WO2011143337A1 - Biomarkers for the identification monitoring and treatment of breast cancer - Google Patents

Abstract

Provided are methods of treating cancer and methods of accessing/monitoring the responsiveness of a cancer cell to a therapeutic compound. Also provided are biomarkers used in said accessing/monitoring.

Description

BLOMARKERS FOR THE IDENTIFICATION MONITORING AND

TREATMENT OF BREAST CANCER

RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. provisional application No.

61/333,429, filed May 11, 2010, which is incorporated herein by reference in its entirety.

FIELD OF THE INVENTION

[0002] This invention relates generally to methods of diagnosing and treating cancer. More specifically, the invention relates to methods of assessing the responsiveness of a cancer cell to a therapeutic compound.

BACKGROUND OF THE INVENTION

[0003] DNA repair refers to a collection of processes by which a cell identifies and corrects damage to the DNA molecules that encode its genome. In human cells, both normal metabolic activities and environmental factors such as UV light can cause DNA damage, resulting in as many as 1 million individual molecular lesions per cell per day. Many of these lesions cause structural damage to the DNA molecule and can alter or eliminate the cell's ability to transcribe the gene that the affected DNA encodes. Other lesions induce potentially harmful mutations in the cell's genome, which will affect the survival of its daughter cells after it undergoes mitosis. Consequently, the DNA repair process must be constantly active so it can respond rapidly to any damage in the DNA structure.

[0004] The rate of DNA repair is dependent on many factors, including the cell type, the age of the cell, and the extracellular environment. A cell that has accumulated a large amount of DNA damage, or one that no longer effectively repairs damage incurred by its DNA, can enter one of three possible states: an irreversible state of dormancy, known as senescence; cell suicide, also known as apoptosis or programmed cell death or unregulated cell division, which can lead to the formation of a tumor.

[0005] Many of the chemotherapeutic agents used for treatment of breast cancer function by causing DNA damage. The level of sensitivity or resistance to these reagents is likely dependent on the function of the different DNA repair pathways. The pathways are known to function in a compensatory manner to repair damaged DNA thus a panel of DNA repair markers could be used to investigate the DNA repair pathways for each patient, creating a clinical test which is prognostic or predictive of chemotherapy efficacy.

[0006] In breast cancer, alterations in several DNA repair proteins are reported including BRCA1, BRCA2, and ATM. Additionally, pathway compensation is shown with PARP1 inhibition being more effective in patients with BRCAlor BRCA2 mutations suggesting the two pathways (base excision repair and homologous recombination) act in a compensatory manner and when both pathways are disrupted, the cancer cells die as they can no longer repair the drug-induced damage.

Summary of the Invention

[0007] The present invention relates in part to the discovery that certain biological markers (referred to herein as "DNARMARKERS"), such as proteins, nucleic acids, polymorphisms, metabolites, protein modifications, nucleic acid modifications,

chromosomes, and other analytes, as well as certain physiological conditions and states, are present or altered in subjects with an increased risk of developing a recurrent breast cancer.

[0008] The present invention also provides methods of assessing the likelihood of a local and/or distant recurrence or overall survival or disease free survival or response to therapy by a patient with breast cancer by measuring the level of an effective amount of one or more DNARMARKERS in a sample from the subject.

[0009] Risk of early local and/or distant recurrence or decreased overall survival or disease free survival or response to therapy by a patient with breast cancer is determined by measuring the level of an effective amount of DNARMARKERS in a sample from the subject. The sample can contain breast tumor or normal breast tissue or a blood sample. The tissue is for example a paraffin embedded tissue, a fresh tissue, or a frozen tissue sample.

[00010] An increased risk of developing a recurrence of breast cancer in the subject is determined by measuring a clinically significant alteration in the level of the

DNARMARKER in the sample. Alternatively, an increased risk of developing a recurrence of breast cancer in the subject is determined by comparing the level of the effective amount of DNARMARKER to a reference value. In some aspects the reference value is an index.

[00011] In another aspect the invention provides a method with a predetermined level of predictability for assessing the progression of a breast cancer in a subject by detecting the level of an effective amount a DNARMARKERS in a first sample from the subject at a first period of time, detecting the level of an effective amount of DNARMARKERS in a second sample from the subject at a second period of time and comparing the level of the

DNARMARKERS detected to a reference value. In some aspects the first sample is taken from the subject prior to being treated for the breast cancer and the second sample is taken from the subject after being treated for the cancer. Treatment can include chemotherapy, radiation therapy, Herceptin therapy, or hormonal therapy.

[00011] In a further aspect the invention provides a method with a predetermined level of predictability for monitoring the effectiveness of treatment or selecting a treatment regimen for breast cancer by detecting the level of an effective amount of

DNARMARKERS in a first sample from the subject at a first period of time and

optionally detecting the level of an effective amount of DNARMARKERS in a second sample from the subject at a second period of time. The level of the effective amount of DNARMARKERS detected at the first period of time is compared to the level detected at the second period of time or alternatively a reference value. Effectiveness of treatment is monitored by a change in the level of the effective amount of DNARMARKERS from the subject.

[00012] Treatment is for example, chemotherapy and /or radiotherapy. Chemotherapeutic agents include cyclophosphamide, anthracycline, 5 fluoro-uracil, methotrexate, taxane, and any combination of these.

[00013] A DNARMARKER includes for example FANCD2, XPF, pMK2, PAR, RAD51 , BRCAl, NQOl, TOP2A, ATM, MREl l, H2AX, NBSl, and RAD50. One, two, three, four, five, ten or more DNARMARKERS are measured. Preferably, at least two

DNARMARKERS selected from FANCD2, XPF, pMK2, PAR, RAD51, BRCAl, NQOl, TOP2A, ATM, MREl l, H2AX, NBSl, and RAD50 are measured.

[00014] In a further aspect the DNARMARKERS are DNA repair proteins belonging to different DNA repair pathways. Alternatively three or more DNARMARKERS are measured where DNARMARKERS belonging to two or more different DNA repair pathways. The level of a DNARMARKER is measured electrophoretically,

immunochemically, or genetically. For example the level of the DNARMARKERS is detected by immunohistochemistry, radioimmunoassay, immunofluorescence assay, by an enzyme-linked immunosorbent assay, by genotyping, or by fluorescence in situ hybridization. [00015] The subject has breast cancer, or a recurrent breast cancer. In some aspects the sample is taken for a subject that has previously been treated for breast cancer. Alternatively, the sample is taken from the subject prior to being treated for breast cancer. The sample is a tumor biopsy such as a core biopsy, a needle biopsy, an excisional tissue biopsy or an incisional tissue biopsy. Additionally, the sample is a tumor cell from blood, lymph nodes or a bodily fluid.

[00016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although methods and materials similar or equivalent to those described herein can be used in the practice of the present invention, suitable methods and materials are described below. All publications, patent applications, patents, and other references mentioned herein are expressly incorporated by reference in their entirety. In cases of conflict, the present specification, including definitions, will control.

[00017] In addition, the materials, methods, and examples described herein are illustrative only and are not intended to be limiting.

[00018] Other features and advantages of the invention will be apparent from and encompassed by the following detailed description and claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[00019] Figure 1 is a series of photographs showing examples of immunohistochemistry staining of one breast cancer specimen stained with DNA Repair Biomarkers BRCA1, RAD51, FANCD2, PAR, pMK2, XPF, and NQ01.

[00020] Figure 2 is a series of graphs showing examples of varying marker expression in breast cancer patients stained with BRCA1, RAD51, FANCD2, PAR, pMK2, XPF, and NQ01.

[00021] Figure 3 is a ROC curve showing that a three Marker Model Separates CEF- treated Patients into Recurrence Groups.

[00022] Figure 4 shows the NQOl protein levels across the patient population.

[00023] Figure 5 shows the estimated survival rates for different values of NQOl expression based on the cox-model.

[00024] Figure 6 shows the estimated survival rates for different ER status as well as NQOl protein status based on the multivariate analysis. DETAILED DESCRIPTION OF THE INVENTION

[00025] The present invention relates to the identification of biomarkers associated with breast cancer. Specifically, these biomarkers are proteins associated in DNA repair pathways. DNA repair pathways are important to the cellular response network to chemotherapy and radiation.

[00026] There are six major DNA repair pathways distinguishable by several criteria which can be divided into three groups those that repair single strand damage and those that repair double stand damage. These pathways include Base-Excision Repair (BER);

Nucleotide Excision Repair (NER); Mismatch Repair (MMR); Homologous

Recombination/Fanconi Anemia pathway (HR/FA); Non-Homologous Endjoining (NHEJ), and Translesion DNA Synthesis repair (TLS).

[00027] BER, NER and MMR repair single strand DNA damage. When only one of the two strands of a double helix has a defect, the other strand can be used as a template to guide the correction of the damaged strand. In order to repair damage to one of the two paired molecules of DNA, there exist a number of excision repair mechanisms that remove the damaged nucleotide and replace it with an undamaged nucleotide complementary to that found in the undamaged DNA strand. BER repairs damage due to a single nucleotide caused by oxidation, alkylation, hydrolysis, or deamination. NER repairs damage affecting longer strands of 2-30 bases. This process recognizes bulky, helix-distorting changes such as thymine dimers as well as single-strand breaks (repaired with enzymes such UvrABC endonuclease). A specialized form of NER known as Transcription- Coupled Repair (TCR) deploys high-priority NER repair enzymes to genes that are being actively transcribed. MMR corrects errors of DNA replication and recombination that result in mispaired nucleotides following DNA replication.

[00028] NHEJ and HR repair double stranded DNA damage. Double stranded damage is particularly hazardous to dividing cells. The NHEJ pathway operates when the cell has not yet replicated the region of DNA on which the lesion has occurred. The process directly joins the two ends of the broken DNA strands without a template, losing sequence information in the process. Thus, this repair mechanism is necessarily mutagenic. However, if the cell is not dividing and has not replicated its DNA, the NHEJ pathway is the cell's only option. NHEJ relies on chance pairings, or microhomologies, between the single-stranded tails of the two DNA fragments to be joined. There are multiple independent "failsafe" pathways for NHEJ in higher eukaryotes. Recombinational repair requires the presence of an identical or nearly identical sequence to be used as a template for repair of the break. The enzymatic machinery responsible for this repair process is nearly identical to the machinery responsible for chromosomal crossover during meiosis. This pathway allows a damaged chromosome to be repaired using the newly created sister chromatid as a template, i.e. an identical copy that is also linked to the damaged region via the centromere. Double-stranded breaks repaired by this mechanism are usually caused by the replication machinery attempting to synthesize across a single-strand break or unrepaired lesion, both of which result in collapse of the replication fork.

[00029] Translesion synthesis is an error-prone bypass method where a DNA lesion is left unrepaired during S phase, and is repaired later in the cell cycle. The DNA replication machinery cannot continue replicating past a site of DNA damage, so the advancing replication fork will stall on encountering a damaged base. The translesion synthesis pathway is mediated by specific DNA polymerases that insert alternative bases at the site of damage and thus allow replication to bypass the damaged base to continue with chromosome duplication. The bases inserted by the translesion synthesis machinery are template- independent, but not arbitrary; for example, one human polymerase inserts adenine bases when synthesizing past a thymine dimer. If this residue is not repaired at a later step, the process is mutagenic.

[00030] Cancer cells accumulate high levels of DNA damage. This damage may result from their heightened proliferative activity or from exposure to chemotherapy or ionizing radiation.

[00031] Alterations in DNA repair genes and proteins have been identified in breast cancer patients and in some cases have been associated with clinical outcome or treatment efficacy. Profiling proteins from multiple pathways is likely to be a more informative approach to predicting the progression of disease or efficacy of the treatment.

[00032] In this study described herein, representatives from several of these pathways were investigated for associations with clinical outcome of individuals with breast cancer. Selected DNA repair protein epitopes in 1491 breast cancer patient samples were evaluated by immunohistochemistry. The DNA repair protein epitopes evaluated included

pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF . The major treatment groups include 673 patients treated with radiation therapy (RT) alone, 114 patients treated with

cyclophosphamide/epirubicin/5-fluorouracil + RT (CEF), and 212 patients treated with cyclophosphamide/methotrexate/5-fluorouracil + RT (CMF). The clinical endpoints measured for the study were 5 year disease free survival and 5 year time to recurrence. Of the patients evaluated, 21% were ER negative and 79% were ER positive, 32% were PR negative and 68% were PR positive, 88% were HER2 negative and 12% were HER2 positive. Approximately 15% of the patients were Triple Negative.

[00033] The DNA repair biomarkers studied were associated with shorter time to cancer recurrence. Specifically, two, three and four marker models were able to segregate high risk and low risk groups based upon time to recurrence in both the training and test cohorts.

[00034] Definitions

[00035] "Accuracy" refers to the degree of conformity of a measured or calculated quantity (a test reported value) to its actual (or true) value. Clinical accuracy relates to the proportion of true outcomes (true positives (TP) or true negatives (TN) versus misclassified outcomes (false positives (FP) or false negatives (FN)), and may be stated as a sensitivity, specificity, positive predictive values (PPV) or negative predictive values (NPV), or as a likelihood, odds ratio, among other measures.

[00036] "Biomarker" in the context of the present invention encompasses, without limitation, proteins, nucleic acids, and metabolites, together with their polymorphisms, mutations, variants, modifications, subunits, fragments, protein-ligand complexes, and degradation products, protein-ligand complexes, elements, related metabolites, and other analytes or sample-derived measures. Biomarkers can also include mutated proteins or mutated nucleic acids. Biomarkers also encompass non-blood borne factors or non-analyte physiological markers of health status, such as "clinical parameters" defined herein, as well as "traditional laboratory risk factors", also defined herein. Biomarkers also include any calculated indices created mathematically or combinations of any one or more of the foregoing measurements, including temporal trends and differences. Where available, and unless otherwise described herein, determinants which are gene products are identified based on the official letter abbreviation or gene symbol assigned by the international Human Genome Organization Naming Committee (HGNC) and listed at the date of this filing at the US National Center for Biotechnology Information (NCBI) web site

[00037] "Clinical parameters" encompasses all non-sample or non-analyte biomarkers of subject health status or other characteristics, such as, without limitation, age (Age), ethnicity (RACE), gender (Sex), or family history (FamHX). [00038] "FN" is false negative, which for a disease state test means classifying a disease subject incorrectly as non-disease or normal.

[00039] "FP" is false positive, which for a disease state test means classifying a normal subject incorrectly as having disease.

[00040] A "formula," "algorithm," or "model" is any mathematical equation, algorithmic, analytical or programmed process, or statistical technique that takes one or more continuous or categorical inputs (herein called "parameters") and calculates an output value, sometimes referred to as an "index" or "index value." Non-limiting examples of "formulas" include sums, ratios, and regression operators, such as coefficients or exponents, biomarker value transformations and normalizations (including, without limitation, those normalization schemes based on clinical parameters, such as gender, age, or ethnicity), rules and guidelines, statistical classification models, and neural networks trained on historical populations. Of particular use in combining DNARMARKERS and other biomarkers are linear and nonlinear equations and statistical classification analyses to determine the relationship between levels of DNARMARKERS detected in a subject sample and the subject's responsivenss to chemotherapy. In panel and combination construction, of particular interest are structural and synactic statistical classification algorithms, and methods of risk index construction, utilizing pattern recognition features, including established techniques such as cross-correlation, Principal Components Analysis (PCA), factor rotation, Logistic Regression (LogReg), Linear Discriminant Analysis (LDA), Eigengene Linear Discriminant Analysis (ELD A), Support Vector Machines (SVM), Random Forest (RF), Recursive Partitioning Tree (RPART), as well as other related decision tree classification techniques, Shrunken Centroids (SC), StepAIC, Kth-Nearest Neighbor, Boosting, Decision Trees, Neural Networks, Bayesian Networks, Support Vector Machines, and Hidden Markov Models, among others. Other techniques may be used in survival and time to event hazard analysis, including Cox, Weibull, Kaplan-Meier and Greenwood models well known to those of skill in the art. Many of these techniques are useful either combined with a DNARMARKER selection technique, such as forward selection, backwards selection, or stepwise selection, complete enumeration of all potential panels of a given size, genetic algorithms, or they may themselves include biomarker selection methodologies in their own technique. These may be coupled with information criteria, such as Akaike's Information Criterion (AIC) or Bayes Information Criterion (BIC), in order to quantify the tradeoff between additional biomarkers and model improvement, and to aid in minimizing overfit. The resulting predictive models may be validated in other studies, or cross-validated in the study they were originally trained in, using such techniques as Bootstrap, Leave-One-Out (LOO) and 10-Fold cross-validation (10-Fold CV). At various steps, false discovery rates may be estimated by value permutation according to techniques known in the art. A "health economic utility function" is a formula that is derived from a combination of the expected probability of a range of clinical outcomes in an idealized applicable patient population, both before and after the introduction of a diagnostic or therapeutic intervention into the standard of care. It encompasses estimates of the accuracy, effectiveness and performance characteristics of such intervention, and a cost and/or value measurement (a utility) associated with each outcome, which may be derived from actual health system costs of care (services, supplies, devices and drugs, etc.) and/or as an estimated acceptable value per quality adjusted life year (QALY) resulting in each outcome. The sum, across all predicted outcomes, of the product of the predicted population size for an outcome multiplied by the respective outcome's expected utility is the total health economic utility of a given standard of care. The difference between (i) the total health economic utility calculated for the standard of care with the intervention versus (ii) the total health economic utility for the standard of care without the intervention results in an overall measure of the health economic cost or value of the intervention. This may itself be divided amongst the entire patient group being analyzed (or solely amongst the intervention group) to arrive at a cost per unit intervention, and to guide such decisions as market positioning, pricing, and assumptions of health system acceptance. Such health economic utility functions are commonly used to compare the cost-effectiveness of the intervention, but may also be transformed to estimate the acceptable value per QALY the health care system is willing to pay, or the acceptable cost-effective clinical performance characteristics required of a new intervention.

[00041] For diagnostic (or prognostic) interventions of the invention, as each outcome (which in a disease classifying diagnostic test may be a TP, FP, TN, or FN) bears a different cost, a health economic utility function may preferentially favor sensitivity over specificity, or PPV over NPV based on the clinical situation and individual outcome costs and value, and thus provides another measure of health economic performance and value which may be different from more direct clinical or analytical performance measures. These different measurements and relative trade-offs generally will converge only in the case of a perfect test, with zero error rate (a.k.a., zero predicted subject outcome misclassifications or FP and FN), which all performance measures will favor over imperfection, but to differing degrees. [00042] "Measuring" or "measurement," or alternatively "detecting" or "detection," means assessing the presence, absence, quantity or amount (which can be an effective amount) of either a given substance within a clinical or subject-derived sample, including the derivation of qualitative or quantitative concentration levels of such substances, or otherwise evaluating the values or categorization of a subject's non-analyte clinical parameters.

[00043] "Negative predictive value" or "NPV" is calculated by TN/(TN + FN) or the true negative fraction of all negative test results. It also is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

[00044] See, e.g., O'Marcaigh AS, Jacobson RM, "Estimating The Predictive Value Of A Diagnostic Test, How To Prevent Misleading Or Confusing Results," Clin. Ped. 1993, 32(8): 485-491, which discusses specificity, sensitivity, and positive and negative predictive values of a test, e.g., a clinical diagnostic test. Often, for binary disease state classification approaches using a continuous diagnostic test measurement, the sensitivity and specificity is summarized by Receiver Operating Characteristics (ROC) curves according to Pepe et al, "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic, Prognostic, or Screening Marker," Am. J. Epidemiol 2004, 159 (9): 882-890, and summarized by the Area Under the Curve (AUC) or c-statistic, an indicator that allows representation of the sensitivity and specificity of a test, assay, or method over the entire range of test (or assay) cut points with just a single value. See also, e.g., Shultz, "Clinical Interpretation Of Laboratory Procedures," chapter 14 in Teitz, Fundamentals of Clinical Chemistry, Burtis and Ashwood (eds.), 4^th edition 1996, W.B. Saunders Company, pages 192-199; and Zweig et al., "ROC Curve Analysis: An Example Showing The Relationships Among Serum Lipid And

Apolipoprotein Concentrations In Identifying Subjects With Coronory Artery Disease," Clin. Chem., 1992, 38(8): 1425-1428. An alternative approach using likelihood functions, odds ratios, information theory, predictive values, calibration (including goodness-of-fit), and reclassification measurements is summarized according to Cook, "Use and Misuse of the Receiver Operating Characteristic Curve in Risk Prediction," Circulation 2007, 115: 928-935.

[00045] Finally, hazard ratios and absolute and relative risk ratios within subject cohorts defined by a test are a further measurement of clinical accuracy and utility. Multiple methods are frequently used to defining abnormal or disease values, including reference limits, discrimination limits, and risk thresholds.

[00046] "Analytical accuracy" refers to the reproducibility and predictability of the measurement process itself, and may be summarized in such measurements as coefficients of variation, and tests of concordance and calibration of the same samples or controls with different times, users, equipment and/or reagents. These and other considerations in evaluating new biomarkers are also summarized in Vasan, 2006.

[00047] "Performance" is a term that relates to the overall usefulness and quality of a diagnostic or prognostic test, including, among others, clinical and analytical accuracy, other analytical and process characteristics, such as use characteristics (e.g., stability, ease of use), health economic value, and relative costs of components of the test. Any of these factors may be the source of superior performance and thus usefulness of the test, and may be measured by appropriate "performance metrics," such as AUC, time to result, shelf life, etc. as relevant.

[00048] "Positive predictive value" or "PPV" is calculated by TP/(TP+FP) or the true positive fraction of all positive test results. It is inherently impacted by the prevalence of the disease and pre-test probability of the population intended to be tested.

[00049] "Risk" in the context of the present invention, relates to the probability that an event will occur over a specific time period, as in the responsiveness to treatmnet, and can can mean a subject's "absolute" risk or "relative" risk. Absolute risk can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative risk refers to the ratio of absolute risks of a subject compared either to the absolute risks of low risk cohorts or an average population risk, which can vary by how clinical risk factors are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(l-p) where p is the probability of event and (1- p) is the probability of no event) to no-conversion.

[00050] "Risk evaluation," or "evaluation of risk" in the context of the present invention encompasses making a prediction of the probability, odds, or likelihood that an event or disease state may occur, the rate of occurrence of the event or conversion from one disease state. Risk evaluation can also comprise prediction of future clinical parameters, traditional laboratory risk factor values, or other indices of cancer, either in absolute or relative terms in reference to a previously measured population. The methods of the present invention may be used to make continuous or categorical measurements of the responsiveness to treatment thus diagnosing and defining the risk spectrum of a category of subjects defined as being at responders or non-responders. In the categorical scenario, the invention can be used to discriminate between normal and other subject cohorts at higher risk for responding. Such differing use may require different DNARMARKER combinations and individualized panels, mathematical algorithms, and/or cut-off points, but be subject to the same aforementioned measurements of accuracy and performance for the respective intended use.

[00051] A "sample" in the context of the present invention is a biological sample isolated from a subject and can include, by way of example and not limitation, tissue biopies, whole blood, serum, plasma, blood cells, endothelial cells, lymphatic fluid, ascites fluid, interstitital fluid (also known as "extracellular fluid" and encompasses the fluid found in spaces between cells, including, inter alia, gingival crevicular fluid), bone marrow, cerebrospinal fluid (CSF), saliva, mucous, sputum, sweat, urine, or any other secretion, excretion, or other bodily fluids.

[00052] "Sensitivity" is calculated by TP/(TP+FN) or the true positive fraction of disease subjects.

[00053] "Specificity" is calculated by TN/(TN+FP) or the true negative fraction of non- disease or normal subjects.

[00054] By "statistically significant", it is meant that the alteration is greater than what might be expected to happen by chance alone (which could be a "false positive"). Statistical significance can be determined by any method known in the art. Commonly used measures of significance include the p-value, which presents the probability of obtaining a result at least as extreme as a given data point, assuming the data point was the result of chance alone. A result is often considered highly significant at a p-value of 0.05 or less.

[00055] A "subject" in the context of the present invention is preferably a mammal. The mammal can be a human, non-human primate, mouse, rat, dog, cat, horse, or cow, but are not limited to these examples. Mammals other than humans can be advantageously used as subjects that represent animal models of cancer. A subject can be male or female.

[00056] "TN" is true negative, which for a disease state test means classifying a non- disease or normal subject correctly.

[00057] "TP" i_s true positive, which for a disease state test means correctly classifying a disease subject.

[00058] DNA REPAIR AND DNA DAMAGE RESPONSE MARKERS

[00059] Patients have varying degrees of responsiveness to therapy and methods are needed to distinguish the capability of the treatment in a dynamic manner. Identification of changes (e.g., active, hyperactive, repressed, downmodulated, or inactive) to the cellular DNA repair pathways are useful in monitoring and predicting the response to a therapeutic compound. Accordingly, included in the invention are biomarkers associated with DNA repair and DNA damage response. The invention features methods for identifying subjects who either are or are pre-disposed to developing resistance or are sensitive to a therapeutic compound, e.g., a chemotherapeutic drug by detection of the biomarkers disclosed herein. These biomarkers are also useful for monitoring subjects undergoing treatments and therapies for cancer and cell proliferative disorders, and for selecting therapies and treatments that would be efficacious in subjects having cancer and cell proliferative disorders.

[00060] The term "biomarker" in the context of the present invention encompasses, without limitation, proteins, nucleic acids, polymorphisms of proteins and nucleic acids, elements, metabolites, and other analytes. Biomarkers can also include mutated proteins or mutated nucleic acids. The term "analyte" as used herein can mean any substance to be measured and can encompass electrolytes and elements, such as calcium.

[00061] Proteins, nucleic acids, polymorphisms, and metabolites whose levels are changed in subjects who have resistance or sensitivity to therapeutic compound, or are predisposed to developing resistance or sensitivity to therapeutic compound are summarized in Table 1 and are collectively referred to herein as, inter alia, "DNA Repair and DNA Damage Response proteins or DNARMARKER". Table 2 summarizes DNARMARKERS associated with Breast Cancer.

[00062] Expression of the DNARMARKERS is determined at the protein or nucleic acid level using any method known in the art. For example, at the nucleic acid level Northern hybridization analysis using probes which specifically recognize one or more of these sequences can be used to determine gene expression. Alternatively, expression is measured using reverse-transcription-based PCR assays, e.g., using primers specific for the

differentially expressed sequence of genes. Expression is also determined at the protein level, i.e. , by measuring the levels of peptides encoded by the gene products described herein, or activities thereof. Such methods are well known in the art and include, e.g. ,

immunoassays based on antibodies to proteins encoded by the genes, aptamers or molecular imprints.. Any biological material can be used for the detection/quantification of the protein or its activity. Alternatively, a suitable method can be selected to determine the activity of proteins encoded by the marker genes according to the activity of each protein analyzed.

[00063] The DNARMARKERS proteins are detected in any suitable manner, but are typically detected by contacting a sample from the patient with an antibody which binds the DNARMARKER protein and then detecting the presence or absence of a reaction product. The antibody may be monoclonal, polyclonal, chimeric, or a fragment of the foregoing, as discussed in detail above, and the step of detecting the reaction product may be carried out with any suitable immunoassay. The sample from the subject is typically a biological fluid as described above, and may be the same sample of biological fluid used to conduct the method described above. The sample may also be in the form of a tissue specimen from a patient where the specimen is suitable for immunohistochemistry in a variety of formats such as paraffin-embedded tissue, frozen sections of tissue, and freshly isolated tissue. The immunodetection methods are antibody-based but there are numerous additional techniques that allow for highly sensitive determinations of binding to an antibody in the context of a tissue. Those skilled in the art will be familiar with various immunohistochemistry strategies.

[00064] Immunoassays carried out in accordance with the present invention may be homogeneous assays or heterogeneous assays. In a homogeneous assay the immunological reaction usually involves the specific antibody (e.g., anti- DNARMARKER protein antibody), a labeled analyte, and the sample of interest. The signal arising from the label is modified, directly or indirectly, upon the binding of the antibody to the labeled analyte. Both the immunological reaction and detection of the extent thereof are carried out in a homogeneous solution. Immunochemical labels which may be employed include free radicals, radioisotopes, fluorescent dyes, enzymes, bacteriophages, or coenzymes.

[00065] In a heterogeneous assay approach, the reagents are usually the sample, the antibody, and means for producing a detectable signal. Samples as described above may be used. The antibody is generally immobilized on a support, such as a bead, plate or slide, and contacted with the specimen suspected of containing the antigen in a liquid phase. The support is then separated from the liquid phase and either the support phase or the liquid phase is examined for a detectable signal employing means for producing such signal. The signal is related to the presence of the analyte in the sample. Means for producing a detectable signal include the use of radioactive labels, fluorescent labels, or enzyme labels. For example, if the antigen to be detected contains a second binding site, an antibody which binds to that site can be conjugated to a detectable group and added to the liquid phase reaction solution before the separation step. The presence of the detectable group on the solid support indicates the presence of the antigen in the test sample. Examples of suitable immunoassays are radioimmunoassays, immunofluorescence methods, chemilumenescence methods, electrochemilumenescence or enzyme-linked immunoassays.

[00066] Those skilled in the art will be familiar with numerous specific immunoassay formats and variations thereof which may be useful for carrying out the method disclosed herein. See generally E. Maggio, Enzyme-Immunoassay, (1980) (CRC Press, Inc., Boca Raton, Fla.); see also U.S. Pat. No. 4,727,022 to Skold et al. titled "Methods for Modulating Ligand- Receptor Interactions and their Application," U.S. Pat. No. 4,659,678 to Forrest et al. titled "Immunoassay of Antigens," U.S. Pat. No. 4,376,110 to David et al., titled

"Immunometric Assays Using Monoclonal Antibodies," U.S. Pat. No. 4,275,149 to Litman et al., titled "Macromolecular Environment Control in Specific Receptor Assays," U.S. Pat. No. 4,233,402 to Maggio et al., titled "Reagents and Method Employing Channeling," and U.S. Pat. No. 4,230,767 to Boguslaski et al., titled "Heterogenous Specific Binding Assay Employing a Coenzyme as Label. "

[00067] Antibodies are conjugated to a solid support suitable for a diagnostic assay (e.g., beads, plates, slides or wells formed from materials such as latex or polystyrene) in accordance with known techniques, such as passive binding. Antibodies as described herein may likewise be conjugated to detectable groups such as radiolabels (e.g., 35 S, 125 I, 131 1), enzyme labels (e.g., horseradish peroxidase, alkaline phosphatase), and fluorescent labels (e.g., fluorescein) in accordance with known techniques.

[00068] The skilled artisan can routinely make antibodies, nucleic acid probes, e.g., oligonucleotides, aptamers, siRNAs against any of the DNARMARKERS in Table 1.

[00069] The invention also includes a DNARMARKER-detection reagent, e.g., nucleic acids that specifically identify one or more DNARMARKER nucleic acids by having homologous nucleic acid sequences, such as oligonucleotide sequences, complementary to a portion of the DNARMARKER nucleic acids or antibodies to proteins encoded by the DNARMARKER nucleic acids packaged together in the form of a kit. The oligonucleotides are fragments of the DNARMARKER genes. For example the olignucleotides are 200, 150, 100, 50, 25, 10 or less nucleotides in length. The kit may contain in separate containers a nucleic acid or antibody (either already bound to a solid matrix or packaged separately with reagents for binding them to the matrix) , control formulations (positive and/or negative), and/or a detectable label. Instructions (e.g., written, tape, VCR, CD-ROM, etc.) for carrying out the assay may be included in the kit. The assay may for example be in the form of a Northern hybridization or a sandwich ELISA as known in the art.

[00070] For example, DNARMARKER detection reagent, is immobilized on a solid matrix such as a porous strip to form at least one DNARMARKER detection site. The measurement or detection region of the porous strip may include a plurality of sites containing a nucleic acid. A test strip may also contain sites for negative and/or positive controls. Alternatively, control sites are located on a separate strip from the test strip.

Optionally, the different detection sites may contain different amounts of immobilized nucleic acids, i.e., a higher amount in the first detection site and lesser amounts in subsequent sites. Upon the addition of test sample, the number of sites displaying a detectable signal provides a quantitative indication of the amount of DNARMARKER present in the sample. The detection sites may be configured in any suitably detectable shape and are typically in the shape of a bar or dot spanning the width of a test strip.

[00071] Alternatively, the kit contains a nucleic acid substrate array comprising one or more nucleic acid sequences. The nucleic acids on the array specifically identify one or more nucleic acid sequences represented by DNARMARKER 1-259. In various embodiments, the expression of 2, 3,4, 5, 6, 7,8, 9, 10, 15, 20, 25, 40 or 50 or more of the sequences represented by DNARMARKER 1-259 are identified by virtue of binding to the array. The substrate array can be on, e.g. , a solid substrate, e.g. , a "chip" as described in U.S. Patent

No.5,744,305. Alternatively the substrate array can be a solution array, e.g., Luminex, Cyvera, Vitra and Quantum Dots' Mosaic. Preferably, the kit contains antibodies for the detection of DN ARM ARKERS .

[00072] THERAPEUTIC METHODS

Responsiveness (e.g., resistance or sensitivity) of a cell to an agent is determined by measuring an effective amount of a DNARMARKER proteins, nucleic acids,

polymorphisms, metabolites, and other analytes (which may be two or more) in a test sample (e.g., a subject derived sample), and comparing the effective amounts to reference or index values, often utilizing mathematical algorithms or formula in order to combine information from results of multiple individual DNARMARKERS and from non-analyte clinical parameters into a single measurement or index. The cell is for example a cancer cell.

Optionally, the cancer is a breast cancer. The DNARMARKER is for example, XPF, FANCD2, pMK2, PAR, BRCA1, RAD51, NQOl, TOP2A.

[00073] By resistance is meant that the failure of a cell to respond to an agent. For example, resistance to a chemotherapeutic drug means the cell is not damaged or killed by the drug. By sensitivity is meant that the cell responds to an agent. For example, sensitivity to a chemotherapeutic drug means the cell is damaged or killed by the drug.

[00074] For example, responsiveness of a cell to a chemotherapeutic agent is identified by determining a decrease in expression or activity of one or more Breast DNARMARKERS. The presence of a deficiency in DNARMARKER indicates that the cell is sensitive to a chemotherapeutic agent. Whereas, the absence of a deficiency indicates that the cell is resistant to a chemotherapeutic agent.

[00075] The methods are useful to treat, alleviate the symptoms of, monitor the progression of or delay the onset of cancer in a subject.

[00076] Expression of an effective amount of DNARMARKER proteins, nucleic acids or metabolites also allows for the course of treatment of cancer or a cell proliferative disorder to be monitored. In this method, a biological sample is provided from a subject undergoing treatment, e.g., chemotherapeutic treatment, for cancer or a cell proliferative disorder. If desired, biological samples are obtained from the subject at various time points before, during, or after treatment. Expression of an effective amount of DNARMARKER proteins, nucleic acids or metabolites is then determined and compared to a reference, e.g. a control individual or population whose cancer or a cell proliferative disorder state is known or an index value. The reference sample or index value may be taken or derived from one or more individuals who have been exposed to the treatment. Alternatively, the reference sample or index value may be taken or derived from one or more individuals who have not been exposed to the treatment. For example, samples may be collected from subjects who have received initial treatment for cancer or a cell proliferative disorder and subsequent treatment for diabetes to monitor the progress of the treatment.

[00077] The amount of the DNARMARKER protein, nucleic acid, polymorphism, metabolite, or other analyte can be measured in a test sample and compared to the "normal control level," utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values. Such normal control level and cutoff points may vary based on whether a DNARMARKER is used alone or in a formula combining with other DNARMARKERS into an index. Alternatively, the normal control level can be a database of DNARMARKER patterns from previously tested subjects who responded to chemotherapy over a clinically relevant time horizon.

[00078] The present invention may be used to make continuous or categorical measurements of the response to chemotherapy or cancer survival, thus diagnosing and defining the risk spectrum of a category of subjects defined as at risk for not responding to chemotherapy. In the categorical scenario, the methods of the present invention can be used to discriminate between treatment responsive and treatment non-responsive subject cohorts. In other embodiments, the present invention may be used so as to discriminate those who have an improved survival potential. Such differing use may require different DNARMARKER combinations in individual panel, mathematical algorithm, and/or cut-off points, but be subject to the same aforementioned measurements of accuracy and other performance metrics relevant for the intended use.

[00079] Identifying the subject who will be responsive to therapy enables the selection and initiation of various therapeutic interventions or treatment regimens in order increase the individual's survival potential. Levels of an effective amount of DNARMARKER proteins, nucleic acids, polymorphisms, metabolites, or other analytes also allows for the course of treatment of a metastatic disease or metastatic event to be monitored. In this method, a biological sample can be provided from a subject undergoing treatment regimens, e.g., drug treatments, for cancer. If desired, biological samples are obtained from the subject at various time points before, during, or after treatment.

[00080] Levels of an effective amount of DNARMARKER proteins, nucleic acids, polymorphisms, metabolites, or other analytes can then be determined and compared to a reference value, e.g. a control subject or population whose therapeutic responsiveness is known or an index value or baseline value. The reference sample or index value or baseline value may be taken or derived from one or more subjects who have been exposed to the treatment, or may be taken or derived from one or more subjects who are at low risk of surviving the cancer, or may be taken or derived from subjects who have shown

improvements in as a result of exposure to treatment. Alternatively, the reference sample or index value or baseline value may be taken or derived from one or more subjects who have not been exposed to the treatment. For example, samples may be collected from subjects who have received initial treatment for cancer or and subsequent treatment for cancer or a metastatic event to monitor the progress of the treatment. A reference value can also comprise a value derived from risk prediction algorithms or computed indices from population studies such as those disclosed herein.

[00081] The DNARMARKERS of the present invention can thus be used to generate a "reference DNARMARKER profile" of those subjects who would or would not be expected respond to cancer treatment. The DNARMARKERS disclosed herein can also be used to generate a "subject DNARMARKER profile" taken from subjects who are responsive cancer treatment. The subject DNARMARKER profiles can be compared to a reference

DNARMARKER profile to diagnose or identify subjects at risk for developing resistance to chemotherapy, to monitor the progression of disease, as well as the rate of progression of disease, and to monitor the effectiveness of treatment modalities. The reference and subject DNARMARKER profiles of the present invention can be contained in a machine-readable medium, such as but not limited to, analog tapes like those readable by a VCR, CD-ROM, DVD-ROM, USB flash media, among others. Such machine-readable media can also contain additional test results, such as, without limitation, measurements of clinical parameters and traditional laboratory risk factors. Alternatively or additionally, the machine-readable media can also comprise subject information such as medical history and any relevant family history. The machine-readable media can also contain information relating to other disease- risk algorithms and computed indices such as those described herein.

[00082] Differences in the genetic makeup of subjects can result in differences in their relative abilities to metabolize various drugs, which may modulate the symptoms or risk factors of cancer or metastatic events. Subjects that have cancer, or at risk for developing cancer or a metastatic event can vary in age, ethnicity, and other parameters. Accordingly, use of the DNARMARKERS disclosed herein, both alone and together in combination with known genetic factors for drug metabolism, allow for a pre-determined level of predictability that a putative therapeutic or prophylactic to be tested in a selected subject will be suitable for treating or preventing cancer in the subject.

[00083] To identify therapeutic that is appropriate for a specific subject a the expression of one or more of DNARMARKER proteins, nucleic acids or metabolites is in a test sample form the subject is determined .

[00084] The pattern of DNARMARKER expression in the test sample is measured and compared to a reference profile, e.g., a therapeutic compound reference expression profile. Comparison can be performed on test and reference samples measured concurrently or at temporally distinct times. An example of the latter is the use of compiled expression information, e.g. , a sequence database, which assembles information about expression levels of DNARMARKERS.

[00085] If the reference sample, e.g., a control sample is from cells that are sensitive to a therapeutic compound then a similarity in the amount of the DNARMARKER proteins in the test sample and the reference sample indicates that treatment with that therapeutic compound will be efficacious. However, a change in the amount of the DNARMARKER in the test sample and the reference sample indicates treatment with that compound will result in a less favorable clinical outcome or prognosis. In contrast, if the reference sample, e.g., a control sample is from cells that are resistant to a therapeutic compound then a similarity in the amount of the DNARMARKER proteins in the test sample and the reference sample indicates that the treatment with that compound will result in a less favorable clinical outcome or prognosis. However, a change in the amount of the DNARMARKER in the test sample and the reference sample indicates that treatment with that therapeutic compound will be efficacious.

[00086] By "efficacious" is meant that the treatment leads to a decrease in the amount of a DNARMARKER protein, or a decrease in size, prevalence, or metastatic potential of cancer in a subject. When treatment is applied prophylactically, "efficacious" means that the treatment retards or prevents cancer or a cell proliferative disorder from forming.

Assessment of cancer and cell proliferative disorders is made using standard clinical protocols.

[00087] The subject is preferably a mammal. The mammal is, e.g. , a human, non-human primate, mouse, rat, dog, cat, horse, or cow. The subject has been previously diagnosed as having cancer or a cell proliferative disorder, and possibly has already undergone treatment for the cancer or a cell proliferative disorder.

[00088] The subject is suffering from or at risk of developing breast cancer. Subjects suffering from or at risk of developing breast cancer are identified by methods known in the art.

[00089] Alternatively, the deficiency is determined by measuring the expression (e.g. increase or decrease relative to a control), detecting a sequence variation or posttranslational modification of one or more DNARMARKERS described herein.

[00090] Posttranslational modification includes for example, phosphorylation, ubiquitination, sumo-ylation, acetylation, alkylation, methylation, glycylation, glycosylation, isoprenylation, lipoylation, phosphopantetheinylation, sulfation, selenation and C-terminal amidation. For example, a deficiency in the Homologous Recombination/FA pathway is determined by detecting the monoubiquitination of FANCD2. Similarly, responsiveness of cancer cell to a MAP2KAP2 inhibitor is determined by detecting phosphorylation of a MAP2KAP2 protein. Phosphorylation indicates the cell is sensitive to a MAP2KAP2 inhibitor. In contrast the absence of phosphorylation indicates the cell is resistant to a MAP2KAP2 inhibitor.

[00091] Sequence variations such as mutations and polymorphisms may include a deletion, insertion or substitution of one or more nucleotides, relative to the wild-type nucleotide sequence. The one or more variations may be in a coding or non-coding region of the nucleic acid sequence and, may reduce or abolish the expression or function of the DNA repair pathway component polypeptide. In other words, the variant nucleic acid may encode a variant polypeptide which has reduced or abolished activity or may encode a wild-type polypeptide which has little or no expression within the cell, for example through the altered activity of a regulatory element. A variant nucleic acid may have one, two, three, four or more mutations or polymorphisms relative to the wild-type sequence.

[00092] The presence of one or more variations in a nucleic acid which encodes a component of a DNA repair pathway, is determined for example by detecting, in one or more cells of a test sample, the presence of an encoding nucleic acid sequence which comprises the one or more mutations or polymorphisms, or by detecting the presence of the variant component polypeptide which is encoded by the nucleic acid sequence.

[00093] Various methods are available for determining the presence or absence in a sample obtained from an individual of a particular nucleic acid sequence, for example a nucleic acid sequence which has a mutation or polymorphism that reduces or abrogates the expression or activity of a DNA repair pathway component. Furthermore, having sequenced nucleic acid of an individual or sample, the sequence information can be retained and subsequently searched without recourse to the original nucleic acid itself. Thus, for example, scanning a database of sequence information using sequence analysis software may identify a sequence alteration or mutation.

[00094] Methods according to some aspects of the present invention may comprise determining the binding of an oligonucleotide probe to nucleic acid obtained from the sample, for example, genomic DNA, RNA or cDNA. The probe may comprise a nucleotide sequence which binds specifically to a nucleic acid sequence which contains one or more mutations or polymorphisms and does not bind specifically to the nucleic acid sequence which does not contain the one or more mutations or polymorphisms, or vice versa. The oligonucleotide probe may comprise a label and binding of the probe may be determined by detecting the presence of the label.

[00095] A method may include hybridization of one or more (e.g. two) oligonucleotide probes or primers to target nucleic acid. Where the nucleic acid is double-stranded DNA, hybridization will generally be preceded by denaturation to produce single-stranded DNA. The hybridization may be as part of a PCR procedure, or as part of a probing procedure not involving PCR. An example procedure would be a combination of PCR and low stringency hybridization. [00096] Binding of a probe to target nucleic acid (e.g. DNA) may be measured using any of a variety of techniques at the disposal of those skilled in the art. For instance, probes may be radioactively, fluorescently or enzymatically labeled. Other methods not employing labeling of probe include examination of restriction fragment length polymorphisms, amplification using PCR, RNase cleavage and allele specific oligonucleotide probing.

Probing may employ the standard Southern blotting technique. For instance, DNA may be extracted from cells and digested with different restriction enzymes. Restriction fragments may then be separated by electrophoresis on an agarose gel, before denaturation and transfer to a nitrocellulose filter. Labeled probe may be hybridized to the DNA fragments on the filter and binding determined.

[00097] Those skilled in the art are well able to employ suitable conditions of the desired stringency for selective hybridization, taking into account factors such as oligonucleotide length and base composition, temperature and so on. Suitable selective hybridization conditions for oligonucleotides of 17 to 30 bases include hybridization overnig ht at 42. °C in 6x SSC and washing in 6.x SSC at a series of increasing temperatures from 42°C to 65°C. Other suitable conditions and protocols are described in Molecular Cloning: a Laboratory Manual: 3rd edition, Sambrook & Russell (2001) Cold Spring Harbor Laboratory Press NY and Current Protocols in Molecular Biology, Ausubel et al. eds. John Wiley & Sons (1992).

[00098] Nucleic acid, which may be genomic DNA, RNA or cDNA, or an amplified region thereof, may be sequenced to identify or determine the presence of polymorphism or mutation therein. A polymorphism or mutation may be identified by comparing the sequence obtained with the database sequence of the component, as set out above. In particular, the presence of one or more polymorphisms or mutations that cause abrogation or loss of function of the polypeptide component, and thus the DNA repair pathway as a whole, may be determined.

[00099] Sequencing may be performed using any one of a range of standard techniques. Sequencing of an amplified product may, for example, involve precipitation with

isopropanol, resuspension and sequencing using a TaqFS+ Dye terminator sequencing kit. Extension products may be electrophoresed on an ABI 377 DNA sequencer and data analyzed using Sequence Navigator software.

[000100] A specific amplification reaction such as PCR using one or more pairs of primers may conveniently be employed to amplify the region of interest within the nucleic acid sequence, for example, the portion of the sequence suspected of containing mutations or polymorphisms. The amplified nucleic acid may then be sequenced as above, and/or tested in any other way to determine the presence or absence of a mutation or polymorphism which reduces or abrogates the expression or activity of the DNA repair pathway component.

Suitable amplification reactions include the polymerase chain reaction (PCR) (reviewed for instance in "PCR protocols; A Guide to Methods and Applications", Eds. Innis et al, 1990, Academic Press, New York, Mullis et al, Cold Spring Harbor Symp. Quant. Biol., 51:263, (1987), Ehrlich (ed), PCR technology, Stockton Press, NY, 1989, and Ehrlich et al, Science, 252: 1643-1650, (1991)).

[000101] Mutations and polymorphisms associated with cancer may also be detected at the protein level by detecting the presence of a variant (i.e. a mutant or allelic variant) polypeptide.

[000102] A method of identifying a cancer cell in a sample from an individual as deficient in DNA repair may include contacting a sample with a specific binding member directed against a variant (e.g. a mutant) polypeptide component of the pathway, and determining binding of the specific binding member to the sample. Binding of the specific binding member to the sample may be indicative of the presence of the variant polypeptide component of the DNA repair pathway in a cell within the sample. Preferred specific binding molecules for use in aspects of the present invention include antibodies and fragments or derivatives thereof ("antibody molecules").

[000103] The reactivities of a binding member such as an antibody on normal and test samples may be determined by any appropriate means. Tagging with individual reporter molecules is one possibility. The reporter molecules may directly or indirectly generate detectable, and preferably measurable, signals. The linkage of reporter molecules may be directly or indirectly, covalently, e.g. via a peptide bond or non-covalently. Linkage via a peptide bond may be as a result of recombinant expression of a gene fusion encoding binding molecule (e.g. antibody) and reporter molecule.

[000104] PERFORMANCE AND ACCURACY MEASURES OF THE INVENTION

[000105] The performance and thus absolute and relative clinical usefulness of the invention may be assessed in multiple ways as noted above. Amongst the various assessments of performance, the invention is intended to provide accuracy in clinical diagnosis and prognosis. The accuracy of a diagnostic or prognostic test, assay, or method concerns the ability of the test, assay, or method to distinguish between subjects responsive to chemotherapeutic treatment and those that are not, is based on whether the subjects have an "effective amount" or a "significant alteration" in the levels of a DNARMARKER. By "effective amount" or "significant alteration," it is meant that the measurement of an appropriate number of DNARMARKERS (which may be one or more) is different than the predetermined cut-off point (or threshold value) for that DNARMARKER(S) and therefore indicates that the subject responsiveness to therapy for which the DNARMARKER(S) is a determinant. The difference in the level of DNARMARKERS between normal and abnormal is preferably statistically significant. As noted below, and without any limitation of the invention, achieving statistical significance, and thus the preferred analytical and clinical accuracy, generally but not always requires that combinations of several DNARMARKERS be used together in panels and combined with mathematical algorithms in order to achieve a statistically significant DNARMARKERS index.

[000106] In the categorical diagnosis of a disease state, changing the cut point or threshold value of a test (or assay) usually changes the sensitivity and specificity, but in a qualitatively inverse relationship. Therefore, in assessing the accuracy and usefulness of a proposed medical test, assay, or method for assessing a subject's condition, one should always take both sensitivity and specificity into account and be mindful of what the cut point is at which the sensitivity and specificity are being reported because sensitivity and specificity may vary significantly over the range of cut points. Use of statistics such as AUC, encompassing all potential cut point values, is preferred for most categorical risk measures using the invention, while for continuous risk measures, statistics of goodness-of-fit and calibration to observed results or other gold standards, are preferred.

[000107] Using such statistics, an "acceptable degree of diagnostic accuracy", is herein defined as a test or assay (such as the test of the invention for determining the clinically significant presence of DNARMARKERS, which thereby indicates the presence of cancer and/or a risk of having a metastatic event) in which the AUC (area under the ROC curve for the test or assay) is at least 0.60, desirably at least 0.65, more desirably at least 0.70, preferably at least 0.75, more preferably at least 0.80, and most preferably at least 0.85.

[000108] By a "very high degree of diagnostic accuracy", it is meant a test or assay in which the AUC (area under the ROC curve for the test or assay) is at least 0.80, desirably at least 0.85, more desirably at least 0.875, preferably at least 0.90, more preferably at least 0.925, and most preferably at least 0.95.

[000109] The predictive value of any test depends on the sensitivity and specificity of the test, and on the prevalence of the condition in the population being tested. This notion, based on Bayes' theorem, provides that the greater the likelihood that the condition being screened for is present in an individual or in the population (pre-test probability), the greater the validity of a positive test and the greater the likelihood that the result is a true positive. Thus, the problem with using a test in any population where there is a low likelihood of the condition being present is that a positive result has limited value (i.e., more likely to be a false positive). Similarly, in populations at very high risk, a negative test result is more likely to be a false negative.

[000110] As a result, ROC and AUC can be misleading as to the clinical utility of a test in low disease prevalence tested populations (defined as those with less than 1% rate of occurrences (incidence) per annum, or less than 10% cumulative prevalence over a specified time horizon). Alternatively, absolute risk and relative risk ratios as defined elsewhere in this disclosure can be employed to determine the degree of clinical utility. Populations of subjects to be tested can also be categorized into quartiles by the test's measurement values, where the top quartile (25% of the population) comprises the group of subjects with the highest relative risk for therapeutic unresponsiveness, and the bottom quartile comprising the group of subjects having the lowest relative risk for therapeutic unresponsiveness Generally, values derived from tests or assays having over 2.5 times the relative risk from top to bottom quartile in a low prevalence population are considered to have a "high degree of diagnostic accuracy," and those with five to seven times the relative risk for each quartile are considered to have a "very high degree of diagnostic accuracy." Nonetheless, values derived from tests or assays having only 1.2 to 2.5 times the relative risk for each quartile remain clinically useful are widely used as risk factors for a disease; such is the case with total cholesterol and for many inflammatory biomarkers with respect to their prediction of future events. Often such lower diagnostic accuracy tests must be combined with additional parameters in order to derive meaningful clinical thresholds for therapeutic intervention, as is done with the aforementioned global risk assessment indices.

[000111] A health economic utility function is an yet another means of measuring the performance and clinical value of a given test, consisting of weighting the potential categorical test outcomes based on actual measures of clinical and economic value for each. Health economic performance is closely related to accuracy, as a health economic utility function specifically assigns an economic value for the benefits of correct classification and the costs of misclassification of tested subjects. As a performance measure, it is not unusual to require a test to achieve a level of performance which results in an increase in health economic value per test (prior to testing costs) in excess of the target price of the test.

[000112] In general, alternative methods of determining diagnostic accuracy are commonly used for continuous measures, when a disease category or risk category has not yet been clearly defined by the relevant medical societies and practice of medicine, where thresholds for therapeutic use are not yet established, or where there is no existing gold standard for diagnosis of the pre-disease. For continuous measures of risk, measures of diagnostic accuracy for a calculated index are typically based on curve fit and calibration between the predicted continuous value and the actual observed values (or a historical index calculated value) and utilize measures such as R squared, Hosmer- Lemeshow P-value statistics and confidence intervals. It is not unusual for predicted values using such algorithms to be reported including a confidence interval (usually 90% or 95% CI) based on a historical observed cohort's predictions, as in the test for risk of future breast cancer recurrence commercialized by Genomic Health, Inc. (Redwood City, California).

[000113] In general, by defining the degree of diagnostic accuracy, i.e., cut points on a ROC curve, defining an acceptable AUC value, and determining the acceptable ranges in relative concentration of what constitutes an effective amount of the DNARMARKERS of the invention allows for one of skill in the art to use the DNARMARKERS to identify, diagnose, or prognose subjects with a pre-determined level of predictability and performance.

[000114] CONSTRUCTION OF DNARMARKER PANELS

[000115] Groupings of DNARMARKERS can be included in "panels." A "panel" within the context of the present invention means a group of biomarkers (whether they are

DNARMARKERS, clinical parameters, or traditional laboratory risk factors) that includes more than one DNARMARKER. A panel can also comprise additional biomarkers, e.g., clinical parameters, traditional laboratory risk factors, known to be present or associated with responsiveness to chemotherapeutic treatement, in combination with a selected group of the DNARMARKERS listed in Table 1 or Table 2.

[000116] As noted above, many of the individual DNARMARKERS, clinical parameters, and traditional laboratory risk factors listed, when used alone and not as a member of a multi- biomarker panel of DNARMARKERS, have little or no clinical use in reliably distinguishing individuals that are responsive to therapeutic treatment and those that are not and thus cannot reliably be used alone in classifying any subject between those two states. Even where there are statistically significant differences in their mean measurements in each of these populations, as commonly occurs in studies which are sufficiently powered, such biomarkers may remain limited in their applicability to an individual subject, and contribute little to diagnostic or prognostic predictions for that subject. A common measure of statistical significance is the p-value, which indicates the probability that an observation has arisen by chance alone; preferably, such p-values are 0.05 or less, representing a 5% or less chance that the observation of interest arose by chance. Such p-values depend significantly on the power of the study performed.

[000117] Despite this individual DNARMARKER performance, and the general performance of formulas combining only the traditional clinical parameters and few traditional laboratory risk factors, the present inventors have noted that certain specific combinations of two or more DNARMARKERS can also be used as multi-biomarker panels comprising combinations of DNARMARKERS that are known to be involved in one or more physiological or biological pathways, and that such information can be combined and made clinically useful through the use of various formulae, including statistical classification algorithms and others, combining and in many cases extending the performance

characteristics of the combination beyond that of the individual DNARMARKERS. These specific combinations show an acceptable level of diagnostic accuracy, and, when sufficient information from multiple DNARMARKERS is combined in a trained formula, often reliably achieve a high level of diagnostic accuracy transportable from one population to another.

[000118] The general concept of how two less specific or lower performing

DNARMARKERS are combined into novel and more useful combinations for the intended indications, is a key aspect of the invention. Multiple biomarkers can often yield better performance than the individual components when proper mathematical and clinical algorithms are used; this is often evident in both sensitivity and specificity, and results in a greater AUC. Secondly, there is often novel unperceived information in the existing biomarkers, as such was necessary in order to achieve through the new formula an improved level of sensitivity or specificity. This hidden information may hold true even for biomarkers which are generally regarded to have suboptimal clinical performance on their own. In fact, the suboptimal performance in terms of high false positive rates on a single biomarker measured alone may very well be an indicator that some important additional information is contained within the biomarker results - information which would not be elucidated absent the combination with a second biomarker and a mathematical formula. [000119] Several statistical and modeling algorithms known in the art can be used to both assist in DNARMARKER selection choices and optimize the algorithms combining these choices. Statistical tools such as factor and cross-biomarker correlation/covariance analyses allow more rationale approaches to panel construction. Mathematical clustering and classification tree showing the Euclidean standardized distance between the

DNARMARKERS can be advantageously used. Pathway informed seeding of such statistical classification techniques also may be employed, as may rational approaches based on the selection of individual DNARMARKERS based on their participation across in particular pathways or physiological functions.

[000120] Ultimately, formula such as statistical classification algorithms can be directly used to both select DNARMARKERS and to generate and train the optimal formula necessary to combine the results from multiple DNARMARKERS into a single index. Often, techniques such as forward (from zero potential explanatory parameters) and backwards selection (from all available potential explanatory parameters) are used, and information criteria, such as AIC or BIC, are used to quantify the tradeoff between the performance and diagnostic accuracy of the panel and the number of DNARMARKERS used. The position of the individual DNARMARKER on a forward or backwards selected panel can be closely related to its provision of incremental information content for the algorithm, so the order of contribution is highly dependent on the other constituent DNARMARKERS in the panel.

[000121] CONSTRUCTION OF CLINICAL ALGORITHMS

[000122] Any formula may be used to combine DNARMARKER results into indices useful in the practice of the invention. As indicated above, and without limitation, such indices may indicate, among the various other indications, the probability, likelihood, absolute or relative chance of responding to chemotherapy. This may be for a specific time period or horizon, or for remaining lifetime risk, or simply be provided as an index relative to another reference subject population.

[000123] Although various preferred formula are described here, several other model and formula types beyond those mentioned herein and in the definitions above are well known to one skilled in the art. The actual model type or formula used may itself be selected from the field of potential models based on the performance and diagnostic accuracy characteristics of its results in a training population. The specifics of the formula itself may commonly be derived from DNARMARKER results in the relevant training population. Amongst other uses, such formula may be intended to map the feature space derived from one or more DNARMARKER inputs to a set of subject classes (e.g. useful in predicting class membership of subjects as normal, responders and non-responders), to derive an estimation of a probability function of risk using a Bayesian approach (e.g. the risk of cancer or a metastatic event), or to estimate the class-conditional probabilities, then use Bayes' rule to produce the class probability function as in the previous case.

[000124] Preferred formulas include the broad class of statistical classification algorithms, and in particular the use of discriminant analysis. The goal of discriminant analysis is to predict class membership from a previously identified set of features. In the case of linear discriminant analysis (LDA), the linear combination of features is identified that maximizes the separation among groups by some criteria. Features can be identified for LDA using an eigengene based approach with different thresholds (ELD A) or a stepping algorithm based on a multivariate analysis of variance (MANOVA). Forward, backward, and stepwise algorithms can be performed that minimize the probability of no separation based on the Hotelling-Lawley statistic.

[000125] Eigengene -based Linear Discriminant Analysis (ELD A) is a feature selection technique developed by Shen et al. (2006). The formula selects features (e.g. biomarkers) in a multivariate framework using a modified eigen analysis to identify features associated with the most important eigenvectors. "Important" is defined as those eigenvectors that explain the most variance in the differences among samples that are trying to be classified relative to some threshold.

[000126] A support vector machine (SVM) is a classification formula that attempts to find a hyperplane that separates two classes. This hyperplane contains support vectors, data points that are exactly the margin distance away from the hyperplane. In the likely event that no separating hyperplane exists in the current dimensions of the data, the dimensionality is expanded greatly by projecting the data into larger dimensions by taking non-linear functions of the original variables (Venables and Ripley, 2002). Although not required, filtering of features for SVM often improves prediction. Features (e.g., biomarkers) can be identified for a support vector machine using a non-parametric Kruskal-Wallis (KW) test to select the best univariate features. A random forest (RF, Breiman, 2001) or recursive partitioning (RPART, Breiman et al., 1984) can also be used separately or in combination to identify biomarker combinations that are most important. Both KW and RF require that a number of features be selected from the total. RPART creates a single classification tree using a subset of available biomarkers. [000127] Other formula may be used in order to pre-process the results of individual DNARMARKER measurement into more valuable forms of information, prior to their presentation to the predictive formula. Most notably, normalization of biomarker results, using either common mathematical transformations such as logarithmic or logistic functions, as normal or other distribution positions, in reference to a population's mean values, etc. are all well known to those skilled in the art. Of particular interest are a set of normalizations based on Clinical Parameters such as age, gender, race, or sex, where specific formula are used solely on subjects within a class or continuously combining a Clinical Parameter as an input. In other cases, analyte -based biomarkers can be combined into calculated variables which are subsequently presented to a formula.

[000128] In addition to the individual parameter values of one subject potentially being normalized, an overall predictive formula for all subjects, or any known class of subjects, may itself be recalibrated or otherwise adjusted based on adjustment for a population's expected prevalence and mean biomarker parameter values, according to the technique outlined in D'Agostino et al, (2001) JAMA 286:180-187, or other similar normalization and recalibration techniques. Such epidemiological adjustment statistics may be captured, confirmed, improved and updated continuously through a registry of past data presented to the model, which may be machine readable or otherwise, or occasionally through the retrospective query of stored samples or reference to historical studies of such parameters and statistics. Additional examples that may be the subject of formula recalibration or other adjustments include statistics used in studies by Pepe, M.S. et al, 2004 on the limitations of odds ratios; Cook, N.R., 2007 relating to ROC curves. Finally, the numeric result of a classifier formula itself may be transformed post-processing by its reference to an actual clinical population and study results and observed endpoints, in order to calibrate to absolute risk and provide confidence intervals for varying numeric results of the classifier or risk formula. An example of this is the presentation of absolute risk, and confidence intervals for that risk, derivied using an actual clinical study, chosen with reference to the output of the recurrence score formula in the Oncotype Dx product of Genomic Health, Inc. (Redwood City, CA). A further modification is to adjust for smaller sub-populations of the study based on the output of the classifier or risk formula and defined and selected by their Clinical Parameters, such as age or sex. [000129] EXAMPLE 1: DISCOVERY STUDY INVESTIGATING THE POTENTIAL UTILITY OF NQOl EXPRESSION AS A PROGNOSTIC OR PREDICTIVE BIOMARKER IN A LARGE

RANDOMIZED BREAST CANCER STUDY

[000130] To investigate the utility of NQOl as a predictive marker for breast cancer patients treated with anthracycline-containing therapy, we applied the marker to a large randomized study. Specifically, tissue microarrays (TMAs) were constructed from 935 of a total of 1,146 patients from two French multicentric randomized trials comparing adjuvant anthracycline-based chemotherapy (CT group) with no chemotherapy (control group) in pre- and postmenopausal, early breast cancer patients (Arriagada, 2005). Survival data were updated in December 2009.

[000131] Immunohistochemistry was performed on the TMAs from this study and digital pathology-based image analysis was performed to develop a Q Score for each analyzable core. The NQOl protein levels were quite variable across the patient population as seen in Figure 4.

[000132] Statistical analysis was performed to determine the predictive potential of NQOl in this study. A total of 600 patients were included in the statistical analysis as some patients dropped out of the study due to insufficient tumor tissue. The patient characteristics for those included in the study are summarized below. For each patient, the maximum value of NQOl expression was used for the analysis.

P atieii is cha r a c ter is tic s

11

Control Qrewp SSi

CT group 3Sii i^' .M.»c ;

Age 5ΰ ^•HS i 2*.«r ;

Age >=5i> Ail · « :>:( ; '-

156 ; 25.90 ;

4 ; 7S.SS ; - Size

<=28

?2S

4

GRADE f 32 ; 5.sr ;

GRADE :t ZTi ; S2.25 J

GRADE US 143 • Si.it

Unknown

:5ii

Positive J.S3 ••^■« 17 ;

ί Si Status

Positive f s«.s? ;

ega ive ?2

Unknown f.4 [000133] In Figure 5, the NQOl score was divided into quartiles to determine if survival was dependent on NQOl expression level in either the chemotherapy (CT) or the no chemotherapy (No CT) cohorts. In the chemotherapy group, higher levels of NQOl correlate with better survival. Alternatively, in the No CT patients, high NQOl expression correlates with poor survival. This data supports earlier data that patients with homozygous common missense variant NQ01*2 faired poorly on anthracyc line-containing therapy relative to patients with wild type NQOl or NQOl heterozygotes (Fagerholm, 2008). The approach in this study was to use immunohistochemistry since the NQ01*2 polymorphism results in reduced stability of NQOl protein. Additionally, it is possible that NQOl heterozygotes may also have a reduced level of NQOl protein.

[000134] In Figure 6, patients have been further divided by ER status (positive or negative) as well as NQOl protein status (high or low). Interestingly, in CT patients that are negative for ER, high NQOl expression correlates with better survival. A similar correlation is seen for no CT patients that are negative for ER though the separation is less. In ER positive patients treated with CT, NQOl expression is not predictive of outcome.

[000135] Based on the multivariate analysis, NQOl status derived from the

immunohistochemistry data is a potential predictor of CT efficacy (p = 0.07). NQOl by ER status interaction improves the model fit compared to a model discarding this interaction (p=0.01). Overall, this data supports the utility of NQOl expression as a predictor of anthracycline-containing therapy efficacy. Additionally, this data provides additional evidence of the utility of NQOl as a predictive biomarker in a large randomized clinical study.

[000136] TABLE 1: DNA Repair and DNA Damage Response Markers

UNG1 7. BER

TDG 8. BER

MUTY 9. BER

MTH1 10. BER

MBD4 11. BER

APE1 12. BER

XPG 13. BER

DNAPOLP 14. BER

XRCC1 15. BER

PARP1 16. BER

DNAPOL51 17. BER

DNAPOL52 18. BER

DNAPOL53 19. BER

DNAPOL54 20. BER

DNAPOL55 21. BER

DNAPOLel 22. BER

DNAPOL82 23. BER

DNAPOL83 24. BER

DNAPOL84 25. BER

DNAPOL85 26. BER

DNALigasel 27. BER

PCNA 28. BER

UBC13 29. BER

MMS2 30. BER

FEN1 31. BER

RFC1 32. BER RFC2 33. BER

PAR 34. BER

RFC4 35. BER

RFC5 36. BER

DNALigasel 37. BER

DNAligase3 38. BER

Aprataxin (Aptx) 39. BER

XRCC1 40. HR

PARP1 41. HR

FEN1 42. HR

DNA ligasel 43. HR

SNM1 44. HR

H2A 45. HR

RPA1 46. HR

RPA2 47. HR

RPA3 48. HR

RAD51 49. HR

XRCC2 50. HR

XRCC3 51. HR

RAD51L1 52. HR

RAD51L2 53. HR

RAD51L3 54. HR

DMC1 55. HR

RAD52 56. HR

RAD54 57. HR

MUS81 58. HR MMS4 59. HR

EMSY 60. HR

BRCA1 61. HR

BARD1 62. HR

BLM 63. HR

BLAP75 64. HR

SRS2 65. HR

SAE2 66. HR

ERCC1 67. HR

TRF2 68. HR/FA

BRC A2/FANCD 1 69. HR/FA

FANCA 70. HR/FA

FANCB 71. HR/FA

FANCC 72. HR/FA

FANCD1 73. HR/FA

FANCD2 74. HR/FA

FANCE 75. HR/FA

FANCF 76. HR/FA

FANCG 77. HR/FA

FANCJ 78. HR/FA

FANCL 79. HR/FA

FANCM 80. HR/FA hHefl 81. HR/FA FANCI 82. HR/FA

USP1 83. HR/FA

PALB2/FANCN 84. HR/FA

DNMT1 85. MMR hMLHl 86. MMR hPMS2 87. MMR hPMSl 88. MMR

GTBP (hMSH6) 89. MMR hMSH2 90. MMR hMSH3 91. MMR

HMGB1 92. MMR

MSH4 93. MMR

MSH5 94. MMR

EXOl 95. MMR

DNAPOL51 96. MMR

DNAPOL52 97. MMR

DNAPOL53 98. MMR

DNAPOL54 99. MMR

DNAPOL55 100. MMR

DNAPOLel 101. MMR

DNAPOL82 102. MMR

DNAPOL83 103. MMR DNAPOL84 104. MMR

DNAPOL85 105. MMR

DNA Ligase I 106. MMR

PCNA 107. MMR

RPA1 108. MMR

RPA2 109. MMR

RPA3 110. MMR

MUTY 111. MMR

MRE11 112. DDR

RAD50 113. DDR

NBS1 114. DDR

H2A 115. DDR

ATM 116. DDR

P53 117. DDR

SMC1 118. DDR

ATF2 119. DDR

CHK1 120. DDR

CHK2 121. DDR

MAPKAP Kinase2 122. DDR

RPA1 123. DDR

RPA2 124. DDR

RPA3 125. DDR RAD 17 126. DDR

RFC1 127. DDR

RFC2 128. DDR

RFC3 129. DDR

RFC4 130. DDR

RFC5 131. DDR

RAD9 132. DDR

RAD1 133. DDR

HUS1 134. DDR

ATRIP 135. DDR

ATR 136. DDR

MDC1 137. DDR

CLASPIN 138. DDR

TOPB1 139. DDR

BRCC36 140. DDR

BLM 141. DDR

SRS2 142. DDR

SAE2 143. DDR

P53BP1 144. DDR

ING1 145. DDR

ING2 146. DDR

SMC1 147. DDR BLAP75 148. DDR

BACH1 149. DDR

BRCA1 150. DDR

BRCA2 151. DDR

BARD1 152. DDR

RAP80 153. DDR

Abraxas 154. DDR

CDT1 155. DDR

RPB8 156. DDR

PPM1D 157. DDR

GADD45 158. DDR

DTL/CDT2 159. DDR

HCLK2 160. DDR

CTIP 161. DDR

BAAT1 162. DDR

HDM2/MDM2 163. DDR

APLF (aprataxin- and PNK- 164. DDR like factor)

14-3-3 σ 165. DDR

Cdc25A 166. DDR

Cdc25B 167. DDR

Cdc25C 168. DDR

PBIP1 169. DDR

H2A 170. NER

XPC 171. NER HR23A 172. NER

HR23B 173. NER

DDB1 174. NER

DDB2 175. NER

XPD 176. NER

XPB 177. NER

XPG 178. NER

CSA 179. NER

CSB 180. NER

XPA 181. NER

XPF 182. NER

ERCC1 183. NER

RNAPolymerase2 184. NER

GTF2H1 185. NER

GTF2H2 186. NER

GTF2H3 187. NER

GTF2H4 188. NER

GTF2H5 189. NER

MNAT1 190. NER

MAT1 191. NER

CDK7 192. NER

CyclinH 193. NER PCNA 194. NER

RFC1 195. NER

RFC2 196. NER

RFC3 197. NER

RFC4 198. NER

RFC5 199. NER

DNAPOL51 200. NER

DNAPOL52 201. NER

DNAPOL53 202. NER

DNAPOL54 203. NER

DNAPOL55 204. NER

DNAPOLel 205. NER

DNAPOL82 206. NER

DNAPOL83 207. NER

DNAPOL84 208. NER

DNAPOL85 209. NER

DNALigasel 210. NER

DNAPOI^i 211. TLS

DNAPOLi 212. TLS

DNAPOLK 213. TLS

REV1 214. TLS

DNAPOLC 215. TLS DNAPC 216. TLS

PCNA 217. TLS

UBC13 218. TLS

MMS2 219. TLS

RAD5 220. TLS hRAD6A 221. TLS hRAD6B 222. TLS

RAD 18 223. TLS

WRN 224. TLS

USP1 225. TLS

SIRT6 226. NHEJ

H2A 227. NHEJ

ARP4 228. NHEJ

ARP8 229. NHEJ

Ino80 230. NHEJ

SWR1 231. NHEJ

KU70 232. NHEJ

KU80 233. NHEJ

DNAPKcs 234. NHEJ

Artemis 235. NHEJ

PS02 236. NHEJ

XRCC4 237. NHEJ DNA LIGASE4 238. NHEJ

XLF 239. NHEJ

DNAPOIA 240. NHEJ

PNK 241. NHEJ

METNASE 242. NHEJ

TRF2 243. NHEJ

MGMT 244. Non-classified

TDP1 245. Non-classified ϋΝΑΡΟΕμ 246. Non-classified hABHl 247. Non-classified hABH2 248. Non-classified hABH3 249. Non-classified hABH4 250. Non-classified hABH5 251. Non-classified hABH6 252. Non-classified hABH7 253. Non-classified hABH8 254. Non-classified

TOPOl 255. Non-classified

TOPOII 256. Non-classified

UBC9 257. Non-classified

UBL1 258. Non-classified

MMS21 259. Non-classified [000137] Table 2 Breast Cancer DNA Repair and DNA Damage Response Markers

NQOl 264 DT

[000138] Table 3 Partition Analysis of Single Markers on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000139] Table 4 Partition Analysis of Single Markers on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000140] TABLE 5 Partition Analysis of Two Marker Models on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each model, p-value, adjusted p-value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000141] Table 6. Partition Analysis of Two Marker Models on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each model, p-value, adjusted p-value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000142] Table 7. Partition Analysis of Three Marker Models on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each model, p-value, adjusted p-value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000143] Table 8. Partition Analysis of Three Marker Models on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each model, p-value, adjusted p-value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

Table 9. Partition Analysis of Four Marker Models on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed

for ability to separate patients into Recurrence and No Recurrence groups. For each model, p-value, adjusted p-value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000144] Table 10. Partition Analysis of Four Marker Models on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each model, p-value, adjusted p-value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000145] Table 11. Probability Analysis of Single Markers on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000146] Table 12. Probability Analysis of Single Markers on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p- value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000147] Table 13. Probability Analysis of Two Marker Models on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

Table 14. Probability Analysis of Two Marker Models on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

Table 15. Probability Analysis of Three Marker Models on CEF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000148] Table 16. Probability Analysis of Three Marker Models on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000149] Table 17. Probability Analysis of Four Marker Models on CMF-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

CMF I CMF I T0P2A. atio; NQ01.Rng;BRCAl.NAS;FANCD 1.78E-01 1.20E+02 0.574368 0.388889 0.518519 0.650794 0.388889 0.759259 1.6 2

CMF CMF TOP2A.Ratio;pMK2.NAS;RAD51;PAR 1.88E-01 1.27E+02 0.618145 0.45045 0.612903 0.525 0.333333 0.777778

CMF CMF T0P2A. Ratio; NQ01.Mean;BRCAl.NAS;XPF 1.89E-01 1.28E+02 0.602551 0.438776 0.607143 0.542857 0.346939 0.77551 1.5

CMF CMF NQ01.Rng;BRCAl.NAS;pMK2.CAS;PAR 1.98E-01 1.34E+02 0.618634 0.439394 0.583333 0.552083 0.328125 0.779412 1

CMF I CMF I T0P2A. Ratio; NQ01.Mean;BRCAl.NAS;pMK 1.99E-01 1.35E+02 0.602504 0.438776 0.62963 0.535211 0.34 0.791667

2. CAS

CMF I CMF I T0P2A. Ratio; NQ01.Mean;pMK2.CAS;FANC 2.10E-01 1.42E+02 0.596817 0.420561 0.551724 0.589744 0.333333 0.779661 1.5

D2

CMF CMF TOP2A.Ratio;NQ01.Rng;RAD51;PAR 2.16E-01 1.46E+02 0.618768 0.443299 0.612903 0.530303 0.38 0.744681 1.4

CMF CMF TOP2A.Ratio;pMK2.CAS;RAD51;XPF 2.25E-01 1.52E+02 0.526357 0.460177 0.612903 0.512195 0.322034 0.777778 1.4

CMF CMF T0P2A. Ratio; NQ01.Rng;BRCAl.NAS;PAR 2.42E-01 1.64E+02 0.586406 0.466667 0.642857 0.483871 0.36 0.75

CMF CMF BRCAl.NAS;pMK2.CAS;RAD51;XPF 2.54E-01 1.72E+02 0.58744 0.403974 0.472222 0.634783 0.288136 0.793478 1.3

CMF CMF NQ01.Rng;pMK2.CAS;RAD51;XPF 2.73E-01 1.84E+02 0.563679 0.422535 0.5 0.603774 0.3 0.780488 1.3

CMF CMF TOP2A.Ratio;NQ01.Rng;RAD51;XPF 2.74E-01 1.85E+02 0.582543 0.444444 0.580645 0.544118 0.367347 0.74 1.4

CMF CMF NQ01.Rng;BRCAl.NAS;pMK2.CAS;XPF 2.93E-01 1.98E+02 0.578515 0.41791 0.5 0.612245 0.321429 0.769231 1.3

CMF CMF TOP2A.Ratio;BRCAl.NAS;pMK2.CAS;RAD51 3.02E-01 2.04E+02 0.571429 0.411765 0.464286 0.635135 0.325 0.758065 1.3

CMF I CMF I T0P2A. Ratio; NQ01.Mean;BRCAl.NAS;RAD5 3.11E-01 2.10E+02 0.582298 0.43299 0.535714 0.57971 0.340909 0.754717 1.

1

CMF I CMF I T0P2A. Ratio; NQ01.Rng;BRCAl. CAS; FANCD 3.12E-01 2.11E+02 0.549254 0.443299 0.533333 0.567164 0.355556 0.730769 1.3

2

CMF CMF TOP2A.Ratio;NQ01.Mean;pMK2.NAS;XPF 3.13E-01 2.11E+02 0.608442 0.439252 0.533333 0.571429 0.326531 0.758621 1.

CMF CMF TOP2A.Ratio;RAD51;PAR;XPF 3.18E-01 2.14E+02 0.607813 0.446429 0.5625 0.55 0.333333 0.758621 1.3

CMF CMF NQ01.Rng;BRCAl.NAS;pMK2.CAS;RAD51 3.24E-01 2.19E+02 0.60319 0.4375 0.545455 0.568421 0.305085 0.782609 1.

CMF CMF NQ01.Mean;BRCAl.NAS;pMK2.NAS;RAD51 3.32E-01 2.24E+02 0.600816 0.428571 0.485714 0.6 0.288136 0.777778 1.

CMF I CMF I TOP2A.Ratio;BRCAl.CAS;pMK2.NAS;FANCD 3.36E-01 2.27E+02 0.578189 0.423423 0.5 0.604938 0.319149 0.765625 1.3

2

CMF I CMF I TOP2A.Ratio;NQ01.Rng;BRCAl.CAS;pMK2. 3.57E-01 2.41E+02 0.584577 0.453608 0.533333 0.552239 0.347826 0.72549 1.2

CAS

CMF CMF NQ01.Rng;BRCAl.CAS;RAD51;XPF 3.63E-01 2.45E+02 0.595469 0.422535 0.487179 0.61165 0.322034 0.759036 1.3

[000150] Table 18. Partition Analysis of Two Marker Models Optimized on CEF- treated Breast Cancer Patients and Applied to All Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p- value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000151] Table 19. Partition Analysis of Two Marker Models Optimized on CMF- treated Breast Cancer Patients and Applied to All Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p- value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000152] Table 20. Partition Analysis of Three Marker Models Optimized on CEF-treated Breast Cancer Patients and Applied to All Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p- value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000153] Table 21. Partition Analysis of Three Marker Models Optimized on CMF-treated Breast Cancer Patients and Applied to All Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p- value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

Table 22. Partition Analysis of Four Marker Models Optimized on CEF-treated Breast Cancer Patients and Applied to All Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p- value, adjusted p- value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed

Table 23. Partition Analysis of Four Marker Models Optimized on CMF-treated Breast Cancer Patients and Applied to All Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCAl.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p- value, adjusted p- value, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

[000154] Table 24. Probability Analysis of Three Marker Models Optimized on CMF-treated Breast Cancer Patients and Applied to All

Treatment Groups. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS,

BRCA1.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

Table 25. Probability Analysis of Three Marker Models on noCT-treated Breast Cancer Patients. The markers in this analysis include pMK2.CAS, NQOl.Rng, FANCD2, BRCAl.CAS, PAR, RAD51, pMK2.NAS, BRCA1.NAS, TOP2A.Ratio, NQOl.Mean, and XPF. The markers are assessed for ability to separate patients into Recurrence and No Recurrence groups. For each marker, p-value, adjusted p-value, AUC, AER, Sensitivity, Specificity, PPV, NPV, and Relative Risk are listed.

Claims

What is claimed is:

1. A method of determining the sensitivity of a breast cancer to a chemotherapeutic agent comprising identifying an alteration in at least one DNARMARKER selected from the group consisting of XPF, FANCD2, pMK2, PAR, BRCA1, PALB2, RAD51, NQOl, TOP2A, CEP17, ATM, CHKl, MREl l, RAD50, NBSl, andH2AX wherein the presence of said alteration indicates said cell is sensitive to a chemotherapeutic agent.

2. A method of determining the resistance of a breast cancer to a chemotherapeutic agent comprising identifying an alteration in at least one DNARMARKER selected from the group consisting of XPF, FANCD2, pMK2, PAR, BRCA1, PALB2, RAD51, NQOl, TOP2A, CEP17, ATM, CHKl, MREll, RAD50, NBSl, andH2AX, wherein the absence of said alteration indicates said cell is resistant to a chemotherapeutic agent.

3. A method of predicting the effectiveness of a chemotherapeutic agent or combination of chemotherapeutic agents of a subject having a breast cancer comprising

a) measuring the level of an effective amount of one or more DNARMARKERS selected from the group consisting of selected from the group consisting of XPF, FANCD2, pMK2, PAR, BRCA1, PALB2, RAD51, NQOl, TOP2A, CEP17, ATM, CHKl, MREl l, RAD50, NBSl, andH2AX in a sample from the subject, and

b) comparing the level of the effective amount of the one or more

DNARMARKERS to a reference value.

4. A method of monitoring the chemotherapeutic agent treatment of a subject with breast cancer comprising

a) detecting the level of an effective amount of one or more DNARMARKERS selected from the group consisting of DNARMARKERS selected from the group consisting of XPF, FANCD2, pMK2, PAR, BRCA1, PALB2, RAD51, NQOl, TOP2A, CEP17, ATM, CHKl, MREl l, RAD50, NBSl, andH2AX in a first sample from the subject at a first period of time;

b) detecting the level of an effective amount of one or more DNARMARKERS in a second sample from the subject at a second period of time; c) comparing the level of the effective amount of one or more DNARMARKERS detected in step (a) to the amount detected in step (b), or to a reference value.

5. A method of predicting a breast cancer patient's likelihood to have an event regardless of treatment comprising

a) measuring the level of an effective amount of one or more DNARMARKERS selected from the group consisting of DNARMARKERS selected from the group consisting of XPF, FANCD2, pMK2, PAR, BRCAl, PALB2, RAD51, NQOl, TOP2A, CEP17, ATM, CHK1, MRE11, RAD50, NBS1, andH2AX in a sample from the subject, and

b) comparing the level of the effective amount of the one or more

DNARMARKERS to a reference value.

6. The method of claims 1 or 2, wherein the one or more DNARMARKERS are selected from the group consisting of pMK2, NQOl, FANCD2, BRCAl, PAR, RAD51, TOP2A. , NQOl, and XPF

7. The method of claims lor 2, wherein further comprising determining an alteration in a DNARMARKER selected from Table 1 or Table 2.

8. The method claims 1 or 2, wherein said alteration is determined by protein levels, post-translational protein modifications, gene copy levels, chromosome copy levels, polymorphisms, nucleic acid modifications.

9. The method claim 8, wherein said post-translational modification is selected from the group consisting of phosphorylation, ubiquitination, sumo-ylation, acetylation, alkylation, methylation, glycylation, glycosylation, hydroxylation, isoprenylation, lipoylation, phosphopantetheinylation, sulfation, selenation and C-terminal amidation.

10. The method of claims 1 or 2, wherein said alteration is an increase or decrease in protein expression of said DNARMARKER.

11. The method of claims 3 or 4, wherein said chemotherapeutic agent is selected from the group consisting of anthracycline, cyclophosphamide, 5-fluorouracil, methotrexate, and taxane or any combination of therof.

12. The method of claim 5, wherein said event is selected from the group consisting of distant recurrence, local recurrence, overall survival, relapse free disease, pathological complete response, partial response, no response.

13. The method any one of claims 1-12, wherein said sample is selected from the group consisting of breast tumor tissue, normal breast tissue, and blood.

14. An algorithm that is derived from the list of biomarkers in Table 1 and Table 2 which specifies how the biomarkers are associated in relation to the other biomarkers in the panel, such that the biomarker algorithm indicates a predictive or prognostic value in treatment response of breast cancer.