

68
218662_PM_s_at
NCAPG
non-SMC condensin I
0.000129807
11.5
14.8
10.7

complex, subunit G

69
201212_PM_at
LGMN
legumain
0.000129933
15.4
18.9
14.2

70
236191_PM_at
—
—
0.000133129
83.4
71.0
76.6

71
33736_PM_at
STOML1
stomatin (EPB72)-like 1
0.000137232
44.9
47.9
37.4

72
221695_PM_s_at
MAP3K2
mitogen-activated protein
0.000139287
76.4
76.8
130.8

kinase kinase kinase 2

73
241692_PM_at
—
—
0.000142595
57.5
44.8
61.8

74
218741_PM_at
CENPM
centromere protein M
0.000142617
13.5
15.9
12.3

75
220684_PM_at
TBX21
T-box 21
0.00014693
272.6
169.0
182.2

76
233700_PM_at
—
—
0.000148072
125.7
74.1
156.3

77
217336_PM_at
RPS10 ///
ribosomal protein S10 ///
0.000149318
76.4
93.5
63.0

RPS10P7
ribosomal protein S10

pseudogene 7

78
224391_PM_s_at
SIAE
sialic acid acetylesterase
0.000152602
28.8
42.0
33.8

79
201220_PM_x_at
CTBP2
C-terminal binding protein 2
0.000155512
1316.8
1225.6
1516.2

80
204589_PM_at
NUAK1
NUAK family, SNF1-like
0.00015593
13.1
10.1
9.6

kinase, 1

81
1565254_PM_s_at
ELL
elongation factor RNA
0.000157726
29.2
24.5
40.4

polymerase II

82
243362_PM_s_at
LOC641518
hypothetical LOC641518
0.000159096
14.3
21.1
13.5

83
219288_PM_at
C3orf14
chromosome 3 open reading
0.000162164
31.1
43.4
28.0

frame 14

84
210797_PM_s_at
OASL
2′-5′-oligoadenylate
0.000167239
268.3
219.6
304.2

synthetase-like

85
243917_PM_at
CLIC5
chloride intracellular
0.00017077
10.9
9.6
10.5

channel 5

86
237538_PM_at
—
—
0.000176359
18.4
21.3
18.0

87
207926_PM_at
GP5
glycoprotein V (platelet)
0.000178057
17.3
19.3
15.7

88
204103_PM_at
CCL4
chemokine (C-C motif)
0.000178791
338.5
265.9
235.5

ligand 4

89
212843_PM_at
NCAM1
neural cell adhesion
0.000180762
28.7
25.8
33.5

molecule 1

90
213629_PM_x_at
MT1F
metallothionein 1F
0.000186273
268.3
348.4
234.3

91
212687_PM_at
LIMS1
LIM and senescent cell
0.000188224
859.6
1115.2
837.3

antigen-like domains 1

92
242898_PM_at
EIF2AK2
eukaryotic translation
0.000189906
82.5
66.4
81.2

initiation factor 2-alpha

kinase 2

93
208228_PM_s_at
FGFR2
fibroblast growth factor
0.000194281
8.9
11.1
8.7

receptor 2

94
219386_PM_s_at
SLAMF8
SLAM family member 8
0.000195762
18.6
23.0
16.5

95
201470_PM_at
GSTO1
glutathione S-transferase
0.000200503
1623.3
1902.3
1495.5

omega 1

96
204326_PM_x_at
MT1X
metallothionein 1X
0.000202494
370.5
471.8
313.0

97
213996_PM_at
YPEL1
yippee-like 1 (Drosophila)
0.00020959
48.9
37.9
40.4

98
203820_PM_s_at
IGF2BP3
insulin-like growth factor 2
0.000210022
21.8
35.5
23.2

mRNA binding protein 3

99
218599_PM_at
REC8
REC8 homolog (yeast)
0.000216761
42.6
43.3
41.1

100
216836_PM_s_at
ERBB2
v-erb-b2 erythroblastic
0.000217714
14.6
12.0
12.9

leukemia viral oncogene

homolog 2,

neuro/glioblastoma derived

o

101
213258_PM_at
TFPI
tissue factor pathway
0.000218458
13.6
24.6
14.2

inhibitor (lipoprotein-

associated coagulation

inhibitor)

102
212859_PM_x_at
MT1E
metallothionein 1E
0.000218994
166.9
238.1
134.5

103
214617_PM_at
PRF1
perforin 1 (pore forming
0.000222846
1169.2
822.3
896.0

protein)

104
38918_PM_at
SOX13
SRY (sex determining
0.000223958
14.1
10.9
11.8

region Y)-box 13

105
209969_PM_s_at
STAT1
signal transducer and
0.00022534
1707.4
1874.3
1574.4

activator of transcription 1,

91 kDa

106
205909_PM_at
POLE2
polymerase (DNA directed),
0.000226803
14.0
16.0
12.7

epsilon 2 (p59 subunit)

107
205612_PM_at
MMRN1
multimerin 1
0.000227425
10.3
15.5
11.1

108
218400_PM_at
OAS3
2′-5′-oligoadenylate
0.000231476
142.6
125.9
170.8

synthetase 3, 100 kDa

109
202503_PM_s_at
KIAA0101
KIAA0101
0.00023183
34.4
65.8
25.5

110
225636_PM_at
STAT2
signal transducer and
0.000234463
1425.0
1422.9
1335.1

activator of transcription 2,

113 kDa

111
226579_PM_at
—
—
0.000234844
97.7
81.1
104.6

112
1555764_PM_s_at
TIMM10
translocase of inner
0.000235756
195.6
204.3
158.7

mitochondrial membrane 10

homolog (yeast)

113
218429_PM_s_at
C19orf66
chromosome 19 open
0.00024094
569.9
524.1
527.4

reading frame 66

114
242155_PM_x_at
RFFL
ring finger and FYVE-like
0.000244391
62.8
46.7
72.0

domain containing 1

115
1556643_PM_at
FAM125A
Family with sequence
0.000244814
173.2
181.8
181.2

similarity 125, member A

116
201957_PM_at
PPP1R12B
protein phosphatase 1,
0.000246874
93.3
63.9
107.9

regulatory (inhibitor)

subunit 12B

117
219716_PM_at
APOL6
apolipoprotein L, 6
0.000248621
86.0
95.2
79.1

118
1554206_PM_at
TMLHE
trimethyllysine hydroxylase,
0.00026882
45.3
41.0
53.4

epsilon

119
207795_PM_s_at
KLRD1
killer cell lectin-like
0.000271145
294.6
201.8
192.5

receptor subfamily D,

member 1

120
210756_PM_s_at
NOTCH2
notch 2
0.000271193
94.0
99.4
142.6

121
219815_PM_at
GAL3ST4
galactose-3-O-
0.00027183
17.3
19.9
16.4

sulfotransferase 4

122
230405_PM_at
C5orf56
chromosome 5 open reading
0.000279441
569.5
563.2
521.9

frame 56

123
228617_PM_at
XAF1
XIAP associated factor 1
0.000279625
1098.8
1162.1
1043.0

124
240733_PM_at
—
—
0.000281133
87.3
54.9
81.2

125
209773_PM_s_at
RRM2
ribonucleotide reductase M2
0.000281144
48.7
88.2
40.4

126
215236_PM_s_at
PICALM
phosphatidylinositol binding
0.000284863
61.6
65.8
113.8

clathrin assembly protein

127
229534_PM_at
ACOT4
acyl-CoA thioesterase 4
0.000286097
17.1
13.2
12.6

128
215177_PM_s_at
ITGA6
integrin, alpha 6
0.000287492
35.2
44.2
34.0

129
210321_PM_at
GZMH
granzyme H (cathepsin G-
0.000293732
1168.2
616.6
532.0

like 2, protein h-CCPX)

130
206194_PM_at
HOXC4
homeobox C4
0.000307767
20.0
17.1
15.1

131
214115_PM_at
VAMP5
Vesicle-associated
0.000308837
11.8
13.2
12.2

membrane protein 5

(myobrevin)

132
211102_PM_s_at
LILRA2
leukocyte immunoglobulin-
0.000310388
94.3
78.0
129.0

like receptor, subfamily A

(with TM domain), member

2

133
201818_PM_at
LPCAT1
lysophosphatidylcholine
0.000311597
662.1
517.3
651.3

acyltransferase 1

134
53720_PM_at
C19orf66
chromosome 19 open
0.000311821
358.7
323.7
319.7

reading frame 66

135
221648_PM_s_at
LOC100507192
hypothetical
0.000312201
68.4
96.2
56.1

LOC100507192

136
236899_PM_at
—
—
0.000318309
9.8
10.5
8.8

137
220467_PM_at
—
—
0.000319714
205.5
124.9
201.6

138
218638_PM_s_at
SPON2
spondin 2, extracellular
0.000320682
168.2
109.2
137.0

matrix protein

139
211287_PM_x_at
CSF2RA
colony stimulating factor 2
0.00032758
173.0
150.9
224.0

receptor, alpha, low-affinity

(granulocyte-macrophage)

140
222058_PM_at
—
—
0.000332098
82.7
61.0
101.6

141
224428_PM_s_at
CDCA7
cell division cycle
0.000332781
22.9
31.5
19.6

associated 7

142
228675_PM_at
LOC100131733
hypothetical
0.000346627
15.2
17.6
14.5

LOC100131733

143
221248_PM_s_at
WHSC1L1
Wolf-Hirschhorn syndrome
0.000354663
25.6
26.9
33.0

candidate 1-like 1

144
227697_PM_at
SOCS3
suppressor of cytokine
0.000354764
103.6
192.4
128.8

signaling 3

145
240661_PM_at
LOC284475
hypothetical protein
0.000355764
79.3
53.9
89.5

LOC284475

146
204886_PM_at
PLK4
polo-like kinase 4
0.000357085
8.9
11.8
8.9

147
216834_PM_at
RGS1
regulator of G-protein
0.00035762
12.4
19.6
11.4

signaling 1

148
234089_PM_at
—
—
0.000359586
10.5
10.1
11.2

149
236817_PM_at
ADAT2
adenosine deaminase,
0.000362076
15.6
14.3
12.0

tRNA-specific 2, TAD2

homolog (S. cerevisiae)

150
225349_PM_at
ZNF496
zinc finger protein 496
0.000363116
11.7
12.0
10.4

151
219863_PM_at
HERC5
hect domain and RLD 5
0.000365254
621.1
630.8
687.7

152
221985_PM_at
KLHL24
kelch-like 24 (Drosophila)
0.000374117
183.6
184.7
216.9

153
1552977_PM_a_at
CNPY3
canopy 3 homolog
0.000378983
351.3
319.3
381.7

(zebrafish)

154
1552667_PM_a_at
SH2D3C
SH2 domain containing 3C
0.000380655
67.1
55.5
82.8

155
223502_PM_s_at
TNFSF13B
tumor necrosis factor
0.000387301
2713.6
3366.3
2999.3

(ligand) superfamily,

member 13b

156
235139_PM_at
GNGT2
guanine nucleotide binding
0.000389019
41.8
35.8
38.6

protein (G protein), gamma

transducing activity

polypeptide

157
239979_PM_at
—
—
0.000389245
361.6
375.0
282.8

158
211882_PM_x_at
FUT6
fucosyltransferase 6 (alpha
0.000392613
11.1
11.6
10.6

(1,3) fucosyltransferase)

159
1562698_PM_x_at
LOC339988
hypothetical LOC339988
0.000394736
156.3
108.5
117.0

160
201890_PM_at
RRM2
ribonucleotide reductase M2
0.000397796
23.6
42.5
21.7

161
243349_PM_at
KIAA1324
KIAA1324
0.000399335
15.4
12.8
20.2

162
243947_PM_s_at
—
—
0.000399873
8.4
9.6
8.9

163
205483_PM_s_at
ISG15
ISG15 ubiquitin-like
0.000409282
1223.6
1139.6
1175.7

modifier

164
202705_PM_at
CCNB2
cyclin B2
0.000409541
14.7
20.9
13.8

165
210835_PM_s_at
CTBP2
C-terminal binding protein 2
0.000419387
992.3
926.1
1150.4

166
210554_PM_s_at
CTBP2
C-terminal binding protein 2
0.000429433
1296.5
1198.0
1519.5

167
207085_PM_x_at
CSF2RA
colony stimulating factor 2
0.000439275
204.5
190.0
290.3

receptor, alpha, low-affinity

(granulocyte-macrophage)

168
204205_PM_at
APOBEC3G
apolipoprotein B mRNA
0.000443208
1115.8
988.8
941.4

editing enzyme, catalytic

polypeptide-like 3G

169
227394_PM_at
NCAM1
neural cell adhesion
0.000443447
19.1
19.4
25.3

molecule 1

170
1568943_PM_at
INPP5D
inositol polyphosphate-5-
0.000450045
127.3
87.7
114.0

phosphatase, 145 kDa

171
213932_PM_x_at
HLA-A
major histocompatibility
0.00045661
9270.0
9080.1
9711.9

complex, class I, A

172
226202_PM_at
ZNF398
zinc finger protein 398
0.000457538
84.5
78.4
98.3

173
233675_PM_s_at
LOC374491
TPTE and PTEN
0.000457898
8.8
8.1
8.5

homologous inositol lipid

phosphatase pseudogene

174
220711_PM_at
—
—
0.000458552
197.6
162.7
209.0

175
1552646_PM_at
IL11RA
interleukin 11 receptor,
0.000463237
18.9
15.9
19.6

alpha

176
227055_PM_at
METTL7B
methyltransferase like 7B
0.000464226
11.1
15.0
11.8

177
223980_PM_s_at
SP110
SP110 nuclear body protein
0.000471467
1330.9
1224.3
1367.3

178
242367_PM_at
—
—
0.000471796
9.1
10.5
9.6

179
218543_PM_s_at
PARP12
poly (ADP-ribose)
0.000476879
513.8
485.7
475.7

polymerase family, member

12

180
204972_PM_at
OAS2
2′-5′-oligoadenylate
0.000480934
228.5
215.8
218.7

synthetase 2, 69/71 kDa

181
205746_PM_s_at
ADAM17
ADAM metallopeptidase
0.000480965
39.0
47.0
60.4

domain 17

182
1570645_PM_at
—
—
0.000482948
9.3
9.1
8.4

183
211286_PM_x_at
CSF2RA
colony stimulating factor 2
0.000484313
261.3
244.7
345.6

receptor, alpha, low-affinity

(granulocyte-macrophage)

184
1557545_PM_s_at
RNF165
ring finger protein 165
0.000489377
17.4
15.4
18.3

185
236545_PM_at
—
—
0.000491065
479.3
367.8
526.2

186
228280_PM_at
ZC3HAV1L
zinc finger CCCH-type,
0.000495768
25.3
36.4
23.7

antiviral 1-like

187
239798_PM_at
—
—
0.000505865
43.9
63.7
48.8

188
208055_PM_s_at
HERC4
hect domain and RLD 4
0.000507283
37.6
34.8
45.8

189
225692_PM_at
CAMTA1
calmodulin binding
0.000515621
244.8
308.6
245.1

transcription activator 1

190
210986_PM_s_at
TPM1
tropomyosin 1 (alpha)
0.000532739
344.0
379.1
391.9

191
205929_PM_at
GPA33
glycoprotein A33
0.00053619
18.3
21.8
16.7

(transmembrane)

192
242234_PM_at
XAF1
XIAP associated factor 1
0.000537429
123.1
133.1
114.9

193
206113_PM_s_at
RAB5A
RAB5A, member RAS
0.000543933
77.5
73.0
111.4

oncogene family

194
242520_PM_s_at
C1orf228
chromosome 1 open reading
0.000547685
30.4
42.5
29.4

frame 228

195
229203_PM_at
B4GALNT3
beta-1,4-N-acetyl-
0.000549855
9.1
9.0
9.7

galactosaminyl transferase 3

196
201601_PM_x_at
IFITM1
interferon induced
0.000554665
6566.1
7035.7
7016.0

transmembrane protein 1 (9-

27)

197
221024_PM_s_at
SLC2A10
solute carrier family 2
0.000559418
8.3
9.7
8.6

(facilitated glucose

transporter), member 10

198
204439_PM_at
IFI44L
interferon-induced protein
0.000570113
343.5
312.4
337.1

44-like

199
215894_PM_at
PTGDR
prostaglandin D2 receptor
0.000571076
343.8
191.2
233.7

(DP)

200
230846_PM_at
AKAP5
A kinase (PRKA) anchor
0.000572655
10.7
10.9
9.6

protein 5

201
210340_PM_s_at
CSF2RA
colony stimulating factor 2
0.000572912
154.2
146.3
200.8

receptor, alpha, low-affinity

(granulocyte-macrophage)

202
237240_PM_at
—
—
0.000573343
9.4
10.7
9.4

203
223836_PM_at
FGFBP2
fibroblast growth factor
0.000574294
792.6
432.4
438.4

binding protein 2

204
233743_PM_x_at
S1PR5
sphingosine-1-phosphate
0.000577598
9.3
8.6
9.6

receptor 5

205
229254_PM_at
MFSD4
major facilitator superfamily
0.000581119
9.4
11.0
9.3

domain containing 4

206
243674_PM_at
LOC100240735 ///
hypothetical
0.00058123
14.5
12.9
12.1

LOC401522
LOC100240735 ///

hypothetical LOC401522

207
208116_PM_s_at
MAN1A1
mannosidase, alpha, class
0.000581644
34.4
39.1
55.0

1A, member 1

208
222246_PM_at
—
—
0.000584363
15.9
13.9
17.9

209
212659_PM_s_at
IL1RN
interleukin 1 receptor
0.000592065
87.2
94.5
116.3

antagonist

210
204070_PM_at
RARRES3
retinoic acid receptor
0.000597748
771.6
780.7
613.7

responder (tazarotene

induced) 3

211
219364_PM_at
DHX58
DEXH (Asp-Glu-X-His)
0.000599299
92.7
85.2
85.3

box polypeptide 58

212
204747_PM_at
IFIT3
interferon-induced protein
0.000601375
603.1
576.7
586.2

with tetratricopeptide

repeats 3

213
240258_PM_at
ENO1
enolase 1, (alpha)
0.000601726
9.0
9.3
10.5

214
210724_PM_at
EMR3
egf-like module containing,
0.000609884
622.3
437.3
795.3

mucin-like, hormone

receptor-like 3

215
204211_PM_x_at
EIF2AK2
eukaryotic translation
0.000611116
168.3
139.2
179.6

initiation factor 2-alpha

kinase 2

216
234975_PM_at
GSPT1
G1 to S phase transition 1
0.000615027
16.6
16.3
21.4

217
228145_PM_s_at
ZNF398
zinc finger protein 398
0.000620533
373.0
329.5
374.3

218
201565_PM_s_at
ID2
inhibitor of DNA binding 2,
0.000627734
1946.2
1798.1
1652.9

dominant negative helix-

loop-helix protein

219
226906_PM_s_at
ARHGAP9
Rho GTPase activating
0.000630617
636.2
516.2
741.5

protein 9

220
228412_PM_at
LOC643072
hypothetical LOC643072
0.00064178
213.5
186.6
282.7

221
233957_PM_at
—
—
0.000644277
33.2
24.7
40.1

222
221277_PM_s_at
PUS3
pseudouridylate synthase 3
0.000649375
86.6
99.3
77.8

223
203911_PM_at
RAP1GAP
RAP1 GTPase activating
0.000658389
106.6
40.1
116.1

protein

224
219352_PM_at
HERC6
hect domain and RLD 6
0.000659313
94.6
87.2
81.8

225
204994_PM_at
MX2
myxovirus (influenza virus)
0.000663904
1279.3
1147.0
1329.9

resistance 2 (mouse)

226
227499_PM_at
FZD3
frizzled homolog 3
0.00066528
11.7
11.0
9.8

(Drosophila)

227
222930_PM_s_at
AGMAT
agmatine ureohydrolase
0.000665618
12.9
14.9
11.4

(agmatinase)

228
204575_PM_s_at
MMP19
matrix metallopeptidase 19
0.000668161
9.6
9.3
9.9

229
221038_PM_at
—
—
0.000671518
8.7
8.2
9.3

230
233425_PM_at
—
—
0.000676591
76.4
70.6
77.9

231
228972_PM_at
LOC100306951
hypothetical
0.000679857
77.8
84.0
60.0

LOC100306951

232
1560999_PM_a_at
—
—
0.000680202
9.8
10.6
10.7

233
225931_PM_s_at
RNF213
ring finger protein 213
0.000685818
339.7
313.2
333.3

234
1559110_PM_at
—
—
0.000686358
11.7
11.5
13.4

235
207538_PM_at
IL4
interleukin 4
0.000697306
8.3
9.5
8.7

236
210358_PM_x_at
GATA2
GATA binding protein 2
0.000702179
22.8
30.8
16.8

237
236341_PM_at
CTLA4
cytotoxic T-lymphocyte-
0.000706875
16.5
22.3
16.8

associated protein 4

238
227416_PM_s_at
ZCRB1
zinc finger CCHC-type and
0.000708438
388.0
422.6
338.2

RNA binding motif 1

239
210788_PM_s_at
DHRS7
dehydrogenase/reductase
0.000719333
1649.6
1559.9
1912.3

(SDR family) member 7

240
213287_PM_s_at
KRT10
keratin 10
0.000721676
557.8
585.1
439.3

241
204026_PM_s_at
ZWINT
ZW10 interactor
0.000724993
23.3
31.1
19.9

242
239223_PM_s_at
FBXL20
F-box and leucine-rich
0.00073241
106.8
75.0
115.9

repeat protein 20

243
234196_PM_at
—
—
0.000742539
140.6
81.3
162.4

244
214931_PM_s_at
SRPK2
SRSF protein kinase 2
0.00074767
30.0
30.9
45.3

245
216907_PM_x_at
KIR3DL1 ///
killer cell immunoglobulin-
0.000748056
18.8
12.6
13.8

KIR3DL2 ///
like receptor, three domains,

LOC727787
long cytoplasmic tail, 1 /// k

246
243802_PM_at
DNAH12
dynein, axonemal, heavy
0.000751054
8.8
9.9
8.4

chain 12

247
212070_PM_at
GPR56
G protein-coupled receptor
0.000760168
338.8
177.5
198.1

56

248
239185_PM_at
ABCA9
ATP-binding cassette, sub-
0.000767347
8.3
9.0
9.8

family A (ABC1), member

9

249
229597_PM_s_at
WDFY4
WDFY family member 4
0.000769378
128.9
96.6
148.4

250
216243_PM_s_at
IL1RN
interleukin 1 receptor
0.000770819
131.4
134.1
180.7

antagonist

251
206991_PM_s_at
CCR5
chemokine (C-C motif)
0.000771059
128.5
128.6
110.5

receptor 5

252
219385_PM_at
SLAMF8
SLAM family member 8
0.000789607
13.8
13.2
11.3

253
240438_PM_at
—
—
0.000801737
10.8
10.4
11.4

254
226303_PM_at
PGM5
phosphoglucomutase 5
0.000802853
11.9
12.6
24.2

255
205875_PM_s_at
TREX1
three prime repair
0.000804871
254.9
251.6
237.6

exonuclease 1

256
1566201_PM_at
—
—
0.000809569
10.4
9.0
10.2

257
211230_PM_s_at
PIK3CD
phosphoinositide-3-kinase,
0.000812288
20.4
20.3
24.6

catalytic, delta polypeptide

258
202566_PM_s_at
SVIL
supervillin
0.000819718
43.9
41.0
67.5

259
244846_PM_at
—
—
0.000821386
75.0
55.1
84.9

260
208436_PM_s_at
IRF7
interferon regulatory factor
0.000826426
264.0
262.4
281.2

7

261
242020_PM_s_at
ZBP1
Z-DNA binding protein 1
0.000828174
87.9
83.1
102.5

262
203779_PM_s_at
MPZL2
myelin protein zero-like 2
0.000830222
10.4
10.0
12.9

263
212458_PM_at
SPRED2
sprouty-related, EVH1
0.000833211
11.5
11.4
13.4

domain containing 2

TABLE 17a

AUCs for the 147 probes to predict AR, HCV and AR + HCV in Liver whole blood samples.

Postive
Negative

Predictive

Predictive
Predictive

Accuracy
Sensitivity
Specificity
Value
Value

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
(%)
(%)

Nearest
147
AR vs. HCV
0.952
96
87
97
95
92

Centroid

Nearest
147
AR vs. HCV + AR
0.821
82
91
92
95
85

Centroid

Nearest
147
HCV vs. HCV + AR
0.944
94
92
97
92
97

Centroid

TABLE 17b

The 147 probesets for distinguishing between AR, HCV and HCV + AR in Liver PAXgene samples

HCV +

p-value
AR -
HCV -
AR -

#
Probeset ID
Gene Symbol
Gene Title
(Phenotype)
Mean
Mean
Mean

1
241038_PM_at
—
—
4.76E−08
21.0
13.2
13.9

2
207737_PM_at
—
—
5.33E−06
8.5
8.4
10.2

3
1557733_PM_a_at
—
—
6.19E−06
116.0
50.8
64.5

4
228290_PM_at
PLK1S1
Polo-like kinase 1 substrate 1
7.97E−06
35.6
48.1
48.5

5
231798_PM_at
NOG
noggin
8.34E−06
25.9
12.6
9.4

6
214039_PM_s_at
LAPTM4B
lysosomal protein
9.49E−06
104.0
58.3
68.5

transmembrane 4 beta

7
241692_PM_at
—
—
9.61E−06
44.8
65.1
78.4

8
230776_PM_at
—
—
1.21E−05
13.7
10.4
9.5

9
217963_PM_s_at
NGFRAP1
nerve growth factor receptor
1.56E−05
713.1
461.2
506.6

(TNFRSF16) associated

protein 1

10
243917_PM_at
CLIC5
chloride intracellular channel
1.67E−05
9.6
10.9
11.6

5

11
219915_PM_s_at
SLC16A10
solute carrier family 16,
1.77E−05
21.8
13.2
12.5

member 10 (aromatic amino

acid transporter)

12
1553873_PM_at
KLHL34
kelch-like 34 (Drosophila)
1.85E−05
12.1
9.6
9.1

13
227645_PM_at
PIK3R5
phosphoinositide-3-kinase,
2.12E−05
824.5
1003.6
1021.4

regulatory subunit 5

14
1552623_PM_at
HSH2D
hematopoietic SH2 domain
2.54E−05
323.9
497.5
445.4

containing

15
227486_PM_at
NT5E
5′-nucleotidase, ecto (CD73)
2.66E−05
18.6
13.4
12.2

16
219659_PM_at
ATP8A2
ATPase, aminophospholipid
4.00E−05
10.8
9.0
8.9

transporter, class I, type 8A,

member 2

17
1555874_PM_x_at
MGC21881
hypothetical locus
4.16E−05
20.0
21.0
31.4

MGC21881

18
202086_PM_at
MX1
myxovirus (influenza virus)
4.52E−05
496.4
1253.1
1074.1

resistance 1, interferon-

inducible protein p78 (mouse)

19
233675_PM_s_at
LOC374491
TPTE and PTEN homologous
4.85E−05
8.1
8.2
9.9

inositol lipid phosphatase

pseudogene

20
219815_PM_at
GAL3ST4
galactose-3-O-
5.37E−05
19.9
17.0
14.3

sulfotransferase 4

21
242898_PM_at
EIF2AK2
eukaryotic translation
6.06E−05
66.4
116.6
108.7

initiation factor 2-alpha

kinase 2

22
215177_PM_s_at
ITGA6
integrin, alpha 6
6.39E−05
44.2
26.9
23.9

23
236717_PM_at
FAM179A
family with sequence
6.43E−05
39.8
51.3
73.3

similarity 179, member A

24
242520_PM_s_at
C1orf228
chromosome 1 open reading
6.67E−05
42.5
29.1
26.4

frame 228

25
207926_PM_at
GP5
glycoprotein V (platelet)
7.03E−05
19.3
14.7
16.0

26
211882_PM_x_at
FUT6
fucosyltransferase 6 (alpha
8.11E−05
11.6
9.8
10.7

(1,3) fucosyltransferase)

27
201656_PM_at
ITGA6
integrin, alpha 6
8.91E−05
112.6
69.0
70.7

28
233743_PM_x_at
S1PR5
sphingosine-1-phosphate
9.26E−05
8.6
10.1
9.2

receptor 5

29
210797_PM_s_at
OASL
2′-5′-oligoadenylate
9.28E−05
219.6
497.2
446.0

synthetase-like

30
243819_PM_at
—
—
9.55E−05
495.1
699.2
769.8

31
209728_PM_at
HLA-DRB4 ///
major histocompatibility
0.000102206
33.8
403.5
55.2

LOC100509582
complex, class II, DR beta

4 /// HLA class II

histocompatibili

32
218638_PM_s_at
SPON2
spondin 2, extracellular
0.000103572
109.2
215.7
187.9

matrix protein

33
224293_PM_at
TTTY10
testis-specific transcript, Y-
0.000103782
8.7
11.1
10.2

linked 10 (non-protein

coding)

34
205660_PM_at
OASL
2′-5′-oligoadenylate
0.000105267
394.6
852.0
878.1

synthetase-like

35
230753_PM_at
PATL2
protein associated with
0.00010873
123.0
168.6
225.2

topoisomerase II homolog 2

(yeast)

36
243362_PM_s_at
LOC641518
hypothetical LOC641518
0.000114355
21.1
13.1
11.2

37
213996_PM_at
YPEL1
yippee-like 1 (Drosophila)
0.00012688
37.9
55.8
59.5

38
232222_PM_at
C18orf49
chromosome 18 open reading
0.000129064
35.7
65.1
53.0

frame 49

39
205612_PM_at
MMRN1
multimerin 1
0.000142028
15.5
9.9
11.2

40
214791_PM_at
SP140L
SP140 nuclear body protein-
0.000150108
223.4
278.8
285.8

like

41
240507_PM_at
—
—
0.000152167
8.4
9.5
8.1

42
203819_PM_s_at
IGF2BP3
insulin-like growth factor 2
0.000174054
75.4
45.9
62.4

mRNA binding protein 3

43
219288_PM_at
C3orf14
chromosome 3 open reading
0.000204911
43.4
29.2
51.0

frame 14

44
214376_PM_at
—
—
0.000213039
8.9
9.6
8.1

45
1568609_PM_s_at
FAM91A2 ///
family with sequence
0.000218802
378.6
472.7
427.1

FLJ39739 ///
similarity 91, member A2 ///

LOC100286793 ///
hypothetical FLJ39739 ///

LOC728855 ///
hypothetica

LOC728875

46
207538_PM_at
IL4
interleukin 4
0.000226354
9.5
8.3
8.9

47
243947_PM_s_at
—
—
0.000227289
9.6
8.4
8.6

48
204211_PM_x_at
EIF2AK2
eukaryotic translation
0.000227971
139.2
222.0
225.5

initiation factor 2-alpha

kinase 2

49
221648_PM_s_at
LOC100507192
hypothetical LOC100507192
0.000230544
96.2
62.4
62.1

50
202016_PM_at
MEST
mesoderm specific transcript
0.000244181
27.5
17.0
19.3

homolog (mouse)

51
220684_PM_at
TBX21
T-box 21
0.000260563
169.0
279.9
309.1

52
219018_PM_s_at
CCDC85C
coiled-coil domain containing
0.000261452
14.9
17.1
17.1

85C

53
204575_PM_s_at
MMP19
matrix metallopeptidase 19
0.00026222
9.3
9.3
11.3

54
1568943_PM_at
INPP5D
inositol polyphosphate-5-
0.000265939
87.7
143.4
133.5

phosphatase, 145 kDa

55
220467_PM_at
—
—
0.000269919
124.9
215.2
206.0

56
207324_PM_s_at
DSC1
desmocollin 1
0.000280239
14.5
11.3
10.3

57
218400_PM_at
OAS3
2′-5′-oligoadenylate
0.000288454
125.9
316.7
299.6

synthetase 3, 100 kDa

58
214617_PM_at
PRF1
perforin 1 (pore forming
0.000292417
822.3
1327.9
1415.4

protein)

59
239798_PM_at
—
—
0.000294263
63.7
39.1
35.3

60
242020_PM_s_at
ZBP1
Z-DNA binding protein 1
0.000303843
83.1
145.8
128.5

61
201786_PM_s_at
ADAR
adenosine deaminase, RNA-
0.000305042
2680.0
3340.9
3194.2

specific

62
234974_PM_at
GALM
galactose mutarotase (aldose
0.000308107
63.1
88.8
93.7

1-epimerase)

63
233121_PM_at
—
—
0.000308702
17.8
23.8
19.4

64
1557545_PM_s_at
RNF165
ring finger protein 165
0.000308992
15.4
24.2
22.1

65
229203_PM_at
B4GALNT3
beta-1,4-N-acetyl-
0.000309508
9.0
10.1
8.6

galactosaminyl transferase 3

66
210164_PM_at
GZMB
granzyme B (granzyme 2,
0.000322925
749.5
1241.7
1374.7

cytotoxic T-lymphocyte-

associated serine esterase 1)

67
222468_PM_at
KIAA0319L
KIAA0319-like
0.000327428
286.7
396.3
401.1

68
223272_PM_s_at
C1orf57
chromosome 1 open reading
0.000342477
69.0
54.6
77.4

frame 57

69
240913_PM_at
FGFR2
fibroblast growth factor
0.00035107
9.6
10.6
11.7

receptor 2

70
230854_PM_at
BCAR4
breast cancer anti-estrogen
0.000352682
10.2
10.2
8.9

resistance 4

71
1562697_PM_at
LOC339988
hypothetical LOC339988
0.000360155
97.8
151.3
142.0

72
222732_PM_at
TRIM39
tripartite motif-containing 39
0.000372812
115.6
135.8
115.4

73
227917_PM_at
FAM85A ///
family with sequence
0.000373226
206.8
154.1
154.9

FAM85B
similarity 85, member A ///

family with sequence

similarity 85, me

74
212687_PM_at
LIMS1
LIM and senescent cell
0.000383722
1115.2
824.0
913.2

antigen-like domains 1

75
216836_PM_s_at
ERBB2
v-erb-b2 erythroblastic
0.000384613
12.0
16.3
14.3

leukemia viral oncogene

homolog 2,

neuro/glioblastoma derived o

76
236191_PM_at
—
—
0.000389259
71.0
95.0
114.3

77
213932_PM_x_at
HLA-A
major histocompatibility
0.000391535
9080.1
10344.2
10116.9

complex, class I, A

78
229254_PM_at
MFSD4
major facilitator superfamily
0.000393739
11.0
9.0
9.5

domain containing 4

79
212843_PM_at
NCAM1
neural cell adhesion molecule
0.000401596
25.8
50.2
37.7

1

80
235256_PM_s_at
GALM
galactose mutarotase (aldose
0.000417617
58.0
79.8
90.2

1-epimerase)

81
1566201_PM_at
—
—
0.000420058
9.0
10.3
8.8

82
204994_PM_at
MX2
myxovirus (influenza virus)
0.000438751
1147.0
1669.1
1518.5

resistance 2 (mouse)

83
237240_PM_at
—
—
0.000440008
10.7
9.2
9.1

84
232478_PM_at
—
—
0.000447263
51.3
96.8
71.5

85
211410_PM_x_at
KIR2DL5A
killer cell immunoglobulin-
0.00045859
24.8
31.7
39.0

like receptor, two domains,

long cytoplasmic tail, 5A

86
1569551_PM_at
—
—
0.00045899
12.7
17.5
17.9

87
222816_PM_s_at
ZCCHC2
zinc finger, CCHC domain
0.00046029
308.7
502.0
404.6

containing 2

88
1557071_PM_s_at
NUB1
negative regulator of
0.000481473
108.5
144.0
155.3

ubiquitin-like proteins 1

89
219737_PM_s_at
PCDH9
protocadherin 9
0.000485253
37.9
76.4
66.9

90
230563_PM_at
RASGEF1A
RasGEF domain family,
0.000488148
86.8
121.7
139.4

member 1A

91
1560080_PM_at
—
—
0.000488309
9.9
11.0
12.2

92
243756_PM_at
—
—
0.000488867
8.5
7.5
8.2

93
212730_PM_at
SYNM
synemin, intermediate
0.000521028
19.5
15.7
27.7

filament protein

94
1552977_PM_a_at
CNPY3
canopy 3 homolog (zebrafish)
0.000521239
319.3
395.2
261.4

95
218657_PM_at
RAPGEFL1
Rap guanine nucleotide
0.000529963
10.4
11.9
11.5

exchange factor (GEF)-like 1

96
228139_PM_at
RIPK3
receptor-interacting serine-
0.000530418
87.8
107.4
102.7

threonine kinase 3

97
38918_PM_at
SOX13
SRY (sex determining region
0.000534735
10.9
13.1
13.1

Y)-box 13

98
207795_PM_s_at
KLRD1
killer cell lectin-like receptor
0.000538523
201.8
309.8
336.1

subfamily D, member 1

99
212906_PM_at
GRAMD1B
GRAM domain containing 1B
0.000540879
51.0
58.3
78.1

100
1561098_PM_at
LOC641365
hypothetical LOC641365
0.000541122
8.7
8.5
10.1

101
209593_PM_s_at
TOR1B
torsin family 1, member B
0.000542383
271.7
392.9
408.3

(torsin B)

102
223980_PM_s_at
SP110
SP110 nuclear body protein
0.000543351
1224.3
1606.9
1561.2

103
1554206_PM_at
TMLHE
trimethyllysine hydroxylase,
0.000545869
41.0
50.6
46.5

epsilon

104
240438_PM_at
—
—
0.000555441
10.4
12.0
13.1

105
212190_PM_at
SERPINE2
serpin peptidase inhibitor,
0.00055869
25.8
18.3
21.4

clade E (nexin, plasminogen

activator inhibitor type 1), me

106
202081_PM_at
IER2
immediate early response 2
0.000568285
1831.1
2155.1
1935.4

107
234089_PM_at
—
—
0.000585869
10.1
12.4
11.9

108
235139_PM_at
GNGT2
guanine nucleotide binding
0.000604705
35.8
50.6
51.5

protein (G protein), gamma

transducing activity

polypeptide

109
235545_PM_at
DEPDC1
DEP domain containing 1
0.00060962
8.7
8.4
10.0

110
242096_PM_at
—
—
0.000618307
8.6
8.7
10.3

111
1553042_PM_a_at
NFKBID
nuclear factor of kappa light
0.000619863
14.9
17.7
16.0

polypeptide gene enhancer in

B-cells inhibitor, delta

112
209368_PM_at
EPHX2
epoxide hydrolase 2,
0.000625958
33.6
25.2
22.3

cytoplasmic

113
1553681_PM_a_at
PRF1
perforin 1 (pore forming
0.000629562
181.7
312.5
312.3

protein)

114
223836_PM_at
FGFBP2
fibroblast growth factor
0.000647084
432.4
739.7
788.9

binding protein 2

115
210812_PM_at
XRCC4
X-ray repair complementing
0.000674811
13.2
15.5
16.5

defective repair in Chinese

hamster cells 4

116
230846_PM_at
AKAP5
A kinase (PRKA) anchor
0.000678814
10.9
9.3
11.2

protein 5

117
214567_PM_s_at
XCL1 ///
chemokine (C motif) ligand
0.000680647
211.0
338.8
347.2

XCL2
1 /// chemokine (C motif)

ligand 2

118
237221_PM_at
—
—
0.00069712
9.9
8.7
9.5

119
232793_PM_at
—
—
0.000698404
10.2
12.5
13.0

120
239479_PM_x_at
—
—
0.000700142
28.1
18.0
20.6

121
1558836_PM_at
—
—
0.000706412
33.2
53.1
45.7

122
1562698_PM_x_at
LOC339988
hypothetical LOC339988
0.000710123
108.5
165.5
158.7

123
1552646_PM_at
IL11RA
interleukin 11 receptor, alpha
0.000716149
15.9
19.4
16.3

124
236220_PM_at
—
—
0.000735209
9.9
8.3
7.7

125
211379_PM_x_at
B3GALNT1
beta-1,3-N-
0.00074606
8.9
8.2
9.7

acetylgalactosaminyl-

transferase 1 (globoside

blood group)

126
222830_PM_at
GRHL1
grainyhead-like 1
0.000766774
14.7
10.5
10.4

(Drosophila)

127
210948_PM_s_at
LEF1
lymphoid enhancer-binding
0.000768363
54.2
36.2
33.1

factor 1

128
244798_PM_at
LOC100507492
hypothetical LOC100507492
0.000800826
48.3
32.0
26.6

129
226666_PM_at
DAAM1
dishevelled associated
0.000828238
64.3
50.3
47.8

activator of morphogenesis 1

130
229378_PM_at
STOX1
storkhead box 1
0.000836722
10.2
8.5
9.6

131
206366_PM_x_at
XCL1
chemokine (C motif) ligand 1
0.000839844
194.1
306.8
324.9

132
214115_PM_at
VAMP5
Vesicle-associated membrane
0.000866755
13.2
12.1
16.6

protein 5 (myobrevin)

133
201212_PM_at
LGMN
legumain
0.00087505
18.9
15.9
13.1

134
204863_PM_s_at
IL6ST
interleukin 6 signal transducer
0.000897042
147.6
107.1
111.1

(gp130, oncostatin M

receptor)

135
232229_PM_at
SETX
senataxin
0.000906105
34.5
45.3
36.9

136
1555407_PM_s_at
FGD3
FYVE, RhoGEF and PH
0.00091116
88.7
103.2
67.0

domain containing 3

137
223127_PM_s_at
C1orf21
chromosome 1 open reading
0.000923068
9.1
10.3
11.0

frame 21

138
202458_PM_at
PRSS23
protease, serine, 23
0.000924141
38.8
74.1
79.3

139
210606_PM_x_at
KLRD1
killer cell lectin-like receptor
0.000931313
289.8
421.9
473.0

subfamily D, member 1

140
212444_PM_at
—
—
0.000935909
10.2
11.6
10.2

141
240893_PM_at
—
—
0.000940973
8.6
9.7
10.3

142
219474_PM_at
C3orf52
chromosome 3 open reading
0.000948853
8.9
10.0
10.2

frame 52

143
235087_PM_at
UNKL
unkempt homolog
0.000967141
10.3
9.8
8.3

(Drosophila)-like

144
216907_PM_x_at
KIR3DL1 ///
killer cell immunoglobulin-
0.000987803
12.6
16.1
19.1

KIR3DL2 ///
like receptor, three domains,

LOC727787
long cytoplasmic tail, 1 /// k

145
238402_PM_s_at
FLJ35220
hypothetical protein
0.000990348
17.2
19.9
15.3

FLJ35220

146
239273_PM_s_at
MMP28
matrix metallopeptidase 28
0.000993809
11.7
9.0
8.7

147
215894_PM_at
PTGDR
prostaglandin D2 receptor
0.000994157
191.2
329.4
283.2

(DP)

TABLE 18a

AUCs for the 320 probes to predict AR, ADNR and TX in Liver biopsy samples.

Postive
Negative

Predictive

Predictive
Predictive

Accuracy
Sensitivity
Specificity
Value
Value

Algorithm
Predictors
Comparison
AUC
(%)
(%)
(%)
(%)
(%)

Nearest
320
AR vs. HCV
0.937
94
84
100
100
89

Centroid

Nearest
320
AR vs. HCV + AR
1.000
100
100
100
100
100

Centroid

Nearest
320
HCV vs. HCV + AR
0.829
82
82
89
75
92

Centroid

TABLE 18b

The 320 probesets that distinguish AR vs. HCV vs. HCV + AR in Liver Biopsies

HCV +

p-value
AR -
HCV -
AR -

#
Probeset ID
Gene Symbol
Gene Title
(Phenotype)
Mean
Mean
Mean

1
219863_PM_at
HERC5
hect domain and RLD 5
1.53E−14
250.4
1254.7
1620.1

2
205660_PM_at
OASL
2′-5′-oligoadenylate
3.30E−14
128.1
1273.7
1760.9

synthetase-like

3
210797_PM_s_at
OASL
2′-5′-oligoadenylate
4.03E−14
62.0
719.3
915.2

synthetase-like

4
214453_PM_s_at
IFI44
interferon-induced protein 44
3.98E−13
342.2
1646.7
1979.2

5
218986_PM_s_at
DDX60
DEAD (Asp-Glu-Ala-Asp)
5.09E−12
352.2
1253.2
1403.0

box polypeptide 60

6
202869_PM_at
OAS1
2′,5′-oligoadenylate synthetase
4.47E−11
508.0
1648.7
1582.5

1, 40/46 kDa

7
226702_PM_at
CMPK2
cytidine monophosphate
5.23E−11
257.3
1119.1
1522.6

(UMP-CMP) kinase 2,

mitochondrial

8
203153_PM_at
IFIT1
interferon-induced protein
5.31E−11
704.0
2803.7
3292.9

with tetratricopeptide repeats

1

9
202086_PM_at
MX1
myxovirus (influenza virus)
5.53E−11
272.4
1420.9
1836.8

resistance 1, interferon-

inducible protein p78 (mouse)

10
242625_PM_at
RSAD2
radical S-adenosyl methionine
9.62E−11
56.2
389.2
478.2

domain containing 2

11
213797_PM_at
RSAD2
radical S-adenosyl methionine
1.43E−10
91.4
619.3
744.7

domain containing 2

12
204972_PM_at
OAS2
2′-5′-oligoadenylate
2.07E−10
88.7
402.1
536.1

synthetase 2, 69/71 kDa

13
219352_PM_at
HERC6
hect domain and RLD 6
2.52E−10
49.5
206.7
272.8

14
205483_PM_s_at
ISG15
ISG15 ubiquitin-like modifier
3.68E−10
629.9
3181.1
4608.0

15
205552_PM_s_at
OAS1
2′,5′-oligoadenylate synthetase
4.08E−10
224.7
868.7
921.2

1, 40/46 kDa

16
204415_PM_at
IFI6
interferon, alpha-inducible
5.83E−10
787.8
4291.7
5465.6

protein 6

17
205569_PM_at
LAMP3
lysosomal-associated
6.80E−10
21.8
91.3
126.2

membrane protein 3

18
219209_PM_at
IFIH1
interferon induced with
8.15E−10
562.3
1246.9
1352.7

helicase C domain 1

19
218400_PM_at
OAS3
2′-5′-oligoadenylate
2.85E−09
87.9
265.2
364.5

synthetase 3, 100 kDa

20
229450_PM_at
IFIT3
interferon-induced protein
4.69E−09
1236.3
2855.3
3291.7

with tetratricopeptide repeats

3

21
226757_PM_at
IFIT2
interferon-induced protein
5.35E−09
442.3
1083.2
1461.9

with tetratricopeptide repeats

2

22
204439_PM_at
IFI44L
interferon-induced protein 44-
5.77E−09
146.3
794.4
1053.5

like

23
227609_PM_at
EPSTI1
epithelial stromal interaction 1
1.03E−08
396.9
1079.8
1370.3

(breast)

24
204747_PM_at
IFIT3
interferon-induced protein
1.59E−08
228.3
698.1
892.7

with tetratricopeptide repeats

3

25
217502_PM_at
IFIT2
interferon-induced protein
1.85E−08
222.9
575.1
745.9

with tetratricopeptide repeats

2

26
228607_PM_at
OAS2
2′-5′-oligoadenylate
2.16E−08
60.9
182.0
225.6

synthetase 2, 69/71 kDa

27
224870_PM_at
KIAA0114
KIAA0114
2.48E−08
156.5
81.8
66.0

28
202411_PM_at
IFI27
interferon, alpha-inducible
4.25E−08
1259.4
5620.8
5634.1

protein 27

29
223220_PM_s_at
PARP9
poly (ADP-ribose)
4.48E−08
561.7
1084.4
1143.1

polymerase family, member 9

30
208436_PM_s_at
IRF7
interferon regulatory factor 7
4.57E−08
58.9
102.9
126.9

31
219211_PM_at
USP18
ubiquitin specific peptidase 18
6.39E−08
51.0
183.6
196.1

32
206133_PM_at
XAF1
XIAP associated factor 1
7.00E−08
463.9
1129.2
1327.1

33
202446_PM_s_at
PLSCR1
phospholipid scramblase 1
1.12E−07
737.8
1317.7
1419.8

34
235276_PM_at
EPSTI1
epithelial stromal interaction 1
1.58E−07
93.5
244.2
279.9

(breast)

35
219684_PM_at
RTP4
receptor (chemosensory)
1.64E−07
189.5
416.3
541.7

transporter protein 4

36
222986_PM_s_at
SPHISA5
shisa homolog 5 (Xenopus
1.68E−07
415.0
586.9
681.4

laevis)

37
223298_PM_s_at
NT5C3
5′-nucleotidase, cytosolic III
2.06E−07
247.6
443.4
474.7

38
228275_PM_at
—
—
2.24E−07
71.6
159.3
138.9

39
228617_PM_at
XAF1
XIAP associated factor 1
2.28E−07
678.3
1412.3
1728.5

40
214022_PM_s_at
IFITM1
interferon induced
2.37E−07
1455.1
2809.3
3537.2

transmembrane protein 1 (9-

27)

41
214059_PM_at
IFI44
Interferon-induced protein 44
2.61E−07
37.1
158.8
182.5

42
206553_PM_at
OAS2
2′-5′-oligoadenylate
2.92E−07
18.9
45.6
53.1

synthetase 2, 69/71 kDa

43
214290_PM_s_at
HIST2H2AA3 ///
histone cluster 2, H2aa3 ///
3.50E−07
563.4
1151.2
1224.7

HIST2H2AA4
histone cluster 2, H2aa4

44
1554079_PM_at
GALNTL4
UDP-N-acetyl-alpha-D-
3.58E−07
69.9
142.6
109.0

galactosamine: polypeptide N-

acetylgalactosaminyltransferase-

like 4

45
202430_PM_s_at
PLSCR1
phospholipid scramblase 1
3.85E−07
665.7
1162.8
1214.5

46
218280_PM_x_at
HIST2H2AA3 ///
histone cluster 2, H2aa3 ///
5.32E−07
299.7
635.3
721.7

HIST2H2AA4
histone cluster 2, H2aa4

47
202708_PM_s_at
HIST2H2BE
histone cluster 2, H2be
7.04E−07
62.4
112.2
115.4

48
222134_PM_at
DDO
D-aspartate oxidase
7.37E−07
76.0
134.9
118.4

49
215071_PM_s_at
HIST1H2AC
histone cluster 1, H2ac
9.11E−07
502.4
1009.1
1019.0

50
209417_PM_s_at
IFI35
interferon-induced protein 35
9.12E−07
145.5
258.9
323.5

51
218543_PM_s_at
PARP12
poly (ADP-ribose)
9.29E−07
172.3
280.3
366.3

polymerase family, member

12

52
202864_PM_s_at
SP100
SP100 nuclear antigen
1.09E−06
372.5
604.2
651.9

53
217719_PM_at
EIF3L
eukaryotic translation
1.15E−06
4864.0
3779.0
3600.0

initiation factor 3, subunit L

54
230314_PM_at
—
—
1.29E−06
36.0
62.5
59.5

55
202863_PM_at
SP100
SP100 nuclear antigen
1.37E−06
500.0
751.3
815.8

56
236798_PM_at
—
—
1.38E−06
143.1
307.0
276.8

57
233555_PM_s_at
SULF2
sulfatase 2
1.38E−06
47.0
133.4
119.0

58
236717_PM_at
FAM179A
family with sequence
1.44E−06
16.5
16.1
24.2

similarity 179, member A

59
228531_PM_at
SAMD9
sterile alpha motif domain
1.54E−06
143.0
280.3
351.7

containing 9

60
209911_PM_x_at
HIST1H2BD
histone cluster 1, H2bd
1.69E−06
543.7
999.9
1020.2

61
238039_PM_at
LOC728769
hypothetical LOC728769
1.77E−06
62.8
95.5
97.2

62
222067_PM_x_at
HIST1H2BD
histone cluster 1, H2bd
1.78E−06
378.1
651.6
661.4

63
201601_PM_x_at
IFITM1
interferon induced
2.00E−06
1852.8
2956.0
3664.5

transmembrane protein 1 (9-

27)

64
213361_PM_at
TDRD7
tudor domain containing 7
2.09E−06
158.5
314.1
328.6

65
224998_PM_at
CMTM4
CKLF-like MARVEL
2.15E−06
42.6
30.0
22.3

transmembrane domain

containing 4

66
222793_PM_at
DDX58
DEAD (Asp-Glu-Ala-Asp)
2.41E−06
93.9
231.9
223.1

box polypeptide 58

67
225076_PM_s_at
ZNFX1
zinc finger, NFX1-type
2.55E−06
185.0
286.0
359.1

containing 1

68
236381_PM_s_at
WDR8
WD repeat domain 8
2.68E−06
41.6
61.5
64.8

69
202365_PM_at
UNC119B
unc-119 homolog B
2.72E−06
383.4
272.7
241.0

(C. elegans)

70
215690_PM_x_at
GPAA1
glycosylphosphatidylinositol
2.75E−06
141.0
103.7
107.5

anchor attachment protein 1

homolog (yeast)

71
211799_PM_x_at
HLA-C
major histocompatibility
2.77E−06
912.3
1446.0
1649.4

complex, class I, C

72
218943_PM_s_at
DDX58
DEAD (Asp-Glu-Ala-Asp)
2.87E−06
153.9
310.7
350.7

box polypeptide 58

73
235686_PM_at
C2orf60
chromosome 2 open reading
3.32E−06
17.2
23.2
20.1

frame 60

74
236193_PM_at
LOC100506979
hypothetical LOC100506979
3.96E−06
24.5
48.1
51.2

75
221767_PM_x_at
HDLBP
high density lipoprotein
4.00E−06
1690.9
1301.2
1248.4

binding protein

76
225796_PM_at
PXK
PX domain containing
4.08E−06
99.2
168.1
154.9

serine/threonine kinase

77
209762_PM_x_at
SP110
SP110 nuclear body protein
4.68E−06
150.5
242.3
282.0

78
211060_PM_x_at
GPAA1
glycosylphosphatidylinositol
4.74E−06
153.1
113.3
116.8

anchor attachment protein 1

homolog (yeast)

79
218019_PM_s_at
PDXK
pyridoxal (pyridoxine,
4.95E−06
304.5
210.8
198.6

vitamin B6) kinase

80
219364_PM_at
DHX58
DEXH (Asp-Glu-X-His) box
5.46E−06
71.5
111.2
113.0

polypeptide 58

81
203281_PM_s_at
UBA7
ubiquitin-like modifier
6.79E−06
80.2
108.2
131.0

activating enzyme 7

82
200923_PM_at
LGALS3BP
lectin, galactoside-binding,
6.99E−06
193.1
401.5
427.4

soluble, 3 binding protein

83
208527_PM_x_at
HIST1H2BE
histone cluster 1, H2be
7.54E−06
307.7
529.7
495.4

84
219479_PM_at
KDELC1
KDEL (Lys-Asp-Glu-Leu)
7.81E−06
74.1
131.5
110.6

containing 1

85
200950_PM_at
ARPC1A
actin related protein 2/3
1.00E−05
1015.8
862.8
782.0

complex, subunit 1A, 41 kDa

86
213294_PM_at
EIF2AK2
eukaryotic translation
1.02E−05
390.4
690.7
651.6

initiation factor 2-alpha kinase

2

87
205943_PM_at
TDO2
tryptophan 2,3-dioxygenase
1.06E−05
7808.6
10534.7
10492.0

88
217969_PM_at
C11orf2
chromosome 11 open reading
1.21E−05
302.6
235.0
214.8

frame 2

89
1552370_PM_at
C4orf33
chromosome 4 open reading
1.24E−05
58.4
124.5
97.2

frame 33

90
211911_PM_x_at
HLA-B
major histocompatibility
1.34E−05
4602.1
6756.7
7737.3

complex, class I, B

91
232563_PM_at
ZNF684
zinc finger protein 684
1.36E−05
131.9
236.2
231.8

92
203882_PM_at
IRF9
interferon regulatory factor 9
1.43E−05
564.0
780.1
892.0

93
225991_PM_at
TMEM41A
transmembrane protein 41A
1.45E−05
122.5
202.1
179.6

94
239988_PM_at
—
—
1.53E−05
11.5
15.4
16.1

95
244434_PM_at
GPR82
G protein-coupled receptor 82
1.55E−05
18.5
32.5
37.0

96
201489_PM_at
PPIF
peptidylprolyl isomerase F
1.58E−05
541.7
899.5
672.9

97
221476_PM_s_at
RPL15
ribosomal protein L15
1.58E−05
3438.3
2988.5
2742.8

98
244398_PM_x_at
ZNF684
zinc finger protein 684
1.65E−05
57.2
96.9
108.5

99
208628_PM_s_at
YBX1
Y box binding protein 1
1.66E−05
4555.5
3911.6
4365.0

100
211710_PM_x_at
RPL4
ribosomal protein L4
1.73E−05
5893.1
4853.3
4955.4

101
229741_PM_at
MAVS
mitochondrial antiviral
1.78E−05
65.2
44.6
34.4

signaling protein

102
206386_PM_at
SERPINA7
serpin peptidase inhibitor,
1.90E−05
3080.8
4251.6
4377.2

clade A (alpha-1

antiproteinase, antitrypsin),

member 7

103
213293_PM_s_at
TRIM22
tripartite motif-containing 22
1.92E−05
1122.0
1829.2
2293.2

104
200089_PM_s_at
RPL4
ribosomal protein L4
1.93E−05
3387.5
2736.6
2823.9

105
235037_PM_at
TMEM41A
transmembrane protein 41A
1.96E−05
134.7
218.5
192.9

106
226459_PM_at
PIK3AP1
phosphoinositide-3-kinase
2.10E−05
2152.4
2747.6
2929.7

adaptor protein 1

107
200023_PM_s_at
EIF3F
eukaryotic translation
2.16E−05
1764.9
1467.2
1365.3

initiation factor 3, subunit F

108
205161_PM_s_at
PEX11A
peroxisomal biogenesis factor
2.17E−05
51.9
87.3
76.9

11 alpha

109
225291_PM_at
PNPT1
polyribonucleotide
2.18E−05
287.0
469.1
455.0

nucleotidyltransferase 1

110
220445_PM_s_at
CSAG2 ///
CSAG family, member 2 ///
2.24E−05
16.3
91.2
120.9

CSAG3
CSAG family, member 3

111
226229_PM_s_at
SSU72
SSU72 RNA polymerase II
2.24E−05
50.4
36.7
32.3

CTD phosphatase homolog

(S. cerevisiae)

112
207418_PM_s_at
DDO
D-aspartate oxidase
2.48E−05
35.2
57.0
50.7

113
201786_PM_s_at
ADAR
adenosine deaminase, RNA-
2.59E−05
1401.5
1867.9
1907.8

specific

114
224724_PM_at
SULF2
sulfatase 2
2.61E−05
303.6
540.1
553.9

115
201618_PM_x_at
GPAA1
glycosylphosphatidylinositol
2.63E−05
131.2
98.1
97.5

anchor attachment protein 1

homolog (yeast)

116
201154_PM_x_at
RPL4
ribosomal protein L4
2.78E−05
3580.5
2915.6
2996.2

117
200094_PM_s_at
EEF2
eukaryotic translation
3.08E−05
3991.6
3248.5
3061.1

elongation factor 2

118
208424_PM_s_at
CIAPIN1
cytokine induced apoptosis
3.17E−05
66.7
94.8
94.8

inhibitor 1

119
204102_PM_s_at
EEF2
eukaryotic translation
3.23E−05
3680.8
3102.7
2853.6

elongation factor 2

120
203595_PM_s_at
IFIT5
interferon-induced protein
3.44E−05
266.9
445.8
450.9

with tetratricopeptide repeats

5

121
228152_PM_s_at
DDX60L
DEAD (Asp-Glu-Ala-Asp)
3.52E−05
136.1
280.8
304.5

box polypeptide 60-like

122
201490_PM_s_at
PPIF
peptidylprolyl isomerase F
3.64E−05
209.2
443.5
251.4

123
217933_PM_s_at
LAP3
leucine aminopeptidase 3
3.81E−05
3145.6
3985.6
4629.9

124
203596_PM_s_at
IFIT5
interferon-induced protein
3.93E−05
195.9
315.8
339.0

with tetratricopeptide repeats

5

125
220104_PM_at
ZC3HAV1
zinc finger CCCH-type,
4.25E−05
23.3
53.1
57.7

antiviral 1

126
213080_PM_x_at
RPL5
ribosomal protein L5
4.28E−05
6986.7
6018.3
5938.6

127
208729_PM_x_at
HLA-B
major histocompatibility
4.58E−05
4720.9
6572.7
7534.4

complex, class I, B

128
32541_PM_at
PPP3CC
protein phosphatase 3,
4.71E−05
63.3
79.7
81.3

catalytic subunit, gamma

isozyme

129
216231_PM_s_at
B2M
beta-2-microglobulin
4.79E−05
13087.7
14063.7
14511.1

130
206082_PM_at
HCP5
HLA complex P5
4.91E−05
129.7
205.7
300.9

131
213275_PM_x_at
CTSB
cathepsin B
4.93E−05
2626.4
2001.3
2331.0

132
200643_PM_at
HDLBP
high density lipoprotein
5.04E−05
404.4
317.8
304.4

binding protein

133
235309_PM_at
RPS15A
ribosomal protein S15a
5.08E−05
98.5
77.4
55.3

134
209761_PM_s_at
SP110
SP110 nuclear body protein
5.33E−05
84.2
145.6
156.0

135
230753_PM_at
PATL2
protein associated with
5.55E−05
42.8
52.1
68.4

topoisomerase II homolog 2

(yeast)

136
225369_PM_at
ESAM
endothelial cell adhesion
5.72E−05
14.9
13.1
11.9

molecule

137
219255_PM_x_at
IL17RB
interleukin 17 receptor B
5.88E−05
334.9
607.9
568.7

138
208392_PM_x_at
SP110
SP110 nuclear body protein
6.05E−05
60.2
96.1
115.5

139
221044_PM_s_at
TRIM34 ///
tripartite motif-containing
6.07E−05
47.0
65.1
70.9

TRIM6-
34 /// TRIM6-TRIM34

TRIM34
readthrough

140
1554375_PM_a_at
NR1H4
nuclear receptor subfamily 1,
6.23E−05
585.8
913.1
791.8

group H, member 4

141
210218_PM_s_at
SP100
SP100 nuclear antigen
6.41E−05
129.0
207.4
222.0

142
206340_PM_at
NR1H4
nuclear receptor subfamily 1,
6.67E−05
983.3
1344.6
1278.4

group H, member 4

143
222868_PM_s_at
IL18BP
interleukin 18 binding protein
7.04E−05
72.0
45.4
90.9

144
204211_PM_x_at
EIF2AK2
eukaryotic translation
7.04E−05
144.8
215.9
229.8

initiation factor 2-alpha kinase

2

145
231702_PM_at
TDO2
Tryptophan 2,3-dioxygenase
7.09E−05
57.9
101.7
83.6

146
204906_PM_at
RPS6KA2
ribosomal protein S6 kinase,
7.10E−05
40.1
28.3
28.7

90 kDa, polypeptide 2

147
218192_PM_at
IP6K2
inositol hexakisphosphate
7.15E−05
84.0
112.5
112.7

kinase 2

148
211528_PM_x_at
HLA-G
major histocompatibility
7.45E−05
1608.7
2230.0
2613.2

complex, class I, G

149
208546_PM_x_at
HIST1H2BB ///
histone cluster 1, H2bb ///
7.82E−05
65.3
131.7
112.0

HIST1H2BC ///
histone cluster 1, H2bc ///

HIST1H2BD ///
histone cluster 1, H2bd /// his

HIST1H2BE ///

HIST1H2BG ///

HIST1H2BH ///

HIST1H2BI

150
204483_PM_at
ENO3
enolase 3 (beta, muscle)
7.85E−05
547.8
1183.9
891.4

151
203148_PM_s_at
TRIM14
tripartite motif-containing 14
7.97E−05
590.8
803.6
862.4

152
1557120_PM_at
EEF1A1
Eukaryotic translation
8.14E−05
20.5
17.4
17.4

elongation factor 1 alpha 1

153
203067_PM_at
PDHX
pyruvate dehydrogenase
8.21E−05
322.0
457.6
413.2

complex, component X

154
224156_PM_x_at
IL17RB
interleukin 17 receptor B
8.48E−05
426.4
755.4
699.9

155
203073_PM_at
COG2
component of oligomeric
9.64E−05
73.6
100.2
96.2

golgi complex 2

156
211937_PM_at
EIF4B
eukaryotic translation
9.68E−05
823.8
617.5
549.7

initiation factor 4B

157
229804_PM_x_at
CBWD2
COBW domain containing 2
9.69E−05
170.0
225.0
229.1

158
225009_PM_at
CMTM4
CKLF-like MARVEL
0.00010207
54.0
40.5
32.3

transmembrane domain

containing 4

159
221305_PM_s_at
UGT1A8 ///
UDP glucuronosyltransferase
0.000109701
214.8
526.8
346.9

UGT1A9
1 family, polypeptide A8 ///

UDP glucuronosyltransferase

1

160
1557820_PM_at
AFG3L2
AFG3 ATPase family gene 3-
0.000112458
1037.9
1315.0
1232.5

like 2 (S. cerevisiae)

161
237627_PM_at
LOC100506318
hypothetical LOC100506318
0.000115046
29.2
22.6
19.1

162
205819_PM_at
MARCO
macrophage receptor with
0.000115755
625.3
467.4
904.8

collagenous structure

163
215313_PM_x_at
HLA-A ///
major histocompatibility
0.000116881
6193.5
8266.5
9636.7

LOC100507703
complex, class I, A /// HLA

class I histocompatibility

antigen

164
226950_PM_at
ACVRL1
activin A receptor type II-like
0.000118584
28.2
25.1
35.5

1

165
213716_PM_s_at
SECTM1
secreted and transmembrane 1
0.000118874
44.7
32.0
50.6

166
207468_PM_s_at
SFRP5
secreted frizzled-related
0.000121583
19.6
25.5
20.2

protein 5

167
218674_PM_at
C5orf44
chromosome 5 open reading
0.000124195
60.4
97.9
77.7

frame 44

168
219691_PM_at
SAMD9
sterile alpha motif domain
0.000126093
29.6
49.5
53.9

containing 9

169
230795_PM_at
—
—
0.00012691
115.4
188.1
164.2

170
200941_PM_at
HSBP1
heat shock factor binding
0.000127149
559.2
643.2
623.6

protein 1

171
230174_PM_at
LYPLAL1
lysophospholipase-like 1
0.000127616
476.3
597.5
471.3

172
214459_PM_x_at
HLA-C
major histocompatibility
0.000131095
4931.4
6208.3
6855.4

complex, class I, C

173
228971_PM_at
LOC100505759
hypothetical LOC100505759
0.000131603
210.7
139.7
91.6

174
217073_PM_x_at
APOA1
apolipoprotein A-I
0.000135801
12423.2
13707.0
13369.3

175
203964_PM_at
NMI
N-myc (and STAT) interactor
0.000138824
641.8
820.4
930.9

176
1556988_PM_s_at
CHD1L
chromodomain helicase DNA
0.000142541
164.4
241.1
226.9

binding protein 1-like

177
214890_PM_s_at
FAM149A
family with sequence
0.000144828
534.0
444.9
342.4

similarity 149, member A

178
209115_PM_at
UBA3
ubiquitin-like modifier
0.000144924
456.2
532.0
555.8

activating enzyme 3

179
212284_PM_x_at
TPT1
tumor protein, translationally-
0.000146465
15764.0
14965.0
14750.6

controlled 1

180
1552274_PM_at
PXK
PX domain containing
0.000150376
24.9
37.1
43.1

serine/threonine kinase

181
214889_PM_at
FAM149A
family with sequence
0.00015075
295.1
236.6
152.6

similarity 149, member A

182
213287_PM_s_at
KRT10
keratin 10
0.000151197
644.2
551.6
509.4

183
213051_PM_at
ZC3HAV1
zinc finger CCCH-type,
0.000152213
635.3
963.0
917.5

antiviral 1

184
219731_PM_at
CC2D2B
Coiled-coil and C2 domain
0.000152224
37.5
50.5
50.5

containing 2B

185
206211_PM_at
SELE
selectin E
0.000156449
76.0
35.1
22.8

186
217436_PM_x_at
HLA-A ///
major histocompatibility
0.000159936
972.4
1408.3
1820.7

HLA-F ///
complex, class I, A /// major

HLA-J
histocompatibility complex,

clas

187
203970_PM_s_at
PEX3
peroxisomal biogenesis factor
0.000164079
387.4
540.4
434.7

3

188
1556643_PM_at
FAM125A
Family with sequence
0.000170998
68.0
107.1
95.8

similarity 125, member A

189
211529_PM_x_at
HLA-G
major histocompatibility
0.000174559
2166.9
3107.2
3708.7

complex, class I, G

190
223187_PM_s_at
ORMDL1
ORM1-like 1 (S. cerevisiae)
0.000182187
784.3
918.4
945.5

191
1566249_PM_at
—
—
0.000182326
15.1
12.7
12.3

192
218111_PM_s_at
CMAS
cytidine monophosphate N-
0.000182338
242.6
418.6
310.9

acetylneuraminic acid

synthetase

193
224361_PM_s_at
IL17RB
interleukin 17 receptor B
0.000183121
231.0
460.8
431.4

194
217807_PM_s_at
GLTSCR2
glioma tumor suppressor
0.000185926
3262.6
2650.0
2523.4

candidate region gene 2

195
222571_PM_at
ST6GALNAC6
ST6 (alpha-N-acetyl-
0.00018814
31.7
24.2
25.0

neuraminyl-2,3-beta-

galactosyl-1,3)-N-

acetylgalactosaminide alpha-2

196
208012_PM_x_at
SP110
SP110 nuclear body protein
0.000189717
245.7
344.1
397.9

197
208579_PM_x_at
H2BFS
H2B histone family, member
0.000192843
352.8
581.2
525.7

S

198
204309_PM_at
CYP11A1
cytochrome P450, family 11,
0.000193276
17.5
27.3
29.2

subfamily A, polypeptide 1

199
211956_PM_s_at
EIF1
eukaryotic translation
0.000193297
6954.0
6412.9
6189.5

initiation factor 1

200
214455_PM_at
HIST1H2BC
histone cluster 1, H2bc
0.000196036
49.9
104.4
101.5

201
232140_PM_at
—
—
0.00019705
25.3
32.7
30.9

202
214054_PM_at
DOK2
docking protein 2, 56 kDa
0.000197843
28.6
25.1
39.9

203
210606_PM_x_at
KLRD1
killer cell lectin-like receptor
0.000201652
59.7
46.6
94.1

subfamily D, member 1

204
211943_PM_x_at
TPT1
tumor protein, translationally-
0.000202842
12849.6
11913.9
11804.6

controlled 1

205
205506_PM_at
VIL1
villin 1
0.000209043
67.1
28.6
21.7

206
210514_PM_x_at
HLA-G
major histocompatibility
0.000214822
715.2
976.4
1100.2

complex, class I, G

207
235885_PM_at
P2RY12
purinergic receptor P2Y, G-
0.000216727
21.1
30.2
49.1

protein coupled, 12

208
212997_PM_s_at
TLK2
tousled-like kinase 2
0.000217726
86.1
108.5
119.7

209
211976_PM_at
—
—
0.000218277
145.9
115.9
104.8

210
231718_PM_at
SLU7
SLU7 splicing factor homolog
0.000221207
185.0
205.3
234.8

(S. cerevisiae)

211
225634_PM_at
ZC3HAV1
zinc finger CCCH-type,
0.000224661
388.3
511.6
490.5

antiviral 1

212
205936_PM_s_at
HK3
hexokinase 3 (white cell)
0.000231343
22.5
19.2
30.2

213
203912_PM_s_at
DNASE1L1
deoxyribonuclease I-like 1
0.000231815
171.2
151.3
183.8

214
224603_PM_at
—
—
0.000232518
562.4
449.5
405.8

215
218085_PM_at
CHMP5
chromatin modifying protein
0.000232702
484.6
584.5
634.2

5

216
204821_PM_at
BTN3A3
butyrophilin, subfamily 3,
0.000235674
245.0
335.6
401.3

member A3

217
217819_PM_at
GOLGA7
golgin A7
0.000242192
845.3
1004.2
967.8

218
200629_PM_at
WARS
tryptophanyl-tRNA synthetase
0.000244656
423.1
279.6
508.5

219
206342_PM_x_at
IDS
iduronate 2-sulfatase
0.000246177
122.3
88.8
95.0

220
1560023_PM_x_at
—
—
0.000247892
14.4
12.5
12.6

221
213706_PM_at
GPD1
glycerol-3-phosphate
0.000254153
124.3
227.8
162.9

dehydrogenase 1 (soluble)

222
204312_PM_x_at
CREB1
cAMP responsive element
0.000257352
28.9
41.8
34.8

binding protein 1

223
230036_PM_at
SAMD9L
sterile alpha motif domain
0.000265574
54.8
75.0
115.7

containing 9-like

224
222730_PM_s_at
ZDHHC2
zinc finger, DHHC-type
0.000270517
96.7
66.7
58.1

containing 2

225
224225_PM_s_at
ETV7
ets variant 7
0.000274744
32.8
55.4
71.0

226
1294_PM_at
UBA7
ubiquitin-like modifier
0.000290256
94.7
122.9
138.8

activating enzyme 7

227
211075_PM_s_at
CD47
CD47 molecule
0.000296663
767.0
998.4
1061.6

228
228091_PM_at
STX17
syntaxin 17
0.000298819
94.3
134.9
110.7

229
205821_PM_at
KLRK1
killer cell lectin-like receptor
0.000299152
95.2
73.8
156.4

subfamily K, member 1

230
1563075_PM_s_at
—
—
0.000300425
41.4
63.6
82.2

231
224701_PM_at
PARP14
poly (ADP-ribose)
0.000301162
367.5
538.6
589.3

polymerase family, member

14

232
209300_PM_s_at
NECAP1
NECAP endocytosis
0.000304084
184.5
246.0
246.0

associated 1

233
200937_PM_s_at
RPL5
ribosomal protein L5
0.00030872
3893.3
3346.0
3136.1

234
208523_PM_x_at
HIST1H2BI
histone cluster 1, H2bi
0.000310294
79.8
114.5
115.8

235
210657_PM_s_at
4-Sep
septin 4
0.000314978
122.1
78.4
61.6

236
239979_PM_at
—
—
0.000315949
40.3
78.8
114.4

237
208941_PM_s_at
SEPHS1
selenophosphate synthetase 1
0.000316337
291.7
228.3
213.0

238
201649_PM_at
UBE2L6
ubiquitin-conjugating enzyme
0.000320318
928.3
1228.3
1623.0

E2L 6

239
211927_PM_x_at
EEF1G
eukaryotic translation
0.000325197
5122.7
4241.7
4215.5

elongation factor 1 gamma

240
225458_PM_at
LOC25845
hypothetical LOC25845
0.000337719
93.6
131.5
110.8

241
208490_PM_x_at
HIST1H2BF
histone cluster 1, H2bf
0.000339692
61.0
96.3
97.7

242
201322_PM_at
ATP5B
ATP synthase, H+
0.000342076
2068.5
2566.2
2543.7

transporting, mitochondrial F1

complex, beta polypeptide

243
221978_PM_at
HLA-F
major histocompatibility
0.00034635
49.8
69.5
100.6

complex, class I, F

244
204031_PM_s_at
PCBP2
poly(rC) binding protein 2
0.000351625
2377.6
2049.5
1911.5

245
243624_PM_at
PIAS2
Protein inhibitor of activated
0.000352892
17.7
15.4
14.1

STAT, 2

246
212998_PM_x_at
HLA-DQB1 ///
major histocompatibility
0.000359233
570.2
339.6
742.5

LOC100133583
complex, class II, DQ beta

1 /// HLA class II

histocompatibili

247
204875_PM_s_at
GMDS
GDP-mannose 4,6-
0.00035965
73.9
41.2
45.5

dehydratase

248
225721_PM_at
SYNPO2
synaptopodin 2
0.000362084
69.1
43.3
32.1

249
229696_PM_at
FECH
ferrochelatase
0.000362327
42.6
34.1
28.8

250
208812_PM_x_at
HLA-C
major histocompatibility
0.000365707
7906.3
9602.6
10311.7

complex, class I, C

251
211666_PM_x_at
RPL3
ribosomal protein L3
0.000376419
4594.1
4006.1
3490.3

252
219948_PM_x_at
UGT2A3
UDP glucuronosyltransferase
0.000376972
219.5
454.5
350.3

2 family, polypeptide A3

253
204158_PM_s_at
TCIRG1
T-cell, immune regulator 1,
0.000384367
217.8
197.5
311.3

ATPase, H+ transporting,

lysosomal V0 subunit A3

254
209846_PM_s_at
BTN3A2
butyrophilin, subfamily 3,
0.000386605
424.5
612.5
703.0

member A2

255
243225_PM_at
LOC283481
hypothetical LOC283481
0.000388527
62.6
42.2
39.2

256
1554676_PM_at
SRGN
serglycin
0.000399135
11.6
12.7
15.0

257
202748_PM_at
GBP2
guanylate binding protein 2,
0.000406447
393.4
258.6
446.1

interferon-inducible

258
238654_PM_at
VSIG10L
V-set and immunoglobulin
0.000411449
15.7
19.5
19.7

domain containing 10 like

259
218949_PM_s_at
QRSL1
glutaminyl-tRNA synthase
0.000413577
154.7
217.8
188.1

(glutamine-hydrolyzing)-like

1

260
230306_PM_at
VPS26B
vacuolar protein sorting 26
0.000420436
80.8
66.4
59.0

homolog B (S. pombe)

261
204450_PM_x_at
APOA1
apolipoprotein A-I
0.000427479
11811.2
13302.5
13014.4

262
213932_PM_x_at
HLA-A
major histocompatibility
0.000435087
7218.3
9083.8
10346.9

complex, class I, A

263
201641_PM_at
BST2
bone marrow stromal cell
0.000438494
217.2
396.5
401.8

antigen 2

264
1552275_PM_s_at
PXK
PX domain containing
0.000438718
24.7
38.6
34.4

serine/threonine kinase

265
210633_PM_x_at
KRT10
keratin 10
0.000438865
535.9
466.6
443.1

266
217874_PM_at
SUCLG1
succinate-CoA ligase, alpha
0.000441648
2582.3
3199.8
3034.6

subunit

267
223192_PM_at
SLC25A28
solute carrier family 25,
0.000456748
157.1
178.0
220.5

member 28

268
204820_PM_s_at
BTN3A2 ///
butyrophilin, subfamily 3,
0.000457313
1264.5
1537.9
1932.9

BTN3A3
member A2 /// butyrophilin,

subfamily 3, member A3

269
32069_PM_at
N4BP1
NEDD4 binding protein 1
0.00045791
320.7
400.4
402.0

270
208870_PM_x_at
ATP5C1
ATP synthase, H+
0.000464012
3210.8
3791.7
3616.3

transporting, mitochondrial F1

complex, gamma polypeptide

1

271
207104_PM_x_at
LILRB1
leukocyte immunoglobulin-
0.000468733
52.9
52.0
80.6

like receptor, subfamily B

(with TM and ITIM domains),

member

272
209035_PM_at
MDK
midkine (neurite growth-
0.000469597
18.5
25.2
30.3

promoting factor 2)

273
230307_PM_at
LOC100129794
similar to hCG1804255
0.000471715
17.3
14.8
13.5

274
225255_PM_at
MRPL35
mitochondrial ribosomal
0.000478299
44.4
59.0
49.3

protein L35

275
229625_PM_at
GBP5
guanylate binding protein 5
0.000478593
243.9
147.4
393.5

276
209140_PM_x_at
HLA-B
major histocompatibility
0.000478945
8305.0
10032.9
11493.8

complex, class I, B

277
210905_PM_x_at
POU5F1P4
POU class 5 homeobox 1
0.000492713
11.9
13.7
13.9

pseudogene 4

278
218480_PM_at
AGBL5
ATP/GTP binding protein-like
0.000494707
23.8
20.7
18.1

5

279
209253_PM_at
SORBS3
sorbin and SH3 domain
0.000495796
97.5
86.2
78.2

containing 3

280
207801_PM_s_at
RNF10
ring finger protein 10
0.000508149
374.0
297.5
327.3

281
212539_PM_at
CHD1L
chromodomain helicase DNA
0.000509089
482.2
677.2
613.0

binding protein 1-like

282
224492_PM_s_at
ZNF627
zinc finger protein 627
0.000513422
127.6
168.3
125.0

283
1557186_PM_s_at
TPCN1
two pore segment channel 1
0.000513966
26.5
21.5
22.4

284
203610_PM_s_at
TRIM38
tripartite motif-containing 38
0.000514783
100.5
139.2
156.0

285
211530_PM_x_at
HLA-G
major histocompatibility
0.000525417
1034.7
1429.2
1621.6

complex, class I, G

286
201421_PM_s_at
WDR77
WD repeat domain 77
0.000527341
114.5
143.9
133.4

287
200617_PM_at
MLEC
malectin
0.000529672
244.8
174.2
147.7

288
1555982_PM_at
ZFYVE16
zinc finger, FYVE domain
0.000550743
27.5
35.4
27.8

containing 16

289
211345_PM_x_at
EEF1G
eukaryotic translation
0.000555581
4011.7
3333.0
3247.8

elongation factor 1 gamma

290
1555202_PM_a_at
RPRD1A
regulation of nuclear pre-
0.000561763
14.0
17.2
14.3

mRNA domain containing 1A

291
218304_PM_s_at
OSBPL11
oxysterol binding protein-like
0.000565559
230.5
347.9
328.7

11

292
219464_PM_at
CA14
carbonic anhydrase XIV
0.000570778
64.9
43.5
32.6

293
204278_PM_s_at
EBAG9
estrogen receptor binding site
0.000570888
482.5
591.0
510.6

associated, antigen, 9

294
218298_PM_s_at
C14orf159
chromosome 14 open reading
0.000571869
411.1
515.6
573.0

frame 159

295
213675_PM_at
—
—
0.000576321
39.1
27.4
25.2

296
1555097_PM_a_at
PTGFR
prostaglandin F receptor (FP)
0.000581257
11.0
12.8
14.0

297
209056_PM_s_at
CDC5L
CDC5 cell division cycle 5-
0.000582594
552.0
682.3
659.9

like (S. pombe)

298
208912_PM_s_at
CNP
2′,3′-cyclic nucleotide 3′
0.00058579
308.8
415.8
392.9

phosphodiesterase

299
227018_PM_at
DPP8
dipeptidyl-peptidase 8
0.000587266
29.6
38.2
41.9

300
224650_PM_at
MAL2
mal, T-cell differentiation
0.000592979
600.4
812.5
665.3

protein 2

301
217492_PM_s_at
PTEN ///
phosphatase and tensin
0.000601775
545.5
511.2
426.0

PTENP1
homolog /// phosphatase and

tensin homolog pseudogene 1

302
211654_PM_x_at
HLA-DQB1
major histocompatibility
0.000608592
538.8
350.2
744.4

complex, class II, DQ beta 1

303
220312_PM_at
FAM83E
family with sequence
0.000609835
16.0
13.9
13.7

similarity 83, member E

304
228230_PM_at
PRIC285
peroxisomal proliferator-
0.00061118
42.0
55.4
57.6

activated receptor A

interacting complex 285

305
215171_PM_s_at
TIMM17A
translocase of inner
0.000624663
1432.1
1905.5
1715.4

mitochondrial membrane 17

homolog A (yeast)

306
228912_PM_at
VIL1
villin 1
0.000630544
53.0
29.5
27.6

307
203047_PM_at
STK10
serine/threonine kinase 10
0.000638877
41.0
39.1
54.7

308
232617_PM_at
CTSS
cathepsin S
0.000640978
1192.9
1083.0
1561.2

309
236219_PM_at
TMEM20
transmembrane protein 20
0.000648505
20.5
38.9
36.1

310
240681_PM_at
—
—
0.000649144
140.6
202.3
192.8

311
1553317_PM_s_at
GPR82
G protein-coupled receptor 82
0.000667359
13.3
20.1
21.2

312
212869_PM_x_at
TPT1
tumor protein, translationally-
0.000669242
14240.7
13447.2
13475.2

controlled 1

313
219356_PM_s_at
CHMP5
chromatin modifying protein
0.000670413
1104.5
1310.4
1322.9

5

314
1552555_PM_at
PRSS36
protease, serine, 36
0.000676354
14.2
12.9
11.8

315
203147_PM_s_at
TRIM14
tripartite motif-containing 14
0.000676359
334.8
419.3
475.4

316
43511_PM_s_at
—
—
0.000678774
70.7
60.9
80.0

317
221821_PM_s_at
C12orf41
chromosome 12 open reading
0.000683679
180.0
213.8
206.9

frame 41

318
218909_PM_at
RPS6KC1
ribosomal protein S6 kinase,
0.000686673
105.8
155.8
151.5

52 kDa, polypeptide 1

319
232724_PM_at
MS4A6A
membrane-spanning 4-
0.000686877
106.7
108.3
160.4

domains, subfamily A,

member 6A

320
218164_PM_at
SPATA20
spermatogenesis associated 20
0.000693114
181.5
130.4
156.0

Number	Date	Country
62029038	Jul 2014	US
62001889	May 2014	US
62001909	May 2014	US
62001902	May 2014	US
61965040	Jan 2014	US
61875276	Sep 2013	US

	Number	Date	Country
Parent	14481167	Sep 2014	US
Child	15898513		US

METHODS AND SYSTEMS FOR ANALYSIS OF ORGAN TRANSPLANTATION

Information

Publication Number

Date Filed

Date Published

Inventors

Original Assignees

CPC

International Classifications

Abstract

Description

Claims

CROSS REFERENCE TO RELATED APPLICATIONS

GOVERNMENT RIGHTS

Provisional Applications (6)

Continuations (1)