A novel prediction model of poly ADP ribose polymerase (PARP) inhibitor sensitivity using genomic analyses

Lead Investigator: Hyung Seok Park, Yonsei University Health System
Title of Proposal Research: A novel prediction model of poly ADP ribose polymerase (PARP) inhibitor sensitivity using genomic analyses
Vivli Data Request: 8769
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

Tumor suppressor genes, which play a crucial role in regulating cell growth and division, prevent the development and progression of tumors by inhibiting abnormal cell growth, promoting cell repair, or inducing cell death. A subset of these tumor suppressor genes is involved in repairing broken DNA and maintaining control over cell growth. When mutations occur in these genes or are inherited from parents, it increases the lifetime risks of breast, ovarian, pancreatic, and prostate cancers.

BRCA1 and BRCA2, well-known tumor suppressor genes, play a crucial role in DNA repair and serve as indicators that a person has a predisposition to particular cancers. 5-10% of all breast cancer cases are attributed to patients with BRCA mutations, and they exhibit selective sensitivity to PARP (poly ADP ribose polymerase) inhibitors. A PARP inhibitor is a type of medication that selectively blocks the activity of PARP enzymes, preventing DNA repair in cancer cells leading to their death. PARP enzymes are involved in DNA repair processes – they detect and bind to damaged DNA sites, recruit other DNA repair proteins, and facilitate the repair process, playing a crucial role in maintaining genomic stability.

Recent preclinical and clinical studies have reported promising antitumor activity of PARP inhibitors in ovarian and breast cancers with a germline BRCA mutation. Germline BRCA mutation refers to the inherited genetic alteration in the BRCA1 or BRCA2 genes that can be passed down from parents, resulting in the mutation being present in every cell of the body. However, germline BRCA mutation testing, which detects inheritable mutations that increase susceptibility to cancers and aid in treatment decisions for breast cancer, still has limitations in predicting the proper response to PARP inhibitors.

Previous clinical studies of PARP inhibitors have shown different drug responses depending on the cancer type (breast, ovarian, and prostate cancers), even though they harbor the same BRCA mutations. In breast cancer with germline BRCA mutations, clinical response rates ranging from 12.9% to 59.9% were observed. Patients with BRCA1/2 wild-type breast cancer, which refers to the non-mutated form of the BRCA1/2 gene, did not experience clinical benefits. However, in ovarian cancer, there was a subgroup of BRCA1/2 wild-type ovarian cancer patients who derived benefits from PARP inhibitors. Despite this unpredictability, several researchers have attempted to verify the predictive value of PARP inhibitors in previous clinical trials. However, These analyses are still insufficient.

In our previous pilot study, we conducted whole genome sequencing of breast cancer samples, which allows for the complete sequencing of genomic DNA, and assessed the predictive value for the response to PARP inhibitors in each sample. Through this study, we confirmed a high correlation between the predictive value and the loss-of-heterozygosity (LOH) of BRCA1/2 genes in germline BRCA-mutated cancers. Loss of heterozygosity (LOH) mutation occurs when a specific region of DNA undergoes a genetic change that leads to the loss of genetic diversity, resulting in the presence of only one type of genetic variant instead of the expected diversity. Based on these findings, we hypothesized that the revised predictive value, which includes information about the LOH status, could serve as a novel candidate as a predictive marker for PARP inhibitor treatment.

Through this research, our team will evaluate the correlation between BRCA mutation and the response to PARP inhibitors in the clinical trial. Thus, our study will enable a greater number of breast cancer patients with BRCA mutation to benefit from PARP inhibitor treatment.

Statistical Analysis Plan:

We will use Whole Genome Sequencing(WGS) data to run the GATK HaplotypeCaller calling the germline mutation to check the gBRCA1/2 mutation. In addition, we will conduct a somatic mutation calling with GATK Mutect2 to obtain somatic Single Nucleotide Variant(SNV) and Insertion–deletion mutations(INDEL) results, and call structure variation(SV) with Manta. Copy number variation(CNV) and Loss of heterozygosity(LOH) profile would be called by sequenza. The above results (somatic SNV, INDEL, SV, CNV and LOH) will be used as input for HRDetect to calculate the HRDetect score, and verify the correlation LOH with this score through the Fisher-Exact test. and missing values will be excluded.

Requested Studies:

Data Contributor: Pfizer Inc.
Study ID: NCT01945775
Sponsor ID: 673-301

Summary of Results:

Our team has not yet begun the analysis as the data we received differed from what we requested.
Specifically, we were expecting to receive whole genome sequencing data and drug response information for PARPi from the clinical study.

Unfortunately, the provided data did not meet these specifications.