WO2011153345A2 - A gene expression profile of brca-ness that correlates with responsiveness to chemotherapy and with outcome in cancer patients - Google Patents

A gene expression profile of brca-ness that correlates with responsiveness to chemotherapy and with outcome in cancer patients Download PDF

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WO2011153345A2
WO2011153345A2 PCT/US2011/038922 US2011038922W WO2011153345A2 WO 2011153345 A2 WO2011153345 A2 WO 2011153345A2 US 2011038922 W US2011038922 W US 2011038922W WO 2011153345 A2 WO2011153345 A2 WO 2011153345A2
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brca
tumor
gene
genes
tumors
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WO2011153345A3 (en
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Stephen Cannistra
Panagiotis Konstantinopoulos
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Beth Israel Deaconess Medical Center Inc
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    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
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    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/106—Pharmacogenomics, i.e. genetic variability in individual responses to drugs and drug metabolism
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/118—Prognosis of disease development
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • certain epithelial cancers like ovarian cancer and breast cancer
  • a subset of tumors exhibit a mutation in breast cancer associated gene 1 or 2 (BRCAl or BRCA2).
  • BRCAl and BRCA2 are involved in cellular DNA repair and, without wishing to be bound by theory, it is believed that impairment of either gene's function is causally related to tumorigenesis.
  • aspects of this invention relate to the surprising discovery that a subset of sporadic epithelial ovarian tumors sensitive to
  • chemotherapeutic compounds can be identified based on the gene expression data obtained from these tumors resembling certain similarities to gene expression data obtained from tumors having a BRCAl or a BRCA2 mutation (regardless of the
  • aspects of this invention relate to methods for the classification of a tumor, for example, an epithelial ovarian tumor, as BRCA-like or non-BRCA-like based on expression profiles obtained from the tumors and in accordance with a predictive algorithm, for example, a diagonal linear discriminant predictor.
  • aspects of this invention relate to methods of identifying genes that are informative, either alone or in combination, as marker genes for the classification of epithelial ovarian tumors as BRCA-like or non-BRCA-like.
  • aspects of this invention relate to kits and reagents useful in the classification of epithelial ovarian tumors as BRCA-like or non- BRCA-like based on expression data.
  • aspects of this invention relate to predictive classifiers useful in the classification of expression profiles as BRCA-like or non-BRCA-like.
  • FIG. 1 illustrates an embodiment of the development of a BRCAness gene expression profile
  • FIG. 2 shows an expression plot of 60 genes that comprise an example of a BRCAness profile
  • aspects of the invention provide compositions, methods, and devices for predicting whether a tumor will be either responsive or resistant to treatment with one or more chemotherapeutic agents.
  • aspects of the invention involve evaluating the expression profiles of one or more identified genes to predict the response of cells to chemotherapeutic agents.
  • Embodiments of the invention may be useful to assist in the diagnosis, prognosis, and/or therapy selection for patients that have or are suspected of having cancer. As described in more detail herein, embodiments of the invention may be useful for evaluating and/or predicting the responsiveness of patients to certain chemotherapeutic agents regardless of the BRCA 1 or 2 status of the patient.
  • chemosensitive tumors For example, in the absence of information about the sensitivity or resistance to chemotherapy, a decision to withhold treatment from a patient may be made if the patient has a marginal health status for chemotherapy, particularly if the overall success rate of such a therapy is low for the patient's cancer type.
  • Some embodiments of this invention address this unmet clinical need and provide methods for accurate prediction of tumor sensitivity to chemotherapeutic compounds based on gene expression data obtained from the tumor.
  • some methods provided herein allow for customizing a therapeutic approach not only to a specific subject, but also to a specific tumor in question, and are useful to increase the success rate of chemotherapeutic interventions and/or to lower the burden of medication side effects on patients carrying
  • chemosensitive and chemoresistant tumors for example, sporadic tumors (e.g., sporadic epithelial ovarian tumors).
  • sporadic tumors e.g., sporadic epithelial ovarian tumors.
  • the identification of informative genes is hampered by non-uniform expression of such genes within a subgroup of tumors, e.g., within chemosensitive tumors, and/or because of the small differences in gene expression between tumor groups, e.g., between chemoresistant and
  • Some embodiments of this invention provide methods for the identification of informative genes for tumor classification as chemosensitive or chemoresistant based on gene expression data. Some embodiments of this invention relate to the discovery of an expression profile in sporadic tumors that correlates with tumor sensitivity to chemotherapeutic agents, for example, platinum compounds or PARP inhibitors, and/or patient outcome. Some embodiments of this invention, accordingly, provide the identities of informative genes for a classification of a tumor, for example, a sporadic epithelial tumor (e.g., ovarian tumor), as chemosensitive or chemoresistant.
  • a sporadic epithelial tumor e.g., ovarian tumor
  • a tumor is classified as BRCA-like or non-BRCA-like based on gene expression data, and chemosensitivity or -resistance is determined based on a tumor's BRCAness.
  • Examples of informative genes for the classification of epithelial ovarian tumors as BRCA-like and non-BRCA-like are shown in Table 1.
  • Some embodiments of this invention provide methods of using a predictive algorithm incorporating informative gene identities for accurate classification of tumors as BRCA-like or non-BRCA-like and/or the classification of tumors as chemosensitive or chemoresistant based on tumor expression data.
  • tumor classification strategies are provided herein that allow the prediction of a tumor's responsiveness to chemotherapy and/or of patient outcome. Some embodiments relate to the identification and/or definition of gene expression profiles for the classification of a tumor as BRCA-like or non-BRCA-like.
  • methods are provided to assign tumors, for example, sporadic epithelial tumors (e.g., ovarian tumors), to a class exhibiting a BRCA-like expression profile or a class exhibiting a non-BRCA-like expression profile.
  • a BRCA-like expression profile is correlated with sensitivity to chemotherapeutic agents, for example, platinum compounds or PARP inhibitors.
  • a non-BRCA-like expression profile is not correlated with chemosensitivity.
  • a non-BRCA-like expression profile is correlated with chemoresistance.
  • Some embodiments relate to the identification of informative genes, the differential expression of which is indicative for a given tumor being chemosensitive or chemoresistant. Some aspects relate to the building of predictive algorithms using the identified informative genes and the use of the resulting predictors in the classification of tumors as chemosensitive or chemoresistant. In some embodiments, specific predictors and methods for their use in classifying sporadic tumors based on expression data as sensitive or resistant to chemotherapeutic agents are provided that allow for a distinction of BRCA-like (BL) from non-BRCA-like (NBL) tumors. In some embodiments, classification of sporadic tumors is performed with at least 94% accuracy.
  • methods and predictors are provided that allow for a distinction between platinum sensitivity and resistance in at least 80% of patient- derived tumor specimens. In some embodiments, methods and predictors are provided that allow for predictions of disease-free survival and overall survival time in subjects with sporadic tumors. In some embodiments, predictors and methods are provided for the classification of tumors based on expression profile data as part of a multivariate disease analysis, for example, with respect to one or more of patient age and health status, and tumor stage, grade, histology, and debulking status.
  • a subject that does not have a mutation or defect in either the BRCA1 or BRCA2 gene may be classified as either responsive or resistant according to methods described herein.
  • a subject e.g., a cancer patient
  • a subject may first be screened to determine whether a BRCA 1 or 2 mutation or defect is present. If the answer is yes, then the subject is likely to be responsive to treatment with one or more chemotherapeutic agents and may be identified as such, and/or prescribed for such treatment, and/or treated (e.g., by administration) with one or more chemotherapeutic agents.
  • such subjects may not need to be evaluated for a BL or NBL expression profile since they are already likely to be sensitive based on their BRCA status.
  • a subject that is BRCA 1 or 2 defective still may be evaluated to determine whether a BL or NBL expression profile is present.
  • patients that have a BRCA 1 or 2 genetic defect may not be candidates for certain chemotherapeutic treatments if they have a NBL gene expression profile as described herein.
  • a subject that is identified as not having a mutation or defect in a BRCA gene may be evaluated for a BL or NBL expression profile. If the subject has a BL expression profile, the subject may be identified and/or treated as responsive to certain chemotherapeutic agents. However, if the subject has a NBL expression profile, the subject may be identified and/or treated as non-responsive to certain chemotherapeutic agents.
  • BRCA proteins BRCA-1 and BRCA-2 are involved in the process of homologous recombination, which mediates repair of double stranded DNA breaks ⁇ Cancer patients, for example, ovarian cancer patients, with germline mutations in either BRCA-1 or BRCA-2 genes exhibit an impaired ability to repair double stranded DNA breaks via homologous recombination, which may partly explain their heightened sensitivity to platinum and their more favorable survival compared to patients not carrying such mutations 2"4 . Furthermore, in the setting of defective homologous recombination, it has been shown that inhibition of a second DNA repair pathway such as base excision repair (BER) is a lethal event 5"7 .
  • BER base excision repair
  • This "BRCAness" phenotype may be due, in part, to defective homologous recombination related to several mechanisms including epigenetic hypermethylation of the BRCA-1 promoter 16 ⁇ 19 , somatic mutation of BRCA-1 or -2 18 ' 20 22 5 or loss of function mutations in other homologous recombination pathway genes 23 ' 24.
  • Some aspects of the invention are based on the discovery of a plurality of informative genes that are differentially expressed in chemosensitive and
  • the identity of informative genes for the classification of tumor as BRCA-like and non-BRCA-like is provided in Table 1.
  • Table 1 Identity of informative genes for the classification of tumors (e.g., epithelial ovarian tumors) as BRCA-like or non-BRCA-like
  • Some embodiments are based on the discovery that tumor expression profiles including expression data for one or more informative genes, allow for accurate diagnostic classification of the tumor as chemosensitive or chemoresistant. Some embodiments relate to diagnostic methods of classifying a tumor as sensitive to a chemotherapeutic compound (chemosensitive) or resistant to a chemotherapeutic compound (chemoresistant) based on gene expression data obtained from the tumor. Some embodiments relate to methods of predicting chemosensitivity or
  • Some embodiments relate to methods of administering chemotherapy to a subject carrying a tumor based on the tumor' s classification as chemosensitive. Some embodiments relate to methods of withholding administration from a subject carrying a tumor based on the tumor being classified as chemoresistant.
  • tumor refers to a neoplastic cell growth, including benign, malignant, pre-cancerous and cancerous cell neoplasms.
  • a tumor may be a liquid tumor, for example, a leukemic tumor, or a solid tumor, for example, an ovarian epithelial tumor, a breast tumor, a colon tumor, a gastric tumor, a prostate tumor, a pancreatic tumor, a lung tumor, a liver tumor, a brain tumor, or a kidney tumor.
  • the tumor is an epithelial tumor.
  • the tumor is a tumor harboring a cell with a defect in homologous recombination.
  • the tumor may be the manifestation of a cancer, for example, blood cancer, ovarian epithelial cancer, breast cancer, colon cancer, gastric cancer, prostate cancer, pancreatic cancer, lung cancer, liver cancer, brain cancer, or kidney cancer.
  • the tumor being classified according to methods provided herein is a primary tumor. In some embodiments, the tumor being classified according to methods provided herein is a secondary, metastatic, or recurrent tumor.
  • subject refers to an individual that may be, but is not limited to, a human, a non-human mammal, for example, a mouse, rat, cow, sheep, cat, dog, or goat.
  • a method for the diagnostic classification of a tumor as chemoresistant or chemosensitive includes obtaining an expression profile of the tumor.
  • An expression profile can be obtained from a cell or a tissue from a tumor, for example, from a tumor biopsy.
  • the term "expression profile", as used herein, refers to a dataset containing gene expression data from a cell or tissue.
  • the expression profile may consist of a single data point, for example, a quantitative or semi-quantitative value of expression of a single gene, for example, reflective of the signal obtained from a quantitative or semi-quantitative assay detecting the abundance of a gene product (e.g., a protein or a nucleic acid transcript).
  • a gene product e.g., a protein or a nucleic acid transcript
  • Suitable assays for the detection of gene expression products are well known to those of skill in the art and include, for example, western blots, ELISA, RT-PCR (e.g. end-point RT-PCR, real-time PCR, or qPCR), protein or nucleic acid microarray, and massive parallel sequencing assays.
  • any suitable assay may be used based on hybridization, specific binding (e.g., antibody binding), or any other technique, as aspects of the invention are not limited in this respect.
  • an expression profile may contain a plurality of gene expression data points, for example, quantitative or semi-quantitative values of expression of two or more genes.
  • the expression profile may comprehensively cover the whole transcriptome or proteome of a given cell, tissue, or organism.
  • Whole-transcriptome or -proteome microarrays containing probes for the detection of substantially all transcript or protein sequences known to those of skill in the art to be transcribed or translated from a cell's genome, e.g., for all sequences in the transcriptome of the respective organism the cell originates from, are well known to those of skill in the art and such microarrays are commercially available for various species including human.
  • Methods for the generation of expression profiles are well known to those in the art and include, for example, western blot, northern blot, reverse northern blot, RT-PCR (e.g.
  • microarray for either protein or transcript detection
  • detection methods see, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, Third Edition (3 Volume Set), Cold Spring Harbor Laboratory Press; 3rd edition (January 15, 2001), ISBN-10: 0879695773; Robert Griitzmann (Editor), Christian Pilarsky (Editor), Cancer Gene Profiling: Methods and Protocols (Methods in Molecular Biology), Humana Press; 1 st edition (November 6, 2009), ISBN-10: 1934115762, both incorporated herein by reference for disclosure of gene product detection and expression profiling methods).
  • methods to generate comprehensive transcript expression profiles from a given cell or tissue that measure the abundance of all transcripts expressed by the cell or tissue are well known in the art and include, for example, whole- transcriptome or whole proteome microarrays and massive parallel sequencing assays (e.g., 454 sequencing or Solexa/Illumina sequencing (see, e.g., Robert Griitzmann (Editor), Christian Pilarsky (Editor), Cancer Gene Profiling: Methods and Protocols (Methods in Molecular Biology), Humana Press; 1 st edition (November 6, 2009), ISBN-10: 1934115762, both incorporated herein by reference for disclosure of whole transcriptome expression profiling methods).
  • massive parallel sequencing assays e.g., 454 sequencing or Solexa/Illumina sequencing (see, e.g., Robert Griitzmann (Editor), Christian Pilarsky (Editor), Cancer Gene Profiling: Methods and Protocols (Methods in Molecular Biology), Humana Press; 1 st edition (November 6, 2009), ISBN-10: 1934
  • a quantitative expression value is a value reflecting the abundance of a gene transcript in the starting sample, for example, a tumor cell or tissue sample.
  • a semi-quantitative expression value is a value reflecting the abundance of a gene transcript in the starting sample in relation to a control or reference quantity.
  • a semi-quantitative value may be a non-numeric indication of gene regulation (e.g., “up”, “down”, “+”, “+ +”, “+ + +” 5 "_ _” 5 or “— ”)_ in some embodiments, a semi-quantitative expression value may give a numeric dimension of gene regulation (e.g., "1.5-fold upregulated", "2.456", "0.32" or "-1.5”).
  • control or reference quantities for the generation of semi-quantitative expression values are well known to those in the art.
  • Appropriate control or reference quantities for the generation of semi-quantitative expression values are well known to those in the art and include, for example, expression values of housekeeping genes (e.g., beta- actin or GAPDH), external controls (e.g., spiked in RNA or DNA controls not usually expressed in the cell to be analyzed), overall expression values (e.g., all expression values obtained from a cell added together), or historic or empiric values.
  • an expression profile used for class prediction of a tumor includes an expression value related to an informative marker gene for the classification of the tumor as chemoresistant or chemosensitive.
  • the informative gene is a gene identified to be informative by methods provided herein.
  • the informative gene is a gene selected from the genes in any of Tables 1-4.
  • the expression profile includes expression values for a subgroup of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or more genes of the genes in any of Tables 1- 4.
  • the expression profile includes expression values for a subgroup of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, or at least 60 genes of the genes in any of Tables 1-4.
  • the expression level of all or fewer of the genes in any of Tables 1-4 are evaluated.
  • the expression level of one or more genes of interest may be evaluated or determined in any suitable biological sample.
  • a biopsy of a tumor may be obtained and an expression profile may be obtained from a biopsy cell or tissue.
  • one or more circulating cells e.g., one or more circulating tumor cells
  • an expression profile may be obtained from the cell or cells.
  • one or more tumor cells may be obtained from ascites fluid, peripheral blood, or from
  • cerebrospinal fluid of a subject cerebrospinal fluid of a subject.
  • a method of diagnostic tumor classification includes processing a tumor expression profile in accordance to a predictive algorithm, for example, a classifier or predictor.
  • a predictive algorithm for example, a classifier or predictor.
  • Bioinformatic methods of classifying tumors based on gene expression data according to a predictive algorithm are well known in the art (see, for example, Dudoit S, Fridlyand J, and Speed TP, Comparison of
  • a predictor can be built from expression data obtained from tumors known to be either chemosensitive or chemoresistant according to methods provided herein or well known to those of skill in the art.
  • a predictor will include a list of genes identified as informative for the class distinction based on a comparison of expression profiles of tumors of both classes.
  • a predictive algorithm will analyze an expression profile obtained from a tumor to be classified by filtering the profile for expression values of informative genes, and calculating a class call (e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant) for each informative gene based on the gene's expression value. For example, if an expression profile obtained from a tumor indicates a downregulation of the expression of an informative gene in the tumor tissue as compared to a reference or control value, and a downregulation of that gene has been determined to be correlated with a BRCA- like/chemosensitive phenotype, then the predictive algorithm would output the respective class call (e.g., BRCA-like or chemosensitive) for this specific informative gene.
  • a class call e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant
  • Some embodiments of this invention provide a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non- BRCA-like.
  • a predictor for the classification of a tumor for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non- BRCA-like.
  • Exemplary predictors for tumor classification as BRCA-like and non- BRCA-like are given in Tables 2 and 3.
  • tumors e.g., epithelial ovarian tumors
  • Table 2 60-gene predictor for classification of tumors (e.g., epithelial ovarian tumors) as BRCA-like and non-BRCA-like.
  • HMGN2 high-mobility group nucleosomal binding domain 2
  • TNF tumor necrosis factor TNF superfamily, member 2
  • SH3BGRL SH3 domain binding glutamic acid-rich protein like
  • SERPINF2 serpin peptidase inhibitor SERPINF2 serpin peptidase inhibitor, clade F (alpha-2 antiplasmin,
  • PSTPIP1 proline-serine-threonine phosphatase interacting protein 1
  • PPP1CC protein phosphatase 1 catalytic subunit, gamma isoform
  • APEX1 APEX nuclease (multifunctional DNA repair enzyme) 1
  • GNAI3 guanine nucleotide binding protein G protein
  • HLA-A major histocompatibility complex class I
  • a GFI1 growth factor independent 1 HLA-A major histocompatibility complex, class I, A GFI1 growth factor independent 1
  • HLA-B major histocompatibility complex class I, B
  • SKP1A S-phase kinase-associated protein 1A (pl9A)
  • PDIA4 protein disulfide isomerase family A member 4
  • PRAME preferentially expressed antigen in melanoma
  • MMP7 matrix metallopeptidase 7 (matrilysin, uterine)
  • APEX1 APEX nuclease multifunctional DNA repair enzyme
  • G protein 28 GNAI3 guanine nucleotide binding protein (G protein), alpha 1.47
  • a predictor for the classification of a tumor as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more genes of the genes listed in any of Tables 1-3.
  • a predictor for the classification of a tumor for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, essentially consists of a subgroup of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more genes of the genes listed in any of Tables 1-3.
  • a predictor for the classification of a tumor for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, consists of a subgroup of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more genes of the genes listed in any of Tables 1-3.
  • a predictor for the classification of a tumor for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, or at least 60 genes of the genes in any of Tables 1-3.
  • a predictor for the classification of a tumor for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non- BRCA-like, essentially consists of up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, up to 10, up to 11, up to 12, up to 13, up to 14, up to 15, up to 20, up to 25, up to 30, up to 35, up to 40, up to 45, up to 50, up to 55, or up to 60 genes of the genes in any of Tables 1-3.
  • a predictor for the classification of a tumor for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, comprises, essentially consists of, or consists of one or more genes listed in any of Tables 1-3 that are upregulated in BRCA-like tumors, and/or of 1 or more genes listed in any of Tables 1-3 that are downregulated in BRCA-like tumors, or a combination thereof.
  • a predictive algorithm classifies a tumor by calculating the sum of all class calls for all informative genes in the predictor and assigning the tumor to the class that received the most calls based on the expression data for these informative genes.
  • the calls of the informative genes included in the predictor are weighed by the predictive algorithm, for example, by assigning more weight to a call of an informative gene differential expression of which strongly correlates with tumor chemosensitivity or
  • a prediction may be based by the inner sum of the weights (w and expression of the genes (x , wherein the weight is the weight assigned to each gene, for example, based on correlation of differential expression of the gene with the class distinction, and expression of the gene is expressed as a quantitative or numeric semi-quantitative value.
  • a class call may be made, in some embodiments, based on the inner sum ⁇ i Wi x; for all or a subset of genes in a predictor exceeding or being lower than a certain threshold level.
  • threshold levels can be calculated from empirical data by methods well known to those of skill in the art.
  • a single threshold level is determinative of a tumor being assigned to one or the other class in a binary class distinction.
  • two threshold levels are determined, and a tumor is assigned to one class, for example, BRCA-like, if ⁇ i Wi x; exceeds the higher threshold level, and to the other class, for example, non-BRCA-like, if ⁇ i Wi x; is less than the lower threshold level.
  • ⁇ i Wi x falls between both threshold levels, the tumor may not be assigned a class.
  • using two threshold levels may result in a predictive algorithm only assigning tumors to a respective class that can be classified with high confidence.
  • An exemplary 30-gene weighted voting predictor is provided in Table 4.
  • a tumor for example, an ovarian epithelial tumor, is classified as BRCA-like if, for example, the inner sum is greater than about 120 ( ⁇ i Wi x; > -120), and as non-BRCA-like, if the inner sum is less than about 120.
  • Weighted voting predictor for classification of tumors for example, epithelial ovarian tumors, as BRCA-like or non-BRCA-like
  • APEX1 1.1607 APEX nuclease (multifunctional DNA repair enzyme) 1
  • GNAI3 1.0425 guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 3 16 SEMA3F 1.0379 sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3F
  • TNF superfamily 18 TNF 0.836 tumor necrosis factor (TNF superfamily
  • class calls for individual genes are weighed based on the level of differential expression detected in the tumor as compared to a control of reference value, for example by giving more weight to a call based on a greater observed fold change in gene expression than to a call based on a smaller fold change (e.g., giving more weight to a call based on a 1.5-fold change than to a call based on 1.1-fold change in gene expression).
  • a predictive algorithm weighs class calls for individual genes based on a combination of phenotype correlation and level of differential expression.
  • a predictive algorithm as provided herein assigns a confidence level to the overall class assignment (e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant).
  • a confidence level to the overall class assignment (e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant).
  • BRCA-like/chemosensitive or non-BRCA-like/chemoresistant e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant.
  • Types of class predictors useful for the classification of tumors in BRCA-like/chemosensitive and non-BRCA-like/chemoresistant include, for example, diagonal linear discriminant predictors, compound covariate predictors, nearest centroid predictors, or support vector machines predictors.
  • a class predictor can be combined with other statistical and/or bioinformatic procedures or algorithms to increase accuracy, for example, with hierarchical clustering of a dataset of unknown class to datasets of known class, or by using aggregation strategies, for example, bagging or boosting schemes.
  • Methods of building and using predictive algorithms using various approaches are well known in the art see, for example, Dudoit S, Fridlyand J, and Speed TP, Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American
  • Gaasenbeek M Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA
  • Bloomfield CD Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999 Oct 15;286(5439):531- rd
  • a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like may comprise, essentially consist of, or consist of 1, 2, 3, 4, or 5 of the top 5 weighted genes in Table 4 (genes 1-5).
  • a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, of the top 10 weighted genes in Table 4 (genes 1-10).
  • a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of the top 15 weighted genes in Table 4 (genes 1-15).
  • a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 of the top 20 weighted genes in Table 4 (genes 1-20).
  • a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 of the top 25 weighted genes in Table 4 (genes 1-25).
  • a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non- BRCA-like may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the top 30 weighted genes in Table 4 (genes 1-30).
  • aspects of the invention relate to methods of interrogating the expression of one or more informative genes, or combinations of genes (e.g., from any of Tables 1-4 or otherwise described herein).
  • One or more expression levels may be evaluated in any suitable sample (or in a nucleic acid or protein preparation from a suitable biological sample).
  • the biological sample is a biopsy of a tumor or other cancerous or precancerous tissue.
  • biological sample may be any suitable sample that contains cells that are informative of the status of a tumor or cancer.
  • a biological sample may be a blood or other fluid sample that contains sufficient tumor or cancer cells to be informative.
  • a subject, or a biopsy or other biological sample obtained from a subject is evaluated to determine whether a BRCA 1 or 2 genetic defect (e.g., mutation) is present.
  • a gene expression analysis is performed only if no BRCA 1 or 2 genetic defect is identified.
  • the presence of a BRCA 1 or 2 genetic defect is not evaluated, or an expression analysis is performed even if a BRCA 1 or 2 is detected, as aspects of the invention are not limited in this respect.
  • any of the genetic and/or expression information described herein may be used alone or in combination, with or without additional patient information to assist in a prognosis, therapeutic recommendation, or other diagnostic or predictive evaluation of the health, outcome, and/or treatment for the patient.
  • the expression level for each of a panel of genes consisting (or consisting essentially) of the genes or combinations of genes described herein (e.g., in any of Tables 1-4 or otherwise described herein) is evaluated to determine the BRCAness of a cell or tissue.
  • the expression of one or a few additional genes may be evaluated to provide a control (e.g., a reference level for normal/high/low expression, an internal control, or other form of control or reference).
  • the expression level for each of a panel of genes comprising the genes or combinations of genes described herein is evaluated to determine the BRCAness of a cell or tissue.
  • the expression level of many additional genes e.g., non- informative genes
  • the evaluation may be based on the expression level of one or more of the informative genes or combinations thereof described herein (e.g., in any of Tables 1-4 or otherwise described herein).
  • the expression level of an informative gene may be compared to the respective level(s) in a chemoresistant cell or tissue, a chemosensitive cell or tissue, or both, or other reference cells or tissues or combinations thereof, in order to determine or classify the cell or tissue in which the expression level(s) was measured. It should be appreciated that a comparison may include a statistical analysis and a conclusion as to the status or classification of the cell or tissue may be based on the presence of statistically significant similarities and/or differences for the expression level of each gene being evaluated relative to one or more reference expression levels.
  • informative gene or "informative marker gene”, as
  • a gene which is measurably differentially expressed in cell populations exhibiting different phenotypes and differential expression of which, alone or in combination with other genes, is correlated with a specific phenotype to a greater degree than expected by chance. For example, if a gene is differentially expressed, e.g.
  • tumor cells e.g., epithelial ovarian tumor cells
  • a chemotherapeutic agent e.g., a platinum compound or a PARP inhibitor
  • such a gene is an "informative gene" if the differential expression is measurable, for example, by expression profiling methods known to those of skill in the art, and differential expression is correlated with chemoresistance and/or chemosensitivity to a degree greater than a degree of correlation expected by chance.
  • Some aspects of this invention provide the identities of informative genes for a classification of a tumor as BRCA-like or non-BRCA-like. Some aspects of this invention provide the identities of informative genes for a classification of a tumor as sensitive to a chemotherapeutic compound (chemosensitive) or resistant to a chemotherapeutic compound (chemoresistant). Examples of informative genes for the classification of epithelial ovarian tumors as BRCA-like and non-BRCA-like are shown in any of Tables 1-4.
  • a class vote is only made by a predictive algorithm for an informative gene, if the level of upregulation or downregulation of the expression of the informative gene is greater than a cutoff value.
  • a class call will be made for a specific informative gene only, if the level of expression of the informative gene is at least a fold change shown in one of the rows of Table 3 for exemplary informative genes.
  • cutoff values may, for example be, values reflecting a minimum fraction (e.g. about 10%, about 20%, about 25%, about 30%, about 35%, about 40%, or bout 50%) of the average fold regulation observed in comparing gene expression profiles from distinct classes.
  • an informative gene is a gene differential expression of which by itself is not correlated to a specific phenotype of a cell to an extent greater than that expected by chance, but differential expression of the gene in the context of differential expression of a different gene is correlated with the phenotype to an extent greater than expected by chance.
  • differentiated expression refers to either up- or downregulation of gene expression to a measurable extent in cells or tissues exhibiting a different phenotype, for example, in tumor cells or tissues that are chemosensitive and tissues that are chemoresistant. Accordingly, a gene that is expressed at a measurably different level in a tumor that is sensitive to
  • chemotherapeutic intervention as compared to the level of expression of the gene in a tumor that is resistant, or not sensitive, to such intervention, is a gene that is differentially expressed in chemosensitive and chemoresistant tumors.
  • Methods for measuring gene expression levels of genes and comparing such measured gene expression levels are well known to those of skill in the art and include, for example, western blot, northern blot, reverse northern blot, transcript or protein microarray,
  • a classification of the tumor is performed after the subject has been diagnosed to have the tumor, but before administration of a chemotherapeutic compound.
  • the tumor is a recurrent tumor, and classification is performed, for example, after a subject underwent initial chemotherapy for the treatment of the initial tumor, but before administration of a chemotherapeutic compound to treat the recurrent tumor.
  • a chemotherapeutic compound for example, a PARP inhibitor or a platinum compound, is administered to the subject based on the classification of the tumor as BRCA-like, or chemosensitive.
  • administration of chemotherapy is omitted based on classification of the tumor as non-BRCA-like or chemoresistant.
  • chemotherapy is administered based on a combination of diagnostic tests, including the classification of the tumor as BRCA-like, or chemosensitive, genetic analysis (e.g. mutation analysis), in vitro tumor cell analysis (e.g., in vitro sensitivity of tumor cells to chemotherapeutic compounds), subject/disease history (e.g. recurrent disease after initial chemotherapy), subject health status, and/or other diagnostic tests or assays well known to those of skill in the art.
  • diagnostic tests including the classification of the tumor as BRCA-like, or chemosensitive, genetic analysis (e.g. mutation analysis), in vitro tumor cell analysis (e.g., in vitro sensitivity of tumor cells to chemotherapeutic compounds), subject/disease history (e.g. recurrent disease after initial chemotherapy), subject health status, and/or other diagnostic tests or assays well known to those of skill in the art.
  • the method of diagnostic tumor classification includes determining whether the tumor expression profile is either similar to a first reference expression profile indicative of tumor sensitivity to a chemotherapeutic compound or to a second reference expression profile indicative of tumor resistance to a chemotherapeutic compound.
  • methods for the identification of appropriate reference expression profiles are provided.
  • a methods of identifying one or more informative marker genes for a class distinction between "BRCA-like” and “non-BRCA-like” of tumors is provided.
  • a tumor is classified as BRCA-like or non-BRCA-like based on whether the inner sum of the weighted expression levels of the genes included in the BRCA-ness predictor is above or below a specific cut-off value, for example, as described in more detail elsewhere herein.
  • aspects of the invention relate to identifying patients that are candidates for one or more chemotherapeutic treatments. In some embodiments, aspects of the invention relate to identifying patients that should not be treated with one or more chemotherapeutics agents.
  • a subject for example, a cancer or tumor, e.g., an epithelial ovarian tumor.
  • a treatment as provided by some aspects of this invention is aimed to eliminate a tumor, to induce a decrease in the size of a tumor, to induce a decrease in the number of tumor cells, or to inhibit or halt the growth of a tumor in a subject.
  • this can be accomplished by various approaches including, but not limited to, chemotherapeutic interventions. Suitable chemotherapeutic methods and administration schedules of chemotherapeutic compounds, alone or in combination with other therapeutics, will be apparent to those of skill in the relevant medical art.
  • Some methods for killing or inhibiting the proliferation of tumor cells feature contacting such cells with a chemotherapeutic agent, for example, a cytotoxic or cytostatic agent.
  • a chemotherapeutic agent for example, a cytotoxic or cytostatic agent.
  • the cells are contacted with a chemotherapeutic agent, for example, a cytotoxic or cytostatic agent, that selectively targets tumor cells.
  • selective targeting is meant that the agent or combination of agents selectively recognizes, binds, or acts upon tumor cells.
  • the agent or combination of agents can effectively kill tumor cells by one or more of several mechanisms, such as by induction of apoptosis, or by attracting other cells such as cytotoxic T lymphocytes or macrophages that can kill or inhibit proliferation of the targeted cells.
  • cytotoxic or cytostatic agent is meant an agent (for example a molecule) that kills or reduces proliferation of cells.
  • cytotoxic agents include, but are not limited to, cytotoxic radionuclides, chemical toxins, and protein toxins.
  • the chemotherapeutic agent is a cytotoxic radionuclide or radiotherapeutic isotope, for example, an alpha-emitting isotope such as 225 Ac, 211 At, 212Bi, 213Bi, 212Pb, 224Ra or 223Ra.
  • the cytotoxic radionuclide may a beta-emitting isotope such as 186Rh, 188Rh, 177Lu, 90Y, 1311, 67Cu, 64Cu, 153Sm or 166Ho.
  • the cytotoxic radionuclide may emit Auger and low energy electrons and may be one of the isotopes 1251, 1231 or 77Br.
  • Chemotherapeutic compounds are well known in the art and non-limiting examples of suitable chemotherapeutic agents include, but are not limited to alkylating agents, for example platinum compounds (e.g., carboplatin, cisplatin and oxaliplatin), mechlorethamine, cyclophosphamide, chlorambucil, and ifosf amide.
  • platinum compounds e.g., carboplatin, cisplatin and oxaliplatin
  • mechlorethamine e.g., carboplatin, cisplatin and oxaliplatin
  • mechlorethamine e.g., mechlorethamine
  • cyclophosphamide cyclophosphamide
  • chlorambucil ifosf amide
  • PARP inhibitors are well known in the art and non-imiting examples of PARP inhibitors include BSI201, AZD2281, ABT888, AG014699, MK48
  • chemotherapeutic compounds are also well known to those of skill in the art and non-limiting examples of such compounds include members of the enediyne family of molecules, such as calicheamicin and esperamicin.
  • Chemical toxins can also be taken from the group consisting of methotrexate, doxorubicin, melphalan, chlorambucil, ARA-C, vindesine, mitomycin C, cis-platinum, etoposide, bleomycin and 5-fluorouracil.
  • antineoplastic agents include, but are not limited to, dolastatins (U.S. Patent Nos.
  • chemotherapeutic compounds and/or combinations of compounds (e.g,. two or more compounds described herein alone or with other compounds) may be used as aspects of the invention are not limited in this respect.
  • compositions of the present invention may be administered in pharmaceutically acceptable preparations.
  • Such preparations may contain
  • the term "pharmaceutically acceptable” means a non-toxic material that does not interfere with the effectiveness of the biological activity of the active ingredients.
  • physiologically acceptable refers to a non-toxic material that is compatible with a biological system such as a cell, cell culture, tissue, or organism. The characteristics of the carrier will depend on the route of
  • physiologically and pharmaceutically acceptable carriers include, without being limited to, diluents, fillers, salts, buffers, stabilizers, solubilizers, and other materials which are well known in the art.
  • carrier denotes an organic or inorganic ingredient, natural or synthetic, with which the active ingredient is combined to facilitate the application.
  • the components of the pharmaceutical compositions also are capable of being co-mingled with the molecules of the present invention, and with each other, in a manner such that there is no interaction which would substantially impair the desired pharmaceutical efficacy.
  • Therapeutics according to some embodiments of the invention can be administered by any conventional route, for example injection or gradual infusion over time.
  • the administration may, for example, be oral, intravenous, intratumoral, intraperitoneal, intramuscular, intracavity, subcutaneous, or transdermal, or by pulmonary aerosol.
  • compositions of some embodiments of the invention are administered in effective amounts.
  • An "effective amount" is that amount of a composition that alone, or together with further doses, produces the desired clinical response.
  • the desired response is inhibiting the progression of the disease, for example, the growth of the tumor or the spread of a primary tumor to secondary sites via metastasis. This may involve slowing the progression of the disease temporarily, although more preferably, it involves halting the progression of the disease permanently.
  • the desired response to treatment is a permanent eradication of tumor cells.
  • the desired response to treatment can be delaying or preventing the manifestation of clinical symptoms, for example, of recurrent tumors.
  • the effect of treatment can be monitored by routine methods or can be monitored according to diagnostic methods of the invention discussed herein.
  • a chemotherapeutic compound or a combination of such compounds will depend, of course, on the particular tumor being treated, the severity of the condition, the individual patient parameters including age, physical condition, size and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment. It will be understood by those of ordinary skill in the art, however, that a patient may insist upon a lower dose or tolerable dose for medical reasons, psychological reasons or for virtually any other reasons.
  • kits comprising reagents useful for determining an expression level of an informative gene, for example, an informative gene listed in any of Tables 1-4.
  • a reagent useful for determining expression of an informative gene may, in some embodiments, be a detectable agent that binds to an expression product of an informative gene.
  • Detectable agents their generation and/or purification and their use are well known to those of skill in the art and non-limiting, exemplary detection agents include detectable binding agents, for example antibodies, antibody fragments, nucleic acids complementary to a sequence comprised in a transcript of the informative gene, aptamers, and adnectins.
  • a kit may comprise a plurality of different nucleic acid molecules that correspond to different informative gene transcripts.
  • the plurality of nucleic acid molecules is attached to a solid support.
  • a kit is provided that includes a focused microarray for the detection of expression levels of all or some of the informative genes described herein, for example, the informative genes listed in any of Tables 1-4.
  • a plurality of primer pairs is provided for determining an expression level of a plurality of informative genes, for example, of all or some of the genes listed in any of Tables 1-4.
  • a publicly available microarray dataset was used that included tumor expression data from 61 patients with pathologically confirmed epithelial ovarian cancer (EOC), including 34 with BRCA-1 or -2 germline mutation (18 BRCA-1 and 16 BRCA-2 mutations), and 27 without either mutation (i.e. sporadic cancers) 28.
  • EOC epithelial ovarian cancer
  • Genome wide hierarchical clustering was used to define BRCA-like and non-BRCA-like tumors as described in detail elsewhere herein and in FIG. 1.
  • Example 2 Patient samples
  • the second patient cohort included 70 patients treated at Beth Israel
  • Standard post- chemotherapy surveillance included serial physical examination, serum CA-125 level, and computed tomography scanning as clinically indicated.
  • RNA isolation Total RNA isolation, microarray hybridization (U133 Plus 2.0 Array GeneChip, Affymetrix, Santa Clara, CA), and data processing were performed previously described ' ' .
  • debulking status optical, less than or equal to 1 cm; or suboptimal, greater than 1 cm residual disease
  • BRCAness profile BRCA-like versus non- BRCA-like
  • a publicly available microarray dataset was used that included tumor expression data from 61 patients with pathologically confirmed epithelial ovarian cancer (EOC), including 34 with BRCA-1 or -2 germline mutation (18 BRCA-1 and 16 BRCA-2 mutations), and 27 without either mutation (i.e. sporadic cancers) 28 .
  • BRCA-like and non-BRCA-like tumors were defined using genome wide hierarchical clustering as illustrated in FIG. 1.
  • FIG. 1 describes the development of the BRCAness gene expression profile.
  • a publicly available microarray dataset was used that included tumor expression data from 61 patients with pathologically confirmed epithelial ovarian cancer (EOC), including 34 with BRCA-1 or -2 germline mutation (18 BRCA-1 and 16 BRCA-2 mutations), and
  • the BRCA-1 cluster contained 22 patients, of which 9 actually had sporadic (non-mutated) disease.
  • the BRCA-2 cluster contained 14 patients, of which 4 had sporadic disease.
  • the sporadic cluster contained 25 patients, of which 6 had BRCA-1 and 5 had BRCA-2 germline mutation.
  • the clustering reproducibility index (R) was 0.934 and the 3 clusters did not change even if clear cell or mucinous samples were excluded from the analysis.
  • these outliers e.g., a BRCA-1 patient contaminating the sporadic cluster, or a sporadic patient contaminating the BRCA cluster
  • a 60-gene diagonal linear discriminant predictor was developed next that distinguished the BRCA clusters (BRCA-like tumors) from the sporadic cluster (non-BRCA-like tumors) (FIG. 2B).
  • the predictor that distinguished BRCA-like from non-BRCA-like tumors was developed using the diagonal linear discriminant algorithm 48.
  • the classifier was trained by selecting genes with the highest fold-change difference between the two classes (BRCA-like and non-BRCA-like tumors). Classifier accuracy and statistical significance were assessed using leave-one-out cross-validation and a 1000 random permutation test to control for over- fitting 49 ' 50 . In order to ascertain that classifier accuracy was not an artifact of the optimal 60 gene predictor, the performance of predictors from a range of 40 to 90 genes was assessed and it was found that they demonstrated very good performance with accuracy of 89-92%.
  • the BRCAness profile was mapped across different platforms using Affymetrix annotation files before being applied to patient and cell line samples
  • Example 7 Patient samples
  • the second patient cohort consisted of 70 EOC patients, in which tumor was obtained at the time of diagnostic exploratory laparotomy. Twenty eight of these patients were diagnosed between November 1994 and June 2005 and treated at Cedars-Sinai Medical Center, and had sporadic EOC as determined by negative BRCA- 1 or -2 sequencing. The remaining 42 patients were diagnosed between January 1995 and October 2000 and treated at Beth Israel Deaconess Medical Center and Memorial Sloan-Kettering Cancer Center, and represent a subset of those previously reported 21 . Seven of the 42 patients were sequenced and found to be negative for a BRCA-1 or -2 mutation. The remaining 35 of these 42 patients were selected on the basis of criteria that are expected to enrich for sporadic disease.
  • these 35 patients had no family history of ovarian cancer, no family history of breast cancer at age ⁇ 50, no family history of more than 1 breast cancer at any age, and were not of Ashkenazi Jewish ethnicity.
  • Ovarian cancer samples from this patient cohort were collected at the time of primary debulking surgery and frozen at -80°C. Tumor samples were pulverized in liquid nitrogen and homogenized in Trizol solution (Invitrogen Corp, Carlsbad, CA), followed by RNA isolation.
  • Capan-1 Twelve cisplatin-resistant clones of the originally cisplatin-sensitive BRCA-2- mutated pancreatic cancer cell line Capan-1 have been previously described 29 .
  • the parent Capan-1 line harbors a 6174delT mutation, associated with loss of
  • heterozygosity 51 As a result of platinum-induced selection pressure, 6 of these 12 clones had acquired secondary genetic events that restored nearly full-length, functional BRCA-2 protein and RAD51 foci formation in response to ionizing radiation (IR) 29 . The remaining 6 clones showed persistent evidence of mutated BRCA-2 (6174delT), lacked BRCA-2 protein expression and exhibited impaired IR- induced RAD51 foci formation (except one which had proficient RAD51 foci formation). Two of the clones with restored functional BRCA-2 protein were tested for PARP inhibitor sensitivity and found to be resistant to PARP inhibition, while two of the clones with restored functional BRCA-2 protein were tested for PARP inhibitor sensitivity and found to be resistant to PARP inhibition 29 .
  • GEO Gene Expression Omnibus
  • Unsupervised hierarchical clustering was performed using the average linkage method and the one minus centered correlation as a distance metric in all cases 52 .
  • the p values of all statistical tests were two-sided.
  • the SPSS version 16.0 and STATA version 10.1 packages were used for statistical tests.
  • All bioinformatic analyses were performed using the BRB- Array Tools Version 3.8 [developed by Dr Richard Simon (Biometrics Research Branch, National Cancer Institute, Bethesda, MD)].
  • FIG. 1 The optimal classifier was a 60-gene diagonal linear discriminant predictor that distinguished BRCA-like from non-BRCA-like tumors with 94% accuracy, as assessed by leave-one-out cross-validation and 1000 random permutations test (FIG. 2)(p ⁇ 0.001).
  • FIG. 2 displays an expression plot of the 60 genes that comprise the BRCAness profile. Columns: Training set samples. Rows: Gene expression levels (normalized). Complete information regarding gene identity is provided in Table 1. Dark shading: Overexpressed genes. Light shading: Underexpressed genes. The gene expression signature that correlates with BRCA-like tumors is defined as the "BL" profile, and the signature that correlates with non-BRCA-like tumors is defined as the "NBL" profile.
  • the gene expression signature that correlates with BRCA-like tumors is defined as the "BL” profile
  • the signature that correlates with non-BRCA-like tumors is defined as the "NBL” profile.
  • the identities of all BRCAness profile genes are provided in Table 1.
  • Example 12 BRCAness profile distinguishes between platinum-sensitive and - resistant tumor biopsy samples It was first investigated whether the BRCAness profile could correlate with platinum responsiveness in patients with known BRCA germline mutation. For this purpose, 10 tumor biopsy specimens from 6 patients with either BRCA-1 or -2 germline mutation were used, four of whom were initially platinum sensitive but eventually developed platinum resistance (with pre and post biopsy pairs). These patients formed the basis of a previous report in which reversion of the BRCA genotype occurred (with re-establishment of BRCA function) upon the development of platinum resistance 29 . Thus, these samples provided an opportunity to determine how the BRCAness profile correlated with both platinum responsiveness and BRCA functional status (e.g., mutant versus revertant BRCA gene).
  • FIG. 3A shows that hierarchical clustering based on the expression pattern of the 60 genes of the BRCAness profile distinguished between platinum resistant and platinum sensitive tumor biopsy samples.
  • FIG. 3B shows the correlation of the BRCAness profile with platinum sensitivity and BRCA germline mutation status in the 10 tumor biopsy specimens from 6 patients.
  • the BRCAness profile accurately distinguished between platinum sensitivity and platinum resistance in 8 out of 10 tumor specimens, which in turn correlated with presence of mutated versus functional BRCA gene status, respectively.
  • the BRCAness profile dynamically tracked the development of platinum resistance over the course of therapy (e.g., the profile changed from BL to NBL following the development of platinum resistance, associated with reversion to functional BRCA-1 or 2).
  • the BRCAness profile could accurately distinguish between platinum sensitive and platinum resistance in 8 out of 10 tumor specimens, which in turn correlated with presence of mutated versus functional BRCA gene status,
  • 29 30 signature were platinum resistant (and had reverted to functional BRCA-1 or -2) ' . Furthermore, patients were observed in which the BRCAness profile dynamically tracked the development of platinum resistance over the course of therapy (e.g., the profile changed from BL to NBL following the development of platinum resistance, associated with reversion to functional BRCA-1 or -2, FIG. 3B).
  • Example 13 BRCAness profile correlates with PARP inhibitor responsiveness and RAD51 foci formation
  • the BRCAness profile correlated with RAD51 foci formation in 9 out of 12 Capan-1 clones, and between presence of mutated versus functional BRCA-2 gene status in 10 out of 12 Capan-1 clones (FIG. 4). Importantly, the BRCAness profile accurately distinguished between 2 PARP inhibitor resistant clones (NBL signature) and 2 PARP inhibitor sensitive clones (BL signature) (FIG. 4).
  • Example 14 The relationship between BRCAness profile and clinical outcome in patients with sporadic EOC
  • the BRCAness profile may correlate with platinum and PARP-inhibitor responsiveness in the context of a known BRCA germline mutation, but they do not address whether the profile correlates with outcome in patients with sporadic disease.
  • the profile was applied to tumor samples from 35 patients with invasive EOC who had been sequenced and known to be wildtype for BRCA-1 and -2, and 35 patients enriched for sporadic disease on the basis of the following characteristics: no family history of ovarian cancer, no family history of breast cancer under the age of 50 years, no family history of more than 1 breast cancer at any age, and not of Ashkenazi Jewish ethnicity 37 ' 38.
  • the clinical and pathologic characteristics of all 70 patients are shown in Table 5.
  • d Debulking status was unknown for 1 patient. Optimal, less than or equal to 1 cm.; suboptimal, greater than 1 cm.
  • NBL non-BRCA-like
  • BL BRCA-like
  • b Debulking status was unknown for 1 patient. Optimal, less than or equal to 1 cm.; suboptimal, greater than 1 cm.
  • DFS median disease free survival
  • FIG. 5B shows DFS in the sequenced patient cohort.
  • FIG. 6B shows OS in the sequenced patient cohort.
  • FIG. 7 shows DFS in the non-sequenced patient cohort.
  • FIG. 7B shows OS in the non-sequenced patient cohort.
  • a Debulking status was unknown for 1 patient. Values in bold are statistically significant at p less than or equal to 0.05. homologous recombination (hazard ratio for death) represented in parentheses (comparing NBL versus BL groups), for statistically significant associations.
  • PARP inhibitors have been evaluated in patients with germline BRCA-1 and - 2 mutations, with impressive results as single agents lo n . In addition to patients with germline BRCA-1 or -2 mutations, however, it has been suggested that PARP inhibition might be a useful therapeutic strategy for the treatment of patients with sporadic cancers that have a BRCAness phenotype, characterized by defective homologous recombination 15 .
  • a number of mechanisms have been identified in sporadic ovarian cancer that might implicate the homologous recombination pathway in pathogenesis and in drug responsiveness. Such mechanisms include mutations or epigenetic silencing of genes involved in the Fanconi Anemia protein complex, intrinsic homologous recombination genes, or other DNA damage response genes 5 ⁇ 15 ⁇ 17 ⁇ 19 ⁇ 23 Amplification of genes that encode for
  • BRCA- 1 promoter methylation has been identified in 5-31%, 21%, and 17% of sporadic EOCs respectively 15 17 19 ⁇ 23 ⁇ 24 5 supporting the notion that at least some patients with sporadic disease might harbor defects in HR, independent of the presence of a germline BRCA-1 or -2 mutation.
  • Teodoridis et al used methylation-specific PCR and showed that BRCA- 1 promoter hypermethylation is associated with improved response to platinum-based chemotherapy 16 .
  • Quinn et al used siRNA knock-down to decrease the expression of the BRCA-1 gene in two separate ovarian cancer cell lines, showing that lower levels of BRCA-1 mRNA correlated with enhanced in vitro sensitivity to cisplatin 39 .
  • the concept of BRCAness has been broadened by identifying a gene expression profile that is associated with platinum and PARP-inhibitor responsiveness, as well as RAD51 foci formation.
  • the profile when applied to a population of patients enriched for sporadic disease, the profile correlated with clinical outcome, independent of standard prognostic factors such as age, grade, histology, stage, and debulking status.
  • the BRCAness profile was developed in ovarian tumors, it was also capable of predicting PARP inhibitor sensitivity and RAD51 foci formation in the pancreatic cancer cell line Capan-1, suggesting that the profile may be detecting a pattern of gene expression that more globally reflects the status of HR, independent of cell lineage.
  • the predictive value of this profile in triple negative breast cancer is currently being investigated, which is thought to be enriched for BRCAness and a high response to platinum-containing chemotherapy 41 .
  • the identification of a gene expression profile that correlates with BRCAness may be useful to identify cancer patients (e.g., patients with epithelial ovarian cancer) to be treated with certain chemotherapeutic agents (including, but not limited to, PARP inhibitors), regardless of the BRCA-1 or -2 mutation status of the patients. This may allow certain chemotherapeutic agents to be used more effectively in a broader range of patients.
  • cancer patients e.g., patients with epithelial ovarian cancer
  • certain chemotherapeutic agents including, but not limited to, PARP inhibitors
  • Ratnam K Low JA: Current development of clinical inhibitors of poly(ADP- ribose) polymerase in oncology. Clin Cancer Res 13:1383-8, 2007
  • Hedenfalk IA Gene expression profiling of hereditary and sporadic ovarian cancers reveals unique BRCA-1 and BRCA-2 signatures. J Natl Cancer Inst 94:960-1, 2002
  • Godwin AK, Meister A, O'Dwyer PJ, et al High resistance to cisplatin in human ovarian cancer cell lines is associated with marked increase of glutathione synthesis.
  • references to a computer program which, when executed, performs the above-discussed functions is not limited to an application program running on a host computer. Rather, the term computer program is used herein in a generic sense to reference any type of computer code (e.g., software or microcode) that can be employed to program a processor to implement the above-discussed aspects of the present invention.
  • the computer implemented processes may, during the course of their execution, receive input manually (e.g., from a user).
  • program or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
  • Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc. that performs particular tasks or implement particular abstract data types.
  • functionality of the program modules may be combined or distributed as desired in various embodiments.
  • data structures may be stored in computer-readable media in any suitable form.
  • data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields.
  • any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
  • Transcripts named herein are well known to those of skill in the art, and the skilled artisan will be able to identify nucleotide sequences associated with the transcripts provided herein, for example, by retrieving transcript-associated database entries from a sequence database (e.g. NCBI or Ensembl at
  • NT2RM1000018 highly similar to Human mRNA for KIAA0066 gene (SEQ ID NO: 15)
  • HDAC1 histone deacetylase 1
  • mRNA SEQ ID NO: 16
  • Homo sapiens high-mobility group nucleosomal binding domain 2 mRNA (cDNA clone IMAGE:3455121) (SEQ ID NO: 18) >gill3435956lgblBC004815.1l Homo sapiens unc-119 homolog B (C. elegans), mRNA (cDNA clone IMAGE:3448346), complete cds (SEQ ID NO: 19)
  • MGST2 Homo sapiens microsomal glutathione S-transferase 2
  • mRNA SEQ ID NO: 24
  • MGST3 Homo sapiens microsomal glutathione S-transferase 3
  • SEQ ID NO: 25 mRNA
  • TNF tumor necrosis factor
  • SEQ ID NO: 27 Homo sapiens tumor necrosis factor
  • IL1RL1 transcript variant 1
  • SEQ ID NO: 28 transcript variant 2
  • Homo sapiens protein disulfide isomerase family A member 4, mRNA (cDNA clone MGC:8346 IMAGE:2819726), complete cds (SEQ ID NO: 31)
  • MTAP methylthioadenosine phosphorylase
  • SEQ ID NO: 34 mRNA
  • WAP four-disulfide core domain 2 WFDC2
  • mRNA SEQ ID NO: 37
  • ITM2C integral membrane protein 2C
  • transcript variant 1 mRNA
  • LSP1 lymphocyte-specific protein 1
  • transcript variant 1 mRNA
  • SEQ ID NO: 39 Homo sapiens lymphocyte-specific protein 1
  • BRCAl early onset
  • transcript variant BRCAlb mRNA
  • VTN vitronectin
  • SEQ ID NO: 42 Homo sapiens vitronectin
  • CD1D CD1D
  • SEQ ID NO: 45 CDld molecule
  • G-rich RNA sequence binding factor 1 GRSF1
  • transcript variant 1 mRNA
  • DAP Homo sapiens death-associated protein
  • SEQ ID NO : 51 Homo sapiens death-associated protein
  • PCTP phosphatidylcholine transfer protein
  • transcript variant 1 mRNA
  • RNASE 1 ribonuclease, RNase A family, 1 (pancreatic) (RNASE 1), transcript variant 4, mRNA (SEQ ID NO: 53)
  • Wiskott-Aldrich syndrome eczema- thrombocytopenia
  • WAS Wiskott-Aldrich syndrome
  • mRNA SEQ ID NO: 57
  • GFI1 growth factor independent 1 transcription repressor
  • SEQ ID NO: 58 transcript variant 1 mRNA
  • N-myc (and STAT) interactor NI
  • mRNA SEQ ID NO: 59
  • enoyl CoA hydratase short chain, 1, mitochondrial (ECHS1), nuclear gene encoding mitochondrial protein, mRNA (SEQ ID NO: 61)
  • mRNA conjuggase, folylpolygammaglutamyl hydrolase (GGH), mRNA (SEQ ID NO: 65) >gil221218992lreflNM_003475.3l Homo sapiens Ras association (RalGDS/AF-6) domain family (N-terminal) member 7 (RASSF7), transcript variant 1, mRNA (SEQ ID NO: 66)
  • Homo sapiens discs large (Drosophila) homolog- associated protein 5 (DLGAP5), transcript variant 1, mRNA (SEQ ID NO: 68) >gil239835753lreflNM_002970.2l Homo sapiens spermidine/spermine Nl- acetyltransferase 1 (SAT1), transcript variant 1, mRNA (SEQ ID NO: 69)
  • CXCR2 Homo sapiens chemokine (C-X-C motif) receptor 2 (CXCR2), transcript variant 1, mRNA (SEQ ID NO: 70) >gil270288734lreflNM_000270.3l Homo sapiens purine nucleoside phosphorylase (PNP), mRNA (SEQ ID NO: 71)
  • BMP1 bone morphogenetic protein 1
  • transcript variant 3 mRNA
  • CSF3 granulocyte
  • transcript variant 1 mRNA
  • Soares_NSF_F8_9W_OT_PA_P_Sl Homo sapiens cDNA clone IMAGE:2365169 3', mRNA sequence (SEQ ID NO: 81)
  • HUMAN contains element LI repetitive element ;
  • mRNA sequence SEQ ID NO: 82
  • Soares_NSF_F8_9W_OT_PA_P_Sl Homo sapiens cDNA clone IMAGE:3523665 3', mRNA sequence (SEQ ID NO: 89)
  • NEUROBLASTOMA Homo sapiens cDNA clone CL0BB030ZH05 5-PRIME, mRNA sequence (SEQ ID NO: 93)
  • Homo sapiens mucosal vascular addressin cell adhesion molecule 1 MADCAM1
  • transcript variant 1 mRNA
  • SEQ ID NO: 96 Homo sapiens transcription elongation factor B (SIII), polypeptide l-like (TCEB1L), mRNA (SEQ ID NO: 97)
  • BRCAl-gene sequences known in the art include, for example: >gil237757283lreflNM_007294.3l Homo sapiens breast cancer 1, early onset
  • the invention may be embodied as a method, of which an example has been provided.
  • the acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative

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Abstract

Aspects of the invention provide methods and compositions for evaluating cells and tissues based on gene expression profiles to determine their sensitivity or resistance to one or more chemotherapeutic agents. A patient having a tumor or cancer can be evaluated using a gene expression profile to assist in determining the disease prognosis, selecting an appropriate therapy, and/or predicting drug resistance or sensitivity.

Description

A GENE EXPRESSION PROFILE OF BRCA-NESS THAT CORRELATES WITH RESPONSIVENESS TO CHEMOTHERAPY AND WITH OUTCOME IN
CANCER PATIENTS RELATED APPLICATIONS
This application claims the benefit under 35 U.S.C. § 119(e) of U.S.
provisional application USSN 61/351,282, filed June 3, 2010, and entitled "A gene expression profile of BRCA-ness that correlates with responsiveness to chemotherapy and with outcome in cancer patients," the entire contents of which are incorporated herein by reference.
BACKGROUND OF THE INVENTION
In certain types of cancers, for example, certain epithelial cancers, like ovarian cancer and breast cancer, a subset of tumors exhibit a mutation in breast cancer associated gene 1 or 2 (BRCAl or BRCA2). Both BRCAl and BRCA2 are involved in cellular DNA repair and, without wishing to be bound by theory, it is believed that impairment of either gene's function is causally related to tumorigenesis.
Importantly, impairment of DNA repair capabilities increases a tumor' s sensitivity to chemotherapeutic agents, which is reflected with a generally favorable prognosis for chemotherapeutic treatment of cancers in subjects with BRCAl or BRCA2 mutations. In contrast, sporadic cancers of the same types develop without BRCA mutations. Until now, there has been no reliable technique to predict whether such sporadic cancers will or will not respond favorably to a chemotherapeutic approach. SUMMARY OF THE INVENTION
In some embodiments, aspects of this invention relate to the surprising discovery that a subset of sporadic epithelial ovarian tumors sensitive to
chemotherapeutic compounds can be identified based on the gene expression data obtained from these tumors resembling certain similarities to gene expression data obtained from tumors having a BRCAl or a BRCA2 mutation (regardless of the
BRCAl/2 genotype of the tumors). In some embodiments, aspects of this invention relate to methods for the classification of a tumor, for example, an epithelial ovarian tumor, as BRCA-like or non-BRCA-like based on expression profiles obtained from the tumors and in accordance with a predictive algorithm, for example, a diagonal linear discriminant predictor. In some embodiments, aspects of this invention relate to methods of identifying genes that are informative, either alone or in combination, as marker genes for the classification of epithelial ovarian tumors as BRCA-like or non-BRCA-like. In some embodiments, aspects of this invention relate to kits and reagents useful in the classification of epithelial ovarian tumors as BRCA-like or non- BRCA-like based on expression data. In some embodiments, aspects of this invention relate to predictive classifiers useful in the classification of expression profiles as BRCA-like or non-BRCA-like.
BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 illustrates an embodiment of the development of a BRCAness gene expression profile;
FIG. 2 shows an expression plot of 60 genes that comprise an example of a BRCAness profile;
FIG. 3 illustrates an example of a BRCAness profile distinguishing between platinum sensitive and resistant tumor biopsy specimens (n=10) from 6 patients with known BRCA germline mutation status;
FIG. 4 illustrates an example of a BRCAness profile distinguishing between RAD51 foci forming CAPAN-1 clones (n=12) and predicting PARP inhibitor sensitivity of different clones (n=4);
FIG. 5 shows an association of a BRCAness profile with DFS and OS in a combined patient cohort (n=70);
FIG. 6 shows an association of a BRCAness profile with DFS and OS in a sequenced patient cohort (n=35); and,
FIG. 7 shows an association of a BRCAness profile with DFS and OS in a non-sequenced patient cohort (n=35).
DETAILED DESCRIPTION
In some embodiments, aspects of the invention provide compositions, methods, and devices for predicting whether a tumor will be either responsive or resistant to treatment with one or more chemotherapeutic agents. In some embodiments, aspects of the invention involve evaluating the expression profiles of one or more identified genes to predict the response of cells to chemotherapeutic agents. Embodiments of the invention may be useful to assist in the diagnosis, prognosis, and/or therapy selection for patients that have or are suspected of having cancer. As described in more detail herein, embodiments of the invention may be useful for evaluating and/or predicting the responsiveness of patients to certain chemotherapeutic agents regardless of the BRCA 1 or 2 status of the patient.
Accurate prediction of tumor sensitivity to chemotherapeutic agents is a cornerstone in the decision for or against the administration of chemotherapy to a subject diagnosed with a tumor. In many cancers, for example, in sporadic epithelial ovarian tumors, such a prediction cannot be made reliably to date, and the decision for or against chemotherapy is mainly based on patient health status and the expected side effects of the therapeutic regimen instead of the sensitivity of the tumor itself. The lack of methods for accurately predicting a chemotherapeutic outcome results in many instances of either i) inappropriate administration of chemotherapy to subjects carrying chemotherapy-resistant (chemoresistant) tumors or ii) withholding of chemotherapy treatment from patients carrying chemotherapy- sensitive
(chemosensitive) tumors. For example, in the absence of information about the sensitivity or resistance to chemotherapy, a decision to withhold treatment from a patient may be made if the patient has a marginal health status for chemotherapy, particularly if the overall success rate of such a therapy is low for the patient's cancer type. Some embodiments of this invention address this unmet clinical need and provide methods for accurate prediction of tumor sensitivity to chemotherapeutic compounds based on gene expression data obtained from the tumor.
Accordingly, some methods provided herein allow for customizing a therapeutic approach not only to a specific subject, but also to a specific tumor in question, and are useful to increase the success rate of chemotherapeutic interventions and/or to lower the burden of medication side effects on patients carrying
chemoresistant tumors.
One major challenge in predicting tumor response to chemotherapeutic intervention from tumor expression data is the identification of informative genes, e.g., of genes the expression level of which is correlated with tumor chemosensitivity or chemoresistance. Informative genes have not been identified for many clinically relevant diagnostic distinctions, for example for the distinction between
chemosensitive and chemoresistant tumors, for example, sporadic tumors (e.g., sporadic epithelial ovarian tumors). In many cases, the identification of informative genes is hampered by non-uniform expression of such genes within a subgroup of tumors, e.g., within chemosensitive tumors, and/or because of the small differences in gene expression between tumor groups, e.g., between chemoresistant and
chemosensitive tumors.
Some embodiments of this invention provide methods for the identification of informative genes for tumor classification as chemosensitive or chemoresistant based on gene expression data. Some embodiments of this invention relate to the discovery of an expression profile in sporadic tumors that correlates with tumor sensitivity to chemotherapeutic agents, for example, platinum compounds or PARP inhibitors, and/or patient outcome. Some embodiments of this invention, accordingly, provide the identities of informative genes for a classification of a tumor, for example, a sporadic epithelial tumor (e.g., ovarian tumor), as chemosensitive or chemoresistant. In some embodiments, a tumor is classified as BRCA-like or non-BRCA-like based on gene expression data, and chemosensitivity or -resistance is determined based on a tumor's BRCAness. Examples of informative genes for the classification of epithelial ovarian tumors as BRCA-like and non-BRCA-like are shown in Table 1.
Some embodiments of this invention provide methods of using a predictive algorithm incorporating informative gene identities for accurate classification of tumors as BRCA-like or non-BRCA-like and/or the classification of tumors as chemosensitive or chemoresistant based on tumor expression data.
In some embodiments, tumor classification strategies are provided herein that allow the prediction of a tumor's responsiveness to chemotherapy and/or of patient outcome. Some embodiments relate to the identification and/or definition of gene expression profiles for the classification of a tumor as BRCA-like or non-BRCA-like. In some embodiments, methods are provided to assign tumors, for example, sporadic epithelial tumors (e.g., ovarian tumors), to a class exhibiting a BRCA-like expression profile or a class exhibiting a non-BRCA-like expression profile. In some embodiments, a BRCA-like expression profile is correlated with sensitivity to chemotherapeutic agents, for example, platinum compounds or PARP inhibitors. In some embodiments, a non-BRCA-like expression profile is not correlated with chemosensitivity. In some embodiments, a non-BRCA-like expression profile is correlated with chemoresistance.
Some embodiments relate to the identification of informative genes, the differential expression of which is indicative for a given tumor being chemosensitive or chemoresistant. Some aspects relate to the building of predictive algorithms using the identified informative genes and the use of the resulting predictors in the classification of tumors as chemosensitive or chemoresistant. In some embodiments, specific predictors and methods for their use in classifying sporadic tumors based on expression data as sensitive or resistant to chemotherapeutic agents are provided that allow for a distinction of BRCA-like (BL) from non-BRCA-like (NBL) tumors. In some embodiments, classification of sporadic tumors is performed with at least 94% accuracy. In some embodiments, methods and predictors are provided that allow for a distinction between platinum sensitivity and resistance in at least 80% of patient- derived tumor specimens. In some embodiments, methods and predictors are provided that allow for predictions of disease-free survival and overall survival time in subjects with sporadic tumors. In some embodiments, predictors and methods are provided for the classification of tumors based on expression profile data as part of a multivariate disease analysis, for example, with respect to one or more of patient age and health status, and tumor stage, grade, histology, and debulking status.
In some embodiments, a subject that does not have a mutation or defect in either the BRCA1 or BRCA2 gene may be classified as either responsive or resistant according to methods described herein. In some embodiments, a subject (e.g., a cancer patient) may first be screened to determine whether a BRCA 1 or 2 mutation or defect is present. If the answer is yes, then the subject is likely to be responsive to treatment with one or more chemotherapeutic agents and may be identified as such, and/or prescribed for such treatment, and/or treated (e.g., by administration) with one or more chemotherapeutic agents. In some embodiments, such subjects may not need to be evaluated for a BL or NBL expression profile since they are already likely to be sensitive based on their BRCA status. However, in some embodiments, a subject that is BRCA 1 or 2 defective still may be evaluated to determine whether a BL or NBL expression profile is present. In some embodiments, patients that have a BRCA 1 or 2 genetic defect may not be candidates for certain chemotherapeutic treatments if they have a NBL gene expression profile as described herein.
In some embodiments, a subject that is identified as not having a mutation or defect in a BRCA gene may be evaluated for a BL or NBL expression profile. If the subject has a BL expression profile, the subject may be identified and/or treated as responsive to certain chemotherapeutic agents. However, if the subject has a NBL expression profile, the subject may be identified and/or treated as non-responsive to certain chemotherapeutic agents. BRCA proteins BRCA-1 and BRCA-2 are involved in the process of homologous recombination, which mediates repair of double stranded DNA breaks \ Cancer patients, for example, ovarian cancer patients, with germline mutations in either BRCA-1 or BRCA-2 genes exhibit an impaired ability to repair double stranded DNA breaks via homologous recombination, which may partly explain their heightened sensitivity to platinum and their more favorable survival compared to patients not carrying such mutations2"4. Furthermore, in the setting of defective homologous recombination, it has been shown that inhibition of a second DNA repair pathway such as base excision repair (BER) is a lethal event 5"7. Based upon this observation, there has been great interest in developing inhibitors of the BER pathway for use as possible therapeutic agents in ovarian cancer patients with germline BRCA- 1 or -2 mutation 8'9. Drugs that target BER typically inhibit poly-ADP ribose polymerase (PARP), an enzyme critical to BER, and have already shown promising activity in patients with recurrent ovarian cancer who harbor germline BRCA-1 or -2 mutation 10,1 \
The promise of PARP inhibitors in the management of epithelial ovarian cancer (EOC) is tempered by the fact that only approximately 10% of such patients have a germline mutation in BRCA-1 or -2 12~14. At first glance, this might imply that 90% of patients with this highly lethal disease would not benefit from this novel class of drugs. However, it has been speculated that a subset of sporadic EOCs may harbor abnormalities in the homologous recombination pathway that could be associated with improved response rate and survival after treatment with platinum compounds, in the absence of germline BRCA-1 or -2 mutation 4'15. This "BRCAness" phenotype may be due, in part, to defective homologous recombination related to several mechanisms including epigenetic hypermethylation of the BRCA-1 promoter 16~19, somatic mutation of BRCA-1 or -2 18'20 22 5 or loss of function mutations in other homologous recombination pathway genes 23 ' 24. At present, however, it has not been possible to reliably identify such patients on the basis of molecular (e.g., transcript- or protein- based) biomarkers, and the concept of BRCAness for patients with the sporadic form of the disease has remained elusive.
Some aspects of the invention are based on the discovery of a plurality of informative genes that are differentially expressed in chemosensitive and
chemoresistant tumors. For example, the identity of informative genes for the classification of tumor as BRCA-like and non-BRCA-like is provided in Table 1. Table 1. Identity of informative genes for the classification of tumors (e.g., epithelial ovarian tumors) as BRCA-like or non-BRCA-like
# Probe set ID GenBank accession # Gene Symbol Unigene Cluster
1 208668. _x_at BC003689 HMGN2 Hs.181163
2 204664. _at NM_001632 ALPP Hs.284255
3 207113. _s_at NM_000594 TNF Hs.241570
4 201311. _s_at AL515318 SH3BGRL Hs.108029
5 205075. _at NM_000934 SERPINF2 Hs.159509
6 219606. _at NM_016018 PHF20L1 Hs.304362
7 202365. _at BC004815 UNC119B Hs.127610
8 204029. _at NM_001408 CELSR2 Hs.57652
9 204531. _s_at NM_007295 BRCA1 Hs.194143
10 204826. _at NM_001761 CCNF Hs.1973
11 214656. _x_at BE790157 MYOIC Hs.286226
12 203817. _at W93728 GUCY1B3 Hs.77890
13 204956. _at NM_002451 MTAP Hs.193268
14 210027. _s_at M80261 APEX1 Hs.73722
15 206832. _s_at NM_004186 SEMA3F Hs.32981
16 207175. _at NM_004797 ADIPOQ Hs.80485
17 209688. _s_at BC005078 CCDC93 Hs.107845
18 201695. _s_at NM_000270 NP Hs.75514
19 205057. _s_at AI762782 IDUA Hs.89560
20 212932. _at AK022494 RAB3GAP1 Hs.306327
21 204534. _at NM_000638 VTN Hs.2257
22 201808. _s_at BE732652 ENG Hs.76753
23 204466. _s_at BG260394 SNCA Hs.271771
24 201403. _s_at NM_004528 MGST3 Hs.191734
25 208037. _s_at NM_007164 M ADC AMI Hs.102598
26 202701. .at NM_006129 BMP1 Hs.1274
27 212224. _at NM_000689 ALDH1A1 Hs.76392
28 210809. _s_at D13665 POSTN Hs.136348
29 203892. _at NM_006103 WFDC2 Hs.2719
30 204259. _at NM_002423 MMP7 Hs.2256
31 200665. _s_at NM_003118 SPARC Hs.111779
32 221004. _s_at NM_030926 ITM2C Hs.111577
33 205974. _at AI168371 HOXD1 Hs.83465
34 204456. _s_at AW611727 GAS1 Hs.65029
35 205479. _s_at NM_002658 PLAU Hs.77274
36 206976. _s_at NM_006644 HSPH1 Hs.36927
37 205849. _s_at NM_006294 UQCRB Hs.131255
38 207008. _at NM_001557 IL8RB Hs.846
39 207526. _s_at NM_003856 IL1RL1 Hs.66
40 200650. _s_at NM_005566 LDHA Hs.2795
41 201858. _s_at J03223 vSRGN Hs.1908
42 203213. _at AL524035 CDC2 Hs.334562
43 206605. _at NM_006025 Pll Hs.997
44 201111. _at AF053641 CSE1L Hs.90073
45 204103. .at NM_002984 CCL4 Hs.75703
46 210512. _s_at AF022375 VEGFA Hs.73793 47 202988. _s_at NM_002922 RGS1 Hs.75256
48 209492. _x_at BC003679 ATP5I Hs.85539
49 201785. _at NM_002933 RNAvSEl Hs.78224
50 205789. _at NM_001766 CD1D Hs.1799
51 202543. _s_at BC005359 GMFB Hs.151413
52 204927. .at NM_003475 RASSF7 Hs.72925
53 203745. _at AI801013 HCCS Hs.211571
54 204086. _at NM_006115 PRAME Hs.30743
55 203764. _at NM_014750 DLG7 Hs.77695
56 201095. .at NM_004394 DAP Hs.75189
57 208658. .at BC000425 PDIA4 Hs.93659
58 201693. _s_at AV733950 EGR1 Hs.326035
59 201641. _at NM_004335 BST2 Hs.118110
60 205365. _at AA527340 H0XB6 Hs.98428
61 201212. _at D55696 LGMN Hs.18069
62 200711. _s_at NM_003197 SKP1A Hs.171626
63 207442. .at NM_000759 CSF3 Hs.2233
64 201501. _s_at NM_002092 GRSF1 Hs.309763
65 203560. _at NM_003878 GGH Hs.78619
66 208729. _x_at D83043 HLA-B Hs.77961
67 202812_ _at NM_000152 GAA Hs.1437
68 201485. _s_at BC004892 RCN2 Hs.79088
69 206589. .at NM_005263 GFI1 Hs.73172
70 213932_ _x_at AI923492 HLA-A Hs.181244
71 201135. _at NM_004092 ECHS1 Hs.76394
72 202269. _x_at BC002666 GBP1 Hs.62661
73 205400_ _at NM_000377 WAS Hs.2157
74 221931_ _s_at AV701173 SEH1L Hs.301048
75 200749_ .at BF112006 RAN Hs.10842
76 218676_ _s_at NM_021213 PCTP Hs.285218
77 213677_ _s_at BG434893 PMS1 Hs.111749
78 201099. _at AA824386 USP9X Hs.77578
79 208716. _s_at AB020980 TMCOl Hs.93832
80 201179. _s_at J03005 GNAI3 Hs.73799
81 200607. _s_at BG289967 RAD21 Hs.81848
82 207569. _at NM_002944 ROS1 Hs.1041
83 201209. _at NM_004964 HDAC1 Hs.88556
84 200726. _at NM_002710 PPP1CC Hs.79081
85 203523. .at NM_002339 LSP1 Hs.56729
86 211178. _s_at AF038602 PSTPIP1 Hs.129758
87 204168. .at NM_002413 MGST2 Hs.81874
88 201289. _at NM_001554 CYR61 Hs.8867
89 200046. at NM_001344 DAD1 Hs.82890
90 209149. _s_at BE899402 TM9SF1 Hs.91586
91 203455. _s_at NM_002970 SAT1 Hs.28491
92 203964. at NM_004688 NMI Hs.54483
93 201589. at D80000 SMC1A Hs.211602 Some embodiments are based on the discovery that tumor expression profiles including expression data for one or more informative genes, allow for accurate diagnostic classification of the tumor as chemosensitive or chemoresistant. Some embodiments relate to diagnostic methods of classifying a tumor as sensitive to a chemotherapeutic compound (chemosensitive) or resistant to a chemotherapeutic compound (chemoresistant) based on gene expression data obtained from the tumor. Some embodiments relate to methods of predicting chemosensitivity or
chemoresistance of a tumor based on tumor classification. Some embodiments relate to methods of administering chemotherapy to a subject carrying a tumor based on the tumor' s classification as chemosensitive. Some embodiments relate to methods of withholding administration from a subject carrying a tumor based on the tumor being classified as chemoresistant.
The term "tumor" as used herein, refers to a neoplastic cell growth, including benign, malignant, pre-cancerous and cancerous cell neoplasms. A tumor may be a liquid tumor, for example, a leukemic tumor, or a solid tumor, for example, an ovarian epithelial tumor, a breast tumor, a colon tumor, a gastric tumor, a prostate tumor, a pancreatic tumor, a lung tumor, a liver tumor, a brain tumor, or a kidney tumor. In some embodiments, the tumor is an epithelial tumor. In some
embodiments, the tumor is a tumor harboring a cell with a defect in homologous recombination. In some embodiments, the tumor may be the manifestation of a cancer, for example, blood cancer, ovarian epithelial cancer, breast cancer, colon cancer, gastric cancer, prostate cancer, pancreatic cancer, lung cancer, liver cancer, brain cancer, or kidney cancer.
In some embodiments, the tumor being classified according to methods provided herein is a primary tumor. In some embodiments, the tumor being classified according to methods provided herein is a secondary, metastatic, or recurrent tumor.
The term "subject", as used herein, refers to an individual that may be, but is not limited to, a human, a non-human mammal, for example, a mouse, rat, cow, sheep, cat, dog, or goat.
In some embodiments, a method for the diagnostic classification of a tumor as chemoresistant or chemosensitive is provided. In some embodiments, the method includes obtaining an expression profile of the tumor. An expression profile can be obtained from a cell or a tissue from a tumor, for example, from a tumor biopsy. The term "expression profile", as used herein, refers to a dataset containing gene expression data from a cell or tissue. In some embodiments, the expression profile may consist of a single data point, for example, a quantitative or semi-quantitative value of expression of a single gene, for example, reflective of the signal obtained from a quantitative or semi-quantitative assay detecting the abundance of a gene product (e.g., a protein or a nucleic acid transcript). Suitable assays for the detection of gene expression products are well known to those of skill in the art and include, for example, western blots, ELISA, RT-PCR (e.g. end-point RT-PCR, real-time PCR, or qPCR), protein or nucleic acid microarray, and massive parallel sequencing assays. However, any suitable assay may be used based on hybridization, specific binding (e.g., antibody binding), or any other technique, as aspects of the invention are not limited in this respect. In some embodiments, an expression profile may contain a plurality of gene expression data points, for example, quantitative or semi-quantitative values of expression of two or more genes. In some embodiments, the expression profile may comprehensively cover the whole transcriptome or proteome of a given cell, tissue, or organism. Whole-transcriptome or -proteome microarrays, containing probes for the detection of substantially all transcript or protein sequences known to those of skill in the art to be transcribed or translated from a cell's genome, e.g., for all sequences in the transcriptome of the respective organism the cell originates from, are well known to those of skill in the art and such microarrays are commercially available for various species including human. Methods for the generation of expression profiles are well known to those in the art and include, for example, western blot, northern blot, reverse northern blot, RT-PCR (e.g. endpoint, real time, or qPCR), microarray (for either protein or transcript detection) (for exemplary detection methods, see, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, Third Edition (3 Volume Set), Cold Spring Harbor Laboratory Press; 3rd edition (January 15, 2001), ISBN-10: 0879695773; Robert Griitzmann (Editor), Christian Pilarsky (Editor), Cancer Gene Profiling: Methods and Protocols (Methods in Molecular Biology), Humana Press; 1st edition (November 6, 2009), ISBN-10: 1934115762, both incorporated herein by reference for disclosure of gene product detection and expression profiling methods).
Further, methods to generate comprehensive transcript expression profiles from a given cell or tissue that measure the abundance of all transcripts expressed by the cell or tissue, are well known in the art and include, for example, whole- transcriptome or whole proteome microarrays and massive parallel sequencing assays (e.g., 454 sequencing or Solexa/Illumina sequencing (see, e.g., Robert Griitzmann (Editor), Christian Pilarsky (Editor), Cancer Gene Profiling: Methods and Protocols (Methods in Molecular Biology), Humana Press; 1st edition (November 6, 2009), ISBN-10: 1934115762, both incorporated herein by reference for disclosure of whole transcriptome expression profiling methods).
In some embodiments, a quantitative expression value is a value reflecting the abundance of a gene transcript in the starting sample, for example, a tumor cell or tissue sample. In some embodiments, a semi-quantitative expression value is a value reflecting the abundance of a gene transcript in the starting sample in relation to a control or reference quantity. In some embodiments, a semi-quantitative value may be a non-numeric indication of gene regulation (e.g., "up", "down", "+", "+ +", "+ + +"5 "_ _"5 or "— ")_ in some embodiments, a semi-quantitative expression value may give a numeric dimension of gene regulation (e.g., "1.5-fold upregulated", "2.456", "0.32" or "-1.5"). Methods of calculating semi-quantitative expression values are well known to those in the art. Appropriate control or reference quantities for the generation of semi-quantitative expression values are well known to those in the art and include, for example, expression values of housekeeping genes (e.g., beta- actin or GAPDH), external controls (e.g., spiked in RNA or DNA controls not usually expressed in the cell to be analyzed), overall expression values (e.g., all expression values obtained from a cell added together), or historic or empiric values.
In some embodiments, an expression profile used for class prediction of a tumor includes an expression value related to an informative marker gene for the classification of the tumor as chemoresistant or chemosensitive. In some
embodiments, the informative gene is a gene identified to be informative by methods provided herein. In some embodiments, the informative gene is a gene selected from the genes in any of Tables 1-4. In some embodiments, the expression profile includes expression values for a subgroup of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or more genes of the genes in any of Tables 1- 4. In some embodiments, the expression profile includes expression values for a subgroup of at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, or at least 60 genes of the genes in any of Tables 1-4. In some embodiments, the expression level of all or fewer of the genes in any of Tables 1-4 are evaluated.
It should be appreciated that the expression level of one or more genes of interest may be evaluated or determined in any suitable biological sample. In some embodiments, a biopsy of a tumor may be obtained and an expression profile may be obtained from a biopsy cell or tissue. In some embodiments, one or more circulating cells (e.g., one or more circulating tumor cells) may be obtained and an expression profile may be obtained from the cell or cells. In some embodiments, one or more tumor cells may be obtained from ascites fluid, peripheral blood, or from
cerebrospinal fluid of a subject.
In some embodiments, a method of diagnostic tumor classification includes processing a tumor expression profile in accordance to a predictive algorithm, for example, a classifier or predictor. Bioinformatic methods of classifying tumors based on gene expression data according to a predictive algorithm are well known in the art (see, for example, Dudoit S, Fridlyand J, and Speed TP, Comparison of
discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97:77-87, 2002, section 2,
"discrimination methods", pages 78-80, incorporated herein by reference for disclosure of discrimination methods based on expression profile data). In general, a predictor can be built from expression data obtained from tumors known to be either chemosensitive or chemoresistant according to methods provided herein or well known to those of skill in the art. Generally, a predictor will include a list of genes identified as informative for the class distinction based on a comparison of expression profiles of tumors of both classes. In some embodiments, a predictive algorithm will analyze an expression profile obtained from a tumor to be classified by filtering the profile for expression values of informative genes, and calculating a class call (e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant) for each informative gene based on the gene's expression value. For example, if an expression profile obtained from a tumor indicates a downregulation of the expression of an informative gene in the tumor tissue as compared to a reference or control value, and a downregulation of that gene has been determined to be correlated with a BRCA- like/chemosensitive phenotype, then the predictive algorithm would output the respective class call (e.g., BRCA-like or chemosensitive) for this specific informative gene. Some embodiments of this invention provide a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non- BRCA-like. Exemplary predictors for tumor classification as BRCA-like and non- BRCA-like are given in Tables 2 and 3.
Table 2. 60-gene predictor for classification of tumors (e.g., epithelial ovarian tumors) as BRCA-like and non-BRCA-like.
Gene Symbol Gene Name
HMGN2 high-mobility group nucleosomal binding domain 2
ALPP alkaline phosphatase, placental (Regan isozyme)
TNF tumor necrosis factor (TNF superfamily, member 2)
SH3BGRL SH3 domain binding glutamic acid-rich protein like
SERPINF2 serpin peptidase inhibitor, clade F (alpha-2 antiplasmin,
pigment epithelium derived factor), member 2
SAT1 spermidine/spermine Nl-acety transferase 1
NMI N-myc (and STAT) interactor
SMC1A structural maintenance of chromosomes 1A
UNC119B unc-119 homolog B (C. elegans)
TM9SF1 transmembrane 9 superfamily member 1
DAD1 defender against cell death 1
CYR61 cysteine-rich, angiogenic inducer, 61
MGST2 microsomal glutathione S-transferase 2
PSTPIP1 proline-serine-threonine phosphatase interacting protein 1
PPP1CC protein phosphatase 1 , catalytic subunit, gamma isoform
HDAC1 histone deacetylase 1
ROS1 v-ros UR2 sarcoma virus oncogene homolog 1 (avian)
GUCY1B3 guanylate cyclase 1, soluble, beta 3
MTAP methylthioadenosine phosphorylase
APEX1 APEX nuclease (multifunctional DNA repair enzyme) 1
SEMA3F sema domain, immunoglobulin domain (Ig), short basic
domain, secreted, (semaphorin) 3F
RAD21 RAD21 homolog (S. pombe)
GNAI3 guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 3
CCDC93 coiled-coil domain containing 93
USP9X ubiquitin specific peptidase 9, X-linked
PMS1 PMS1 postmeiotic segregation increased 1 (S. cerevisiae)
PCTP phosphatidylcholine transfer protein
RAN RAN, member RAS oncogene family
IDUA iduronidase, alpha-L-
SEH1L SEHl-like (S. cerevisiae)
WAS Wiskott-Aldrich syndrome (eczema-thrombocytopenia)
GBP1 guanylate binding protein 1, interferon-inducible, 67kDa
VTN vitronectin
ECHS1 enoyl Coenzyme A hydratase, short chain, 1 , mitochondrial
HLA-A major histocompatibility complex, class I, A GFI1 growth factor independent 1
RCN2 reticulocalbin 2, EF-hand calcium binding domain
ENG endoglin (Osler-Rendu- Weber syndrome 1)
SNCA synuclein, alpha (non A4 component of amyloid precursor)
MGST3 microsomal glutathione S-transferase 3
HLA-B major histocompatibility complex, class I, B
GGH gamma-glutamyl hydrolase (conjugase,
folylpolygammaglutamyl hydrolase)
M ADC AMI mucosal vascular addressin cell adhesion molecule 1
SKP1A S-phase kinase-associated protein 1A (pl9A)
LGMN legumain
BST2 bone marrow stromal cell antigen 2
PDIA4 protein disulfide isomerase family A, member 4
PRAME preferentially expressed antigen in melanoma
CD1D CD Id molecule
RGS1 regulator of G-protein signaling 1
VEGFA vascular endothelial growth factor A
CCL4 chemokine (C-C motif) ligand 4
Pll 26 serine protease
CDC2 cell division cycle 2, Gl to S and G2 to M
SRGN serglycin
LDHA lactate dehydrogenase A
SPARC secreted protein, acidic, cysteine-rich (osteonectin)
MMP7 matrix metallopeptidase 7 (matrilysin, uterine)
WFDC2 WAP four-disulfide core domain 2
POSTN periostin, osteoblast specific factor
Table 3. 30-gene predictor for informative genes in core classifier
Figure imgf000015_0001
16 BST2 bone marrow stromal cell antigen 2 1.45
17 SEMA3F sema domain, immunoglobulin domain (Ig), short 1.22
basic domain, secreted, (semaphorin) 3F
18 WFDC2 WAP four-disulfide core domain 2 1.63
19 MMP7 matrix metallopeptidase 7 (matrilysin, uterine) 2.17
20 APEX1 APEX nuclease (multifunctional DNA repair enzyme) 1 1.24
21 LDHA lactate dehydrogenase A 1.28
22 GGH gamma-glutamyl hydrolase (conjugase, 1.51
folylpolygammaglutamyl hydrolase)
23 IDUA iduronidase, alpha-L- 1.24
24 SMC1A structural maintenance of chromosomes 1A 1.25
25 TM9SF1 transmembrane 9 superfamily member 1 1.37
26 DAD1 defender against cell death 1 1.35
27 PPP1CC protein phosphatase 1, catalytic subunit, gamma 1.17
isoform
28 GNAI3 guanine nucleotide binding protein (G protein), alpha 1.47
inhibiting activity polypeptide 3
29 0S1 c-ros oncogene 1 , receptor tyrosine kinase 1.34
30 PDIA4 protein disulfide isomerase family A, member 4 1.73
In some embodiments, a predictor for the classification of a tumor as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, comprises 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more genes of the genes listed in any of Tables 1-3. In some embodiments, a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, essentially consists of a subgroup of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more genes of the genes listed in any of Tables 1-3. In some embodiments, a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, consists of a subgroup of 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, or more genes of the genes listed in any of Tables 1-3.
In some embodiments, a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, comprises at least 2, at least 3, at least 4, at least 5, at least 6, at least 7, at least 8, at least 9, at least 10, at least 11, at least 12, at least 13, at least 14, at least 15, at least 20, at least 25, at least 30, at least 35, at least 40, at least 45, at least 50, at least 55, or at least 60 genes of the genes in any of Tables 1-3. In some embodiments, a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non- BRCA-like, essentially consists of up to 2, up to 3, up to 4, up to 5, up to 6, up to 7, up to 8, up to 9, up to 10, up to 11, up to 12, up to 13, up to 14, up to 15, up to 20, up to 25, up to 30, up to 35, up to 40, up to 45, up to 50, up to 55, or up to 60 genes of the genes in any of Tables 1-3.
In some embodiments, a predictor for the classification of a tumor, for example, an epithelial ovarian tumor, as chemosensitive or chemoresistant, or chemosensitive or not chemosensitive, or BRCA-like or non-BRCA-like, comprises, essentially consists of, or consists of one or more genes listed in any of Tables 1-3 that are upregulated in BRCA-like tumors, and/or of 1 or more genes listed in any of Tables 1-3 that are downregulated in BRCA-like tumors, or a combination thereof.
In some embodiments, a predictive algorithm, as provided herein, classifies a tumor by calculating the sum of all class calls for all informative genes in the predictor and assigning the tumor to the class that received the most calls based on the expression data for these informative genes. In some embodiments, the calls of the informative genes included in the predictor are weighed by the predictive algorithm, for example, by assigning more weight to a call of an informative gene differential expression of which strongly correlates with tumor chemosensitivity or
chemoresistance than to a call of an informative gene that shows only weak correlation with either phenotype. Statistical methods of determining correlation between differential expression and observed phenotype are well known in the art. In some embodiments employing a weighted voting scheme, a prediction may be based by the inner sum of the weights (w and expression of the genes (x , wherein the weight is the weight assigned to each gene, for example, based on correlation of differential expression of the gene with the class distinction, and expression of the gene is expressed as a quantitative or numeric semi-quantitative value. A class call may be made, in some embodiments, based on the inner sum∑ i Wi x; for all or a subset of genes in a predictor exceeding or being lower than a certain threshold level. Such threshold levels can be calculated from empirical data by methods well known to those of skill in the art. In some embodiments, a single threshold level is determinative of a tumor being assigned to one or the other class in a binary class distinction. In some embodiments, two threshold levels are determined, and a tumor is assigned to one class, for example, BRCA-like, if ∑ i Wi x; exceeds the higher threshold level, and to the other class, for example, non-BRCA-like, if ∑ i Wi x; is less than the lower threshold level. In some embodiments, if ∑ i Wi x; falls between both threshold levels, the tumor may not be assigned a class. In some embodiments, using two threshold levels may result in a predictive algorithm only assigning tumors to a respective class that can be classified with high confidence.
An exemplary 30-gene weighted voting predictor is provided in Table 4. In some embodiments employing the predictor described in Table 4, a tumor, for example, an ovarian epithelial tumor, is classified as BRCA-like if, for example, the inner sum is greater than about 120 (∑ i Wi x; > -120), and as non-BRCA-like, if the inner sum is less than about 120.
Table 4. Weighted voting predictor for classification of tumors, for example, epithelial ovarian tumors, as BRCA-like or non-BRCA-like
# Gene symbol Weight Gene name
1 GFI1 -2.7315 growth factor independent 1 transcription
repressor
2 PPP1CC 2.3313 protein phosphatase 1, catalytic subunit, gamma isoform
3 ROS1 2.285 c-ros oncogene 1 , receptor tyrosine kinase
4 SMC1A 1.5789 structural maintenance of chromosomes 1A
5 PDIA4 1.5138 protein disulfide isomerase family A, member 4
6 IDUA 1.4871 iduronidase, alpha-L¬
7 CD1D -1.4667 CD Id molecule
8 Pll -1.3085 26 serine protease
9 DAD1 1.213 defender against cell death 1
10 CCDC93 -1.2001 coiled-coil domain containing 93
11 APEX1 1.1607 APEX nuclease (multifunctional DNA repair enzyme) 1
12 TM9SF1 1.1594 transmembrane 9 superfamily member 1
13 LDHA 1.0736 lactate dehydrogenase A
14 WAS -1.0483 Wiskott-Aldrich syndrome (eczema- thrombocytopenia)
15 GNAI3 1.0425 guanine nucleotide binding protein (G protein), alpha inhibiting activity polypeptide 3 16 SEMA3F 1.0379 sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3F
17 SNCA -0.9457 synuclein, alpha (non A4 component of amyloid precursor)
18 TNF 0.836 tumor necrosis factor (TNF superfamily,
member 2)
19 SH3BGRL 0.7711 SH3 domain binding glutamic acid-rich protein like
20 GGH 0.7583 gamma-glutamyl hydrolase (conjugase,
folylpolygammaglutamyl hydrolase)
21 LGMN -0.548 legumain
22 VEGFA 0.4834 vascular endothelial growth factor A
23 CCL4 -0.4713 chemokine (C-C motif) ligand 4
24 BST2 0.4623 bone marrow stromal cell antigen 2
25 RGS1 -0.4598 regulator of G-protein signaling 1
26 WFDC2 0.4489 WAP four-disulfide core domain 2
27 NMI 0.4457 N-myc (and STAT) interactor
28 GBP1 0.411 guanylate binding protein 1 , interferon- inducible, 67kDa
29 MMP7 0.3115 matrix metallopeptidase 7 (matrilysin, uterine)
30 CYR61 -0.2718 cysteine-rich, angiogenic inducer, 61
In some embodiments, class calls for individual genes are weighed based on the level of differential expression detected in the tumor as compared to a control of reference value, for example by giving more weight to a call based on a greater observed fold change in gene expression than to a call based on a smaller fold change (e.g., giving more weight to a call based on a 1.5-fold change than to a call based on 1.1-fold change in gene expression). In some embodiments, a predictive algorithm, as provided herein, weighs class calls for individual genes based on a combination of phenotype correlation and level of differential expression. In some embodiments, a predictive algorithm as provided herein assigns a confidence level to the overall class assignment (e.g., BRCA-like/chemosensitive or non-BRCA-like/chemoresistant). Those of skill in the art will be aware of a variety of methods and algorithms to build and use a class predictor. Types of class predictors useful for the classification of tumors in BRCA-like/chemosensitive and non-BRCA-like/chemoresistant include, for example, diagonal linear discriminant predictors, compound covariate predictors, nearest centroid predictors, or support vector machines predictors. Further, a class predictor can be combined with other statistical and/or bioinformatic procedures or algorithms to increase accuracy, for example, with hierarchical clustering of a dataset of unknown class to datasets of known class, or by using aggregation strategies, for example, bagging or boosting schemes. Methods of building and using predictive algorithms using various approaches are well known in the art see, for example, Dudoit S, Fridlyand J, and Speed TP, Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American
Statistical Association 97:77-87, 2002, section 2, "discrimination methods", pages 78- 80, incorporated herein by reference for disclosure of discrimination methods based on expression profile data; and Golub TR, Slonim DK, Tamayo P, Huard C,
Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA,
Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999 Oct 15;286(5439):531- rd
7, e.g., FIG. 1 and description, and text passage from page 531, 3 columns, last line to page 532, second full paragraph, incorporated herein by reference for disclosure of building and using a weighted voting scheme class predictor based on expression data).
In some embodiments, a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like, may comprise, essentially consist of, or consist of 1, 2, 3, 4, or 5 of the top 5 weighted genes in Table 4 (genes 1-5). In some embodiments, a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like, may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10, of the top 10 weighted genes in Table 4 (genes 1-10). In some embodiments, a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like, may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 of the top 15 weighted genes in Table 4 (genes 1-15). In some embodiments, a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like, may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 of the top 20 weighted genes in Table 4 (genes 1-20). In some embodiments, a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non-BRCA-like, may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, or 25 of the top 25 weighted genes in Table 4 (genes 1-25). In some embodiments, a predictor for the classification of tumors as chemosensitive or chemoresistant, or BRCA-like or non- BRCA-like, may comprise, essentially consist of, or consist of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the top 30 weighted genes in Table 4 (genes 1-30).
Accordingly, in some embodiments, aspects of the invention relate to methods of interrogating the expression of one or more informative genes, or combinations of genes (e.g., from any of Tables 1-4 or otherwise described herein). One or more expression levels may be evaluated in any suitable sample (or in a nucleic acid or protein preparation from a suitable biological sample). In some embodiments, the biological sample is a biopsy of a tumor or other cancerous or precancerous tissue. However, in some embodiments, biological sample may be any suitable sample that contains cells that are informative of the status of a tumor or cancer. For example, a biological sample may be a blood or other fluid sample that contains sufficient tumor or cancer cells to be informative.
In some embodiments, a subject, or a biopsy or other biological sample obtained from a subject, is evaluated to determine whether a BRCA 1 or 2 genetic defect (e.g., mutation) is present. In some embodiments, a gene expression analysis is performed only if no BRCA 1 or 2 genetic defect is identified. However, in other embodiments, the presence of a BRCA 1 or 2 genetic defect is not evaluated, or an expression analysis is performed even if a BRCA 1 or 2 is detected, as aspects of the invention are not limited in this respect. It should be appreciated that any of the genetic and/or expression information described herein may be used alone or in combination, with or without additional patient information to assist in a prognosis, therapeutic recommendation, or other diagnostic or predictive evaluation of the health, outcome, and/or treatment for the patient.
In some embodiments, the expression level for each of a panel of genes consisting (or consisting essentially) of the genes or combinations of genes described herein (e.g., in any of Tables 1-4 or otherwise described herein) is evaluated to determine the BRCAness of a cell or tissue. In some embodiments, the expression of one or a few additional genes (e.g., non- informative genes) may be evaluated to provide a control (e.g., a reference level for normal/high/low expression, an internal control, or other form of control or reference). In some embodiments, the expression level for each of a panel of genes comprising the genes or combinations of genes described herein (e.g., in any of Tables 1-4 or otherwise described herein) is evaluated to determine the BRCAness of a cell or tissue. In some embodiments, the expression level of many additional genes (e.g., non- informative genes) may be determined, but the evaluation may be based on the expression level of one or more of the informative genes or combinations thereof described herein (e.g., in any of Tables 1-4 or otherwise described herein).
It should be appreciated that the expression level of an informative gene (or set of genes) may be compared to the respective level(s) in a chemoresistant cell or tissue, a chemosensitive cell or tissue, or both, or other reference cells or tissues or combinations thereof, in order to determine or classify the cell or tissue in which the expression level(s) was measured. It should be appreciated that a comparison may include a statistical analysis and a conclusion as to the status or classification of the cell or tissue may be based on the presence of statistically significant similarities and/or differences for the expression level of each gene being evaluated relative to one or more reference expression levels.
The term "informative gene" or "informative marker gene", as
interchangeably used herein, refers to a gene which is measurably differentially expressed in cell populations exhibiting different phenotypes and differential expression of which, alone or in combination with other genes, is correlated with a specific phenotype to a greater degree than expected by chance. For example, if a gene is differentially expressed, e.g. upregulated or downregulated, in tumor cells (e.g., epithelial ovarian tumor cells) susceptible, or sensitive to a chemotherapeutic agent (e.g., a platinum compound or a PARP inhibitor), as compared to cells of the same cell type (e.g., the same tumor type) resistant to the chemotherapeutic agent, such a gene is an "informative gene", if the differential expression is measurable, for example, by expression profiling methods known to those of skill in the art, and differential expression is correlated with chemoresistance and/or chemosensitivity to a degree greater than a degree of correlation expected by chance.
Some aspects of this invention provide the identities of informative genes for a classification of a tumor as BRCA-like or non-BRCA-like. Some aspects of this invention provide the identities of informative genes for a classification of a tumor as sensitive to a chemotherapeutic compound (chemosensitive) or resistant to a chemotherapeutic compound (chemoresistant). Examples of informative genes for the classification of epithelial ovarian tumors as BRCA-like and non-BRCA-like are shown in any of Tables 1-4.
In some embodiments, a class vote is only made by a predictive algorithm for an informative gene, if the level of upregulation or downregulation of the expression of the informative gene is greater than a cutoff value. For example, in some embodiments, a class call will be made for a specific informative gene only, if the level of expression of the informative gene is at least a fold change shown in one of the rows of Table 3 for exemplary informative genes. Those of skill in the art will be able to determine appropriate cut off values for individual informative gene class calls, and such cutoff values may, for example be, values reflecting a minimum fraction (e.g. about 10%, about 20%, about 25%, about 30%, about 35%, about 40%, or bout 50%) of the average fold regulation observed in comparing gene expression profiles from distinct classes.
In some embodiments, an informative gene is a gene differential expression of which by itself is not correlated to a specific phenotype of a cell to an extent greater than that expected by chance, but differential expression of the gene in the context of differential expression of a different gene is correlated with the phenotype to an extent greater than expected by chance.
The term "differential expression", as used herein, refers to either up- or downregulation of gene expression to a measurable extent in cells or tissues exhibiting a different phenotype, for example, in tumor cells or tissues that are chemosensitive and tissues that are chemoresistant. Accordingly, a gene that is expressed at a measurably different level in a tumor that is sensitive to
chemotherapeutic intervention as compared to the level of expression of the gene in a tumor that is resistant, or not sensitive, to such intervention, is a gene that is differentially expressed in chemosensitive and chemoresistant tumors. Methods for measuring gene expression levels of genes and comparing such measured gene expression levels are well known to those of skill in the art and include, for example, western blot, northern blot, reverse northern blot, transcript or protein microarray,
RT-PCR, and massive parallel sequencing assays. However, other techniques may be used as aspects of the invention are not limited in this respect.
Some aspects of this invention relate to methods of assisting in the evaluation of a subject having a tumor for treatment with a chemotherapeutic compound. In some embodiments, a classification of the tumor is performed after the subject has been diagnosed to have the tumor, but before administration of a chemotherapeutic compound. In some embodiments, the tumor is a recurrent tumor, and classification is performed, for example, after a subject underwent initial chemotherapy for the treatment of the initial tumor, but before administration of a chemotherapeutic compound to treat the recurrent tumor. In some embodiments, a chemotherapeutic compound, for example, a PARP inhibitor or a platinum compound, is administered to the subject based on the classification of the tumor as BRCA-like, or chemosensitive. In some embodiments, administration of chemotherapy is omitted based on classification of the tumor as non-BRCA-like or chemoresistant. In some
embodiments, chemotherapy is administered based on a combination of diagnostic tests, including the classification of the tumor as BRCA-like, or chemosensitive, genetic analysis (e.g. mutation analysis), in vitro tumor cell analysis (e.g., in vitro sensitivity of tumor cells to chemotherapeutic compounds), subject/disease history (e.g. recurrent disease after initial chemotherapy), subject health status, and/or other diagnostic tests or assays well known to those of skill in the art.
In some embodiments, the method of diagnostic tumor classification includes determining whether the tumor expression profile is either similar to a first reference expression profile indicative of tumor sensitivity to a chemotherapeutic compound or to a second reference expression profile indicative of tumor resistance to a chemotherapeutic compound. In some embodiments, methods for the identification of appropriate reference expression profiles are provided. In some embodiments, a methods of identifying one or more informative marker genes for a class distinction between "BRCA-like" and "non-BRCA-like" of tumors is provided. In some embodiments, a tumor is classified as BRCA-like or non-BRCA-like based on whether the inner sum of the weighted expression levels of the genes included in the BRCA-ness predictor is above or below a specific cut-off value, for example, as described in more detail elsewhere herein.
In some embodiments, aspects of the invention relate to identifying patients that are candidates for one or more chemotherapeutic treatments. In some embodiments, aspects of the invention relate to identifying patients that should not be treated with one or more chemotherapeutics agents.
The terms "therapy", "therapeutic", "treat" or "treatment" refer to , but are not limited to, one or more clinical intervention with an intent to prevent, ameliorate, or cure a condition or symptoms of the condition in a subject, for example, a cancer or tumor, e.g., an epithelial ovarian tumor.
In some embodiments, a treatment as provided by some aspects of this invention is aimed to eliminate a tumor, to induce a decrease in the size of a tumor, to induce a decrease in the number of tumor cells, or to inhibit or halt the growth of a tumor in a subject. Apparent to those skilled in the relevant medical arts, this can be accomplished by various approaches including, but not limited to, chemotherapeutic interventions. Suitable chemotherapeutic methods and administration schedules of chemotherapeutic compounds, alone or in combination with other therapeutics, will be apparent to those of skill in the relevant medical art.
Some methods for killing or inhibiting the proliferation of tumor cells, according to some embodiments of this invention, feature contacting such cells with a chemotherapeutic agent, for example, a cytotoxic or cytostatic agent. In some embodiments, the cells are contacted with a chemotherapeutic agent, for example, a cytotoxic or cytostatic agent, that selectively targets tumor cells. By "selectively targeting" is meant that the agent or combination of agents selectively recognizes, binds, or acts upon tumor cells. In some embodiments, the agent or combination of agents can effectively kill tumor cells by one or more of several mechanisms, such as by induction of apoptosis, or by attracting other cells such as cytotoxic T lymphocytes or macrophages that can kill or inhibit proliferation of the targeted cells. By
"cytotoxic or cytostatic agent" is meant an agent (for example a molecule) that kills or reduces proliferation of cells. Some examples of cytotoxic agents include, but are not limited to, cytotoxic radionuclides, chemical toxins, and protein toxins.
In some embodiments, the chemotherapeutic agent is a cytotoxic radionuclide or radiotherapeutic isotope, for example, an alpha-emitting isotope such as 225 Ac, 211 At, 212Bi, 213Bi, 212Pb, 224Ra or 223Ra. Alternatively, the cytotoxic radionuclide may a beta-emitting isotope such as 186Rh, 188Rh, 177Lu, 90Y, 1311, 67Cu, 64Cu, 153Sm or 166Ho. Further, the cytotoxic radionuclide may emit Auger and low energy electrons and may be one of the isotopes 1251, 1231 or 77Br.
Chemotherapeutic compounds are well known in the art and non-limiting examples of suitable chemotherapeutic agents include, but are not limited to alkylating agents, for example platinum compounds (e.g., carboplatin, cisplatin and oxaliplatin), mechlorethamine, cyclophosphamide, chlorambucil, and ifosf amide. PARP inhibitors are well known in the art and non-imiting examples of PARP inhibitors include BSI201, AZD2281, ABT888, AG014699, MK4827, INO-1001, NU1025.
Other chemotherapeutic compounds are also well known to those of skill in the art and non-limiting examples of such compounds include members of the enediyne family of molecules, such as calicheamicin and esperamicin. Chemical toxins can also be taken from the group consisting of methotrexate, doxorubicin, melphalan, chlorambucil, ARA-C, vindesine, mitomycin C, cis-platinum, etoposide, bleomycin and 5-fluorouracil. Examples of antineoplastic agents include, but are not limited to, dolastatins (U.S. Patent Nos. 6,034,065 and 6,239,104) and derivatives thereof, for example, dolastatin 10 (dolavaline-valine-dolaisoleuine-dolaproine- dolaphenine) and the derivatives auristatin PHE (dolavaline-valine-dolaisoleuine- dolaproine-phenylalanine-methyl ester) (Pettit, G.R. et al., Anticancer Drug Des. 13(4):243-277, 1998; Woyke, T. et al., Antimicrob. Agents Chemother. 45(12):3580- 3584, 2001), and aurastatin E and the like. Other chemotherapeutic agents are known to those skilled in the art.
However, it should be appreciated that other chemotherapeutic compounds, and/or combinations of compounds (e.g,. two or more compounds described herein alone or with other compounds) may be used as aspects of the invention are not limited in this respect.
Therapeutic compositions of the present invention may be administered in pharmaceutically acceptable preparations. Such preparations may contain
pharmaceutically acceptable concentrations of salt, buffering agents, preservatives, compatible carriers, supplementary immune potentiating agents such as adjuvants and cytokines, and optionally other therapeutic agents.
As used herein, the term "pharmaceutically acceptable" means a non-toxic material that does not interfere with the effectiveness of the biological activity of the active ingredients. The term "physiologically acceptable" refers to a non-toxic material that is compatible with a biological system such as a cell, cell culture, tissue, or organism. The characteristics of the carrier will depend on the route of
administration. Examples of physiologically and pharmaceutically acceptable carriers include, without being limited to, diluents, fillers, salts, buffers, stabilizers, solubilizers, and other materials which are well known in the art. The term "carrier" denotes an organic or inorganic ingredient, natural or synthetic, with which the active ingredient is combined to facilitate the application. The components of the pharmaceutical compositions also are capable of being co-mingled with the molecules of the present invention, and with each other, in a manner such that there is no interaction which would substantially impair the desired pharmaceutical efficacy.
Therapeutics according to some embodiments of the invention can be administered by any conventional route, for example injection or gradual infusion over time. The administration may, for example, be oral, intravenous, intratumoral, intraperitoneal, intramuscular, intracavity, subcutaneous, or transdermal, or by pulmonary aerosol.
The compositions of some embodiments of the invention are administered in effective amounts. An "effective amount" is that amount of a composition that alone, or together with further doses, produces the desired clinical response. In some cases of treating a particular disease or condition, for example, a cancer manifested in a tumor, the desired response is inhibiting the progression of the disease, for example, the growth of the tumor or the spread of a primary tumor to secondary sites via metastasis. This may involve slowing the progression of the disease temporarily, although more preferably, it involves halting the progression of the disease permanently. In some cases, the desired response to treatment is a permanent eradication of tumor cells. In some cases, the desired response to treatment can be delaying or preventing the manifestation of clinical symptoms, for example, of recurrent tumors.
The effect of treatment can be monitored by routine methods or can be monitored according to diagnostic methods of the invention discussed herein.
The effective amount of a chemotherapeutic compound or a combination of such compounds will depend, of course, on the particular tumor being treated, the severity of the condition, the individual patient parameters including age, physical condition, size and weight, the duration of the treatment, the nature of concurrent therapy (if any), the specific route of administration and like factors within the knowledge and expertise of the health practitioner. These factors are well known to those of ordinary skill in the art and can be addressed with no more than routine experimentation. It is generally preferred that a maximum dose of the individual components or combinations thereof be used, that is, the highest safe dose according to sound medical judgment. It will be understood by those of ordinary skill in the art, however, that a patient may insist upon a lower dose or tolerable dose for medical reasons, psychological reasons or for virtually any other reasons.
In some embodiments, a kit is provided, comprising reagents useful for determining an expression level of an informative gene, for example, an informative gene listed in any of Tables 1-4. A reagent useful for determining expression of an informative gene may, in some embodiments, be a detectable agent that binds to an expression product of an informative gene. Detectable agents, their generation and/or purification and their use are well known to those of skill in the art and non-limiting, exemplary detection agents include detectable binding agents, for example antibodies, antibody fragments, nucleic acids complementary to a sequence comprised in a transcript of the informative gene, aptamers, and adnectins. In some embodiments, a kit may comprise a plurality of different nucleic acid molecules that correspond to different informative gene transcripts. In some embodiments, the plurality of nucleic acid molecules is attached to a solid support. In some embodiments, a kit is provided that includes a focused microarray for the detection of expression levels of all or some of the informative genes described herein, for example, the informative genes listed in any of Tables 1-4. In some embodiments, a plurality of primer pairs is provided for determining an expression level of a plurality of informative genes, for example, of all or some of the genes listed in any of Tables 1-4.
These and other aspects of the invention are illustrated by the following non- limiting examples.
EXAMPLES
Given the heterogeneous mechanism(s) by which an ovarian cancer cell might develop defective HR, it was reasoned that a broad-based approach that makes few assumptions about mechanism might have the highest chance of identifying patients with a BRCAness phenotype. Microarray gene expression profiling lends itself to this goal, because it is not mechanism based and has already been proposed in ovarian cancer as both a prognostic as well as predictive tool 25~27. In this study it is shown that it is possible to define a gene expression profile of BRCAness, associated with responsiveness to platinum and PARP inhibitors, and that this profile is correlated with important outcome measures in patients with the sporadic form of the disease.
Example 1: Development of a gene expression profile of BRCAness
For the purpose of profile development, a publicly available microarray dataset was used that included tumor expression data from 61 patients with pathologically confirmed epithelial ovarian cancer (EOC), including 34 with BRCA-1 or -2 germline mutation (18 BRCA-1 and 16 BRCA-2 mutations), and 27 without either mutation (i.e. sporadic cancers) 28. Genome wide hierarchical clustering was used to define BRCA-like and non-BRCA-like tumors as described in detail elsewhere herein and in FIG. 1. Example 2: Patient samples
Two patient cohorts were used in this study. The first included 6 EOC patients with BRCA- 1 or -2 germline mutations and have been previously described
29 ' 30. Four patients had paired samples, pre and post the development of platinum resistance, and 2 had samples obtained only at the time of platinum sensitive disease.
The second patient cohort included 70 patients treated at Beth Israel
Deaconess Medical Center, Memorial Sloan Kettering Medical Center, and Cedars- Sinai Medical Center, who underwent exploratory laparotomy for diagnosis, staging, and debulking, followed by first-line platinum-based chemotherapy. Standard post- chemotherapy surveillance included serial physical examination, serum CA-125 level, and computed tomography scanning as clinically indicated.
The study protocol for collection of tissue and clinical information for all patients was approved by the institutional review boards at all three institutions, and patients provided written informed consent authorizing the collection and use of the tissue for study purposes. Additional details are provided elsewhere herein.
Example 3: Cell lines
Twelve cisplatin-resistant clones of the BRCA-2-mutated pancreatic cell line Capan-1 have been previously described 29.
Example 4: RNA Isolation and Affvmetrix GeneChip Hybridization
Total RNA isolation, microarray hybridization (U133 Plus 2.0 Array GeneChip, Affymetrix, Santa Clara, CA), and data processing were performed previously described ' ' .
Example 5: Statistical Analysis
The statistical significance of the association between the BRCAness profile and various clinicopathologic factors was assessed by the Fisher's exact test. The p values of all statistical tests were two-sided. Overall and disease free survival curves were generated by the Kaplan-Meier method, and differences between survival curves were assessed for statistical significance with the log-rank test. Multivariate analyses to adjust for known prognostic factors were performed using a Cox proportional hazards regression model that included grade (1-2 versus 3), age (< 65 years versus >65 years), stage (2 versus 3 or 4), histology (clear cell, papillary serous,
endometrioid), debulking status (optimal, less than or equal to 1 cm; or suboptimal, greater than 1 cm residual disease) and BRCAness profile (BRCA-like versus non- BRCA-like).
Example 6: Development of a gene expression profile of BRCAness
For the purpose of profile development, a publicly available microarray dataset was used that included tumor expression data from 61 patients with pathologically confirmed epithelial ovarian cancer (EOC), including 34 with BRCA-1 or -2 germline mutation (18 BRCA-1 and 16 BRCA-2 mutations), and 27 without either mutation (i.e. sporadic cancers) 28. BRCA-like and non-BRCA-like tumors were defined using genome wide hierarchical clustering as illustrated in FIG. 1. FIG. 1 describes the development of the BRCAness gene expression profile. A publicly available microarray dataset was used that included tumor expression data from 61 patients with pathologically confirmed epithelial ovarian cancer (EOC), including 34 with BRCA-1 or -2 germline mutation (18 BRCA-1 and 16 BRCA-2 mutations), and
27 without either mutation (i.e. sporadic cancers) 28. Although previous investigators have described gene expression differences between BRCA-mutated and sporadic cancers, these studies grouped all sporadic tumors together without taking into consideration that some of these sporadic tumors might indeed have a BRCAness phenotype 4053. Thus, the group of sporadic patients in such analyses is
"contaminated" by patients who may exhibit a gene expression profile more consistent with a BRCA-1 or -2 germline mutation carrier, and vice versa. In order to address this issue, genome-wide hierarchical clustering of all 61 tumors was performed first. It was found that patients clustered into three groups, representing BRCA-1, BRCA-2, and sporadic clusters, respectively (FIG. 2A). The BRCA-1 cluster contained 22 patients, of which 9 actually had sporadic (non-mutated) disease. The BRCA-2 cluster contained 14 patients, of which 4 had sporadic disease. The sporadic cluster contained 25 patients, of which 6 had BRCA-1 and 5 had BRCA-2 germline mutation. The clustering reproducibility index (R) was 0.934 and the 3 clusters did not change even if clear cell or mucinous samples were excluded from the analysis. For the purpose of defining the profile, these outliers (e.g., a BRCA-1 patient contaminating the sporadic cluster, or a sporadic patient contaminating the BRCA cluster) were removed from the analysis. A 60-gene diagonal linear discriminant predictor was developed next that distinguished the BRCA clusters (BRCA-like tumors) from the sporadic cluster (non-BRCA-like tumors) (FIG. 2B).
The predictor that distinguished BRCA-like from non-BRCA-like tumors was developed using the diagonal linear discriminant algorithm 48. The classifier was trained by selecting genes with the highest fold-change difference between the two classes (BRCA-like and non-BRCA-like tumors). Classifier accuracy and statistical significance were assessed using leave-one-out cross-validation and a 1000 random permutation test to control for over- fitting 49'50. In order to ascertain that classifier accuracy was not an artifact of the optimal 60 gene predictor, the performance of predictors from a range of 40 to 90 genes was assessed and it was found that they demonstrated very good performance with accuracy of 89-92%. The BRCAness profile was mapped across different platforms using Affymetrix annotation files before being applied to patient and cell line samples
(www.affymetrix.com/analysis/index.affx). Non-biologic experimental variation ("batch effect") across datasets was adjusted using empirical Bayes methods as previously described 36.
Example 7: Patient samples
Two patient cohorts were used in this study. The first was comprised of 6 EOC patients with BRCA-1 or BRCA-2 germline mutations treated at Cedars-Sinai
Medical Center and have been previously described 29 ' 30. Four patients from this group had paired samples, pre and post the development of platinum resistance (two patients with germline BRCA-1 mutation and two with germline BRCA-2 mutation). In each case, the development of platinum resistance was associated with reversion to functional BRCA-1 and -2 protein 23'30. Tumor from the two other patients was obtained at the time of platinum sensitive disease, without a follow-up specimen at the time of platinum resistance. In both of these cases the tumor specimen contained a germline mutation in BRCA-1.
The second patient cohort consisted of 70 EOC patients, in which tumor was obtained at the time of diagnostic exploratory laparotomy. Twenty eight of these patients were diagnosed between November 1994 and June 2005 and treated at Cedars-Sinai Medical Center, and had sporadic EOC as determined by negative BRCA- 1 or -2 sequencing. The remaining 42 patients were diagnosed between January 1995 and October 2000 and treated at Beth Israel Deaconess Medical Center and Memorial Sloan-Kettering Cancer Center, and represent a subset of those previously reported 21. Seven of the 42 patients were sequenced and found to be negative for a BRCA-1 or -2 mutation. The remaining 35 of these 42 patients were selected on the basis of criteria that are expected to enrich for sporadic disease.
Specifically, these 35 patients had no family history of ovarian cancer, no family history of breast cancer at age<50, no family history of more than 1 breast cancer at any age, and were not of Ashkenazi Jewish ethnicity. Ovarian cancer samples from this patient cohort were collected at the time of primary debulking surgery and frozen at -80°C. Tumor samples were pulverized in liquid nitrogen and homogenized in Trizol solution (Invitrogen Corp, Carlsbad, CA), followed by RNA isolation.
Example 8: Cell lines
Twelve cisplatin-resistant clones of the originally cisplatin-sensitive BRCA-2- mutated pancreatic cancer cell line Capan-1 have been previously described 29. The parent Capan-1 line harbors a 6174delT mutation, associated with loss of
heterozygosity 51. As a result of platinum-induced selection pressure, 6 of these 12 clones had acquired secondary genetic events that restored nearly full-length, functional BRCA-2 protein and RAD51 foci formation in response to ionizing radiation (IR) 29. The remaining 6 clones showed persistent evidence of mutated BRCA-2 (6174delT), lacked BRCA-2 protein expression and exhibited impaired IR- induced RAD51 foci formation (except one which had proficient RAD51 foci formation). Two of the clones with restored functional BRCA-2 protein were tested for PARP inhibitor sensitivity and found to be resistant to PARP inhibition, while two of the clones with restored functional BRCA-2 protein were tested for PARP inhibitor sensitivity and found to be resistant to PARP inhibition 29.
Example 9: RNA Isolation and Affvmetrix GeneChip Hybridization
Total RNA was isolated from patient tumor samples using Trizol reagent (Invitrogen, Carlsbad, CA) and from Capan- 1 cell lines using the RNeasy Mini Kit (Qiagen Valencia, CA, USA) according to manufacturer's instructions. cDNA synthesis and hybridization on oligonucleotide microarrays (U133 Plus 2.0 Array GeneChip, Affymetrix, Inc., Santa Clara, C A) containing approximately 54,700 transcripts were carried out using standard protocols. Microarray experiments were performed at the Dana Farber Cancer Institute Microarray Core Facility (chip.dfci.harvard.edu/). Raw data were processed using Robust Multi-Array (RMA) analysis. All raw microarray data are provided in GEO (Gene Expression Omnibus).
Example 10: Statistical Analysis
The statistical significance of the association between the BRCAness profile and various clinicopathologic factors was assessed by the Fisher's exact test.
Unsupervised hierarchical clustering was performed using the average linkage method and the one minus centered correlation as a distance metric in all cases 52. The p values of all statistical tests were two-sided. The SPSS version 16.0 and STATA version 10.1 packages were used for statistical tests. All bioinformatic analyses were performed using the BRB- Array Tools Version 3.8 [developed by Dr Richard Simon (Biometrics Research Branch, National Cancer Institute, Bethesda, MD)].
Example 11: Characteristics of the BRCAness profile
The strategy for developing the BRCAness profile is described in detail in
FIG. 1. The optimal classifier was a 60-gene diagonal linear discriminant predictor that distinguished BRCA-like from non-BRCA-like tumors with 94% accuracy, as assessed by leave-one-out cross-validation and 1000 random permutations test (FIG. 2)(p<0.001). FIG. 2 displays an expression plot of the 60 genes that comprise the BRCAness profile. Columns: Training set samples. Rows: Gene expression levels (normalized). Complete information regarding gene identity is provided in Table 1. Dark shading: Overexpressed genes. Light shading: Underexpressed genes. The gene expression signature that correlates with BRCA-like tumors is defined as the "BL" profile, and the signature that correlates with non-BRCA-like tumors is defined as the "NBL" profile.
Other predictive algorithms performed similarly, such as compound covariate predictor (92%), nearest centroid (92%) and support vector machines (92%) 32~35. For the analyses described below, the gene expression signature that correlates with BRCA-like tumors is defined as the "BL" profile, and the signature that correlates with non-BRCA-like tumors is defined as the "NBL" profile. The identities of all BRCAness profile genes are provided in Table 1.
Example 12: BRCAness profile distinguishes between platinum-sensitive and - resistant tumor biopsy samples It was first investigated whether the BRCAness profile could correlate with platinum responsiveness in patients with known BRCA germline mutation. For this purpose, 10 tumor biopsy specimens from 6 patients with either BRCA-1 or -2 germline mutation were used, four of whom were initially platinum sensitive but eventually developed platinum resistance (with pre and post biopsy pairs). These patients formed the basis of a previous report in which reversion of the BRCA genotype occurred (with re-establishment of BRCA function) upon the development of platinum resistance 29. Thus, these samples provided an opportunity to determine how the BRCAness profile correlated with both platinum responsiveness and BRCA functional status (e.g., mutant versus revertant BRCA gene).
The BRCAness profile was applied to these 10 platinum sensitive and resistant tumors specimens, with the results shown in FIG. 3. FIG. 3A shows that hierarchical clustering based on the expression pattern of the 60 genes of the BRCAness profile distinguished between platinum resistant and platinum sensitive tumor biopsy samples. Abbreviations: NBL, non BRCA-like; BL, BRCA-like. Five out of 6 tumor samples with the BL signature were platinum-sensitive, whereas 3 out of 4 tumor samples with the NBL signature were platinum-resistant. FIG. 3B shows the correlation of the BRCAness profile with platinum sensitivity and BRCA germline mutation status in the 10 tumor biopsy specimens from 6 patients. The BRCAness profile accurately distinguished between platinum sensitivity and platinum resistance in 8 out of 10 tumor specimens, which in turn correlated with presence of mutated versus functional BRCA gene status, respectively. In the first two patients, the BRCAness profile dynamically tracked the development of platinum resistance over the course of therapy (e.g., the profile changed from BL to NBL following the development of platinum resistance, associated with reversion to functional BRCA-1 or 2).
The BRCAness profile could accurately distinguish between platinum sensitive and platinum resistance in 8 out of 10 tumor specimens, which in turn correlated with presence of mutated versus functional BRCA gene status,
respectively. Specifically, 5 out of 6 tumors with the BL signature were platinum sensitive (and were BRCA-1 or -2 mutated), whereas 3 out of 4 tumors with the NBL
29 30 signature were platinum resistant (and had reverted to functional BRCA-1 or -2) ' . Furthermore, patients were observed in which the BRCAness profile dynamically tracked the development of platinum resistance over the course of therapy (e.g., the profile changed from BL to NBL following the development of platinum resistance, associated with reversion to functional BRCA-1 or -2, FIG. 3B).
Example 13: BRCAness profile correlates with PARP inhibitor responsiveness and RAD51 foci formation
The above data suggest that the BRCAness profile might correlate with platinum responsiveness in patient-derived tumor samples. As another surrogate of BRCAness, it was next investigated whether the profile could also correlate with the ability to form RAD51 foci after ionizing radiation, which is a surrogate of intact homologous recombination, as well as with responsiveness to PARP inhibitors. For this purpose, 12 clones of the BRCA-2-mutated pancreatic cancer cell line Capan-1 was used, a cell line previously characterized 29. These clones were generated by exposing the parent Capan-1 cell line to platinum-selection pressure, eventually isolating 12 platinum resistant clones. Seven of these clones formed intact RAD51 foci after IR (6 of these regained functional BRCA-2 due to secondary BRCA-2 mutations which cancel the effect of the inherited BRCA-2 mutation), and the remaining 5 exhibited deficient RAD51 foci formation (all of which were BRCA-2 deficient). PARP inhibitor sensitivity had been determined for 4 of these clones, 2 being PARP inhibitor sensitive, and 2 being PARP inhibitor resistant (Sakai W, Swisher EM, Karlan BY, et al: Secondary mutations as a mechanism ofcisplatin resistance in BRCA2-mutated cancers. Nature 451: 1116-20, 2008). When applied to these cell lines, the BRCAness profile correlated with RAD51 foci formation in 9 out of 12 Capan-1 clones, and between presence of mutated versus functional BRCA-2 gene status in 10 out of 12 Capan-1 clones (FIG. 4). Importantly, the BRCAness profile accurately distinguished between 2 PARP inhibitor resistant clones (NBL signature) and 2 PARP inhibitor sensitive clones (BL signature) (FIG. 4).
Example 14: The relationship between BRCAness profile and clinical outcome in patients with sporadic EOC
The above data suggest that the BRCAness profile may correlate with platinum and PARP-inhibitor responsiveness in the context of a known BRCA germline mutation, but they do not address whether the profile correlates with outcome in patients with sporadic disease. In order to test this, the profile was applied to tumor samples from 35 patients with invasive EOC who had been sequenced and known to be wildtype for BRCA-1 and -2, and 35 patients enriched for sporadic disease on the basis of the following characteristics: no family history of ovarian cancer, no family history of breast cancer under the age of 50 years, no family history of more than 1 breast cancer at any age, and not of Ashkenazi Jewish ethnicity 37 ' 38. The clinical and pathologic characteristics of all 70 patients are shown in Table 5.
Table 5. Clinical and Pathological Characteristics
Figure imgf000036_0001
a All 35 patients underwent sequencing and were wildtype for BRCA-1 and -2.
b Not sequenced but enriched for sporadic disease on the basis of the following: no family history of ovarian cancer, no family history of breast cancer under the age of 50 years, no family history of more than 1 breast cancer at any age, and not of Ashkenazi Jewish ethnicity.
c All patients received first line platinum-based chemotherapy
d Debulking status was unknown for 1 patient. Optimal, less than or equal to 1 cm.; suboptimal, greater than 1 cm.
e Difference in debulking status between sequenced and non-sequenced cohorts was not statistically significant (p=0.19).
Overall, 20 of the 70 patient cohort (29%) demonstrated the BL profile (8 out of 35 in the sequenced group, and 12 out of 35 in the non-sequenced group, p=0.43). Compared to the non-sequenced cohort, the sequenced cohort was enriched with patients with optimally debulked disease although this did not reach statistical significance (two-sided Fisher's exact p = 0.19). As shown in Table 6, there were no differences in age, stage, grade, histology, or debulking status between the BL and the NBL signature groups. The ability to achieve a clinical remission for the BL and NBL groups was 90% compared to 74%, although this did not reach statistical significance (two-sided Fisher's exact p = 0.2).
Table 6. Association of BRCAness profile with clinical characteristics and remission status after first line therapy
Figure imgf000038_0001
aNBL, non-BRCA-like; BL, BRCA-like.
b Debulking status was unknown for 1 patient. Optimal, less than or equal to 1 cm.; suboptimal, greater than 1 cm.
c CR, complete response.
For the entire 70 patient cohort, the BRCAness profile was capable of discriminating between long and short median disease free survival (DFS), with the BL and NBL patients having a median DFS of 34 months and 15 months, respectively (log-rank P = 0.013) (FIG. 5A). In addition, the percent of patients disease-free at 18 months for the BL and NBL groups was 65% and 29%, respectively (two-sided p = 0.007). Similarly, the percent disease free at 24 months for the BL and NBL groups was 50% and 22%, respectively (two-sided p=0.042). Finally, the BRCAness profile distinguished between long and short median overall survival (OS), with the BL and NBL patients having a median OS of 72 and 41 months, respectively (log-rank P = 0.006) (FIG. 5B). Similar findings were observed when applying the profile separately to the group of 35 sequenced patients (Table 5) who had undergone formal mutation testing and were found to have wildtype BRCA-1 and -2 genes, or to the group of the 35 non-sequenced patients. FIG. 6A shows DFS in the sequenced patient cohort. The median DFS for patients with the BL and NBL profile was not yet reached at a median follow up of 53.5 months and 20 months, respectively (log-rank P = 0.03). FIG. 6B shows OS in the sequenced patient cohort. The median OS for patients with the BL and NBL profile was 72 months and 48 months, respectively (log-rank P = 0.1). FIG. 7 shows DFS in the non-sequenced patient cohort. The median DFS for patients with the BL and NBL profile was 22 and 7 months, respectively (log-rank P = 0.013). FIG. 7B shows OS in the non-sequenced patient cohort. The median OS for patients with the BL and NBL profile was not yet reached at a median follow up of 39 months and 30 months, respectively (log-rank P = 0.009). In univariate analysis, the hazard ratio for recurrence (NBL versus BL group) was 2.47 (P = 0.018, 95% CI, 1.17 to 5.2) and the hazard ratio for death (NBL versus BL group) was 3.29 (P = 0.009, 95% CI, 1.34 to 8.09) (Table 7). Multivariate analysis including the BRCAness profile, age, stage, grade, histology, and debulking status, demonstrated that the profile maintained an independent association with DFS and OS. The hazard ratio for recurrence (NBL versus BL group) was 2.65 (P = 0.016, 95% CI, 1.2 to 5.86) and the hazard ratio for death (NBL versus BL group) was 3.39 (P = 0.009, 95% CI, 1.35 to 8.5) (Table 7). The fact that characteristics such as stage, grade, and histology did not correlate with outcome in either univariate or
multivariate analysis is likely related to the fact that the vast majority of patients were stage III (81%), grade III (89%), and serous histology (94%). Table 7. Predictive value of BRCAness profile adjusted for Grade, Age, Stage and Debulking Status
Figure imgf000040_0001
a Debulking status was unknown for 1 patient. Values in bold are statistically significant at p less than or equal to 0.05. homologous recombination (hazard ratio for death) represented in parentheses (comparing NBL versus BL groups), for statistically significant associations.
PARP inhibitors have been evaluated in patients with germline BRCA-1 and - 2 mutations, with impressive results as single agents lo n. In addition to patients with germline BRCA-1 or -2 mutations, however, it has been suggested that PARP inhibition might be a useful therapeutic strategy for the treatment of patients with sporadic cancers that have a BRCAness phenotype, characterized by defective homologous recombination 15. In this regard, a number of mechanisms have been identified in sporadic ovarian cancer that might implicate the homologous recombination pathway in pathogenesis and in drug responsiveness. Such mechanisms include mutations or epigenetic silencing of genes involved in the Fanconi Anemia protein complex, intrinsic homologous recombination genes, or other DNA damage response genes 5·15·17·19·23 Amplification of genes that encode for
24 proteins that inactivate BRCA-2 function, such as EMSY, has also been described . BRCA- 1 promoter methylation, FANCF promoter methylation, and EMSY amplification have been identified in 5-31%, 21%, and 17% of sporadic EOCs respectively 15 17 19·23·24 5 supporting the notion that at least some patients with sporadic disease might harbor defects in HR, independent of the presence of a germline BRCA-1 or -2 mutation.
Although it is possible to identify individual molecular mechanisms by which the homologous recombination pathway might be disrupted in sporadic ovarian cancer, only a handful of studies have explored the relationship between homologous recombination and response to platinum or PARP-inhibitors. D' Andrea et al showed that inhibition of the FANCF gene in ovarian cancer cell lines through promoter methylation is associated with enhanced sensitivity to DNA damaging agents such as platinum, while demethylation of the FANCF promoter results in platinum resistance 23. Mccabe et al showed that cells deficient in the expression of genes involved in homologous recombination (e.g., RAD51, ATR, ATM, CHK2) are sensitive to PARP inhibitors 5. Teodoridis et al used methylation-specific PCR and showed that BRCA- 1 promoter hypermethylation is associated with improved response to platinum-based chemotherapy 16. In addition, Quinn et al used siRNA knock-down to decrease the expression of the BRCA-1 gene in two separate ovarian cancer cell lines, showing that lower levels of BRCA-1 mRNA correlated with enhanced in vitro sensitivity to cisplatin 39.
In the experiments described herein, the concept of BRCAness has been broadened by identifying a gene expression profile that is associated with platinum and PARP-inhibitor responsiveness, as well as RAD51 foci formation. In addition, when applied to a population of patients enriched for sporadic disease, the profile correlated with clinical outcome, independent of standard prognostic factors such as age, grade, histology, stage, and debulking status.
The present data does not determine with certainty whether the correlation between the BL signature and improved survival is indicative of enhanced platinum responsiveness, or conversely might identify patients with a more indolent natural history. In this regard, it is intriguing that the proportion of patients rendered into a complete clinical remission at the end of first line chemotherapy was higher in patients with a BL signature (90%), compared to those with the NBL signature (74%), although this was not statistically significant (p = 0.2). Accordingly, without wishing to be bound by theory, in some emobdiments, improved survival and/or complete remission in patients with a tumor classified as BRCA-like is, at least partially, due to increased responsiveness to chemotherapeutic compounds.
Although previous microarray studies have reported gene expression differences between BRCA-mutated and sporadic breast and ovarian cancers, none of them was specifically designed to identify a BRCAness profile with predictive or prognostic potential in patients with sporadic disease 28'40. In order to define the profile described herein, hierarchical clustering was first used to identify those sporadic patients who migrated with the BRCA-mutated group, these were then re- categorized them appropriately, prior to generating the profile (FIG. 1). From the standpoint of profile development, this provided two comparison groups, based not simply on the presence or absence of BRCA germline mutation, but rather based upon how patients clustered with one another, partly independent of their mutation status. Not only was the resulting profile capable of tracking with platinum response, PARP- inhibitor response, and clinical outcome, but it contained genes such as APEX1, MGST3 and PMS 1 , that have been previously associated with platinum resistance or DNA repair (FIG. 2) 41-46 '. It is noteworthy that neither BRCA-1 or -2 was part of the gene expression profile, perhaps indirectly supporting the notion that at least for some patients, genes other than BRCA-1 or 2 may sometimes be responsible for BRCAness in sporadic disease.
Although the BRCAness profile was developed in ovarian tumors, it was also capable of predicting PARP inhibitor sensitivity and RAD51 foci formation in the pancreatic cancer cell line Capan-1, suggesting that the profile may be detecting a pattern of gene expression that more globally reflects the status of HR, independent of cell lineage. In this regard, the predictive value of this profile in triple negative breast cancer is currently being investigated, which is thought to be enriched for BRCAness and a high response to platinum-containing chemotherapy 41. Ultimately, it should be possible to apply this profile in the context of a clinical trial involving sporadic ovarian cancer patients treated with a PARP inhibitor, in order to gain further insight into the predictive value of this approach. In this regard, studies are currently being performed to explore the potential value of PARP inhibitors in patients with ovarian cancer, independent of BRCA mutation status. These include a placebo-controlled, randomized phase II trial of maintenance AZD2281 in patients with relapsed ovarian cancer who are responding to platinum-containing chemotherapy (NCT00753545), as well as a phase II trial of ABT888 in combination with topotecan in patients with relapsed disease (NCT01012817). Accordingly, gene expression profiling may be useful as an eligibility criterion in such studies, to enrich for sporadic patients that may benefit the most from this novel class of agents. Although further study may provide additional information, in some embodiments the identification of a gene expression profile that correlates with BRCAness may be useful to identify cancer patients (e.g., patients with epithelial ovarian cancer) to be treated with certain chemotherapeutic agents (including, but not limited to, PARP inhibitors), regardless of the BRCA-1 or -2 mutation status of the patients. This may allow certain chemotherapeutic agents to be used more effectively in a broader range of patients.
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In addition, it should be appreciated that the reference to a computer program which, when executed, performs the above-discussed functions, is not limited to an application program running on a host computer. Rather, the term computer program is used herein in a generic sense to reference any type of computer code (e.g., software or microcode) that can be employed to program a processor to implement the above-discussed aspects of the present invention.
It should be appreciated that in accordance with several embodiments of the present invention wherein processes are implemented in a computer readable medium, the computer implemented processes may, during the course of their execution, receive input manually (e.g., from a user).
The terms "program" or "software" are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of the present invention as discussed above. Additionally, it should be appreciated that according to one aspect of this embodiment, one or more computer programs that when executed perform methods of the present invention need not reside on a single computer or processor, but may be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present invention.
Computer-executable instructions may be in many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that performs particular tasks or implement particular abstract data types. Typically the functionality of the program modules may be combined or distributed as desired in various embodiments.
Also, data structures may be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures may be shown to have fields that are related through location in the data structure. Such relationships may likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that conveys relationship between the fields. However, any suitable mechanism may be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
Transcripts named herein are well known to those of skill in the art, and the skilled artisan will be able to identify nucleotide sequences associated with the transcripts provided herein, for example, by retrieving transcript-associated database entries from a sequence database (e.g. NCBI or Ensembl at
http://www.ncbi.nlm.nih.gov/ and http://uswest.ensembl.org/index.html, respectively). Non-limiting examples of representative sequences related to some transcripts disclosed herein are given below:
>gil3lemblA00003.1l B.taurus DNA sequence 2 from patent application EP0238993 (SEQ ID NO: 1)
>gill78742lgblM80261.1IHUMAPE Human apurinic endonuclease (APE) mRNA, complete cds (SEQ ID NO: 2)
>gill83183lgblJ03005.1IHUMGIAB Human alternative guanine nucleotide-binding regulatory protein (G) alpha-inhibitory-subunit mRNA, complete cds (SEQ ID NO: 3) >gill90419lgblJ03223.1IHUMPROLEU Human secretory granule proteoglycan peptide core mRNA, complete cds (SEQ ID NO: 4)
>gil393318ldbjlD13665.1IHUMOSF2Pl Homo sapiens osf-2 mRNA for osteoblast specific factor 2 (OSF-2pl), complete cds (SEQ ID NO: 5)
>gill871135ldbjlD83043.1l Homo sapiens HLA-B mRNA, allele A*2711, complete cds (SEQ ID NO: 6)
>gill890049ldbj ID55696. il Homo sapiens mRNA for cysteine protease, complete cds (SEQ ID NO: 7)
>gil2921548lgblAF038602.1l Homo sapiens CD2 binding protein 1 short form mRNA, complete cds (SEQ ID NO: 8) >gil3560556lgbl AF053641.11 Homo sapiens brain cellular apoptosis susceptibility protein (CSE1) mRNA, complete cds (SEQ ID NO: 9)
>gil3719220lgbl AF022375.11 Homo sapiens vascular endothelial growth factor mRNA, complete cds (SEQ ID NO: 10)
>gil6005805lreflNM_007164.1l Homo sapiens mucosal vascular addressin cell adhesion molecule 1 (MADCAM1), mRNA (SEQ ID NO: 11)
>gil6006030lreflNM_003197.21 Homo sapiens transcription elongation factor B (SIII), polypeptide l-like (TCEB 1L), mRNA (SEQ ID NO: 12)
>gil6467174ldbj IAB020980. il Homo sapiens mRNA for putative membrane protein, complete cds (SEQ ID NO: 13)
>gil7262372lreflNM_004335.2l Homo sapiens bone marrow stromal cell antigen 2 (BST2), mRNA (SEQ ID NO: 14)
>gill0433912ldbj IAK022494. il Homo sapiens cDNA FLJ12432 fis, clone
NT2RM1000018, highly similar to Human mRNA for KIAA0066 gene (SEQ ID NO: 15)
>gill3128859lreflNM_004964.2l Homo sapiens histone deacetylase 1 (HDAC1), mRNA (SEQ ID NO: 16)
>gill3277543lgblBC003679.1l Homo sapiens ATP synthase, H+ transporting, mitochondrial F0 complex, subunit E, mRNA (cDNA clone MGC: 12532
IMAGE:4052832), complete cds (SEQ ID NO: 17)
>gill3277559lgblBC003689.1l Homo sapiens high-mobility group nucleosomal binding domain 2, mRNA (cDNA clone IMAGE:3455121) (SEQ ID NO: 18) >gill3435956lgblBC004815.1l Homo sapiens unc-119 homolog B (C. elegans), mRNA (cDNA clone IMAGE:3448346), complete cds (SEQ ID NO: 19)
>gill3477224lgblBC005078.1l Homo sapiens coiled-coil domain containing 93, mRNA (cDNA clone IMAGE: 3609481) (SEQ ID NO: 20)
>gill3529184lgblBC005359.1l Homo sapiens glia maturation factor, beta, mRNA (cDNA clone MGC: 12462 IMAGE:3681628), complete cds (SEQ ID NO: 21) >gill9924164lreflNM_002944.2l Homo sapiens c-ros oncogene 1 , receptor tyrosine kinase (ROS1), mRNA (SEQ ID NO: 22)
>gil20521835ldbjlD80000.2l Homo sapiens KIAA0178 mRNA, complete cds (SEQ ID NO: 23)
>gil22035639lreflNM_002413.3l Homo sapiens microsomal glutathione S-transferase 2 (MGST2), mRNA (SEQ ID NO: 24) >gil22035640lreflNM_004528.2l Homo sapiens microsomal glutathione S-transferase 3 (MGST3), mRNA (SEQ ID NO: 25)
>gil25777722lreflNM_000689.3l Homo sapiens aldehyde dehydrogenase 1 family, member Al (ALDH1A1), mRNA (SEQ ID NO: 26)
>gil25952110lreflNM_000594.2l Homo sapiens tumor necrosis factor (TNF), mRNA (SEQ ID NO: 27)
>gil27894327lreflNM_003856.2l Homo sapiens interleukin 1 receptor-like 1
(IL1RL1), transcript variant 2, mRNA (SEQ ID NO: 28)
>gil33873105lgblBC004892.2l Homo sapiens reticulocalbin 2, EF-hand calcium binding domain, mRNA (cDNA clone MGC: 1650 IMAGE:3505241), complete cds (SEQ ID NO: 29)
>gil33876989lgblBC002666.2l Homo sapiens guanylate binding protein 1, interferon- inducible, 67kDa, mRNA (cDNA clone MGC:3949 IMAGE:3606865), complete cds (SEQ ID NO: 30)
>gil38197148lgblBC000425.2l Homo sapiens protein disulfide isomerase family A, member 4, mRNA (cDNA clone MGC:8346 IMAGE:2819726), complete cds (SEQ ID NO: 31)
>gil42544158lreflNM_006644.2l Homo sapiens heat shock 105kDa/l lOkDa protein 1 (HSPH1), mRNA (SEQ ID NO: 32)
>gil46249365lreflNM_006115.31 Homo sapiens preferentially expressed antigen in melanoma (PRAME), transcript variant 1, mRNA (SEQ ID NO: 33)
>gil47132621lreflNM_002451.3l Homo sapiens methylthioadenosine phosphorylase (MTAP), mRNA (SEQ ID NO: 34)
>gil48675809lreflNM_003118.21 Homo sapiens secreted protein, acidic, cysteine-rich (osteonectin) (SPARC), mRNA (SEQ ID NO: 35)
>gil56682943lreflNM_002922.3l Homo sapiens regulator of G-protein signaling 1 (RGS1), mRNA (SEQ ID NO: 36)
>gil56699494lreflNM_006103.3l Homo sapiens WAP four-disulfide core domain 2 (WFDC2), mRNA (SEQ ID NO: 37)
>gil60302915lreflNM_030926.4l Homo sapiens integral membrane protein 2C (ITM2C), transcript variant 1, mRNA (SEQ ID NO: 38)
>gil61742787lreflNM_002339.2l Homo sapiens lymphocyte-specific protein 1 (LSP1), transcript variant 1, mRNA (SEQ ID NO: 39) >gil63252872lreflNM_007295.2l Homo sapiens breast cancer 1, early onset (BRCAl), transcript variant BRCAlb, mRNA (SEQ ID NO: 40)
>gil75709180lreflNM_002423.3l Homo sapiens matrix metallopeptidase 7 (matrilysin, uterine) (MMP7), mRNA (SEQ ID NO: 41)
>gil88853068lreflNM_000638.3l Homo sapiens vitronectin (VTN), mRNA (SEQ ID NO: 42)
>gil90704850lreflNM_002984.2l Homo sapiens chemokine (C-C motif) ligand 4 (CCL4), transcript variant 1, mRNA (SEQ ID NO: 43)
>gil94721245lreflNM_001632.3l Homo sapiens alkaline phosphatase, placental (Regan isozyme) (ALPP), mRNA (SEQ ID NO: 44)
>gill l0618228lreflNM_001766.3l Homo sapiens CDld molecule (CD1D), mRNA (SEQ ID NO: 45)
>gill l l l20330lreflNM_016018.4l Homo sapiens PHD finger protein 20-like 1 (PHF20L1), transcript variant 1, mRNA (SEQ ID NO: 46)
>gill 18572587lreflNM_001761.2l Homo sapiens cyclin F (CCNF), mRNA (SEQ ID NO: 47)
>gill l9393890lreflNM_000152.3l Homo sapiens glucosidase, alpha; acid (GAA), transcript variant 1, mRNA (SEQ ID NO: 48)
>gill24053441lreflNM_000934.3l Homo sapiens serpin peptidase inhibitor, clade F (alpha-2 antiplasmin, pigment epithelium derived factor), member 2 (SERPINF2), transcript variant 1, mRNA (SEQ ID NO: 49)
>gill49193320lreflNM_002092.3l Homo sapiens G-rich RNA sequence binding factor 1 (GRSF1), transcript variant 1, mRNA (SEQ ID NO: 50)
>gill49999367lreflNM_004394.2l Homo sapiens death-associated protein (DAP), mRNA (SEQ ID NO : 51 )
>gill56151414lreflNM_021213.2l Homo sapiens phosphatidylcholine transfer protein (PCTP), transcript variant 1, mRNA (SEQ ID NO: 52)
>gill56938332lreflNM_002933.4l Homo sapiens ribonuclease, RNase A family, 1 (pancreatic) (RNASE 1), transcript variant 4, mRNA (SEQ ID NO: 53)
>gill57389013lreflNM_001408.2l Homo sapiens cadherin, EGF LAG seven-pass G- type receptor 2 (flamingo homolog, Drosophila) (CELSR2), mRNA (SEQ ID NO: 54) >gill68693661lreflNM_001344.2l Homo sapiens defender against cell death 1 (DAD1), mRNA (SEQ ID NO: 55) >gill69658386lreflNM_006294.3l Homo sapiens ubiquinol-cytochrome c reductase binding protein (UQCRB), nuclear gene encoding mitochondrial protein, mRNA (SEQ ID NO: 56)
>gill87608471lreflNM_000377.2l Homo sapiens Wiskott-Aldrich syndrome (eczema- thrombocytopenia) (WAS), mRNA (SEQ ID NO: 57)
>gill87761347lreflNM_005263.3l Homo sapiens growth factor independent 1 transcription repressor (GFI1), transcript variant 1, mRNA (SEQ ID NO: 58) >gill88219598lreflNM_004688.2l Homo sapiens N-myc (and STAT) interactor (NMI), mRNA (SEQ ID NO: 59)
>gill88497702lreflNM_004186.3l Homo sapiens sema domain, immunoglobulin domain (Ig), short basic domain, secreted, (semaphorin) 3F (SEMA3F), mRNA (SEQ ID NO: 60)
>gill94097322lreflNM_004092.3l Homo sapiens enoyl CoA hydratase, short chain, 1, mitochondrial (ECHS1), nuclear gene encoding mitochondrial protein, mRNA (SEQ ID NO: 61)
>gill94097416lreflNM_002710.2l Homo sapiens protein phosphatase 1, catalytic subunit, gamma isozyme (PPP1CC), mRNA (SEQ ID NO: 62)
>gill97313774lreflNM_001554.4l Homo sapiens cysteine-rich, angiogenic inducer, 61 (CYR61), mRNA (SEQ ID NO: 63)
>gil207028465lreflNM_005566.3l Homo sapiens lactate dehydrogenase A (LDHA), transcript variant 1, mRNA (SEQ ID NO: 64)
>gil215422332lreflNM_003878.2l Homo sapiens gamma-glutamyl hydrolase
(conjugase, folylpolygammaglutamyl hydrolase) (GGH), mRNA (SEQ ID NO: 65) >gil221218992lreflNM_003475.3l Homo sapiens Ras association (RalGDS/AF-6) domain family (N-terminal) member 7 (RASSF7), transcript variant 1, mRNA (SEQ ID NO: 66)
>gil222537757lreflNM_002658.3l Homo sapiens plasminogen activator, urokinase (PLAU), transcript variant 1, mRNA (SEQ ID NO: 67)
>gil226371666lreflNM_014750.4l Homo sapiens discs, large (Drosophila) homolog- associated protein 5 (DLGAP5), transcript variant 1, mRNA (SEQ ID NO: 68) >gil239835753lreflNM_002970.2l Homo sapiens spermidine/spermine Nl- acetyltransferase 1 (SAT1), transcript variant 1, mRNA (SEQ ID NO: 69)
>gil269973857lreflNM_001557.3l Homo sapiens chemokine (C-X-C motif) receptor 2 (CXCR2), transcript variant 1, mRNA (SEQ ID NO: 70) >gil270288734lreflNM_000270.3l Homo sapiens purine nucleoside phosphorylase (PNP), mRNA (SEQ ID NO: 71)
>gil289063431lreflNM_006025.3l Homo sapiens endonuclease, polyU- specific (ENDOU), transcript variant 2, mRNA (SEQ ID NO: 72)
>gil291463261lreflNM_006129.4l Homo sapiens bone morphogenetic protein 1 (BMP1), transcript variant 3, mRNA (SEQ ID NO: 73)
>gil295317373lreflNM_004797.3l Homo sapiens adiponectin, C1Q and collagen domain containing (ADIPOQ), transcript variant 2, mRNA (SEQ ID NO: 74) >gil296011050lreflNM_000759.3l Homo sapiens colony stimulating factor 3
(granulocyte) (CSF3), transcript variant 1, mRNA (SEQ ID NO: 75)
>gill422918lgblW93728.1IW93728 zd96all.sl Soares_fetal_heart_NbHH19W Homo sapiens cDNA clone IMAGE:357308 3', mRNA sequence (SEQ ID NO: 76)
>gil2269409lgblAA527340.1IAA527340 ng36d08.sl NCI_CGAP_Co3 Homo sapiens cDNA clone IMAGE:936879 3', mRNA sequence (SEQ ID NO: 77)
>gil2896270lgblAA824386.1IAA824386 aj29c05.sl Soares_testis_NHT Homo sapiens cDNA clone 1391720 3', mRNA sequence (SEQ ID NO: 78)
>gil3701541lgblAI168371.1IAI168371 qa24h02.sl Soares_NhHMPu_S 1 Homo sapiens cDNA clone IMAGE: 1687731 3', mRNA sequence (SEQ ID NO: 79) >gil5178449lgblAI762782.1IAI762782 wi04c05.xl NCI_CGAP_CLL1 Homo sapiens cDNA clone IMAGE:2389256 3' similar to gb:M74715 ALPHA-L-IDURONIDASE PRECURSOR (HUMAN);, mRNA sequence (SEQ ID NO: 80)
>gil5366485lgblAI801013.1IAI801013 wgl5d09.xl
Soares_NSF_F8_9W_OT_PA_P_Sl Homo sapiens cDNA clone IMAGE:2365169 3', mRNA sequence (SEQ ID NO: 81)
>gil5659456lgblAI923492.1IAI923492 wn86a02.xl NCI_CGAP_Utl Homo sapiens cDNA clone IMAGE:2452682 3' similar to gb:X13111 HLA CLASS I
HISTOCOMPATIBILITY ANTIGEN, A-l l A*1101/A*1102 ALPHA
(HUMAN); contains element LI repetitive element ;, mRNA sequence (SEQ ID NO: 82)
>gil7316913lgblAW611727.1IAW611727 hg86h08.xl NCI_CGAP_Kidll Homo sapiens cDNA clone IMAGE:2952543 3' similar to SW:GAS1_HUMAN P54826 GROWTH- ARREST-SPECIFIC PROTEIN 1 ;, mRNA sequence (SEQ ID NO: 83) >gill0146644lgblBE732652.1IBE732652 601571266F1 NIH_MGC_21 Homo sapiens cDNA clone IMAGE:3925601 5', mRNA sequence (SEQ ID NO: 84) >gill0211355lgblBE790157.1IBE790157 601482981F1 NIH_MGC_68 Homo sapiens cDNA clone IMAGE:3885236 5', mRNA sequence (SEQ ID NO: 85)
>gill0367069lgblBE899402.1IBE899402 601681419F1 NIH_MGC_9 Homo sapiens cDNA clone IMAGE:3951724 5', mRNA sequence (SEQ ID NO: 86)
>gill0717503lgblAV701173.1IAV701173 AV701173 ADA Homo sapiens cDNA clone ADAAGH04 5', mRNA sequence (SEQ ID NO: 87)
>gill0851495lgblAV733950.1IAV733950 AV733950 cdA Homo sapiens cDNA clone cdAADG12 5', mRNA sequence (SEQ ID NO: 88)
>gill0941619lgblBF112006.1IBFl 12006 7137e05.xl
Soares_NSF_F8_9W_OT_PA_P_Sl Homo sapiens cDNA clone IMAGE:3523665 3', mRNA sequence (SEQ ID NO: 89)
>gill2770210lgblBG260394.1IBG260394 602371523F1 NIH_MGC_93 Homo sapiens cDNA clone IMAGE:4479556 5', mRNA sequence (SEQ ID NO: 90) >gill3046289lgblBG289967.1IBG289967 602381386F1 NIH_MGC_93 Homo sapiens cDNA clone IMAGE:4499085 5', mRNA sequence (SEQ ID NO: 91) >gill3341399lgblBG434893.1IBG434893 602507842F1 NIH_MGC_79 Homo sapiens cDNA clone IMAGE:4604891 5', mRNA sequence (SEQ ID NO: 92) >gil45652030lgblAL515318.3IAL515318 AL515318 Homo sapiens
NEUROBLASTOMA Homo sapiens cDNA clone CL0BB030ZH05 5-PRIME, mRNA sequence (SEQ ID NO: 93)
>gil45699297lgblAL524035.3IAL524035 AL524035 Homo sapiens
NEUROBLASTOMA COT 25 -NORMALIZED Homo sapiens cDNA clone
CS0DC003YN06 3-PRIME, mRNA sequence (SEQ ID NO: 94)
>gil6005805lreflNM_007164.1l Homo sapiens mucosal vascular addressin cell adhesion molecule 1 (MADCAM1), mRNA (SEQ ID NO: 95)
Below is an additional sequence representative of MADCAM1:
>gil 109633021 lreflNM_130760.21 Homo sapiens mucosal vascular addressin cell adhesion molecule 1 (MADCAM1), transcript variant 1, mRNA (SEQ ID NO: 96) >gil6006030lreflNM_003197.21 Homo sapiens transcription elongation factor B (SIII), polypeptide l-like (TCEB1L), mRNA (SEQ ID NO: 97)
>gil63252872lreflNM_007295.2l Homo sapiens breast cancer 1, early onset (BRCAl), transcript variant BRCAlb, mRNA (SEQ ID NO: 98)
Other representative BRCAl-gene sequences known in the art include, for example: >gil237757283lreflNM_007294.3l Homo sapiens breast cancer 1, early onset
(BRCA1), transcript variant 1, mRNA (SEQ ID NO: 99)
>gil237681126lreflNR_027676.1l Homo sapiens breast cancer 1, early onset
(BRCA1), transcript variant 6, non-coding RNA (SEQ ID NO: 100)
>gil237681124lreflNM_007299.3l Homo sapiens breast cancer 1, early onset
(BRCA1), transcript variant 5, mRNA (SEQ ID NO: 101)
>gil237681122lreflNM_007298.3l Homo sapiens breast cancer 1, early onset
(BRCA1), transcript variant 4, mRNA (SEQ ID NO: 102)
>gil237681120lreflNM_007297.3l Homo sapiens breast cancer 1, early onset
(BRCA1), transcript variant 3, mRNA (SEQ ID NO: 103)
>gil237681118lreflNM_007300.3l Homo sapiens breast cancer 1, early onset
(BRCA1), transcript variant 2, mRNA (SEQ ID NO: 104)
Various aspects of the present invention may be used alone, in combination, or in a variety of arrangements not specifically discussed in the embodiments described in the foregoing and is therefore not limited in its application to the details and arrangement of components set forth in the foregoing description or illustrated in the drawings. For example, aspects described in one embodiment may be combined in any manner with aspects described in other embodiments.
Also, the invention may be embodied as a method, of which an example has been provided. The acts performed as part of the method may be ordered in any suitable way. Accordingly, embodiments may be constructed in which acts are performed in an order different than illustrated, which may include performing some acts simultaneously, even though shown as sequential acts in illustrative
embodiments.
Use of ordinal terms such as "first," "second," "third," etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.
Also, the phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. The use of "including,"
"comprising," or "having," "containing," "involving," and variations thereof herein, is meant to encompass the items listed thereafter and equivalents thereof as well as additional items.
Having described several embodiments of the invention in detail, various modifications and improvements will readily occur to those skilled in the art. Such modifications and improvements are intended to be within the spirit and scope of the invention. Accordingly, the foregoing description is by way of example only, and is not intended as limiting. The invention is limited only as defined by the following claims and the equivalents thereto. What is claimed is:

Claims

1. A method of assisting in the evaluation of a subject having a tumor for treatment with a chemotherapeutic compound, the method comprising
(a) obtaining an expression profile of the tumor, the profile comprising at least the expression level of one gene selected from the group of genes consisting of all or of some of the genes listed in any of Tables 1-4; and
(b) determining whether the tumor expression profile is either similar to a first reference expression profile indicative of tumor sensitivity to the chemotherapeutic compound or to a second reference expression profile indicative of tumor resistance to the chemotherapeutic compound.
2. The method of claim 1, wherein the tumor is an epithelial ovarian tumor.
3. The method of claim 1 or 2, further comprising
(c) administering the chemotherapeutic compound to the subject based on the tumor expression profile being similar to the first reference expression profile, or
(d) not administering the chemotherapeutic compound to the subject based on the tumor expression profile being similar to the second reference expression profile.
4. The method of any of claims 1-3, wherein the chemotherapeutic compound is a platinum compound.
5. The method of any of claims 1-4, wherein the chemotherapeutic compound is a PARP inhibitor.
6. The method of any of claims 1-5, wherein the subject does not carry a germline mutation of the BRCA1 or the BRCA2 gene.
7. A method of classifying a tumor as "BRCA-like" or "non-BRCA-like", the method comprising,
(a) obtaining an expression profile of the tumor, the profile comprising at least one gene selected from the group of genes consisting of the genes listed in any of Tables 1-4; (b) assigning the tumor to the class "BRCA-like" or the class "non-BRCA- like" based on the expression profile in accordance with a classifier comprising the at least one gene.
8. The method of claim 7, wherein the classifier is a diagonal linear discriminant predictor.
9. The method of claim 7 or 8, wherein the classifier is a compound covariate predictor.
10. The method of any of claims 7-9, wherein the classifier is a nearest centroid predictor.
11. The method of any of claims 7-10, wherein the classifier is a support vector machines predictor.
12. The method of any of claims 7-11, further comprising
(c) assigning a confidence value to the classification of the tumor as "BRCA- like" or "non-BRCA-like".
13. The method of claim 12, wherein
if the tumor is classified as "BRCA-like", then the tumor is indicated to be sensitive to a chemotherapeutic compound, or
if the tumor is classified as "non-BRCA-like", then the tumor is indicated to be resistant to the chemotherapeutic compound.
14. The method of claim 12, wherein
if the tumor is classified as "BRCA-like" with a confidence of at least about 25%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, at least about 95%, or at least about 99%, then the tumor is indicated to be sensitive to a chemotherapeutic compound, or
if the tumor is classified as "non-BRCA-like" with a confidence at least about 25%, at least about 30%, at least about 40%, at least about 50%, at least about 60%, at least about 70%, at least about 80%, at least about 90%, at least about 95%, or at least about 99%, then the tumor is indicated to be resistant to the chemotherapeutic compound.
15. The method of claim 13, wherein the chemotherapeutic compound is a platinum compound.
16. The method of claim 13, wherein the chemotherapeutic compound is a PARP inhibitor.
17. The method of any of claims 7-16, wherein the tumor is an epithelial ovarian tumor.
18. A method of identifying one or more informative marker genes for a class distinction between "BRCA-like" and "non-BRCA-like" of tumors, the method comprising
(a) obtaining expression profiles from a plurality of tumors known to harbor a BRCAl or BRCA2 mutation or harbor neither BRCAl or BRCA2 mutation (sporadic tumors) wherein the expression profiles comprise a plurality of candidate marker genes
(b) performing hierarchical clustering analysis to define
i) which sporadic tumors co-cluster with BRCAl or BRCA2 tumors ii) which BRCAl or BRCA2 tumors co-cluster with sporadic tumors
(c) excluding tumors identified in i)+ii) above
(d) defining as "BRCA-like" tumors, the remaining BRCAl or BRCA2 after exclusion of ii)
(e) defining as "non-BRCA-like" tumors, the remaining sporadic tumors after exclusion of i)
(f) sorting the candidate marker genes using a neighborhood analysis algorithm, wherein the genes are sorted by degree to which their expression in the profiled tumors correlates with the class distinction; and
(g) determining whether said correlation is stronger than expected by chance, wherein marker genes for which the correlation with the class distinction is stronger than expected by chance are informative marker genes.
19. The method of claim 18, further comprising
(d) building a classifier comprising at least one of the informative marker genes.
20. The method of claim 19, wherein the classifier is a diagonal linear discriminant predictor.
21. The method of claim 19, wherein the classifier is a compound covariate predictor.
22. The method of claim 19, wherein the classifier is a nearest centroid predictor.
23. The method of claim 19, wherein the classifier is a support vector machines predictor.
24. The method of claim 19, further comprising
(e) obtaining an expression profile from a tumor of unknown class, wherein the expression profile comprises the at least one informative marker gene, and
(f) classifying the tumor as either "BRCA-like" or "non-BRCA-like" based on the expression profile in accordance to the classifier.
25. A kit for the generation of a tumor expression profile for the classification of a tumor as "BRCA-like" or "non-BRCA-like", the kit comprising
a plurality of nucleic acids corresponding to a plurality of informative marker genes for the classification of a tumor as "BRCA-like" or "non-BRCA-like", wherein the plurality of informative marker genes is chosen from the group consisting of the genes listed in any of Tables 1-4.
26. A kit for the generation of a tumor expression profile for the classification of a tumor as "BRCA-like" or "non-BRCA-like", the kit comprising
a detection agent for the determining the expression level of at least one of the genes of any of Tables 1-4, or
a binding agent that binds to a gene product expressed from an informative gene, wherein the informative gene is a gene listed in any of Tables 1-4.
27. The kit of claim 25 or 26, wherein the detection agent is an antibody or a fragment thereof that selectively binds to a protein expressed from the at least one gene.
28. The kit of claim 25 or 26, wherein the detection agent is a nucleic acid molecule that selectively binds to a nucleic acid expressed from the at least one gene.
29. The kit of any of claims 25-28, wherein the kit comprises
a plurality of of nucleic acids corresponding to a plurality of informative marker genes for the classification of a tumor as "BRCA-like" or "non-BRCA-like", wherein the plurality of informative marker genes is chosen from the group consisting of the genes listed in any of Tables 1-4.
30. The kit of any of claims 25, 28 or 29, wherein the nucleic acids are immobilized on a solid support.
31. The kit of any of claims 25-30, further comprising
reagents for the generation of the tumor expression profile, the reagents comprising RNA extraction reagents, a reverse transcriptase, a DNA polymerase, a buffering reagent, nucleotides, a fluorescent label, and/or a hybridization reagent.
PCT/US2011/038922 2010-06-03 2011-06-02 A gene expression profile of brca-ness that correlates with responsiveness to chemotherapy and with outcome in cancer patients Ceased WO2011153345A2 (en)

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WO2016185406A1 (en) 2015-05-19 2016-11-24 Nadathur Estates Pvt. Ltd. Method for identification of a deficient brca1 function
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