# Patent

## Title
Copy number aberration driven endocrine response gene signature

## Abstract

Disclosed are methods of predicting the likelihood of long-term survival without recurrence of breast cancer for a subject having estrogen receptor-positive (ER+) breast cancer treated with adjuvant endocrine monotherapy. In various embodiments, these methods comprise performing a gene expression profile of a breast tissue sample of substantially all of the genes of the “CADER set” described herein; calculating a risk score using a regression model; and applying a double median cutoff classification to assign the subject to a sensitive, indeterminate or resistant group, wherein assignment to a sensitive group predicts longer relapse-free survival compared to the median relapse-free survival of ER+ breast cancer patients treated with adjuvant endocrine monotherapy.

## Background
CROSS REFERENCE TO RELATED APPLICATION
This application claims priority from U.S. Provisional Application Ser. No. 61/658,517 filed Jun. 12, 2012, which is incorporated herein by reference in its entirety.


STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH
This invention was made with government support under CA114722, CA095614, and CA068438 awarded by the National Institutes of Health. The government has certain rights in the invention.


FIELD
This work relates generally to breast cancer and, more particularly, to clinically useful methods and devices for assessing breast cancer prognosis.
INTRODUCTION
Genomic models based upon gene-expression signatures can provide clinically useful information for patient management. Nevertheless, patients are often assigned to intermediate risk groups in which the clinical decisions cannot be made confidently (Paik, S., et al. N. Engl. J. Med. 351:2817-26, 2004; Nielsen, T. O., et al. Clin. Cancer Res. 16:5222-32, 2010). Furthermore, besides HER2 and ER, most genes in these signatures are not amplicon-driven.
There are multiple studies that use gene expression markers for diagnosis, prognosis, determining treatment options, and monitoring relapse free survival.
Cobleigh et al., U.S. Pat. No. 7,569,345 ('345) provides a gene set for diagnosis and/or prognosis of breast cancer. This patent discloses a method of predicting the likelihood of long-term survival without recurrence of breast cancer for a patient having ER-positive breast cancer.
Bertucci et al., US Patent Application No. US 2011/0014191, discloses 16 serine/threonine kinases for determining poor clinical outcome or increased risk of recurrence following treatment when these genes are overexpressed.
Fuqua et al., US Patent Application No. US 2007/0059720, discloses RNA profiling for predicting resistance to chemotherapy in breast cancer.
Erlander et al., U.S. Pat. No. 7,504,214 ('214), discloses a gene signature of 149 genes that predicts tamoxifen treatment outcome in ER-positive breast cancer.
Perou et al., US Patent Application No. US 2011/0145176 (“Perou”) discloses methods for classifying and evaluating the prognosis of a subject having breast cancer. Perou further provides methods for predicting outcome or response to therapy of a subject diagnosed with breast cancer using the PAM50 classification model.
Some of these tests are used in a clinical setting such as OncotypeDx Assay (Genomic Health, Inc.) and PAM50 assay (University Genomics, St. Louis, Mo.). OncotypeDx predicts recurrence of tamoxifen-treated, node-negative breast cancer (Paik, S., et al. N. Engl. J. Med. 351:2817-26, 2004), while PAM50 assay predicts variable response to chemotherapy (Parker, J. S., et al. J. Clin. Oncol. 27:1160-7, 2009).
While these studies predict and provide clinically useful information in certain groups of subjects, they are still limited in their ability to predict risk of relapse, i.e., many patients will be deemed to be of indeterminate risk of relapse using existing prognostic models. Thus, there is a need for methods for assigning greater percentages of subjects to either high risk of relapse or low risk of relapse in view of present prognostic models or methods that further dissect intermediate risk of relapse deemed by an existing model, for example, PAM50.
SUMMARY
Accordingly, the present inventors have succeeded in integrating gene expression and gene copy data in estrogen receptor positive (ER+) breast cancer to develop a new prognostic model that is superior to models derived from gene expression data alone. The mRNA expression from genes that are both prognostic in ER+ disease and modulated by copy number aberration (CNA) provide superior assay performance as well as new biological and clinical insights.
Thus, the present teachings are drawn to methods to determine relapse risk in a human subject afflicted with estrogen receptor-positive breast cancer treated with adjuvant endocrine monotherapy. In various embodiments, the methods comprise obtaining a breast cancer tissue sample from a human subject, determining gene expression levels in the sample of most, substantially all, or all the genes listed in the Copy number Aberration Driven Endocrine Response (CADER) set. The CADER set comprises, consists essentially of or consists of 27 treatment sensitivity genes and 27 treatment resistant genes listed in Table 1. The endocrine therapy sensitivity genes include PARP3, AZGP1, ZNF18, EPHX2, IGFBP4, NUDT18, FM05, C1orf66, COL14A1, PLAT, PCM1, PHYHD1, ZBTB20, NFKB1, TK2, ABAT, ACP6, TSPAN7, TNFRSF10B, GSTM1, CHDH, KCTD9, EVL, MAP2K4, RPL21 and STC2 and the endocrine therapy resistance genes include VDAC2, KIFC1, EIF2S2, EIF2C2, CCNB1, RAD54B, RACGAP1, CDC2, CDCA5, BIRC5, C8orf76, MCM10, TDG, UBE2C, TPX2, C20orf24, FBXO45, KIF4A, NUP107, DSC2, KIF18A, ZWINT, TMPO, CCT6A, TOP2A, CENPE and XPOT. The methods further comprise scaling the expression levels to have similar distribution of a matching prototype dataset and assigning the subject to a risk group for relapse based on the measured gene expression values. In various embodiments of the present teachings, a subject can be deemed to be at low risk of relapse if the subject has an up-regulated sensitive gene centroid value (relative to median zero) and a down-regulated resistant gene centroid value. In addition, in various embodiments, the subject can be deemed to be at high risk of relapse if the subject has a down-regulated sensitive gene centroid value (relative to median zero) and an up-regulated resistant gene centroid value. In various embodiments, a subject having an expression profile that does not lead to a determination of either low risk of relapse or high risk of relapse can be deemed to be at indeterminate risk of relapse.
In various embodiments, assigning the subject to a sensitive, indeterminate or resistant group comprises representing the expression levels as a coordinate in a quadrant in a 1-dimensional space by the resistant gene centroid and the sensitive gene centroid, and determining the Euclidean distance of the expression levels to the gene centroid of each of sensitive, resistant and indeterminate response groups, wherein the subject is assigned to the group with the shortest distance.
In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can include calculating the double median cutoff classification scheme to categorize the risk score. In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can comprise calculating the average of the expression levels of treatment-resistant genes and treatment-sensitive genes separately, and comparing them to the median zero (if genes are each median-centered) to give the risk score response categories.
In various embodiments, the adjuvant endocrine monotherapy can be tamoxifen treatment. In some embodiments, the adjuvant endocrine monotherapy can be aromatase inhibitor treatment.
In various embodiments, the CADER gene set can be significantly associated with copy number aberration. In some embodiments, a copy number aberration can be a copy number loss. In other embodiments, a copy number aberration can be a copy number gain, such as, in non-limiting example, a tandem duplication. In other embodiments, the copy number aberration can be copy number amplification.
Various embodiments of the present teachings include a microarray comprising, consisting essentially of, or consisting of a solid support and probe sets for each gene of the CADER set. In some configurations, the solid support can be, in non-limiting example, an Agilent 4*44 K platform (Agilent Technologies, Inc., Santa Clara Calif.) or a NanoString nCounter platform (NanoString Technologies, Inc., Seattle, Wash.). In some configurations, the probe sets can comprise, consist of or consist essentially of probes for each gene of the CADER set. In some configurations, the probe sets can comprise, consist essentially of, or consist of probes for each gene of the CADER set, plus probe sets for one or more housekeeping genes. In various embodiments, a microarray consisting essentially of a solid support and probe sets for each gene of the CADER set can also include additional probes and probe sets such as positive and negative controls, as well as proteins, buffers and salts that may be needed to conduct sample analysis.
In various embodiments, the present teachings include a microarray-based methods of predicting the likelihood of long-term survival without recurrence of cancer for a subject having estrogen receptor-positive breast cancer treated with endocrine monotherapy. The methods comprise obtaining a breast cancer tissue sample from a human subject, determining gene expression levels in the sample of most, substantially all, or all the genes listed in the CADER set. The CADER set comprises, consists essentially of or consists of 27 treatment sensitivity genes and 27 treatment resistant genes listed in Table 1. The endocrine therapy sensitivity genes include PARP3, AZGP1, ZNF18, EPHX2, IGFBP4, NUDT18, FMO5, C1orf66, COL14A1, PLAT, PCM1, PHYHD1, ZBTB20, NFKB1, TK2, ABAT, ACP6, TSPAN7, TNFRSF10B, GSTM1, CHDH, KCTD9, EVL, MAP2K4, RPL21 and STC2 and the endocrine therapy resistance genes include VDAC2, KIFC1, EIF2S2, EIF2C2, CCNB1, RAD54B, RACGAP1, CDC2, CDCA5, BIRC5, C8orf76, MCM10, TDG, UBE2C, TPX2, C20orf24, FBXO45, KIF4A, NUP107, DSC2, KIF18A, ZWINT, TMPO, CCT6A, TOP2A, CENPE and XPOT. The methods further comprise scaling the expression levels to have similar distribution of a matching prototype dataset and assigning the subject to a risk group for relapse based on the measured gene expression values. In various embodiments of the present teachings, a subject can be deemed to be at low risk of relapse if the subject has an up-regulated sensitive gene centroid value (relative to median zero) and a down-regulated resistant gene centroid value. In addition, in various embodiments, the subject can be deemed to be at high risk of relapse if the subject has a down-regulated sensitive gene centroid value (relative to median zero) and an up-regulated resistant gene centroid value. In various embodiments, a subject having an expression profile that does not lead to a determination of either low risk of relapse or high risk of relapse can be deemed to be at indeterminate risk of relapse.
In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can include calculating the double median cutoff classification scheme to categorize the risk score. In some embodiments, determining the gene centroid of the treatment-resistant endocrine-response modifier genes and the gene centroid of the treatment-sensitive endocrine-response modifier genes can comprise calculating the average of the expression levels of treatment-resistant genes and treatment-sensitive genes separately, and comparing them to the median zero (if genes are each median-centered) to give the risk score response categories.
In various embodiments, the adjuvant endocrine monotherapy can be tamoxifen treatment. In some embodiments, the adjuvant endocrine monotherapy can be aromatase inhibitor treatment.



BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1A-1B illustrate the combined remark diagram for the POL and Z1031 cohort (A) and analysis steps flow chart (B).
FIG. 2A-D illustrate Kaplan Meier (KM) curves of the CADER categories.
FIG. 3A-C illustrate KM curves of RFS on CADER response groups based on the 54-ERMGs in each of the three public cohorts: Symmans (A), Pawitan (B) and Zhang (C).
FIG. 4 illustrates a quadrant in a 2-dimensional space by the resistant and the sensitive gene centroid.
FIG. 5 illustrates KM curves for sensitive/indeterminate/resistance groups using eighteen chromosome 8q resistance genes and six chromosome 8p sensitivity genes.



DETAILED DESCRIPTION
The present inventors disclose putative endocrine-response-modifier genes (ERMGs) that were identified by statistical associations between mRNA expression, copy number aberrations (CNA) and the anti-proliferative effects of neoadjuvant aromatase inhibitor (AI) therapy. Twenty-seven “treatment-sensitivity” and 27 “treatment-resistance” ERMGs were further selected through association with relapse-free survival (RFS) in patients uniformly treated with adjuvant tamoxifen monotherapy. The present teachings include 54 ERMGs and the derived copy number aberration driven endocrine response signature (CADER) that were further validated in three independent public datasets and compared with an established prognostic model (PAM50 ROR subtype-based score).
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present teachings belong. Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), and March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992), provide a person of skill in the art with general guides to many of the terms used in the present application.
One skilled in the art will recognize many methods and materials similar or equivalent to those described herein, which could be used in the practice of the present teachings. The present teachings are in no way limited to the methods and materials described. For purposes of the present teachings, the following terms are defined below.
As used herein, “microarray” refers to an ordered arrangement of hybridizable array elements such as polynucleotide probes, on a substrate.
As used herein, “treatment sensitive genes” are genes that are up-regulated in patients who are sensitive to endocrine treatment.
As used herein, “treatment resistance genes” are genes that are up-regulated in patients who are resistant to endocrine treatment.
As used herein, “gene amplification” refers to abnormal multiple copies of a gene or gene fragment comprised by a cell or cell line.
As used herein, “amplicon” refers to an amplified stretch of DNA.
As used herein, “prediction” refers to the likelihood that a subject will respond either favorably or unfavorably to a drug or set of drugs, and also the extent of those responses, or that a patient will survive, following surgical removal or the primary tumor and/or chemotherapy for a certain period of time without cancer recurrence.
As used herein, “long-term” survival means survival for at least 5 years following surgery or other treatment.
As used herein, “tumor” refers to cancerous tumors.
As used herein, “pathology” of cancer refers to phenomena that compromise the health of a cancer patient. These include, without limitation, abnormal cell growth, metastasis, interference with the normal functioning of neighboring cells, release of cytokines or other secretory products at abnormal levels, suppression or aggravation of inflammatory or immunological responses, neoplasia, premalignancy, malignancy, and invasion of surrounding or distant tissues or organs.
As used herein, “biological sample” is any sampling of cells, tissues, or bodily fluids containing cells. Examples of biological samples include, but are not limited to, biopsies, smears, and bodily fluids such as blood, lymph, urine, saliva, nipple aspirates, and gynecological fluids.
As used herein, “blood” includes whole blood, plasma, and serum.
Methods and compositions described herein utilize laboratory techniques well known to skilled artisans. Such techniques can be found in laboratory manuals such as Sambrook, J., et al., Molecular Cloning: A Laboratory Manual, 3rd ed. Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 2001; Spector, D. L. et al., Cells: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, N.Y., 1998; Methods in Enzymology (Academic Press, Inc.); Handbook of Experimental Immunology, 4th edition (D. M. Weir & C. C. Blackwell, eds., Blackwell Science Inc., 1987); Current Protocols in Molecular Biology (F. M. Ausubel et al., eds., 1987); and PCR: The Polymerase Chain Reaction, (Mullis et al., eds., 1994).
Gene Expression Profiling
In general, methods of gene expression profiling can be divided into two large groups: methods based on hybridization analysis of polynucleotides, and methods based on sequencing of polynucleotides. Methods known in the art for the quantification of mRNA expression in a sample include northern blotting and in situ hybridization (Parker & Barnes, Methods in Molecular Biology 106:247-283 (1999)); RNAse protection assays (Hod, Biotechniques 13:852-854 (1992)); and reverse transcription-polymerase chain reaction (RT-PCR) (Weis et al., Trends in Genetics 8:263-264 (1992)). Alternatively, antibodies can be employed that can recognize specific duplexes, including DNA duplexes, RNA duplexes, and DNA-RNA hybrid duplexes or DNA-protein duplexes. Representative methods for sequencing-based gene expression analysis include Serial Analysis of Gene Expression (SAGE), and gene expression analysis by massively parallel signature sequencing (MPSS). For example, RT-PCR can be used to compare mRNA levels in different sample populations, in normal and tumor tissues, with or without drug treatment, to characterize patterns of gene expression, to discriminate between closely related mRNAs, and/or to analyze RNA structure.
A first step for an RT-PCR analysis can be extraction and/or isolation of mRNA from a sample. In some embodiments, starting material can be total RNA isolated from a human tumor, a tumor cell line, and/or corresponding normal tissues or cell lines. RNA can be isolated from a variety of primary tumors, such as, without limitation, breast, lung, colon, prostate, brain, liver, kidney, pancreas, spleen, thymus, testis, ovary, uterus, etc., tumor, or tumor cell lines. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.
Methods for mRNA extraction are well known in the art and are disclosed in standard textbooks of molecular biology, including Ausubel et al., Current Protocols of Molecular Biology, John Wiley and Sons (1997). 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 18:42044 (1995). In particular, RNA isolation can be performed using a purification kit, buffer set and protease from commercial manufacturers, such as Qiagen, 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 MasterPureTM Complete DNA and RNA Purification Kit (EPICENTRE®, Madison, Wis.), and Paraffin Block RNA Isolation Kit (Ambion, Inc.). Total RNA from tissue samples can be isolated using RNA Stat-60 (Tel-Test). RNA prepared from a tumor can be isolated, for example, by cesium chloride density gradient centrifugation.
A first step in gene expression profiling by RT-PCR can be the reverse transcription of the RNA template into cDNA, followed by amplification in a PCR reaction. For example, extracted RNA can be reverse-transcribed using a GeneAmp RNA PCR kit (Perkin Elmer, Calif., USA), following the manufacturer's instructions. The cDNA can then be used as template in a subsequent PCR amplification and quantitative analysis using, for example, a TaqMan® (Life Technologies, Inc., Grand Island, N.Y.) assay.
TaqMan® RT-PCR can be performed using commercially available equipment, such as, for example, an ABI PRISM 7700TM Sequence Detection SystemTM (Perkin-Elmer-Applied Biosystems, Foster City, Calif., USA), or Lightcycler (Roche Molecular Biochemicals, Mannheim, Germany).
RT-PCR can be performed using an internal standard such as mRNA for glyceraldehyde-3-phosphate-dehydrogenase (GAPDH) and/or β-actin as a control (see, e.g., Held et al., Genome Research 6: 986-994, 1996).
Representative protocols for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are given in various published journal articles (for example: T. E. Godfrey et al., J. Molec. Diagnostics 2: 84-91, 2000; K. Specht et al., Am. J. Pathol. 158: 419-429, 2001). In various configurations, a representative process can start with cutting about 10 μm-thick sections of paraffin-embedded tumor tissue samples. The RNA can be extracted, and protein and DNA can be removed. RNA repair and/or amplification steps can be included.
In some aspects of the present teachings, PCR primers and probes can be designed based upon intron sequences present in the gene to be amplified. In such aspects, a first step in the primer/probe design can be the delineation of intron sequences within the genes. This can be accomplished using publicly available software, such as the DNA BLAST software (Kent, W. J., Genome Res. 12(4): 656-664, 2002). Subsequent steps can follow well established methods of PCR primer and probe design.
In some configurations, in order to avoid non-specific signals, repetitive sequences within the introns can be masked when designing the primers and probes. This can be accomplished by using software such as the Repeat Masker program available on-line through the Baylor College of Medicine. This program can be used to screen DNA sequences against a library of repetitive elements and returns a query sequence in which the repetitive elements are masked. The masked intron sequences can then be used to design primer and probe sequences using a commercially or otherwise publicly available primer/probe design package, such as Primer Express (Applied Biosystems); MGB assay-by-design (Applied Biosystems); Primer3 (Steve Rozen and Helen J. Skaletsky (2000) Primer3 on the WWW for general users and for biologist programmers. In: Krawetz S, Misener S (eds) Bioinformatics Methods and Protocols: Methods in Molecular Biology. Humana Press, Totowa, N.J., pp 365-386).
Factors considered in PCR primer design include primer length, melting temperature (Tm), and G/C content, specificity, complementary primer sequences, and 3′-end sequence. In general, optimal PCR primers are generally 17-30 bases in length, and contain about 20% from 10% to 80%, or about 80% G+ C bases, such as, for example, about 50%, from 50 to 60%, or about 60% G+C bases. In various configurations, Tm's between 50 and 80° C., e.g. about 50 to 70° C. can be preferred.
Further guidelines for PCR primer and probe design can be found in various published sources, e.g. Dieffenbach, C. W. et al., “General Concepts for PCR Primer Design” in: PCR Primer, A Laboratory Manual, Cold Spring Harbor Laboratory Press, New York, 1995, pp. 133-155; Innis and Gelfand, “Optimization of PCRs” in: PCR Protocols, A Guide to Methods and Applications, CRC Press, London, 1994, pp. 5-11; and Plasterer, T. N. Primerselect: Primer and probe design. Methods Mol. Biol. 70:520-527 (1997), the entire disclosures of which are hereby expressly incorporated by reference.
Microarrays
In some embodiments, differential gene expression can also be identified, or confirmed using a microarray technique. Thus, the expression profile of breast cancer-associated genes can be measured in either fresh or paraffin-embedded tumor tissue, using microarray technology. In these methods, polynucleotide sequences of interest (including cDNAs and oligonucleotides) can be plated, or arrayed, on a microchip substrate. The arrayed sequences are then hybridized with specific DNA probes from cells or tissues of interest. Just as in the RT-PCR method, the source of mRNA typically can be total RNA isolated from human tumors or tumor cell lines, and corresponding normal tissues or cell lines. Thus RNA can be isolated from a variety of primary tumors or tumor cell lines. If the source of mRNA is a primary tumor, mRNA can be extracted, for example, from frozen or archived paraffin-embedded and fixed (e.g. formalin-fixed) tissue samples.
In an embodiment of the microarray technique, PCR-amplified inserts of cDNA clones can be applied to a substrate in a dense array. The microarrayed genes, immobilized on the microchip, can be suitable for hybridization under stringent conditions.
In some embodiments, fluorescently labeled cDNA probes can be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from tissues of interest. Labeled cDNA probes applied to the chip can hybridize with specificity to loci of DNA on the array. After washing to remove non-specifically bound probes, the chip can be scanned by confocal laser microscopy or by another detection method, such as a CCD camera. Quantification of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance.
In some configurations, dual color fluorescence can be used. With dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pairwise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. In various configurations, the miniaturized scale of the hybridization can afford a convenient and rapid evaluation of the expression pattern for large numbers of genes. In various configurations, such methods can have sensitivity required to detect rare transcripts, which are expressed at fewer than 1000, fewer than 100, or fewer than 10 copies per cell. In various configurations, such methods can detect at least approximately two-fold differences in expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2): 106-149 (1996)). In various configurations, microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GenChip technology, or Incyte's microarray technology.
Serial Analysis of Gene Expression (SAGE)
SAGE is a method that allows simultaneous and quantitative analysis of a large number of gene transcripts, without the need of providing an individual hybridization probe for each transcript, hi various configurations of these methods, a short sequence tag (about 10-14 bp) is generated that contains sufficient information to uniquely identify a transcript, provided that the tag is obtained from a unique position within each transcript. Then, many transcripts are linked together to form long serial molecules that can be sequenced, revealing the identity of the multiple tags simultaneously. The expression pattern of any population of transcripts can be quantitatively evaluated by determining the abundance of individual tags, and identifying the gene corresponding to each tag (Velculescu et al., Science 270:484-487 (1995); Velculescu et al., Cell 88:243-51 (1997).
MassARRAY Technology
The MassARRAY (Sequenom, San Diego, Calif.) technology is an automated, high-throughput method of gene expression analysis using mass spectrometry (MS) for detection. According to this method, following the isolation of RNA, reverse transcription and PCR amplification, the cDNAs are subjected to primer extension. The cDNA-derived primer extension products are purified, and dispensed on a chip array that is pre-loaded with the components needed for MALTI-TOF MS sample preparation. The various cDNAs present in the reaction are quantitated by analyzing the peak areas in the mass spectrum obtained.
Gene Expression Analysis by Massively Parallel Signature Sequencing (MPSS)
This method, described by Brenner et al., Nature Biotechnology 18:630-634 (2000), is a sequencing approach that combines non-gel-based signature sequencing with in vitro cloning of millions of templates on separate 5 μm diameter microbeads. First, a microbead library of DNA templates is constructed by in vitro cloning. This is followed by the assembly of a planar array of the template-containing microbeads in a flow cell at a high density (typically greater than 3.times.106 microbeads/cm2). The free ends of the cloned templates on each microbead are analyzed simultaneously, using a fluorescence-based signature sequencing method that does not require DNA fragment separation. This method has been shown to simultaneously and accurately provide, in a single operation, hundreds of thousands of gene signature sequences from a yeast cDNA library.
General Description of the mRNA Isolation, Purification and Amplification
The steps of a representative protocol for profiling gene expression using fixed, paraffin-embedded tissues as the RNA source, including mRNA isolation, purification, primer extension and amplification are given in various published journal articles {for example: T. E. Godfrey et al. J. Molec. Diagnostics 2: 84-91 [2000]; K. specht et al., Am. J. Pathol. 158: 419-29 [2001]}. Briefly, a representative process starts with cutting about 10 urn thick sections of paraffin-embedded tumor tissue samples. The RNA is then extracted, and protein and DNA are removed. After analysis of the RNA concentration, RNA repair and/or amplification steps may be included, if necessary, and RNA is reverse transcribed using gene specific promoters followed by RT-PCR. Finally, the data are analyzed to identify the best treatment option(s) available to the patient on the basis of the characteristic gene expression pattern identified in the tumor sample examined.
Breast Cancer Gene Set, Assayed Gene Subsequences, and Clinical Application of Gene Expression Data
Some embodiments of the present teachings comprise measuring expression levels of certain genes by breast cancer tissue to provide prognostic information. In various configurations, expression levels can be normalized regarding both differences in the amount of RNA assayed and variability in the quality of the RNA used. Therefore, an assay can involve measurement of the expression of certain “normalizing” genes, including housekeeping genes such as, for example, GAPDH and/or Cyp1. In some configurations, normalization can be based on the mean or median signal (Ct) of all of the assayed genes or a subset thereof (global normalization approach). On a gene-by-gene basis, measured normalized amount of a patient tumor mRNA can be compared to the amount found in a breast cancer tissue reference set. The number (N) of breast cancer tissues in this reference set can be sufficiently high to ensure that different reference sets (as a whole) behave essentially the same way. In various configurations, if this condition is met, the identity of the individual breast cancer tissues present in a particular set can have no significant impact on the relative amounts of the genes assayed. In some configurations, the breast cancer tissue reference set can consist of or consist essentially of at least about 30, preferably at least about 40, more preferably at least about 50, different FPE breast cancer tissue specimens. Unless noted otherwise, normalized expression levels for each mRNA/tested tumor/patient can be expressed as a percentage of the expression level measured in the reference set. More specifically, the reference set of a sufficiently high number (e.g. at least 40) of tumors can yield a distribution of normalized levels of each mRNA species. The level measured in a particular tumor sample to be analyzed falls at some percentile within this range, which can be determined by methods well known in the art. Below, unless noted otherwise, reference to expression levels of a gene assume normalized expression relative to a reference set although unless stated otherwise.
In some embodiments, gene expression data can be pre-processed, by addressing for example but without limitation, missing data, translation, scaling, normalization, and/or weighting. In some configurations, multivariate projection methods, such as principal component analysis (PCA) and partial least squares analysis (PLS), can be used as scaling-sensitive methods. In these configurations, 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. In some embodiments, scaling and weighting can be used to place the data in the correct metric, thereby revealing patterns inherent in the data.
In some configurations, missing data, for example gaps in column values, can be replaced or “filled” with, for example but without limitation, 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”).
As used herein, “translation” of descriptor coordinate axes can include normalization and mean centering. In some configurations, “normalization” can be used to remove sample-to-sample variation. In various embodiments, for microarray data, the process of normalization can be used to remove systematic errors by balancing the fluorescence intensities of the two labeling dyes. In various configurations, methods for calculating normalization factor can 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 (2002) Nat. Genet. 32 (Suppl.), 496-501). In one embodiment, 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, PUM1, ACTB, GAPD, GUSB, RPLP0, 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.
A gene centroid represents the average expression levels of a set of predefined genes for an individual relative to control levels. The CADER classification centers on two centroids, the centroid of the 27 CADER resistant genes and the centroid of the 27 CADER sensitive genes.
In various configurations, many normalization approaches are possible, which can be applied at any of several points in the analysis. In a configuration, microarray data can be normalized using the LOWESS method (Yang, Y. H., et al. Nucleic Acids Res. 30:e15, 2002). In another embodiment, qPCR data can be normalized to the geometric mean of a set of housekeeping genes.
In some embodiments, “mean centering” can also be used to simplify interpretation. In these embodiments, for each descriptor, the average value of that descriptor for all samples can be subtracted. In this way, the mean of a descriptor coincides with the origin, and all descriptors can be “centered” at zero. In “unit variance scaling,” data can be scaled to equal variance. In various configurations, the value of each descriptor can be scaled by 1/StDev, where StDev is the standard deviation for that descriptor for all samples.
In some embodiments, “Pareto scaling” can also be used to simplify interpretation (van den Berg, R. A., et al., BMC Genomics. 7:142, 2006). In pareto scaling, the value of each descriptor is scaled by 1/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. In various configurations, pareto scaling can be performed, for example, on raw data or mean centered data.
In some embodiments, “logarithmic scaling” can 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. In these embodiments, a value is replaced by the logarithm of that value.
In some embodiments, “equal range scaling” can be used to assist interpretation. In these embodiments, each descriptor is divided by the range of that descriptor for all samples. In this way, all descriptors have the same range, that is, 0-1. However, this method can be sensitive to the presence of outlier points.
In some embodiments, “autoscaling,” can be used to assist interpretation. In these embodiments, each data vector is mean centered and unit variance scaled. This technique can be used to weigh each descriptor equally.
In various embodiments, a double median cut-off scheme can be used to classify subjects into the three response groups (sensitive, resistant and indeterminate). In various configurations, each gene can be normalized to have a zero median and a unit inter-quartile range (IQR). The gene centroid of 27 CADER resistant genes and the gene centroid of the 27 CADER sensitive genes can be separately calculated. Because of this normalization, the double median cut-off can be equivalent to double zero cut-off since all genes and centroids have a zero median. Patients with a greater-than-zero resistant gene centroid value and a less-than-zero sensitive gene centroid can be assigned into the therapy resistant group while the patients with less-than-zero resistant gene centroid value and a greater-than-zero sensitive gene centroid can be assigned into the therapy sensitive group. In various configurations, detection of up-regulation of resistant genes can lead to a bad prognosis, and detection of up-regulation of the sensitive genes can lead to good prognosis. The remaining patients can have indeterminate responses to therapy. (FIG. 4)
Methods
The methods and compositions described herein utilize laboratory techniques well known to skilled artisans, and can be found in references such as Sambrook and Russel (2006), Condensed Protocols from Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN 0879697717; Sambrook and Russel (2001) Molecular Cloning: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN 0879695773; Ausubel et al. (2002) Short Protocols in Molecular Biology, Current Protocols, ISBN 0471250929; Spector et al. (1998) Cells: A Laboratory Manual, Cold Spring Harbor Laboratory Press, ISBN 0879695226.
EXAMPLES
The present teachings including descriptions provided in the Examples that are not intended to limit the scope of any claim or aspect. Unless specifically presented in the past tense, an example can be a prophetic or an actual example. The following non-limiting examples are provided to further illustrate the present teachings. Those of skill in the art, in light of the present disclosure, will appreciate that many changes can be made in the specific embodiments that are disclosed and still obtain a like or similar result without departing from the spirit and scope of the present teachings.
Example 1
This example illustrates the identification of Endocrine Response Modifier Genes (ERMG).
Study samples to identify ERMG were used from previously described neoadjuvant endocrine therapy studies, preoperative letrozole Phase 2 (POL) trial and ACOSOG Z1031 neoadjuvant aromatase inhibitor (AI) trial (Van Tine, B. A., et al. 2011; Olson, J.A., Jr., et al. 2009). A 10% Ki67 cut-point in the surgical specimen was used to define AI sensitivity and resistance, as this cut point is a reliable surrogate for relapse-free survival (RFS). Fifty-two and 179 patients from the POL and Z1031 cohort respectively were used for discovery (FIG. 1A). Total sample size (sample size for POL/sample size for Z1031) are noted in each box. Baseline PAM50 subtype and baseline and surgical specimen ER and Ki67 levels were similarly distributed between the two cohorts. Public gene expression data were accessed on 263 patients from the Loi study (Loi, S., et al. 2007, referred to herein as “Loi”), 195 from the Symmans study (Symmans, W. F., et al. 2010, referred to herein as “Symmans”), 136 from the Zhang study (Zhang, Y., et al. 2009, referred to herein as “Zhang”) and 87 from the Pawitan study (Pawitan, Y., et al. 2005, referred to herein as “Pawitan”), restricting to ER+ breast cancer patients treated with adjuvant endocrine monotherapy (i.e. no chemotherapy) (FIG. 1B). Sequential analysis steps that lead to the CADER signature are shown in this flow chart (FIG. 1B). In each step, analyzed cohorts, analysis end points, genomic profiling and statistical methods, and number of retained treatment resistance and sensitivity signature genes are presented.
ERMGs were identified in study samples by significance analysis of microarrays (SAM) as genes differentially expressed between the sensitive and resistant tumors (defined by the 10% Ki67 cut point) at the 5% false discovery rate (FDR). There were 1927 putative ERMGs that fit these criteria. Of these, 1047 are up-regulated in the resistant tumors with a fold-change (relative to sensitive) ranging from 1.29 ̃1.80 while 780 ERMGs were up-regulated in the sensitive tumors with a fold change (relative to resistant) between 1.32 and 1.81.
Two exercises were conducted in parallel to focus verification and validation efforts on the most relevant genes. First, genes were excluded whose mRNA levels were not significantly associated with CNA, based on 137 POL-Z1031 cases with paired gene-expression and aCGH, comparative genomic hybrid, data. This analysis revealed that 617 (460 resistant and 157 sensitive) of the original 1927 ERMGs were differentially expressed between copy number gain/amplification versus neutral/LOH samples. Correlation matrixes demonstrated that copy number data in contiguous chromosomal locations were highly correlated. Chromosomes (Chr) 6, 1, 20, 3 and 8 harbored the greatest number of CNA-associated resistance-ERMGs (n=64, 60, 43, 40, 40 respectively) while sensitivity-ERMGs were largely located on Chr17, 4, 1, 5, 11 and 8 (n=25, 18, 15, 14, 13, and 12 respectively), hi general, the sensitivity ERMGs were predominantly affected by LOH whereas resistance ERMGs were dominantly affected by gene gain and amplification (Table 1). Table 1 presents the list of the 54 ERMGs including official gene symbols, gene descriptions, Cox survival P-values from Loi, copy number P values are shown for each ERMG.







TABLE 1







Listing of the 54 ERMGs.












CNV Percentage





Symbol
(del/neutral/gain/amp)
Locus
CNV P
Cox P










Endocrine Therapy Sensitivity Genes











PARP3
36.1/60.6/3.2/—
3p21.31
0.0418
1.80E−05


AZGP1
31.2/58.4/10.4/—
7q22.1
0.0013
3.81E−05


ZNF18
54.2/44.5/1.3/—
17p11.2
0.0416
1.13E−04


EPHX2
48.0/40.9/11.0/—
8p21
0.0004
6.57E−04


IGFBP4
34.6/45.8/17.0/2.6
17q12
0.0239
0.0010


NUDT18
53.6/36.6/9.8/—
8p21.3
0.0003
0.0010


FMO5C
0.6/38.1/55.5/5.8
1q21.1
0.0008
0.0018


C1orf66W
2.6/34.8/57.4/5.2
1q23.1
2.68E−09
0.0021


COL14A1
2.6/47.7/27.4/22.2
8q23
0.0003
0.0031


PLAT
16.2/50.6/22.7/10.4
8p12
0.0001
0.0033


PCM1C
43.2/45.8/10.3/0.6
8p22
1.8E−05
0.0051


PHYHD1
44.0/52.0/4.0/—
9q34.11
0.0013
0.0072


ZBTB20
4.7/82.4/12.2/0.7
3q13.2
0.0344
0.0081


NFKB1
6.5/84.5/8.4/0.6
4q24
0.0214
0.0081


TK2
67.3/26.1/6.5/—
16q22
0.0019
0.0081


ABAT
4.5/49.7/43.9/1.9
16p13.2
1.56E−05
0.0083


ACP6
0.6/38.1/54.8/6.5
1q21
0.0023
0.0094


TSPAN7
17.4/66.5/14.8/1.3
Xp11.4
0.0482
0.0097


TNFRSF10B
50.3/40.6/9.0/—
8p22
0.0004
0.0131


GSTM1PK
27.1/69.0/3.2/0.6
1p13.3
0.0245
0.0196


CHDH
23.4/73.4/3.2/—
3p21.1
0.0115
0.0244


OSBPL1A
25.2/66.5/7.7/0.6
18q11.1
0.0423
0.0272


KCTD9
45.8/43.9/10.3/—
8p21.1
0.0051
0.0285


EVL
21.9/65.6/11.3/1.3
14q32.2
0.0001
0.0294


MAP2K4
54.5/44.2/1.3/—
17p11.2
0.0170
0.0308


RPL21
27.7/61.9/8.4/1.9
13q12.2
0.0006
0.0333


STC2C
9.0/70.3/20.6/—
5q35.1
0.0070
0.0466







Endocrine Therapy Resistance Genes











VDAC2
5.2/78.7/14.2/1.9
10q22
3.2E−07
3.44E−08


KIFC1
17.4/73.5/9.0/—
6p21.3
0.0039
1.63E−06


EIF2S2
11.1/63.4/25.5/—
20q11.2
2.3E−04
3.57E−06


EIF2C2
7.2/48.4/26.8/17.6
8q24
5.4E−14
4.80E−06


CCNB1PKP
24.5/58.1/17.4/—
5q12
4.5E−04
7.55E−06


RAD54B
2.0/47.1/33.3/17.6
8q22.1
2.4E−15
1.12E−05


RACGAP1
11.6/76.1/12.3/—
12q13.12
0.0187
2.23E−05


CDC2C; P
5.2/80.0/14.2/0.6
10q21.1
0.0014
2.27E−05


CDCA5
30.3/58.1/10.3/1.3
11q12.1
6.2E−04
2.29E−05


BIRC5PK; P
33.5/38.1/27.1/1.3
17q25
7.2E−07
2.67E−05


C8orf76
5.8/46.1/28.6/19.5
8q24.13
1.4E−18
3.27E−05


MCM10
9.7/78.1/11.0/1.3
10p13
1.2E−04
3.52E−05


TDG
11.0/72.9/15.5/0.6
12q24.1
1.4E−04
3.68E−05


UBE2CC; P
4.5/54.5/36.4/4.5
20q13.12
4.6E−04
3.70E−05


TPX2
11.0/63.9/25.2/—
20q11.2
7.0E−03
3.71E−05


C20orf24
13.6/57.4/25.2/3.9
20q11.23
5.2E−08
4.45E−05


FBXO45
13.6/70.3/14.8/1.3
3q29
3.6E−05
4.46E−05


KIF4A
22.2/69.9/7.8/—
Xq13.1
3.5E−02
4.51E−05


NUP107
2.0/69.5/25.3/3.2
12q15
2.6E−07
6.62E−05


DSC2
14.2/71.6/13.6/0.6
18q12.1
2.5E−02
6.91E−05


KIF18A
6.6/77.0/15.1/1.3
11p14.1
3.5E−07
7.09E−05


ZWINT
5.2/80.7/13.6/0.6
10q21
3.2E−02
7.24E−05


TMPO
12.6/68.9/17.2/1.3
12q22
1.0E−02
7.71E−05


CCT6A
14.2/69.7/16.1/—
7p11.2
2.6E−04
8.91E−05


TOP2A
43.8/40.5/15.0/0.6
17q21
2.7E−07
9.03E−05


CENPE
6.5/85.2/7.7/0.6
4q24
1.9E−04
1.31E−04


XPOT
3.9/68.4/25.2/2.6
12q14.2
2.0E−03
1.32E−04









In the second exercise, the 1927 putative ERMGs were simultaneously screened for association with RFS using the independent Loi cohort (Loi, S., et al. 2007). In univariate survival analysis, a total of 431 ERMGs (317 resistant and 114 sensitive) were significantly associated with RFS in the same resistance/sensitivity direction (by hazard ratio estimation) as observed in POL-Z1031. Only fifty two of these genes (12%) overlapped with those contained in the five gene signatures previously published as prognostic in ER+ breast cancer (Paik, S., et al. N. Engl. J. Med. 351:2817-26, 2004; Nielsen, T. O., et al. Clin. Cancer. Res. 16:5222-32, 2010; Chanrion, M., et al. Clin. Cancer. Res. 14:1744-52, 2008; van de Vijver, M. J., et al. N. Engl. J. Med. 347:1999-2009, 2002; Wang, Y., et al. Lancet. 365:671-9, 2005). Takingthe results from both exercises, two hundred and twenty four ERMGs (197 resistant and 27 sensitivity) survived.
Example 2
This example illustrates the prognostic power of the 54 ERMGs individually.
Three independent public microarray data sets on ER+ breast cancer tumors treated with adjuvant tamoxifen only were accessed (Symmans, Zhang and Pawitan). The hazard ratios (HRs), 95% CIs and P-values estimated from univariate Cox regression models were calculated for each ERMG within each public cohort. By meta-analysis, 25 among the 48 genes present in at least one cohort showed significant association with RFS and in the expected direction as from the discovery. Hierarchical cluster analyses on the 54 ERMGs subsequently demonstrated that the sensitivity and resistant genes separated consistently and cleanly in all cohorts. Based on the joint expression pattern of treatment sensitivity and resistance ERMGs, three groups of samples were definable: a group with high-expression of resistance-ERMGs and low-expression of sensitivity-ERMGs (‘resistant’ group), a second group with the opposite expression pattern (‘sensitive’ group) and a third with a mixed pattern (‘indeterminate’ group).
A CADER signature through application of a double median cutoff was developed to classify patients into the three groups. Categorical CADER calls (sensitive/indeterminate/resistant) can be separately made on each cohort. The three categories of CADER calls exhibited a balanced distribution on the three public cohorts (Chi-square p-value p=0.48), especially between Symmans and Pawitan (Chi-square p-value p=0.83). The association between CADER and RFS can be tested individually within each cohort. Assembled together, the CADER calls from all the three cohorts can also be tested for a combined association analysis with RFS. The CADER categories successfully produced differing risk of relapse predictions in the combined data (FIG. 2A, Log-rank test P=1.84e-07) as well as within each cohort separately (FIG. 3A-C Log-rank tests, Symmans p=0.004, Pawitan P=0.001, Zhang P=0.044). Survival probabilities (FIG.) of the CADER-sensitive group were nearly 25% higher compared to the resistance group at year 8 (94% vs. 70%) in the combined dataset with an estimated HR (resistant vs. sensitive) of 5.18 (95% CI:2.67 ̃10.08) while the HR of indeterminate to sensitive was 2.31 (95% CI: 1.06 ̃5.02). Kaplan Meier (KM) curves of RFS in the combined public cohorts are displayed in FIG. 2, showing data from all patients (A), the patient subset of intermediate-risk designated by the PAM50 ROR-S model (B), node positive subset of patients (C), and node negative subset of patients (D). CADER groups are indicated as follows: sensitive indicated by solid lines, indeterminate indicated by dashed lines and resistant indicated by dotted lines. The P-values are based on log rank tests. This illustrates that patient stratification into endocrine therapy response groups using the CADER model is significantly associated with survival, even by stratification of node status and within the ROR-S median risk subset.
The CADER assignments were highly concordant with the PAM50 ROR-S (Fisher exact test P=3.50E-44) and the PAM50 intrinsic subtype calls (see Parker, J. S., et al. 2009, referred to herein as “PAM50”) (Fisher exact test P=8.22E-37) in the combined dataset and individually (Table 2).







TABLE 2





Concordance between CADER response groups and PAM50-based ROR-S and breast


cancer intrinsic subtypes.























CADER








Response



Set
Group
N
Node−
Node+
Node.P







POL-Z1031
Sensitive
86


—




Indeterminate
53




Resistant
92



Loi (N = 263)
Sensitive
99
47
48
0.5228




Indeterminate
69
28
38




Resistant
95
39
54



MDACC (N = 195)
Sensitive
79
52
27
0.1371




Indeterminate
55
33
22




Resistant
61
30
31



Zhang (N = 136)
Sensitive
54
54
0
—




Indeterminate
28
28
0




Resistant
54
54
0



Pawitan (N = 87)
Sensitive
35
26
8
0.2592




Indeterminate
22
11
9




Resistant
30
20
9



All (N = 418)
Sensitive
168
132
35
0.1881




Indeterminate
105
72
31




Resistant
145
104
40















CADER





Response
ROR_S














Set
Group
Low
Med
High
RORS.P







POL-Z1031
Sensitive
57
25
0
1.73E−23




Indeterminate
14
32
4




Resistant
4
55
31



Loi (N = 263)
Sensitive
47
18
0
3.26E−22




Indeterminate
17
28
4




Resistant
1
31
30



MDACC (N = 195)
Sensitive
67
12
0
1.07E−21




Indeterminate
26
26
3




Resistant
6
32
23



Zhang (N = 136)
Sensitive
45
9
0
2.72E−16




Indeterminate
7
19
2




Resistant
6
30
18



Pawitan (N = 87)
Sensitive
26
9
0
1.85E−11




Indeterminate
12
10
0




Resistant
2
11
17



All (N = 418)
Sensitive
138
30
0
3.50E−44




Indeterminate
44
55
5




Resistant
14
73
58















CADER





Response
Subtype














Set
Group
LumA
LumB
Her2
Basal
Normal
Subtype.P





POL-Z1031
Sensitive
68
18



1.16E−22



Indeterminate
19
34



Resistant
5
87


Loi (N = 263)
Sensitive
70
15
2
3
9
1.44E−26



Indeterminate
15
36
3
5
10



Resistant
2
58
17
14
4


MDACC (N = 195)
Sensitive
61
4
0
0
14
2.59E−19



Indeterminate
26
17
1
0
11



Resistant
10
33
11
6
1


Zhang (N = 136)
Sensitive
43
4
0
0
7
2.15E−11



Indeterminate
13
11
0
1
3



Resistant
10
33
5
3
3


Pawitan (N = 87)
Sensitive
27
6
0
0
2
6.21E−08



Indeterminate
10
8
0
0
4



Resistant
3
17
5
4
1


All (N = 418)
Sensitive
131
14
0
0
23
8.22E−37



Indeterminate
49
36
1
1
18



Resistant
23
83
21
13
5









Analyzed cohorts include POL-Z1031, Loi, Symmans, Zhang, Pawitan and “All” (combining Symmans, Zhang and Pawitan) in Table 2. High agreement between CADER response groups, ROR-S and breast cancer subtypes can be observed within each individual cohort, as well as the combination of the three public cohorts. The association between CADER classification and RFS was still significant after stratification by nodal status in the combined analysis (FIG. 2C-D, Log rank test P=0.038 in node positive and P=1.82e-05 in node negative). Despite the high concordance with the PAM50-defined groups, the CADER classification stratified the risk of relapse within the subset of patients who were assigned medium risk by the ROR-S model (FIG. 2B, Log rank test P=0.0005). To confirm the independent prognostic ability of CADER, multivariate Cox analysis can be applied. The results (Table 3) show a strong independent prognostic ability of CADER to classic clinical variables (likelihood ratio test P=5.62E-05), with the predicted resistant patients experiencing a HR of 5.32 (95% CI: 2.41 ̃11.76) relative to sensitive patients.








TABLE 3







Multivariate survival analysis of the combined Public cohort


(Symmans and Pawitan) for RFS.









Variable
HR (95% CI)
P










CADER (N = 238)









Age (≧65 vs. <65)
 2.4 (1.33~4.35)
0.0038


Grade (3 vs. 1~2)
0.62 (0.32~1.19)
0.1508


Node (Positive vs. Negative)
 2.1 (1.17~3.75)
0.0126


Tumor Size (≧2.0 cm vs. <2.0 cm)
2.73 (1.15~6.46)
0.0227




5.62E-05


CADER
1.91 (0.81~4.51)
0.138


indeterminate vs. sensitive
 5.32 (2.41~11.76)
3.59E-05


resistant vs. sensitive










Harrell's C-index
0.7851







ROR-S (N = 238)









Age (≧65 vs. <65)
2.11 (1.15~3.87)
0.016


Grade (3 vs. 1~2)
0.92 (0.47~1.8) 
0.8054


Node (Positive vs. Negative)
2.52 (1.42~4.48)
0.0015


Tumor Size (3 vs. 1~2)
2.59 (1.12~5.98)
0.0263


ROR_S

0.1104


med vs. low
1.84 (0.99~3.4) 
0.0537


high vs. low
1.94 (0.83~4.55)
0.1283








Harrell's C-index
0.7252







CADER + ROR-S (N = 238)









Age (≧65 vs. <65)
2.27 (1.24~4.15)
0.0078


Grade (3 vs. 1~2)
0.66 (0.34~1.31)
0.2349


Node (Positive vs. Negative)
 2.1 (1.17~3.77)
0.0132


Tumor Size (≧2.0 cm vs. <2.0 cm)
2.78 (1.17~6.6) 
0.0201




0.6284


ROR_S
 0.9 (0.44~1.81)
0.765


med vs. low
0.64 (0.25~1.64)
0.3498


high vs. low

0.0002


CADER
  2 (0.82~4.86)
0.1279


indeterminate vs. sensitive
6.31 (2.51~15.9)
9.23E−05


resistant vs. sensitive










Harrell's C-index
0.7857
















TABLE 4





Survival Probability estimation of CADER and ROR-S


in Public cohorts.




















5 yr survival
10 yr survival prob.


Set
CADER
prob.
(8 yr for all cohort)





All cohort
sensitive
0.96 (0.93-0.99)
0.94 (0.91-0.98)



(N = 168)




(Symmans,
indeterminate
0.90 (0.85-0.96)
0.85 (0.78-0.93)


Zhang,
(N = 105)
0.77 (0.7-0.84) 
 0.7 (0.63-0.78)


Pawitan)
resistant





(N = 145







5 yr survival
10 yr survival prob.


CADER
ROR S
prob.
(8 yr for all cohort)





sensitive
low (N = 63)
0.94 (0.91-0.98)
0.91 (0.87-0.95)


(N = 168)





indeterminate
med (N = 197)
0.85 (0.80-0.91)
0.79 (0.73-0.86)


(N = 105)





resistant
high (N = 158)
0.74 (0.64-0.86)
0.73 (0.62-0.85)


(N = 145)









When both CADER and ROR-S were included in the multivariate model, CADER remained significant (likelihood ratio test P-value=0.0002) but ROR-S did not. These results illustrate the prognostic effect of CADER response groups is independent of classic clinical variables and ROR-S.
Example 3
This example illustrates a single sample predictor.
A single sample predictor for CADER classification can proceed as the following. Patient can provide a breast cancer tissue sample. The sample can be subject to measurement of the microarray gene expression on the 54 CADER genes using either the Agilent 4*44 K platform or the NanoString nCounter platform. Next, the patient's 54 CADER genes' expression values (along with other patients') can be scaled to have similar distribution as the matching prototype dataset by using the “distance-weighted-discrimination-single sample predictor (DWD-SSP)” tool (for example, Benito, M., et al, Bioinformatics, 2004, 20, 105-114.) or other software developed for this purpose. Then, the patient's gene expression data can be adjusted toward the prototype data, and the patient can be assigned to the one of the three groups based on the nearest neighbor principle. More specifically a patient in each of the three groups in the prototype dataset can be represented as a coordinate in a quadrant in a 2-dimensional space by the resistant and the sensitive gene centroid (FIG. 4). Subsequently, the Euclidean distance of the new patient's gene expression data can be compared to the gene centroid of each of the three groups. The patient can then be assigned to the group with the shortest distance.
Example 4
This example illustrates the complex interplay between gene expression, gene copy and prognostic effects.
Examination of Chromosome 8 (Chr8) can show the complex interplay between gene expression, gene copy and prognostic effects. Multiple Chr8 genes were identified as ERMGs. Loss of a large portion of Chr8p and gene copy gain of both the remaining fragment of Chr8p and often concomitant gain of the entire Chr8q arm had a strong influence on gene expression and prognosis. A CADER classification focused only on 24 Chr8 ERMGs in the public data sets produced significant risk stratification in the merged data (FIG. 5, log-rank P=0.005). Notably neither the 21 gene recurrence score (Paik, S., et al. 2004) nor the PAM50 model (Parker, J. S., et al. 2009) includes genes on Chr8. The CADER gene expression signature can successfully parse this complexity and can translate the information into a prognostic test.
Example 5
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, a patient was diagnosed at age 69 as grade 1 node negative with a tumor size of 1.2 cm. Gene expression profile of patient's CADER gene signature is presented in table 5.








TABLE 5







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
9.089031301
CENPE
5.714622364


AZGP1
11.35771279
CCNB1
8.154402347


EPHX2
9.070143362
KIFC1
6.484647472


IGFBP4
12.32944125
CCT6A
9.620321527


NUDT18
8.382194264
EIF2C2
8.044701081


FMO5
8.142263278
RAD54B
5.756407083


C1orf66
9.247242841
VDAC2
11.7508889


COL14A1
7.416577951
MCM10
6.069573765


PLAT
9.831019817
ZWINT
9.805901534


PCM1
11.28875579
KIF18A
6.147591126


ZBTB20
9.688345972
RACGAP1
8.457413969


NKFB1
9.990544209
TDG
9.108421015


TK2
8.328267011
NUP107
9.937813942


ABAT
10.16352859
TMPO
8.183558895


ACP6
10.39248656
XPOT
9.450319913


TSPAN7
8.854489277
BIRC5
8.129717848


TNFRSF10B
7.479798286
TOP2A
8.069509653


GSTM1
10.09219086
DSC2
6.432188782


OSBPL1A
10.03427376
EIF2S2
7.637794024


KCTD9
7.904455623
UBE2C
9.62721325


EVL
12.75017457
TPX2
8.319388569


MAP2K4
9.256905695
C20orf24
11.46484768


RPL21
13.97244515
KIF4A
7.598866178


STC2
12.7561502





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk. This patient exhibited a relapse-free survival time of 10.22 years.

Example 6
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 78 as grade 2 node positive. Gene expression profile of patient's CADER gene signature is presented in table 6.








TABLE 6







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
7.706061643
CENPE
5.047042347


AZGP1
12.80261593
CCNB1
9.559859894


EPHX2
4.993816896
KIFC1
6.711240139


IGFBP4
10.79447333
CCT6A
9.205403494


NUDT18
9.07085503
EIF2C2
8.538651152


FMO5
7.532417562
RAD54B
4.523797903


C1orf66
8.820325116
VDAC2
12.01035138


COL14A1
6.679366713
MCM10
5.446321834


PLAT
11.65087922
ZWINT
11.22262799


PCM1
11.07051999
KIF18A
3.237846496


ZBTB20
8.711204359
RACGAP1
9.157539202


NKFB1
11.27566382
TDG
8.564709433


TK2
9.063859097
NUP107
9.936110469


ABAT
9.086053214
TMPO
8.40781558


ACP6
9.703784979
XPOT
8.679222508


TSPAN7
9.435145555
BIRC5
8.666361558


TNFRSF10B
5.94832483
TOP2A
8.640228015


GSTM1
9.75245794
DSC2
7.280523855


OSBPL1A
9.601367587
EIF2S2
8.042112414


KCTD9
7.885565166
UBE2C
10.55005182


EVL
12.93028836
TPX2
9.792737142


MAP2K4
10.1175172
C20orf24
11.34355343


RPL21
15.49653705
KIF4A
8.988270499


STC2
11.67771198





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk. This patient exhibited a relapse-free survival time of 13.28 years.

Example 7
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 82.6 as grade 1 node negative with a tumor size of 2.5 cm. Gene expression profile of patient's CADER gene signature is presented in table 7.








TABLE 7







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
8.174546595
CENPE
5.183835158


AZGP1
13.16491313
CCNB1
9.329909721


EPHX2
8.94232765
KIFC1
8.159754674


IGFBP4
10.73512082
CCT6A
10.11615574


NUDT18
7.614608355
EIF2C2
6.716000941


FMO5
9.016646233
RAD54B
6.687388038


C1orf66
9.343330199
VDAC2
12.02040261


COL14A1
5.412112391
MCM10
4.450809873


PLAT
9.611437635
ZWINT
10.05941182


PCM1
11.00268588
KIF18A
6.517618437


ZBTB20
8.412640663
RACGAP1
9.053757653


NKFB1
10.31368648
TDG
8.283186412


TK2
9.063293162
NUP107
10.06027055


ABAT
11.24702673
TMPO
8.534868246


ACP6
10.6358907
XPOT
8.725274164


TSPAN7
7.772079521
BIRC5
8.690569453


TNFRSF10B
7.675838903
TOP2A
9.108931542


GSTM1
9.794034203
DSC2
5.869174656


OSBPL1A
9.989288869
EIF2S2
8.030215231


KCTD9
8.509371583
UBE2C
9.983460223


EVL
12.56118174
TPX2
9.441747358


MAP2K4
10.41218633
C20orf24
11.19135574


RPL21
15.08960134
KIF4A
9.086006485


STC2
10.7705123





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 4.62 years.

Example 8
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 50 as grade 1 node negative with a tumor size of 2 cm. Gene expression profile of patient's CADER gene signature is presented in table 8.








TABLE 8







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
8.423019389
CENPE
5.208923128


AZGP1
12.14689386
CCNB1
9.067919062


EPHX2
7.179973326
KIFC1
7.803930949


IGFBP4
10.96270429
CCT6A
10.10034748


NUDT18
8.093034532
EIF2C2
8.355983071


FMO5
8.509914885
RAD54B
5.578315416


C1orf66
9.268922232
VDAC2
11.98112025


COL14A1
7.402107354
MCM10
5.096041201


PLAT
10.2412839
ZWINT
9.400614412


PCM1
10.80954268
KIF18A
4.320711476


ZBTB20
8.216998421
RACGAP1
8.426501711


NKFB1
10.37867582
TDG
8.424581655


TK2
8.642152449
NUP107
10.34239592


ABAT
10.99556805
TMPO
8.238047033


ACP6
9.716986412
XPOT
9.722447929


TSPAN7
8.5971143
BIRC5
8.154255818


TNFRSF10B
7.789496624
TOP2A
8.426551678


GSTM1
9.07138847
DSC2
7.916892991


OSBPL1A
9.709580369
EIF2S2
8.062037251


KCTD9
8.12069555
UBE2C
9.351723036


EVL
11.57919319
TPX2
8.857336669


MAP2K4
9.476622768
C20orf24
11.54843739


RPL21
14.8847084
KIF4A
7.999559725


STC2
11.89892093





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 9.48 years.

Example 9
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 72 as grade 2 node negative with a tumor size of 2.5 cm. Gene expression profile of patient's CADER gene signature is presented in table 9.








TABLE 9







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
8.771670864
CENPE
3.651962054


AZGP1
14.12907741
CCNB1
8.842648054


EPHX2
8.224530958
KIFC1
9.934962727


IGFBP4
13.25201221
CCT6A
8.541236904


NUDT18
9.132402527
EIF2C2
7.812369431


FMO5
8.573554651
RAD54B
3.771356866


C1orf66
10.38687407
VDAC2
11.48336871


COL14A1
6.226066377
MCM10
6.289920626


PLAT
9.894345609
ZWINT
9.856885203


PCM1
6.610539309
KIF18A
4.323664316


ZBTB20
9.658834161
RACGAP1
8.678725078


NKFB1
9.515187228
TDG
8.78362627


TK2
9.00909589
NUP107
9.291523701


ABAT
8.774114776
TMPO
8.070476677


ACP6
10.57558001
XPOT
8.035070874


TSPAN7
9.544470732
BIRC5
9.867229559


TNFRSF10B
5.75209336
TOP2A
8.268887116


GSTM1
10.61241092
DSC2
8.0597748


OSBPL1A
9.063677782
EIF2S2
7.677085789


KCTD9
6.62967452
UBE2C
11.33116967


EVL
12.21472727
TPX2
9.862771273


MAP2K4
7.983384073
C20orf24
12.76892279


RPL21
13.50547562
KIF4A
8.868607027


STC2
10.77093214





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 10.37 years.

Example 10
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 60 as grade 1 node negative with a tumor size of 1.5 cm. Gene expression profile of patient's CADER gene signature is presented in the table 10.








TABLE 10







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
8.195331371
CENPE
5.632172111


AZGP1
13.74984022
CCNB1
8.055483336


EPHX2
6.696227011
KIFC1
6.489111331


IGFBP4
12.09832399
CCT6A
10.38135864


NUDT18
8.270420346
EIF2C2
8.755586257


FMO5
7.021118002
RAD54B
6.958867796


C1orf66
9.662189619
VDAC2
11.30180572


COL14A1
7.293557331
MCM10
5.917221042


PLAT
9.920697333
ZWINT
9.7205269


PCM1
10.02811491
KIF18A
3.444838554


ZBTB20
10.19466269
RACGAP1
8.419747507


NKFB1
9.603847806
TDG
9.456320243


TK2
8.924649251
NUP107
9.984285983


ABAT
8.896633344
TMPO
7.949108266


ACP6
9.285473956
XPOT
9.54292892


TSPAN7
8.695560913
BIRC5
7.063289261


TNFRSF10B
6.320138873
TOP2A
7.086963896


GSTM1
9.681219679
DSC2
8.003530478


OSBPL1A
9.970328855
EIF2S2
7.436955491


KCTD9
7.911145356
UBE2C
9.291612864


EVL
12.0387619
TPX2
8.426273521


MAP2K4
8.50845927
C20orf24
11.48997945


RPL21
14.66438421
KIF4A
6.462304872


STC2
10.34574607





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 9.82 years.

Example 11
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 76 as grade 2 node negative with a tumor size of 2 cm. Gene expression profile of patient's CADER gene signature is presented in table 11.








TABLE 11







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
9.220276473
CENPE
4.890147366


AZGP1
13.63396186
CCNB1
9.357511666


EPHX2
7.63440817
KIFC1
9.213362515


IGFBP4
10.90988711
CCT6A
8.961784315


NUDT18
9.043535413
EIF2C2
8.739433283


FMO5
7.432979371
RAD54B
6.007633509


C1orf66
9.643784067
VDAC2
11.81497055


COL14A1
7.599356611
MCM10
6.617340391


PLAT
11.32894871
ZWINT
10.78246017


PCM1
7.474273878
KIF18A
5.558831023


ZBTB20
9.525264933
RACGAP1
8.939562764


NKFB1
9.83374884
TDG
8.734711926


TK2
8.840877327
NUP107
9.38846082


ABAT
10.29856415
TMPO
8.103172514


ACP6
10.5667799
XPOT
8.466124368


TSPAN7
8.74103966
BIRC5
9.011353969


TNFRSF10B
7.94094245
TOP2A
8.059448642


GSTM1
11.16294174
DSC2
6.733968514


OSBPL1A
9.990399182
EIF2S2
7.133801494


KCTD9
7.503238991
UBE2C
10.34231341


EVL
9.296777305
TPX2
8.956665888


MAP2K4
8.788801129
C20orf24
12.14857435


RPL21
13.81407231
KIF4A
7.816111893


STC2
12.93997443





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 8.82 years.

Example 12
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 71 as grade 2 node negative with a tumor size of 3.8 cm. Gene expression profile of patient's CADER gene signature is presented in table 12.








TABLE 12







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
7.225232241
CENPE
5.356683467


AZGP1
11.10551416
CCNB1
8.69014881


EPHX2
7.608820719
KIFC1
6.627439879


IGFBP4
10.14052865
CCT6A
9.503117742


NUDT18
8.790224261
EIF2C2
8.276676063


FMO5
8.352492368
RAD54B
7.296602326


C1orf66
9.095903836
VDAC2
12.07803136


COL14A1
6.801220806
MCM10
4.90426628


PLAT
12.1254784
ZWINT
9.963178365


PCM1
10.80643214
KIF18A
5.297509956


ZBTB20
8.655693668
RACGAP1
8.675716832


NKFB1
9.950598064
TDG
8.856305995


TK2
8.142178994
NUP107
9.825879761


ABAT
12.07425458
TMPO
7.898770909


ACP6
9.934738407
XPOT
9.409422485


TSPAN7
8.615270886
BIRC5
7.920768251


TNFRSF10B
5.930584997
TOP2A
8.274852202


GSTM1
10.48712907
DSC2
7.740213072


OSBPL1A
9.748346575
EIF2S2
7.64317594


KCTD9
7.917730095
UBE2C
9.971378017


EVL
11.05681921
TPX2
8.827280345


MAP2K4
9.304427564
C20orf24
11.53656485


RPL21
14.34440973
KIF4A
7.655959994


STC2
12.86487322





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 9.77 years.

Example 13
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 71 as grade 2 node negative with a tumor size of 1.1 cm. Gene expression profile of patient's CADER gene signature is presented in table 13.








TABLE 13







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
9.291704969
CENPE
5.382882804


AZGP1
12.78955459
CCNB1
8.440426299


EPHX2
9.635858316
KIFC1
6.829062874


IGFBP4
11.53492614
CCT6A
9.324394803


NUDT18
8.580021659
EIF2C2
8.891052037


FMO5
9.367852626
RAD54B
5.385472494


C1orf66
9.531665745
VDAC2
11.42239554


COL14A1
7.198780142
MCM10
6.408723154


PLAT
8.965633637
ZWINT
10.00026781


PCM1
11.73299647
KIF18A
3.071083725


ZBTB20
9.995356409
RACGAP1
9.064077065


NKFB1
10.39137003
TDG
8.955158771


TK2
8.610248982
NUP107
9.991926076


ABAT
10.16325203
TMPO
8.339618599


ACP6
9.899745943
XPOT
9.205077535


TSPAN7
8.232730392
BIRC5
8.003442603


TNFRSF10B
8.125270272
TOP2A
8.044071277


GSTM1
9.57469389
DSC2
5.813682345


OSBPL1A
9.742077303
EIF2S2
7.924900627


KCTD9
8.122019716
UBE2C
9.793971887


EVL
12.81661697
TPX2
8.571855639


MAP2K4
8.464554411
C20orf24
11.58041449


RPL21
13.78632737
KIF4A
7.9808711


STC2
9.597783917





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 11.03 years.

Example 14
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 64 as grade 2 node negative with a tumor size of 4 cm. Gene expression profile of patient's CADER gene signature is presented in table 14.








TABLE 14







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
8.672237007
CENPE
5.698169245


AZGP1
10.03974419
CCNB1
8.499274857


EPHX2
7.997469912
KIFC1
8.528740811


IGFBP4
9.855627665
CCT6A
9.52592378


NUDT18
8.405623241
EIF2C2
8.373179934


FMO5
9.240328855
RAD54B
6.843287473


C1orf66
8.995817157
VDAC2
11.54992641


COL14A1
7.932292908
MCM10
4.90418841


PLAT
8.852188372
ZWINT
10.28260367


PCM1
11.08708954
KIF18A
6.428181192


ZBTB20
9.793639913
RACGAP1
9.049262721


NKFB1
9.967037219
TDG
9.118005266


TK2
8.642951231
NUP107
9.94434022


ABAT
9.276640765
TMPO
8.588766178


ACP6
10.29122674
XPOT
9.116196563


TSPAN7
7.719768947
BIRC5
8.143036369


TNFRSF10B
7.640641107
TOP2A
8.619855173


GSTM1
9.281695426
DSC2
7.808699225


OSBPL1A
10.05216708
EIF2S2
7.959857094


KCTD9
8.239843842
UBE2C
9.996023676


EVL
11.57660733
TPX2
8.505614443


MAP2K4
8.738524985
C20orf24
11.67011271


RPL21
15.28974011
KIF4A
7.810522036


STC2
11.64642431





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 9.45 years.

Example 15
This example illustrates a patient classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 62 as grade 1 node negative with a tumor size of 2 cm. Gene expression profile of patient's CADER gene signature is presented in table 15.








TABLE 15







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
7.95404528
CENPE
5.889816181


AZGP1
12.96281502
CCNB1
8.977058202


EPHX2
3.925829388
KIFC1
6.860790579


IGFBP4
13.98951479
CCT6A
10.07424594


NUDT18
7.342338206
EIF2C2
8.819670918


FMO5
8.755710391
RAD54B
6.850474828


C1orf66
8.502083292
VDAC2
10.70361673


COL14A1
6.885825359
MCM10
4.556044807


PLAT
8.958129179
ZWINT
9.90352554


PCM1
9.263851333
KIF18A
2.43819653


ZBTB20
8.760094598
RACGAP1
8.91534784


NKFB1
9.944393215
TDG
9.143939972


TK2
8.550001609
NUP107
10.22940838


ABAT
10.65823825
TMPO
8.625364575


ACP6
9.717129716
XPOT
10.04425538


TSPAN7
8.234606874
BIRC5
8.244907696


TNFRSF10B
6.783398571
TOP2A
9.529961049


GSTM1
10.27668464
DSC2
7.958312793


OSBPL1A
11.07866444
EIF2S2
7.901577226


KCTD9
7.495327704
UBE2C
10.12534313


EVL
11.12621928
TPX2
9.347041926


MAP2K4
8.92875982
C20orf24
12.26651445


RPL21
14.3166023
KIF4A
7.967112657


STC2
14.76346489





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as low risk.

This patient exhibited a relapse-free survival time of 9.4 years.

Example 16
This example refers to examples 5-15 of patients that were classified in the indeterminate group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In the Symmans dataset, twelve patients were classified as medium risk by PAM50 but low risk by CADER. Relapse free survival was observed in 11 out of the 12 patients that were re-characterized by CADER therefore the present teaching are able to predict relapse free survival.
Example 17
This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 61 as grade 2 node negative with a tumor size of 2.2 cm. Gene expression profile of patient's CADER gene signature is presented in table 16.








TABLE 16







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
7.744600437
CENPE
7.544588013


AZGP1
10.66904972
CCNB1
9.47591913


EPHX2
4.805292308
KIFC1
9.506767063


IGFBP4
11.29608728
CCT6A
10.21427538


NUDT18
8.002058823
EIF2C2
8.7960344


FMO5
6.155200313
RAD54B
6.375895604


C1orf66
9.333179208
VDAC2
12.00687248


COL14A1
6.288615109
MCM10
7.501949976


PLAT
9.537949138
ZWINT
10.95987061


PCM1
9.588582125
KIF18A
6.051795069


ZBTB20
8.790479168
RACGAP1
9.24241408


NKFB1
9.725030872
TDG
9.575761169


TK2
8.531978461
NUP107
10.32938969


ABAT
8.159354409
TMPO
8.790588323


ACP6
9.584543206
XPOT
10.49669192


TSPAN7
8.693409574
BIRC5
9.336064812


TNFRSF10B
6.091732191
TOP2A
9.421322801


GSTM1
9.06286858
DSC2
7.776095411


OSBPL1A
9.151000584
EIF2S2
8.190102297


KCTD9
7.87786112
UBE2C
10.78982172


EVL
10.55251978
TPX2
10.15418118


MAP2K4
8.27281105
C20orf24
11.90394725


RPL21
13.95732984
KIF4A
8.570767398


STC2
9.01194817





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.

This patient exhibited distant relapse and had survived without distant relapse for 6.98 years.

Example 18
This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 53 as grade 2 node positive with a tumor size of 3.5 cm. Gene expression profile of patient's CADER gene signature is presented in table 17.








TABLE 17







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
6.804051671
CENPE
5.37205207


AZGP1
10.39856245
CCNB1
8.991964499


EPHX2
5.513231806
KIFC1
6.771636713


IGFBP4
11.7797415
CCT6A
10.3483913


NUDT18
7.862417652
EIF2C2
8.904665998


FMO5
5.658509257
RAD54B
7.079699025


C1orf66
8.564056693
VDAC2
13.68322287


COL14A1
8.03407683
MCM10
6.122997443


PLAT
10.11372685
ZWINT
9.949184652


PCM1
9.443642359
KIF18A
6.485956244


ZBTB20
8.531888877
RACGAP1
8.632956402


NKFB1
10.66976642
TDG
8.135705715


TK2
8.928895236
NUP107
10.29069575


ABAT
8.16839595
TMPO
7.526540447


ACP6
9.360656124
XPOT
9.91958533


TSPAN7
8.640596844
BIRC5
7.847795729


TNFRSF10B
6.051866381
TOP2A
8.010053062


GSTM1
9.84184812
DSC2
8.152131803


OSBPL1A
9.022350499
EIF2S2
8.122990969


KCTD9
8.251784271
UBE2C
10.09858581


EVL
11.45450604
TPX2
9.211925163


MAP2K4
8.798858799
C20orf24
12.24436801


RPL21
14.78763383
KIF4A
8.680433487


STC2
11.53819169





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.

This patient exhibited distant relapse and had survived without distant relapse for 5.22 years.

Example 19
This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 68 as grade 1 node positive with a tumor size of 9.9 cm. Gene expression profile of patient's CADER gene signature is presented in table 18.








TABLE 18







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
7.941656208
CENPE
5.820982523


AZGP1
11.91844546
CCNB1
9.080828313


EPHX2
11.09221285
KIFC1
8.443053537


IGFBP4
11.09921988
CCT6A
9.912108147


NUDT18
7.291000984
EIF2C2
9.014789327


FMO5
5.507258787
RAD54B
7.373821752


C1orf66
8.052387123
VDAC2
12.24316711


COL14A1
6.619255837
MCM10
5.481657234


PLAT
9.179447346
ZWINT
9.928994375


PCM1
9.999879743
KIF18A
7.056975017


ZBTB20
8.664983154
RACGAP1
9.481396294


NKFB1
9.962166703
TDG
8.90556312


TK2
8.363256706
NUP107
10.45249963


ABAT
8.00208168
TMPO
7.768463425


ACP6
9.287822071
XPOT
9.563085024


TSPAN7
8.75268169
BIRC5
8.48871999


TNFRSF10B
7.582667467
TOP2A
9.319611162


GSTM1
10.13714896
DSC2
6.847939442


OSBPL1A
10.21907084
EIF2S2
8.340508305


KCTD9
8.544638002
UBE2C
9.971599409


EVL
10.3354868
TPX2
9.405812825


MAP2K4
7.800937453
C20orf24
12.00273341


RPL21
14.15711663
KIF4A
8.013343028


STC2
10.27067709





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.

This patient exhibited distant relapse and had survived without distant relapse for 4.66 years.

Example 20
This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 66 as grade 2 node negative with a tumor size of 4 cm. Gene expression profile of patient's CADER gene signature is presented in table 19.








TABLE 19







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
8.575552586
CENPE
5.702899308


AZGP1
11.37810078
CCNB1
9.954936246


EPHX2
5.625112756
KIFC1
7.746900849


IGFBP4
11.49885025
CCT6A
9.318846576


NUDT18
8.252384776
EIF2C2
9.173067959


FMO5
5.640726866
RAD54B
6.204679593


C1orf66
9.838671483
VDAC2
12.33336871


COL14A1
7.175991287
MCM10
4.978675499


PLAT
11.14152372
ZWINT
9.799826718


PCM1
8.676624207
KIF18A
5.066342911


ZBTB20
9.359683135
RACGAP1
9.958406158


NKFB1
9.804029895
TDG
9.03871544


TK2
8.627974937
NUP107
9.704097868


ABAT
7.87688799
TMPO
8.42129297


ACP6
10.06691172
XPOT
9.736253402


TSPAN7
8.465600958
BIRC5
8.988490932


TNFRSF10B
5.909433537
TOP2A
9.10676166


GSTM1
12.05894345
DSC2
8.097231854


OSBPL1A
10.15966192
EIF2S2
8.45169494


KCTD9
6.895008503
UBE2C
11.9116952


EVL
12.22035136
TPX2
10.42247756


MAP2K4
8.843494166
C20orf24
13.05330118


RPL21
14.22740117
KIF4A
7.893017097


STC2
12.64230526





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.

This patient exhibited distant relapse and had survived without distant relapse for 5.68 years.

Example 21
This example illustrates a patient classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In this example, patient was diagnosed at age 61 as grade 3 node positive with a tumor size of 3 cm. Gene expression profile of patient's CADER gene signature is presented in table 20.








TABLE 20







Endocrine Therapy
Endocrine Therapy


Sensitivity Genes
Resistance Genes










Gene Symbol
Expression Level
Gene Symbol
Expression Level













PARP3
7.381832762
CENPE
7.085017638


AZGP1
10.64694672
CCNB1
8.638235879


EPHX2
7.549320403
KIFC1
8.129562479


IGFBP4
12.1897795
CCT6A
8.923314956


NUDT18
7.802321466
EIF2C2
8.834816089


FMO5
7.751416609
RAD54B
5.881147478


C1orf66
8.943595771
VDAC2
11.87057155


COL14A1
7.984848322
MCM10
7.072996991


PLAT
10.83283341
ZWINT
9.926258074


PCM1
10.86560731
KIF18A
5.780102567


ZBTB20
10.32743519
RACGAP1
8.843596679


NKFB1
10.14861856
TDG
9.445600595


TK2
8.511326627
NUP107
9.962609964


ABAT
8.19708022
TMPO
8.355612197


ACP6
9.416616338
XPOT
9.200544014


TSPAN7
8.395774951
BIRC5
8.314366606


TNFRSF10B
7.110230031
TOP2A
8.881611607


GSTM1
9.235942407
DSC2
7.863153004


OSBPL1A
9.309367829
EIF2S2
7.712268984


KCTD9
7.720045847
UBE2C
10.07626817


EVL
12.13976416
TPX2
8.850223895


MAP2K4
8.417635977
C20orf24
12.08210918


RPL21
14.25344008
KIF4A
8.257021285


STC2
8.652446485





*missing gene symbols are ZNF18, PHYHD1, CHDH, which are sensitivity genes, and CDC2, CDCA5, C8orf76, FBXO45, which are resistance genes. These probes were not included in the PAM50 study.






Analyzing the data with the methods of the present teachings, categorizes this patient as resistant thus high risk for relapse.

This patient exhibited distant relapse and had survived without distant relapse for 10.8 years.

Example 22
This example refers to examples 17-21 of patients that were classified in the low risk group using the PAM50 study can be better characterized using the CADER gene expression signature of the present teachings.
In the Symmans dataset, six patients were classified as low risk for relapse by PAM50 but high risk for relapse by CADER. Distant relapse was observed in 5 out of the 6 patients that were re-characterized by CADER therefore the present teachings are able to predict distant relapse.
All references cited are hereby incorporated by reference, each in its entirety.

## Claims

1. A method of predicting the likelihood of long-term survival without recurrence of cancer for a subject having estrogen receptor positive (ER+) breast cancer treated with endocrine monotherapy, the method comprising:
a) obtaining a breast cancer tissue sample from a human subject;
b) generating cDNA from the sample;
c) detecting expression levels of a set of genes consisting of (i) endocrine therapy sensitivity genes PARP3, AZGP1, EPHX2, IGFBP4, NUDT18, FOM5, C1orf66, COL14A1, PLAT, PCM1, ABTB20, NFKB1, TK2, ABAT, ANF18, PHYHD1, and CHDH and (ii) endocrine monotherapy resistance genes CENPE, CCNB1, KIFC1, CCT6A, EIF2C2, RAD54B, VDAC2, MCM10, ZWINT, KIF18A, RACGAP1, TDG, NUP107, TMPO, XPOT, BIRC5, TOP2A, DSC2, EIP2S2, UBEC2C, TPX2, C20orf24, KIF4A, CDC2, CDCA5, C8orf76, and FBXO45 from the cDNA; and
d) assigning the subject to a sensitive, indeterminate or resistant group, wherein assignment to the sensitive group predicts longer relapse-free survival compared to the resistant group, wherein the assigning the subject to a sensitive, indeterminate or resistant group comprises determining the gene centroid of the endocrine therapy sensitivity genes and the gene centroid of the endocrine therapy resistance genes.

2. The method of claim 1, wherein the assigning the subject to a sensitive, indeterminate or resistant group comprises representing the expression levels as a coordinate in a quadrant in a 2-dimensional space comprising the resistant gene centroid and the sensitive gene centroid and determining a Euclidean distance of the expression levels to a gene centroid of each of the sensitive, resistant and indeterminate groups, wherein the subject is assigned to the group with the shortest distance.

3. The method of claim 1, wherein the endocrine monotherapy comprises a therapy selected from the group consisting of tamoxifen treatment and aromatase inhibitor treatment.

4. The method of claim 1, further consisting of detecting expression levels of one or more housekeeping genes.

5. The method of claim 4, wherein the one or more housekeeping genes are selected from the group consisting of MRPL19, SF3A1, and PUM1.

6. A The method of claim 4, wherein the one or more housekeeping genes are selected from the group consisting of MRPL19, SF3A1, PUM1, ACTB, GAPD, GUSB, RPLP0, and TFRC.

7. A method of predicting the likelihood of long-term survival without recurrence of cancer for a subject having estrogen receptor positive (ER+) breast cancer treated with endocrine monotherapy, the method comprising:
a) obtaining a breast cancer tissue sample from a human subject;
b) generating cDNA from the sample;
c) detecting expression levels of a set of genes consisting of (i) endocrine therapy sensitivity genes PARP3, AZGP1, EPHX2, IGFBP4, NUDT18, FOM5, C1orf66, COL14A1, PLAT, PCM1, ABTB20, NFKB1, TK2, ABAT, ANF18, PHYHD1, and CHDH and (ii) endocrine monotherapy resistance genes CENPE, CCNB1, KIFC1, CCT6A, EIF2C2, RAD54B, VDAC2, MCM10, ZWINT, KIF18A, RACGAP1, TDG, NUP107, TMPO, XPOT, BIRC5, TOP2A, DSC2, EIP2S2, UBEC2C, TPX2, C20orf24, KIF4A, CDC2, CDCA5, C8orf76, and FBXO45 from the cDNA;
d) scaling the expression levels to have similar distribution of a matching prototype dataset in which patients belong to a sensitive, an indeterminate or a resistant group based on the centroids of the sensitive genes and the resistant genes; and
e) assigning the subject to the sensitive, indeterminate or resistant group, wherein assignment to the sensitive group predicts longer relapse-free survival compared to the resistant group, wherein the assigning the subject to a sensitive, indeterminate or resistant group comprises determining the gene centroid of the endocrine therapy sensitivity genes and the gene centroid of the endocrine therapy resistance genes.

8. The method of claim 7, wherein detecting the expression levels comprises performing a quantitative RT-PCR assay.

9. The method of claim 7, further comprising providing a microarray consisting of probes for (i) endocrine therapy sensitivity genes PARP3, AZGP1, EPHX2, IGFBP4, NUDT18, FMO5, C1orf66, COL14A1, PLAT, PCM1, ZBTB20, NFKB1, TK2, ABAT, ACP6, TSPAN7, TNFRSF10B, OSBPL1A, KCTD9, EVL, MAP2K4, RPL21, STC2, ZNF18, PHYHD1, and CHDH, and (ii) endocrine therapy resistance genes CENPE, CCNB1, KIFC1, CCT6A, EIF2C2, RAD54B, VDAC2, MCM10, ZWINT, KIF18A, RACGAP1, TDG, NUP107, TMPO, XPOT, BIRC5, TOP2A, DSC2, EIF2S2, UBE2C, TPX2, C20orf24, KIF4A, CDC2, CDCA5, C8orf76, and FBXO45.

10. The method of claim 7, wherein the assigning the subject to a sensitive, indeterminate or resistant group comprises representing the expression levels as a coordinate in a quadrant in a 2-dimensional space comprising the resistant gene centroid and the sensitive gene centroid and determining a Euclidean distance of the expression levels to a gene centroid of each of the sensitive, resistant and indeterminate groups, wherein the subject is assigned to the group with the shortest distance.

11. The method of claim 7, wherein the endocrine monotherapy comprises a therapy selected from the group consisting of tamoxifen treatment and aromatase inhibitor treatment.