PURITY INDEPENDENT SUBTYPING OF TUMORS (PURIST), A PLATFORM AND SAMPLE TYPE INDEPENDENT SINGLE SAMPLE CLASSIFIER FOR TREATMENT DECISION MAKING IN PANCREATIC CANCER

Information

  • Patent Application
  • 20220170109
  • Publication Number
    20220170109
  • Date Filed
    April 01, 2020
    4 years ago
  • Date Published
    June 02, 2022
    2 years ago
Abstract
Provided are methods for identifying pancreatic cancer subtypes in a subject and treating the same. In some embodiments, the method comprise obtaining gene expression levels for each of the following genes in the biological sample: GPR87, KRT6A, BCAR3, PTGES, 1TGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3; performing pair-wise comparisons of gene expression levels for combinations of these genes, and calculating a Raw Score for the biological sample, wherein the Raw Score is indicative of the pancreatic cancer subtype in the subject. Also provided are methods for identifying differential treatment strategies for subjects diagnosed with PDAC, methods for treating PDAC patients based on the subtype of PD AC the patients have; and methods for classifying subjects diagnosed with PDAC as having a basal-like subtype or a classical subtype of PDAC.
Description
BACKGROUND

Recent treatment advances, including FOLFIRINOX (Conroy et al., 2011), gemcitabine plus nab-paclitaxel (Von Hoff et al., 2013), and olaparib for BRCA-mutant patients (Kindler et al., 2019), have provided patients and providers with better options. With the substantial progress in molecular subtyping for pancreatic cancer (Collisson et al., 2011; Moffitt et al., 2015; Bailey et al., 2016; Cancer Genome Atlas Research Network, 2017; Puleo et al., 2018; Maurer et al., 2019), there is now an opportunity to determine the optimal choice of therapy given a patient's molecular subtype and other biomarker information, enabling “precision medicine” approaches in pancreatic cancer (Aguirre et al., 2018; Aung et al., 2018).


Transcriptomic molecular subtyping in pancreatic cancer is currently an area of active development, where multiple subtyping schemas for pancreatic cancer have been proposed. For example, three molecular subtypes with potential clinical and therapeutic relevance were first described by Collisson and colleagues (Collisson et al., 2011), leveraging a combination of cell line, bulk, and laser capture microdissected (LCM) patient samples: Collisson (i) quasi-mesenchymal (QM-PDA), (ii) classical, and (iii) exocrine-like. A subsequent study of patients with pancreatic cancer (Bailey et al., 2016), based on more diverse pancreatic cancer histologies in addition to the most common pancreatic ductal adenocarcinoma (PDAC), found four molecular subtypes: Bailey (i) squamous, (ii) pancreatic progenitor, (iii) immunogenic, and (iv) aberrantly differentiated endocrine exocrine (ADEX). More recently, Puleo and colleagues describe five subtypes that are based on features specific to tumor cells and the local microenvironment (Puleo et al., 2018). Maurer and colleagues performed LCM of both tumor and stroma and showed the contribution of each to the three schemas above (Maurer et al., 2019). Finally, we have previously shown two tumor-intrinsic subtypes of PDAC (Moffitt et al., 2015), which we called Moffitt (i) basal-like, given the similarities with basal breast and basal bladder cancer, and (ii) classical, given the overlap with the Collisson classical subtype.


However, consensus regarding proposed subtypes for clinical decision making in PDAC has been elusive. In addition, each proposed schema utilized independent cohorts of patients to demonstrate clinical relevance. As a result, the generalizability, robustness, and relative clinical utility of each proposed subtyping schema remains unclear. Comparative evaluations of these proposed subtyping systems have been limited, partially due to the difficulty in curating and applying these diverse subtyping approaches in new datasets.


SUMMARY

This Summary lists several embodiments of the presently disclosed subject matter, and in many cases lists variations and permutations of these embodiments. This Summary is merely exemplary of the numerous and varied embodiments. Mention of one or more representative features of a given embodiment is likewise exemplary. Such an embodiment can typically exist with or without the feature(s) mentioned; likewise, those features can be applied to other embodiments of the presently disclosed subject matter, whether listed in this Summary or not. To avoid excessive repetition, this Summary does not list or suggest all possible combinations of such features.


The presently disclosed subject matter provides in some embodiments methods for determining a subtype of a pancreatic tumor in a biological sample comprising, consisting essentially of, or consisting of pancreatic tumor cells obtained from a subject. In some embodiments, the methods comprise obtaining gene expression levels for each of the following genes in the biological sample: GPR87, KRT6A, BCAR3, PTGES, ITGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3; performing a pair-wise comparison of the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H, wherein Gene Pairs 1-8 and Gene Pairs A-H are presented in Table 1, and calculating a Raw Score for the biological sample, wherein the calculating comprises assigning a value of 1 for each Gene Pair for which Gene A of the Gene Pair has a higher expression level than Gene B of the Gene Pair, and a value of 0 for each Gene Pair for which Gene A of the Gene Pair has a lower expression level than Gene B of the Gene Pair; multiplying each assigned value by the coefficient listed above for the corresponding Gene Pair to calculate eight individual Gene Pair scores; and adding the eight individual Gene Pair scores together along with a baseline effect to calculate a Raw Score for the biological sample, wherein the baseline effect is −6.815 for Gene Pairs 1-8 and −12.414 for Gene Pairs A-H, wherein if the calculated Raw Score is greater than or equal to 0, the tumor subtype is determined to be a basal-like subtype, and if the calculated Raw Score if less than 0, the tumor subtype is determined to be a classical subtype. In some embodiments, the method further comprises converting the Raw Score to a predicted basal-like probability (PBP) using the inverse-logit transformation






PBP=e
Raw score/(1+eRaw Score),


wherein if the PBP is greater than 0.5, the tumor subtype is determined to be a basal-like subtype and if the PBP if less than or equal to 0.5, the tumor subtype is determined to be a classical subtype. In some embodiments, the pancreatic tumor is a pancreatic ductal adenocarcinoma (PDAC). In some embodiments, the biological sample comprises a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or comprises a frozen or archival sample derived therefrom. In some embodiments, the obtaining employs a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof. In some embodiments, the technique comprises NanoString and employs probes comprising the SEQ ID NOs. as set forth in Table 2. In some embodiments, the subject is a human.


The presently disclosed subject matter also provides in some embodiments methods for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC). In some embodiments, the methods comprise obtaining gene expression levels for each of the following genes in a biological sample comprising PDAC cells isolated from the subject: GPR87, KRT6A, BCAR3, PTGES, ITGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3; performing a pair-wise comparison of the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H, wherein Gene Pairs 1-8 and Gene Pairs A-H are as defined herein above, calculating a Raw Score for the biological sample, wherein the calculating comprises assigning a value of 1 for each Gene Pair for which Gene A of the Gene Pair has a higher expression level than Gene B of the Gene Pair, and a value of 0 for each Gene Pair for which Gene A of the Gene Pair has a lower expression level than Gene B of the Gene Pair; multiplying each assigned value by the coefficient listed above for the corresponding Gene Pair to calculate eight individual Gene Pair scores; and adding the eight individual Gene Pair scores together along with a baseline effect to calculate a Raw Score for the biological sample, wherein the baseline effect is −6.815 for Gene Pairs 1-8 and −12.414 for Gene Pairs A-H, wherein if the calculated Raw Score is greater than or equal to 0, the tumor subtype is determined to be a basal-like subtype, and if the calculated Raw Score if less than 0, the tumor subtype is determined to be a classical subtype; identifying a differential treatment strategy for the subject based on the subtype assigned, wherein if the assigned subtype is a basal-like subtype, the differential treatment strategy comprises treatment with gemcitabine, optionally in combination with nab-paclitaxel; and if the assigned subtype is a classical subtype, the different treatment strategy comprises treatment with FOLFIRINOX. In some embodiments, the biological sample comprises a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or comprises a frozen or archival sample derived therefrom. In some embodiments, the obtaining employs a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof. In some embodiments, the technique comprises NanoString and employs probes comprising the SEQ ID NOs: identified herein above. In some embodiments, the subject is a human.


The presently disclosed subject matter also provides in some embodiments methods for treating patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). In some embodiments, the methods comprise identifying a subtype of the patient's PDAC via any method disclosed herein; and treating the patient with gemcitabine, optionally in combination with nab-paclitaxel, if the assigned subtype is a basal-like subtype and treating the patient with FOLFIRINOX if the assigned subtype is classical. In some embodiments, the treating comprises at least one additional anti-PDAC treatment. In some embodiments, the at least one additional anti-PDAC treatment is surgery, radiation, administration of an additional chemotherapeutic agent, and any combination thereof. In some embodiments, the additional chemotherapeutic agent is a CCR2 inhibitor, a checkpoint inhibitor, or any combination thereof. In some embodiments, the patient is a human.


The presently disclosed subject matter also provides in some embodiments methods for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having a basal-like subtype or a classical subtype of PDAC. In some embodiments, the methods comprise performing a pair-wise comparison of gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H in a sample comprising PDAC cells isolated from the subject, wherein Gene Pairs 1-8 and Gene Pairs A-H are as defined herein above; and calculating a Raw Score for the sample, wherein the calculating comprises assigning a value of 1 for each Gene Pair for which Gene A of the Gene Pair has a higher expression level than Gene B of the Gene Pair, and a value of 0 for each Gene Pair for which Gene A of the Gene Pair has a lower expression level than Gene B of the Gene Pair; multiplying each assigned value by the coefficient listed above for the corresponding Gene Pair to calculate eight individual Gene Pair scores; and adding the eight individual Gene Pair scores together along with a baseline effect to calculate a Raw Score for the biological sample, wherein the baseline effect is −6.815 for Gene Pairs 1-8 and −12.414 for Gene Pairs A-H, wherein if the calculated Raw Score is greater than or equal to 0, the PDAC subtype is determined to be a basal-like subtype, and if the calculated Raw Score if less than 0, the PDAC subtype is determined to be a classical subtype. In some embodiments, the methods further comprise converting the Raw Score to a predicted basal-like probability (PBP) using the inverse-logit transformation






PBP=e
Raw score/(1+eRaw Score)


wherein if the PBP is greater than 0.5, the PDAC subtype is determined to be a basal-like subtype and if the PBP if less than or equal to 0.5, the PDAC subtype is determined to be a classical subtype. In some embodiments, the sample comprises a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or comprises a frozen or archival sample derived therefrom. In some embodiments, the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H in a sample are determined using a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof. In some embodiments, the technique comprises NanoString and employs probes comprising the SEQ ID NOs: identified herein above. In some embodiments, the subject is a human.


Thus, it is an object of the presently disclosed subject matter to provide methods for classifying PDAC cancers into basal-like or classical subtypes, which in some embodiments can be used to differentially treat the PDAC cancers based on the subtype identified. An object of the presently disclosed subject matter having been stated hereinabove, and which is achieved in whole or in part by the presently disclosed subject matter, other objects will become evident as the description proceeds when taken in connection with the accompanying EXAMPLES and Figures as best described herein below.





BRIEF DESCRIPTION OF THE FIGURES


FIGS. 1A-1C are Kaplan-Meier plots showing subtype performance in predicting patient prognosis in pooled datasets from the survival group (see Table 7). Kaplan-Meier plots of OS in the context of the subtyping schemas of Collisson (FIG. 1A), Bailey (FIG. 1B), and Moffitt (FIG. 1C). Log-rank P values for overall association were determined from stratified Cox proportional hazards models, where dataset was used as a stratification factor to account for variation in baseline hazard across studies. BIC was calculated to compare the three subtyping schemas.



FIG. 2 shows the results of development and validation of the PurIST SSC classifier. It provides an overview of the PurIST prediction procedure. Gene expression for genes pertaining to each PurIST TSP is first measured in a new sample. Values are assigned for each TSP given the relative expression of each gene in the TSP (1 if gene A>gene B expression in the pair, 0 otherwise). Given the set of estimated PurIST TSP coefficients, a TSP score is calculated by summing the product of each TSP and its corresponding TSP coefficient, adjusting for the model intercept. This value is finally transformed into a predicted probability of belonging to the basal-like subtype for classification (inverse logit function).



FIGS. 3A-3G show clinical relevance of PurIST SSC in datasets belonging to the treatment group. FIGS. 3A and 3B are Kaplan-Meier plots of OS in pooled datasets (FIG. 3A) belonging to the survival group minus datasets belonging to the training group and Yeh Seq FNA samples (FIG. 3B). P value and HRs for overall association were estimated by stratified Cox proportional hazards model in FIG. 3A, where dataset of origin was used as a stratification factor. FIGS. 3C and 3D are waterfall plots showing the percent change (% change) in size of tumor target lesions from baseline in the context of PurIST subtypes in the COMPASS (FIG. 3C) and Linehan trials (FIG. 3D). +20% and −30% of size change are marked by dashed lines. In FIG. 3C, gray vs. black bars denote PurIST subtype calls of the patient tumors. Patients marked with * were treated with gemcitabine/nab-paclitaxel (GP)-based therapy, and the rest were treated with modified FOLFIRINOX (m-FOLFIRINOX). In FIG. 3D, gray vs. black bars denote PurIST subtype calls of pretreatment samples. Colored tracks below to compare subtype calls for samples pre- and posttreatment of PurIST subtyping and the Moffitt schema. Patients marked with * were treated with FOLFIRINOX, and the rest were treated with FOLFIRINOX+PF-04133309. FIG. 3E is a plot of correlation between the PurIST score (basal-like probability) for patient samples pre- and posttreatment in the Linehan trial. Basal-like samples are denoted with light gray triangles and classical samples are denoted with black triangles. FIGS. 3F and 3G are plots showing correlation between the percentage of change (% change) of tumors and the PurIST score (basal-like probability) derived from PurIST in basal-like (FIG. 3F) and classical samples (FIG. 3G), excluding a basal-like sample with an unstable DNA subtype.





BRIEF DESCRIPTION OF THE SEQUENCE LISTING

SEQ ID NOs: 1-58 are exemplary biosequences corresponding to certain human gene products as disclosed herein and summarized herein below. For each of SEQ ID NOs: 1-58, the odd numbered SEQ ID NO: encodes the immediately following even numbered SEQ ID NO. as set forth in Table 3.


SEQ ID NOs: 59-102 are exemplary NanoString probes for certain gene products disclosed herein, which are as follows: ANXA10 (SEQ ID NO: 59), C16orf74 (SEQ ID NO: 60), CDH17 (SEQ ID NO: 61), DCBLD2 (SEQ ID NO: 62), DDC (SEQ ID NO: 63), GPR87 (SEQ ID NO: 64), KRT6A (SEQ ID NO: 65), KRT15 (SEQ ID NO: 66), KRT17 (SEQ ID NO: 67), LGALS4 (SEQ ID NO: 68), PLA2G10 (SEQ ID NO: 69), PTGES (SEQ ID NO: 70), REG4 (SEQ ID NO: 71), S100A2 (SEQ ID NO: 72), TFF1 (SEQ ID NO: 73), TSPAN8 (SEQ ID NO: 74), CTSE (SEQ ID NO: 75), LYZ (SEQ ID NO: 76), MUC17 (SEQ ID NO: 77), MYOIA (SEQ ID NO: 78), NR1I2 (SEQ ID NO: 79), PIP5K1B (SEQ ID NO: 80), BCAR3 (SEQ ID NO: 81), GATA6 (SEQ ID NO: 82), CLRN3 (SEQ ID NO: 83), CLDN18 (SEQ ID NO: 84), ITGA3 (SEQ ID NO: 85), SLC40A1 (SEQ ID NO: 86), KRT5 (SEQ ID NO: 87), RPLP0 (SEQ ID NO: 88), B2M (SEQ ID NO: 89), ACTB (SEQ ID NO: 90), RPL19 (SEQ ID NO: 91), GAPDH (SEQ ID NO: 92), LDHA (SEQ ID NO: 93), PGK1 (SEQ ID NO: 94), TUBB (SEQ ID NO: 95), SDHA (SEQ ID NO: 96), CLTC (SEQ ID NO: 97), HPRT1 (SEQ ID NO: 98), ABCF1 (SEQ ID NO: 99), GUSB (SEQ ID NO: 100), TBP (SEQ ID NO: 101), and ALAS1 (SEQ ID NO: 102).


Genes listed among SEQ ID NOs: 59-102 that are not included in those among SEQ ID NOs: 1-59 (e.g., those corresponding to SEQ ID NOs: 75-80 and 88-102) can be employed in some embodiments as internal controls for any of the gene expression techniques disclosed herein.


DETAILED DESCRIPTION
I. General Considerations

Molecular subtyping for pancreatic cancer has made substantial progress in recent years, facilitating the optimization of existing therapeutic approaches to improve clinical outcomes in pancreatic cancer. Disclosed herein are assessments of three major subtype classification schemas in the context of results from two clinical trials and by meta-analysis of publicly available expression data to assess statistical criteria of subtype robustness and overall clinical relevance. We then developed a single-sample classifier (SSC) using penalized logistic regression based on the most robust and replicable schema.


Demonstrated herein is that a tumor-intrinsic two-subtype schema is most robust, replicable, and clinically relevant. We developed Purity Independent Subtyping of Tumors (PurIST), a SSC with robust and highly replicable performance on a wide range of platforms and sample types. We show that PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response to FOLIFIRNOX.


We show that a tumor-intrinsic two-subtype schema is the most replicable and clinically robust across different subtype schemas, with basal-like subtype tumors showing resistance to FOLFIRINOX-based regimens in two independent clinical trials. Our results strongly support the need to evaluate molecular subtyping in treatment decision-making for patients with PDAC in the context of future clinical trials. We present PurIST, a clinically usable single-sample classifier that is robust and highly replicable across different gene expression platforms and sample collection types, and may be utilized in future clinical trials.


As such, present herein is a clinically usable SSC that may be used on any type of gene expression data including RNAseq, microarray, and NanoString, and on diverse sample types including FFPE, core biopsies, FNAs, and bulk frozen tumors. Although results of the association of FOLFIRINOX resistance in patients with basal-like subtype tumors is compelling, future prospective clinical trials in patients with PDAC will be needed to evaluate the utility of PurIST in treatment decision making, and in the context of different therapies. The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for patients with pancreatic ductal adenocarcinoma and should be considered in the context of future clinical trials.


II. Definitions

All technical and scientific terms used herein, unless otherwise defined below, are intended to have the same meaning as commonly understood by one of ordinary skill in the art. References to techniques employed herein are intended to refer to the techniques as commonly understood in the art, including variations on those techniques or substitutions of equivalent techniques that would be apparent to one of skill in the art. While the following terms are believed to be well understood by one of ordinary skill in the art, the following definitions are set forth to facilitate explanation of the presently disclosed subject matter.


Following long-standing patent law convention, the terms “a,” “an,” and “the” mean “one or more” when used in this application, including the claims. Thus, the phrase “a cell” refers to one or more cells, unless the context clearly indicates otherwise.


As used herein, the term “and/or” when used in the context of a list of entities, refers to the entities being present singly or in combination. Thus, for example, the phrase “A, B, C, and/or D” includes A, B, C, and D individually, but also includes any and all combinations and subcombinations of A, B, C, and D.


The term “comprising,” which is synonymous with “including,” “containing,” and “characterized by,” is inclusive or open-ended and does not exclude additional, unrecited elements and/or method steps. “Comprising” is a term of art that means that the named elements and/or steps are present, but that other elements and/or steps can be added and still fall within the scope of the relevant subject matter.


As used herein, the phrase “consisting of” excludes any element, step, and/or ingredient not specifically recited. For example, when the phrase “consists of” appears in a clause of the body of a claim, rather than immediately following the preamble, it limits only the element set forth in that clause; other elements are not excluded from the claim as a whole.


As used herein, the phrase “consisting essentially of” limits the scope of the related disclosure or claim to the specified materials and/or steps, plus those that do not materially affect the basic and novel characteristic(s) of the disclosed and/or claimed subject matter.


With respect to the terms “comprising,” “consisting essentially of,” and “consisting of,” where one of these three terms is used herein, the presently disclosed and claimed subject matter can include the use of either of the other two terms. For example, it is understood that the methods of the presently disclosed subject matter in some embodiments comprise the steps that are disclosed herein and/or that are recited in the claims, in some embodiments consist essentially of the steps that are disclosed herein and/or that are recited in the claims, and in some embodiments consist of the steps that are disclosed herein and/or that are recited in the claim.


The term “subject” as used herein refers to a member of any invertebrate or vertebrate species. Accordingly, the term “subject” is intended to encompass any member of the Kingdom Animalia including, but not limited to the phylum Chordata (i.e., members of Classes Osteichythyes (bony fish), Amphibia (amphibians), Reptilia (reptiles), Aves (birds), and Mammalia (mammals)), and all Orders and Families encompassed therein. In some embodiments, the presently disclosed subject matter relates to human subjects.


Similarly, all genes, gene names, and gene products disclosed herein are intended to correspond to orthologs from any species for which the compositions and methods disclosed herein are applicable. Thus, the terms include, but are not limited to genes and gene products from humans. It is understood that when a gene or gene product from a particular species is disclosed, this disclosure is intended to be exemplary only, and is not to be interpreted as a limitation unless the context in which it appears clearly indicates. Thus, for example, the genes and/or gene products disclosed herein are also intended to encompass homologous genes and gene products from other animals including, but not limited to other mammals, fish, amphibians, reptiles, and birds.


The methods and compositions of the presently disclosed subject matter are particularly useful for warm-blooded vertebrates. Thus, the presently disclosed subject matter concerns mammals and birds. More particularly provided is the use of the methods and compositions of the presently disclosed subject matter on mammals such as humans and other primates, as well as those mammals of importance due to being endangered (such as Siberian tigers), of economic importance (animals raised on farms for consumption by humans) and/or social importance (animals kept as pets or in zoos) to humans, for instance, carnivores other than humans (such as cats and dogs), swine (pigs, hogs, and wild boars), ruminants (such as cattle, oxen, sheep, giraffes, deer, goats, bison, and camels), rodents (such as mice, rats, and rabbits), marsupials, and horses. Also provided is the use of the disclosed methods and compositions on birds, including those kinds of birds that are endangered, kept in zoos, as well as fowl, and more particularly domesticated fowl, e.g., poultry, such as turkeys, chickens, ducks, geese, guinea fowl, and the like, as they are also of economic importance to humans. Thus, also provided is the application of the methods and compositions of the presently disclosed subject matter to livestock, including but not limited to domesticated swine (pigs and hogs), ruminants, horses, poultry, and the like.


The term “about,” as used herein when referring to a measurable value such as an amount of weight, time, dose, etc., is meant to encompass variations of in some embodiments ±20%, in some embodiments ±10%, in some embodiments ±5%, in some embodiments ±1%, and in some embodiments ±0.1% from the specified amount, as such variations are appropriate to perform the disclosed methods and/or to employ the presently disclosed arrays.


As used herein the term “gene” refers to a hereditary unit including a sequence of DNA that occupies a specific location on a chromosome and that contains the genetic instruction for a particular characteristic or trait in an organism. Similarly, the phrase “gene product” refers to biological molecules that are the transcription and/or translation products of genes. Exemplary gene products include, but are not limited to mRNAs and polypeptides that result from translation of mRNAs. Any of these naturally occurring gene products can also be manipulated in vivo or in vitro using well known techniques, and the manipulated derivatives can also be gene products. For example, a cDNA is an enzymatically produced derivative of an RNA molecule (e.g., an mRNA), and a cDNA is considered a gene product. Additionally, polypeptide translation products of mRNAs can be enzymatically fragmented using techniques well known to those of skill in the art, and these peptide fragments are also considered gene products.


As used herein, the term “ANXA10” refers to the annexin A10 (ANXA10) gene and its transcription and translation products. Exemplary ANXA 10 nucleic acid and amino acid sequences are presented in Accession Nos. NM_007193.5 and NP_009124.2 of the GENBANK® biosequence database, respectively, and are also set forth in SEQ ID NOs: 1 and 2, respectively.


As used herein, the term “BCAR3” refers to the BCAR3 adaptor protein, NSP family member (BCAR3), gene and its transcription and translation products. Exemplary BCAR3 nucleic acid and amino acid sequences are presented in Accession Nos. NM_001261408.2 and NP_001248337.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 3 and 4, respectively.


As used herein, the term “C16orf74” refers to the Homo sapiens chromosome 16 open reading frame 74 (C16orf14) gene and its transcription and translation products. Exemplary C16orf74 nucleic acid and amino acid sequences are presented in Accession Nos. NM_206967.3 and NP_996850.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 5 and 6, respectively.


As used herein, the term “CDH17” refers to the cadherin 17 (CDH17) gene and its transcription and translation products. Exemplary CDH17 nucleic acid and amino acid sequences are presented in Accession Nos. NM_004063.4 and NP_004054.3 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 7 and 8, respectively.


As used herein, the term “CLDN18” refers to the claudin 18 (CLDN18) gene and its transcription and translation products. Exemplary CLDN18 nucleic acid and amino acid sequences are presented in Accession Nos. NM_016369.4 and NP_057453.1 of the GENBANK@ biosequence database, and are also set forth in SEQ ID NOs: 9 and 10, respectively.


As used herein, the term “CLRN3” refers to the clarin 3 (CLRN3) gene and its transcription and translation products. Exemplary CLRN3 nucleic acid and amino acid sequences are presented in Accession Nos. NM_152311.5 and NP_689524.1 of the GENBANK@ biosequence database, and are also set forth in SEQ ID NOs: amino acid and 12, respectively.


As used herein, the term “CTSE” refers to the cathepsin E (CTSE) gene and its transcription and translation products. Exemplary CTSE nucleic acid and amino acid sequences are presented in Accession Nos. NM_001910.4 and NP_001901.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 13 and 14, respectively.


As used herein, the term “DCBLD2” refers to the discoidin, CUB and LCCL domain containing 2 (DCBLD2) gene and its transcription and translation products. Exemplary DCBLD2 nucleic acid and amino acid sequences are presented in Accession Nos. NM_080927.4 and NP_563615.3 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 15 and 16, respectively.


As used herein, the term “DDC” refers to the dopa decarboxylase (DDC) gene and its transcription and translation products. Exemplary DDC nucleic acid and amino acid sequences are presented in Accession Nos. NM_000790.4 and NP_000781.2 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 17 and 18, respectively.


As used herein, the term “GATA6” refers to the GATA binding protein 6 (GATA6) gene and its transcription and translation products. Exemplary GATA6 nucleic acid and amino acid sequences are presented in Accession Nos. NM_005257.6 and NP_005248.2 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 19 and 20, respectively.


As used herein, the term “GPR87” refers to the G protein-coupled receptor 87 (GPR87) gene and its transcription and translation products. Exemplary GPR87 nucleic acid and amino acid sequences are presented in Accession Nos. NM_023915.4 and NP_076404.3 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 21 and 22, respectively.


As used herein, the term “ITGA3” refers to the integrin subunit alpha 3 (ITGA3) gene and its transcription and translation products. Exemplary ITGA3 nucleic acid and amino acid sequences are presented in Accession Nos. NM_002204.4 and NP_002195.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 23 and 24, respectively.


As used herein, the term “KRT5” refers to the keratin 5 (KRT5) gene and its transcription and translation products. Exemplary KRT5 nucleic acid and amino acid sequences are presented in Accession Nos. NM_000424.4 and NP_000415.2 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 25 and 26, respectively.


As used herein, the term “KRT6A” refers to the keratin 6A (KRT6A) gene and its transcription and translation products. Exemplary KRT6A nucleic acid and amino acid sequences are presented in Accession Nos. NM_005554.4 and NP_005545.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 27 and 28, respectively.


As used herein, the term “KRT15” refers to the keratin 15 (KRT15) gene and its transcription and translation products. Exemplary KRT15 nucleic acid and amino acid sequences are presented in Accession Nos. NM_002275.4 and NP_002266.3 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 29 and 30, respectively.


As used herein, the term “KRT17” refers to the keratin 17 (KRT17) gene and its transcription and translation products. Exemplary KRT17 nucleic acid and amino acid sequences are presented in Accession Nos. NM_000422.3 and NP_000413.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 31 and 32, respectively.


As used herein, the term “LGALS4” refers to the galectin 4 (LGALS4) gene and its transcription and translation products. Exemplary LGALS4 nucleic acid and amino acid sequences are presented in Accession Nos. NM_006149.4 and NP_006140.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 33 and 34, respectively.


As used herein, the term “LYZ” refers to the lysozome (LYZ) gene and its transcription and translation products. Exemplary LYZ nucleic acid and amino acid sequences are presented in Accession Nos. NM_000239.3 and NP_000230.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 35 and 36, respectively.


As used herein, the term “MUC17” refers to the mucin 17, cell surface associated (MUC17) gene and its transcription and translation products. Exemplary MUC17 nucleic acid and amino acid sequences are presented in Accession Nos. NM_001040105.2 and NP_001035194.1 of the GENBANK@ biosequence database, and are also set forth in SEQ ID NOs: 37 and 38, respectively.


As used herein, the term “MYOIA” refers to the myosin 1A (MYOIA) gene and its transcription and translation products. Exemplary MYOIA nucleic acid and amino acid sequences are presented in Accession Nos. NM_005379.4 and NP_005370.1 of the GENBANK@ biosequence database, and are also set forth in SEQ ID NOs: 39 and 40, respectively.


As used herein, the term “NR1I2” refers to the nuclear receptor subfamily 1 group I member 2 (NR1I2) gene and its transcription and translation products. Exemplary NR1I2 nucleic acid and amino acid sequences are presented in Accession Nos. NM_022002.2 and NP_071285.1 of the GENBANK@ biosequence database, and are also set forth in SEQ ID NOs: 41 and 42, respectively.


As used herein, the term “PIP5K1B” refers to the phosphatidylinositol-4-phosphate 5-kinase, type I, beta (PIP5K1B) gene and its transcription and translation products. Exemplary PIP5K1B nucleic acid and amino acid sequences are presented in Accession Nos. NM_003558.4 and NP_003549.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 43 and 44, respectively.


As used herein, the term “PLA2G10” refers to the phospholipase A2 group X (PLA2G10) gene and its transcription and translation products. Exemplary PLA2G10 nucleic acid and amino acid sequences are presented in Accession Nos. NM_003561.3 and NP_003552.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 45 and 46, respectively.


As used herein, the term “PTGES” refers to the prostaglandin E synthase (PTGES) gene and its transcription and translation products. Exemplary PTGES nucleic acid and amino acid sequences are presented in Accession Nos. NM_004878.5 and NP_004869.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 47 and 48, respectively.


As used herein, the term “REG4” refers to the regenerating family member 4 (REG4) gene and its transcription and translation products. Exemplary REG4 nucleic acid and amino acid sequences are presented in Accession Nos. NM_032044.4 and NP_114433.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 49 and 50, respectively.


As used herein, the term “S100A2” refers to the S100 calcium binding protein A2 (S100A2) gene and its transcription and translation products. Exemplary S100A2 nucleic acid and amino acid sequences are presented in Accession Nos. NM_005978.4 and NP_005969.2 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 51 and 52, respectively.


As used herein, the term “SLC40A1” refers to the solute carrier family 40 member 1 (SLC40A1) gene and its transcription and translation products. Exemplary SLC40A1 nucleic acid and amino acid sequences are presented in Accession Nos. NM_014585.6 and NP_055400.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 53 and 54, respectively.


As used herein, the term “TFF1” refers to the trefoil factor 1 (TFF1) gene and its transcription and translation products. Exemplary TFF1 nucleic acid and amino acid sequences are presented in Accession Nos. NM_003225.3 and NP_003216.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 55 and 56, respectively.


As used herein, the term “TSPAN8” refers to the tetraspanin 8 (TSPAN8) gene and its transcription and translation products. Exemplary TSPAN8 nucleic acid and amino acid sequences are presented in Accession Nos. NM_004616.3 and NP_004607.1 of the GENBANK® biosequence database, and are also set forth in SEQ ID NOs: 57 and 58, respectively.


The term “isolated,” as used in the context of a nucleic acid or polypeptide (including, for example, a nucleotide sequence, a polypeptide, and/or a peptide), indicates that the nucleic acid or polypeptide exists apart from its native environment. An isolated nucleic acid or polypeptide can exist in a purified form or can exist in a non-native environment.


Further, as used for example in the context of a cell, nucleic acid, polypeptide, or peptide, the term “isolated” indicates that the cell, nucleic acid, polypeptide, or peptide exists apart from its native environment. In some embodiments, “isolated” refers to a physical isolation, meaning that the cell, nucleic acid, polypeptide, or peptide has been removed from its native environment (e.g., from a subject).


The terms “nucleic acid molecule” and “nucleic acid” refer to deoxyribonucleotides, ribonucleotides, and polymers thereof, in single-stranded or double-stranded form. Unless specifically limited, the term encompasses nucleic acids containing known analogues of natural nucleotides that have similar properties as the reference natural nucleic acid. The terms “nucleic acid molecule” and “nucleic acid” can also be used in place of “gene,” “cDNA,” and “mRNA.” Nucleic acids can be synthesized, or can be derived from any biological source, including any organism.


As used herein, the terms “peptide” and “polypeptide” refer to polymers of at least two amino acids linked by peptide bonds. Typically, “peptides” are shorter than “polypeptides,” but unless the context specifically requires, these terms are used interchangeably herein.


As used herein, a cell, nucleic acid, or peptide exists in a “purified form” when it has been isolated away from some, most, or all components that are present in its native environment, but also when the proportion of that cell, nucleic acid, or peptide in a preparation is greater than would be found in its native environment. As such, “purified” can refer to cells, nucleic acids, and peptides that are free of all components with which they are naturally found in a subject, or are free from just a proportion thereof.


II. Methods

In some embodiments, the presently disclosed subject matter relates to methods for determining a subtype of a pancreatic tumor in a biological sample comprising, consisting essentially of, or consisting of pancreatic tumor cells obtained from a subject. As used herein, the phrase “subtype of a pancreatic tumor” refers to classifications wherein the underlying nature of the pancreatic tumor and/or cells thereof are classified differentially with respect to gene expression, prognosis, treatment decisions, etc. Various subtypes for pancreatic tumors and cells thereof have been described in the literature, including those set forth in, for example, U.S. Patent Application Publication No. 2017/0233827; Moffitt et al., 2015; Bailey et al., 2016; Nywening et al., 2016; Aung et al., 2017; Cancer Genome Atlas Research Network, 2017; Connor et al., 2017; and Aguirre et al., 2018; each of which is incorporated herein by reference in its entirety.


In some embodiments, the pancreatic tumor is classified as being of the basal-like subtype or of the classical subtype. The classification with respect to basal-like vs. classical can be made on the basis of the methods disclosed herein. By way of example and not limitation, a method for classifying a pancreatic tumor as being of the classical vs. the basal-like subtype can comprise obtaining gene expression levels for each of the following genes in the biological sample: GPR87, KRT6A, BCAR3, PTGES, ITGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, GENE SLC40A1, CLRN3; performing a pair-wise comparison of the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H, wherein Gene Pairs 1-8 and Gene Pairs A-H are as shown in Table 1; and calculating a Raw Score for the biological sample. In some embodiments, the calculating comprises assigning a value of 1 for each Gene Pair for which Gene A of the Gene Pair has a higher expression level than Gene B of the Gene Pair, and a value of 0 for each Gene Pair for which Gene A of the Gene Pair has a lower expression level than Gene B of the Gene Pair; multiplying each assigned value by the coefficient listed in Table 1 for the corresponding Gene Pair to calculate eight individual Gene Pair scores; and adding the eight individual Gene Pair scores together along with a baseline effect to calculate a Raw Score for the biological sample, wherein the baseline effect is −6.815 for Gene Pairs 1-8 and −12.414 for Gene Pairs A-H (i.e., the intercepts identified in Tables 25 and 26). To assign a subtype to the biological sample, if the calculated Raw Score is greater than or equal to 0, the tumor subtype is determined to be a basal-like subtype, and if the calculated Raw Score if less than 0, the tumor subtype is determined to be a classical subtype.


In some embodiments, the Raw Score that is calculated is further converted to a predicted basal-like probability (PBP) using the inverse-logit transformation






PBP=e
Raw Score/(1+eRaw score.


The PBP is another way to classify pancreatic tumor subtypes as being basal-like or classical. When a PBP is calculated, the threshold value for classifying basal-like vs. classical is slightly modified. In these cases, if the PBP is greater than 0.5, the tumor subtype is determined to be a basal-like subtype, and if the PBP if less than or equal to 0.5, the tumor subtype is determined to be a classical subtype.


As used herein, the terms “biological sample” and “sample” refer to a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or a frozen or archival sample derived therefrom, that comprises pancreatic tumor (in some embodiments, pancreatic ductal adenocarcinoma (PDAC)) cells that have been isolated from a patient with a pancreatic tumor and/or nucleic acids and/or proteins that have been isolated from such a sample. Depending on the type of gene expression analysis to be employed (discussed in more detail herein below), the sample should comprise DNA, RNA (in some embodiments messenger RNA; mRNA), or protein.


Given that the methods disclosed herein relate to pairwise comparisons of multiple genes with respect to expression levels of the corresponding gene products in the biological samples, comparisons of nucleic acid gene products or protein gene products can be employed. As would be understood by one of ordinary skill in the art, quantitative assays can be desirable to determine relative expression levels. With respect to nucleic acids, particularly mRNA gene products, a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof can be employed. Non-limiting examples of such techniques include whole transcriptome RNAseq, targeted RNAseq, SAGE, RT-PCR (particularly QRT-PCR), cDNA microarray analyses, and NanoString analysis. Techniques for assaying gene expression levels using RT-PCR, nucleic acid and/or protein microarray hybridization, and RNA-Seq are known in the art (see e.g., U.S. Pat. Nos. 5,800,992; 6,004,755; 6,013,449; 6,020,135; 6,033,860; 6,040,138; 6,177,248; 6,251,601; 6,309,822, 7,824,856; 9,920,367; 10,227,584; each of which is incorporated by reference in its entirety. See also U.S. Patent Application Publication Nos. 2010/0120097; 2011/0189679; 2014/0113333; 2015/0307874; each of which is incorporated by reference in its entirety.


In some embodiments, the assay involves use of NanoString. The basic NanoString technology is described in PCT International Patent Application Publication No. WO 2019/226514 and U.S. Pat. No. 9,181,588, each of which is incorporated herein by reference in its entirety. For use with Gene Pairs 1-8 and A-H, one of ordinary skill in the art can design appropriate NanoString probes based on the sequences of the corresponding gene products. Exemplary NanoString probes are identified in Table 6. In some embodiments, and particularly wherein different assay techniques are employed with different samples, an internal control can be employed to normalize and/or harmonize gene expression data. In some embodiments, an internal control comprises a housekeeping gene. Exemplary housekeeping genes include the CTSE, LYZ, MUC17, MYO1A, NR1I2, PIP5K1B, RPLP0, B2M, ACTB, RPL19, GAPDH, LDHA, PGK1, TUBB, SDHA, CLTC, HPRT1, ABCF1, GUSB, TBP, and ALAS1, and exemplary NanoString probes that can be employed with these genes are disclosed in SEQ ID NOs: 75-102, respectively.


In some embodiments, a gene product is a protein gene product, and gene expression is determined by quantifying an amount of protein present in a sample. Methods for quantifying gene expression at the protein level are known, and include but are not limited to enzyme-linked immunosorbent assay (ELISA), immunoprecipitation (IP), radioimmunoassay (RIA), mass spectroscopy (MS), quantitative western blotting, protein and/or peptide microarrays, etc. See e.g., U.S. Pat. Nos. 7,595,159; 8,008,025; 8,293,489; and 10,060,912; each of which is incorporated by reference herein in its entirety. For those assays that require the use of antibodies, various commercial sources of antibodies, including monoclonal antibodies, exist, including but not limited to ProMab Biotechnologies, Inc. (Richmond, Calif., United States of America), abcam plc (Cambridge, United Kingdom), Santa Cruz Biotechnology, Inc. (California, United States of America), etc.


In some embodiments, the determination of subtype of a pancreatic tumor sample, optionally a PDAC sample, can be employed in making a differential treatment decision with respect to the subject since basal-like and classical subtypes respond differently to different treatments. By way of example and not limitation, if the assigned subtype is a basal-like subtype, a differential treatment strategy for that subject/patient could be with gemcitabine (i.e., 4-amino-1-[(2R,4R,5R)-3,3-difluoro-4-hydroxy-5-(hydroxymethyl)oxolan-2-yl]pyrimidin-2-one, which is often administered as a hydrochloride; see U.S. Patent Application Publication No. 2008/0262215 and U.S. Pat. No. 8,299,239), optionally in combination with paclitaxel (i.e., [(1S,2S,3R,4S,7R,9S,10S,12R,15S)-4,12-diacetyloxy-15-[(2R,3S)-3-benzamido-2-hydroxy-3-phenylpropanoyl]oxy-1,9-dihydroxy-10,14,17,17-tetramethyl-11-oxo-6-oxatetracyclo[11.3.1.03,10.04,7]heptadec-13-en-2-yl] benzoate; see U.S. Pat. No. 6,753,006) or nab-paclitaxel (i.e., ABRAXANE® brand nanoparticle albumin-bound paclitaxel; see U.S. Pat. No. 7,758,891). Methods for treating pancreatic cancer with gemcitabine and/or paclitaxel/nab-paclitaxel are known (see e.g., U.S. Patent Application Publication No. 2017/0020824, which is incorporated herein by reference in its entirety).


If the subtype of the pancreatic tumor sample is classical, then in some embodiments the subject/patient is treated with FOLFIRINOX (composed of folinic acid (leucovorin), fluorouracil, irinotecan, and oxaliplatin; Conroy et al., 2011). In some embodiments, FOLFIRINOX can be combined with other treatments, including but not limited to the CCR2 inhibitor PF-04136309 (see Nywening et al., 2016).


In some embodiments, additional anti-pancreatic cancer/tumor strategies can be employed, including but not limited to surgery, radiation, or administration of other chemotherapeutics. Exemplary chemotherapeutics that can be employed in the methods of the presently disclosed subject matter include, but are not limited to protein kinase inhibitors (PKIs). A listing of exemplary PKIs, their targets, and their associations with basal-like and classical tumor subtypes is presented in Table 28. In some embodiments, a PKI that is associated with overexpression in basal-like subtypes tumors is employed in a combination therapy for samples that are of a basal-like subtype. In some embodiments, a PKI that is associated with overexpression in classical subtype tumors is employed in a combination therapy for samples that are of the classical subtype.


In some embodiments, the presently disclosed subject matter also provides methods for treating patients diagnosed with PDAC. In some embodiments, the methods comprise determining a subtype of the patient's PDAC as being basal-like or classical, and treating the subject as disclosed herein. In some embodiments, basal-like subtype patients are treated with gemcitabine, optionally in combination with nab-paclitaxel, and classical subtype patients are treated with FOLFIRINOX, optionally in combination with a CCR2 inhibitor. The combination therapies discussed herein above can also be employed in the treatment methods of the presently disclosed subject matter.


EXAMPLES

The following EXAMPLES provide illustrative embodiments. In light of the present disclosure and the general level of skill in the art, those of skill will appreciate that the following EXAMPLES are intended to be exemplary only and that numerous changes, modifications, and alterations can be employed without departing from the scope of the presently disclosed subject matter.


Materials and Methods for the Examples

Public datasets. Archival data were obtained from public sources (see Moffitt et al., 2015; Aung et al., 2017; Aguirre et al., 2018; Bailey et al., 2016; Nywening et al., 2016; Connor et al., 2017; and Cancer Genome Atlas Research Network, 2017) and are summarized in Table 4. For the public datasets, expression was used “as-is” with respect to the original publication; that is, RNAseq data were not realigned and gene-level expression estimates were provided in terms of fragments per kilobase per million reads (FPKM) or transcripts per million (TPM), depending on the study.


Sample collection. Deidentified bulk and FNA samples (see Table 5) were collected from the Institutional Review Board (IRB)-approved University of North Carolina Lineberger Comprehensive Cancer Center Tissue Procurement Core Facility after IRB exemption in accordance with the U.S. Common Rule and were flash frozen in liquid nitrogen. FNA samples were collected ex vivo at the time of resection. The FNA technique used mirrors standard cytopathology procedures, where three passes were performed using a 22-gauge needle. Palpation was used to localize the tumor. Samples were frozen in either PBS or RNALATER@ brand stabilizing reagent (Sigma-Aldrich Corp., St. Louis, Mo., United States of America). FFPE samples were prepared, hematoxylin and eosin stained, and reviewed by a single pathologist who was blinded to the results as described herein. See below for data processing and analysis of Yeh_Seq samples. RNAseq (GSE131050) and NanoString (GSE131051) data generated from these samples are deposited in Gene Expression Omnibus (GEO).


RNAseq. Samples for Yeh_Seq were sequenced on a NEXTSEQ® 500 brand sequencing system (Illumina, inc., San Diego, Calif., United States of America). We converted BCL files to FASTQ using bcl2fastq2 Conversion Software 2.20.0 (Illumina, Inc.). Individual lane files were combined into one FASTQ for each sample. FASTQ of PDX samples were split into human and mouse reads using bbmap v37.90 (Bushnell, 2014). The total expected read counts per gene were quantified by Salmon 0.9.1 (Patro et al., 2017) using arguments “—gcBias—seqBias”. For human samples, Genome Reference Consortium Human Build 38 (GRCh38) was used. For PDX samples, GRCh38 was involved in quantification for human reads, while the mouse reference genome GRCm38/mm10 (December 2011) available at the website of the University of California Santa Cruz (UCSC) Genomics Institute was used to quantify mouse reads. The expression of each gene was measured by the Transcripts per Million (TPM), which was subjected to downstream analysis.


Customized quality control guidelines were used for low-input (FNA) and degraded (FFPE) samples (Adiconis et al., 2013). Bulk or FNA samples were flagged if the proportion of bases mapped to coding regions fell below 30%. For FFPE samples, samples were flagged if the proportion fell below 10%. We also checked the total number of unique reads after deduplication. Bulk and FFPE samples were flagged if the total number of unique reads were below 1 million. FNA samples were flagged if the total number of unique reads were below half a million. We also checked the uniformity of transcript coverage by assessing 5′-to-3′ bias using gene body plots, and insert size distribution, so that any sample that clearly distinguished itself as an outlier was flagged.


For the Linehan dataset, total RNA was isolated from matched patient tumor biopsies collected at baseline and post-treatment cycle two as part of clinical study NCT01413022 testing the efficacy of PF-04136309 in combination with FOLFIRINOX as previously described (Nywening et al., 2016). RNA expression libraries were generated with TruSeq Stranded mRNA kits according to the manufacturer's instructions and sequencing was performed on the HiSeq 2500 Sequencing System (Illumina, Inc.). BCL files were converted to FASTQ with bcltofastq software v2.19.0 (Illumina, Inc.). The total expected read counts per gene were quantified by Salmon 0.9.1 using arguments “—gcBias—seqBias” and reference genome GRCh38, which were normalized to TPM as described above.


CC based subtype calling. Unsupervised CC was applied for each of the subtyping schemas (Collisson, Bailey, and Moffitt) on all public datasets included in our study as previously described using the ConsensusClusterPlus package in R (Aguirre et al., 2018), subsequent to sample filtering. In brief, 62 genes identified by Collisson (Collisson et al., 2011), 613 differentially expressed genes from the multiclass SAM analysis by Bailey (Bailey et al., 2016), and 50 tumor specific genes from Moffitt (Moffitt et al., 2015) were utilized for subtyping analysis, seeking the presence of 3, 4, and 2 clusters respectively. For the Bailey and Collisson schemas and using published calls as the gold standard (Bailey subtypes in the PACA_AU_array and PACA_AU_seq datasets, and Bailey and Collisson subtypes in the TCGA_PAAD dataset), we found a better concordance of the subtype calls by applying row-scaling than without row-scaling prior to consensus clustering (CC). Therefore, for the Bailey and Collisson schemas, each dataset was subjected to gene-wise (row) scaling across samples so that expressions were normalized to z-scores for each gene as the input for CC. Row-scaling was not applied to the Moffitt schema. For the COMPASS and Connor datasets, the 10 least variable signature genes were dropped in subtype calling for the Bailey schema since, in these two datasets, the CC found subsamples with 0 variance which led to termination of the function in R.


PurIST Single Sample Classifier.

    • Data pre-processing. For each RNAseq dataset, we first removed genes in the bottom 20% percentile in expression on average in that dataset. This is to remove consistently low expressing genes that may be unhelpful for prediction later. For microarray data, due to probe-specific effects, it is more difficult to assume that measured expression is correlated with actual biological expression, so we do not apply this filtering here. We then further reduced the list of remaining genes in each dataset to those belonging to a list of 500 Moffitt tumor-specific genes determined previously (Moffitt et al., 2015). Finally, we retained only those genes that were in common across all nine datasets after these filtering steps. At the end of this process, we had 412 genes out of 500 tumor-specific genes remaining that were in common across all 9 data datasets.
    • Training Datasets and candidate gene ranking. Training labels and expression values from the genes in our tumor-specific gene list served as the basis for our building the PurIST model. Training labels for PurIST were a subset of the Moffitt CC in the Training Group datasets (Aguirre, Moffitt_GEO_array and TCGA_PAAD; Table 7) were utilized. These samples were further filtered to provide final training labels for the PurIST algorithm by dropping poorly clustered samples on the clustered dendrogram in each dataset based on visual inspection. We considered these filtered calls as “training labels”. Because not all genes may be consistent in their relationship with tumor subtype across training datasets or may be strongly discriminatory between subtypes, we ranked candidate genes in based on the consistency of their Differential Expression (DE) between subtypes in each individual Training Group dataset, as well as the consistency in the direction of their DE for utilization in subsequent steps (Lusa et al., 2007; Paquet & Hallett, 2015). We applied the Wilcoxon Rank Sum test to each gene in a given study to test for differences in mean expression between basal-like and classical subjects. We then obtained a cross-study DE consistency score by summing the −log10 p-values for differential expression across studies. In general, genes that were consistently differentially expressed were most likely to have higher scores. Then, we ranked genes based on this score from largest to smallest. We then considered the top 10% of this list for model training. Lastly, we removed genes where the sign of the difference in mean subtype expression was not the same in all Training Group datasets. The remaining genes then formed our final candidate gene list for downstream steps in PurIST model training.
    • kTSP selection for prediction: overview. Let us define a gene pair (gdis, gdit), where gdis is the raw expression of gene s for subject i in study d, and gdit is defined similarly with respect to some gene t. A TSP is an indicator variable based on this gene pair, I(gdis>gdit)-1/2, where its value represents which gene in the pair has higher expression in subject i from study d (1/2 if gdis>gdit, and −1/2 otherwise). In traditional applications (k=1), a single TSP is selected out of the set of all possible gene pairs such that if I(gdis>gdit)-1/2>0, this implies subtype A with high probability in the training data, otherwise implying subtype B (Geman et al., 2004). Therefore, in a new subject, binary class prediction is performed by checking whether I(gdis,1>gdit,1)-1/2>0 vs otherwise. We view such binary variables as “biological switches” indicating how pairs of genes are expressed relative to some clinical outcome. TSPs were originally proposed in the context of binary classification (Geman et al., 2004; Tan et al., 2005; Afsari et al., 2014). In the kTSP setting, class prediction reduces to verifying whether the sum across k selected TSPs is greater than 0:











l
=
1

k



I


(


g

dis
,
l


>

g

dit
,
l



)



-

1
2


>
0




This reduces to a majority vote across the selected k TSPs, where the contribution of each of the k TSPs are equally weighted to select subtype A if the above sum is greater than 0, and subtype B otherwise.


We describe this approach to select TSPs in the next section. However, several studies have found that equal weighting of TSPs in majority voting may be suboptimal, as some TSPs may be more informative than others (Shi et al., 2011). Therefore, we utilized penalized logistic regression (Breheny & Huang, 2011) to jointly estimate the effect of each of the k selected TSPs in predicting binary subtype, and to further remove TSPs with weak or redundant effects. Predicted probabilities of basal-like subtype membership may then be obtained from the fitted model logistic regression model on our training samples, where values greater than 0.5 indicate predicted membership to the basal-like subtype and classical otherwise.

    • Horizontal data integration and kTSP selection via switchbox. To apply the top scoring pairs transformation, we utilized the switchBox R package (Afsari et al., 2015) to enumerate all possible gene pairs based on our final candidate gene list and training samples (function SWAP.KTSP.Train, with optimal parameters featureNo=1000, krange=50, FilterFunc=NULL). Given the large number of potential gene pairs based on this list, in addition to the strong correlation between gene pairs sharing the same gene, the switchBox package utilized a greedy algorithm to select from this list a subset of gene pairs that were helpful for prediction, given the set of training labels. We merged data from each Training Group dataset without normalization prior to applying switchBox, as the method only looked at the relative gene expression ranking within each sample from each study. The method then selected a subset of k TSPs, where k is determined through a greedy optimization procedure.
    • Model training based on selected kTSP list. To remove redundant TSPs and to jointly estimate their contribution in predicting subtype in our training samples, we utilized the ncvreg R package (Breheny & Huang, 20111) to fit a penalized logistic regression model based upon the selected TSPs from switchBox. Our design matrix was an N× (k+1) matrix, where the first column pertained to the intercept and the remaining k columns pertained to the k selected TSPs from switchBox. Here N was the total number of training samples from each dataset employed for training. Each TSP in the design matrix was represented as a binary vector, taking on the value of 1 if gene A's expression was greater than gene B's expression. Our outcome variable here was binary subtype (1=Basal, 0 otherwise). We utilized optional parameters alpha=0.5 and nfolds=N. We allowed for correlation between TSPs by setting the ncvreg alpha parameter to 0.5 in order to shrink the coefficients of highly correlated TSPs and also remove correlated uninformative TSPs from the model. We set nfolds=N to apply leave one out cross validation in order to choose the optimal MCP penalty tuning parameter for variable selection, where the optimal tuning parameter was the one that minimized the cross-validation error of the fitted model. Our final model then reported the set of coefficients estimated for each of the kTSPs, where each coefficient may be interpreted as the change in log odds of a patient being part of the basal-like subtype when the 1th TSP is equal to 1, given the others in the model. TSPs with coefficient of 0 were those that have been removed from the model for either weak effect or redundancy with other TSPs. Predicted probabilities of Basal subtype membership may be obtained by computing the inverse logit of the linear predictor Xi,new{circumflex over (β)}(the Raw Score), where Xi,new was a 1×(k+1) TSP predictor vector from a new sample, and P was our estimated set of coefficients from the fitted penalized logistic regression model. Then, predicted probabilities of basal-like subtype membership for this new sample can be computed through the inverse logit function:






{circumflex over (p)}
i,new=exp(Xi,new{circumflex over (β)})(1+exp(Xi,new{circumflex over (β)}))


{circumflex over (p)}i,new values greater than 0.5 indicated predicted membership basal-like subtype, and those less than 0.5 were those that were predicted those be of the classical subtype. This was equivalent to determining whether Xi,new{circumflex over (β)}>0 (basal-like subtype) vs Xi,new{circumflex over (β)}<0 (classical subtype), where Xi,new{circumflex over (β)} may also be utilized as a continuous score for classification (“PurIST Score”). Therefore, prediction in new samples, such as from our validation datasets, reduced to simply checking the relative expression of each gene within the set of TSPs. Those TSPs with selected 0 coefficient can be ignored in this setting.


For all discussions regarding classifier performance, we obtained the predicted subtypes in the manner described above. The level of confidence in the prediction can be determined based upon the distance of {circumflex over (p)}i,new from 0.5, where values closer to {circumflex over (p)}i,new indicated lower confidence in the predicted subtype and higher confidence otherwise. Specifically, values of {circumflex over (p)}i,new between 0.5 and 0.6 indicated the lean basal-like prediction category, 0.6 and 0.9 represented the likely basal-like prediction category, and values greater than 0.9 indicated the strong basal-like prediction category. Values of {circumflex over (p)}i,new between 0.5 and 0.4 indicated the lean classical prediction category, 0.6 and 0.1 represented the likely classical prediction category, and values less than 0.1 indicated the strong classical prediction category.

    • NanoString and PurIST-n. We repeated the above procedure with a subset of genes using NanoString probes (PurIST-n; see Table 6). We then retrained our model in given our training datasets limiting to these genes, rebuilding candidate TSPs and applying our penalized logistic regression model to obtain our PurIST-n classifier. Matched samples from RNAseq were run on the NanoString nCounter platform as per manufacturers instruction. In brief, for each sample, RNA was combined with the NanoString master mix and the Capture Probe set. Hybridization of the RNA with the Capture Probe set took place overnight while incubating at 65° C. After hybridization completed, the samples were added to the NanoString nCounter cartridge and placed in the nCounter Prep Station using the high sensitivity setting. After the Prep Station run was complete, the cartridge was removed and placed in the NanoString Digital Analyzer for scanning.


Sample inclusion for consensus clustering analysis and PurIST training. For treatment response and survival analysis, samples with available clinical and RNAseq data were used. Specifically, for the pooled survival analysis, samples from the following datasets with RNAseq data and CC calls were utilized: Linehan, Moffitt_GEO_array, PACA_AU_seq, PACA_AU_array, and TCGA_PAAD (survival group; Table 7). Duplicated samples in PACA_AU_seq and PACA_AU_array datasets were only used once, with the subtypes called in PACA_AU_array used when mismatches of subtype calls were found between the two datasets. To train PurIST, Moffitt schema CC calls from the datasets in the training group (Aguirre, Moffitt_GEO_array, and TCGA_PAAD; Table 7) were utilized. These samples were further filtered to provide final training labels for the PurIST algorithm by dropping poorly clustered samples on the clustered dendrogram in each dataset based on visual inspection. We considered these filtered calls as “training labels.” Model training for PurIST is described herein above.


Statistical Analysis. Overall survival estimates were calculated using the Kaplan-Meier method. Association between overall survival and individual covariates such as subtype were evaluated via the cox proportional hazards (coxph) models using the coxph function from the ‘survival’ R package, where a given subtyping schema was considered as a multi-level categorical predictor. The logrank p-value was utilized to evaluate overall association of a subtyping system with overall survival. In the pooled analyses, a stratified coxph model was utilized, where dataset of origin was used as a stratification factor to account for variation in baseline hazard across studies. To test for differences in survival between individual subtypes within a schema, linear contrasts were utilized in conjunction with the fitted stratified coxph model to construct a general linear hypothesis test. BIC pertaining to each fitted stratified coxph model was calculated for each schema using the “BIC” function in R, where smaller BIC values indicate better model fit. Agreement between subtype calls in patients within matched samples were performed using Cohen's Kappa via the “kappa2” function from the irr package in R. Hypothesis tests evaluating the null hypothesis that Kappa=0, indicating random agreement, was also performed using the kappa2 function. Kappa values of 1 indicate perfect agreement. Association between categorical response, defined by RECIST 1.1 criteria (PD, SD, PR, CR), and called subtypes from in a given clinical trial with treatment response was evaluated using the Generalized Cochran-Mantel-Haenszel test, with trial arm utilized as the stratification factor and assuming categorical treatment response as an ordinal variable. This is to correct for potential confounding due to differences between arms. This test was carried out using the “cmh_test” function from the coin R package. We determined an empirical null distribution for this test using permutation testing, assuming 5 million permutations to ensure robustness against any deviations from test assumptions. In modeling response as a continuous variable (% change in tumor volume from baseline) with respect to a given schema, two-way ANOVA was utilized, where schema subtype and arm were utilized as categorical factors, and BIC was calculated similar to before. When categorical response was utilized, a multinomial regression model utilizing schema subtypes as a categorical prediction was fit using the “polr” from the MASS R package, and BIC was calculated as mentioned previously. For the permutation test to compare correlation among various gene sets, we first evaluated the Spearman correlations between each of the PurIST TSP genes in FFPE vs. bulk, FFPE vs. FNA, and also bulk vs. FNA. This was also repeated for each of the Bailey ADEX genes and Bailey immunogenic genes. We then calculated paired Wilcoxon signed-rank statistic of to test if the 18 correlations among TSP genes was significantly higher than that of ADEX genes (or immunogenic genes). Since the 18 correlations were not independent observations, the null distribution was approximated using permutations. The permutation of the FFPE and FNA matches for the 6 bulk samples was done 10,000 times and the paired Wilcoxon statistic was likewise computed for each permutation. This generated the distribution of the statistic under the null hypothesis that the paired difference between correlations among TSP genes versus those among ADEX genes (or immunogenic genes) are centered around zero, which allowed us to derive a p-value for the observed statistic before permutation.


Example 1
The Moffitt Tumor-intrinsic Two-subtype Schema has Important Implications for Treatment Response

To evaluate the potential impact of molecular subtypes on treatment response, we utilized transcriptomic and treatment response data from two independent clinical trials, and performed a systematic analysis of treatment response with respect to CC calls from each of the three different subtyping schemas (described herein above)) for PDAC: Collisson, Bailey, and Moffitt (Collisson et al., 2011; Bailey et al., 2016; Moffitt et al., 2015). We first examined the association of the subtypes from each schema with treatment response using patient samples from a promising phase Ib trial by Nywening and colleagues (“Linehan,” Linehan_seq dataset; Tables 8-17) of FOLFIRINOX in combination with a CCR2 inhibitor (PF-04136309) in patients with locally advanced PDAC, where an objective response was seen in 49% of patients (Nywening et al., 2016). Enrolled patients had no prior treatment, and underwent core biopsies prior to the start of therapy. Twenty-eight patients with RNAseq and treatment data were available for analysis.


We found a significant overall association between categorical treatment response (based on RECIST 1.1 criteria) and pretreatment subtype classifications from the Moffitt schema (p=0.0117; Tables 18-21), where basal-like tumors showed no response to FOLFIRINOX alone or FOLFIRINOX plus PF-04136309 after stratifying by arm [overall response rate (ORR)=0%; disease control rate (DCR)=33%; Tables 18-21, generalized Cochran-Mantel-Haenszel test], whereas classical tumors showed a much stronger response overall (ORR=40%; DCR=100%). In contrast, we were unable to identify a relationship between subtype and treatment response under the Collisson (p=0.428) and Bailey (p=0.113) schemas (Tables 18-21). As the sample size in this phase Ib trial (n=28 patients) was small, we similarly reanalyzed the COMPASS trial results (n=40 patients) in the context of the three subtyping schemas.


Patients enrolled in COMPASS underwent core-needle biopsies and were treated with one of two standard first-line therapies, modified-FOLFIRINOX or gemcitabine plus nanoparticle albumin-bound paclitaxel (nab-paclitaxel). Collected patient samples in COMPASS underwent laser capture microdissection (LCM) followed by whole genome sequencing and RNAseq. Subtypes for each schema were determined as mentioned previously. Similar to our findings in the Linehan phase Ib trial, we found a significant association between the Moffitt two subtype schema with categorical treatment response stratifying by arm (P=0.00098, generalized Cochran-Mantel-Haenszel test), where the basal-like subtype had much lower response to either treatment (ORR=10%; DCR=50%) relative to the classical subtype (ORR=36.7%; DCR=100%). We also found significant associations between treatment response and the subtypes from the Collisson (p=0.0024) and Bailey (p=0.0067) schemas. However, we notably observe that the Bailey squamous subtype strongly overlaps with the Moffitt basal-like subtype, and the remaining nonsquamous Bailey subtypes appear to overlap strongly with the Moffitt classical subtype (Cohen Kappa=1.0, p=2.54×10−10). We similarly found that the Collisson QM-PDA and the remaining non-QM-PDA subtypes correspond strongly with the Moffitt basal-like and classical subtypes, respectively (Cohen Kappa=0.875, p=2.44×10−8), a fact also mirrored in the Linehan trial.


Given these observations, we formally evaluated the relative clinical utility of each subtyping system using non-nested model selection criteria such as Bayesian information criterion (BIC; Schwarz, 1978). Briefly, such criteria evaluate model fit relative to the complexity of the model, as models with more predictors (subtypes) may simply have better fit due to overfitting, and also may contain excess predictors (additional subtypes) that do not contribute meaningfully in differentiating clinical outcomes. The model with the lowest BIC in a series of competing candidate models is preferred in statistical applications, and is agnostic to the magnitude of the difference (Kass et al., 1995). Considering response as a continuous outcome (% change in tumor volume), we find that the Moffitt schema had the best (lowest) BIC score in both datasets (Linehan BIC=247.37, COMPASS BIC=378.75, two-way ANOVA model; Tables 18-21), compared with the Collisson (Linehan BIC=254.63, COMPASS BIC=382.8) and Bailey (Linehan BIC=250.75, COMPASS BIC=385.66) schemas. This result similarly held if we considered response as a categorical variable (ordinal regression model; Tables 18-21). This finding was also reflected among the non-QM-PDA and nonsquamous subtypes (Tables 18-21), where little difference in response can be seen between these subtypes. Our results using BIC suggested that the additional subtypes found in the Collisson and Bailey schemas do not demonstrate additional benefit in differentiating treatment response over the Moffitt two-subtype schema. Taken together, these results suggest that the Moffitt basal-like and classical subtypes strongly and parsimoniously explained treatment response relative to other schemas in both clinical trials.


The Linehan phase Ib trial captured both pre- and posttreatment biopsies, providing a unique opportunity to evaluate the stability of molecular subtypes after treatment. As pre- and post-treatment biopsies were unlikely to be obtained from the same location, these samples may also provide an opportunity to evaluate intrapatient tumor heterogeneity. Interestingly, we found strong stability in the Moffitt schema subtypes in pre- and post-treatment biopsies (Cohen Kappa=1.0; p=2.54 10−10), suggesting that not only may there be less tumor-intrinsic subtype heterogeneity within a tumor, but also that the Moffitt schema subtypes are not affected by treatment, either with FOLFIRINOX or with the addition of the CCR2 inhibitor. In contrast, we found higher rates of switching in Collisson subtypes pre- to posttreatment (Tables 23 and 24), where changes in the exocrine-like and classical subtypes were more common. Similarly, the nonsquamous Bailey subtypes appeared to show the highest rate of subtype switching pre- and posttreatment, with the ADEX subtype demonstrating the highest rate of switching among these subtypes (Tables 23 and 24).


It was unclear whether there is any clinical significance to such subtype transitions. Prior studies had suggested that the Bailey ADEX, Bailey immunogenic, and Collisson exocrine-like subtypes are confounded by tumor purity in contrast to the Moffitt subtypes (Cancer Genome Atlas Research Network, 2017; Puleo et al., 2018; Maurer et al., 2019), which may explain some of the increased heterogeneity in subtypes pre- and posttreatment in these schemas. In contrast, the Collisson QM-PDA and Bailey squamous subtypes, which were shown to overlap strongly with the Moffitt basal-like subtype, were observed to be much more stable between the two time points.


Example 2
The Tumor-intrinsic Two-subtype Schema Strongly and Replicably Differentiates Patient Survival Across Multiple Studies

Given the paucity of available genomic data in the context of treatment response in PDAC, we also performed a meta-analysis of five independent patient cohorts with OS data available: Linehan_seq, Moffitt GEO array (GSE71729), ICGC PACA_AU array, ICGC PACA_AU seq, and TCGA PAAD (survival group; Table 7). To determine the potential replicability of the different subtyping schemas (Collisson, Bailey, Moffitt) in differentiating clinical outcomes, we utilized CC subtype calls from each schema.


We found that the Moffitt tumor-intrinsic two-subtype schema reliably differentiated survival across individual datasets (Table 22), showing significant associations with OS in the majority of individual studies in contrast to other schemas. After pooling datasets, we found that patients with Moffitt basal-like subtype tumors had significantly worse prognosis compared with the Moffitt classical subtype (FIG. 1C, stratified HR=1.98, p<0.0001, stratified Cox proportional hazards model). We also observed similar trends in the Bailey squamous and Collisson QM-PDA subtypes relative to other subtypes in the same schemas (FIGS. 1A and 1B), mirroring our treatment response results described herein above. However, overall subtype-specific survival differences were most pronounced within the two-subtype schema across studies (Table 22), compared with the Collisson (p=0.069) and Bailey (p=0.076) schemas.


Moreover, we found that nonsquamous subtypes in the Bailey schema had very similar OS to one another (FIG. 1B), where a direct overall comparison of these subtypes showed no statistically significant differences in survival in our pooled dataset (immunogenic vs. ADEX stratified HR=1.07, pancreatic progenitor vs. ADEX HR=1.01, overall p=0.82). We found a similar result when comparing survival among patients from the non-QM-PDA subtypes in the Collisson schema in the pooled data (FIG. 1A; exocrine-like vs. classical stratified HR=1.17; p=0.344).


In our pooled dataset, strong correspondence was again found between the Bailey squamous, Collisson QM-PDA, and Moffitt basal-like subtypes, and between the Moffitt classical subtype and the remaining subtypes in the Bailey (Cohen Kappa=0.56, p=0) and Collisson (Cohen Kappa=0.4, p=0) schemas. In TCGA PAAD, where estimates of tumor purity were available, Moffitt classical patients that were also classified as QM-PDA in the Collisson schema had much lower tumor purity than other samples (p=0.0016). The Bailey ADEX and immunogenic samples also had lower tumor purity, regardless of whether they were called Moffitt classical or basal-like. These findings were similar to other studies (Cancer Genome Atlas Research Network, 2017; Puleo et al., 2018; Maurer et al., 2019), and suggested that the discordance in subtype assignment between schemas may be driven by tumor purity.


To determine the best fitting model for OS, we calculated BIC with respect to the stratified Cox proportional hazards model pertaining to each schema. Similar to our analysis of treatment response, we found that the Moffitt two-subtype schema had the best (lowest) BIC and therefore had the best and most parsimonious fit to the pooled survival data (FIGS. 1A-1C; Table 22). We also found this to be the case in the majority of individual studies, replicated across each of our validation datasets (Table 22). These results reflected our finding that no difference in OS can be observed among the Collisson non-QM-PDA and Bailey nonsquamous subtypes in our pooled analysis.


Taken together, these findings supported the conclusion that the Moffitt two-subtype schema strongly and parsimoniously explained differences in OS as compared to alternate subtyping schemas. Our results further suggested that the additional subtypes found in the Collisson and Bailey schemas did not demonstrate additional clinical benefit in terms of predicting OS relative to the simpler Moffitt two-subtype schema, based on BIC and direct statistical comparison of the Collisson non-QM-PDA and Bailey nonsquamous subtypes. Given the robustness and highly replicable clinical utility of the Moffitt schema, we next developed a SSC based on this tumor-intrinsic two-subtype schema to avoid reliance on CC-based analysis.


Example 3
PurIST SSC

The ability to resolve and assign subtypes via clustering is limited when applied to individual patients. Reclustering new samples with existing training samples may also change existing subtype assignments. Thus, we developed a robust SSC, PurIST, to predict subtype in individual patients, based on our three largest bulk gene expression datasets (TCGA PAAD, Aguirre Biopsies, and Moffitt GSE71729, training group). A key element of our method includes the utilization of tumor-intrinsic genes previously identified (Moffitt et al., 2015) to avoid the possible confounding of tumor gene expression with those from other tissue types. For model training, we designated training labels as described herein above. We used rank-derived quantities as predictors in our final SSC model instead of the raw expression values, utilizing the k Top Scoring Pair (kTSP) approach to generate these predictors (described herein above). The motivation of this approach was that while the raw values of gene expression may be on different scales in different studies, their relative magnitudes can be preserved by ranks.


We found that this type of rank transformation of the raw expression data had several advantages. First, a single predictor (TSP) only depends on the ranks of raw gene expression of a gene pair in a sample. Hence, its value is robust to overall technical shifts in raw expression values (i.e., due to variation in sequencing depth), and, as a result, is less sensitive to common between-sample normalization procedures of data preprocessing (Leek, 2009; Afsari et al., 2014; Patil et al., 2015). Second, it simplifies data integration over different training studies as data are on the same scale. Finally, prediction in new patients is also simplified, as normalizing new patient data to the training set is no longer necessary, which may further affect the accuracy of model predictions (Patil et al., 2015).


Example 4
Development and External Validation of PurIST Classifier

We applied the systematic procedure described herein implementing the above approach to derive our PurIST model for prediction in the tumor-intrinsic two-subtype schema given the training labels and ranked transformed predictors for each training samples. The selected eight gene pairs (TSP), fitted model, and model coefficients are given in Tables 25 and 26. The validation that is performed in a hypothetical new patient comprises computing the values of each of the eight selected TSPs in that patient, where a value of 1 is assigned if the first gene in a TSP—gene A—has greater expression than the second gene—gene B—in that patient (and assigned 0 value otherwise). These values are then multiplied by the corresponding set of estimated TSP model coefficients, summing these values to get the patient “TSP Score” after correction for estimated baseline effects. This score is then converted to a predicted probability of belonging to the basal-like subtype, where values greater than 0.5 suggest basal-like subtype membership and the classical subtype otherwise.


To assess the quality of our prediction model, we evaluated the cross-validation error of the final model in our training group. We found that the internal leave-one-out cross-validation error for PurIST on the training group was low (3.1%).


To validate this model, we applied it to the validation group datasets and determined whether PurIST predictions recapitulated the CC subtypes in each study. We found that pooled validation samples strongly segregated by CC subtype when sorted by their predicted basal-like probability, despite diverse studies of origin. These suggested that our methodology avoided potential study-level batch effects. The relative expression of classifier genes within each classifier TSP (paired rows) strongly discriminated between subtypes in each sample, forming the basis of our robust TSP-oriented approach for subtype prediction. We also found that, visually, predicted subtypes from PurIST had strong correspondence with independently determined CC subtypes.


Overall, the PurIST classifier predicted subtypes with high levels of confidence with most basal-like subtype predictions having predicted basal-like probabilities >0.9 (strong basal-like) and most classical subtype predictions with predicted basal probabilities of <0.1 (strong classical). Among these high confidence predictions, the majority of these calls corresponded with subtypes obtained independently via CC. Lower confidence calls (likely/lean basal-like/classical categories of prediction) had higher rates of misclassification, although these less confident calls were more rare in our validation datasets.


To evaluate the overall classification performance of PurIST across studies, we applied a nonparametric meta-analysis approach to obtain a consensus ROC curve based on the individual ROC curves from each validation study (Martinez-Camblor, 2017). We found that the overall consensus AUC was high, with a value of 0.993. ROC curves from individual studies were also consistent. In addition, we found that the estimated interstudy variability of these ROC curves with respect to predicted basal-like probability threshold t was low overall, with relatively higher variance at low thresholds and almost no variability at our standard threshold of 0.5 or greater. These reflected the similarity of individual ROC curves that were observed.


We found that within our validation datasets, the prediction accuracy rates were in general 90% or higher, and individual study AUCs were 0.95 or greater (see Table 27). Furthermore, sensitivities and specificities were often high and in some cases equal to 1, reflecting near perfect classification accuracy. These results suggested that PurIST was robust across multiple datasets and platforms and recapitulated the subtypes independently obtained via CC, which we have shown to have high clinical utility.


Example 5
Replicability of PurIST in Archival Formalin-fixed and Paraffin-embedded and FNA Samples

Because frozen bulk tumor samples are not commonly available in routine clinical practice, we next looked at the replicability of PurIST predictions across sample types that are more widely collected in clinical practice. Notably, nearly all preoperative and metastatic biopsies are obtained using either FNA or core biopsy techniques. Prior studies have shown the feasibility of performing RNAseq on core biopsies (Aguirre et al., 2018) and endoscopic ultrasound guided FNAs, both of which are commonly utilized in the diagnosis of pancreatic cancer (Rodriguez et al., 2016). We therefore evaluated the performance of PurIST in both formalin-fixed and paraffin embedded (FFPE) and FNA samples.


Among 47 pairs of matched FNA and bulk samples that passed quality control (Yeh_Seq dataset), we found significant agreement between the PurIST subtype calls of the matched FNA and bulk samples (Cohen Kappa=0.544; p=2.8×105). Only three pairs of samples (6.4%) show disagreement in subtype calling results using PurIST. CC calls of the bulk samples are also shown as a comparison.


We performed a similar evaluation with tumors that we had matched FFPE, FNA, and bulk samples available. We found complete agreement among PurIST subtype predictions among FFPE, FNA, and bulk samples in patients that had all three sample types available (five sets total), further supporting that PurIST was robust across different sample preparations.


We also found that the genes pertaining to PurIST TSPs are comparatively less variable than genes not designated as tumor-intrinsic. For example, PurIST TSP genes, originally selected from our tumor-intrinsic gene list, had significantly higher Spearman correlation between sample types than Bailey immunogenic (p=0.0149) or ADEX genes (p=0.0083) using a permutation test described herein above. The stability of TSP genes across sample types supported their robustness and their ability to identify tumor-intrinsic signals in samples that may be confounded by low-input or degradation.


Example 6
Replicability of PurIST Predictions on a NanoString Platform

RNAseq assays in Clinical Laboratory Improvement Amendments (CLIA)-certified laboratories are still in their infancy. Thus, we evaluated the performance of PurIST on samples using NCOUNTER@ brand detection technology (NanoString Technologies, Inc., Seattle, Wash., United States of America), a gene expression quantification system that directly quantifies molecular barcodes. This platform has been widely used in cancer molecular subtyping (Veldman-Jones et al., 2015), and is more widely available in CLIA-certified laboratories.


In samples with both RNAseq and NanoString platform expression data available, we evaluated the consistency between subtype calls based on their RNAseq and NanoString expression data using PurIST-n. This updated classifier was trained in a manner similar to PurIST, with the exception that genes were limited to those in common between the two platforms, as a more limited set of genes were available for our NanoString probeset. We found that there was strong agreement between PurIST-n calls in 51 patients with matched RNAseq/NanoString samples (Cohen Kappa=0.879; p=2.25×10−11), where only one sample showed disagreement in its PurIST-n call. This discrepancy may have been due to the relatively lower read count in the RNAseq sample for this patient. In addition, it is noteworthy that the PurIST-n call for this sample was a low confidence call (“lean classical”). These results supported the replicability of PurIST on the NanoString platform and suggested that NanoString may be more robust at overcoming the hurdles of low input or degraded samples.


Example 7
Applicability of PurIST to Treatment Decision Making

We next evaluated the potential utility of using PurIST for clinical decision making. In basal-like and classical samples that were classified by PurIST, we found significant survival differences in both the pooled public (with all training group samples removed) and the Yeh_Seq FNA datasets, with basal-like samples showing shorter OS (FIGS. 3A and 3B; Table 22).


We then looked at the relevance of PurIST to treatment response in the COMPASS and Linehan trials (FIGS. 3C and 3D). PurIST recapitulated 48 of 49 PDAC subtype calls compared with the previous CC-based calls in the COMPASS dataset, and 66 of 66 subtype calls in the Linehan dataset. Only one patient with a CC classical tumor was called basal-like by PurIST and had stable disease (SD, % change >−30% and <20%) in the COMPASS trial. Notably, the only PR seen in a PurIST basal-like tumor was in a patient with an unstable DNA subtype (Aung et al., 2018).


In agreement with our CC analysis, we found that PurIST-predicted subtype tumors had similar associations with treatment response (FIGS. 3C and 3D; Tables 18-21). We also found no change in PurIST subtype or the confidence of the call after treatment, suggesting that PurIST tumor subtypes were unchanged after treatment with FOLFIRINOX and PF-04136300 (FIGS. 3D and 3E). Finally, after excluding the sample with an unstable-DNA-subtype, we showed a positive correlation between PurIST basal-like predicted class probabilities and worse treatment response in basal-like tumors (FIG. 3F). No association of PurIST classical confidence and treatment response was seen (FIG. 3G).


DISCUSSION OF THE EXAMPLES

The availability of next-generation sequencing has facilitated a wealth of genomic studies in pancreatic cancer (Collisson et al., 2011; Moffitt et al., 2015; Bailey et al., 2016; Cancer Genome Atlas Research Network, 2017; Puleo et al., 2018; Maurer et al., 2019). Paired with the increasing availability of promising treatment options for patients with pancreatic ductal adenocarcinomas (PDAC), the ability to predict optimal treatment regimens for patients is becoming ever more critical. Treatments such as FOLFIRINOX have nearly doubled median overall survival (OS) from 6.8 to 11.1 months (Conroy et al., 2011), and gemcitabine plus nab-paclitaxel has increased median OS to 8.5 months (Von Hoff et al., 2013) in patients with metastatic disease. Determining the optimal choice of therapy given a patient's individual clinical or molecular characteristics, thereby enabling “precision medicine” approaches (Ashley, 2016) in PDAC, may improve these outcomes further.


The ongoing multi-center study of changes and characteristics of genes in patients with pancreatic cancer for better treatment selection (COMPASS) was the first study to show treatment ramifications with two molecular subtypes (Aung et al., 2018) first introduced by Moffitt and co-workers in 2015 (Moffitt et al., 2015). Patients enrolled in COMPASS underwent percutaneous core needle biopsies and were treated with one of two standard first-line therapies, modified-FOLFIRINOX or gemcitabine plus nab-paclitaxel according to physician choice. Collected patient samples in COMPASS underwent laser capture microdissection (LCM) followed by whole genome and RNA sequencing, providing an essential opportunity to evaluate genomic associations with treatment response. The findings from COMPASS demonstrated strong associations of molecular subtypes derived from consensus clustering (CC) with treatment response, and further support the need for a clinically usable subtyping system that can be integrated into future clinical studies.


While the development of subtype-based precision medicine approaches is advanced for some cancers (Parker, 2009; Hood, 2011; Vargas, 2016; Dienstmann, 2017), consensus regarding such molecular subtypes for clinical decision-making in pancreatic ductal adenocarcinoma (PDAC) has been elusive. Multiple molecular subtyping systems for pancreatic cancer have been recently proposed in the literature with some studies isolated to PDAC and others that include additional histologies that fall under pancreatic cancer. For example, three molecular subtypes with potential clinical and therapeutic relevance (Collisson classical, quasi-mesenchymal and exocrine-like) were first described in Collisson et al., 2011, leveraging a combination of cell line, bulk, and microdissected patient samples. In contrast, a subsequent study of pancreatic cancer patients later found four molecular subtypes (Bailey et al., 2016) based upon the more diverse pancreatic cancer types: PDAC, adenosquamous, colloid, IPMN with invasive cancer, acinar cell and undifferentiated cancers (Bailey pancreatic progenitor, squamous, immunogenic, and aberrantly differentiated endocrine exocrine (ADEX)). More recently, Puleo et al., described five subtypes which are based on features specific to tumor cells and the local microenvironment (Puleo et al., 2018). Maurer et al. experimentally demonstrated the epithelial and stromal origin of many these transcripts with a cohort of microdissected samples (Maurer et al., 2019). Using non-negative matrix factorization to virtually microdissect tumor samples, we previously have shown two tumor-specific subtypes of PDAC (Moffitt et al., 2015) that we called basal-like, given the similarities with basal breast and basal bladder cancer, and classical, given the overlap with Collisson classical.


Comparative evaluations of these proposed subtyping systems have been limited, partially due to the difficulty in curating and applying these diverse subtyping approaches in new datasets. In one study, The Cancer Genome Atlas (TCGA) pancreatic cancer (PAAD) working group showed that the Collisson quasi-mesenchymal, Bailey immunogenic, and Bailey ADEX subtypes are enriched in low molecular purity PDAC samples (Cancer Genome Atlas Research Network, 2017). In samples of sufficient purity, Collisson classical/Moffitt classical/Bailey pancreatic progenitor and Collisson quasi-mesenchymal/Moffitt basal-like/Bailey squamous were most closely aligned. However, no other independent molecular or clinical evaluations of alternate subtyping systems have been proposed.


Through the careful curation of a large number of publicly available PDAC gene expression datasets, we perform, for the first time, a systematic interrogation of the aforementioned subtyping systems based upon their molecular fidelity and clinical utility across multiple validation datasets. We describe herein that the two-tumor subtype model developed by Moffitt et al. (Moffitt et al., 2015) is robust to confounders such as purity and best explains clinical outcomes across multiple validation datasets. Given the performance of this two-tumor subtype model, we have developed a single sample classifier that we call Purity Independent Subtyping of Tumors (PurIST) to perform subtype calling for clinical use. We showed that PurIST performs well on multiple gene expression platforms including microarray, RNA sequencing, and NanoString. In addition, we demonstrated its potential utility for small sample volumes such as fine needle aspirations (FNAs), given the preponderance of non-surgical biopsies in the neoadjuvant and metastatic settings. Lastly, we confirmed the stability of PurIST subtypes after treatment, and augmented the prior findings in COMPASS that subtypes are associated with treatment response. Particularly, we showed that PurIST basal-like subtype tumors were associated with treatment resistance to FOLFIRINOX, strongly supporting the need to incorporate subtyping into clinical trials of patients with PDAC.


Several subtyping systems for pancreatic cancer have now been proposed. Despite this, several limitations remain before they can be clinically usable. Here we leverage the wealth of transcriptomic studies that have been performed in pancreatic cancer to determine the molecular subtypes that may be most clinically useful and replicable across studies. Our results show that while multiple molecular subtypes may be used to characterize patient samples, the two tumor-intrinsic subtypes from the Moffitt schema: basal-like (overlaps with Bailey squamous/Collisson QM-PDA) and classical (overlaps with non-Bailey squamous/non-Collisson QMPDA) are the most concordant and clinically robust. The compelling findings of basal-like tumors showing resistance to FOLFIRINOX and the lack of objective studies comparing current first-line therapies FOLFIRINOX versus gemcitabine plus nab-paclitaxel strongly support the need to evaluate the role of molecular subtyping in treatment decision making for patients with PDAC. Therefore, we have developed a SSC based on the two tumor-intrinsic subtypes that avoids the instability associated with current strategies of clustering multiple samples and the low tumor purity issues in PDAC samples.


Prior studies have shown that merging samples from multiple studies (horizontal data integration) can improve the performance of prediction models, relative to training on individual studies (Richardson et al., 2016). However, systematic differences in the scales of the expression values in each dataset are often observed, as some may have been separately normalized prior to their publication or were generated from a variety of expression platforms. Complicated cross-platform normalizations are often employed in such situations prior to model training. Furthermore, new samples must be normalized to the training dataset prior to prediction to obtain relevant predicted values. This often results in a “test-set bias” (Patil et al., 2015), where predictions may change due to the samples in the test set or the normalization approach used. In addition, prediction models may change with the addition of new training samples, as renormalizations may be warranted among training samples. In all, this leads to potential complications for data merging, stability of prediction, and model accuracy (Lusa et al., 2007; Paquet & Hallett, 2015).


These drawbacks are largely addressed by the presently disclosed PurIST approach, which is not dependent on cross-study normalization, and is robust to platform type and sample collection differences. We showed that the sensitivity and specificity of PurIST calls are high across multiple independent studies, demonstrating that the PurIST classifier recapitulated the tumor-intrinsic subtype calling obtained initially by CC. Given the significant clinical relevance of the two tumor-intrinsic subtypes for both prognosis and treatment response and the high accuracy of predicted subtype calls in our validation datasets, PurIST would appear to have tremendous clinical value. Specifically, PurIST worked for gene expression data assayed across multiple platforms, including microarrays, RNAseq, and NanoString. Furthermore, the algorithm provided replicable classification for matched samples from snap-frozen bulk tissue as well as FNA, core biopsies, and archival tissues.


Thus, PurIST may be flexibly used on low input and more degraded samples and may be performed with targeted gene expression platforms such as NanoString, avoiding the need for a CLIA RNAseq assay. Our enduring findings that basal-like subtype tumors were significantly less likely to respond to FOLFIRINOX-based regimens strongly supported the need for the incorporation of molecular subtyping in treatment decision making to determine the association of molecular subtypes with this and other therapies. In addition, the stability of PurIST subtypes after treatment is a noteworthy finding and may point to fundamental biological differences in the tumor subtypes. Our ability to subtype based on either core or FNA biopsies considerably increases the flexibility and practicality of integrating PDAC molecular subtypes into future clinical trials in the metastatic and neoadjuvant setting where bulk specimens are rarely available.


Summarily, several genomic studies in pancreatic cancer suggest clinically relevant expression-based subtypes. However, consensus subtypes remain unclear. Using the explosion of publicly available data, the relationships of the different subtypes were examined and it has been demonstrated that a two-tumor subtype schema was most robust and clinically relevant. A single-sample classifier (SSC) that is referred to herein as Purity Independent Subtyping of Tumors (PurIST) with robust and highly replicable performance on a wide range of platforms and sample types has been produced and is described herein. That PurIST subtypes have meaningful associations with patient prognosis and have significant implications for treatment response has been demonstrated. The flexibility and utility of PurIST on low-input samples such as tumor biopsies allows it to be used at the time of diagnosis to facilitate the choice of effective therapies for PDAC patients and should be considered in the context of future clinical trials.


REFERENCES

All references cited in the instant disclosure, including but not limited to all patents, patent applications and publications thereof, scientific journal articles, and database entries (including but not limited to GENBANK® biosequence database entries and all annotations available therein) are incorporated herein by reference in their entireties to the extent that they supplement, explain, provide a background for, or teach methodology, techniques, and/or compositions employed herein.

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It will be understood that various details of the presently disclosed subject matter can be changed without departing from the scope of the presently disclosed subject matter. Furthermore, the foregoing description is for the purpose of illustration only, and not for the purpose of limitation.









TABLE 1







Gene Pairs and Related Coefficients for PurIST and PurIST-n










GENE PAIR
GENE A
GENE B
Coefficient





1
GPR87
REG4
1.994


2
KRT6A
ANXA10
2.031


3
BCAR3
GATA6
1.618


4
PTGES
CLDN18
0.922


5
ITGA3
LGALS4
1.059


6
C16orf74
DDC
0.929


7
S100A2
SLC40A1
2.505


8
KRT5
CLRN3
0.485


A
GPR87
REG4
3.413


B
KRT6A
ANXA10
3.437


C
KRT17
LGALS4
2.078


D
S100A2
TFF1
2.651


E
C16orf74
DDC
0.901


F
KRT15
PLA2G10
2.677


G
PTGES
CDH17
2.911


H
DCBLD2
TSPAN8
1.903
















TABLE 2







Exemplary NanoString Probes and SEQ ID NOs.











GENE PAIR
GENE A
SEQ ID NO:
GENE B
SEQ ID NO:





1
GPR87
64
REG4
71


2
KRT6A
65
ANXA10
59


3
BCAR3
81
GATA6
82


4
PTGES
70
CLDN18
84


5
ITGA3
85
LGALS4
68


6
C16orf74
60
DDC
63


7
S100A2
72
SLC40A1
86


8
KRT5
87
CLRN3
83


A
GPR87
64
REG4
71


B
KRT6A
65
ANXA10
59


C
KRT17
67
LGALS4
68


D
S100A2
72
TFF1
73


E
C16orf74
60
DDC
63


F
KRT15
66
PLA2G10
69


G
PTGES
70
CDH17
61


H
DCBLD2
62
TSPAN8
74
















TABLE 3







Listing of Exemplary Nucleic acid and Amino acid Sequences


with GENBANK ® Accession Nos.








Gene Name
Nucleic Acid and Amino Acid


(Coding Nucleotides*)
Accession Nos.** (SEQ ID NO:)





ANXA10 (165-1139)
NM_007193.5 (1); NP_009124.2 (2)


BCAR3 (359-2836)
NM_001261408.2 (3);



NP_001248337.1 (4)


C16orf74 (190-420)
NM_206967.3 (5); NP_996850.1 (6)


CDH17 (94-2592)
NM_004063.4 (7); NP_004054.3 (8)


CLDNI8 (62-847)
NM_016369.4 (9); NP_057453.1 (10)


CLRN3 (158-838)
NM_152311.5 (11); NP_689524.1 (12)


CTSE (105-1295)
NM_001910.4 (13); NP_001901.1(14)


DCBLD2 (370-2697)
NM_080927.4 (15); NP_563615.3 (16)


DDC (87-1529)
NM_000790.4 (17); NP_000781.2 (18)


GATA6 (132-1919)
NM_005257.6 (19); NP_005248.2 (20)


GPR87 (334-1410)
NM_023915.4 (21); NP_076404.3 (22)


ITGA3 (331-3486)
NM_002204.4 (23); NP_002195.1 (24)


KRT5 (99-1871)
NM_000424.4 (25); NP_000415.2 (26)


KRT6A (70-1764)
NM_005554.4 (27); NP_005545.1 (28)


KRT15 (64-1434)
NM_002275.4 (29); NP_002266.3 (30)


KRT17 (67-1365)
NM_000422.3 (31); NP_000413.1 (32)


LGALS4 (60-1031)
NM_006149.4 (33); NP_006140.1 (34)


LYZ (29-475)
NM_000239.3 (35); NP_000230.1 (36)


MUC17 (56-13537)
NM_001040105.2 (37);



NP_001035194.1 (38)


MYO1A (264-3395)
NM_005379.4 (39); NP_005370.1 (40)


NR1I2 (49-1470)
NM_022002.2 (41); NP_071285.1 (42)


PIP5K1B (766-2388)
NM_003558.4 (43); NP_003549.1 (44)


PLA2G10 (80-577)
NM_003561.3 (45); NP_003552.1 (46)


PTGES (31-489)
NM_004878.5 (47); NP_004869.1 (48)


REG4 (147-623)
NM_032044.4 (49); NP_114433.1 (50)


S100A2 (350-646)
NM_005978.4 (51); NP_005969.2 (52)


SLC40A1 (327-2042)
NM_014585.6 (53); NP_055400.1 (54)


TFF1 (41-295)
NM_003225.3 (55); NP_003216.1 (56)


TSPAN8 (180-893)
NM_004616.3 (57); NP_004607.1 (58)





*nucleotide positions in the corresponding an Accession No.


**Accession Nos. in the GENBANK ® biosequence database.













TABLE 4







Summary of Public Datasets














Sample





Dataset
Platform
Collection
Sample Types
Samples
Reference















MoffittGEO
microarray
Bulk
Primary PDAC, PDAC
357
Moffitt et


(G5E71729)


metastases, normal

al., 2015





tissues




COMPASS
RNAseq
Core
Primary PDAC, PDAC
50
Auna et




biopsies,
metastases

al., 2017




LCM





Aguirre
RNAseq
Core
Primary PDAC, PDAC
73
Aguirre et


Biopsies

biopsies,
metastases, acinar cell

al., 2018




FNA
carcinoma




ICGC
RNAseq
Bulk,
Primary pancreatic
92
Bailey et


PACA-AU

>12%
cancers: PDAC,

al., 2016


seq

celluarity
adenosquamous, colloid,







IPMN with invasive







cancer, acinar cell and







undifferentiated




ICGC
microarray
Bulk,
Primary pancreatic
131
Bailey et


PACA-

>12%
cancers: PDAC,

al., 2016


AU array

celluarity
adenosquamous, colloid,







IPMN with invasive







cancer, acinar cell and







undifferentiated,







mucinous non-cystic







carcinoma, and signet







ring




Moffitt
RNAseq
Bulk
PDX, PDAC cell lines,
61
Moffitt et





CAFS

al., 2015


Linehan seq
RNAseq
Core
Primary PDAC
66
Nywening




biopsies,


et al.,




bulk


2016


Connor
RNAseq
LCM
Primary PDAC, PDAC
74
Connor et





metastases

al., 2017


TCGA
RNAseq
Bulk
Primary PDAC
181
CGARN,


PAAD




2017





*Cancer Genome Atlas Research Network













TABLE 5







Yeh_Seq Samples













Platform














RNA-seq
NanoString














Sample type

Primary
PDX
Primary
PDX

















Bulk
FF*
47
18
16
18




FFPE
5
7
1
7



FNA

45
3
16
0





*FF: flash frozen













TABLE 6







Genes and Probes Analyzed by NanoString











GENE PAIR
GENE A
SEQ ID NO:
GENE B
SEQ ID NO:





A
GPR87
64
REG4
71


B
KRT6A
65
ANXA10
59


C
KRT17
67
LGALS4
68


D
S100A2
77
TFF1
73


E
C16orf74
60
DDC
63


F
KRT15
66
PLA2G10
69


G
PTGES
70
CDH17
61


H
DCRLD2
62
TSPAN8
74
















TABLE 7







Group Membership












Treatment
Survival
Training
Validation


Public Dataset
Group
Group
Group
Group


and Citation
(#)
(#)
(#)
(#)





Moffitt GEO (GSE71729);
N
Y
Y
N


Moffitt et al. 2015

(125)
(139)



COMPASS
Y
N
N
Y


Aung et al., 2017
(40)


(49)


Aguirre Biopsies;
N
N
Y
N


Aguirre et al., 2018


(46)



ICGC PACA-AU seq;
N
Y
N
Y


Bailey et al., 2016

(57)

(65)


ICGC PACA-AU array;
N
Y
N
Y


Bailey et al., 2016

(71)

(97)


Moffitt;
N
N
N
Y


Moffitt et al., 2015



(37)


Linehan seq;
Y
Y
N
Y


Nywening et al., 2016
(28)
(28)

(66)


Connor;
N
N
N
Y


Connor et al., 2017



(66)


TCGA PAAD;
N
Y
Y
N


CGARN*, 2017

(146)
(136)



Pooled

376
321
378


Group Notes
A
B
C
D


(see below)





#: number of samples in Group


*: CGARN: Cancer Genome Atlas Research Network


A: Only samples with RNA-seq AND treatment response were considered.


B: duplicated samples between ICGC PACA-AU seq and ICGC PACA-AU array were removed when pooling.


C: Training Samples used here are a subset of the CC subtypes derived on each dataset.


D: Samples with CC labels were considered for validation.













TABLE 8







Aguirre_seq













ID and Method
Collisson
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob
















0400001_T1; resection



FALSE
Classical
0.032063


0400003_T1; resection



FALSE
Basal
0.991223


0400005_T1; resection



FALSE
Classical
0.0055


0400008_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.001779




Progenitor






0400009_T1; resection
exocrine-like
Immunogenic
classical
TRUE
Classical
0.002749


0400010_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.013709


0400017_T1; resection



FALSE
Classical
0.146883


0400025_T1; resection



FALSE
Classical
0.001096


0400027_T1; biopsy



FALSE
Classical
0.012753


0400040_T1; resection



FALSE
Classical
0.023545


0400047_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.001096




Progenitor






0400047_T2; resection



FALSE
Classical
0.001779


0400049_T1; resection



FALSE
Basal
0.785925


0400050_T1; resection



FALSE
Classical
0.008293


0400055_T1; biopsy
exocrine-like
Immunogenic
classical
TRUE
Classical
0.019979


0400062_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.001096


0400067_T1; biopsy
exocrine-like
Immunogenic
classical
TRUE
Classical
0.001096


0400068_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400069_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.001096




Progenitor






0400070_T1; resection



FALSE
Classical
0.001096


0400071_T1; resection



FALSE
Classical
0.019979


0400075_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.001096




Progenitor






0400078_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400081_T1; resection



FALSE
Classical
0.013709


0400083_T1; biopsy



FALSE
Classical
0.013709


0400087_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.00799




Progenitor






0400088_T1; resection



FALSE
Classical
0.003153


0400089_T1; biopsy
exocrine-like
Squamous
classical
TRUE
Classical
0.00693


0400091_T1; biopsy
exocrine-like
Squamous
basal
TRUE
Basal
0.902224


0400096_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.002749


0400097_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.001096


0400098_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.002769




Progenitor






0400123_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.001096




Progenitor






0400124_T1; biopsy
classical
Squamous
basal
TRUE
Basal
0.784733


0400127_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.280897




Progenitor






0400127_T2; resection



FALSE
Classical
0.020585


0400129_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400136_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400137_T1; biopsy
exocrine-like
ADEX
classical
TRUE
Classical
0.236376


0400142_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400148_T1; biopsy
exocrine-like
ADEX
classical
TRUE
Classical
0.001096


0400151_T2; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.002769




Progenitor






0400164_T1; biopsy
classical
ADEX
classical
TRUE
Classical
0.001096


0400165_T1; biopsy
exocrine-like
ADEX
classical
TRUE
Classical
0.001096


0400167_T1; biopsy
exocrine-like
ADEX
basal
TRUE
Basal
0.784733


0400171_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400172_T1; biopsy
exocrine-like
ADEX
classical
TRUE
Classical
0.426918


0400174_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.001096




Progenitor






0400177_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.032865




Progenitor






0400179_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.001096


0400192_T1; biopsy
QM
ADEX
basal
TRUE
Basal
0.975101


0400193_T1; biopsy



FALSE
Classical
0.001096


0400195_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.985816


0400198_T1; biopsy



FALSE
Classical
0.002749


0400202_T1; biopsy



FALSE
Classical
0.280897


0400203_T1; biopsy



FALSE
Classical
0.002749


0400208_T1; biopsy
QM
Squamous
classical
TRUE
Classical
0.032865


0400214_T1; biopsy
exocrine-like
ADEX
classical
TRUE
Classical
0.001779


0400215_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.002749




Progenitor






0400220_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.850276


0400231_T1; biopsy
QM
Squamous
classical
FALSE
Classical
0.211492


0400233_T1; biopsy
QM
Squamous
basal
TRUE
Basal
0.991223


0400235_T1; biopsy
exocrine-like
ADEX
basal
FALSE
Basal
0.96486


0400237_T1; biopsy
exocrine-like
ADEX
classical
TRUE
Classical
0.001096


0400242_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.437577


0400243_T1; biopsy
QM
ADEX
basal
TRUE
Basal
0.991223


0400245_T1; biopsy



FALSE
Classical
0.092608


0400251_T1; biopsy
classical
Pancreatic
classical
TRUE
Classical
0.002769




Progenitor






0400253_T1; biopsy



FALSE
Classical
0.193605


0400267_T1; biopsy



FALSE
Classical
0.002749


0400268_T1; biopsy



FALSE
Classical
0.092608


0400270_T1; biopsy
classical
Immunogenic
classical
TRUE
Classical
0.00799


0400278_T1; biopsy



FALSE
Classical
0.205302
















TABLE 9







COMPASS

















ID
Histology
Change
RECIST
Treatment
Collisson
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob




















COMP0014

−8.7
SD
FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.013405








Progenitor






COMP0001
Adenoca.
−30.6
PR
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.005113


COMP0002
Adenoca.
−45.1
PR
GP
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096


COMP0004
Adenoca.
−15.6
SD
FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.002749








Progenitor






COMP0005
Adenoca.
−4.2
SD
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.037269


COMP0006
Adenoca.
−54
PR
FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0007
Adenoca.


GP
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.691693


COMP0008

−22.3
SD
GP
Classical
Immunogenic
Classical
FALSE
Classical
0.008293


COMP0010
Adenoca.
5.4
SD
GP
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0009

−27.8
SD
FFX
Classical
ADEX
Classical
FALSE
Classical
0.008906


COMP0011
Adenoca.


FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.902224


COMP0012
Adenoca.


GP
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0013
Adenosq.
75
PD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.936765


COMP0015
Adenoca.
25
PD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0017
Adenoca.
9.5
SD
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.013405


COMP0018

44.7
PD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0019
Adenoca.
−45.9
PR
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.001096


COMP0020
Adenoca.
17.5
SD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0021
Adenosq.
−45.8
PR
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.854066


COMP0023

−42.1
PR
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.005113


COMP0025
Adenoca.


FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0026
Adenoca.
−8.6
SD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.784733


COMP0028

14
SD
FFX
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.008293








Progenitor






COMP0030
Adenoca.
−4.3
SD
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.002749


COMP0029
Adenoca.
−15
SD
FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0032
Adenoca.
6.6
SD
FFX
Classical
ADEX
Classical
FALSE
Classical
0.001779


COMP0033



GP
Classical
Pancreatic
Classical
FALSE
Classical
0.008293








Progenitor






COMP0034

24.5
PD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0035

−33.3
PR
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.037703


COMP0036
Adenoca.
4.9
SD
FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0037

−43.8
PR
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.007887


COMP0038
Adenoca.
7.4
SD
FFX
Classical
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0039
Adenoca.


FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0041



FFX
Exocrine-like
Pancreatic
Classical
FALSE
Classical
Classical








Progenitor






COMP0042
Adenoca.
−17.5
SD
FFX
Classical
Immunogenic
Classical
FALSE
Classical
0.223407


COMP0043

−20
SD
FFX
Exocrine-like
ADEX
Classical
FALSE
Classical
0.223407


COMP0044
Adenoca.
−24.1
SD
FFX
Exocrine-like
Immunogenic
Classical
FALSE
Classical
0.037703


COMP0045

34.4
PD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0046
Adenoca.
−11.1
SD
FFX
QM-PDA
ADEX
Classical
FALSE
Classical
0.419634


COMP0047

−54.5
PR
FFX
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






COMP0048

6.9
SD
FFX
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223


COMP0050

−12.4
SD
GP
Classical
Immunogenic
Classical
FALSE
Classical
0.001096


COMP0049
Adenoca.
−19.2
SD
FFX
Classical
ADEX
Classical
FALSE
Basal-like
0.591897


COMP0052
Adenoca.


Classical
Immunogenic
Classical
FALSE
Classical
0.00446
0.001096


COMP0055
Acinar
−8.1
SD
FFX
Exocrine-like
ADEX

FALSE
Classical
0.005113


COMP0056
Adenoca.
−54.5
PR
FFX
Exocrine-like
ADEX
Classical
FALSE
Classical
0.211492


COMP0057

−5.6
SD
FFX
QM-PDA
ADEX
Classical
FALSE
Classical
0.419634


COMP0058
Adenoca.
−51
PR
GP
Exocrine-like
Immunogenic
Classical
FALSE
Classical
0.013405


COMP0059
Adenoca.
−41.3
PR
GP
Classical
Immunogenic
Classical
FALSE
Classical
0.013405


COMP0060
Adenoca.

GP +
Classical
Immunogenic
Classical
FALSE
Classical
0.001096






Medi +












Tremi
















TABLE 10







Connor














ID
SampleType
Collisson
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob

















PCSI_0083
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Basal-like
0.936765


PCSI_0103
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0132
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.005113





Progenitor






PCSI_0142
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0145
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Classical
0.150416


PCSI_0173
Primary
QM-PDA
Squamous
Classical
FALSE
Classical
0.325049


PCSI_0226
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Basal-like
0.978228


PCSI_0233
Primary
Exocrine-like
Squamous
Classical
FALSE
Classical
0.005113


PCSI_0235
Primary
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001779


PCSI_0240
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Basal-like
0.978077


PCSI_0261
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096


PCSI_0263
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.020585


PCSI_0264
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0268
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.008293





Progenitor






PCSI_0269
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001779


PCSI_0274
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0279
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0280
Primary
Classical
ADEX
Classical
FALSE
Classical
0.001779


PCSI_0283
Primary
Classical
Immunogenic
Classical
FALSE
Classical
0.001779


PCSI_0284
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Classical
0.089648


PCSI_0285
Primary
Classical
Immunogenic
Classical
FALSE
Classical
0.001779


PCSI_0286
Primary
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001779


PCSI_0287
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0290
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0292
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Basal-like
0.946668


PCSI_0302
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.005113





Progenitor






PCSI_0303
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0305
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0307
Primary
QM-PDA
Squamous
Classical
FALSE
Basal-like
0.556881


PCSI_0309
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096


PCSI_0310
Primary
QM-PDA
Squamous
Classical
FALSE
Classical
0.001096


PCSI_0311
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0312
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096


PCSI_0324
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0325
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0326
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001779





Progenitor






PCSI_0328
Primary
Classical
Immunogenic
Classical
FALSE
Classical
0.001096


PCSI_0329
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0330
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096


PCSI_0334
Primary
Classical
Immunogenic
Classical
FALSE
Classical
0.001096


PCSI_0337
Primary
Classical
ADEX
Classical
FALSE
Classical
0.06411


PCSI_0338
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0340
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0341
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.00799





Progenitor






PCSI_0345
Primary
Exocrine-like
ADEX
Classical
FALSE
Classical
0.013405


PCSI_0350
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0353
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.023545


PCSI_0355
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0403
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0453
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.012753


PCSI_0456
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001779





Progenitor






PCSI_0457
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001779





Progenitor






PCSI_0458
Primary
Classical
ADEX
Classical
FALSE
Classical
0.033022


PCSI_0477
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Classical
0.090712


PCSI_0489
Liver



FALSE
Classical
0.001096



Metastasis








PCSI_0506
Primary
Classical
Immunogenic
Classical
FALSE
Classical
0.019841


PCSI_0508
Primary
QM-PDA
ADEX
Classical
FALSE
Classical
0.001779


PCSI_0509
Primary
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001779


PCSI_0511
Primary
QM-PDA
Pancreatic
Classical
FALSE
Classical
0.013405





Progenitor






PCSI_0528
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.00799


PCSI_0531
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0537
Primary
Classical
Pancreatic
Classical
FALSE
Classical
0.001096





Progenitor






PCSI_0572
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Classical
0.309466


RAMP_0002
Lymph Node



FALSE
Classical
0.146883



Metastasis








RAMP_0002
Liver



FALSE
Classical
0.001096



Metastasis








RAMP_0004
Lymph Node



FALSE
Basal-like
0.991223



Metastasis








RAMP_0004
Liver



FALSE
Basal-like
0.978077



Metastasis








RAMP_0004
Primary
Exocrine-hike
Squamous
Basal-like
FALSE
Basal-like
0.991223


RAMP_0006
Liver



FALSE
Classical
0.037703



Metastasis








RAMP_0006
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Classical
0.419634


RAMP_0007
Primary
QM-PDA
ADEX
Basal-like
FALSE
Basal-like
0.556881


RAMP_0008
Lymph Node



FALSE
Classical
0.013405



Metastasis








RAMP_0008
Liver



FALSE
Classical
0.001096



Metastasis








RAMP_0008
Primary
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.037703
















TABLE 11







Linehan_seq

















ID
Treatment
Pre.Post
Change
RECIST
Collisson
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob




















S1124.02.01
FOLF
Pre


Classical
Immunogenic
Classical
FALSE
Classical
0.001096


S1124.02.02
FOLF
Post


Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096


S1124.03.01
FOLF
Pre
4
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.001096


S1124.03.02
FOLF
Post
4
SD
Exocrine-like
Immunogenic
Classical
FALSE
Classical
0.00799


S1124.04.01
FOLF
Pre


Exocrine-like
Squamous
Classical
FALSE
Classical
0.054078


S1124.07.01
FOLF
Pre
20.40816
PD
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.860249


S1124.07.02
FOLF
Post
20.40816
PD
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.679704


S1124.08.01
FOLF
Pre
0
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.00693


S1124.08.02
FOLF
Post
0
SD
Classical
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






S1124.09.01
FOLF
Pre


Exocrine-like
Squamous
Classical
FALSE
Classical
0.052325


S1124.11.01
FOLF
Pre


Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.0055








Progenitor






S1124.12.01
FOLF
Pre


QM-PDA
Squamous
Classical
FALSE
Classical
0.001779


S1124.13.01
FOLF
Pre
−16.2791
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.001096


S1124.13.02
FOLF
Post
−16.2791
SD
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.001096








Progenitor






S1124.14.01
FOLF +
Pre
−31.8182
PR
Exocrine-like
ADEX
Classical
FALSE
Classical
0.0055



PF











S1124.14.02
FOLF +
Post
−31.8182
PR
Exocrine-like
ADEX
Classical
FALSE
Classical
0.0055



PF











S1124.15.01
FOLF +
Pre
−32.1429
PR
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.15.02
FOLF +
Post
−32.1429
PR
Classical
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.16.01
FOLF +
Pre
−8.82353
SD
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF











S1124.16.02
FOLF +
Post
−8.82353
SD
Classical
Pancreatic
Classical
FALSE
Classical
0.001096



PF




Progenitor






S1124.17.01
FOLF +
Pre
0
SD
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF











S1124.17.02
FOLF +
Post
0
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.21.01
FOLF +
Pre


Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF











S1124.23.01
FOLF +
Pre


Classical
Immunogenic
Classical
FALSE
Classical
0.002769



PF











S1124.24.01
FOLF +
Pre
−40.625
PR
Classical
Pancreatic
Classical
FALSE
Classical
0.001096



PF




Progenitor






S1124.24.02
FOLF +
Post
−40.625
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.25.01
FOLF +
Pre
−19.697
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.25.02
FOLF +
Post
−19.697
SD
QM-PDA
Squamous
Classical
FALSE
Classical
0.001096



PF











S1124.28.01
FOLF +
Pre
−37.037
PR
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.0055



PF











S1124.28.02
FOLF +
Post
−37.037
PR
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.002769



PF











S1124.30.01
FOLF +
Pre
−46.875
PR
QM-PDA
Squamous
Classical
FALSE
Classical
0.002749



PF











S1124.30.02
FOLF +
Post
−46.875
PR
QM-PDA
Squamous
Classical
FALSE
Classical
0.147772



PF











S1124.31.01
FOLF +
Pre
25.64103
PD
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.783895



PF











S1124.31.02
FOLF +
Post
25.64103
PD
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.783895



PF











S1124.32.01
FOLF +
Pre
−17.1429
SD
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.991223



PF











S1124.32.02
FOLF +
Post
−17.1429
SD
QM-PDA
Squamous
Basal-like
FALSE
Basal-like
0.842116



PF











S1124.33.01
FOLF +
Pre
−8.16327
SD
Classical
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.33.02
FOLF +
Post
−8.16327
SD
QM-PDA
Squamous
Classical
FALSE
Classical
0.0055



PF











S1124.34.01
FOLF +
Pre
−40
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.34.02
FOLF +
Post
−40
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.002769



PF











S1124.35.01
FOLF +
Pre
−26.9841
SD
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.002769



PF











S1124.35.02
FOLF +
Post
−26.9841
SD
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.002769



PF











S1124.37.01
FOLF +
Pre
−30
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.002769



PF











S1124.37.02
FOLF +
Post
−30
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.004491



PF











S1124.38.01
FOLF +
Pre
8.571429
SD
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.38.02
FOLF +
Post
8.571429
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.40.01
FOLF +
Pre


Classical
Pancreatic
Classical
FALSE
Classical
0.001096



PF




Progenitor






S1124.41.01
FOLF +
Pre
6.451613
SD
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.41.02
FOLF +
Post
6.451613
SD
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF











S1124.42.01
FOLF +
Pre
−29.6296
SD
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.42.02
FOLF +
Post
−29.6296
SD
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.43.01
FOLF +
Pre
−35.7143
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.43.02
FOLF +
Post
−35.7143
PR
QM-PDA
Squamous
Classical
FALSE
Classical
0.0055



PF











S1124.46.01
FOLF +
Pre
−35.5556
PR
QM-PDA
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.46.02
FOLF +
Post
−35.5556
PR
Classical
Immunogenic
Classical
FALSE
Classical
0.001096



PF











S1124.48.01
FOLF +
Pre
0
SD
Classical
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.48.02
FOLF +
Post
0
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.0055



PF











S1124.51.01
FOLF +
Pre
−13.5135
SD
Classical
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.51.02
FOLF +
Post
−13.5135
SD
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.001096



PF




Progenitor






S1124.52.01
FOLF +
Pre


QM-PDA
Squamous
Classical
FALSE
Classical
0.002769



PF











S1124.53.01
FOLF +
Pre
−13.5135
SD
Exocrine-like
Pancreatic
Classical
FALSE
Classical
0.0055



PF




Progenitor






S1124.53.02
FOLF +
Post
−13.5135
SD
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF











S1124.54.01
FOLF +
Pre
−5.71429
SD
QM-PDA
Squamous
Classical
FALSE
Classical
0.002769



PF











S1124.54.02
FOLF +
Post
−5.71429
SD
Classical
Immunogenic
Classical
FALSE
Classical
0.001779



PF











S1124.57.01
FOLF +
Pre
−33.3333
PR
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF











S1124.57.02
FOLF +
Post
−33.3333
PR
Exocrine-like
ADEX
Classical
FALSE
Classical
0.001096



PF
















TABLE 12







Moffitt_GEO_array














ID
SampleType
Collisson
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob

















53862-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.007943


49360-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.766498


54249-Primary-Pancreas
Primary
QM-PDA
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


48661-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


49071-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001779


53838-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.002749


49073-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.050513


48556-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.936765


48558-Primary-Pancreas
Primary
Classical
Squamous
Classical
TRUE
Classical
0.146264


52042-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.002769


52043-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


48562-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


48564-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
FALSE
Classical
0.429034


48567-Primary-Pancreas
Primary
Classical
Immunogenic
Classical
TRUE
Classical
0.019979


48568-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Basal-like
0.755533


49388-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


46648-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46649-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


46650-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46651-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.019841


47702-Primary-Pancreas
Primary
Classical
Squamous
Classical
TRUE
Classical
0.001096


46652-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46987-Primary-Pancreas
Primary
Exocrine-like
Immunogenic
Classical
TRUE
Classical
0.002769


46653-Primary-Pancreas
Primary
Classical
Squamous
Basal-like
TRUE
Basal-like
0.827009


46832-Primary-Pancreas
Primary
Exocrine-like
Squamous
Basal-like
TRUE
Basal-like
0.975101


46831-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.991223


46985-Primary-Pancreas
Primary
Classical
Squamous
Classical
TRUE
Classical
0.013247


46828-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.089725


47692-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.991223


46986-Primary-Pancreas
Primary
Classical
Squamous
Classical
TRUE
Classical
0.001096


47590-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


47969-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.002769


47989-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


46581-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001779


46582-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46830-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


46584-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


47703-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.991223


47708-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


46450-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


47695-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


48550-Primary-Pancreas
Primary
Exocrine-like
Immunogenic
Classical
TRUE
Classical
0.001096


46339-Primary-Pancreas
Primary
Classical
Squamous
Basal-like
TRUE
Basal-like
0.975101


46578-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46585-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.052325


46337-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.048743


46587-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


47700-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.002749


46826-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46592-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


46452-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


46460-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
FALSE
Classical
0.288464


47983-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.00799


46642-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


47701-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.199683


46643-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


47965-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.00693


46644-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.002769


46645-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.019841


46646-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


49390-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


49392-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.837062


64482-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.00799


64500-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


72613-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


64501-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


72616-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.002749


64502-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.13805


64503-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.021347


64504-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


64505-Primary-Pancreas
Pdmary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


64507-Primary-Pancreas
Pdmary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.204595


64508-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


64509-Primary-Pancreas
Primary
Exocrine-like
ADEX
Basal-like
TRUE
Basal-like
0.898468


64510-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


46647-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.991223


48569-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


64498-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.957264


64490-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
FALSE
Classical
0.020585


64491-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001779


64492-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


64494-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.957264


64495-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


56525-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.671478


56527-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


56536-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.978228


56537-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


56538-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


56539-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


56367-Primary-Pancreas
Primary
QM-PDA
Pancreatic Progenitor
Classical
TRUE
Classical
0.019841


56369-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


56528-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


56529-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.001096


56530-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.001096


56540-Primary-Pancreas
Primary
Exocrine-like
Squamous
Classical
TRUE
Classical
0.001779


56377-Primary-Pancreas
Primary
Exocrine-like
ADEX
Basal-like
FALSE
Classical
0.012917


56373-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


56374-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.00799


56541-Primary-Pancreas
Primary
Exocrine-like
Squamous
Basal-like
FALSE
Classical
0.005113


56542-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


56375-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


56535-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


54175-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


54301-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001779


54291-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.089725


54302-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.991223


54303-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54172-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


54304-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54305-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.013247


54309-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


54306-Primary-Pancreas
Primary
Exocrine-like
Squamous
Basal-like
TRUE
Basal-like
0.754774


54307-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.00799


54292-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


54243-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54308-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.019979


54293-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.002769


54310-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


54315-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.936765


54311-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.0055


54312-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54299-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


54313-Primary-Pancreas
Primary
Exocrine-like
Squamous
Basal-like
TRUE
Basal-like
0.978077


54314-Primary-Pancreas
Primary
QM-PDA
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


54294-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


54295-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


54173-Primary-Pancreas
Primary
Exocrine-like
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54316-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.859538


54317-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54297-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.001096


54300-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.089725


54318-Primary-Pancreas
Primary
Classical
Immunogenic
Classical
TRUE
Classical
0.013247


54296-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54174-Primary-Pancreas
Primary
Classical
Pancreatic Progenitor
Classical
TRUE
Classical
0.001096


54298-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.00446


54171-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.004491


64496-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
FALSE
Classical
0.031663


56322-Primary-Pancreas
Primary
Exocrine-like
Immunogenic
Classical
TRUE
Classical
0.013247


56326-Primary-Pancreas
Primary
QM-PDA
Squamous
Basal-like
TRUE
Basal-like
0.790185


56534-Primary-Pancreas
Primary
QM-PDA
Pancreatic Progenitor
Classical
TRUE
Classical
0.013247


56531-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


56523-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.001096


56316-Primmy-Pancreas
Primary
QM-PDA
ADEX
Classical
TRUE
Classical
0.001096


56320-Primary-Pancreas
Primary
QM-PDA
Squamous
Classical
TRUE
Classical
0.052325


64497-Primary-Pancreas
Primary
Exocrine-like
ADEX
Classical
TRUE
Classical
0.002749
















TABLE 13







Moffitt_S2














ID
SampleType
Collisson
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob

















PDX-1
PDX


Classical
FALSE
Classical
0.001096


PDX-2
PDX


Classical
FALSE
Classical
0.001096


PDX-3
PDX


Classical
FALSE
Basal-like
0.514077


PDX-4
PDX


Classical
FALSE
Classical
0.001096


PDX-5
PDX


Basal-like
FALSE
Basal-like
0.991223


PDX-6
PDX


Classical
FALSE
Classical
0.078689


PDX-7
PDX


Classical
FALSE
Classical
0.001096


PDX-8
PDX


Classical
FALSE
Classical
0.001096


PDX-9
PDX


Classical
FALSE
Classical
0.001096


PDX-10
PDX


Classical
FALSE
Classical
0.001096


PDX-11
PDX


Classical
FALSE
Classical
0.002769


PDX-12
PDX


Classical
FALSE
Classical
0.001096


PDX-13
PDX


Classical
FALSE
Classical
0.001096


PDX-14
PDX


Classical
FALSE
Classical
0.001096


PDX-15
PDX


Classical
FALSE
Classical
0.020585


PDX-16
PDX


Classical
FALSE
Classical
0.001096


PDX-17
PDX


Classical
FALSE
Classical
0.001096


PDX-18
PDX


Classical
FALSE
Classical
0.013805


PDX-19
PDX


Classical
FALSE
Classical
0.013247


PDX-20
PDX


Classical
FALSE
Classical
0.013805


PDX-21
PDX


Classical
FALSE
Classical
0.001096


PDX-22
PDX


Classical
FALSE
Classical
0.0055


PDX-23
PDX


Classical
FALSE
Classical
0.02224


PDX-24
PDX


Classical
FALSE
Classical
0.001096


PDX-25
PDX


Classical
FALSE
Classical
0.002749


PDX-26
PDX


Basal-like
FALSE
Basal-like
0.691693


PDX-27
PDX


Classical
FALSE
Classical
0.001096


PDX-28
PDX


Classical
FALSE
Classical
0.001096


PDX-29
PDX


Classical
FALSE
Classical
0.013247


PDX-30
PDX


Basal-like
FALSE
Basal-like
0.960163


PDX-31
PDX


Classical
FALSE
Classical
0.001096


PDX-32
PDX


Classical
FALSE
Classical
0.001096


PDX-33
PDX


Classical
FALSE
Classical
0.001096


PDX-34
PDX


Basal-like
FALSE
Basal-like
0.991223


PDX-35
PDX


Classical
FALSE
Classical
0.001096


PDX-36
PDX


Classical
FALSE
Classical
0.001096


PDX-37
PDX


Basal-like
FALSE
Basal-like
0.991223
















TABLE 14







PACA_AU_array















ID
SampleType
Collisson
Bailey_original
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob


















SA407779
Primary tumour
Exocrine-like

ADEX

FALSE
Classical
0.014


SA407918
Primary tumour
Exocrine-like
Immunogenic
ADEX
Classical
FALSE
Classical
0.014


SA407946
Cell line
Exocrine-like

ADEX

FALSE
Classical
0.005


SA408003
Primary tumour
Exocrine-like
Squamous
Squamous

FALSE
Classical
0.427


SA408106
Primary tumour
Classical

Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA408266
Cell line
Exocrine-like

ADEX

FALSE
Classical
0.014


SA408314
Primary tumour
Exocrine-like

ADEX
Classical
FALSE
Classical
0.005


SA408414
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Classical
0.005


SA408530
Primary tumour
Exocrine-like
Squamous
Squamous
Basal-like
FALSE
Classical
0.211


SA408570
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.014


SA408650
Metastatic tumour
QM-PDA

ADEX

FALSE
Classical
0.014


SA408706
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.014


SA408726
Cell line
QM-PDA

Immunogenic

FALSE
Classical
0.014


SA408758
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.014


SA408774
Cell line
QM-PDA

ADEX

FALSE
Classical
0.014


SA408806
Primary tumour
QM-PDA

Immunogenic

FALSE
Classical
0.005


SA408843
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.093


SA408867
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Classical
0.427


SA408891
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.975


SA408946
Primary tumour
Exocrine-like

Squamous
Classical
FALSE
Classical
0.412


SA408963
Cell line
Classical

Pancreatic Progenitor

FALSE
Classical
0.005


SA409186
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.005


SA409258
Primary tumour
QM-PDA
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA409310
Primary tumour
Exocrine-like
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.975


SA409342
Primary tumour
QM-PDA
Pancreatic Progenitor
ADEX

FALSE
Classical
0.034


SA409398
Primary tumour
Exocrine-like

ADEX
Classical
FALSE
Classical
0.022


SA409446
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA409498
Primary tumour
QM-PDA
Immunogenic
ADEX

FALSE
Classical
0.034


SA409527
Cell line
Exocrine-like

ADEX

FALSE
Classical
0.022


SA409543
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.014


SA409590
Primary tumour
Exocrine-like

Squamous
Classical
FALSE
Classical
0.014


SA409622
Primary tumour
QM-PDA
Pancreatic Progenitor
Immunogenic
Classical
FALSE
Classical
0.014


SA409662
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.005


SA409678
Cell line
Exocrine-like

Squamous

FALSE
Classical
0.054


SA409711
Primary tumour
Exocrine-like
ADEX
Squamous
Classical
FALSE
Classical
0.438


SA409775
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.034


SA409818
Cell line
Exocrine-like

ADEX

FALSE
Classical
0.005


SA409838
Primary tumour
Classical

Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA409891
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.005


SA409923
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.014


SA410030
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.005


SA410054
Primary tumour
QM-PDA

ADEX
Classical
FALSE
Classical
0.205


SA410103
Primary tumour
QM-PDA
Immunogenic
ADEX
Classical
FALSE
Classical
0.039


SA410118
Primary tumour
Exocrine-like
Immunogenic
Immunogenic
Classical
FALSE
Classical
0.014


SA410207
Primary tumour
Classical
ADEX
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410234
Primary tumour
Exocrine-like
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA410263
Primary tumour
QM-PDA
Immunogenic
Squamous
Classical
FALSE
Basal-like
0.548


SA410286
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.014


SA410310
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410382
Primary tumour
Exocrine-like

Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410383
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.986


SA410410
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.009


SA410503
Primary tumour
Exocrine-like
Pancreatic Progenitor
ADEX
Classical
FALSE
Classical
0.014


SA410535
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410559
Cell line
Exocrine-like

ADEX

FALSE
Classical
0.005


SA410566
Primary tumour
Classical

Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410582
Primary tumour
QM-PDA
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410606

QM-PDA

Squamous

FALSE
Classical
0.205


SA410687
Primary tumour
QM-PDA
Pancreatic Progenitor
Immunogenic

FALSE
Classical
0.211


SA410742
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.014


SA410750
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410758
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410763
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410859
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.837


SA410883
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.014


SA410899
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410911
Primary tumour
Exocrine-like
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA410933
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA411001
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.205


SA411029
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA411042
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.211


SA411189
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.054


SA411209
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.093


SA411241
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.014


SA411261
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.205


SA411305
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.034


SA411360
Cell line
QM-PDA

Immunogenic

FALSE
Classical
0.014


SA411397
Primary tumour
Classical
Squamous
Immunogenic
Classical
FALSE
Classical
0.014


SA411406
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA411430
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.005


SA411454
Primary tumour
Exocrine-like

Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA411557
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.005


SA411578
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.296


SA411721
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA411745
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.014


SA411769
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA411797
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA411833
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA411907
Cell line
QM-PDA

Pancreatic Progenitor

FALSE
Classical
0.014


SA411923
Primary tumour
Exocrine-like
Squamous
Immunogenic
Classical
FALSE
Classical
0.301


SA412003
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.034


SA412076
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.093


SA412212
Primary tumour
QM-PDA
Pancreatic Progenitor
Immunogenic
Classical
FALSE
Classical
0.014


SA412299
Primary tumour
Exocrine-like
ADEX
ADEX

FALSE
Classical
0.039


SA412367
Primary tumour
Exocrine-like

ADEX
Classical
FALSE
Classical
0.034


SA412455
Primary tumour
Exocrine-like

Squamous
Classical
FALSE
Classical
0.412


SA518603
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.014


SA518614
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.014


SA518615
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518624

Classical

Pancreatic Progenitor

FALSE
Classical
0.005


SA518630
Primary tumour
Exocrine-like
ADEX
ADEX

FALSE
Classical
0.005


SA518633
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA518637
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.975


SA518665
Primary tumour
Exocrine-like
ADEX
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518689
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518695
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518701
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA518704
Primary tumour
Exocrine-like
ADEX
Immunogenic
Classical
FALSE
Classical
0.014


SA518709
Primary tumour
Exocrine-like

Immunogenic
Classical
FALSE
Classical
0.034


SA518712
Primary tumour
Exocrine-like

Immunogenic
Classical
FALSE
Classical
0.014


SA518716
Primary tumour
Exocrinc-like
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518724
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518765
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.014


SA518806
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.205


SA518817
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518851
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA518854
Primary tumour
Exocrine-like

Squamous
Classical
FALSE
Classical
0.034


SA518868
Primary tumour
QM-PDA
Immunogenic
Immunogenic
Classical
FALSE
Classical
0.093


SA518878
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.039


SA528670
Primary tumour
Exocrine-like

Squamous
Basal-like
FALSE
Basal-like
0.975


SA528675
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.014


SA528676
Primary tumour
QM-PDA

Immunogenic
Classical
FALSE
Classical
0.034


SA528687
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA528693
Primary tumour
QM-PDA
ADEX
Squamous
Basal-like
FALSE
Basal-like
0.991


SA528695
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA528697
Primary tumour
Exocrine-like

Immunogenic
Classical
FALSE
Classical
0.014


SA528709
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA528712
Cell line
QM-PDA

Immunogenic

FALSE
Classical
0.022


SA528713
Metastatic tumour
Exocrine-like

Squamous

FALSE
Classical
0.034


SA528755
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA528761
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.558
















TABLE 15







PACA_AU_seq















ID
SampleType
Collisson
Balley_original
Bailey
Moffitt
PurIST.training
PurIST
PurIST.basal.prob


















SA407858
Primary tumour
Classical

Pancreatic Progenitor

FALSE
Classical
0.001


SA408414
Primary tumour
QM-PDA
Squamous
Immunogenic
Classical
FALSE
Classical
0.002


SA408530
Primary tumour
Exocrine-like
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA408570
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.096


SA408758
Primary tumour
Exocrine-like
ADEX
Immunogenic
Classical
FALSE
Classical
0.001


SA408867
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Classical
0.427


SA409775
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Basal-like
0.850


SA409923
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA409990
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.991


SA410103
Primary tumour
QM-PDA
Immunogenic
Immunogenic
Classical
FALSE
Classical
0.138


SA410118
Primary tumour
Exocrine-like
Immunogenic
ADEX
Classical
FALSE
Classical
0.014


SA410263
Primary tumour
QM-PDA
Immunogenic
Squamous
Basal-like
FALSE
Basal-like
0.991


SA410311
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA410566
Primary tumour
Classical

Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410742
Primary tumour
Classical
Pancreatic Progenitor
Immunogenic
Classical
FALSE
Classical
0.008


SA410750
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA410758
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA410763
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA410859
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA410883
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.014


SA410899
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.008


SA410911
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA410933
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.001


SA410977
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.937


SA411001
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.014


SA411025
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.991


SA411029
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.005


SA411189
Primary tumour
QM-PDA
Squamous
Squamous
Classical
FALSE
Basal-like
0.860


SA411209
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.093


SA411241
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA411305
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Basal-like
0.680


SA411397
Primary tumour
Classical
Squamous
Immunogenic
Classical
FALSE
Classical
0.099


SA411406
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA411430
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.001


SA411557
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.001


SA411578
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Basal-like
0.902


SA411682
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.937


SA411709
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.937


SA411721
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA411745
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA411769
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA411797
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.001


SA411833
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA411841
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.937


SA411923
Primary tumour
Exocrine-like
Squamous
Squamous
Classical
FALSE
Classical
0.301


SA412003
Primary tumour
QM-PDA

ADEX
Classical
FALSE
Classical
0.092


SA412060
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.991


SA412076
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Classical
0.020


SA412268
Metastatic tumour
QM-PDA

Squamous

FALSE
Basal-like
0.991


SA412299
Primary tumour
Exocrine-like
ADEX
ADEX

FALSE
Classical
0.020


SA518492
Cell line
QM-PDA

Squamous

FALSE
Basal-like
0.901


SA518603
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518614
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.001


SA518615
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518624

Classical

Pancreatic Progenitor

FALSE
Classical
0.001


SA518630
Primary tumour
Exocrine-like
ADEX
ADEX

FALSE
Classical
0.001


SA518633
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA518637
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA518665
Primary tumour
Exocrine-like
ADEX
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518689
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518695
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.005


SA518701
Primary tumour
Exocrine-like
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.001


SA518704
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.013


SA518712
Primary tumour
Exocrine-like

ADEX
Classical
FALSE
Classical
0.013


SA518716
Primary tumour
Exocrine-like
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518724
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518750
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518765
Primary tumour
Classical
Pancreatic Progenitor
Immunogenic
Classical
FALSE
Classical
0.001


SA518806
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Classical
0.142


SA518817
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA518851
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA518854
Primary tumour
Exocrine-like

ADEX
Classical
FALSE
Classical
0.092


SA518868
Primary tumour
QM-PDA
Immunogenic
ADEX
Classical
FALSE
Classical
0.014


SA518873

QM-PDA

Squamous

FALSE
Basal-like
0.991


SA518878
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Classical
0.001


SA528675
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor

FALSE
Classical
0.001


SA528676
Primary tumour
QM-PDA

ADEX
Basal-like
FALSE
Classical
0.025


SA528677
Primary tumour
Exocrine-like
ADEX
ADEX
Classical
FALSE
Classical
0.003


SA528679
Primary tumour
QM-PDA
Immunogenic
Squamous
Basal-like
FALSE
Basal-like
0.991


SA528687
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA528695
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA528701
Metastatic tumour
Classical

Pancreatic Progenitor

FALSE
Classical
0.014


SA528709
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.991


SA528711
Primary tumour
QM-PDA
Pancreatic Progenitor
ADEX
Classical
FALSE
Classical
0.014


SA528755
Primary tumour
Classical
Immunogenic
Pancreatic Progenitor
Classical
FALSE
Classical
0.001


SA528761
Primary tumour
QM-PDA
Squamous
Squamous
Basal-like
FALSE
Basal-like
0.762


SA528763
Primary tumour
QM-PDA

ADEX

FALSE
Classical
0.036


SA528766
Primary tumour
Exocrine-like

ADEX
Classical
FALSE
Basal-like
0.548


SA528767
Primary tumour
QM-PDA
Squamous
ADEX
Classical
FALSE
Classical
0.211


SA528768
Primary tumour
Classical
Pancreatic Progenitor
Pancreatic Progenitor

FALSE
Classical
0.002


SA528769
Primary tumour
Classical

ADEX
Classical
FALSE
Classical
0.001


SA528771
Primary tumour
Classical

ADEX
Classical
FALSE
Classical
0.005
















TABLE 16







TCGA_PAAD





















PurIST.train-

PurIST.bas-


ID
Collisson_original
Collisson
Bailey_original
Bailey
Moffitt
ing
PurIST
al.prob





TCGA-2L-AAQE-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
FALSE
Classical
0.436


TCGA-XD-AAUL-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.148


TCGA-2L-AAQJ-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-2L-AAQI-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.038


TCGA-3A-A9IB-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.548


TCGA-3A-A9IU-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.548


TCGA-FB-AAPS-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Classical
TRUE
Classical
0.032


TCGA-HV-AA8X-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-LB-A9Q5-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.165


TCGA-HZ-A9TJ-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-3A-A9IH-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Basal-like
0.785


TCGA-RB-AA9M-01A
Classical
Classical
Immunogenic
Immunogenic
Basal-like
FALSE
Classical
0.308


TCGA-IB-AAUQ-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.939


TCGA-3A-A9J0-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.008


TCGA-FB-AAQ3-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-FB-AAQ1-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
TRUE
Basal-like
0.991


TCGA-2J-AAB9-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
FALSE
Classical
0.090


TCGA-2J-AABA-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.034


TCGA-2J-AABR-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
FALSE
Basal-like
0.957


TCGA-FB-AAQ6-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-2J-AABE-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.013


TCGA-2J-AABT-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.003


TCGA-FB-AAPQ-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-HV-AA8V-01A
Classical
Classical
ADEX
Squamous
Basal-like
FALSE
Classical
0.087


TCGA-2J-AABV-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.001


TCGA-2J-AABF-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-2J-AABU-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.939


TCGA-FB-AAPU-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-2J-AABH-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.005


TCGA-FB-AAPY-01A
Exocrine-like
Exoctine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-2J-AAB1-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-XD-AAUG-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-2J-AAB4-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-2J-AABI-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-FB-AAPZ-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Classical
TRUE
Classical
0.142


TCGA-XD-AAUH-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
TRUE
Classical
0.003


TCGA-3A-A9IX-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.013


TCGA-2J-AABK-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-2J-AAB6-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-FB-AAQ0-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-XD-AAUI-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Classical
TRUE
Classical
0.096


TCGA-2J-AAB8-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.001


TCGA-3A-A9IZ-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.054


TCGA-2J-AABO-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Classical
FALSE
Classical
0.064


TCGA-Z5-AAPL-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
TRUE
Classical
0.004


TCGA-FB-AAQ2-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-F2-6879-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-HZ-7925-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.234


TCGA-IB-7651-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.008


TCGA-HZ-7926-01A
Exocrine-like
Exocrine-like
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.008


TCGA-IB-7885-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
FALSE
Classical
0.325


TCGA-IB-7652-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-IB-7644-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-IB-7887-01A
Classical
Classical
Immunogenic
Immunogenic
Basal-like
FALSE
Classical
0.087


TCGA-IB-7889-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.016


TCGA-IB-7646-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-IB-7886-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.005


TCGA-IB-7893-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.855


TCGA-HZ-7919-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.005


TCGA-HZ-8001-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.978


TCGA-IB-7647-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.001


TCGA-IB-7897-01A
QM-PDA
QM-PDA
Squamous
ADEX
Classical
TRUE
Classical
0.036


TCGA-IB-7888-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.001


TCGA-HZ-8002-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.200


TCGA-HZ-7922-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.014


TCGA-HZ-8003-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.001


TCGA-IB-7649-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-IB-7645-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.001


TCGA-IB-7890-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.978


TCGA-IB-7891-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.005


TCGA-H6-8124-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.937


TCGA-HZ-8315-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.034


TCGA-HZ-8317-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.038


TCGA-HZ-8519-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.008


TCGA-HZ-8636-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.005


TCGA-HZ-8637-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.001


TCGA-IB-8127-01A
Exocrine-like
Exocrine-like
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.165


TCGA-IB-8126-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.001


TCGA-F2-A44H-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.005


TCGA-FB-A4P6-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.013


TCGA-FB-A4P5-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.038


TCGA-H6-A45N-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-HV-A5A3-01A
Classical
Classical
Immunogenic
Immunogenic
Basal-like
TRUE
Basal-like
0.902


TCGA-HV-A5A5-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-HV-A5A4-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-HZ-A49H-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-HV-A5A6-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.021


TCGA-HZ-A49G-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.038


TCGA-HZ-A4BH-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.013


TCGA-HZ-A49I-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.003


TCGA-HZ-A4BK-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-M8-A5N4-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.229


TCGA-F2-A44G-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.003


TCGA-HZ-8005-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.902


TCGA-PZ-A5RE-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.005


TCGA-FB-A78T-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-FB-A5VM-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-US-A774-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.003


TCGA-OE-A75W-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-US-A779-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-IB-A5SP-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-IB-A5SQ-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.965


TCGA-US-A77G-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-US-A77E-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-Q3-A5QY-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
FALSE
Basal-like
0.557


TCGA-IB-A5ST-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
TRUE
Classical
0.096


TCGA-IB-A5SO-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.005


TCGA-IB-A5SS-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.902


TCGA-IB-A6UF-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-HV-A7OL-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-IB-A6UG-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
TRUE
Basal-like
0.557


TCGA-HZ-A77P-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-HZ-A77O-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
FALSE
Basal-like
0.548


TCGA-LB-A7SX-01A
Classical
Classical
Immunogenic
Immunogenic
Basal-like
TRUE
Classical
0.388


TCGA-RB-A7B8-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-US-A776-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.005


TCGA-HZ-A8P0-01A
Exocrine-like
Exocrine-like
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.004


TCGA-IB-A7LX-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.991


TCGA-HZ-A77Q-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
TRUE
Classical
0.051


TCGA-IB-A7M4-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
FALSE
Basal-like
0.860


TCGA-XN-A8T5-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
FALSE
Classical
0.055


TCGA-LB-A8F3-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.024


TCGA-YB-A89D-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-YY-A8LH-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-S4-A8RP-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-XN-A8T3-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
TRUE
Basal-like
0.902


TCGA-F2-A8YN-01A
Classical
Classical
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.022


TCGA-S4-A8RO-01A
Classical
Classical
Immunogenic
Immunogenic
Basal-like
FALSE
Basal-like
0.707


TCGA-HZ-A8P1-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-IB-AAUM-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.001


TCGA-IB-AAUP-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
TRUE
Classical
0.004


TCGA-IB-AAUT-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-YH-A8SY-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.779


TCGA-IB-AAUU-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-IB-AAUS-01A
Exocrine-like
Exocrine-like
ADEX
Squamous
Classical
TRUE
Classical
0.090


TCGA-Q3-AA2A-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-S4-A8RM-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-2L-AAQA-01A
Classical
Classical
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.002


TCGA-2L-AAQL-01A
Exocrine-like
Exocrine-like
Progenitor
Pancreatic Progenitor
Classical
TRUE
Classical
0.001


TCGA-3A-A9I5-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
TRUE
Classical
0.008


TCGA-3A-A9I9-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.003


TCGA-3A-A9I7-01A
Exocrine-like
Exocrine-like
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.064


TCGA-3E-AAAY-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Classical
TRUE
Classical
0.002


TCGA-3E-AAAZ-01A
Exocrine-like
Exocrine-like
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.003


TCGA-F2-A7TX-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
TRUE
Basal-like
0.902


TCGA-IB-AAUN-01A
Exocrine-like
Exocrine-like
Squamous
ADEX
Basal-like
FALSE
Basal-like
0.786


TCGA-M-AAUO-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.899


TCGA-3A-A9IC-01A
QM-PDA
QM-PDA
Immunogenic
Immunogenic
Classical
TRUE
Classical
0.009


TCGA-IB-AAUR-01A
QM-PDA
QM-PDA
ADEX
Squamous
Classical
TRUE
Classical
0.013


TCGA-FB-A545-01A
QM-PDA
QM-PDA
ADEX
Squamous
Basal-like
TRUE
Basal-like
0.855
















TABLE 17







Yeh_seq































Neoadj.





Sam-


PurIST.
Survi-




Tx.



Path-
Tis-
ple


basal.
valAna-
Clini-
Adj.
Adj. Tx.
Neoadj.
Regi-


ID
ology
sueType
Type
Moffitt
PurIST
prob
lysis
calType
Tx
Regimen
Tx
men





S001.FNA.Pi.0422T1
adeno
Primary
FNA
Basal-
Basal-
0.762
TRUE
Panc
yes
gemcitabine
no





PDAC

like
like









S002.FNA.Pi.0825T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE
Panc
yes
gemcitabine
no





PDAC

cal
cal


Tumor






S003.FNA.Pi.1119T1
adeno
Primary
FNA
Basal-
Basal-
0.991
TRUE

yes
(gem w compl
no





PDAC

like
like




and difficulty














tol)




S004.FNA.Pi.0517T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE

yes
gem + erlotinib
no





PDAC

cal
cal









S005.FNA.Pi.0818T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
5FU/RT
no





PDAC

cal
cal









S006.FNA.Pi.1012T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

LTFO

no





PDAC

cal
cal









S007.FNA.Pi.1118T1
adeno
Primary
FNA
Classi-
Classi-
0.039
TRUE

yes
gem
no





PDAC

cal
cal









S008.FNA.Pi.0105T1
adeno
Primary
FNA
Classi-
Classi-
0.013
TRUE

yes
gem
no





PDAC

cal
cal









S009.FNA.Pi.0119T1
adeno
Primary
FNA
Basal-
Classi-
0.064
TRUE

yes
unknown
no





PDAC

like
cal




systemic




S010.FNA.Pi.0417T1
adeno
Primary
FNA
Classi-
Classi-
0.013
TRUE

yes
gem
no





PDAC

cal
cal









S011.FNA.Pi.0503T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + 5fu/rt
no





PDAC

cal
cal









S012.FNA.Pi.0921T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE

yes
5FU/RT
no





PDAC

cal
cal









S013.FNA.Pi.1109T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE
Panc
yes
gem
no





PDAC

cal
cal


Tumor






S014.FNA.Pi.1129T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE
Panc
yes
gem + 5FU/RT
no





PDAC

cal
cal


Tumor






S015.FNA.Pi.1206T1
adeno
Primary
FNA
Classi-
Classi-
0.003
TRUE
Panc
yes
Folfirinox
yes
folfiri-




PDAC

cal
cal


Tumor



nox


S016.FNA.Pi.1214T1
adeno
Primary
FNA
Classi-
Classi-
0.008
TRUE
Panc
DOO

no





PDAC

cal
cal


Tumor






S017.FNA.Pi.0124T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE
Panc
yes
gem + 5FU/RT
no





PDAC

cal
cal


Tumor






S018.FNA.Pi.0221T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE
Panc
yes
gem
no





PDAC

cal
cal


Tumor






S019.FNA.Pi.0222T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE
Panc
yes
gem
no





PDAC

cal
cal


Tumor






S020.FNA.Pi.0327T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE
Panc
yes
gem
yes
5FU/




PDAC

cal
cal


Tumor



RT


S021.FNA.Pi.0328T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
RT/gem + gem
no





PDAC

cal
cal









S022.FNA.Pi.0411T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem
yes
5FU/




PDAC

cal
cal






RT


S023.FNA.Pi.0417T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S024.FNA.Pi.0425T1
adeno
Primary
FNA
Classi-
Classi-
0.003
TRUE
Panc
yes
gem + 5FU/RT
no





PDAC

cal
cal


Tumor






S025.FNA.Pi.0502T2
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S026.FNA.Pi.0508T1
adeno
Primary
FNA
Classi-
Classi-
0.040
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S027.FNA.Pi.0523T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE
Panc
no

no





PDAC

cal
cal


Tumor






S028.FNA.Pi.0524T1
ampul-
Primary
FNA
Classi-
Classi-
0.001
FALSE


gem + 5FU





lary
PDAC

cal
cal









S029.FNA.Pi.0605T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem
no





PDAC

cal
cal









S030.FNA.Pi.0607T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE

yes
5FU/RT
no





PDAC

cal
cal









S031.FNA.Pi.0614T1
adeno
Primary
FNA
Classi-
Classi-
0.008
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S032.FNA.Pi.0710T2
adeno
Primary
FNA
Classi-
Classi-
0.001
FALSE
Panc
yes
gem + 5fu/RT
no





PDAC

cal
cal


Normal,














IPMN














patient






S033.FNA.Pi.0711T1
adeno
Primary
FNA
Classi-
Classi-
0.013
TRUE

LTFO

no





PDAC

cal
cal









S034.FNA.Pi.0904T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + RT/5FU
no





PDAC

cal
cal









S035.FNA.Pi.1009T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

no

yes
5FU/




PDAC

cal
cal






RT


S036.FNA.Pi.1119T1
adeno
Primary
FNA
Basal-
Classi-
0.165
TRUE

yes
Folfirinox
no





PDAC

like
cal









S037.FNA.Pi.1204T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem
no





PDAC

cal
cal









S038.FNA.Pi.1205T1
adeno
Primary
FNA
Classi-
Classi-
0.090
TRUE

yes
gem + 5FU/RT
yes
gem




PDAC

cal
cal









S039.FNA.Pi.0129T1
adeno
Primary
FNA
Classi-
Classi-
0.024
TRUE

yes
gem + RT
no





PDAC

cal
cal









S040.FNA.Pi.0417T1
adeno
Primary
FNA
Classi-
Classi-
0.008
TRUE

yes
Gem + 5FU/RT
no





PDAC

cal
cal









S041.FNA.Pi.0424T1
adeno
Primary
FNA
Basal-
Classi-
0.002
TRUE

yes
gem + 5FU/RT
no





PDAC

like
cal









S042.FNA.Pi.0806T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S043.FNA.Pi.0121T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S044.FNA.Pi.0608T1
adeno
Primary
FNA
Classi-
Classi-
0.001
TRUE

yes
gem + 5fu/rt
no





PDAC

cal
cal









S045.FNA.Pi.1207T1
adeno
Primary
FNA
Classi-
Classi-
0.002
TRUE

yes
gem + 5FU/RT
no





PDAC

cal
cal









S046.FNA.PDX.0616T1
adeno
PDX
FNA
Classi-
Classi-
0.001
FALSE











cal
cal









S047.FNA.PDX.0508T1
adeno
PDX
FNA
Classi-
Classi-
0.013
FALSE











cal
cal









S048.FNA.PDX.0902T1B

PDX
FNA
Classi-
Classi-
0.001
FALSE











cal
cal









S049.FFPE.PDX.1222T1
adeno
PDX
FFPE
Classi-
Classi-
0.001
FALSE











cal
cal









S050.FFPE.PDX.0113T1

PDX
FFPE
Basal-
Classi-
0.223
FALSE











like
cal









S051.FFPE.PDX.1108T1
adeno
PDX
FFPE
Classi-
Classi-
0.005
FALSE











cal
cal









S052.FFPE.PDX.1109T1
adeno
PDX
FFPE
Classi-
Classi-
0.002
FALSE











cal
cal









S053.FFPE.PDX.1109T1
adeno
PDX
FFPE
Classi-
Classi-
0.001
FALSE











cal
cal









S054.FFPE.PDX.0417T1
adeno
PDX
FFPE
Classi-
Classi-
0.003
FALSE











cal
cal









S055.FFPE.PDX.0910T1
adeno
PDX
FFPE
Classi-
Classi-
0.001
FALSE











cal
cal









S056.FF.PDX.1222T1
adeno
PDX
FF
Classi-
Classi-
0.003
FALSE











cal
cal









S057.FF.PDX.0113T1

PDX
FF
Classi-
Classi-
0.021
FALSE











cal
cal









S058.FF.PDX.1108T1
adeno
PDX
FF
Classi-
Classi-
0.014
FALSE











cal
cal









S059.FF.PDX.1108T1
adeno
PDX
FF
Classi-
Classi-
0.014
FALSE











cal
cal









S060.FF.PDX.0411T1
adeno
PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S061.FF.PDX.0523T1
adeno
PDX
FF
Basal-
Classi-
0.093
FALSE











like
cal









S062.FF.PDX.0319T1

PDX
FF
Classi-
Classi-
0.005
FALSE











cal
cal









S063.FF.PDX.0119T1

PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S064.FF.PDX.0218T2

PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S065.FF.PDX.0225T1
adeno-
PDX
FF
Basal-
Basal-
0.991
FALSE








squa-


like
like










mous













S066.FF.PDX.0616T1
adeno
PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S067.FF.PDX.1109T1
adeno
PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S068.FF.PDX.0806T1
adeno
PDX
FF
Classi-
Classi-
0.003
FALSE











cal
cal









S069.FF.PDX.0508T1
adeno
PDX
FF
Classi-
Classi-
0.013
FALSE











cal
cal









S070.FF.PDX.0902T1B

PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S071.FF.PDX.1112T1

PDX
FF
Classi-
Classi-
0.001
FALSE











cal
cal









S072.FF.PDX.1125T2

PDX
FF
Basal-
Basal-
0.902
FALSE











like
like









S073.FF.PDX.PancT6

PDX
FF
Basal-
Basal-
0.991
FALSE











like
like









S074.FFPE.Pi.0517T1
adeno
Primary
FFPE
Classi-
Classi-
0.024
FALSE









PDAC

cal
cal









S075.FFPE.Pi.0503T1
adeno
Primary
FFPE
Classi-
Classi-
0.038
FALSE









PDAC

cal
cal









S076.FFPE.Pi.0417T1
adeno
Primary
FFPE
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S077.FFPE.Pi.0523T1
adeno
Primary
FFPE
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S078.FFPE.Pi.0806T1
adeno
Primary
FFPE
Classi-
Classi-
0.013
FALSE









PDAC

cal
cal









S079.FF.Pi.0422T1
adeno
Primary
FF
Basal-
Basal-
0.991
FALSE









PDAC

like
like









S080.FF.Pi.0825T1
adeno
Primary
FF
Classi-
Classi-
0.003
FALSE









PDAC

cal
cal









S081.FF.Pi.1119T1
adeno
Primary
FF
Basal-
Basal-
0.991
FALSE









PDAC

like
like









S082.FF.Pi.0517T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S083.FF.Pi.0818T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S084.FF.Pi.1012T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S085.FF.Pi.1118T1
adeno
Primary
FF
Classi-
Classi-
0.093
FALSE









PDAC

cal
cal









S086.FF.Pi.0105T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S087.FF.Pi.0119T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S088.FF.Pi.0417T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S089.FF.Pi.0503T1
adeno
Primary
FF
Classi-
Classi-
0.004
FALSE









PDAC

cal
cal









S090.FF.Pi.1109T1
adeno
Primary
FF
Classi-
Classi-
0.014
FALSE









PDAC

cal
cal









S091.FF.Pi.1129T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S092.FF.Pi.1206T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S093.FF.Pi.1214T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S094.FF.Pi.0124T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S095.FF.Pi.0221T1
adeno
Primary
FF
Classi-
Classi-
0.003
FALSE









PDAC

cal
cal









S096.FF.Pi.0222T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S097.FF.Pi.0327T1
adeno
Primary
FF
Classi-
Classi-
0.004
FALSE









PDAC

cal
cal









S098.FF.Pi.0328T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S099.FF.Pi.0411T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S100.FF.Pi.0417T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S101.FF.Pi.0425T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S102.FF.Pi.0502T2
adeno
Primary
FF
Classi-
Classi-
0.003
FALSE









PDAC

cal
cal









S103.FF.Pi.0508T1
adeno
Primary
FF
Classi-
Basal-
0.557
FALSE









PDAC

cal
like









S104.FF.Pi.0523T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S105.FF.Pi.0523T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S106.FF.Pi.0524T1
ampul-
Primary
FF
Classi-
Classi-
0.001
FALSE








lary
PDAC

cal
cal









S107.FF.Pi.0605T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S108.FF.Pi.0607T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S109.FF.Pi.0614T1
adeno
Primary
FF
Classi-
Classi-
0.008
FALSE









PDAC

cal
cal









S110.FF.Pi.0710T2
adeno
Primary
FF
Classi-
Classi-
0.005
FALSE









PDAC

cal
cal









S111.FF.Pi.0711T1
adeno
Primary
FF
Classi-
Basal-
0.860
FALSE









PDAC

cal
like









S112.FF.Pi.0904T1
adeno
Primary
FF
Classi-
Classi-
0.034
FALSE









PDAC

cal
cal









S113.FF.Pi.1009T1
adeno
Primary
FF
Classi-
Classi-
0.033
FALSE









PDAC

cal
cal









S114.FF.Pi.1119T1
adeno
Primary
FF
Classi-
Classi-
0.021
FALSE









PDAC

cal
cal









S115.FF.Pi.1204T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S116.FF.Pi.1205T1
adeno
Primary
FF
Classi-
Classi-
0.013
FALSE









PDAC

cal
cal









S117.FF.Pi.0129T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S118.FF.Pi.0417T1
adeno
Primary
FF
Classi-
Classi-
0.013
FALSE









PDAC

cal
cal









S119.FF.Pi.0424T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S120.FF.Pi.0806T1
adeno
Primary
FF
Classi-
Classi-
0.005
FALSE









PDAC

cal
cal









S121.FF.Pi.0806T1
adeno
Primary
FF
Classi-
Classi-
0.001
FALSE









PDAC

cal
cal









S122.FF.Pi.0121T1
adeno
Primary
FF
Classi-
Classi-
0.002
FALSE









PDAC

cal
cal









S123.FF.Pi.0608T1
adeno
Primary
FF
Classi-
Classi-
0.005
FALSE









PDAC

cal
cal









S124.FF.Pi.1207T1
adeno
Primary
FF
Classi-
Basal-
0.671
FALSE









PDAC

cal
like









S125.FF.Pi.PancT6

Primary
FF
Basal-
Basal-
0.991
FALSE









PDAC

like
like
















TABLE 18







Collisson























Cochran-
Two-Way
Ordinal









Mantel-
ANOVA
Regression







SD

Haenszel test
Model BIC
Model BIC






PD
(>−30%)
PR
stratified by
(smaller is
(smaller is


Dataset
Treatment
# of samples
Collisson
(>=20%)
& <20%)
(<=−30%)
treatment
better)
better)



















COMPASS
FFX
34
Classical
0
12
6
0.0024
382.8
75.77





Exocrine-like
0
3
2








QM-PDA
5
5
1






GP
6
Classical
0
2
1








Exocrine-like
0
0
2








QM-PDA
0
1
0





Linehan
FOLFIRINOX +
24
Classical
0
4
4
0.4278
254.63
61.57



PF-04136309

Exocrine-like
0
5
3








QM-PDA
1
4
3






FOLFIRINOX
4
Classical
0
3
0








Exocrine-like
0
0
0








QM-PDA
1
0
0
















TABLE 19







Bailey























Cochran-
Two-Way
Ordinal









Mantel-
ANOVA
Regression







SD

Haenszel test
Model BIC
Model BIC






PD
(>−30%)
PR
stratified by
(smaller is
(smaller is


Dataset
Treatment
# of samples
Bailey
(>=20%)
& <20%)
(<=−30%)
treatment
better)
better)



















COMPASS
FFX
34
Squamous
5
3
1
0.0067
385.66
78.68





Immunogenic
0
5
5








Pancreatic
0
6
2








Progenitor











ADEX
0
6
1






GP
6
Squamous
0
1
0








Immunogenic
0
2
2








Pancreatic
0
0
0








Progenitor











ADEX
0
0
1





Linehan
FOLFIRINOX +
24
Squamous
1
2
1
0.1126
250.75
60.64



PF-04136309

Immunogenic
0
3
5








Pancreatic
0
6
2








Progenitor











ADEX
0
2
2






FOLFIRINOX
4
Squamous
1
0
0








Immunogenic
0
3
0








Pancreatic
0
0
0








Progenitor











ADEX
0
0
0
















TABLE 20







Moffitt























Cochran-
Two-Way
Ordinal









Mantel-
ANOVA
Regression







SD

Haenszel test
Model BIC
Model BIC






PD
(>−30%)
PR
stratified by
(smaller is
(smaller is


Dataset
Treatment
# of samples
Moffitt
(>=20%)
& <20%)
(<=−30%)
treatment
better)
better)



















COMPASS
FFX
34
Basal-like
5
3
1
0.00098
378.75
73.07





Classical
0
17
8






GP
6
Basal-like
0
1
0








Classical
0
2
3





Linehan
FOLFIRINOX +
24
Basal-like
1
1
0
0.01183
247.37
47.47



PF-04136309

Classical
0
12
10






FOLFIRINOX
4
Basal-like
1
0
0








Classical
0
3
0
















TABLE 21







SSC



















SD

Cochran-Mantel-Haenszel






PD
(>−30%
PR
test stratified


Dataset
Treatment
# of samples
PurIST
(>=20%)
& <20%)
(<=−30%)
by treatment

















COMPASS
FFX
34
Basal-like
5
4
1
1.20E−03





Classical
0
16
8




GP
6
Basal-like
0
1
0






Classical
0
2
3



Linehan
FOLFIRINOX +
24
Basal-like
1
1
0
0.0118



PF-04136309

Classical
0
12
10




FOLFIRINOX
4
Basal-like
1
0
0






Classical
0
3
0
















TABLE 22







Summary of Subtype Calls by Schema















Median







Follow-







up time (m)



















Cen-
Overall
Subtypes




















#

All
sored
Survival (m)

# of
% of


HR






















Ana-
#
pa-
pa-

95%

sam-
sam-
Log-

95%



Dataset
lyzed
Events
tients
tients
Median
CI
Subtype
ples
ples
rank
HR
CI
BIC
























Linehan_Seq
28
7
16.5
18
NA
[25,
Collis-
Classical
10
35.7%
0.67


44.503


(FOLFIRINOX +





NA]
son
Exocrine-
9
32.1%






PF-04136309)







like
















QM-PDA
9
32.1%













Bailey
ADEX
5
17.9%
0.35


44.321










Immimo-
9
32.1%














genic
















Pancreatic
9
32.1%














Progenitor
















Squamous
5
17.9%













Moffitt
Basal-like
2
7.1%
0.05
6.937
[0.707,
41.442










Classical
26
92.9%


68.027]










SSC
Basal-like
2
7.1%
0.05
6.937
[0.707,
41.442










Classical
26
92.9%


68.027]



Moffitt_GEO_array
125
84
13
18
17
[13,
Collis-
Classical
43
34.4%
0.79


683.915








20]
son
Exocrine-
48
38.4%














like
















QM-PDA
34
27.2%













Bailey
ADEX
27
21.6%
<0.0001


677.403










Immuno-
3
2.4%














genic
















Pancreatic
47
37.6%














Progenitor
















Squamous
48
38.4%
















%













Moffitt
Basal-like
24
19.2%
0.034
1.737
[1.038,
675.985










Classical
101
80.8%


2.906]










SSC
Basal-like
20
16.0%
0.14
1.502
[0.870,
678.021










Classical
105
84.0%


2.595]



PACA_AU_array
71
43
14
21.5
16.6
[13.7,
Collis-
Classical
27
38.0%
0.019


305.231








30.0]
son
Exocrine-
18
25.4%














like
















QM-PDA
26
36.6%













Bailey
ADEX
12
16.9%
0.12


311.789










Immuno-
19
26.8%














genic
















Pancreatic
17
23.9%














Progenitor
















Squamous
23
32.4%













Moffitt
Basal-like
13
18.3%
0.009
2.516
[1.228,
304.408










Classical
58
81.7%


5.155]










SSC
Basal-like
12
16.9%
0.038
2.218
[1.022,
306.343










Classical
59
83.1%


4.815]



PACA_AU_seq
57
33
13.2
17.5
15
[13.2,
Collis-
Classical
24
42.1%
0.006


211.127








NA]
son
Exocrine-
11
19.3%














like
















QM-PDA
22
38.6%













Bailey
ADEX
7
12.3%
0.47


222.647










Immuno-
16
28.1%














genic
















Pancreatic
14
24.6%














Progenitor
















Squamous
20
35.1%













Moffitt
Basal-like
11
19.3%
0.014
2.835
[1.188,
213.574










Classical
46
80.7%


6.766]










SSC
Basal-like
14
24.6%
0.072
2.016
[0.921,
215.414










Classical
43
75.4%


4.417]



TCGA_PAAD
146
75
14.2
15.1
20.2
[16.6,
Collis-
Classical
52
35.6%
0.41


623.054








23.4]
son
Exocrine-
61
41.8%














like
















QM-PDA
33
22.6%













Bailey
ADEX
38
26.0%
0.54


626.808










Immuno-
26
17.8%














genic
















Pancreatic
51
34.9%














Progenitor
















Squamous
31
21.2%













Moffitt
Basal-like
37
25.3%
0.0064
1.941
[1.194,
613.865










Classical
109
74.7%


3.156]










SSC
Basal-like
33
22.6%
0.0031
2.113
[1.271,
612.969










Classical
113
77.4%


3.512]



Pooled public
376
214
14.1
17
19
[16.6,
Collis-
Classical
134
35.6%
0.0692


1654.238


datasets





22.0]
son
Exocrine-
137
36.4%






of primary







like








samples







QM-PDA
105
27.9%













Bailey
ADEX
83
22.1%
0.0768


1658.276










Immuno-
58
15.4%














genic
















Pancreatic
127
33.8%














Progenitor
















Squamous
108
28.7%













Moffitt
Basal-like
77
20.5%
1.43E−05
1.982
[1.447,
1637.78










Classical
299
79.5%


2.715]










SSC
Basal-like
68
18.1%
0.0001
1.896
[1.361,
1641.295










Classical
308
81.9%


2.640]




124
63
15
17.5
23.3
[16.6,
SSC
Basal-like
21
16.9%
0.0107
2.436
[1.2086,
384.622








35.8]
exclud-
Classical
103
83.1%


4.9116]










ing
















training
















samples)









Aguirre_seq
48
35
10
15
11.5
[9.73,
SSC
Basal-like
15
31.3%
0.14
1.688
[0.835,
219.125








19.80]

Classical
33
68.8%


3.407]



Yeh_seq_FNA
42
30
12.9
24.4
17.1
[10.2,
SSC
Basal-like
2
4.8%
0.017
5.289
[1.151,
178.179








24.6]

Classical
40
95.2%


24.31]
















TABLE 23





Collisson Transition Rates





















Pre-treatment
QM
0.22
0
0.78




Exocrine
0.5
0.5
0




Classical
0.45
0.27
0.27





Classical
Exocrine
QM













Post-treatment

















TABLE 24





Bailey Transition Rates




















Pre-treatment
Squamous
0
0.2
0
0.8



PP
0.25
0.38
0.25
0.12



Immuno.
0
0.64
0.18
0.18



ADEX
0.5
0.25
0.25
0




Squamous
PP
Immuno.
ADEX











Post-treatment
















TABLE 25







PurIST Coefficients











Intercept: −6.815


Gene A
Gene B
Coefficient












GPR87
REG4
1.994


KRT6A
ANXA10
2.031


BCAR3
GATA6
1.618


PTGES
CLDN18
0.922


ITGA3
LGALS4
1.059


C16orf74
DDC
0.929


S100A2
SLC40A1
2.505


KRT5
CLRN3
0.485
















TABLE 26







PurIST-n Coefficients











Intercept: −12.414


Gene A
Gene B
Coefficients












GPR87
REG4
3.413


KRT6A
ANXA10
3.437


KRT17
LGALS4
2.078


S100A2
TFF1
2.651


C16orf74
DDC
0.901


KRT15
PLA2G10
2.677


PTGES
CDH17
2.911


DCBLD2
TSPAN8
1.903


PIP5K1B
MUC17
0.036


NR1I2
MYO1A
−0.638


CTSE
LYZ
0.977
















TABLE 27







Validation Dataset Individual Study Areas Under the Curves













Dataset
N
Basal-like
Accuracy
Sensitivity
Specificity
AUC
















PACA_AU_seq
65
12
0.892
0.833
0.906
0.965


PACA_AU_array
95
14
0.958
0.929
0.963
0.973


Moffitt
37
56
0.973
11
0.969
11


Linehan_Seq
66
11
1
0.545
1
0.984


Connor
66
13
0.909
1
0.982
1


COMPASS
49
12
0.98
0.833
0.972
0.965
















TABLE 28







Exemplary PKIs and Their Targets










Gene


Overexpressed in


Name
Aliases
Compounds
Subtype





AAK1
KIAA1048, DKF2p686K16132
GSK3236425A; LP-935509; UNC-AA-1-0013
Basal-like




(SGC-AAK1-1); UNC-AA-1-0017



ABL1
JTK7, c-ABL, p150
asciminib; canertinib, CI-1033; erlotinib, OSI-744;
Basal-like




GNF-5; imatinib; LDN-214117; masitinib AB1010;





XMD-17-51



CDK1
CDC28A, CDC2, P34CDC2
GW276655; GW300657X; GW300660X;
Basal-like




GW416981X



CDK16
PCTAIRE, PCTAIRE1,
CAF-204; SNS-032
Basal-like



PCTGAIRE, FLJ16665,





PCTK1




CDK17
PCTAIRE2, PCTK2
YL-206; SNS-032
Basal-like


CDK4
CMM3; PSK-J3
abetnaciclib; LY2857785; palbociclib; ribociclib;
Basal-like




PFE-PKIS 32; PFE-PKIS 44; SIHR CDK4/6





compound 83; SIHR CDK4/6 compound 91



CDK7
CAK1, CDKN7, MO15, STK1,
BMS-387032/SNS-032; BS-181; THZ1
Basal-like



CAK, HCAK, p39MO15




CSNK2A2
CSNK2A1, CK2α, CK2A2,
G59973, entospletinib; GO289; CX-4945,
Basal-like



CK2a2, CK2α2
silmasertib; AZ-G



DDR1
RTK6, CD167, CAK, CD167,
AC220, quizartinib; DDR1 compound 7ae; DDR1-1N-1;
Basal-like



DDR, EDDR1, HGK2, MCK10,
imatinib; LY2801653; masitinib AB1010;




NEP, NTRK4, PTK3, PTK3A,
PD173074; RAF-265, CHIR-265; DDR-TRK-1;




TRKE
GW832467; TPKI-39



EPHA2
ARCC2, CTPA, CTPP1,
LY3009120; MLN8237/Alisertib; GW693917A; ALW-
Basal-like



CTRCT6, ECK, EphA2
II-41-27



FER
PPP1R74, TYK3, p94-Fer
GSK1838705A; PF-06463922, Lorlatinib; GSK1904529
Basal-like


FRK
RAK, GTK, PTK5
Abbott Compound 530; PF-06463922, Lorlatinib;
Classical




XMD8-87; GSK1904529; TPKI-113



GSK3A/G

BAY-61-3606; Carna compound 13; CHIR-99021;
Basal-like


SK3B

EHT5372; GW784752X; SB-742609; TPKI-91;





ARA014418; LY-317615, enzastaurin;





GW513184X; GW810372X; SB-725317; TPKI-85



INSR
CD220, HHF5, IR
OSI-906, linsitinib; GSK1392956A; GSK1904529;
Basal-like




GSK2219385



LIMK1
LIMK, LIMK-1
CRT0105446; LIMKi compound 3; Amakem
Basal-like




tetrahydropyrimido-indole compound 3; Scripps FL





18b; LX7101; TH-257; R10015



LYN
JTK8, p53Lyn, p56Lyn
masitinib AB1010; saturated ibrutinib; Maly LYN
Classical



compound 19




MAP2K2
CFC4, 1VIAPKK2, MEK2,
Trametinib (GSK1120212); cobimetinib/GDC0973;




MKK2, PRKMK2
binimetinib; refametinib; ESD0001937



MAP3K11
SPRK, MEKK11, MLK-3,
PFE-PKIS18; SGK1 Sanofi 14n
Classical



PTK1




MAP3K2
MEKK2B, MEKK2
MRKI-19; GSK2656157; AKI00000018a;
Basal-like




AK100000021a



MAP3K5
MAPKKK5, ASK1,
Compound 10.HCl; MSC 2032964A; PF3644022;
Basal-like



MEKK5
TPKI-58



MAP4K5
KHSqqq1, GCKR, KHS,
FRAX1036; G-5555
Basal-like



MAPKKKK5




MAPK1
ERK, ERK2, p41mapk,
Carna compound 13; SCH772984; Vertex 11e; AZ
Basal-like



MAPK2, PRKM2, PRKM1,
compound 35




ERT1, ERK-2, P42MAPK,





PRKM1, p38, p40, p41,





p42-MAPK, Erk2




MAPK3
ERK1, p44mapk, p44erk1,
SCH+E:E772984;
Classical



PRKM3, ERK-1, ERT2,
GAN-305074X (aka GW5074)




HS44KDAP, HUMKER1A,





P44ERK1, P44MAPK,





p44-ERK1, p44-MAPK, Erk1




PAK4

GenentechPAK compound 13;
Basal-like




Novartis compound 11



PIP4K2C
PIP5K2C
G1T28
Basal-like


PKM

TLN-232 (aka CAP-232)



PRKCD
ALPS3, CVID9, Al 71, PKCD,
LY-317615; enzastaurn; uprosertib, GSK2141795
Classical



nPKC-delta PKCd




PTK2B
CAKB, PYK2, RAFTK, PTK,
PF-06463922; Lorlatinib; GSK1392956A
Basal-like



CADTK, FADK2, FAK2, PKB




PIK6
BtK, p21cdc42Hs,
PLX-4720; Vemurafenib; saturated ibrutinib; XMD8-87;
Classical




21a; PF-6698840



RIPK2
RICK, RIP2, CARDIAK,
LDN-214117; Novartis Compound 2; OD36; OD38;
Basal-like



CARD3, CCK, GIG30
saturated ibrutinib; SB-203580; SB-590885; WEHI-





345; GSK583; GSK RIPK2 inhibitor 7



ROCK2
ROCK-II
GSK269962A; GSK429286; SB-747651A;
Classical




Scripps compound 35; Netarsudil; netarsudil hydrolysis





product; Abbvie ROCK compound 16; Abbvie





ROCK compound 58



SRC
ASV, c-src,ASV1, THC6,
many inhibitors, usually src family
Classical



c-SRC, p60-Src




sTK10
LOK, PRO2729
erlotinib, OSI-744; GSK461364A; RAF-265,
Basal-like




CHIR-265; GSK204607; SB-633825



TBK1
NAK, FTDALS4; T2K
WEHI-112; GSK8612
Basal-like


YES1
Yes, c-yes, HsT441, P61-YES
PF-477736; saturated ibrutinib; GW621970X
Basal-like








Claims
  • 1. A method for determining a subtype of a pancreatic tumor in a biological sample comprising, consisting essentially of, or consisting of pancreatic tumor cells obtained from a subject, the method comprising: (a) obtaining gene expression levels for each of the following genes in the biological sample: GPR87, KRT6A, BCAR3, PTGES, ITGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3;(b) performing a pair-wise comparison of the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H, wherein Gene Pairs 1-8 and Gene Pairs A-H are as follows:
  • 2. The method of claim 1, further comprising converting the Raw Score to a predicted basal-like probability (PBP) using the inverse-logit transformation PBP=eRaw Score/(1eRaw Score),
  • 3. The method of claim 1, wherein the pancreatic tumor is a pancreatic ductal adenocarcinoma (PDAC).
  • 4. The method of claim 1, wherein the biological sample comprises a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or comprises a frozen or archival sample derived therefrom.
  • 5. The method of claim 1, wherein the obtaining employs a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof.
  • 6. The method of claim 5, wherein the technique comprises NanoString and employs probes comprising the following SEQ ID NOs:
  • 7. The method of claim 1, wherein the subject is a human.
  • 8. A method for identifying a differential treatment strategy for a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC), the method comprising: (a) obtaining gene expression levels for each of the following genes in a biological sample comprising PDAC cells isolated from the subject: GPR87, KRT6A, BCAR3, PTGES, ITGA3, C16orf74, S100A2, KRT5, REG4, ANXA10, GATA6, CLDN18, LGALS4, DDC, SLC40A1, CLRN3;(b) performing a pair-wise comparison of the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H, wherein Gene Pairs 1-8 and Gene Pairs A-H are as follows:
  • 9. The method of claim 8, wherein the biological sample comprises a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or comprises a frozen or archival sample derived therefrom.
  • 10. The method of claim 8, wherein the obtaining employs a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof.
  • 11. The method of claim 10, wherein the technique comprises NanoString and employs probes comprising the following SEQ ID NOs:
  • 12. The method of claim 8, wherein the subject is a human.
  • 13. A method for treating a patient diagnosed with pancreatic ductal adenocarcinoma (PDAC), the method comprising: (a) identifying a subtype of the patient's PDAC via the method of any one of claims 1-6; and(b) treating the patient with gemcitabine, optionally in combination with nab-paclitaxel, if the assigned subtype is a basal-like subtype and treating the patient with FOLFIRINOX if the assigned subtype is classical.
  • 14. The method of claim 13, where the treating comprises at least one additional anti-PDAC treatment.
  • 15. The method of claim 14, where the at least one additional anti-PDAC treatment is surgery, radiation, administration of an additional chemotherapeutic agent, administration of a protein kinase inhibitor (PKI), and any combination thereof.
  • 16. The method of claim 15, wherein the additional chemotherapeutic agent is a CCR2 inhibitor, a checkpoint inhibitor, or any combination thereof.
  • 17. The method of claim 13, wherein the patient is a human.
  • 18. A method for classifying a subject diagnosed with pancreatic ductal adenocarcinoma (PDAC) as having a basal-like subtype or a classical subtype of PDAC, the method comprising: (a) performing a pair-wise comparison of gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H in a sample comprising PDAC cells isolated from the subject, wherein Gene Pairs 1-8 and Gene Pairs A-H are as follows:
  • 19. The method of claim 18, further comprising converting the Raw Score to a predicted basal-like probability (PBP) using the inverse-logit transformation PBP=eRaw Score/(1+eRaw Score),
  • 20. The method of claim 18, wherein the sample comprises a biopsy sample, optionally a fine needle biopsy aspiration or a percutaneous core needle biopsy, or comprises a frozen or archival sample derived therefrom.
  • 21. The method of claim 18, wherein the gene expression levels for each of Gene Pairs 1-8 or for each of Gene Pairs A-H in a sample are determined using a technique selected from the group consisting of microarray analysis, RNAseq, quantitative RT-PCR, NanoString, or any combination thereof.
  • 22. The method of claim 21, wherein the technique comprises NanoString and employs probes comprising the following SEQ ID NOs:
  • 23. The method of claim 18, wherein the subject is a human.
CROSS REFERENCE TO RELATED APPLICATION

The presently disclosed subject matter claims the benefit of U.S. Provisional Patent Application Ser. No. 62/827,473, filed Apr. 1, 2019, the disclosure of which incorporated herein by reference in its entirety.

GOVERNMENT INTEREST

This invention was made with government support under grant numbers CA199064 and CA211000 awarded by the National Institutes of Health. The government has certain rights in the invention.

PCT Information
Filing Document Filing Date Country Kind
PCT/US2020/026209 4/1/2020 WO 00
Provisional Applications (1)
Number Date Country
62827473 Apr 2019 US