Patent Application
20200332368
This disclosure relates generally to the field of cancer biology, and specifically, to the fields of detection and identification of specific cancer cell phenotypes and correlation with appropriate therapies.
Current approaches to treating early breast cancer, including adjuvant therapy, have indeed improved survival and reduced recurrence. However, the risk of recurrence may be underestimated in some patients, but overestimated in others.
While the risk of recurrence does diminish somewhat over time, ongoing risk has been observed in many studies, some of them involving tens of thousands of patients with breast cancer. In fact, some of the patients who experienced recurrence after five years in these studies had previously been considered “low risk”—for example, their cancer had not spread to the lymph nodes at the time of their initial diagnosis, or their estrogen receptor status was positive. In one of these studies, a substantial number of recurrences occurred more than five years post-treatment. Thus, there is a need in the art to determine risk of recurrence and determine therapies which reduce that risk and improve overall survival.
The present invention provides a method of predicting outcome in a subject having breast cancer comprising: providing a tumor sample from the subject; determining the expression of the genes in the NANO46 intrinsic gene list of Table 1 in the tumor sample; measuring the similarity of the tumor sample to an intrinsic subtype based on the expression of the genes in the NANO46 intrinsic gene list, wherein the intrinsic subtype consists of at least Basal-like, Luminal A, Luminal B or HER2-enriched; determining a proliferation score based on the expression of a subset of proliferation genes in the NANO46 intrinsic gene list; determining the size of the tumor, calculating a risk of recurrence score using a weighted sum of said intrinsic subtype, proliferation score and tumor size; and determining whether the subject has a low or high risk of recurrence based on the recurrence score. In one embodiment a low score indicates a more favorable outcome and high score indicates a less favorable outcome.
The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the NANO46 intrinsic genes recited in Table 1. In some embodiments, the methods of the present invention can include determining the expression of at least one of, a combination of, or each of, the NANO46 intrinsic genes selected from ANLN, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. The expression of the members of the NANO46 intrinsic gene list can be determined using the nanoreporter code system (nCounter® Analysis system).
The methods of the present invention can include determining at least one of, a combination of, or each of, the following: tumor size, tumor grade, nodal status, intrinsic subtype, estrogen receptor expression, progesterone receptor expression, and HER2/ERBB2 expression
The sample can be a sampling of cells or tissues. The sample can be a tumor. The tissue can be obtained from a biopsy. The sample can be a sampling of bodily fluids. The bodily fluid can be blood, lymph, urine, saliva or nipple aspirate.
While the disclosure has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the disclosure, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
The patent and scientific literature referred to herein establishes the knowledge that is available to those with skill in the art. All United States patents and published or unpublished United States patent applications cited herein are incorporated by reference. All published foreign patents and patent applications cited herein are hereby incorporated by reference. Genbank and NCBI submissions indicated by accession number cited herein are hereby incorporated by reference. All other published references, documents, manuscripts and scientific literature cited herein are hereby incorporated by reference.
While this disclosure has been particularly shown and described with references to preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the scope of the disclosure encompassed by the appended claims.
The disclosure presents a method of predicting outcome in a subject having breast cancer comprising: providing a tumor sample from the subject; determining the expression of the genes in the NANO46 intrinsic gene list of Table 1 in the tumor sample; determining the intrinsic subtype of the tumor sample based on the expression of the genes in the NANO46 intrinsic gene list, wherein the intrinsic subtype consists of at least Basal-like, Luminal A, Luminal B or HER2-enriched; determining a proliferation score based on the expression of a subset of proliferation genes in the NANO46 intrinsic gene list; determining the size of the tumor, calculating a risk of recurrence score using a weighted sum of said intrinsic subtype, proliferation score and tumor size; and determining whether the subject has a low or high risk of recurrence based on the recurrence score. In one embodiment a low score indicates a more favorable outcome and high score indicates a less favorable outcome.
Intrinsic genes are statistically selected to have low variation in expression between biological sample replicates from the same individual and high variation in expression across samples from different individuals. Thus, intrinsic genes are used as classifier genes for breast cancer classification. Although clinical information was not used to derive the breast cancer intrinsic subtypes, this classification has proved to have prognostic significance. Intrinsic gene screening can be used to classify breast cancers into five molecular distinct intrinsic subtypes, Luminal A (LumA), Luminal B (LumB), HER2-enriched (Her-2-E), Basal-like, and Normal-like (Perou et al. Nature, 406(6797):747-52 (2000); Sorlie et al. PNAS, 98(19):10869-74 (2001)).
A NANO46 gene expression assay, as described herein, can identify intrinsic subtype from a biological sample, e.g., a standard formalin fixed paraffin embedded tumor tissue. The methods utilize a supervised algorithm to classify subject samples according to breast cancer intrinsic subtype. This algorithm, referred to herein as the NANO46 classification model, is based on the gene expression profile of a defined subset of intrinsic genes that has been identified herein as superior for classifying breast cancer intrinsic subtypes. The subset of genes, along with primers target-specific sequences utilized for their detection, is provided in Table 1. Table 1A provides the sequences of target specific probe sequences for detecting each gene utilized in Table 1. The sequences provided in Table 1A are merely representative and are not meant to limit the invention. The skilled artisan can utilize any target sequence-specific probe for detecting any of (or each of) the genes in Table 1.
Table 2 provides select sequences for the NANO46 genes of Table 1.
At least 40, at least 41, at least 42, at least 43, at least 44, at least 46 or all 46 of the genes in Table 1 can be utilized in the methods of the present invention. Preferably, the expression of each of the 46 genes is determined in a biological sample. The prototypical gene expression profiles (i.e. centroid) of the four intrinsic subtypes were pre-defined from a training set of FFPE breast tumor samples using hierarchical clustering analysis of gene expression data. A heatmap of the prototypical gene expression profiles (i.e. centroids) of these four subtypes is shown in
After performing the Breast Cancer Intrinsic Subtyping test with a test breast cancer tumor sample and the reference sample provided as part of the test kit, a computational algorithm based on a Pearson's correlation compares the normalized and scaled gene expression profile of the NANO46 intrinsic gene set of the test sample to the prototypical expression signatures of the four breast cancer intrinsic subtypes. The intrinsic subtype analysis is determined by determining the expression of a NANO50 set of genes (which is determining the expression of the NANO46 set of genes and further includes determining the expression of MYBL2, BIRC5, GRB7 and CCNB1) and the risk of recurrence (“ROR”) is determined using the NANO46 set of genes). Specifically, the intrinsic subtype is identified by comparing the expression of the NANO50 set of genes in the biological sample with the expected expression profiles for the four intrinsic subtypes. The subtype with the most similar expression profile is assigned to the biological sample. The ROR score is an integer value on a 0-100 scale that is related to an individual patient's probability of distant recurrence within 10 years for the defined intended use population. The ROR score is calculated by comparing the expression profiles of the NANO46 genes in the biological sample with the expected profiles for the four intrinsic subtypes, as described above, to calculate four different correlation values. These correlation values are then combined with a proliferation score (and optionally one or more clinicopathological variables, such as tumor size) to calculate the ROR score. Preferably, the ROR score is calculated by comparing only the expression profiles of the NANO46 genes.
Independent testing on a cohort of node negative, estrogen receptor positive patients treated with tamoxifen shows predominantly Luminal A and B subtype patients with Luminal A patients exhibiting better outcome than Luminal B patients,
The training set of FFPE breast tumor samples, which had well defined clinical characteristics and clinical outcome data, were used to establish a continuous Risk of Recurrence (ROR) score. The score is calculated using coefficients from a Cox model that includes correlation to each intrinsic subtype, a proliferation score (mean gene expression of a subset of 18 of the 46 genes), and tumor size, Table 4.
The test variables in Table 4 are multiplied by the corresponding coefficients and summed to produce a risk score (“ROR-PT”).
ROR-PT equation=−0.0067*A+0.4317*B+−0.3172*C+0.4894*D+0.1981*E+0.1133*F
In previous studies, the ROR score provided a continuous estimate of the risk of recurrence for ER-positive, node-negative patients who were treated with tamoxifen for 5 years (Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009)). This result was verified on ER-positive, node-negative patients from the same cohort,
The gene set contains many genes that are known markers for proliferation. The methods of the present invention provide for the determination of subsets of genes that provide a proliferation signature. The methods of the present invention can include determining the expression of at least one of, a combination of, or each of, a 18-gene subset of the NANO46 intrinsic genes selected from ANLN, CCNE1, CDC20, CDC6, CDCA1, CENPF, CEP55, EXO1, KIF2C, KNTC2, MELK, MKI67, ORC6L, PTTG1, RRM2, TYMS, UBE2C and/or UBE2T. Preferably, the expression of each of the 18-gene subset of the NANO46 gene set is determined to provide a proliferation score. The expression of one or more of these genes may be determined and a proliferation signature index can be generated by averaging the normalized expression estimates of one or more of these genes in a sample. The sample can be assigned a high proliferation signature, a moderate/intermediate proliferation signature, a low proliferation signature or an ultra-low proliferation signature. Methods of determining a proliferation signature from a biological sample are as described in Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009) and supplemental online material (these documents are incorporated herein, by reference, in their entireties).
Description of Intrinsic Subtype Biology
Luminal subtypes: The most common subtypes of breast cancer are the luminal subtypes, Luminal A and Luminal B. Prior studies suggest that luminal A comprises approximately 30% to 40% and luminal B approximately 20% of all breast cancers, but they represent over 90% of hormone receptor positive breast cancers (Nielsen et al. Clin. Cancer Res., 16(21):5222-5232 (2009)). The gene expression pattern of these subtypes resembles the luminal epithelial component of the breast. These tumors are characterized by high expression of estrogen receptor (ER), progesterone receptor (PR), and genes associated with ER activation, such as LIV1, GATA3, and cyclin D1, as well as expression of luminal cytokeratins 8 and 18 (Lisa Carey & Charles Perou (2009). Gene Arrays, Prognosis, and Therapeutic Interventions. Jay R. Harris et al. (4th ed.), Diseases of the breast (pp. 458-472). Philadelphia, Pa.: Lippincott Williams & Wilkins).
Luminal A: Luminal A (LumA) breast cancers exhibit low expression of genes associated with cell cycle activation and the ERBB2 cluster resulting in a better prognosis than Luminal B. The Luminal A subgroup has the most favorable prognosis of all subtypes and is enriched for endocrine therapy-responsive tumors.
Luminal B: Luminal B (LumB) breast cancers also express ER and ER-associated genes. Genes associated with cell cycle activation are highly expressed and this tumor type can be HER2(+) (˜20%) or HER2(−). The prognosis is unfavorable (despite ER expression) and endocrine therapy responsiveness is generally diminished relative to LumA.
HER2-enriched: The HER2-enriched subtype is generally ER-negative and is HER2-positive in the majority of cases with high expression of the ERBB2 cluster, including ERBB2 and GRB7. Genes associated with cell cycle activation are highly expressed and these tumors have a poor outcome.
Basal-like: The Basal-like subtype is generally ER-negative, is almost always clinically HER2-negative and expresses a suite of “basal” biomarkers including the basal epithelial cytokeratins (CK) and epidermal growth factor receptor (EGFR). Genes associated with cell cycle activation are highly expressed.
Clinical Variables
The NANO46 classification model described herein may be further combined with information on clinical variables to generate a continuous risk of recurrence (ROR) predictor. As described herein, a number of clinical and prognostic breast cancer factors are known in the art and are used to predict treatment outcome and the likelihood of disease recurrence. Such factors include, for example, lymph node involvement, tumor size, histologic grade, estrogen and progesterone hormone receptor status, HER-2 levels, and tumor ploidy. In one embodiment, risk of recurrence (ROR) score is provided for a subject diagnosed with or suspected of having breast cancer. This score uses the NANO46 classification model in combination with clinical factors of lymph node status (N) and tumor size (T). Assessment of clinical variables is based on the American Joint Committee on Cancer (AJCC) standardized system for breast cancer staging. In this system, primary tumor size is categorized on a scale of 0-4 (TO: no evidence of primary tumor; Tl: <2 cm; T2: >2 cm-<5 cm; T3: >5 cm; T4: tumor of any size with direct spread to chest wall or skin). Lymph node status is classified as N0-N3 (NO: regional lymph nodes are free of metastasis; N1: metastasis to movable, same-side axillary lymph node(s); N2: metastasis to same-side lymph node(s) fixed to one another or to other structures; N3: metastasis to same-side lymph nodes beneath the breastbone). Methods of identifying breast cancer patients and staging the disease are well known and may include manual examination, biopsy, review of patient's and/or family history, and imaging techniques, such as mammography, magnetic resonance imaging (MRI), and positron emission tomography (PET).
Sample Source
In one embodiment of the present disclosure, breast cancer subtype is assessed through the evaluation of expression patterns, or profiles, of the intrinsic genes listed in Table 1 in one or more subject samples. For the purpose of discussion, the term subject, or subject sample, refers to an individual regardless of health and/or disease status. A subject can be a subject, a study participant, a control subject, a screening subject, or any other class of individual from whom a sample is obtained and assessed in the context of the disclosure. Accordingly, a subject can be diagnosed with breast cancer, can present with one or more symptoms of breast cancer, or a predisposing factor, such as a family (genetic) or medical history (medical) factor, for breast cancer, can be undergoing treatment or therapy for breast cancer, or the like. Alternatively, a subject can be healthy with respect to any of the aforementioned factors or criteria. It will be appreciated that the term “healthy” as used herein, is relative to breast cancer status, as the term “healthy” cannot be defined to correspond to any absolute evaluation or status. Thus, an individual defined as healthy with reference to any specified disease or disease criterion, can in fact be diagnosed with any other one or more diseases, or exhibit any other one or more disease criterion, including one or more cancers other than breast cancer. However, the healthy controls are preferably free of any cancer.
In particular embodiments, the methods for predicting breast cancer intrinsic subtypes include collecting a biological sample comprising a cancer cell or tissue, such as a breast tissue sample or a primary breast tumor tissue sample. By “biological sample” is intended any sampling of cells, tissues, or bodily fluids in which expression of an intrinsic gene can be detected. Examples of such biological samples include, but are not limited to, biopsies and smears. Bodily fluids useful in the present disclosure include blood, lymph, urine, saliva, nipple aspirates, gynecological fluids, or any other bodily secretion or derivative thereof. Blood can include whole blood, plasma, serum, or any derivative of blood. In some embodiments, the biological sample includes breast cells, particularly breast tissue from a biopsy, such as a breast tumor tissue sample. Biological samples may be obtained from a subject by a variety of techniques including, for example, by scraping or swabbing an area, by using a needle to aspirate cells or bodily fluids, or by removing a tissue sample (i.e., biopsy). Methods for collecting various biological samples are well known in the art. In some embodiments, a breast tissue sample is obtained by, for example, fine needle aspiration biopsy, core needle biopsy, or excisional biopsy. Fixative and staining solutions may be applied to the cells or tissues for preserving the specimen and for facilitating examination. Biological samples, particularly breast tissue samples, may be transferred to a glass slide for viewing under magnification. In one embodiment, the biological sample is a formalin-fixed, paraffin-embedded breast tissue sample, particularly a primary breast tumor sample. In various embodiments, the tissue sample is obtained from a pathologist-guided tissue core sample.
Expression Profiling
In various embodiments, the present disclosure provides methods for classifying, prognosticating, or monitoring breast cancer in subjects. In this embodiment, data obtained from analysis of intrinsic gene expression is evaluated using one or more pattern recognition algorithms. Such analysis methods may be used to form a predictive model, which can be used to classify test data. For example, one convenient and particularly effective method of classification employs multivariate statistical analysis modeling, first to form a model (a “predictive mathematical model”) using data (“modeling data”) from samples of known subtype (e.g., from subjects known to have a particular breast cancer intrinsic subtype: LumA, LumB, Basal-like, HER2-enriched, or normal-like), and second to classify an unknown sample (e.g., “test sample”) according to subtype. Pattern recognition methods have been used widely to characterize many different types of problems ranging, for example, over linguistics, fingerprinting, chemistry and psychology. In the context of the methods described herein, pattern recognition is the use of multivariate statistics, both parametric and non-parametric, to analyze data, and hence to classify samples and to predict the value of some dependent variable based on a range of observed measurements. There are two main approaches. One set of methods is termed “unsupervised” and these simply reduce data complexity in a rational way and also produce display plots which can be interpreted by the human eye. However, this type of approach may not be suitable for developing a clinical assay that can be used to classify samples derived from subjects independent of the initial sample population used to train the prediction algorithm.
The other approach is termed “supervised” whereby a training set of samples with known class or outcome is used to produce a mathematical model which is then evaluated with independent validation data sets. Here, a “training set” of intrinsic gene expression data is used to construct a statistical model that predicts correctly the “subtype” of each sample. This training set is then tested with independent data (referred to as a test or validation set) to determine the robustness of the computer-based model. These models are sometimes termed “expert systems,” but may be based on a range of different mathematical procedures. Supervised methods can use a data set with reduced dimensionality (for example, the first few principal components), but typically use unreduced data, with all dimensionality. In all cases the methods allow the quantitative description of the multivariate boundaries that characterize and separate each subtype in terms of its intrinsic gene expression profile. It is also possible to obtain confidence limits on any predictions, for example, a level of probability to be placed on the goodness of fit. The robustness of the predictive models can also be checked using cross-validation, by leaving out selected samples from the analysis.
The NANO46 classification model described herein is based on the gene expression profile for a plurality of subject samples using the intrinsic genes listed in Table 1. The plurality of samples includes a sufficient number of samples derived from subjects belonging to each subtype class. By “sufficient samples” or “representative number” in this context is intended a quantity of samples derived from each subtype that is sufficient for building a classification model that can reliably distinguish each subtype from all others in the group. A supervised prediction algorithm is developed based on the profiles of objectively-selected prototype samples for “training” the algorithm. The samples are selected and subtyped using an expanded intrinsic gene set according to the methods disclosed in International Patent Publication WO 2007/061876 and US Patent Publication No. 2009/0299640, which is herein incorporated by reference in its entirety. Alternatively, the samples can be subtyped according to any known assay for classifying breast cancer subtypes. After stratifying the training samples according to subtype, a centroid-based prediction algorithm is used to construct centroids based on the expression profile of the intrinsic gene set described in Table 1.
In one embodiment, the prediction algorithm is the nearest centroid methodology related to that described in Narashiman and Chu (2002) PNAS 99:6567-6572, which is herein incorporated by reference in its entirety. In the present disclosure, the method computes a standardized centroid for each subtype. This centroid is the average gene expression for each gene in each subtype (or “class”) divided by the within-class standard deviation for that gene. Nearest centroid classification takes the gene expression profile of a new sample, and compares it to each of these class centroids. Subtype prediction is done by calculating the Spearman's rank correlation of each test case to the five centroids, and assigning a sample to a subtype based on the nearest centroid.
Detection of Intrinsic Gene Expression
Any methods available in the art for detecting expression of the intrinsic genes listed in Table 1 are encompassed herein. By “detecting expression” is intended determining the quantity or presence of an RNA transcript or its expression product of an intrinsic gene. Methods for detecting expression of the intrinsic genes of the disclosure, that is, gene expression profiling, include methods based on hybridization analysis of polynucleotides, methods based on sequencing of polynucleotides, immunohistochemistry methods, and proteomics-based methods. The methods generally detect expression products (e.g., mRNA) of the intrinsic genes listed in Table 1. In preferred embodiments, PCR-based methods, such as reverse transcription PCR (RT-PCR) (Weis et al., TIG 8:263-64, 1992), and array-based methods such as microarray (Schena et al., Science 270:467-70, 1995) are used. By “microarray” is intended an ordered arrangement of hybridizable array elements, such as, for example, polynucleotide probes, on a substrate. The term “probe” refers to any molecule that is capable of selectively binding to a specifically intended target biomolecule, for example, a nucleotide transcript or a protein encoded by or corresponding to an intrinsic gene. Probes can be synthesized by one of skill in the art, or derived from appropriate biological preparations. Probes may be specifically designed to be labeled. Examples of molecules that can be utilized as probes include, but are not limited to, RNA, DNA, proteins, antibodies, and organic molecules.
Many expression detection methods use isolated RNA. The starting material is typically total RNA isolated from a biological sample, such as a tumor or tumor cell line, and corresponding normal tissue or cell line, respectively. If the source of RNA is a primary tumor, RNA (e.g., mRNA) can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g., formalin-fixed) tissue samples (e.g., pathologist-guided tissue core samples).
General methods for RNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., ed., Current Protocols in Molecular Biology, John Wiley & Sons, New York 1987-1999. Methods for RNA extraction from paraffin embedded tissues are disclosed, for example, in Rupp and Locker, Lab Invest. 56:A67, (1987); and De Andres et al. Biotechniques 18:42-44, (1995). In particular, RNA isolation can be performed using a purification kit, a buffer set and protease from commercial manufacturers, such as Qiagen (Valencia, Calif.), according to the manufacturer's instructions. For example, total RNA from cells in culture can be isolated using Qiagen RNeasy mini-columns. Other commercially available RNA isolation kits include MASTERPURE™ Complete DNA and RNA Purification Kit (Epicentre, Madison, Wis.) and Paraffin Block RNA Isolation Kit (Ambion, Austin, Tex.). Total RNA from tissue samples can be isolated, for example, using RNA Stat-60 (Tel-Test, Friendswood, Tex.). Total RNA from FFPE can be isolated, for example, using High Pure FFPE RNA Microkit, Cat No. 04823125001 (Roche Applied Science, Indianapolis, Ind.). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation. Additionally, large numbers of tissue samples can readily be processed using techniques well known to those of skill in the art, such as, for example, the single-step RNA isolation process of Chomczynski (U.S. Pat. No. 4,843,155).
Isolated RNA can be used in hybridization or amplification assays that include, but are not limited to, PCR analyses and probe arrays. One method for the detection of RNA levels involves contacting the isolated RNA with a nucleic acid molecule (probe) that can hybridize to the mRNA encoded by the gene being detected. The nucleic acid probe can be, for example, a full-length cDNA, or a portion thereof, such as an oligonucleotide of at least 7, 15, 30, 60, 100, 250, or 500 nucleotides in length and sufficient to specifically hybridize under stringent conditions to an intrinsic gene of the present disclosure, or any derivative DNA or RNA. Hybridization of an mRNA with the probe indicates that the intrinsic gene in question is being expressed.
In one embodiment, the mRNA is immobilized on a solid surface and contacted with a probe, for example by running the isolated mRNA on an agarose gel and transferring the mRNA from the gel to a membrane, such as nitrocellulose. In an alternative embodiment, the probes are immobilized on a solid surface and the mRNA is contacted with the probes, for example, in an Agilent gene chip array. A skilled artisan can readily adapt known mRNA detection methods for use in detecting the level of expression of the intrinsic genes of the present disclosure.
An alternative method for determining the level of intrinsic gene expression product in a sample involves the process of nucleic acid amplification, for example, by RT-PCR (U.S. Pat. No. 4,683,202), ligase chain reaction (Barany, PNAS USA 88: 189-93, (1991)), self sustained sequence replication (Guatelli et al., Proc. Natl. Acad. Sci USA 87: 1874-78, (1990)), transcriptional amplification system (Kwoh et al., Proc. Natl. Acad. ScL USA 86: 1173-77, (1989)), Q-Beta Replicase (Lizardi et al., Bio/Technology 6:1197, (1988)), rolling circle replication (U.S. Pat. No. 5,854,033), or any other nucleic acid amplification method, followed by the detection of the amplified molecules using techniques well known to those of skill in the art. These detection schemes are especially useful for the detection of nucleic acid molecules if such molecules are present in very low numbers.
In particular aspects of the disclosure, intrinsic gene expression is assessed by quantitative RT-PCR. Numerous different PCR or QPCR protocols are known in the art and exemplified herein below and can be directly applied or adapted for use using the presently-described compositions for the detection and/or quantification of the intrinsic genes listed in Table 1. Generally, in PCR, a target polynucleotide sequence is amplified by reaction with at least one oligonucleotide primer or pair of oligonucleotide primers. The primer(s) hybridize to a complementary region of the target nucleic acid and a DNA polymerase extends the primer(s) to amplify the target sequence. Under conditions sufficient to provide polymerase-based nucleic acid amplification products, a nucleic acid fragment of one size dominates the reaction products (the target polynucleotide sequence which is the amplification product). The amplification cycle is repeated to increase the concentration of the single target polynucleotide sequence. The reaction can be performed in any thermocycler commonly used for PCR. However, preferred are cyclers with real time fluorescence measurement capabilities, for example, SMARTCYCLER® (Cepheid, Sunnyvale, Calif.), ABI PRISM 7700® (Applied Biosystems, Foster City, Calif.), ROTOR-GENE™ (Corbett Research, Sydney, Australia), LIGHTCYCLER® (Roche Diagnostics Corp, Indianapolis, Ind.), ICYCLER® (Biorad Laboratories, Hercules, Calif.) and MX4000® (Stratagene, La Jolla, Calif.).
In another embodiment of the disclosure, microarrays are used for expression profiling. Microarrays are particularly well suited for this purpose because of the reproducibility between different experiments. DNA microarrays provide one method for the simultaneous measurement of the expression levels of large numbers of genes. Each array consists of a reproducible pattern of capture probes attached to a solid support. Labeled RNA or DNA is hybridized to complementary probes on the array and then detected by laser scanning. Hybridization intensities for each probe on the array are determined and converted to a quantitative value representing relative gene expression levels. See, for example, U.S. Pat. Nos. 6,040,138, 5,800,992 and 6,020,135, 6,033,860, and 6,344,316. High-density oligonucleotide arrays are particularly useful for determining the gene expression profile for a large number of RNAs in a sample.
In a preferred embodiment, the nCounter® Analysis system is used to detect intrinsic gene expression. The basis of the nCounter® Analysis system is the unique code assigned to each nucleic acid target to be assayed (International Patent Application Publication No. WO 08/124847, U.S. Pat. No. 8,415,102 and Geiss et al. Nature Biotechnology. 2008. 26(3): 317-325; the contents of which are each incorporated herein by reference in their entireties). The code is composed of an ordered series of colored fluorescent spots which create a unique barcode for each target to be assayed. A pair of probes is designed for each DNA or RNA target, a biotinylated capture probe and a reporter probe carrying the fluorescent barcode. This system is also referred to, herein, as the nanoreporter code system.
Specific reporter and capture probes are synthesized for each target. Briefly, sequence-specific DNA oligonucleotide probes are attached to code-specific reporter molecules. Preferably, each sequence specific reporter probe comprises a target specific sequence capable of hybriding to no more than one NANO46 gene of Table 1 and optionally comprises at least two, at least three, or at least four label attachment regions, said attachment regions comprising one or more label monomers that emit light. Capture probes are made by ligating a second sequence-specific DNA oligonucleotide for each target to a universal oligonucleotide containing biotin. Reporter and capture probes are all pooled into a single hybridization mixture, the “probe library”. Preferably, the probe library comprises a probe pair (a capture probe and reporter) for each of the NANO46 genes in Table 1.
The relative abundance of each target is measured in a single multiplexed hybridization reaction. The method comprises contacting a biological sample with a probe library, the library comprising a probe pair for the NANO46 genes in Table 1, such that the presence of the target in the sample creates a probe pair—target complex. The complex is then purified. More specifically, the sample is combined with the probe library, and hybridization occurs in solution. After hybridization, the tripartite hybridized complexes (probe pairs and target) are purified in a two-step procedure using magnetic beads linked to oligonucleotides complementary to universal sequences present on the capture and reporter probes. This dual purification process allows the hybridization reaction to be driven to completion with a large excess of target-specific probes, as they are ultimately removed, and, thus, do not interfere with binding and imaging of the sample. All post hybridization steps are handled robotically on a custom liquid-handling robot (Prep Station, NanoString Technologies).
Purified reactions are deposited by the Prep Station into individual flow cells of a sample cartridge, bound to a streptavidin-coated surface via the capture probe, electrophoresed to elongate the reporter probes, and immobilized. After processing, the sample cartridge is transferred to a fully automated imaging and data collection device (Digital Analyzer, NanoString Technologies). The expression level of a target is measured by imaging each sample and counting the number of times the code for that target is detected. Data is output in simple spreadsheet format listing the number of counts per target, per sample.
This system can be used along with nanoreporters. Additional disclosure regarding nanoreporters can be found in International Publication No. WO 07/076129 and WO 07/076132, and US Patent Publication No. 2010/0015607 and 2010/0261026, the contents of which are incorporated herein in their entireties. Further, the term nucleic acid probes and nanoreporters can include the rationally designed (e.g. synthetic sequences) described in International Publication No. WO 2010/019826 and US Patent Publication No. 2010/0047924, incorporated herein by reference in its entirety.
Data Processing
It is often useful to pre-process gene expression data, for example, by addressing missing data, translation, scaling, normalization, weighting, etc. Multivariate projection methods, such as principal component analysis (PCA) and partial least squares analysis (PLS), are so-called scaling sensitive methods. By using prior knowledge and experience about the type of data studied, the quality of the data prior to multivariate modeling can be enhanced by scaling and/or weighting. Adequate scaling and/or weighting can reveal important and interesting variation hidden within the data, and therefore make subsequent multivariate modeling more efficient. Scaling and weighting may be used to place the data in the correct metric, based on knowledge and experience of the studied system, and therefore reveal patterns already inherently present in the data.
If possible, missing data, for example gaps in column values, should be avoided. However, if necessary, such missing data may replaced or “filled” with, for example, the mean value of a column (“mean fill”); a random value (“random fill”); or a value based on a principal component analysis (“principal component fill”).
“Translation” of the descriptor coordinate axes can be useful. Examples of such translation include normalization and mean centering. “Normalization” may be used to remove sample-to-sample variation. For microarray data, the process of normalization aims to remove systematic errors by balancing the fluorescence intensities of the two labeling dyes. The dye bias can come from various sources including differences in dye labeling efficiencies, heat and light sensitivities, as well as scanner settings for scanning two channels. Some commonly used methods for calculating normalization factor include: (i) global normalization that uses all genes on the array; (ii) housekeeping genes normalization that uses constantly expressed housekeeping/invariant genes; and (iii) internal controls normalization that uses known amount of exogenous control genes added during hybridization (Quackenbush Nat. Genet. 32 (Suppl.), 496-501 (2002)). In one embodiment, the intrinsic genes disclosed herein can be normalized to control housekeeping genes. For example, the housekeeping genes described in U.S. Patent Publication 2008/0032293, which is herein incorporated by reference in its entirety, can be used for normalization. Exemplary housekeeping genes include MRPL19, PSMC4, SF3A1, PUMl, ACTB, GAPD, GUSB, RPLPO, and TFRC. It will be understood by one of skill in the art that the methods disclosed herein are not bound by normalization to any particular housekeeping genes, and that any suitable housekeeping gene(s) known in the art can be used.
Many normalization approaches are possible, and they can often be applied at any of several points in the analysis. In one embodiment, microarray data is normalized using the LOWESS method, which is a global locally weighted scatter plot smoothing normalization function. In another embodiment, qPCR data is normalized to the geometric mean of set of multiple housekeeping genes.
“Mean centering” may also be used to simplify interpretation. Usually, for each descriptor, the average value of that descriptor for all samples is subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors are “centered” at zero. In “unit variance scaling,” data can be scaled to equal variance. Usually, the value of each descriptor is scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples. “Pareto scaling” is, in some sense, intermediate between mean centering and unit variance scaling. In pareto scaling, the value of each descriptor is scaled by l/sqrt(StDev), where StDev is the standard deviation for that descriptor for all samples. In this way, each descriptor has a variance numerically equal to its initial standard deviation. The pareto scaling may be performed, for example, on raw data or mean centered data.
“Logarithmic scaling” may be used to assist interpretation when data have a positive skew and/or when data spans a large range, e.g., several orders of magnitude. Usually, for each descriptor, the value is replaced by the logarithm of that value. In “equal range scaling,” each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 1. However, this method is sensitive to presence of outlier points. In “autoscaling,” each data vector is mean centered and unit variance scaled. This technique is a very useful because each descriptor is then weighted equally, and large and small values are treated with equal emphasis. This can be important for genes expressed at very low, but still detectable, levels.
In one embodiment, data is collected for one or more test samples and classified using the NANO46 classification model described herein. When comparing data from multiple analyses (e.g., comparing expression profiles for one or more test samples to the centroids constructed from samples collected and analyzed in an independent study), it will be necessary to normalize data across these data sets. In one embodiment, Distance Weighted Discrimination (DWD) is used to combine these data sets together (Benito et al. (2004) Bioinformatics 20(1): 105-114, incorporated by reference herein in its entirety). DWD is a multivariate analysis tool that is able to identify systematic biases present in separate data sets and then make a global adjustment to compensate for these biases; in essence, each separate data set is a multi-dimensional cloud of data points, and DWD takes two points clouds and shifts one such that it more optimally overlaps the other.
The methods described herein may be implemented and/or the results recorded using any device capable of implementing the methods and/or recording the results. Examples of devices that may be used include but are not limited to electronic computational devices, including computers of all types. When the methods described herein are implemented and/or recorded in a computer, the computer program that may be used to configure the computer to carry out the steps of the methods may be contained in any computer readable medium capable of containing the computer program. Examples of computer readable medium that may be used include but are not limited to diskettes, CD-ROMs, DVDs, ROM, RAM, and other memory and computer storage devices. The computer program that may be used to configure the computer to carry out the steps of the methods and/or record the results may also be provided over an electronic network, for example, over the internet, an intranet, or other network.
Calculation of Risk of Recurrence
Provided herein are methods for predicting breast cancer outcome within the context of the intrinsic subtype and optionally other clinical variables. Outcome may refer to overall or disease-specific survival, event-free survival, or outcome in response to a particular treatment or therapy. In particular, the methods may be used to predict the likelihood of long-term, disease-free survival. “Predicting the likelihood of survival of a breast cancer patient” is intended to assess the risk that a patient will die as a result of the underlying breast cancer. “Long-term, disease-free survival” is intended to mean that the patient does not die from or suffer a recurrence of the underlying breast cancer within a period of at least five years, or at least ten or more years, following initial diagnosis or treatment.
In one embodiment, outcome is predicted based on classification of a subject according to subtype. In addition to providing a subtype assignment, the NANO46 bioinformatics model provides a measurement of the similarity of a test sample to all four subtypes which is translated into a Risk of Recurrence (ROR) score that can be used in any patient population regardless of disease status and treatment options. The intrinsic subtypes and ROR also have value in the prediction of pathological complete response in women treated with, for example, neoadjuvant taxane and anthracycline chemotherapy (Rouzier et al., J Clin Oncol 23:8331-9 (2005), incorporated herein by reference in its entirety). Thus, in various embodiments of the present disclosure, a risk of recurrence (ROR) model is used to predict outcome. Using these risk models, subjects can be stratified into low, medium, and high risk of recurrence groups. Calculation of ROR can provide prognostic information to guide treatment decisions and/or monitor response to therapy.
In some embodiments described herein, the prognostic performance of the NANO46-defined intrinsic subtypes and/or other clinical parameters is assessed utilizing a Cox Proportional Hazards Model Analysis, which is a regression method for survival data that provides an estimate of the hazard ratio and its confidence interval. The Cox model is a well-recognized statistical technique for exploring the relationship between the survival of a patient and particular variables. This statistical method permits estimation of the hazard (i.e., risk) of individuals given their prognostic variables (e.g., intrinsic gene expression profile with or without additional clinical factors, as described herein). The “hazard ratio” is the risk of death at any given time point for patients displaying particular prognostic variables. See generally Spruance et al., Antimicrob. Agents & Chemo. 48:2787-92 (2004).
The NANO46 classification model described herein can be trained for risk of recurrence using subtype distances (or correlations) alone, or using subtype distances with clinical variables as discussed supra. In one embodiment, the risk score for a test sample is calculated using intrinsic subtype distances alone using the following equation:
ROR=0.05*Basal+0.1 l*Her2+−0.25*LumA+0.07*LumB+−0.1 l*Normal, where the variables “Basal,” “Her2,” “LumA,” “LumB,” and “Normal” are the distances to the centroid for each respective classifier when the expression profile from a test sample is compared to centroids constructed using the gene expression data deposited with the Gene Expression Omnibus (GEO).
Risk score can also be calculated using a combination of breast cancer subtype and the clinical variables tumor size (T) and lymph nodes status (N) using the following equation: ROR (full)=0.05*Basal+0.1*Her2+−0.19*LumA+0.05*LumB+−0.09*Normal+0.16*T+0.08*N, again when comparing test expression profiles to centroids constructed using the gene expression data deposited with GEO as accession number GSE2845.
In yet another embodiment, risk score for a test sample is calculated using intrinsic subtype distances alone using the following equation:
ROR-S=0.05*Basal+0.12*Her2+−0.34*LumA+0.0.23*LumB, where the variables “Basal,” “Her2,” “LumA,” and “LumB” are as described supra and the test expression profiles are compared to centroids constructed using the gene expression data deposited with GEO as accession number GSE2845. In yet another embodiment, risk score can also be calculated using a combination of breast cancer subtype and the clinical variable tumor size (T) using the following equation (where the variables are as described supra): ROR-C=0.05*Basal+0.1 l*Her2+−0.23*LumA+0.09*LumB+0.17*T.
In yet another embodiment, risk score for a test sample is calculated using intrinsic subtype distances in combination with the proliferation signature (“Prolif”) using the following equation:
ROR-P=−0.001*Basal+0.7*Her2+−0.95*LumA+0.49*LumB+0.34*Prolif, where the variables “Basal,” “Her2,” “LumA,” “LumB” and “Prolif” are as described supra and the test expression profiles are compared to centroids constructed using the gene expression data deposited with GEO as accession number GSE2845.
In yet another embodiment, risk score can also be calculated using a combination of breast cancer subtype, proliferation signature and the clinical variable tumor size (T) using the ROR-PT described in conjunction with Table 3 supra.
Detection of Subtypes
Immunohistochemistry for estrogen (ER), progesterone (PgR), HER2, and Ki67 was performed concurrently on serial sections with the standard streptavidin-biotin complex method with 3,3′-diaminobenzidine as the chromogen. Staining for ER, PgR, and HER2 interpretation can be performed as described previously (Cheang et al., Clin Cancer Res. 2008; 14(5):1368-1376.), however any method known in the art may be used.
For example, a Ki67 antibody (clone SP6; ThermoScientific, Fremont, Calif.) can be applied at a 1:200 dilution for 32 minutes, by following the Ventana Benchmark automated immunostainer (Ventana, Tucson Ariz.) standard Cell Conditioner 1 (CC1, a proprietary buffer) protocol at 98° C. for 30 minutes. An ER antibody (clone SP1; ThermoFisher Scientific, Fremont Calif.) can be used at 1:250 dilution with 10-minute incubation, after an 8-minute microwave antigen retrieval in 10 mM sodium citrate (pH 6.0). Ready-to-use PR antibody (clone 1E2; Ventana) can be used by following the CC1 protocol as above. HER2 staining can be done with a SP3 antibody (ThermoFisher Scientific) at a 1:100 dilution after antigen retrieval in 0.05 M Tris buffer (pH 10.0) with heating to 95° C. in a steamer for 30 minutes. For HER2 fluorescent in situ hybridization (FISH) assay, slides can be hybridized with probes to LSI (locus-specific identifier) HER2/neu and to centromere 17 by use of the PathVysion HER-2 DNA Probe kit (Abbott Molecular, Abbott Park, Ill.) according to manufacturer's instructions, with modifications to pretreatment and hybridization as previously described (Brown L A, Irving J, Parker R, et al. Amplification of EMSY, a novel oncogene on 11q13, in high grade ovarian surface epithelial carcinomas. Gynecol Oncol. 2006; 100(2):264-270). Slides can then be counterstained with 4′,6-diamidino-2-phenylindole, stained material was visualized on a Zeiss Axioplan epifluorescent microscope, and signals were analyzed with a Metafer image acquisition system (Metasystems, Altlussheim, Germany). Biomarker expression from immunohistochemistry assays can then be scored by two pathologists, who were blinded to the clinicopathological characteristics and outcome and who used previously established and published criteria for biomarker expression levels that had been developed on other breast cancer cohorts.
Tumors were considered positive for ER or PR if immunostaining was observed in more than 1% of tumor nuclei, as described previously. Tumors were considered positive for HER2 if immunostaining was scored as 3+ according to HercepTest criteria, with an amplification ratio for fluorescent in situ hybridization of 2.0 or more being the cut point that was used to segregate immunohistochemistry equivocal tumors (scored as 2+) (Yaziji, et al., JAMA, 291(16):1972-1977 (2004)). Ki67 was visually scored for percentage of tumor cell nuclei with positive immunostaining above the background level by two pathologists.
Other methods can also be used to detect subtypes. These techniques include ELISA, Western blots, Northern blots, or FACS analysis.
Kits
The present disclosure also describes kits useful for classifying breast cancer intrinsic subtypes and/or providing prognostic information to identify risk of recurrence These kits comprise a set of capture probes and/or primers specific for the intrinsic genes listed in Table 1. The kit may further comprise a computer readable medium.
In one embodiment of the present disclosure, the capture probes are immobilized on an array. By “array” is intended a solid support or a substrate with peptide or nucleic acid probes attached to the support or substrate. Arrays typically comprise a plurality of different capture probes that are coupled to a surface of a substrate in different, known locations. The arrays of the disclosure comprise a substrate having a plurality of capture probes that can specifically bind an intrinsic gene expression product. The number of capture probes on the substrate varies with the purpose for which the array is intended. The arrays may be low-density arrays or high-density arrays and may contain 4 or more, 8 or more, 12 or more, 16 or more, 32 or more addresses, but will minimally comprise capture probes for the 46 intrinsic genes listed in Table 1.
Techniques for the synthesis of these arrays using mechanical synthesis methods are described in, e.g., U.S. Pat. No. 5,384,261, incorporated herein by reference in its entirety for all purposes. The array may be fabricated on a surface of virtually any shape or even a multiplicity of surfaces. Arrays may be probes (e.g., nucleic-acid binding probes) on beads, gels, polymeric surfaces, fibers such as fiber optics, glass or any other appropriate substrate, see U.S. Pat. Nos. 5,770,358, 5,789,162, 5,708,153, 6,040,193 and 5,800,992, each of which is hereby incorporated in its entirety for all purposes. Arrays may be packaged in such a manner as to allow for diagnostics or other manipulation on the device. See, for example, U.S. Pat. Nos. 5,856,174 and 5,922,591 herein incorporated by reference.
In another embodiment, the kit comprises a set of oligonucleotide primers sufficient for the detection and/or quantitation of each of the intrinsic genes listed in Table 1. The oligonucleotide primers may be provided in a lyophilized or reconstituted form, or may be provided as a set of nucleotide sequences. In one embodiment, the primers are provided in a microplate format, where each primer set occupies a well (or multiple wells, as in the case of replicates) in the microplate. The microplate may further comprise primers sufficient for the detection of one or more housekeeping genes as discussed infra. The kit may further comprise reagents and instructions sufficient for the amplification of expression products from the genes listed in Table 1.
In order to facilitate ready access, e.g., for comparison, review, recovery, and/or modification, the molecular signatures/expression profiles are typically recorded in a database. Most typically, the database is a relational database accessible by a computational device, although other formats, e.g., manually accessible indexed files of expression profiles as photographs, analogue or digital imaging readouts, spreadsheets, etc. can be used. Regardless of whether the expression patterns initially recorded are analog or digital in nature, the expression patterns, expression profiles (collective expression patterns), and molecular signatures (correlated expression patterns) are stored digitally and accessed via a database. Typically, the database is compiled and maintained at a central facility, with access being available locally and/or remotely.
Devices and Tests
General—
The NanoString nCounter Analysis System delivers direct, multiplexed measurements of gene expression through digital readouts of the relative abundance of hundreds of mRNA transcripts. The nCounter Analysis System uses gene-specific probe pairs (
After hybridization, all of the sample processing steps are automated on the nCounter Prep Station. First, excess capture and reporter probes are removed (
Finally, probe/target complexes are aligned and immobilized (
After sample processing has completed, cartridges are placed in the nCounter Digital Analyzer for data collection. Each target molecule of interest is identified by the “color code” generated by six ordered fluorescent spots present on the reporter probe. The Reporter Probes on the surface of the cartridge are then counted and tabulated for each target molecule (
Reagents and Test Components—
The Breast Cancer test will simultaneously measure the expression levels of NANO46 plus eight housekeeping genes in a single hybridization reaction using an nCounter CodeSet designed specifically to those genes. Each assay also includes positive assay controls comprised of a linear titration of in vitro transcribed RNA transcripts and corresponding probes, and a set of probes with no sequence homology to human RNA sequences which are used as negative controls. Each assay run includes a reference sample consisting of in vitro transcribed RNA's of the targets and housekeeping genes for normalization purposes. The normalized gene expression profile of a breast tumor sample is correlated to prototypical gene expression profiles of the four breast cancer intrinsic subtypes (Luminal A, Luminal B, HER2-enriched, or Basal-like) that were identified from a training set of breast tumors. The gene expression profile, in combination with selected clinical variables, is used as part of a trained algorithm as a prognostic indicator of risk of distant recurrence of breast cancer.
FFPE Tissue Extraction—
The Breast Cancer Test will use RNA extracted from Formalin-fixed, Paraffin-embedded (FFPE) tissue that has been diagnosed as invasive carcinoma of the breast. A pathologist first performs an H & E stain of a tumor section mounted onto a slide to identify the region of viable invasive breast carcinoma containing tumor content above a minimum threshold. The pathologist circles the region on the H & E slide. The pathologist then mounts unstained tissue sections onto slides and marks the area of the slides containing invasive tumor. For larger tumors (>100 mm2 of viable invasive carcinoma on the H&E slide), the test requires only a single 10 μm section. For smaller tumors (<100 mm2), the test requires 3 sections. The identified region of viable invasive breast carcinoma containing sufficient tumor content on the slides is macro-dissected prior to RNA extraction. Procedures for shipping FFPE tissue slides from the collection site to a testing site will be defined as part of the procedure.
Following extraction of total RNA and removal of genomic DNA, the optical density is measured at wavelengths of 260 nm and 280 nm to determine both yield and purity. The assay procedure requires an input range of 125-500 ng of total RNA for the subsequent hybridization step. NanoString plans to validate that this input range of RNA is sufficient to reproducibly perform the assay on the nCounter Analysis System. Additionally, the RNA quality will be measured using an OD 260/280 reading, with a target ratio of no less than 1.7 with an upper limit of 2.5. Procedures for storing RNA will be provided to the user so that downstream processing can be performed at a later point in time if desired.
Requirements for Spectrophotometer to Measure Yield and Purity Post RNA Extraction—
RNA isolations from the FFPE sample result in a final sample volume of 30 μL. This volume is too low for the quantitation of nucleic acid abundance using absorbance measurements in a cuvette-type UV-Vis spectrophotometer; therefore, NanoString's protocol includes a step for quantitating total RNA using a low volume spectrophotometer such as the NanoDrop™ spectrophotometer. NanoString will define performance specifications for the spectrophotometer so that the range of RNA input recommended for the test is above the limit of detection of the low volume spectrophotometer and is reproducibly measurable.
Hybridization—
For each set of up to 10 RNA samples, the user will pipette the specified amount of RNA into separate tubes within a 12 reaction strip tube and add the CodeSet and hybridization buffer. A reference sample is pipetted into the remaining two tubes with CodeSet and hybridization buffer. The CodeSet consists of probes for each gene that is targeted, additional probes for endogenous “housekeeping” normalization genes and positive and negative controls. The probes within the CodeSet pertaining to each of these genes within the four groups (target genes, housekeeping genes, and positive and negative controls) are each assigned a unique code and are therefore individually identifiable within each run. The reference sample consists of in vitro transcribed RNA for the targeted genes and housekeeping genes. Once the hybridization reagents are added to the respective tubes, the user transfers the strip tube into a heated-lid heatblock for a specified period of time at a set temperature.
Requirement for Heat Block with Heated Lid for Hybridization Step—
The nCounter assay includes an overnight hybridization under isothermal conditions. Because the overnight hybridization is performed in a small volume at elevated temperature, care must be taken to avoid evaporation. Many commercial PCR thermocyclers are equipped with heated lids that will prevent the evaporation of small volumes of liquid. Because the assay does not require any fine control of temperature ramping, any heat block with a programmable heated lid and a block with dimensions that fit the NanoString tubes will work with the NanoString assay. NanoString plans to provide specifications for heat blocks that meet the assay requirements.
Purification and Binding on the Prep Station—
Upon completing hybridization, the user will then transfer the strip tube containing the set of 10 assays and 2 reference samples into the nCounter Prep Station along with the required prepackaged reagents and disposables described in Table 1. The Prep Plates contain the necessary reagents for purification of excess probes and binding to the cartridge (see section IIIC below for detailed description of purification process). The prep plates are centrifuged in a swinging bucket centrifuge prior to placement on the deck of the Prep Station. An automated purification process then removes excess capture and reporter probe through two successive hybridization-driven magnetic bead capture steps. The nCounter Prep Station then transfers the purified target/probe complexes into an nCounter cartridge for capture to a glass slide. Following completion of the run, the user removes the cartridge from the Prep Station and seals it with an adhesive film.
Imaging and Analysis on the Digital Analyzer—
The sealed cartridge is then inserted into the nCounter Digital Analyzer which counts the number of probes captured on the slide for each gene, which corresponds to the amount of target in solution. Automated software then checks thresholds for the housekeeping genes, reference sample, and positive and negative controls to qualify each assay and ensure that the procedure was performed correctly. The housekeeping genes provide a measure of RNA integrity, and the thresholds indicate when a tested RNA sample is too degraded to be analyzed by the test due to improper handling or storage of tissue or RNA (e.g. improper tumor fixation, FFPE block storage, RNA storage, RNA handling introducing RNase). The positive and negative assay controls indicate a failure of the assay process (e.g. error in assay setup such as sample mixing with CodeSet, or sample processing such as temperature). The signals of each sample are next normalized using the housekeeping genes to control for input sample quality. The signals are then normalized to the reference sample within each run to control for run-to-run variations. The resulting normalized data is entered in the Breast Cancer Intrinsic Subtyping algorithm to determine tumor intrinsic subtype, risk of relapse score, and risk classification.
Instrumentation—
The nCounter Analysis System is comprised of two instruments, the nCounter Prep Station used for post-hybridization processing, and the Digital Analyzer used for data collection and analysis.
nCounter Prep Station—
The nCounter Prep Station (
Hybridization to the target RNA is driven by excess NanoString probes. To accurately analyze these hybridized molecules they are first purified from the remaining excess probes in the hybridization reaction. The Prep Station isolates the hybridized mRNA molecules from the excess Reporter and Capture probes using two sequential magnetic bead purification steps. These affinity purifications utilize custom oligonucleotide-modified magnetic beads that retain only the tripartite complexes of mRNA molecules that are bound to both a Capture probe and a Reporter probe.
Next, this solution of tripartite complexes is washed through a flow cell in the NanoString sample cartridge. One surface of this flow cell is coated with a polyethylene glycol (PEG) hydrogel that is densely impregnated with covalently bound streptavidin. As the solution passes through the flow cell, the tripartite complexes are bound to the streptavidin in the hydrogel through biotin molecules that are incorporated into each Capture probe. The PEG hydrogel acts not only to provide a streptavidin-dense surface onto which the tripartite complexes can be specifically bound, but also inhibits the non-specific binding of any remaining excess reporter probes.
After the complexes are bound to the flow cell surface, an electric field is applied along the length of each sample cartridge flow cell to facilitate the optical identification and order of the fluorescent spots that make up each reporter probe. Because the reporter probes are charged nucleic acids, the applied voltage imparts a force on them that uniformly stretches and orients them along the electric field. While the voltage is applied, the Prep Station adds an immobilization reagent that locks the reporters in the elongated configuration after the field is removed. Once the reporters are immobilized the cartridge can be transferred to the nCounter Digital Analyzer for data collection. All consumable components and reagents required for sample processing on the Prep Station are provided in the nCounter Master Kit. These reagents are ready to load on the deck of the nCounter Prep Station which can process up to 10 samples and 2 reference samples per run in approximately 2.5 hours.
nCounter Digital Analyzer—
The nCounter Digital Analyzer (
The Digital Analyzer captures hundreds of consecutive fields-of-view (FOV) that can each contain hundreds or thousands of discrete Reporter Probes. Each FOV is a combination of four monochrome images captured at different wavelengths. The resulting overlay can be thought of as a four-color image in blue, green, yellow, and red. Each 4-color FOV is processed in real time to provide a “count” for each fluorescent barcode in the sample. Because each barcode specifically identifies a single mRNA molecule, the resultant data from the Digital Analyzer is a precise measure of the relative abundance of each mRNA of interest in a biological sample.
Software—
The Prep Station and the Digital Analyzer are stand-alone units that do not require connection to an external PC, but must be networked to one another using a Local Area Network (LAN). The nCounter System software securely manages operations through user accounts and permissions. Both instruments use setup and process wizards on an embedded touch screen user interface to guide the user through the sample processing and data collection steps of the assay. The user is led through the procedure by step-by-step instructions on the Prep Station and Digital Analyzer. The instrument touch screen uses a pressure sensitive method for controlling operations and enables the user to interact with the system by touching a selection on the screen. Because the touchscreen provides a limited human interface for data entry, the system also hosts a web-based application for user accounts management, sample batch definition, and sample status tracking.
When samples are processed, the system software tracks the user account and reagent lots for each sample in a centralized data repository. After expression data for a sample is acquired by the Digital Analyzer, it is first analyzed to ensure that all pre-specified quality control metrics are met. The qualified data are then processed through a locked PAM50 algorithm to generate a report containing intrinsic subtype and risk of recurrence (ROR) score. The sample report is transferred to the central repository where it can be securely accessed for download by a user with the correct permissions.
The Breast Cancer Intrinsic Subtyping Algorithm—
The nCounter system will be used to identify the intrinsic subtype of an excised invasive carcinoma of the breast using a 50 gene classifier algorithm originally named the PAM50 (Parker J. S., et al. Supervised Risk Predictor of Breast Cancer Based on Intrinsic Subtypes. Journal of Clinical Oncology, 27: 1160-1167 (2009)). The gene expression profile will assign a breast cancer to one of four molecular classes or intrinsic subtypes: Basal-like, Luminal A, Luminal B, and HER2 enriched. A brief description of each subtype is provided below.
Luminal subtypes: The most common subtypes of breast cancer are the luminal subtypes in the hormone-receptor positive population, Luminal A and Luminal B. Prior studies suggest that luminal A comprises approximately 30% to 40% and luminal B approximately 20% of breast cancers and over 90% of hormone receptor-positive breast cancers. The gene expression pattern of these subtypes resembles the luminal epithelial component of the breast (Nielsen, T O et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor positive breast cancer. Clinical Cancer Research, 16:5222-5232 (2010)). These tumors are characterized by high expression of estrogen receptor (ER), progesterone receptor (PR), and genes associated with ER activation such as LIV1, GATA3, and cyclin D1, as well as expression of luminal cytokeratins 8 and 18.
Luminal A: Luminal A (LumA) breast cancers exhibit low expression of genes associated with cell cycle activation and the ERBB2 cluster resulting in a better prognosis than luminal B. The Luminal A subgroup has the most favorable prognosis of all subtypes and is enriched for endocrine therapy-responsive tumors.
Luminal B: Luminal B (LumB) breast cancers express ER and ER-associated genes, but to a lower extent than LumA. Genes associated with cell cycle activation are highly expressed and this tumor type can be HER2(+) or HER2(−). The prognosis is unfavorable (despite ER expression) and endocrine therapy responsiveness is generally diminished relative to LumA.
Basal-like: The Basal-like subtype is generally ER-negative, is almost always clinically HER2-negative and expresses a suite of “basal” biomarkers including the basal epithelial cytokeratins (CK) and epidermal growth factor receptor (EGFR). Genes associated with cell cycle activation are highly expressed.
HER2-enriched: The HER2-enriched subtype is generally ER-negative and is HER2-positive in the majority of cases with high expression of the ERBB2 cluster, including ERBB2 and GRB7. Genes associated with cell cycle activation are highly expressed and these tumors have a poor outcome.
Cutoffs for the intrinsic subtyping algorithm are pre-defined from training sets that defined the following: 1) intrinsic subtype centroids (i.e. the prototypical gene expression profile of each subtype), 2) coefficients for Risk of Recurrence (ROR) score, and 3) risk classification (Low/Intermediate/High). The intrinsic subtype centroids (Luminal A, Luminal B, Her2-enriched, Basal-like) were trained using a clinically representative set of archived FFPE breast tumor specimens collected from multiple sites. Hierarchical clustering analysis of gene expression data from the FFPE breast tumor samples was combined with breast tumor biology (i.e. gene expression of previously defined intrinsic subtypes) to define the prototypical expression profile (i.e. centroid) of each subtype. A computational algorithm correlates the normalized 50 gene expression profile of an unknown breast cancer tumor sample to each of the prototypical expression signatures of the four breast cancer intrinsic subtypes. The tumor sample is assigned the subtype with the largest positive correlation to the sample.
304 unique tumor samples with well-defined clinical characteristics and clinical outcome data were used to establish the ROR score. The ROR score is calculated using coefficients from a Cox model that includes the Pearson correlation (R) to each intrinsic subtype, a proliferation score (P), and tumor size (T), as shown in the equation below.
ROR=aRLumA+bRLumB+cRHer2e+dRbasal+eP+fT
To classify tumor samples into specific risk groups (Low Risk/Intermediate Risk/High Risk) based on their calculated ROR score, cutoffs were set based on probability of recurrence free survival in a patient population consisting of hormone receptor positive, post-menopausal patients treated with endocrine therapy alone.
Anticipated Use of NanoString Breast Cancer Test in Clinical Practice—
Oncologists currently use a series of tests to develop a treatment protocol for breast cancer patients. Included in these are the IHC/FISH tests such as ER/PR IHC and HER2 IHC/FISH, and the Agendia MammaPrint® assay and the Genomic Health Oncotype Dx® test. These tests offer the oncologist additional information regarding the patient's prognosis and recommended treatment regimens.
These tests, however, have limitations. ER, PgR, and Her2 testing is done locally by pathologists and reference labs, but the challenges with widespread standardization of IHC and FISH testing is well documented (Lester, J et al. Assessment of Tissue Estrogen and Progesterone Receptor Levels: A Survey of Current Practice, Techniques, and Quantitation Methods. The Breast Journal, 6:189-196 (2000); Wolff, A et al. American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Human Epidermal Growth Factor Receptor 2 Testing in Breast Cancer. Archives of Pathology and Laboratory Medicine, 131:18-43 (2007)). The MammaPrint test is FDA cleared for use only with frozen or fresh-preserved tissue samples, yet most of the tumor samples collected in the United States are FFPE rather than fresh-frozen. This test is also not distributed and is only available through the Agendia reference labs. The Oncotype Dx test can be used to predict the risk of relapse for stage I/II, node negative, estrogen receptor-positive patients receiving adjuvant Tamoxifen therapy as well as response to cyclophosphamide/methotrexate/5-fluorouracil (CMF) chemotherapy. However this test is only offered as a lab-developed test (LDT) through Genomic Health's CLIA laboratory and is not FDA cleared for prognostic use, or FDA approved for predicting chemotherapy response.
NanoString envisions a model that would have the Breast Cancer test used in conjunction with other sources of clinical data currently available to oncologists for breast cancer prognosis in selected patient segments. The Breast Cancer Test would be an additional source of prognostic information adding significant value to established clinical parameters (i.e tumor size, nodal status) used by oncologists in managing a patient with breast cancer.
Methods, Assays and Kits
The methods, assays and kits of the present invention include a series of quality control metrics that are automatically applied to each sample during analysis. These metrics evaluate the performance of the assay to determine whether the results fall within expected values. Upon successful analysis of these quality control metrics, the Assay gives the following results:
Intrinsic Subtypes
The Intrinsic Subtype of a breast cancer tumor has been shown to be related to prognosis in Early Stage Breast Cancer. On average, patients with a Luminal A tumor have significantly better outcomes than patients with Luminal B, HER2-Enriched, or Basal-like tumors.
The Intrinsic Subtype is identified by comparing the gene expression profile of 50 genes in an unknown sample with the expected expression profiles for the four intrinsic subtypes. The subtype with the most similar profile is assigned to the unknown sample.
The most common subtypes of breast cancer are the luminal subtypes, Luminal A (LumA) and Luminal B (LumB). Prior studies suggest that Luminal A comprises approximately 30% to 40% and Luminal B approximately 20% of breast cancers. However, greater than 90% of hormone-receptor positive patients have luminal tumors. The gene expression pattern of these subtypes resembles the luminal epithelial component of the breast tissue. These tumors are characterized by high expression of estrogen receptor (ER), progesterone receptor (PR), and genes associated with ER activation, such as LIV1, GATA3, and cyclin D1, as well as expression of luminal cytokeratins 8 and 18. Luminal A breast cancers exhibit lower expression of genes associated with cell cycle activation when compared to Luminal B breast cancers resulting in a better prognosis.
Prior studies suggest that the HER2-Enriched subtype (Her2E) comprises approximately 20% of breast cancers. However, HER2-Enriched tumors are generally ER-negative, so only 5% of the tested ER-positive patient population was found to have HER2-Enriched breast cancer. Regardless of ER-status, HER2-Enriched tumors are HER2-positive in the majority of cases with high expression of the ERBB2 cluster, including ERBB2 and GRB7. Genes associated with cell cycle activation are also highly expressed.
Published data suggest that the Basal-like subtype comprises approximately 20% of breast cancers. However, Basal-like tumors are generally ER-negative, so only 1% of hormone receptor-positive patients have Basal-like breast cancer. The Basal-like subtype is almost always clinically HER2-negative and expresses a suite of “basal” biomarkers including the basal epithelial cytokeratins (CK) and epidermal growth factor receptor (EGFR). Genes associated with cell cycle activation are highly expressed.
ROR Score
The ROR score is an integer value on a 0-100 scale that is related to an individual patient's probability of distant recurrence within 10 years for the defined intended use population. The ROR score is calculated by comparing the expression profiles of 46 genes in an unknown sample with the expected profiles for the four intrinsic subtypes, as described above, to calculate four different correlation values. These correlation values are then combined with a proliferation score and the tumor size to calculate the ROR score.
Probability of 10-Year Distant Recurrence
The ROR scores for a cohort of post-menopausal women with hormone receptor-positive early stage breast cancer were compared to distant recurrence-free survival following surgery and treatment with 5 years of adjuvant endocrine therapy followed by 5 years of observation. This study resulted in a model relating the ROR score to the probability of distant recurrence in this tested patient population including a 95% confidence interval.
Risk Classification
Risk classification is also provided to allow interpretation of the ROR score by using cutoffs related to clinical outcome in tested patient populations.
Risk Classification by ROR Range and Nodal Status
Quality Control
Each lot of the Assay components is tested using predetermined specifications. All kit-level items are lot tracked, and the critical components contained within each kit are tested together and released as a Master Lot.
The assay kit includes a series of internal controls that are used to assess the quality of each run set as a whole and each sample individually. These controls are listed below.
Batch Control Set: In Vitro Transcribed RNA Reference Sample
A synthetic RNA Reference Sample is included as a control within the Assay kit. The reference sample is comprised of in-vitro transcribed RNA targets from the 50 algorithm and 8 housekeeping genes. The Reference Sample is processed in duplicate in each assay run along with a set of up to 10 unknown breast tumor RNA samples in a 12 reaction strip tube. The signal from the Reference Sample is analyzed against pre-defined thresholds to qualify the run.
The signal from each of the 50 algorithm genes of the breast tumor RNA sample is normalized to the corresponding genes of the Reference Sample.
Positive Control Set: In Vitro Transcribed RNA Targets and Corresponding Capture and Reporter Probes
Synthetic RNA targets are used as positive controls (PCs) for the assay. The PC target sequences are derived from the External RNA Control Consortium (ERCC) DNA sequence library. The RNA targets are in-vitro transcribed from DNA plasmids. Six RNA targets are included within the assay kit in a 4-fold titration series (128-0.125 fM final concentration in hybridization reaction) along with the corresponding Capture and Reporter Probes. The PCs are added to each breast tumor RNA sample and Reference RNA Sample tested with the Prosigna Assay. A sample will be disqualified from further analysis if the signal intensities from the PCs do not meet pre-defined thresholds.
Negative Control Set: Exogenous Probes without Targets
Negative control (NC) target sequences are derived from the ERCC DNA sequence library. The probes designed to detect these target sequences are included as part of the assay kit without the corresponding target sequence. The negative controls (NCs) are added to each breast tumor RNA sample and Reference Sample tested with the Prosigna Assay as a quality control measure. The sample will be disqualified from further analysis if the signal intensities from the NCs do not meet pre-defined thresholds.
RNA Integrity Control Set: Housekeeping Genes
Capture and Reporter Probes designed to detect 8 housekeeping genes and 50 algorithm genes are included as part of the kit. The expression levels of the 8 housekeeping genes are analyzed to determine the quality of RNA extracted from the FFPE tissue sample and input into the assay. The sample will be disqualified from further analysis if the expression level of the housekeeping genes falls below pre-defined thresholds.
The housekeeping genes are also used to normalize for any differences in the intact RNA amount in a sample prior to Reference Sample normalization.
For the purposes of the present disclosure, “breast cancer” includes, for example, those conditions classified by biopsy or histology as malignant pathology. The clinical delineation of breast cancer diagnoses is well known in the medical arts. One of skill in the art will appreciate that breast cancer refers to any malignancy of the breast tissue, including, for example, carcinomas and sarcomas. Particular embodiments of breast cancer include ductal carcinoma in situ (DCIS), lobular carcinoma in situ (LCIS), or mucinous carcinoma. Breast cancer also refers to infiltrating ductal (IDC) or infiltrating lobular carcinoma (ILC). In most embodiments of the disclosure, the subject of interest is a human patient suspected of or actually diagnosed with breast cancer.
The article “a” and “an” are used herein to refer to one or more than one (i.e., to at least one) of the grammatical object of the article. By way of example, “an element” means one or more element.
Throughout the specification the word “comprising,” or variations such as “comprises” or “comprising,” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.
FFPE Tissue Review/Procurement and RNA Extraction: The Breast Cancer Intrinsic Subtyping Test will use RNA extracted from Formalin-fixed, Paraffin-embedded (FFPE) tissue that has been diagnosed as invasive carcinoma of the breast. A Pathologist reviews an H & E stained slide to identify the tissue area containing sufficient tumor tissue content for the test. Unstained slide mounted tissue sections are processed by macro-dissecting the identified tumor area on each slide to remove any adjacent normal tissue. RNA is then isolated from the tumor tissue, and DNA is removed from the sample.
Assay Setup and Initiation of Hybridization: For each batch of up to 10 RNA samples isolated from a breast tumor, the user will set up a run using the nCounter Analysis x5 system software, which tracks sample processing, reagent lots, and results for each sample. To initiate the assay, the user will pipette the specified amount of RNA into separate tubes within a 12 reaction strip tube and add the CodeSet and hybridization buffer. A reference sample is pipetted into the remaining two tubes with CodeSet and hybridization buffer. The CodeSet consists of probes for each gene that is targeted, additional probes for endogenous “housekeeping” normalization genes and positive and negative controls that are spiked into the assay. The reference sample consists of in vitro transcribed RNA for the targeted genes and housekeeping genes. Once the hybridization reagents are added to the respective tubes, the user transfers the strip tube into a heated-lid heatblock for a specified period of time at a set temperature.
Purification and Binding on the Prep Station: Upon completing hybridization, the user will transfer the strip tube containing the set of 10 assays and 2 reference samples onto the nCounter Prep Station along with the required prepackaged reagents and disposables. An automated purification process then removes excess capture and reporter probe through two successive hybridization-driven magnetic bead capture steps. The nCounter Prep Station then transfers the purified target/probe complexes into an nCounter cartridge for capture to a glass slide. Following completion of the run, the user removes the cartridge from the Prep Station and seals it with an adhesive film.
Imaging and Analysis on the Digital Analyzer: The cartridge is then sealed and inserted into the nCounter Digital Analyzer which counts the number of probes captured on the slide for each gene, which corresponds to the amount of target in solution. Automated software will then check thresholds for the housekeeping genes, reference sample, and positive and negative controls to qualify each assay and ensure that the procedure was performed correctly. The signals of each sample are next normalized using the housekeeping genes to control for input sample quality. The signals are then normalized to the reference sample within each run to control for run-to-run variations. The resulting normalized data is entered in the Breast Cancer Intrinsic Subtyping algorithm to determine tumor intrinsic subtype and risk of recurrence score.
The aim of the study is to assess the performance of the ROR score in predicting distal recurrence for postmenopausal patients with hormone receptor positive early breast cancer (HR+ EBC) treated with tamoxifen or tamoxifen followed by anastrozole when the NANO46 test is performed in a routine hospital pathology lab. Does the ROR score add prognostic information (Distant RFS) beyond the Clinical Treatment Score in all patients (CTS includes: nodes, grade, tumor size, age, treatment)? Do the ROR-based risk groups at prognostic information (Distant RFS) beyond the Clinical Treatment Score in all patients?
Study Overview: 3,714 patients were enrolled in a ABCSG8. Patients were postmenopausal women with HR+ EBC (node negative and note positive), grade one or two, with no prior treatment. 1,671 patients re-consented for long-term follow-up or are deceased. The median follow-up was 11 years. 1,620 FFPE blocks were collected. 25 had insufficient cancer in the block on path review, 73 had insufficient RNA included, 44 failed QC specs for the NanoString device. 1,478 patients (91.2%) passed the NANO46 analysis.
Methods: Three unstained 10 micron sections and 1 H&E slide for each patient was sent to an independent academic pathology laboratory at BCCA where tissue review, manual micro-dissection and RNA extraction were performed. NANO46 analysis was then conducted on 250 ng of the extracted RNA using the NanoString nCounter Analysis System; both intrinsic subtype and ROR score were calculated.
Results: The ROR Score adds statistically significant prognostic information (Distant RFS) beyond CTS in all patients (Likelihood ratio test ΔLRχ2=53.5, p<0.0001). The ROR-based risk groups add statistically significant prognostic information (Distant RFS) beyond CTS in all patients (Likelihood ratio test ΔLRχ2=34.1, p<0.0001). Differentiation between Luminal A and Luminal B adds statistically significant prognostic information (Distant RFS) beyond CTS in all patients (Luminal B vs. A: HR=2.38, 95% CI; 1.69-3.35, p<0.0001). Results in the node-negative and node-positive subgroups are similar to the results for all patients that are reported in the study.
Conclusions: The results show that both the ROR score and the ROR-based risk groups add statistically significant prognostic information beyond the Clinical Treatment Score. The results demonstrate that a complex, multi-gene-expression test can be performed in a hospital pathology laboratory and meet the same quality metrics as a central reference laboratory. The results of the TransATAC and ABCSG8 studies together provide Level 1 evidence for the clinical validity of the NANO46 test for predicting the risk of distant recurrence in postmenopausal women with HR+ EBC treated with endocrine therapy alone. The results also show that Luminal A subtypes have better outcomes than Luminal B subtypes in postmenopausal women with HR+ EBC treated with endocrine therapy alone.
This application claims the benefit of U.S. Provisional Application No. 61/650,209, filed May 22, 2012 and U.S. Provisional Application No. 61/753,673, filed Jan. 17, 2013. The contents of each of these applications are incorporated herein by reference in their entireties.
| Number | Date | Country | |
|---|---|---|---|
| 61650209 | May 2012 | US | |
| 61753673 | Jan 2013 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 13899656 | May 2013 | US |
| Child | 16792051 | US |
