Patent Description:
Epithelial ovarian cancer (OC) is the second leading cause of death among gynecologic cancers worldwide as there were <NUM>,<NUM> incident cases and mortality of <NUM>,<NUM> reported in <NUM> (<NUM>). Since screening tools are ineffective and early clinical warning signs are rare, the majority of OC cases present in late clinical stages. Even though therapeutic strategies improved within the past years, the prognosis is still poor, with an average five-year survival-rate of <NUM>% (<NUM>).

Cytoreductive surgery aiming for complete resection, followed by platinum-based chemotherapy, has been the backbone of OC treatment for decades (<NUM>,<NUM>). Carboplatin, combined with paclitaxel +/- bevacizumab, as an initial systemic regimen leads to a response rate of approximately <NUM>%, especially in high-grade serous ovarian cancer (HGSOC). Nevertheless, the disease of most patients recurs over time (<NUM>).

Recently, Poly (ADP-ribose) polymerase inhibitors (PARPi) were added to the therapeutic arsenal. Both the mainstay carboplatin/paclitaxel and these new strategies yield high responses in the overall population, which could be explained by homologous recombination deficiency (HRD) in a substantial proportion of OC (<NUM>).

Platinum compounds and PARPi exploit the HRD, by inducing DNA double-strand breaks or impeding its repair via synthetic lethality, leading to cell cycle arrest or death. The breast and ovarian cancer germline predisposition genes BRCA1 and BRCA2 play crucial roles in homologous recombination (<NUM>, <NUM>), an essential, highly accurate DNA-repair process fixing double-strand breaks. Deleterious germline mutations with subsequent loss of heterozygosity (LOH) in BRCA1/<NUM> can explain a subset of HR-deficient ovarian cancers, resulting in the registration of BRCA mutation analyses as companion diagnostics in specific OC (<NUM>) and metastasized breast cancer (<NUM>) settings. However, it was found that a larger proportion of OC displays a phenotype similar to germline (g) BRCA1/<NUM>-mutated cancers, so-called BRCAness (<NUM>). This is supported by preclinical analyses (<NUM>) and clinical trials of the three registered PARPi in OC (<NUM>-<NUM>) demonstrating survival benefits for a larger subgroup, and has led to their approval, independent of mutation status in most indications. Since these additional patients might benefit from a specific therapy, various potential biomarkers are under investigation, including single gene methylation, gene expression (profiles), copy number/ LOH based assays, mutational signatures, and combinations (<NUM>-<NUM>) as well as the identification of BRCAness status by analyzing whole-exome deep sequence data from wildtype OC cases; see<NPL>.

For determining BRCA-deficient breast cancer tumors, mutational signatures have been established as described in<NPL>, wherein for OC,, the LOH-score (<NUM>) (Foundation Medicine, Cambridge, U. ) and MyChoice® (<NUM>) (Myriad, Salt Lake City, U. ) were applied within the trials mentioned above and demonstrated the ability to narrow the respective subgroup. This led to the first approval of an HRD assay (MyChoice®) as a companion diagnostic for applying niraparib in heavily pretreated OC patients (<NUM>). Two recent clinical trials (<NUM>, <NUM>) evaluated this biomarker in the first-line setting, showing a benefit of a PARPi predominantly in the HRD-positive, but also in the HRD-negative subgroup. In contrast, within the PAOLA-<NUM> trial (<NUM>), the test defined a subpopulation beyond BRCA mutation-carriers benefitting most from the addition of olaparib to bevacizumab maintenance therapy after carboplatin and paclitaxel. Only recently, the EMA recommended the approval of this combination therapy for HR-deficient OC defined by the presence of BRCA-mutations or genomic instability for first-line maintenance treatment accordingly (<NUM>). Nonetheless, the exploration of further transparent tests that can easily be implemented and further elaborated on decentral platforms is still ongoing to improve the quest for predictive markers.

In general, the present invention relates to materials and methods involved in assessing cancer cells, in particular ovarian cancer cells, for homologous recombination deficiency (HRD). In particular, the present invention relates to a method of determining or predicting HRD status, i.e. the presence or absence of a HRD signature, in a subject having ovarian cancer and to a method of diagnosing the presence or absence of HRD in a patient sample, respectively. The method comprises the determination of copy number variation (CNV) of a genomic locus in a test sample from a subject comprising cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising or consisting of the loci set forth in Table <NUM> and/or in Table <NUM>; and wherein similarity between the CNVs of the pool of genomic loci in the test sample from the subject and the CNVs of a corresponding pool of genomic loci in the DNA of a reference sample of BRCA-like mutated cancer cells and a control sample of BRCA non-mutated cancer cells, is indicative for the HR deficiency status in the subject, wherein (i) similarity between the CNVs of the test sample and the CNVs of the reference sample identifies the subject as HR deficient; and/or (ii) similarity between the CNVs of the test sample and the CNVs of the control sample identifies the subject as not being HR deficient. CNVs include losses and gains of genomic segments. The similarity is defined as distance measure between the centroids of the two classes, i.e. the control and reference sample, and the centroid of the test sample.

A centroid of a class, which is in the present case the control and reference sample, respectively, is constructed by shrinking the class centroid by an optimization routine towards the overall centroid of both classes after standardizing by the within-class standard deviation for each CNVs.

Preferably, a similarity score, i.e. a posterior probability score, is computed by providing a shrunken centroid value which is derived from the CNV of the genomic locus and wherein the comparison of the shrunken centroids of the test sample from the subject with the shrunken centroids of the reference sample and the shrunken centroids of the control sample is converted into a posterior probability score according to Tibshirani PNAS <NUM> (<NUM>). This score ranges from <NUM>-<NUM>, with <NUM>-<NUM> being non-BRCA-like and <NUM>-<NUM> being BRCA-like, wherein this definition is set during the training, wherein it has been defined that the BRCA-like class is the one of interest.

Thus, in one embodiment the method of the present invention comprises (a) determining copy number variation (CNV) of a genomic locus in a test sample from a subject comprising cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising or consisting of the loci set forth in Table <NUM> and/or Table <NUM>; and (b) providing a shrunken centroid value which is derived from the CNV of the genomic locus, and wherein a posterior probability score of ≥ <NUM> identifies the subject as being HRD; and/or wherein a posterior probability score of < <NUM> identifies the subject as not being HRD, wherein the posterior probability score is obtainable by comparison of the shrunken centroid values derived from the CNVs of the pool of genomic loci in the DNA of the cancer cells in the test sample from the subject with the shrunken centroid values derived from the CNVs of a reference set of samples of BRCA-like mutated cancer cells and of control samples of BRCA non-mutated cancer cells.

The method of the present invention optionally comprises the transmission of the result to the subject or to a third party like a physician or genetic counselor.

Furthermore, a posterior probability score of ≥ <NUM> representing a higher similarity between the CNVs of the test sample and the CNVs of the reference sample than the control sample identifies the subject as being a likely responder to an anti-cancer therapy. A posterior probability score of < <NUM> representing a higher similarity between the CNVs of the test sample and the CNVs of the control sample than the reference sample, respectively identifies the subject as not being a likely responder to an anti-cancer therapy.

The present invention is also directed to a method of predicting the response of a subject having cancer, in particular ovarian cancer, to an anti-cancer therapy, i.e. to a cancer treatment regimen, wherein the method comprises the steps as defined above. In particular, a posterior probability score of ≥ <NUM> and respectively a similarity between the CNVs of the test sample and the CNVs of the reference sample indicates an increased likelihood that the subject will respond to the anti-cancer therapy, and/or a posterior probability score of < <NUM> and respectively a similarity between the CNVs of the test sample and the CNVs of the control sample indicates an increased likelihood that the subj ect will not respond to the anti-cancer therapy. The method of the present optionally comprises the transmission of the result to the subject or to a third party like a physician or genetic counselor.

In a preferred embodiment, the anti-cancer therapy/the cancer treatment regime is selected from homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, and drugs that indirectly cause double strand DNA breaks or where the drug or dose of the drug has been identified in mechanistic studies to target homologous recombination deficiency, like for example (but not limited to) interstrand crosslinking agents, platinum compounds, PARP inhibitors, anthracyclines, as well as topoisomerase I and II inhibitors, preferably the anti-cancer therapy/the cancer treatment regime comprises PARP inhibitors or DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents.

In one embodiment of the method of the present invention, the determination of the CNV is performed by DNA sequencing, preferably by low coverage whole genome sequencing (LG-WGS). In one embodiment of the method of the present invention, determination of the CNVs is performed prior to administration of the anti-cancer therapy.

Optionally, the method of the present invention further comprises recommending a subject which has been identified as being HRD and/or having an increased likelihood of responding to the anti-cancer therapy, a treatment regimen comprising homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks; and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency, for example interstrand crosslinking agents, platinum compounds, PARP inhibitors, anthracyclines, and topoisomerase I and II inhibitors, preferably PARP inhibitors or DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents. Alternatively, the method of the present invention may optionally comprise recommending a subject which has been identified as not being HRD and/or not having an increased likelihood of responding to the anti-cancer therapy, a standard treatment in the specific clinical setting of the patient, which might not include homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks, and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency.

The invention further relates to the above-mentioned drugs, namely PARP inhibitors, DNA double strand inducing agents, preferably platinum compounds or an interstrand crosslinking agents, anthracyclines, topoisomerase I inhibitors, or a topoisomerase II inhibitors for use in a method of treating cancer, in particular ovarian cancer. Such method is in general analogous to the methods described above and differs in that a particular treatment regimen is administered (recommended, prescribed, initiated, continued, etc.) based at least in part of the calculated posterior probability scores and the similarity determinations as explained above. The treatment regime is preferably the treatment regime as defined above.

Furthermore, the present invention relates to a treatment regime comprising homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks, or that otherwise have been identified as specifically targeting homologous recombination deficient tumors for use in treating ovarian cancer in a subject, wherein said subject has been identified as HRD and/or identified to have an increased likelihood of responding to the anti-cancer treatment regimen according to the method as described above.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The details of one or more embodiments of the invention are set forth in the description and accompanying drawings as well as in the Examples below.

Tumors with HRD have been shown to be particularly sensitive to DNA crosslinking agents, such as alkylators, platinum drugs or platinating agents as well as to PARP inhibitors. The breast and ovarian cancer germline predisposition genes BRCA1 and BRCA2 play crucial roles in homologous recombination. Thus, ovarian cancers associated with a germline BRCA1 or BRCA2 mutation derive benefit of targeted therapy, for example with PARP inhibitors or specific classes of chemotherapy, such as platinum compounds as mentioned above. Furthermore, up to approximately <NUM>% of tumors in ovarian cancer share characteristics with germline mutated cancers, because of other alterations in the BRCA1/BRCA2 pathway of homologous recombination, and therefore those benefit of such therapies as well. This phenotype is called BRCAness or BRCA-like.

Breast cancer BRCA1-like and BRCA2-like copy number profile classifiers predictive for mutation status and response to therapy, targeting HRD have been developed before, but breast cancer classifiers did not sufficiently predict mutation status in ovarian cancer. Hence, new classifiers had to be developed.

Accordingly, the present invention provides predictive biomarkers to identify the germline mutated as well as said BRCAness group for therapy specifically targeting BRCAness in ovarian cancer. In other words, the method of the present invention is useful for identifying patients with ovarian cancer, wherein the cancer cells have a defective DNA repair system, and thus, which are responsive to the mentioned targeted anti-cancer therapy.

Furthermore, the method of the present invention is also useful for identifying a group of patients that is non-BRCA1/<NUM> and non-BRCA1/<NUM>-like, respectively, and that does not benefit of this therapy. Until now, no reliably method has been described that also identifies the nonbenefitting population correctly.

Cancers and cell lines with BRCA mutations or mutations in the BRCA pathway exhibit genomic instability, manifesting in abnormal copy number profiles. Thus, in general, the present invention is based on the discovery that certain chromosomal copy number aberrations or variations, respectively (CNVs) in tumor cells allow tumors to be classified as either BRCA-associated-/ BRCA-like tumors or sporadic tumors. This classification of a tumor allows for the prospective prediction of responsiveness of the patient from which the tumor was removed to anti-cancer therapy.

In the literature, other assays have been described measuring HRD in ovarian cancer (<NUM>, <NUM>, <NUM>, <NUM>), some of which are also partly based on derivatives of SNP/copy number profiles. The approach of the present invention differs inter alia from other tests because it uses genomic location-specific aberrations. This leads to additional information being retained, such as aberrations that collaborate with the underlying HRD mechanism. Additionally, the presented classifiers are tissue-specific, which is an advantage compared to other HRD assays, as a response to targeted strategies can depend on the tissue of origin.

The terms copy number aberrations, copy number variants/variations and CNVs are used interchangeable herein. Furthermore, when reference is made to BRCA1-like or BRCA2-like, for example BRCA1-like tumors, or BRCA2-like tumors, if not indicated otherwise, this includes tumors having a mutation in the BRCA1 gene or BRCA2 gene itself and tumors which share characteristics with those germline mutated cancers, because of other alterations in the BRCA1/BRCA2 pathway of homologous recombination.

HRD is a defect in DNA repair by hampered homologous recombination repair (HRR), which is a form of DNA recombination often used to repair DNA double strand breaks. In cancers, this is often caused by loss of function mutations in BRCA1, BRCA2, RAD51C, RAD51D or PALB2, promoter hypermethylation of the BRCA1 gene promoter (leading to reduced expression of BRCA1) or a series of as yet to be defined causes. HRD is also characterized by the cellular sensitivity to for example PARP inhibitors, topoisomerase inhibitors or platinum salts.

The method of the present invention comprises the determination of a CNV of a genomic locus in a test sample from a subject comprising cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising the loci set forth in Table <NUM> or in Table <NUM>, and wherein similarity between the CNVs in the pool of genomic loci in the test sample from the subject and the CNVs in a corresponding pool of genomic loci in the DNA of a reference sample of BRCA-like mutated cancer cells and a control sample of BRCA non-mutated cancer cells, is indicative for the HR deficiency status in the subject, wherein similarity is the distance measure between centroids of the two classes, i.e., the control and the reference sample, and the centroid of the test sample, wherein the centroid of a class is constructed by shrinking the class centroid by an optimization routine towards the overall centroid of both classes after standardizing by the within-class standard deviation for each CNV.

The test sample is a sample of material comprising cancer cells, in particular ovarian cancer cells. Samples include, but are not limited to material obtained from the subject of interest, i.e. from the ovarian cancer patient and may be directly obtained from the source, for example via tumor biopsy, or indirectly after culturing and/or further processing of the tumor cells. In particular, the test sample is a DNA sample or a sample that comprises DNA from the tumor cells of the subject of interest. Accordingly, the test sample is derived from a subject that is to be diagnosed of having HRD status or not and that is to be diagnosed of being responsive to a particular anti-cancer therapy or not. Any appropriate type of sample can be assessed to determine if the genome of the cancer cells contains an HRD signature, or lacks an HRD signature. Examples of samples containing cancer cells, in particular ovarian cancer cells that can be assessed as described herein include, without limitation, tumor biopsy samples (e.g., ovarian tumor biopsy samples), formalin-fixed, paraffin-embedded tissue samples containing cancer cells, core needle biopsies, fine needle aspirates, and samples containing cancer cells shed from a tumor. For formalin-fixed, paraffin-embedded tissue samples, the sample can be prepared by DNA extraction using a genomic DNA extraction kit optimized for FFPE tissue, for example Qiagen DNA mini kit (Qiagen, cat nr. <NUM>), QuickExtract™ FFPE DNA Extraction Kit (Epicentre™), and QIAamp™ DNA FFPE Tissue Kit (Qiagen™)).

In some cases, laser dissection techniques can be performed on a tissue sample to minimize the number of non-cancer cells within a cancer cell sample to be assessed. In some cases, antibody based purification methods can be used to enrich for cancer cells and/or deplete non-cancer cells. Examples of antibodies that could be used for cancer cell enrichment include, without limitation, anti-EpCAM, anti-TROP-<NUM>, anti-c-Met, anti-Folate binding protein, anti-N-Cadherin, anti-CD318, anti-antimesencymal stem cell antigen, anti-Her2, anti-MUCl, anti-EGFR, anti-cytokeratins (e.g., cytokeratin <NUM>, cytokeratin <NUM>, etc.), anti-Caveolin-<NUM>, anti-PSA, anti-CA125, and anti-surfactant protein antibodies. Even if dynamic ranges of the profiles are influenced, validation can be performed by the person skilled in the art as described herein.

The control sample used in the method of the present invention is a corresponding sample comprising ovarian cancer cells and the corresponding DNA, respectively, which are not associated with BRCA1-like or BRCA2-like mutations and do not have a HRD signature.

The reference sample is a corresponding sample comprising BRCA mutated ovarian cancer cells and the corresponding DNA, respectively. Based on such reference sample and a plurality of such reference samples, respectively, ovarian cancer specific DNA copy number aberration classifiers have been developed that identify a population enriched for BRCA1 and HRD-associated gene mutations (BRCA1-like), i.e. BRCA1-like classifiers, and a population enriched for BRCA2 and HR-associated gene mutations (BRCA2-like), i.e. BRCA2-like classifiers and a population enriched for BRCA1-like and BRCA2-like mutations. Those classifiers are listed in Table <NUM> (BRCA1-like) and Table <NUM> (BRCA2-like).

In particular, those classifiers have been developed by generating copy number profiles of control samples, i.e. samples derived from patients without a family history of ovarian or breast cancer, of gBRCA1m samples, i.e. samples derived from patients having ovarian cancer and having mutations in the BRCA1 gene, and of gBRCA2m samples, i.e. samples derived from patients having ovarian cancer and having mutations in the BRCA2 gene. <FIG> presents the average profiles of gBRCA1m and gBRCA2m versus control ovarian cancers. The generation of the copy number profiles is described in Example <NUM> in more detail. Thus, the present invention also relates to the development of BRCA1-like and BRCA2-like classifiers by generating copy number profiles of control samples, of gBRCA1m samples and of gBRCA2m samples, respectively.

After generating the copy number profiles, the ovarian cancer specific shrunken-centroid classifiers have been trained on ovarian cancer data. These shrunken centroids select genomic regions and weights that are discriminative for the BRCA-like and non-BRCA-like class. For this, ten-fold cross-validation has been carried out and training of the shrunken centroid classifiers was performed on the class labels gBRCA1m vs. control and gBRCA2m vs. control to classify ovarian cancer as being similar to gBRCA1m (BRCA1-like) or gBRCA2m (BRCA2-like) or controls (C). Subsequently, samples were classified as BRCA-like if the predicted probability was > <NUM> and non-BRCA-like if the predicted probability was <= <NUM>, as was pre-defined in the training set. This is described in more detail in Example <NUM>. Thus, the present invention also relates to the training of the above-mentioned classifiers on ovarian cancer data. In principle any data can be used as long as they comprise data of control samples and of gBRCA1m samples and gBRCA2m samples, respectively.

The training was followed by validation of the classifiers as for example described in Examples <NUM> and <NUM>. As described in Example <NUM> validation was performed by using the TCGA dataset. In particular, <NUM> of <NUM> samples have been classified as having a BRCA1-like profile. Within these <NUM>, <NUM>/<NUM> gBRCA1, <NUM>/<NUM> somatic BRCA1-mutated tumors, and <NUM>/<NUM> BRCA1-methylated tumors were classified as BRCA1-like, resulting in an overall sensitivity of <NUM>% (<NUM>% CI: <NUM>-<NUM>) and a specificity of <NUM>% (<NUM>% Cl: <NUM>-<NUM>). Furthermore, <NUM> of <NUM> samples were assigned to be BRCA2-like. Within these <NUM>, <NUM>/<NUM> gBRCA2m <NUM> and <NUM>/<NUM> sBRCA2m samples were classified as BRCA2-like, resulting in a sensitivity of <NUM>% (<NUM>% CI: <NUM>-<NUM>) and a specificity of <NUM>% (<NUM>% CI: <NUM>-<NUM>).

Validation was also performed within AGOTR1 study samples, wherein in total <NUM> samples with complete genetic and epigenetic information available, were analyzed. The results of the validation are presented in Example <NUM>. In particular, focus was first put on the detection-rate of tumors derived from deleterious germline mutations in BRCA1/<NUM> (see Table <NUM>, <FIG>). <NUM> of <NUM> BRCA1-mutated cases were BRCA1-like (detection rate of <NUM>%), whereas <NUM> of <NUM> samples with a BRCA2 class4/<NUM> variant in germline were BRCA2-like (detection rate of <NUM>%). All remaining BRCA2-associated cases were identified by combining both classifiers, i.e. being BRCA1- or BRCA2-like. Regarding deleterious somatic variants, <NUM> of <NUM> (<NUM>%) samples with a BRCA1 mutation were identified by the BRCA1-like classifier. As the other two tumors displayed a BRCA2-like phenotype, both classifiers' application detected <NUM>% of the mutated cases. The BRCA2-like classifier confirmed <NUM> of <NUM> sBRCA2m cases. The BRCA1-like classifier found no additional sample. Of the <NUM> examined samples with a BRCA1 promoter hypermethylation, <NUM> displayed a BRCA1- like profile (<NUM>%), one was classified as non-BRCA-like. Thus, in one embodiment, the present invention relates to the validation of the classifiers. In principle, validation can be performed with any samples or dataset which comprise samples and data, respectively comprising a BRCA-like (BRCA1-like and BRCA2-like) profile and a control profile. Furthermore, the validation set also comprises samples and data, respectively for which the class is known, i.e. with pathogenic variants/mutations in for example the BRCA1 and BRCA2 gene, respectively or other HRD related genes as mentioned below. Optionally, the validation data set further comprises samples and data, respectively that are non-BRCA-like i.e. that do not comprise a mutation in any of the HRD pathway related genes. Alternatively or in addition, validation is also possible by determining the therapy response, i.e. whether a patient responds to a treatment with a treatment regimen comprising homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks; and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency.

As mentioned above, HRD is not only associated with alterations in the BRCA genes. Thus, alterations in further HR genes (ATM, BARD1, BRIP1, CHEK1, CHEK2, FAM175A, FANCM, MRE11A, NBN, PALB2, RAD50, RAD51C, RAD51D, and XRCC2) are being classified as BRCA-like and as yet unknown genes. Within the study cohort, <NUM> tumors without BRCA mutation or BRCA1 methylation displayed a genetic alteration in another OC-risk- or HR-gene (except sTP53 mutations), of which <NUM> were classified as BRCA-like (<NUM>%; only BRCA1-like n=<NUM>, only BRCA2-like n=<NUM>, BRCA1- and BRCA2-like n=<NUM>). The detailed results are presented in Example <NUM>.

Thus, the present invention also relates to the development and/or training and optionally validation of the classifiers which are used in the method of the present invention, i.e. the method of determining HRD status in a subject having ovarian cancer and the method of predicting the response of a subject having ovarian cancer to an anti-cancer therapy, respectively.

The method of the present invention comprises the determination of a CNV of a genomic locus in a test sample from a subject comprising cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising the loci set forth in Table <NUM> or in Table <NUM>, and wherein similarity between the CNVs in the pool of genomic loci in the test sample from the subject and the CNVs in a corresponding pool of genomic loci in the DNA of a reference sample of BRCA-like mutated cancer cells and a control sample of BRCA non-mutated cancer cells, is indicative for the HRD status in the subject, i.e. whether the cancer cells have a HRD signature or not.

In particular, similarity between the CNVs of the test sample and the CNVs of the reference sample identifies the subject as HR deficient, wherein similarity between the CNVs of the test sample and the CNVs of the control sample identifies the subject as not being HR deficient. Furthermore, non-similarity between the CNVs of the test sample and the CNVs of the reference sample can also identify a subject as not being HR deficient.

Determining CNVs is usually performed at one or more genomic loci. In principle, any appropriate technique can be used to determine genotypes at loci of interest within the genome of a cell. For example, array-based comparative genomic hybridization (array-CGH) can be used. CGH refers generally to molecular-cytogenetic techniques for the analysis of copy number changes, gains or losses, in the DNA content of a given subject's DNA. CGH can be used to identify chromosomal alterations, such as unbalanced chromosomal changes, in any number of cells including, for example ovarian cancer cells. CGH can be utilized to detect one or more chromosomal amplifications and/or deletions of regions between a test sample and a reference/control sample. In particular, nucleic acids from a test sample and nucleic acids from a reference/control sample are labelled differentially, hybridized to an array comprising a plurality of probes, preferentially covering the whole genome, and wherein the ratio of the signal intensity of the test sample to that of the reference/control sample is calculated to measure the copy number changes for a particular location in the genome. This is exemplarily described in <CIT> as well as in the references cited therein. Furthermore, single nucleotide polymorphism (SNP) arrays, molecular inversion probe (MIP) assays, targeted sequencing of loci of interest, and large-scale sequencing (e.g., whole exome, transcriptome, or genome sequencing) can be used to identify CNVs (as long as they cover or sufficiently approximate the loci of interest). Preferably, low coverage whole genome sequencing (LC-WGS) is used, preferably single-read sequencing, wherein the read length can vary and is for example between <NUM> to <NUM> bp, preferably between <NUM> and <NUM> bp and most preferably about <NUM> bp. Furthermore, the LC-WGS can be performed with a coverage of for example <NUM> - 3X, and preferably with a coverage of <NUM>. The sequencing data can then by analyzed regarding the copy numbers of the DNA in the genomic loci.

Due to the development of the classifiers set forth in Tables <NUM> and <NUM> and as explained above, it was possible to establish the method of the present invention which detects highly reliable HRD status in a subject. However, the person skilled in the art is well aware of the fact that not all genomic loci set forth in Table <NUM> and Table <NUM>, respectively need to be considered in order to receive a reliable result on the HRD status of the subjects. In particular, as soon as the BRCA1-like classifiers as set forth in Table <NUM> and the BRCA2-like classifiers set forth in Table <NUM> have been developed, the person skilled in the art knows how to vary the method so that still reliable results are achieved. For example, it is possible that the CNVs in additional loci are considered, or that the CNVs in less loci are considered, or that some different loci are considered in comparison to those listed in Table <NUM> and Table <NUM>, respectively. In particular, variations can be made as long as the method still reaches an appropriate sensitivity, preferably a sensitivity of <NUM>% to <NUM> %, more preferably of <NUM> % to <NUM>%, that allows to make a reliable statement about the HRD status of the subject to be tested.

As regards the BRCA1-like classifiers, the pool of genomic loci can vary as long as the method of the present invention still shows a sensitivity of at least <NUM>%, i.e. a sensitivity between <NUM>% and <NUM>%, preferably of at least <NUM> % to <NUM>%, i.e. a sensitivity between <NUM> to <NUM>% and <NUM>%. In one embodiment, the pool of genomic loci comprises at least <NUM>, preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, and most preferably all of those loci listed in Table <NUM> as long as the above-mentioned sensitivities are reached. Of course any numbers of loci in between are also encompassed and just not mentioned for simplicity.

As regards the BRCA2-like classifiers, the pool of genomic loci can vary as long as the method of the present invention still shows a sensitivity of at least <NUM>%, i.e. a sensitivity between <NUM>% and <NUM>%, preferably of at least <NUM>% to <NUM>%, i.e. a sensitivity between <NUM> to <NUM>% and <NUM>%. In one embodiment, the pool of genomic loci comprises at least <NUM>, preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, and most preferably all of those loci listed in Table <NUM> as long as the above-mentioned sensitivities are reached. Of course any numbers of loci in between are also encompassed and just not mentioned for simplicity.

Thus, similarity or non-similarity between the CNVs in a pool of genomic loci in the test sample from the subject and the CNVs in a corresponding pool of genomic loci in the DNA of a reference sample of BRCA-like mutated cancer cells and a control sample of BRCA non-mutated cancer cells, can be determined in the method of the present invention by considering at least <NUM>, preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, and most preferably all of those loci listed in Table <NUM> and/or by considering at least <NUM>, preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, and most preferably all of those loci listed in Table <NUM>, respectively.

The method of the present invention also encompasses the determination of CNV of one genomic locus in a test sample from a subject comprising cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising the loci set forth in Table <NUM>, wherein determination of the CNV and the CNVs, respectively, can be made in one of the genomic loci, in two, three, four, five [. ], or in all <NUM> of the genomic loci. ] represents all numbers from six to <NUM>. The method of the present invention also encompasses the determination of a CNV of one genomic locus in a test sample from a subject comprising cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising the loci set forth in Table <NUM>, wherein determination of the CNV and the CNVs, respectively, can be made in one of the genomic loci, in two, three, four, five [. ], or in all <NUM> of the genomic loci. ] represents all numbers from six to <NUM>.

As mentioned above, CNVs include losses and gains of genomic segments. The similarity is defined as distance measure between the centroids of the two classes, i.e. the control and reference sample, and the centroid of the test sample. A centroid of a class, which is in the present case the control and reference sample, respectively, is constructed by shrinking the class centroid by an optimization routine towards the overall centroid of both classes after standardizing by the within-class standard deviation for each CNVs.

A similarity score, i.e. a posterior probability score, is computed by providing a shrunken centroid value which is derived from the CNV of the genomic locus and wherein the comparison of the shrunken centroids of the test sample from the subject with the shrunken centroids of the reference sample and the shrunken centroids of the control sample is converted into a posterior probability score according to Tibshirani PNAS <NUM> (<NUM>). This score ranges from <NUM>-<NUM>, with <NUM>-<NUM> being non-BRCA-like and <NUM>-<NUM> being BRCA-like.

Thus, in one embodiment the method of the present invention comprises, or essentially consists of the determination of a CNV of a genomic locus in a test sample from a subject, wherein the test sample comprises cancer cells, e.g. a DNA test sample of cancer cells, wherein the locus is selected from a pool of genomic loci comprising the loci set forth in Table <NUM>, the loci set forth in Table <NUM>, or the loci set forth in Tables <NUM> and <NUM>; and the provision of a shrunken centroid value which is derived from the CNV of the genomic locus, and wherein a posterior probability score of ≥ <NUM> identifies the subject as HRD; and/or wherein a posterior probability score of < <NUM> identifies the subject as not being HRD.

As mentioned above, the determination of the CNV and the CNVs, respectively, can be made in one of the genomic loci, in two, three, four, five [. ], or in all <NUM> of the genomic loci listed in Table <NUM> ([. ] represents all numbers from six to <NUM>), and/or in one of the genomic loci, in two, three, four, five [. ], or in all <NUM> of the genomic loci listed in Table <NUM> ([. ] represents all numbers from six to <NUM>).

The posterior probability score is generally obtainable by comparing the shrunken centroid values derived from the CNVs of a pool of genomic loci in the test sample from the subject with the shrunken centroid values derived from the CNVs of a reference sample of BRCA-like mutated cancer cells and of a control sample of BRCA non-mutated cancer cells. In general, the loci comprised in the pool of genomic loci as regards the BRCA1-like classifiers are those listed in Table <NUM> and the loci comprised in the pool of genomic loci as regards the BRCA2-like classifiers are those listed in Table <NUM>. The CNV profile of the reference and the control sample can be a profile that has been established before performing the method of the present invention or it can in principle also be established in the course of the execution of the method of the present invention. Thus, optionally, the method of the present invention further comprises the determination of the posterior probability score by comparison of the shrunken centroid values derived from the CNVs of the pool of genomic loci in the DNA of the cancer cells in the test sample from the subject with the shrunken centroid values derived from the CNVs of a reference set of samples of BRCA-like mutated cancer cells and of control samples of BRCA non-mutated cancer cells.

As mentioned above and as regards the BRCA1-like classifiers, the loci in the pool of genomic loci can vary as long as the method still shows a sensitivity of at least <NUM>%, i.e. a sensitivity between <NUM>% and <NUM>%, preferably of at least <NUM> % to <NUM>%, i.e. a sensitivity between <NUM> to <NUM>% and <NUM>%. In one embodiment, the pool of genomic loci comprises at least <NUM>, preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, and most preferably all of those loci listed in Table <NUM> as long as the above-mentioned sensitivities are reached. Of course any numbers of loci in between are also encompassed and just not mentioned for simplicity. Furthermore, as regards the BRCA2-like classifiers, the loci in the pool of genomic loci can vary as long as the method still shows a sensitivity of at least <NUM>%, i.e. a sensitivity between <NUM>% and <NUM>%, preferably of at least <NUM>% to <NUM>%, i.e. a sensitivity between <NUM> to <NUM>% and <NUM>%. In one embodiment, the pool of genomic loci comprises at least <NUM>, preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, more preferably <NUM>, and most preferably all of those loci listed in Table <NUM> as long as the above-mentioned sensitivities are reached. Of course any numbers of loci in between are also encompassed and just not mentioned for simplicity.

A posterior probability score of ≥ <NUM> identifies the subject as HRD and thus, it identifies the subject as being a likely responder to an anti-cancer therapy. A posterior probability score of < <NUM> identifies the subject as not being HRD and thus, it identifies the subject a not being a likely responder to an anti-cancer therapy. In other words, similarity between the CNVs of the test sample and the CNVs of the reference sample identifies the subject as HR deficient and thus, identifies the subject as being a likely responder to an anti-cancer therapy, wherein similarity between the CNVs of the test sample and the CNVs of the control sample identifies the subject as not being HR deficient and thus, it identifies the subject a not being a likely responder to an anti-cancer therapy.

The present invention is thus also directed to a method of predicting the response of a subject having cancer, in particular ovarian cancer, to an anti-cancer therapy, i.e. to a cancer treatment regimen, wherein the method comprises the steps as defined above. In particular, a posterior probability score of ≥ <NUM> indicates an increased likelihood that the subject will respond to the anti-cancer therapy, and/or a posterior probability score of < <NUM> indicates an increased likelihood that the subject will not respond to the anti-cancer therapy. In other words, similarity between the CNVs of the test sample and the CNVs of the reference sample indicates an increased likelihood that the subject will respond to the anti-cancer therapy, wherein similarity between the CNVs of the test sample and the CNVs of the control sample indicates an increased likelihood that the subject will not respond to the anti-cancer therapy.

Thus, patients having cancer cells (or samples derived therefrom), and in particular ovarian cancer cells, identified as having an HRD signature status can be classified, based at least in part on such HRD signature, as being likely to respond to a particular cancer treatment regimen. For example, patients having cancer cells with an HRD signature can be classified as being likely to respond to a cancer treatment regimen that includes the use of drugs that target homologous recombination deficiency, that directly or indirectly cause double strand DNA breaks, and/or where the drug has been identified in mechanistic studies to target homologous recombination deficiency like DNA damaging agents, synthetic lethality agents (e.g., a PARP inhibitor), radiation, or a combination thereof. Examples of DNA damaging agents include, without limitation, platinum-based chemotherapy drugs (e.g., cisplatin, carboplatin, oxaliplatin, and picoplatin), anthracyclines (e.g., epirubicin and doxorubicin), topoisomerase I inhibitors (e.g. campothecin, topotecan, and irinotecan), DNA crosslinkers such as mitomycin C, and triazene compounds (e.g., dacarbazine and temozolomide). Preferably, the cancer treatment regime includes PARP inhibitors or DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents.

Synthetic lethality therapeutic approaches typically involve administering an agent that inhibits at least one critical component of a biological pathway that is especially important to a particular tumor cell's survival. For example, when a tumor cell has a deficient homologous repair pathway (e.g., as determined according to the present invention), inhibitors of poly ADP ribose polymerase (or platinum drugs, double strand break repair inhibitors, etc.) can be especially potent against such tumors because two pathways critical to survival become obstructed (one biologically, e.g., by BRCA1/<NUM>-like mutation, and the other synthetically, e.g., by administration of a pathway drug). Synthetic lethality approaches to cancer therapy are described in, e.g.,<NPL>. Examples of synthetic lethality agents include, without limitation, PARP inhibitors or double strand break repair inhibitors in homologous repairdeficient tumor cells, PARP inhibitors in PTEN-deficient tumor cells, methotrexate in MSH2-deficient tumor cells, etc. Examples of PARP inhibitors include, without limitation, olaparib, iniparib, veliparib and niraparib. Examples of double strand break repair inhibitors include, without limitation, KU55933 (ATM inhibitor) and NU7441 (DNA-PKcs inhibitor). Examples of information that can be used in addition to the presence of an HRD signature to base a classification of being likely to respond to a particular cancer treatment regimen include, without limitation, previous treatment results, germline or somatic DNA mutations, gene or protein expression profiling (e.g. BRCA1 and/or BRCA2 status), tumor histology (e.g., epithelial ovarian tumors like serous, mucinous, endometrioid, clear cell, transitional cell tumors (Brenner tumors), carcinosarcoma, mixed epithelial tumor, undifferentiated carcinoma, etc.), disease stage, tumor or cancer grade, number of previous courses of treatment, etc. Preferably, the information that can be used in addition to the presence of an HRD signature to base a classification of being likely to respond to a particular cancer treatment regimen is the tumor histology, and in particular of serous tumors.

Treatment regimens can also comprise the addition of olaparib to bevacizumab maintenance therapy after carboplatin and paclitaxel treatment. In particular, a treatment regime comprises hyperthermic intraperitoneal chemotherapy with cisplatin (<NUM> per square meter) at interval debulking after standard neoadjuvant carbotaxol, i.e. paclitaxel and carboplatin, in stage III epithelial ovarian cancer.

Once classified as being likely to respond to a particular cancer treatment regimen as defined above, a treatment regime can be recommended to the patient, and/or the cancer patient can be treated with such a cancer treatment regimen. The invention thus relates to a method of the present invention which further comprises recommending a subject which has been identified as being HRD and/or having an increased likelihood of responding to the anti-cancer therapy, a treatment regimen comprising the above-mentioned drugs, i. homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks; and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency. On the other hand, the method may also comprise recommending a subject which has been identified as not being HRD and/or not having an increased likelihood of responding to the anti-cancer therapy, a standard treatment in the specific clinical setting of the patient, which might not include the above-mentioned drugs, i. homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks, and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency. In turn, such a subject can be classified as likely to respond to a cancer treatment regimen that includes the use of one or more cancer treatment agents not associated with HRD, such as (but not limited to) a taxane agent (e.g., paclitaxel, docetaxel, abraxane), and/or a growth factor receptor inhibitor (e.g., bevacizumab). However, in case no other treatment approach is available, the standard treatment may still comprise homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks, and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency.

Accordingly, the present invention further relates to a treatment regime comprising homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, and/or drugs that indirectly cause double strand DNA breaks, or that otherwise have been identified as specifically targeting homologous recombination deficient tumors like a DNA damaging agent, a synthetically lethal agent (e.g., a PARP inhibitor), or a combination thereof for use in treating ovarian cancer in a subject, wherein said subject has been identified as having an HRD signature and/or identified to have an increased likelihood of responding to the anti-cancer treatment regimen as described before.

Furthermore, the present invention relates to homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, and/or drugs that indirectly cause double strand DNA breaks, or that otherwise have been identified as specifically targeting homologous recombination deficient tumors, i.e., PARP inhibitors or DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents or any combination thereof for use in a method of treating a patient comprising detecting an HRD signature as described herein, wherein the method comprises administering (or recommending or prescribing) a treatment regimen comprising the above-mentioned drugs. Any appropriate method for treating the cancer at issue can be used to treat a cancer patient identified as having cancer cells having an HRD signature. For example, platinum-based chemotherapy drugs or a combination of platinum-based chemotherapy drugs can be used to treat cancer as described elsewhere (see, e.g., <CIT>,<CIT>,<CIT>, <CIT>and <CIT>). In some cases, anthracyclines or a combination of anthracyclines can be used to treat cancer as described elsewhere (see, e.g., <CIT>,<CIT>, <CIT>, <CIT>,<CIT>, and <CIT>). In some cases, topoisomerase I inhibitors or a combination of topoisomerase I inhibitors can be used to treat cancer as described elsewhere (see, e.g., <CIT> and <CIT>). In some cases, PARP inhibitors or a combination of PARP inhibitors can be used to treat cancer as described elsewhere (see, e.g., <CIT>,<CIT>, and<CIT>). In some cases, radiation can be used to treat cancer as described elsewhere (see, e.g., <CIT>). In some cases, a combination comprising different agents (e.g., a combination comprising any of platinum-based chemotherapy drugs, anthracyclines, topoisomerase I inhibitors, and/or PARP inhibitors) with or without radiation treatments can be used to treat cancer. In some cases, a combination treatment may comprise any of the above agents or treatments (e.g., PARP inhibitors or DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents) together with another agent or treatment, e.g., a taxane agent (e.g., doxetaxel, paclitaxel, abraxane, preferably paclitaxel), and/or a growth factor receptor inhibitor (e.g., bevacizumab).

Furthermore, patients identified as having cancer cells lacking an HRD signature can be classified, based at least in part on a sample lacking an HRD signature, as being less likely to respond to a treatment regimen that includes a homologous recombination deficiency-targeting drug, drug that directly causes double strand DNA breaks, drug that indirectly causes double strand DNA breaks, and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency, like PARP inhibitors, DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents or a combination thereof. In turn, such a patient can be classified as likely to respond to a cancer treatment regimen that includes the use of one or more cancer treatment agents not associated with HRD, such as a taxane agent (e.g., doxetaxel, paclitaxel, abraxane), and/or a growth factor receptor inhibitor (e.g., bevacizumab). However, in case no other treatment approach is available, the standard treatment may still comprise homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks, and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency.

Once classified as being likely to respond to a particular cancer treatment regimen (e.g., a cancer treatment regimen that includes the use of a cancer treatment agent not associated with HRD), the cancer patient can be treated with such a cancer treatment regimen. The invention thus provides a treatment regimen not comprising the use of a homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks, and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency, like PARP inhibitors, DNA double strand break inducing agents, e.g. platinum compounds and interstrand crosslinking agents, or a combination thereof for use in a method of treating a patient comprising detecting the absence of an HRD signature as described herein and administering (or recommending or prescribing) said treatment regimen. In some embodiments the treatment regimen comprises one or more of a taxane agent (e.g., doxetaxel, paclitaxel, abraxane, preferably paclitaxel), and/or a growth factor receptor inhibitor (e.g., bevacizumab). Any appropriate method for the cancer being treated can be used to treat a cancer patient identified as having cancer cells lacking an HRD signature. Examples of information that can be used in addition to the presence of an HRD signature to base a classification of being likely to respond to a particular cancer treatment regimen include, without limitation, previous treatment results, germline or somatic DNA mutations, gene or protein expression profiling (e.g. BRCA1 and/or BRCA2 status), tumor histology (e.g., epithelial ovarian tumors like serous, mucinous, endometrioid, clear cell, transitional cell tumors (Brenner tumors), carcinosarcoma, mixed epithelial tumor, undifferentiated carcinoma, etc.), disease stage, tumor or cancer grade, number of previous courses of treatment, etc. Preferably, the information that can be used in addition to the presence of an HRD signature to base a classification of being likely to respond to a particular cancer treatment regimen is the tumor histology, and in particular of serous tumors.

Once treated for a particular period of time (e.g., between one to six months), the patient can be assessed to determine whether or not the treatment regimen has an effect. If a beneficial effect is detected, the patient can continue with the same or a similar cancer treatment regimen. If a minimal or no beneficial effect is detected, then adjustments to the cancer treatment regimen can be made. For example, the dose, frequency of administration, or duration of treatment can be increased. In some cases, additional anti-cancer agents can be added to the treatment regimen or a particular anti-cancer agent can be replaced with one or more different anti-cancer agents. The patient being treated can continue to be monitored as appropriate, and changes can be made to the cancer treatment regimen as appropriate.

In addition to predicting likely treatment response or selecting desirable treatment regimens, an HRD signature can be used to determine a patient's prognosis. Thus, in one aspect, a method is provided for determining a patient's prognosis based at least in part of detecting the presence or absence of an HRD signature in a sample from the patient. The method comprises, or consists essentially of, (a) determining whether a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature as described herein, and (b)(<NUM>) determining, based at least in part on the presence of the HRD signature, that the patient has a relatively good prognosis, or (b)(<NUM>) determining, based at least in part on the absence of the HRD signature, that the patient has a relatively poor prognosis. Prognosis may include the patient's likelihood of survival (e.g., progression-free survival, overall survival), wherein a relatively good prognosis would include an increased likelihood of survival as compared to some reference population (e.g., average patient with this patient's cancer type/subtype, average patient not having an HRD signature, etc.). Conversely, a relatively poor prognosis in terms of survival would include a decreased likelihood of survival as compared to some reference population (e.g., average patient with this patient's cancer type/subtype, average patient having an HRD signature, etc.).

As described herein, the present invention relates to methods for assessing patients for ovarian cancer cells having an HRD signature. In some embodiments, one or more clinicians or medical professionals can determine whether a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature. In some cases, one or more clinicians or medical professionals can determine if a patient contains cancer cells having an HRD signature by obtaining a cancer cell sample from the patient and assessing the DNA of cancer cells of the cancer cell sample to determine the presence or absence of an HRD signature as described herein.

In some cases, one or more clinicians or medical professionals can obtain a cancer cell sample from a patient and provide that sample to a testing laboratory having the ability to assess DNA of cancer cells of the cancer cell sample to provide an indication about the presence or absence of an HRD signature as described herein. In such cases, the one or more clinicians or medical professionals can determine if a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature by receiving information about the presence or absence of an HRD signature as described herein directly or indirectly from the testing laboratory. For example, a testing laboratory, after assessing DNA of cancer cells for presence or absence of an HRD signature as described herein, can provide a clinician or medical professional with, or access to, a written, electronic, or oral report or medical record that provides an indication about the presence or absence of an HRD signature for a particular patient (or patient sample) being assessed. Such a written, electronic, or oral report or medical record can allow the one or more clinicians or medical professionals to determine if a particular patient being assessed contains cancer cells having an HRD signature.

Once a clinician or medical professional or group of clinicians or medical professionals determines that a particular patient being assessed contains cancer cells having an HRD signature, the clinician or medical professional (or group) can classify that patient as having cancer cells whose genome contains the presence of an HRD signature. In some cases, a clinician or medical professional or group of clinicians or medical professionals can diagnose a patient determined to have cancer cells whose genome contains the presence of an HRD signature as having cancer cells deficient in (or likely to be deficient in) HRD. Such a diagnosis can be based solely on a determination that a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature or can be based at least in part on a determination that a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature. For example, a patient determined to have cancer cells having an HRD signature can be diagnosed as likely to be deficient in HRD based on the combination of the presence of an HRD signature and deficient status in one or more tumor suppressor genes (e.g., BRCA1/<NUM>, RAD51C), a family history of cancer, or the presence of behavioral risk factors (e.g., smoking). Thus, patients can be detected that have germline mutations in HRD associated genes.

In some cases, a clinician or medical professional or group of clinicians or medical professionals can diagnose a patient determined to have cancer cells whose genome contains the presence of an HRD signature as having cancer cells which can contain genetic mutations in one or more genes in the HRD pathway. Such a diagnosis can be based solely on a determination that a particular patient being assessed contains cancer cells having a genome containing an HRD signature or can be based at least in part on a determination that a particular patient being assessed contains cancer cells having a genome containing an HRD signature. For example, a patient determined to have cancer cells whose genome contains the presence of an HRD signature can be diagnosed as having cancer cells likely to contain genetic mutations in one or more genes in the HRD pathway based on the combination of the presence of an HRD signature and a family history of cancer, or the presence of behavioral risk factors (e.g., smoking).

In some cases, a clinician or medical professional or group of clinicians or medical professionals can diagnose a patient determined to have cancer cells having an HRD signature as having cancer cells likely to respond to a particular cancer treatment regimen. Such a diagnosis can be based solely on a determination that a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature or can be based at least in part on a determination that a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having an HRD signature. For example, a patient determined to have cancer cells having an HRD signature can be diagnosed as being likely to respond to a particular cancer treatment regimen based on the combination of the presence of an HRD signature and deficient status in one or more tumor suppressor genes (e.g., BRCA1/<NUM>, RAD51), a family history of cancer, or the presence of behavioral risk factors (e.g., smoking). As described herein, a patient determined to have cancer cells having an HRD signature can be diagnosed as likely to respond to a cancer treatment regimen that includes the use of homologous recombination deficiency-targeting drugs, drugs that directly cause double strand DNA breaks, drugs that indirectly cause double strand DNA breaks; and/or drugs which have been identified in mechanistic studies to target homologous recombination deficiency, for example interstrand crosslinking agents, platinum compounds, PARP inhibitors, anthracyclines, and topoisomerase I and II inhibitors, preferably PARP inhibitors or DNA double strand break inducing agents, e.g. platinum compounds such as cisplatin, carboplatin, oxaliplatin, or picoplatin, and interstrand crosslinking agents, a combination thereof, or a combination of any of the preceding with another anti-cancer agent.

Once a clinician or medical professional or group of clinicians or medical professionals determines that a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) having a genome lacking an HRD signature, the clinician or medical professional (or group) can classify that patient as having cancer cells whose genome lacks an HRD signature. In some cases, a clinician or medical professional or group of clinicians or medical professionals can diagnose a patient determined to have cancer cells containing a genome lacking an HRD signature as having cancer cells likely to have functional HRD. In some cases, a clinician or medical professional or group of clinicians or medical professionals can diagnose a patient determined to have cancer cells containing a genome lacking an HRD signature as having cancer cells that do not likely contain genetic mutations in one or more genes in the HRD pathway. In some cases, a clinician or medical professional or group of clinicians or medical professionals can diagnose a patient determined to have cancer cells containing a genome lacking an HRD signature and that are less likely to respond to a platinum-based chemotherapy drug such as cisplatin, carboplatin, oxalaplatin, or picoplatin, an anthracycline such as epirubicin or doxorubicin, a topoisomerase I inhibitor such as campothecin, topotecan, or irinotecan, a PARP inhibitor, or radiation and/or more likely to respond to a cancer treatment regimen that includes the use of a cancer treatment agent not associated with HRD such as one or more taxane agents, growth factor or growth factor receptor inhibitors, anti-metabolite agents, etc..

The subjects/patients who are tested, diagnosed, treated, etc. are either treatment naive subjects or subjects that have already undergone treatment. For example, the subjects display primary or platinum-sensitive relapsed ovarian cancer defined as relapse after a platinum-free interval of at least <NUM> months.

The present invention also provides methods for performing a diagnostic analysis of a nucleic acid sample (e.g., a genomic nucleic acid sample or nucleic acids amplified therefrom) of a cancer patient to determine if a sample from the patient comprises cancer cells (or whether a sample comprises DNA derived from such cells) containing an HRD signature. In some cases, one or more laboratory technicians or laboratory professionals can detect the presence or absence of an HRD signature in the genome of cancer cells of the patient by (a) receiving a cancer cell sample obtained from the patient, receiving a genomic nucleic acid sample obtained from cancer cells obtained from the patient, or receiving a sample containing nucleic acids enriched and/or amplified from such a genomic nucleic acid sample obtained from cancer cells obtained from the patient and (b) performing an analysis (e.g. , microarray-based assay or a sequencing-based assay, preferably LC-WGS) using the received material to detect the presence or absence of an HRD signature as described herein. In some cases, one or more laboratory technicians or laboratory professionals can receive a sample to be analyzed (e.g., a cancer cell sample obtained from the patient, a genomic nucleic acid sample obtained from cancer cells obtained from the patient, or a sample containing nucleic acids enriched and/or amplified from such a genomic nucleic acid sample obtained from cancer cells obtained from the patient) directly or indirectly from a clinician or medical professional.

Once a laboratory technician or laboratory professional or group of laboratory technicians or laboratory professionals detects the presence of an HRD signature as described herein, the laboratory technician or laboratory professional (or group) can associate that HRD signature or the result (or results or a summary of results) of the performed diagnostic analysis with the corresponding patient's name, medical record, symbolic/numerical identifier, or a combination thereof. Such identification can be based solely on detecting the presence of an HRD signature or can be based at least in part on detecting the presence of an HRD signature. For example, a laboratory technician or laboratory professional can identify a patient having cancer cells that were detected to have an HRD signature as having cancer cells potentially deficient in HRD (or as having an increased likelihood of responding to a particular treatment as described herein) based on a combination of the presence of an HRD signature and the results of other genetic and biochemical tests performed at the testing laboratory.

The converse of the preceding is also true. Namely, once a laboratory technician or laboratory professional or group of laboratory technicians or laboratory professionals detects the absence of an HRD signature, the laboratory technician or laboratory professional (or group) can associate the absence of an HRD signature or the result (or results or a summary of results) of the performed diagnostic analysis with the corresponding patient's name, medical record, symbolic/numerical identifier, or a combination thereof. In some cases, a laboratory technician or laboratory professional or group of laboratory technicians or laboratory professionals can identify a patient having cancer cells that were detected to lack an HRD signature as having cancer cells with potentially intact HRD (or having a decreased likelihood of responding to a particular treatment as described herein) either based solely on the absence of an HRD signature or based on a combination of the presence of an HRD signature and the results of other genetic and biochemical tests performed at the testing laboratory.

The results of any analyses according to the invention will often be communicated to physicians, genetic counselors, or a clinic and/or patients (or other interested parties such as researchers) in a transmittable form that can be communicated or transmitted to any of the above parties. Such a form can vary and can be tangible or intangible. The results can be embodied in descriptive statements, diagrams, photographs, charts, images or any other visual forms. For example, graphs or diagrams showing genotype or HRD status information can be used in explaining the results. The statements and visual forms can be recorded on a tangible medium such as papers, computer readable media such as floppy disks, compact disks, flash memory, etc., or in an intangible medium, e.g., an electronic medium in the form of email or website on internet or intranet. In addition, results can also be recorded in a sound form and transmitted through any suitable medium, e.g., analog or digital cable lines, fiber optic cables, etc., via telephone, facsimile, wireless mobile phone, internet phone and the like.

Thus, the information and data on a test result can be produced anywhere in the world and transmitted to a different location. As an illustrative example, when an assay is conducted outside Germany, the Netherlands, or the EU, the information and data on a test result may be generated, cast in a transmittable form as described above, and then imported into Germany, the Netherlands or the EU. Accordingly, the present invention also encompasses a method for producing a transmittable form of information on an HRD signature for at least one patient sample. The method comprises the steps of (<NUM>) determining an HRD signature according to methods of the present invention; and (<NUM>) embodying the result of the determining step in a transmittable form. The transmittable form is a product of such a method.

Several embodiments of the invention described herein involve a step of correlating the presence of an HRD signature according to the present invention (e.g., the CNVs in comparison to reference and control samples) to a particular clinical feature (e.g., an increased likelihood of HRD deficiency; an increased likelihood of response to a treatment regimen comprising a DNA damaging agent, an anthracycline, a topoisomerase I inhibitor, radiation, and/or a PARP inhibitor; etc.) and optionally correlating the absence of a HRD signature to one or more other clinical features. Throughout this document, wherever such an embodiment is described, another embodiment of the invention may involve, in addition to or instead of a correlating step, one or both of the following steps: (a) concluding that the patient has the clinical feature based at least in part on the presence or absence of the HRD signature; or (b) communicating that the patient has the clinical feature based at least in part on the presence or absence of the HRD signature.

As used herein, "communicating" a particular piece of information means to make such information known to another person or transfer such information to a thing (e.g., a computer). In some methods of the invention, a patient's prognosis or likelihood of response to a particular treatment is communicated. In some embodiments, the information used to arrive at such a prognosis or response prediction (e.g., HRD signature according to the present invention, etc.) is communicated. This communication may be auditory (e.g., verbal), visual (e.g., written), electronic (e.g., data transferred from one computer system to another), etc. In some embodiments, communicating a cancer classification (e.g., prognosis, likelihood of response, appropriate treatment, etc.) comprises generating a report that communicates the cancer classification. In some embodiments the report is a paper report, an auditory report, or an electronic record. In some embodiments the report is displayed and/or stored on a computing device (e.g., handheld device, desktop computer, smart device, website, etc.). In some embodiments the cancer classification is communicated to a physician (e.g., a report communicating the classification is provided to the physician). In some embodiments the cancer classification is communicated to a patient (e.g., a report communicating the classification is provided to the patient). Communicating a cancer classification can also be accomplished by transferring information (e.g., data) embodying the classification to a server computer and allowing an intermediary or end-user to access such information (e.g., by viewing the information as displayed from the server, by downloading the information in the form of one or more files transferred from the server to the intermediary or end-user's device, etc.).

A system for detecting HRD in a sample comprises, or consists essentially of, (a) a sample analyzer configured to produce a plurality of signals about genomic DNA in the test sample, and (b) a computer sub-system programmed to calculate, based on the plurality of signals, the CNVs in the pool of genomic loci. The computer sub-system can be programmed to compare the CNVs of the test sample with the CNVs in a corresponding pool of genomic loci in the DNA of a reference sample of BRCA-like mutated cancer cells and a control sample of BRCA non-mutated cancer cells, respectively, and/or to provide a shrunken centroid value which is derived from the CNVs of the genomic locus, by comparison of the shrunken centroid values derived from the CNVs of the pool of genomic loci in the DNA of the cancer cells in the test sample from the subject with the shrunken centroid values derived from the CNVs of a reference set of samples of BRCA-like mutated cancer cells and of control samples of BRCA non-mutated cancer cells, to detect (a) HRD or likelihood of HRD (e.g. , an HRD signature) in the sample, or (b) an increased likelihood that the cancer patient will respond to a cancer treatment regimen as defined above. The system can comprise an output module configured to display (a) or (b). The system can comprise an output module configured to display a recommendation for the use of the cancer treatment regimen.

A corresponding computer sub-system, i.e. computer program product embodied in a computer readable medium that, when executing on a computer, can be used to perform the calculations as described above.

Several documents are cited throughout the text of this specification.

A more complete understanding can be obtained by reference to the following specific Examples.

Fifty confirmed gBRCA1-mutated (m), <NUM> confirmed gBRCA2m cases and <NUM> patients without a family history of ovarian or breast cancer (controls) were identified through the Netherlands Cancer Institute (NKI) tumor registration database and the Erasmus Medical Center (see Table <NUM>). This study was conducted in concordance with Dutch law and national guidelines that allow for the analysis of residual tissue specimens obtained for diagnostic purposes and anonymized publication of the results (<NUM>).

Histological classification and grading, according to the <NUM>-tiered system, were performed by two expert gynaecopathologists. We isolated DNA from formalin-fixed paraffin-embedded (FFPE) tumor slides, using (micro)-dissection to obtain <NUM>% tumor percentage with the Qiagen DNA mini kit (Qiagen, cat nr. <NUM>) and for BRCA1 methylation, using an MLPA kit (MRC-Holland) as described before (<NUM>). In particular, hypermethylation of the BRCA1 promoter was assessed using a custom methylation specific MLPA set according to the manufacturer's protocol (ME005-custom; MRC-Holland, Amsterdam, the Netherlands). Probe sequences of the MLPA set are available on request (info@mlpa. DNA fragments were analyzed on a <NUM> DNA Analyzer (Applied Biosystems, Foster City, CA, USA). For normalization and analysis, the Coffalyzer program was used (MRC-Holland, Amsterdam, The Netherlands); peak heights below <NUM> were excluded from further analyses. When the BRCA1 probes showed methylation (threshold of <NUM>; MRC-Holland), we classified the result as BRCA1 promoter methylation.

A PCR-based methylation assay was performed centrally (Institute of Pathology, University Hospital Bonn, Germany), as previously described (<NUM>). Quantitative methylation-specific PCR (qMSP) assays were designed to allow specific amplification of the bisulfite-converted (convertion using the innuCONVERT Bisulfite Basic Kit; Analytik Jena, Jena, Germany) methylated gene promoter sequences of BRCA1, PALB2 and RAD51C. Triplicate measurements were carried out for each sample, and median methylation levels were computed with values ≥<NUM>% considered positive.

We obtained NimbleGen <NUM> aCGH profiles from the training set samples. The <NUM> array data were mapped to the BAC aCGH platform for dimension and noise reduction by averaging probes covered by the BAC clone, as described before (<NUM>,<NUM>). The data discussed here have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE111688 (https://www. gov/geo/query/acc. cgi?acc=GSE111688).

We carried out nested double loop <NUM>-fold cross validation of shrunken centroid classifiers to classify OC as being similar to gBRCA1m (BRCA1-like) or gBRCA2m (BRCA2-like) or controls (C) (<NUM>). We obtained the delta threshold by optimizing the classification error and selecting the sparsest model within one standard error of the optimal solution (<NUM>). Subsequently, we used the model at the selected threshold to predict the samples in the outer loop. The area under the curve (AUC) of the receiver/operator curve (ROC) of our predictions of the class labels is computed using samples that are left out of the training procedure. After estimating this unbiased performance, we trained the full dataset's final model using the inner loop. See Supplementary Methods for pseudocode.

We downloaded the Infinium HumanMethylation27 BeadChip methylation, segmented genome-wide human SNP6. <NUM> copy number, and gene expression data from the firebrowse. org archives of the ovarian cancer TCGA data (version <NUM>. BRCA1 and BRCA2 mutation status were obtained from cBioPortal, which stores the somatic and germline mutation status used in the original TCGA manuscripts (<NUM>). The BRCA1 promoter methylation status was obtained by correlating the methylation and gene expression data. We used methylation probe cg10893007 because of the strongest Pearson correlation with log BRCA1 gene expression ratio among the BRCA1 promoter probes and close location to the MLPA probe used in the previous study (<NUM>) among those covering the BRCA1 promoter in the TCGA dataset.

TCGA data were mapped to the NKI BAC array CGH positions, as described before (<NUM>). In short, SNP6. <NUM> probes within the start and end positions of the BAC clone were averaged (mapped TCGA). We adjusted for differences in scaling and centering by using a method similar to quantile normalization. Briefly, we performed linear regression by fitting a generalized linear Gaussian model with identity link function to the sorted location-wise average DNA copy number values of the NimbleGen data and the sorted location-wise DNA copy number averages of the mapped TGCA dataset. Subsequently, we used the obtained alpha coefficient to correct the centering and the obtained beta coefficient to correct the scaling of the mapped TCGA data, followed by classification. We validated this method on samples that had been analyzed before (<NUM>) both on NimbleGen <NUM> aCGH and SNP6. Subsequently, samples were classified as BRCA-like if the predicted probability was > <NUM> and non-BRCA-like if the predicted probability was <= <NUM>, as was pre-defined in the training set.

The sensitivity and specificity of detecting the class labels gBRCA1/BRCA2 mutation, sBRCA1/BRCA2 mutation, and BRCA1 promoter hypermethylation were calculated. The Youden index, (sensitivity+specificity)-<NUM>, was used as a balanced measure to assess performance. This readout resembles the equal weighting of sensitivity and specificity in the training process.

Within the consecutive cohort study AGO-TR1 (NCT02222883) <NUM> OC patients were counseled and enrolled in <NUM> AGO study group centers in Germany. The ethical committee approved the study protocol of the Landesaerztekammer Nordrhein (Nr. Written informed consent was obtained before any study-related procedure. All individuals were <NUM> years or older and displayed a primary (PR: n=<NUM>) or platinum-sensitive relapsed (RE: n=<NUM>) OC, defined as relapse after a platinum-free interval of at least <NUM> months. The AGO study group documented clinical data including demographics, medical and family history as well as disease characteristics.

The Center of Hereditary Breast and Ovarian Cancer in Cologne performed the genetic analyses on blood samples of all participants and FFPE-tumor samples of <NUM> patients as previously described (<NUM>,<NUM>). In short, a paired multi-gene panel sequencing of germline and tumor samples analyzing <NUM> OC related and DNA repair genes was performed with a complementary use of CNV-analysis for the detection of large genomic rearrangements in germline samples. In particular, paired mutation sequencing of germline and tumor samples was performed at the Center of Hereditary Breast and Ovarian Cancer, Cologne, using a customer-tailored SureSelect gene panel (TruRisk® Panel, Agilent, Santa Clara, U. ) covering the entire coding and exonflanking regions (+/- 15nt) of the below specified <NUM> genes. A hybridization capture-based NGS protocol, suitable for DNA derived from blood as well as FFPE-tissue, was applied (Agilent SureSelect XT; optimized for 200ng genomic DNA input). Sequencing was carried out on Mi-Seq and Hi-Seq <NUM> devices (Illumina, San Diego, U. For bioinformatical analyses we applied the VARBANK version <NUM> pipeline of the Cologne Center for Genomics and the SOPHiA DDM® platform (Sophia Genetics, Saint-Sulpice, Switzerland). For the germline samples CNV-detection via MLPA (multiple Ligation-dependent Probe Amplification, MRC Holland, Amsterdam, The Netherlands), CNV-tool of the SOPHIA DDM® platform, and arrayCGH (array comparative genomic hybridization) were applied, if appropriate. The analyses of all germline and <NUM> of <NUM> FFPE samples was successfully performed.

HR genes: ATM, NM_000051. <NUM>; BARD1 NM_000465. <NUM>; BRCA1 NM_007294. <NUM>; BRCA2, NM_000059. <NUM>; BRIP1 NM_032043. <NUM>; CHEK1, NM_001330427. <NUM>; CHEK2, NM_007194. <NUM>; FAM175A, NM_139076. <NUM>; FANCM, NM_020937. <NUM>; MRE11A, NM_005591. <NUM>; NBN, NM_002485. <NUM>; PALB2, NM_024675. <NUM>; RAD50, NM_005732. <NUM>; RAD51C, NM_058216. <NUM>; RAD51D, NM_002878. <NUM>; XRCC2, NM_005431.

All variants were classified using a five-tier variant classification system as proposed by the International Agency for Research on Cancer Unclassified Genetic Variants Working Group, namely, deleterious=class <NUM>, likely deleterious=class <NUM>, variant of uncertain significance=class <NUM>,likely benign=class <NUM> and benign=class <NUM>. For somatic variants, the My Cancer Genome database (http://www. mycancergenome. org), the IARC TP53 database (https://p53. fr) and the ClinVar database (https://www. gov/clinvar/) were also considered for variant classification. Variants reported to occur in large outbred control reference groups at an allele frequency of ><NUM>% were generally considered benign. Class <NUM>/<NUM> variants were subsequently defined as 'deleterious variants'. Variants were considered somatic if they were not identified in a paired germline analysis of the corresponding blood sample. Also, quantitative methylation assays analyzing BRCA1-, PALB2-, and RAD51C- promoter regions were carried out as described before (<NUM>). In total, the complete data of <NUM> individuals were successfully generated (<NUM>) (see CONSORT-like flow diagram, <FIG>).

The obtained copy number profiles were visually assessed supported by signal to noise ratios and noise variance as quality measures. The unsegmented <NUM>log ratios were assumed to represent the noise, whereas the segmented profiles were assumed to represent the signal. Both values were calculated per profile in R using the base function var as follows in pseudocode: <MAT> <MAT>.

As observed earlier (<NUM>), cutoffs are experiment dependent, therefore these measures formed support for the visual assessment. In general, samples with a signal to noise ratio lower than <NUM> are suspicious for lower quality and with higher level of noise variance. However, determination of too low quality was done visually by identifying those samples for which visually present large aberrations were not called by the segmentation algorithm and thus resulted in an incorrect 'flat' copy number profile. Flat copy number profiles in general were not excluded as they might contain biology if not based on high noise variance. In a post-hoc analysis, flat profiles associated with non-HGS histology and non-HR-associated mutations. Samples with higher noise but also visually correctly segmented profiles were retained.

We selected <NUM> samples with matching germline and somatic mutation status and successful performed methylation analyses for BRCA-like classification (see Table <NUM>), including all available samples with deleterious germline and somatic variants (IARC class <NUM>/<NUM>) in BRCA1/<NUM> (n=<NUM>), other HR-related and hereditary non-polyposis colorectal cancer (HNPCC) genes (n=<NUM>), and somatic (s) BRCA1/<NUM>-VUS (variants of unknown significance) (n=<NUM>). As controls, we randomly selected a similarly sized group (n=<NUM>) from the rest of the main cohort taking PR-/RE-status, age at diagnosis, and sTP53 mutations into account. Samples with only gBRCA1/<NUM> VUS (n=<NUM>) were excluded.

Hematoxylin and eosin-stained <NUM> tissue sections were centrally investigated (Institute of Pathology, University Hospital Bonn, Germany). Tumor areas containing ><NUM>% tumor nuclei were chosen and dissected for DNA isolation. DNA isolation from FFPE tumor samples was conducted using standard procedures, as previously described (<NUM>).

LcWGS was centrally performed (NKI), as described earlier (<NUM>). Library preparation was performed with an input of 200ng double-stranded DNA derived from FFPE-tumor samples using the TruSeq®DNA LT Sample Preparation kit (Illumina, San Diego, U. Ten explicit indexed samples were equimolarly pooled and sequenced in one lane on an Illumina HiSeq <NUM> device (Illumina, San Diego, U. Single-read sequencing (read-length 65bp) was performed with an aimed coverage of <NUM>. 5X and sequences were aligned to reference genome GRCh38. Reads were counted in 20kb non-overlapping bins, corrected for CG bias and corrected for local alignment-bases estimated mappability, resulting in <NUM>log count ratios. The 20kb resolution <NUM>log ratios were mapped to the <NUM> MB resolution input for the classifier (mapped AGO-TR1). This was done by averaging the <NUM>log count ratios within the <NUM> MB bins (surrounding the BAC clone locations of the BAC platform).

Since the classifier's training was performed on oligonucleotide array CGH data, we performed a correction of centering and scaling of the data with the next-generation sequencing (NGS) platform in this study. This correction is akin to quantile normalization and was performed by fitting a linear regression model with Gaussian distribution and identity link function using the R glm function to the sorted location-wise average of the training set and the mapped AGOTR1 dataset. Subsequently, we used the obtained alpha coefficient to correct the centering and the obtained beta coefficient to correct the scaling of the new data. We validated this method on samples that had been analyzed both on NimbleGen <NUM> array CGH and NGS in the cross-platform robustness dataset we previously described (<NUM>). Subsequently, samples were classified as BRCA-like if the predicted probability was > <NUM> and non-BRCA-like if the predicted probability was <= <NUM> as was pre-defined in the training set. The cross validation algorithm, pseudo code for platform correction, analysis of misclassified samples and error curves and the validation of platform correction is described in the following:.

In the cross validation loops a sample of the data is used to obtain the unbiased performance measures. The final classifier, which uses all the data rather than only a sample, was trained on all the data using the inner loop described above.

We used the cross-platform comparison dataset described before (<NUM>). In this dataset we analyzed the same sample with multiple techniques. Since we are interested in the direct comparison between NGS (NGS) and NimbleGen <NUM> array CGH (NG135), we intersected the dataset for overlap between these samples. For <NUM> samples we obtained a copy number profile with NGS and NG135. Without applying the platform correction we obtained an accuracy of <NUM> (<NUM>% CI: <NUM>-<NUM>) and a kappa value of <NUM> (<NUM>% CI: <NUM>-<NUM>) for the BRCA1-like classifier and an accuracy of <NUM> (<NUM>% CI: <NUM>-<NUM>) and a kappa of <NUM> (<NUM>% CI: <NUM>-<NUM>) for the BRCA2-like classifier. With applying the platform correction, we obtained an accuracy of NGS <NUM> (<NUM>% CI: <NUM>-<NUM>) and a kappa of <NUM> (<NUM>% CI: <NUM>-<NUM>) for the BRCA1-like classifier and an accuracy of <NUM> (<NUM>% CI: <NUM>-<NUM>) and a kappa of <NUM> (<NUM>% CI <NUM> -<NUM>) for the BRCA2-like classifier. For SNP6 we retained an accuracy of <NUM> (<NUM>% CI <NUM>-<NUM>) for BRCA1-like classification and <NUM> (<NUM>% CI <NUM>-<NUM>) for BRCA2-like classification.

Fisher's exact- and χ<NUM>-square-test were applied, where appropriate, to calculate the level of significance. All tests were two-sided, and a p-value <<NUM> after correction for multiple testing using the Benjamini-Hochberg approach was considered significant.

We generated copy number profiles of <NUM> control, <NUM>BRCA1m and <NUM>BRCA2m OC, and show the pathological characteristics of these tumors in Table <NUM>. <FIG> presents the average profiles of gBRCA1m and gBRCA2m versus control ovarian cancers.

First, we classified the ovarian cancer copy number profiles with the breast cancer BRCA1-like and BRCA2-like classifiers (<NUM>,<NUM>) and obtained an AUC of <NUM> (<NUM>% CI: <NUM>-<NUM>) for gBRCA1m and an AUC of <NUM> (<NUM>%CI: <NUM>-<NUM>) for gBRCA2m OC in the training set. Given this low performance, we trained shrunken centroid classifiers on ovarian cancer data to investigate whether the performance could be improved. These shrunken centroids select genomic regions and weights that are discriminative for the BRCA-like and non-BRCA-like class. We used ten-fold cross-validation and trained on the class labels gBRCA1m vs. control and gBRCA2m vs. control. We observed a cross-validated AUC of <NUM> (<NUM>-<NUM>) and <NUM> (<NUM>-<NUM>) respectively for BRCA1-like and BRCA2-like classification (Table <NUM>). Since the NimbleGen aCGH platform is not available anymore, we validated cross-platform compatibility of our classifier using the same methods and dataset as previously described (<NUM>), see the Methods section above.

We used the TCGA ovarian cancer data as an external validation set for our newly trained ovarian cancer classifiers. We classified <NUM> of <NUM> samples as having a BRCA1-like profile. Within these <NUM>, <NUM>/<NUM>BRCA1, <NUM>/<NUM> somatic BRCA1-mutated tumors, and <NUM>/<NUM> BRCA1-methylated tumors were classified as BRCA1-like, resulting in an overall sensitivity of <NUM>% (<NUM>% CI: <NUM>-<NUM>). Specificity, however, is lower, at <NUM>% (<NUM>% CI: <NUM>-<NUM>). <NUM> of <NUM> samples were assigned to be BRCA2-like. Within these <NUM>, <NUM>/<NUM>BRCA2m and <NUM>/<NUM> sBRCA2m samples were classified as BRCA2-like, resulting in a sensitivity of <NUM>% (<NUM>% CI: <NUM>-<NUM>) and a specificity of <NUM>% (<NUM>% CI: <NUM>-<NUM>).

We further analyzed both the germline/somatic mutation status of BRCA1/<NUM> and BRCA1 promoter hypermethylation in the AGO TR1 study. In addition, we aimed to characterize those patients called BRCA-like without a mutation in BRCA1 or BRCA2.

In total, <NUM> samples with complete genetic and epigenetic information available, were analyzed. <NUM> germline and <NUM> somatic deleterious variants in BRCA1/<NUM> and other HR genes were present in <NUM> OC samples (see Table <NUM>). The majority displayed a BRCA1 mutation (g: n=<NUM>, s: n=<NUM>), a BRCA2 mutation (g: n=<NUM>, s: n=<NUM>) or a RAD51C mutation (g: n=<NUM>). One of these samples presented a germline double mutation of gBRCA1 and -<NUM> and was analyzed with the gBRCA1m group; one somatic BRCA2 mutation coincided with a gBRCA2 mutation and was assigned to the gBRCA2 group. Eight other samples with a BRCA mutation (g: n=<NUM>, s: n=<NUM>) displayed a class <NUM>/<NUM> in another HR gene as well and were analyzed with the respective BRCA-mutated group.

Most OCs presented with high-grade serous histology (<NUM>%), and a tumor TP53 mutation was found in <NUM>%. Promoter hypermethylation was detected in <NUM>% of samples for BRCA1 (n=<NUM>) and in <NUM>% of samples (n=<NUM>) for RAD51C. The mean age at diagnosis was <NUM> years, and <NUM> primary OC and <NUM> platinum-sensitive recurrent cases were included. <NUM> tumors showed a BRCA1-like or BRCA2-like classification (<NUM>%). <NUM> OC thereof were BRCA1-like, <NUM> BRCA2-like, and <NUM> were both BRCA1- and BRCA2-like.

As there is no established gold standard to define BRCAness in general, we first focused on the detection-rate of tumors derived from deleterious germline mutations in BRCA1/<NUM> (see Table <NUM>, <FIG>). <NUM> of <NUM> BRCA1-mutated cases had a BRCA1-like (detection rate of <NUM>%), whereas <NUM> of <NUM> samples with a BRCA2 class4/<NUM> variant in germline were BRCA2-like (detection rate of <NUM>%). All remaining BRCA2-associated cases were identified by combining both classifiers, i.e. being BRCA1- or BRCA2-like.

Regarding deleterious somatic variants, <NUM> of <NUM> (<NUM>%) samples with a BRCA1 mutation were identified by the BRCA1-like classifier. As the other two tumors displayed a BRCA2-like phenotype, both classifiers' application detected <NUM>% of the mutated cases. The BRCA2-like classifier confirmed <NUM> of <NUM> sBRCA2m cases. The BRCA1-like classifier found no additional sample. Of the <NUM> examined samples with aBRCA1 promoter hypermethylation, <NUM> displayed a BRCA1-like profile (<NUM>%), one was classified as non-BRCA-like.

Alterations in further HR genes (ATM, BARD1, BRIP1, CHEK1, CHEK2, FAM175A, FANCM, MRE11A, NBN, PALB2, RAD50, RAD51C, RAD51D, XRCC2) varied in being classified as BRCA-like (see Table <NUM>). Within the study cohort, <NUM> tumors without BRCA mutation or BRCA1 methylation displayed a genetic alteration in another OC-risk- or HR-gene (except sTP53 mutations), of which <NUM> were classified as BRCA-like (<NUM>%; only BRCA1-like n=<NUM>, only BRCA2-like n=<NUM>, BRCA1- and BRCA2-like n=<NUM>). A distinct statistical association was observed for the largest subgroup, the RAD51C-associated tumors. All germline mutationcarriers developed a BRCA-like OC (only BRCA1-like n=<NUM>, BRCA1- and BRCA2-like n=<NUM>). No clear statistical association could be observed for most other HR gene mutated samples and samples with RAD51C methylation (see Table <NUM>, <FIG>) possibly due to small numbers and coinciding aberrations.

Samples with a class <NUM>/<NUM> variant in RAD51D (g: n=<NUM>, s: n=<NUM>) presented a BRCA1- and <NUM>-like profile in <NUM> of <NUM> cases and FANCM-associated OC (g: n=<NUM>, s: n=<NUM>) in <NUM> of <NUM> samples, one sample further presented with only a BRCA<NUM>-like phenotype. All four AIM-associated samples showed a BRCA-like profile (only BRCA1-like: n=<NUM>, only BRCA2-like: n=<NUM>, BRCA1- and <NUM>-like: n=<NUM>). Three of <NUM> PALB2-associated cancers presented a BRCA-like profile, including two samples with each an ATM mutation (see above) and a BRCA1 methylation. Therefore, the underlying mechanism leading to a BRCA-like phenotype was unclear. Samples with each a mutation in CHEK1 (g), FAM175A (g), and BARD1 (s) as well as three tumors with a germline mutation in HNPCC genes (MSH2 n=<NUM>, MSH6 with additional somatic MSH6- and BRIP1 mutation, n=<NUM>) were examined and showed no BRCA-like profile. Epigenetic alterations in RAD51C were rare. Four samples with a RAD51C promoter hypermethylation were analyzed, of which one was derived from a germline BRCA1 mutation carrier, presenting a BRCA1- and BRCA2-like profile, assumingly due to the known mutation. Of the three remaining samples, one was classified as BRCA1- and BRCA2-like; the other two (one of them from a gMRE11A mutation-carrier) displayed a non-BRCA-like phenotype.

Within the cohort, <NUM> samples displayed a BRCA1/<NUM>-like phenotype. These profiles were associated with a germline mutation in BRCA1/<NUM> in <NUM> and a somatic mutation in BRCA1/<NUM> respectively a BRCA1 promoter hypermethylation in <NUM> of these samples. In another <NUM> different samples, a somatic or germline class <NUM>/<NUM> variant in another HR gene (esp. RAD51C) or a RAD51C promoter hypermethylation can explain the presence of a BRCA-like phenotype. For the remaining <NUM> BRCA1/<NUM>-like samples (<NUM>%) no aberration affecting the HR could be detected by gene panel or methylation analyses (see <FIG>).

Regarding all OC without a genetic or epigenetic alteration in BRCA1/<NUM> (n=<NUM>), <NUM>% (n=<NUM>) display a BRCA1/<NUM>-like phenotype. Excluding all HR gene mutated cases (n=<NUM>), as the study cohort was enriched for those, the BRCA1/<NUM>-like rate was <NUM>% (n=<NUM>). Furthermore, a BRCA-like phenotype was correlated with high-grade serous histology, sTP53 mutations and could be observed more often, when the patient was included with platinum-sensitive recurring disease than at primary diagnosis. A non-BRCA-like profile was seen in association with low-grade serous histology, in combination with PTEN- or PIK3CA mutations and with gHNPCC mutations (see Table <NUM>).

In summary, BRCA1-like and BRCA2-like copy number classifiers have been identified and these signatures identify patients with germline and somatic mutations and promoter hypermethylation in BRCA1/<NUM>. In addition, we were able to investigate underlying molecular mechanisms in BRCA-like cases without a deleterious variant in BRCA1 or BRCA2.

Another advantage of this study is that after initial promising results within cross-validation of the training set and in the TCGA test dataset were obtained, the AGO-TR1 study provided a large and well characterized cohort in which we could validate the prediction of mutation status. Overall, the BRCA1-like classifier showed a convincing performance with detecting BRCA1-mutated and -methylated cancers in more than <NUM>% of the cases. When applying both classifiers in the AGO-TR1 trial, more than <NUM>% of all BRCA1/<NUM>-associated cancers in the cohort were detected. Extensive germline and tumor genetic analysis provided additional support for the BRCA-like class showing alterations in BRCA1/<NUM> or other HR genes in <NUM>% of cases. Thus, a convincing performance of the BRCA1-like classifier and the combination of both classifiers in detecting BRCA mutations/methylation has been shown. Additionally, around half of the non-BRCA-associated OC cases displayed a BRCA-like phenotype as well.

Claim 1:
An in vitro method of determining homologous recombination (HR) deficiency status in a subject having ovarian cancer, wherein the method comprises
(a) determining copy number variation (CNV) of a genomic locus in a DNA test sample of cancer cells from the subject, wherein the genomic locus is selected from a pool of genomic loci comprising or consisting of the loci set forth in Table <NUM> and/or Table <NUM>;
wherein similarity between the CNVs of the pool of genomic loci in the DNA test sample and the CNVs of the pool of genomic loci of a reference sample of BRCA-like mutated cancer cells identifies the subject as HR deficient; and/or similarity between the CNVs in the pool of genomic loci in the DNA of the test sample and the CNVs in the pool of genomic loci of the control sample of BRCA non-mutated cancer cells identifies the subject as not being HR deficient, wherein similarity is the distance measure between centroids of the two classes, i.e., the control and the reference sample, and the centroid of the test sample, wherein the centroid of a class is constructed by shrinking the class centroid by an optimization routine towards the overall centroid of both classes after standardizing by the within-class standard deviation for each CNV; and optionally
(b) transmitting the result to the subject or a third party, preferably a physician or genetic counselor.