METHODS AND SYSTEMS FOR PREDICTING CANCER HOMOLOGOUS RECOMBINATION PATHWAY DEFICIENCY, AND DETERMINING TREATMENT RESPONSE

1 . A method ( 100 ) for providing a homologous recombination DNA repair deficiency (HRD) score for a cancer patient, comprising:

receiving ( 120 ) information about the cancer patient, the information comprising at least mRNA expression data obtained from a tumor of the cancer patient;

analyzing ( 130 ), using a trained HRD score model, the received information about the cancer patient to generate an HRD score for the cancer patient; and

providing ( 140 ), via a user interface, the generated HRD score for the cancer patient;

wherein the HRD score model is trained by:

(i) Identifying ( 310 ) a plurality of HR pathway genes;

(ii) generating ( 320 ) a plurality of candidate HR deficiency (HRD) features using (i) DNA mutation data; (ii) DNA copy number variation (CNV) data; (iii) DNA methylation data; and (iv) mRNA expression data to define an activity of each of the plurality of HR pathway genes;

(iii) receiving ( 330 ) a training dataset comprising records for a plurality of historical cancer patients, at least some of whom were HR deficient;

(iv) determining ( 340 ), using the training dataset, a subset of candidate HRD features based on an association between each of the plurality of candidate HRD features and historical cancer patient survival;

(v) identifying ( 350 ) HRD expression signatures (HRDES) for a plurality of genes for each of a plurality of the historical cancer patients in the training dataset, wherein identifying comprises: (a) classifying, based on the subset of candidate HRD features, the historical cancer patients into either a HRD low group or an HRD high group; and (b) comparing mRNA expression data from the HRD low group to mRNA expression data from the HRD high group;

(vi) calculating ( 360 ), for each of the plurality of genes for which a HRDES was identified, a distance between the gene and a plurality of HR pathway genes within a constructed molecular causal network for the cancer type;

(vii) weighting ( 370 ), based on the calculated distance, one or more of the plurality of genes in the HRDES; and

(viii) training ( 380 ), using training dataset, the HRD score model to identify a set of final HRD features and their associated weights.

2 . The method of claim 1 , wherein the generated HRD score for the cancer patient indicates that the tumor is HR deficient.

3 . The method of claim 2 , further comprising the step of implementing ( 150 ), when the generated HRD score for the cancer patient indicates that the tumor is HR deficient, a treatment to target the HR deficiency.

4 . The method of claim 3 , wherein the treatment to target the HR deficiency is chemotherapy, and/or a poly ADP ribose polymerase (PARP) inhibitor.

5 . The method of claim 1 , wherein the set of final HRD features comprises one or more of the genes in TABLE 1.

6 . A method ( 100 ) for treating a cancer patient, comprising:

receiving ( 140 ) a generated HRD score for the cancer patient indicating that the tumor is HR deficient; and

administering ( 150 ) a treatment to the cancer patient;

wherein the HRD score is generated by:

receiving ( 120 ) information about the cancer patient, the information comprising at least mRNA expression data obtained from a tumor of the cancer patient;

analyzing ( 130 ), using a trained HRD score model, the received information about the cancer patient to generate an HRD score for the cancer patient;

wherein the HRD score model is trained by:

(i) identifying ( 310 ) a plurality of HR pathway genes;

(ii) generating ( 320 ) a plurality of candidate HR deficiency (HRD) features using (i) DNA mutation data; (ii) DNA copy number variation (CNV) data; (iii) DNA methylation data; and (iv) mRNA expression data to define an activity of each of the plurality of HR pathway genes;

(iii) receiving ( 330 ) a training dataset comprising records for a plurality of historical cancer patients, at least some of whom were HR deficient;

(iv) determining ( 340 ), using the training dataset, a subset of candidate HRD features based on an association between each of the plurality of candidate HRD features and historical cancer patient survival;

(v) identifying ( 350 ) HDR expression signatures (HRDES) for a plurality of genes for each of a plurality of the historical cancer patients in the training dataset, wherein identifying comprises: (a) classifying, based on the subset of candidate HRD features, the historical cancer patients into either a HRD low group or an HRD high group; and (b) comparing mRNA expression data from the HRD low group to mRNA expression data from the HRD high group;

(vi) calculating ( 360 ), for each of the plurality of genes for which a HRDES was identified, a distance between the gene and a plurality of HR pathway genes within a constructed molecular causal network for the cancer type;

(vii) weighting ( 370 ), based on the calculated distance, one or more of the plurality of genes in the HRDES is utilized to generate an HR score;

(viii) training ( 380 ), using training dataset the HR score model to identify a set of final HRD features and their associated weights.

7 . The method of claim 7 , wherein the treatment is chemotherapy, and/or a poly ADP ribose polymerase (PARP) inhibitor.

8 . The method of claim 7 , wherein the set of final HRD features comprises one or more of the genes in TABLE 1.

9 . The method of claim 1 , wherein the subject has been diagnosed with cancer, is at risk of having cancer, or is suspected of having cancer.

10 . The method claim 1 , wherein the cancer is selected from the group consisting of triple negative breast cancer, human epidermal growth factor receptor 2-negative breast cancer, estrogen receptor-dependent breast cancer, ovarian cancer, prostate cancer, lung cancer, colorectal cancer, and/or other solid cancer, leukemia, lymphoma and/or other blood cell cancer, and any combination thereof.

11 . A system ( 200 ) configured to provide a homologous recombination DNA repair deficiency (HRD) score for a cancer patient, comprising:

information about the cancer patient, the information comprising at least mRNA expression data obtained from a tumor of the breast cancer patient;

a trained HRD score model ( 262 );

a processor ( 220 ) configured to analyze, using the trained HRD score model, the received information about the cancer patient to generate an HRD score for the cancer patient; and

a user interface ( 240 ) configured to provide the generated HRD score for the cancer patient;

wherein the HRD score model is trained by:

(i) identifying a plurality of HR pathway genes;

(ii) generating a plurality of candidate HR deficiency (HRD) features using (i) DNA mutation data; (ii) DNA copy number variation (CNV) data; (iii) DNA methylation data; and (iv) mRNA expression data to define an activity of each of the plurality of HR pathway genes;

(iii) receiving a training dataset comprising records for a plurality of historical cancer patients, at least some of whom were HR deficient;

(iv) determining, using the training dataset, a subset of candidate HRD features based on an association between each of the plurality of candidate HRD features and historical cancer patient survival;

(v) identifying HDR expression signatures (HRDES) for a plurality of genes for each of a plurality of the historical cancer patients in the training dataset, wherein identifying comprises: (a) classifying, based on the subset of candidate HRD features, the historical cancer patients into either a HRD low group or an HRD high group; and (b) comparing mRNA expression data from the HRD low group to mRNA expression data from the HRD high group;

(vi) calculating, for each of the plurality of genes for which a HRDES was identified, a distance between the gene and a plurality of HR pathway genes within a constructed molecular causal network for the cancer type;

(vii) weighting, based on the calculated distance, one or more of the plurality of genes in the HRDES is utilized to generate an HR score; and

(viii) training, using training dataset the HR score model to identify a set of final HRD features and their associated weights.

12 . The system of claim 11 , wherein the generated HR score for the cancer patient indicates that the tumor is HRD deficient.

13 . The system of claim 12 , wherein the system is further configured to recommend, when the generated HRD score for the cancer patient indicates that the tumor is HR deficient, a treatment to target the HR deficiency.

14 . The system of claim 13 , wherein the treatment to target the HR deficiency is chemotherapy, and/or a poly ADP ribose polymerase (PARP) inhibitor.

15 . The system of claim 11 , wherein the set of final HRD features comprises one or more of the genes in TABLE 1.