LATE ER+ BREAST CANCER ONSET ASSESSMENT AND TREATMENT SELECTION
20170247766 · 2017-08-31
Assignee
Inventors
Cpc classification
G16B20/00
PHYSICS
International classification
Abstract
A method for determining the likelihood of late ER− breast cancer disease relapse/recurrence is disclosed. Late ER+ breast cancer disease onset and/or recurrence is determined for a period of 5 to 20 years after an initial ER+ breast cancer disease onset in a patient. An ER+ breast cancer patient is assigned a risk score that is compared to a defined threshold value, and identifies the risk score as low risk or high risk for late breast cancer recurrence. A late ER+ breast cancer gene panel of 8 to 15 genes is provided. Subjects having a risk score greater than or equal to that of the threshold value are at a relatively high risk of recurrent disease, and are determined to benefit from aggressive therapeutic intervention, whereas subjects having a risk score less than the threshold value are at a relatively low risk of recurrent disease, and could forego treatment.
Claims
1. An assessment tool for late ER+ breast cancer recurrence in an at risk human ER+ breast cancer patient comprising a threshold value that defines a reference heterogeneous late ER+ breast cancer marker of heterogeneous late ER+ breast cancer survivor population gene panel levels, wherein the assessment tool partitions an at risk human ER+ breast cancer tissue score into a high risk or a low risk ER+ breast cancer recurrence group.
2. The assessment tool of claim 1 wherein the heterogeneous late ER+ breast cancer survivor population gene panel comprises at least 8 genes selected from the group consisting of: ZNF652, PKD1, ZNF786, SPDYE7P, TSC2, ZNF692, DMWD, MBD4, HSD17B7, RGS1, GNA11, PHKA2, EGR1, CDC42, TNRC6A, MARCH6, GPR34, IL18, MRPL20, BHLHE41, FOS, ARID4B, EIF2AK4, TTC14, DAAM1, KLHL8, PDCD7, GFOD1, CRAMP1L, ANKS1B, GLI3, SLC4A5, ATP6AP1L, AVP, TUBB6, DENR, TRADD, PPA2, RPL7L1 and ADAM17.
3. The late ER+ breast cancer recurrence assessment tool of claim 1 wherein a low risk human ER+ breast cancer tissue score below an about 60.sup.th percentile of the score values in a heterogeneous ER+ breast cancer population indicates a patient with a statistically lower probability of developing late ER+ breast cancer recurrence from 5 to 20 years after an initial ER+ breast cancer occurrence.
4. The late ER+ breast cancer recurrence assessment tool of claim 1 wherein a high risk human ER+ breast cancer tissue score at least above an about 60.sup.th percentile or higher of the threshold score values in a heterogeneous ER+ breast cancer population indicates a patient with a statistically higher probability of developing late ER+ breast cancer recurrence from 5 to 20 years after an initial ER+ breast cancer occurrence.
5. The late ER+ breast cancer recurrence assessment tool of claim 1 wherein the level of each gene comprising the heterogeneous late ER+ breast cancer survivor population gene panel is identified with a cDNA, mRNA, cRNA or other nucleotide that is specific for the gene.
6. A set of probes or a set of oligonucleotide primer pairs, wherein each probe or set of oligonucleotide primer pairs is a detectably labeled single-stranded polynucleotide having specific binding affinity for at least 8 of the genes comprising: ZNF652, PKD1, ZNF786, SPDYE7P, TSC2, ZNF692, DMWD, MBD4, HSD17B7, RGS1, GNA11, PHKA2, EGR1, CDC42, TNRC6A, MARCH6, GPR34, IL18, MRPL20, BHLHE41, FOS, ARID4B, EIF2AK4, TTC14, DAAM1, KLHL8, PDCD7, GFOD1, CRAMP1L, ANKS1B, GLI3, SLC4A5, ATP6AP1L, AVP, TUBB6, DENR, TRADD, PPA2, RPL7L1 and ADAM17, wherein said detectable label is a non-naturally occurring polynucleotide label.
7. The set of probes or set of oligonucleotide primer pairs of claim 6 wherein set of probes or set of oligonucleotide primer pairs are provided on a solid substrate.
8. The set of probes or set of oligonucleotide primer pairs of claim 7 wherein the solid substrate is a microchip.
9. A method for determining patient risk for late ER+ breast cancer recurrence comprising: measuring a patient breast cancer tissue sample from an at risk ER+ breast cancer patient for levels of a heterogeneous late ER+ breast cancer survivor population gene panel comprising at least 8 genes; calculating a patient gene risk score between 0 and 1 for each gene of the gene panel measured in the patient breast cancer tissue sample; calculating a patient cumulative cancer test score between 0 to 100 from the patient gene risk score values for each gene of the gene panel; and comparing said patient cumulative cancer test score to a reference heterogeneous ER+ breast cancer population threshold value; wherein a patient cumulative cancer test score below about a 60.sup.th percentile of the score values in a heterogeneous ER+ breast cancer population indicates a patient with a statistically lower probability of developing late ER+ breast cancer recurrence from 5 to 20 years after an initial ER+ breast cancer occurrence; and wherein a patient cumulative cancer test score at least above about a 60.sup.th percentile or higher of the score values in a heterogeneous ER+ breast cancer population indicates a patient with a statistically higher probability of developing late ER+ breast cancer recurrence from 5 to 20 years after an initial ER+ breast cancer occurrence.
10. The method of claim 9 wherein the patient breast tissue sample is a frozen tissue, formalin fixed, paraffin embedded (FFPE) tissue, or a fresh tissue sample, and the levels of the heterogeneous late ER+ breast cancer survivor population gene panel are provided by measure of a cDNA or cRNA prepared from the patient breast tissue sample.
11. The method of claim 9 wherein an ER+ breast cancer patient having a higher probability of ate ER+ breast cancer recurrence is administered an aggressive anti-cancer therapeutic treatment, and an ER+ breast cancer patient having a lower probability of late ER+ breast cancer recurrence is not administered an aggressive anti-cancer therapeutic treatment.
12. The method of claim 9 wherein the heterogeneous late ER+ breast cancer survivor population gene panel comprises at least 8 genes selected from the group consisting of: ZNF652, PKD1, ZNF786, SPDYE7P, TSC2, ZNF692, DMWD, MBD4, HSD17B7, RGS1, GNA11, PHKA2, EGR1, CDC42, TNRC6A, MARCH6, GPR34, IL18, MRPL20, BHLHE41, FOS, ARID4B, EIF2AK4, TI C14, DAAM1, KLHL8, PDCD7, GFOD1, CRAMP1L, ANKS1B, GLI3, SLC4A5, ATP6AP1L, AVP, TUBB6, DENR, TRADD, PPA2, RPL7L1, and ADAM17.
13. The method of claim 9 further comprising the step of administering an aggressive anti-cancer therapeutic regimen to an ER+ breast cancer patient having a cumulative cancer test score at least within an about 60.sup.th percentile or higher of the score values of a reference heterogeneous ER+ breast cancer population , or not administering an aggressive anti-cancer therapeutic regimen to an ER+ breast cancer patient not demonstrating a cumulative cancer test score at least above an about 60.sup.th percentile or higher of the score values of a reference heterogeneous ER+ breast cancer population
14. The method of claim 9 wherein the breast tissue sample is a frozen tissue, a formalin fixed, paraffin-embedded (FFPE) tissue or a fresh tissue sample and the levels of the heterogeneous late ER+ breast cancer survivor population gene panel are provided by measure of a cDNA or cRNA prepared from the patient breast tissue sample.
15. The method of claim 9 wherein the heterogeneous late ER+ breast cancer survivor population gene panel comprises at least 8 genes selected from the group consisting of: ZNF652, PKD1, ZNF786, SPDYE7P, TSC2, ZNF692, DMWD, MBD4, HSD17B7, RGS1, GNA11, PHKA2, EGR1, CDC42, TNRC6A, MARCH6, GPR34, IL18, MRPL20, BHLHE41, FOS, ARID4B, EIF2AK4, TTC14, DAAM1, KLHL8, PDCD7, GFOD1, CRAMP1L, ANKS1B, GLI3, SLC4A5, ATP6AP1L, AVP, TUBB6, DENR, TRADD, PPA2, RPL7L1, and ADAM17.
16. The set of probes of claim 6 comprising a kit for assessing late onset ER+ breast cancer in a human wherein said probes detectably labeled probes or a set of oligonucleotide primer pairs having specific binding affinity for at least 8 of the genes comprising: ZNF652, PKD1, ZNF786, SPDYE7P, TSC2, ZNF692, DMWD, MBD4, HSD17B7, RGS1, GNA11, PHKA2, EGR1, CDC42, TNRC6A, MARCH6, GPR34, IL18, MRPL20, BHLHE41, FOS, ARID4B, EIF2AK4, TTC14, DAAM1, KLHL8, PDCD7, GFOD1, CRAMP1L, ANKS1B, GLI3, SLC4A5, ATP6AP1L, AVP, TUBB6, DENR, TRADD, PPA2, RPL7L1 and ADAM17, wherein said detectable label is a non-naturally occurring polynucleotide label.
17. The set of probes of claim 16 wherein the set of detectably labeled probes or a set of oligonucleotide primer pairs is provided on a solid substrate.
18. The set of probes of claim 16 wherein said kit further comprises an instructional insert.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0031]
[0032]
[0033]
[0034]
DETAILED DESCRIPTION
[0035] 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 instant disclosure belongs. 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 one skilled in the art with a general guide to many of the terms used in the present application.
[0036] The instant disclosure provides a method for predicting the probability of cancer relapse after at least eight years post-diagnosis and the likelihood that a patient will benefit from aggressive chemotherapeutic intervention. The method is based on (1) identifying a panel of gene that correlates with the occurrence of a late ER+ breast cancer disease or recurrence of cancer, (2) determining a risk score for a patient sample, and comparing that risk score to a threshold that stratifies a population of patients into poor prognosis and good prognosis, (3) using that measurement to determine if a patient would benefit from aggressive chemotherapeutic intervention. The method can be used to make treatment decisions concerning the therapy of cancer patients.
[0037] 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 disclosure. Indeed, the present disclosure is in no way limited to the methods and materials described. For purposes of the present disclosure, the following terms are defined.
[0038] As used herein, “expression” refers to the process by which DNA is transcribed into mRNA and/or the process by which the transcribed mRNA is subsequently translated into peptides, polypeptides or proteins. If the polynucleotide is derived from genomic DNA, expression may include splicing of the mRNA in a eukaryotic cell.
[0039] A “gene expression profile” refers to a pattern of expression of at least one biomarker that recurs in multiple samples and reflects a property shared by those samples, such as tissue type, response to a particular treatment, or activation of a particular biological process or pathway in the cells. Furthermore, a gene expression profile differentiates between samples that share that common property and those that do not with better accuracy than would likely be achieved by assigning the samples to the two groups at random. A gene expression profile may be used to predict whether samples of unknown status share that common property or not. Some variation between the levels of at least one biomarker and the typical profile is to be expected, but the overall similarity of the expression levels to the typical profile is such that it is statistically unlikely that the similarity would be observed by chance in samples not sharing the common property that the expression profile reflects.
[0040] The term “tag” or “label” is defined as a detectable tag or label, that may be used to detect, monitor, quantify, and otherwise identify the presence or absence of a particular oligonucleotide or specific nucleic acid sequence, and may be used to label or tag a cDNA, cRNA, mRNA, DNA, or any other type of nucleic acid probe or primer. These tags or labels include, by way of example and not limitation, visually detectable labels, such as, e.g., dyes, fluorophores, and radioactive labels, as well as biotin to provide biotinylated species of oligonucleotide, mRNA, cRNA, etc. In addition, the invention contemplates the use of magnetic beads and electron dense substances, such as metals, e.g., gold, as labels. A wide variety of radioactive isotopes may be used including, e.g., 14C, 3H, 99mTc, 123I, 131I, 32P, 192Ir, 103Pd 198AU, 111In, 67Ga, 201TI, 153SM, 18F and 90Sr. Other radioisotopes that may be used include, e.g., thallium-201 or technetium 99m. In other embodiments, the detectable agent is a fluorophore, such as, e.g., fluorescein or rhodamine. A variety of biologically compatible fluorophores are commercially available.
[0041] The term “cDNA” refers to complementary DNA, i.e. mRNA molecules present in a cell or organism made into cDNA with an enzyme such as reverse transcriptase. A “cDNA library” is a collection of all of the mRNA molecules present in a cell or organism, all turned into cDNA molecules with the enzyme reverse transcriptase, then inserted into “vectors” (other DNA molecules that can continue to replicate after addition of foreign DNA). Exemplary vectors for libraries include bacteriophage (also known as “phage”), viruses that infect bacteria, for example, lambda phage. The library can then be probed for the specific eDNA (and thus mRNA) of interest.
[0042] The term “cRNA” refers to complementary ribonucleic acid, i.e., a synthetic RNA produced by transcription from a specific DNA single stranded template. The cRNA can be labeled with radioactive uracil and then used as a probe. (King & Stansfield, A Dictionary of Genetics, 4th ed.). Alternatively, a non-radioactive label, such as biotin or other non-radioactive label, may be used to label the cRNA probe. cRNA is also described as a single-stranded RNA whose base sequence is complementary to specific DNA sequences (e.g., genes) or, more rarely, another single-stranded RNA, usually conveys an artificial hybridization probe or antisense genetic inhibitor.
[0043] As an example, transcriptional activity can be assessed by measuring levels of messenger RNA using a gene chip such as the Affymetrix.RTM. HG-U133-Plus-2 GeneChips. High-throughput, real-time quantitation of RNA of a large number of genes of interest thus becomes possible in a reproducible system.
[0044] Particular combinations of markers may be used that show optimal function with different ethnic groups or sex, different geographic distributions, different stages of disease, different degrees of specificity or different degrees of sensitivity. Particular combinations may also be developed which are particularly sensitive to the effect of therapeutic regimens on disease progression. Subjects may be monitored after a therapy and/or course of action to determine the effectiveness of that specific therapy and/or course of action.
[0045] The term “late ER+ breast cancer recurrence” is used in the description of the present invention to mean an ER+ breast cancer that manifests in an ER+ breast cancer patient at least 5 to 20 years after an initial ER+ breast cancer diagnosis.
[0046] The term “late ER+-recurrence threshold” as used in the description of the present invention relates to a value that demarcates a high risk late ER+ recurrence group and a low risk late ER+ recurrence group.
[0047] The term “microarray” refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate.
[0048] The term “polynucleotide,” when used in singular or plural, generally refers to any polyribonucleotide or polydeoxribonucleotide, which may be unmodified RNA or DNA or modified RNA or DNA. Thus, for instance, polynucleotides as defined herein include, without limitation, single- and double-stranded DNA, DNA including single- and double-stranded regions, single- and double-stranded RNA, and RNA including single- and double-stranded regions, hybrid molecules comprising DNA and RNA that may be single-stranded or, more typically, double-stranded or include single- and double-stranded regions. In addition, the term “polynucleotide” as used herein refers to triple-stranded regions comprising RNA or DNA or both RNA and DNA. The strands in such regions may be from the same molecule or from different molecules. The regions may include all of one or more of the molecules, but more typically involve only a region of some of the molecules. One of the molecules of a triple-helical region often is an oligonucleotide. The term “polynucleotide” specifically includes cDNAs. The term includes DNAs (including cDNAs) and RNAs that contain one or more modified bases. Thus, DNAs or RNAs with backbones modified for stability or for other reasons are “polynucleotides” as that term is intended herein. Moreover, DNAs or RNAs comprising unusual bases, such as inosine, or modified bases, such as tritiated bases, are included within the term “polynucleotides” as defined herein. In general, the term “polynucleotide” embraces all chemically, enzymatically and/or metabolically modified forms of unmodified polynucleotides, as well as the chemical forms of DNA and RNA characteristic of viruses and cells, including simple and complex cells.
[0049] The term “oligonucleotide” refers to a polynucleotide, including, without limitation, single-stranded deoxyribonucleotides, single- or double-stranded ribonucleotides, RNA:DNA hybrids and double-stranded DNAs. Oligonucleotides, such as single-stranded DNA probe oligonucleotides, are often synthesized by chemical methods, for example using automated oligonucleotide synthesizers that are commercially available. However, oligonucleotides can be made by a variety of other methods, including in vitro recombinant DNA-mediated techniques and by expression of DNAs in cells and organisms.
[0050] The terms “differentially expressed gene,” “differential gene expression,” and their synonyms, which are used interchangeably, refer to a gene whose expression is activated to a higher or lower level in a subject suffering from a disease, specifically cancer, such as breast cancer, relative to its expression in a normal or control subject. The terms also include genes whose expression is activated to a higher or lower level at different stages of the same disease. It is also understood that a differentially expressed gene may be either activated or inhibited at the nucleic acid level or protein level, or may be subject to alternative splicing to result in a different polypeptide product. Such differences may be evidenced by a change in mRNA levels, surface expression, secretion or other partitioning of a polypeptide, for example. Differential gene expression may include a comparison of expression between two or more genes or their gene products, or a comparison of the ratios of the expression between two or more genes or their gene products, or even a comparison of two differently processed products of the same gene, which differ between normal subjects and subjects suffering from a disease, specifically cancer, or between various stages of the same disease. Differential expression includes both quantitative, as well as qualitative, differences in the temporal or cellular expression pattern in a gene or its expression products among, for example, normal and diseased cells, or among cells which have undergone different disease events or disease stages. For the purpose of the instant disclosure, “differential gene expression” is considered to be present when there is at least an about two-fold, preferably at least about four-fold, more preferably at least about six-fold, most preferably at least about ten-fold difference between the expression of a given gene in normal and diseased subjects, or between various stages of disease development in a diseased subject.
[0051] The term “prognosis” is used herein to refer to the prediction of the likelihood of cancer-attributable death or progression, including recurrence, metastatic spread, and drug resistance, of a neoplastic disease, such as breast cancer.
[0052] The term “prediction” is used herein to refer to the likelihood that a patient 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. The predictive methods of the instant disclosure can be used clinically to make treatment decisions by choosing the most appropriate treatment modalities for any particular patient. The predictive methods of the instant disclosure are valuable tools in predicting if a patient is likely to respond favorably to a treatment regimen, such as surgical intervention, chemotherapy with a given drug or drug combination, and/or radiation therapy, or whether long-term survival of the patient, following surgery and/or termination of chemotherapy or other treatment modalities is likely.
[0053] The term “long-term” survival is used herein to refer to survival for at least 5 years, more preferably for at least 8 years, most preferably for at least 10 years following initial surgery or other treatment.
[0054] The term “tumor,” as used herein, refers to all neoplastic cell growth and proliferation, whether malignant or benign, and all pre-cancerous and cancerous cells and tissues.
[0055] The terms “cancer” and “cancerous” refer to or describe the physiological condition in mammals that is typically characterized by unregulated cell growth. Examples of cancer include, but are not limited to, breast cancer, ovarian cancer, colon cancer, lung cancer, prostate cancer, hepatocellular cancer, gastric cancer, pancreatic cancer, cervical cancer, liver cancer, bladder cancer, cancer of the urinary tract, thyroid cancer, renal cancer, carcinoma, melanoma, and brain cancer.
[0056] The “pathology” of cancer includes all phenomena that compromise the well-being of the patient. This includes, without limitation, abnormal or uncontrollable 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 response, neoplasia, premalignancy, malignancy, invasion of surrounding or distant tissues or organs, such as lymph nodes, etc.
[0057] In the context of the present invention, reference to “at least eight,” “at least ten,” “at least fifteen,” etc. of the genes listed in any particular gene set means any one or any and all combinations of the genes listed.
[0058] The term “node negative” cancer, such as, for example, “node negative” breast cancer, is used herein to refer to cancer that has not spread to the lymph nodes.
[0059] The term “sample material” is also designated as a “sample” or as a “specimen” such as a tissue specimen that is fresh frozen, preserved (i.e., FFPE), or otherwise provided in a fresh, preserved or semi-preserved state.
[0060] “Biologically homogeneous” refers to the distribution of an identifiable protein, nucleic acid, gene or genes, the expression product(s) of those genes, or any other biologically informative molecule such as a nucleic acid (DNA, RNA, mRNA, iRNA, cDNA etc.), protein, metabolic byproduct, enzyme, mineral etc. of interest that provides a statistically significant identifiable population or populations that may be correlated with an identifiable disease state of interest.
[0061] “Low expression,” or “low expression level(s),” “relatively low expression,” or “lower expression level(s)” and synonyms thereof, according to one embodiment of the instant disclosure, refers to expression levels, that based on a mixture model fit of density distribution of expression levels for a particular multi-state gene of interest falls below a threshold “c”, whereas “high expression,” “relatively high,” “high expression level(s)” or “higher expression level(s)” refers to expression levels failing above a threshold “c” in the density distribution. The threshold “c” is the value that separates the two components or modes of the mixture model fit.
[0062] The term “gene expression profiling” is used in the broadest sense, and includes methods of quantification of mRNA and/or protein levels in a biological sample.
[0063] The term “adjuvant therapy” is generally used to describe treatment that is given in addition to a primary (initial) treatment. In cancer treatment, the term “adjuvant therapy” is used to refer to chemotherapy, hormonal therapy and/or radiation therapy following surgical removal of a tumor, with the primary goal of reducing the risk of cancer recurrence.
[0064] “Neoadjuvant therapy” is adjunctive or adjuvant therapy given prior to surgery to remove the tumor. Neoadjuvant therapy includes, for example, chemotherapy, radiation therapy, and hormone therapy. Thus, chemotherapy may be administered prior to surgery to shrink the tumor, so that surgery can be more effective, or, in the case of previously inoperable tumors, possible.
[0065] The term “cancer-related biological function” is used herein to refer to a molecular activity that impacts cancer success against the host, including, without limitation, activities regulating cell proliferation, programmed cell death (apoptosis), differentiation, invasion, metastasis, tumor suppression, susceptibility to immune surveillance, angiogenesis, maintenance or acquisition of immortality.
[0066] The late relapse score identifies patients at risk for relapse between five and twenty years after diagnosis with ER+ breast cancer, independent of the risk of early relapse (before 5 years), and describes a novel gene expression state of breast cancer tumors (the late relapse high risk group) that exhibit low protein production and other features of a dormant population. Combining the resulting signature with a genomic test for late recurrence of breast cancer provides physicians with a 20-year prognosis to guide long-term treatment decisions. A signature that predicts late recurrence independent of early relapse serves the dual purpose of isolating the biological processes that promote late recurrence and potentially points to more effective treatments.
[0067] In one embodiment, the late relapse score comprises expression of a minimum of eight genes to predict the risk of relapse in ER+ breast cancer eight years post-diagnosis. The genes were identified using the Metabric microarray dataset (Curtis et al., 2012) using statistical methods for genomic panel discovery (Bauer, Hummon, & Buechler, 2012; Buechler, 2009). The survival endpoint in the Metabric dataset is breast cancer specific death (BSD).
[0068] In another embodiment, a risk score is constructed from gene expression measurements. A gene is considered multistate (Buechler, 2009) if its distribution of expression across a population is sufficiently bimodal, which is formalized with the statistical concept of a mixture model. In building prognostic models, the continuous vector of expression values for a multistate gene is replaced by a binary variable representing the two states, or component groups. As defined herein, the state or component enriched with poor prognosis cases is given the value 1 and the other state or component is given the value 0.
[0069] In the instant disclosure, a binary classification variable is replaced with a continuous score that measures the probability of membership in a component; i.e., numbers near 0, 1, or in between, depending on the likelihood that the sample is in the poor prognosis component. This risk score for a gene is calculated by the mixture model methods. The risk score for a gene derived from the mixture model fit in a training set is generalized to a validation set using the statistical method of fitting the same mixture model to the new data.
[0070] A prognostic score for a panel of multistate genes is defined as the sum of the risk scores of these genes, resealed to a range of 0-100. This contrasts with the method described by Buechler (Buechler, 2009) in which the multigene prognostic variable is 1 if any of the single-gene variables is 1, and 0 otherwise. Here, samples considered low risk by all of the genes will have a score near 0, and the score increases with the number of genes that classify the sample as high risk.
EXAMPLES
Example 1
Characteristics of Training And Validation Subsets of The ER+ Metabric Microarray Dataset
[0071] The present example is provided to define the statistical tools, models and data sets employed to derive the present methods.
[0072] All statistical analyses were performed using R (http://www.r-project.org). Mixture models were fit using the package mclust (Fraley & Raftery, 2002; 2012) and survival analysis was performed with the survival package. The significance of a Cox proportional hazard (CPH) model was assessed with the P value of the logrank score test. The significance of a multivariate CPH over a CPH using a subset of the variables was measured with a Chi-squared test of the log-likelihoods. The proportional hazard condition was tested with the cox.zph function.
[0073] The Monte Carlo cross-validation (Kuhn & Johnson, 2013) was used to estimate parameters in the development of a predictive model. This method, applied within the training set of model construction, identified models that generalize better than models defined without cross-validation.
[0074] The ER+ Metabric dataset (Table 1, (Curtis et al., 2012)) contains gene expression values hybridized to the illuminaHumanv3 array platform. Death due to breast cancer (BSD) is the survival endpoint in this dataset. Cohort I and Cohort II (Table 1) consists of the training and validation cohorts, respectively (Curtis et al., 2012). The training cohort (Cohort I), defined as the sample population with events prior to 8 years excluded (represented by * on Table 1); the validation cohort (Cohort II) defined as the sample population with at least 8 years of BSD-free survival (represented by † on Table 1).
TABLE-US-00001 TABLE 1 Characteristics of training and validation subsets of the ER+ Metabric microarray dataset Late Late Relapse t Relapse Cohort I Cohort II training validation (n = 798) (n = 720) (n = 485)* (n = 366)† Death by breast 137/48/14 109/47/2 0/48/0 0/47/0 cancer (time <8 years/ time ≧8 years/NA) LN−/LN+ 432/366 397/323 277/208 223/143 Grade 70/392/336/0 96/320/234/70 53/277/155/0 49/163/ (1/2/3/NA) 111/43 Tamoxifen 578/220 510/210 349/136 234/132 (yes/no) Size 354/444/0 315/391/14 236/249/0 194/164/8 (≦2 cm/>2 cm/ NA) Age (<50/≧50) 143/655 104/616 97/388 64/302 INDUCT 565/233 509/211 485/0 273/93 (low/high)
Example 2
Methodology for the Derivation and Validation of the Late Relapse Gene Signature
[0075] The following algorithm details the steps used herein. The algorithm was used with Monte Carlo cross-validation to select the parameters n and c, as well as in the ultimate derivation of Late Relapse. [0076] Late relapse training-validation algorithm [0077] An instance of model training and validation is executed with the following [0078] Inputs: [0079] A training set of low INDUCT samples with no relapse events before 8 years; [0080] A validation set with all follow-up times greater than 8 years (hence no relapse events before 8 years), disjoint from the training set; [0081] A number n=the number of genes to use for the panel; [0082] A number c, between 0 and 100, =value of the late relapse score separating the low risk and high risk samples; [0083] A set of multistate genes from which the panel is selected. [0084] Discovery process: [0085] For each candidate multistate variable, the chi-square statistic between the multistate gene's binary variable and the BSD event vector in the training set was computed; [0086] The panel variables P, the genes with the n largest chi-square statistics were selected; [0087] The late relapse score S was formed by adding the individual risk scores of the genes in P and scaling for 0 to 100; [0088] A binary late relapse test T was formed using the value c: the low risk samples were those with S less than c and the high risk samples were those with S greater than or equal to c. [0089] Validation process: [0090] The binary test variable T was computed using a Cox proportional hazard model in the variable T on the assessment set; [0091] The assessment process reported the p-value of the CPH.
Example 3
Derivation of the Late Relapse Score and Risk Stratification
[0092] The derivation of the late relapse risk stratification required multiple steps to select all of the necessary components. In summary, a panel of multistate genes was selected, a continuous multigene score was constructed, and finally samples were divided into low risk and high-risk groups by comparing the late relapse score value to a threshold value, (c). As detailed in the late relapse Training-Validation Algorithm, the panel of genes was selected as the (n) multistate genes most predictive of late relapse in the training set, for a particular number (n). The execution of the algorithm required first selecting the necessary inputs: (1) training and validation sets, (2) a candidate set of multistate genes, and the numbers (n) and (c).
[0093] Samples in the Metabric cohort I (Table 1) were chosen as the training set excluding those with relapse events before 8 years. The restriction in cohort I samples minimized effects of early relapse processes that may have extended beyond eight years. This set consisted of 485 samples with 48 late BSD events. The late relapse validation set consisted of ER+ samples in the Metabric cohort II with follow-up time at least eight years (366 samples with 47 late BSD events).
[0094] The pool of multistate genes (i.e., array probes) from which the late relapse gene panel was selected was filtered to exclude probes that (1) were not annotated to a gene and (2) were not contained in a weighted gene coexpression network analysis (WGCNA) module. These restrictions aided the analysis of the biological processes underlying late relapse. In the training step, a multistate gene's level of significance to predict late relapse was measured with the chi-squared statistic of the gene's binary variable and the late relapse event vector. The chi-squared statistic was chosen over a CPH because in the discovery stage there was difficulty with isolating late relapse events (assessed by the chi-squared statistic), while a CPH model gives greater weight to earlier events.
[0095] The parameters (n) and (c) required by the algorithm were selected using Monte Carlo cross-validation. A family of 100 training sets, Ti, i <100, were randomly chosen so that each Ti consists of ⅔ of the late relapse training set, for balance. For each i≦100, a validation set, Vi, disjoint from Ti and consisting of ER+ samples in the Metabric cohort I with at least eight years of follow-up was chosen. Note that the Vi's were disjoint from the overall late relapse validation set. Each Ti contained 325 samples with 32 late relapse cases and each Vi contained 124 samples with 17 late events. Candidate values of (n), specifically 5, 10, 15, 20, 30, and candidate values of (c)od, namely integers ranging from 20 to 45, were tested by applying the late relapse derivation algorithm to each pair Ti-Vi, i≦100, and each candidate pair of (n) and (c). From each application the p-values of CPH models were collected and evaluated in Vi for the derived continuous late relapse score and the binary late relapse risk stratification defined using (c). The suitability of the candidate parameters (n) and (c) were assessed using the median p-values ranging over all Ti-Vi, and the median rates of events in the low risk groups.
TABLE-US-00002 TABLE 2 Candidate genes for late relapse panel WGCNA High Risk Probe Symbol Gene Id Module Comp* ILMN_2155322 ZNF652 22834 1 High ILMN_2339028 PKD1 5310 13 High ILMN_1713706 ZNF786 136051 1 High ILMN_1656233 SPDYE7P 441251 1 High ILMN_1714216 TSC2 7249 13 High ILMN_1800750 ZNF692 55657 1 High ILMN_1714352 DMWD 1762 13 High ILMN_2055310 MBD4 8930 11 High ILMN_1671661 HSD17B7 51478 11 High ILMN_1656011 RGS1 5996 12 Low ILMN_1802397 GNA11 2767 13 High ILMN_1814074 PHKA2 5256 1 High ILMN_1762899 EGR1 1958 20 Low ILMN_1738424 CDC42 998 2 Low ILMN_1714622 TNRC6A 27327 13 High ILMN_1757106 MARCH6 10299 1 High ILMN_1701947 GPR34 2857 12 Low ILMN_1778457 IL18 3606 1 High ILMN_2189424 MRPL20 55052 3 Low ILMN_1726809 BHLHE41 79365 7 High ILMN_1669523 FOS 2353 20 Low ILMN_2269564 ARID4B 51742 1 High ILMN_1755114 EIF2AK4 440275 1 High ILMN_2390472 TTC14 151613 1 High ILMN_1787251 DAAM1 23002 1 High ILMN_2189222 KLHL8 57563 1 High ILMN_2148290 PDCD7 10081 1 High ILMN_1778240 GFOD1 54438 1 High ILMN_1660551 CRAMP1L 57585 13 High ILMN_1758392 ANKS1B 56899 1 High ILMN_1771962 GLI3 2737 2 High ILMN_2273224 SLC4A5 57835 1 High ILMN_1755990 ATP6AP1L 92270 1 High ILMN_1811443 AVP 551 13 High ILMN_1702636 TUBB6 84617 10 Low ILMN_2168952 DENR 8562 1 High ILMN_1793831 TRADD 8717 1 Low ILMN_2342455 PPA2 27068 1 High ILMN_2220320 RPL7L1 285855 1 High ILMN_2121066 ADAM17 6868 1 High
Example 4
Density Distribution of the Continuous Later Score
[0096] Assessment of the binary late relapse score risk variables showed that panels using 15 variables performed better than those using fewer variables, but no increase in performance was found with more than 15 variables. For panels with 15 genes, binary tests defined by cuts of 30 to 35 performed equivalently well, with lowest event rates in the low risk groups occurring for cuts 29-33. For these reasons, we chose 15 as the panel size and 31 as the score threshold separating low risk and high risk. The continuous late relapse scores derived in the Ti performed poorly in the Vi in CPH models, so the binary risk stratification was chosen for generalization.
[0097] The prioritized set of possible panel of genes (Table 2, ranked by significance) was generated by executing the late relapse training-validation algorithm using the late relapse training set and the multistate candidate probes described above. The fifteen most significant probes were used to define the continuous late relapse score. The late relapse score was extended to the late relapse validation set; the binary late relapse risk stratification was defined using a threshold of 31. The late relapse score had similar distributions in the training and validation sets (
Example 5:
Validation of Late Relapse Prediction Using Later and Long-Term Prognosis Using Induct+Later
[0098] The present example is provided to demonstrate the utility of the present assessment tools, late ER+ breast cancer genetic indicator panel, kits, and methods of using these elements, for successfully identifying almost half of the population (48%) at some risk of developing a recurrent form of an ER+ breast cancer, who may successfully opt out of toxic and expensive anti-cancer treatment, without any appreciable increase in mortality. The tools and methods described herein identify 48% of previously positively diagnosed ER+ breast cancer patient survivors, who are at low risk of cancer recurrence after at least 5, 8 or even 20 years of disease-free survival. Patients who have a low risk (LateR) score and are also lymph node (LN) negative have less than and about 0.5% chance of recurrence after 8 years of disease-free survival (Table 3), even with no Tamoxifen or chemotherapy treatment. These patients can be declared “cured” of any recurrent cancer employing the present techniques after 8 years, and thus spared the side effects and expense of treatment. In this way, the present tools and methods may be used to significantly reduce suffering for tens of thousands of women a year.
[0099] The late relapse score risk stratification (48% low risk, LateR<31) significantly predicts breast cancer specific death events after eight years BSD-free survival in ER+ breast cancer in the validation set (p=0.03,
[0100] The late relapse score combined with a test to predict the probability of early relapse predicts long-term survival in ER+ breast cancer with consistent significance over 20 years. The stratification of patients into groups that have low or high risk of early relapse and low or high risk of late relapse produces a tool for long-term survival prediction. Expected survival over 20 years for the four strata computed in the validation set, segregated by lymph node status (
TABLE-US-00003 TABLE 3 Survival characteristics of subgroups defined by the combined test for early relapse and late relapse groups in the Metabric Cohort I. 20-Year 8-Year BSD BSD-Free Early BSD Free Survival Late BSD Survival Risk Group Size Events (95% Cl) Events (95% Cl) LN− long-term validation set (p = 6.24 × 10.sup.−5) low INDUCT, 165 8 0.94 (0.90-0.98) 0 0.94 (0.90-0.98) low LateR low INDUCT, 124 7 0.94 (0.89-0.98) 8 0.83 (0.75-0.92) high LateR high INDUCT, 51 9 0.78 (0.67-0.92) 1 0.63 (0.39-1.0) low LateR high INDUCT, 57 9 0.83 (0.73-0.94) 9 0.45 (0.27-0.74) high LateR LNs+ long-term validation set (p = 5.71 × 10−6) low INDUCT, 143 18 0.84 (0.77-0.91) 6 0.72 (0.63-0.84) low LateR low INDUCT, 77 19 0.72 (0.62-0.84) 10 0.49 (0.36-0.65) high LateR high INDUCT, 54 22 0.50 (0.37-0.68) 4 0.19 (0.04-0.80) low LateR high INDUCT, 49 17 0.61 (0.48-0.78) 9 0.29 (0.14-0.58) high LateR
Example 6
Validation of Late Relapse Score as a Predictor of Late Relapse Independent of Clinical Parameters And PAM50
[0101] In the validation set of samples with at least eight years of relapse-free survival, LN, tumor grade, PAM50 and INDUCT were found to be significant in univariate CPH models using eight years as the baseline time (Table 4). The late relapse risk signature is significant as a late relapse risk factor in multivariate survival analysis including other risk factors identified above (Table 5), verifying late relapse as an independent test for late relapse risk and supporting the assertion that the different processes drive early and late relapse.
TABLE-US-00004 TABLE 4 Significance of clinical variables PAM50 and INDUCT (a test to predict relapse prior to eight years) as predictors of late relapse in the validation set Variable p-value Lymph node status (LN+/LN−) 0.0004 grade 0.02 Grade (excluding grade 1) 0.71 Size 2 cm/>2 cm) 0.33 Age (<50/50) 0.91 PAM50 0.002 INDUCT 0.0007 (p-value of a Cox proportional hazard model with 8 years as a baseline)
TABLE-US-00005 TABLE 5 Significance of late relapse signature as a late relapse risk factor independent of clinical variables, PAM50 and INDUCT in the validation set. (p value computed using 2-times the difference of the log-likelihood of a CPH using only the variable in the first column and a CPH including the variable and the LateR). Variable p-value LN 0.004 LN + grade 0.017 LN + PAM50 0.003 LN INDUCT 0.015
Example 7
Premalignant Lesion and Pre-Invasive Tumor Risk Assessment for Late ER+ Breast Cancer Occurrence
[0102] The LateR score predicts recurrence of cancer my measuring gene expression in biopsy tissue that has been confirmed to be ER+ breast cancer. Tissue that is pathologically classified as a pre-malignant lesion, or a pre-invasive tumor, have significant genomic similarity to cancer (Ma et al., 2003). Applied to these pre-cancerous lesions, LateR will predict the onset of invasive breast cancer years hence. (See Ma, X.-J., Salunga, R., Tuggle, J. T., Gaudet, J., Enright, E., McQuary, P., et al. (2003). Gene expression profiles of human breast cancer progression. Proceedings of the National Academy of Sciences of the United States of America, 100(10), 5974-5979. http://doi.org/10.1073/pnas.0931261100).
[0103] All of the patents, patent applications, patent application publications and other publications recited herein are hereby incorporated by reference as if set forth in their entirety. The present invention has been described in connection with what are presently considered to be the most practical and preferred embodiments. However, the invention has been presented by way of illustration and is not intended to be limited to the disclosed embodiments. Accordingly, one of skill in the art will realize that the invention is intended to encompass all modifications and alternative arrangements within the spirit and scope of the invention as set forth in the appended claims.