April 17, 2024
PARP Inhibitor Biomarkers

Enhancing Precision Oncology: Exploring PARP Inhibitor Biomarkers For Tailored Cancer Therapy

Precision medicine has revolutionized cancer treatment by targeting therapies based on a patient’s individual tumor biomarkers. Poly (ADP-ribose) polymerase (PARP) inhibitors represent an important class of targeted therapies for certain cancers with specific genetic mutations. However, not all patients respond equally well to PARP inhibitors. Identifying additional predictive biomarkers can help optimize PARP inhibitor treatment and improve patient outcomes.

BRCA Mutation Status

The most definitive predictive biomarker for PARP Inhibitor Biomarkers response is mutation status in the BRCA1 and BRCA2 genes. Cancers with germline or somatic mutations in these genes that are involved in DNA repair via homologous recombination are highly sensitive to PARP inhibition. PARP normally aids in DNA repair, but inhibiting it in BRCA-mutated cells causes synthetic lethality – meaning the cancer cells cannot survive the combined loss of BRCA and PARP function. PARP inhibitors, such as olaparib, rucaparib and niraparib, have gained FDA approval for treating BRCA-mutated ovarian andbreast cancers. Testing tumor tissue for BRCA mutations is now standard in selecting appropriate patients for PARP inhibitor therapy.

Homologous Recombination Deficiency

While BRCA mutation status is an extremely important predictive biomarker, not all homologous recombination deficient (HRD) cancers have detectable BRCA mutations. Other genetic and epigenetic factors can impair the HR DNA repair pathway. Newer biomarkers aim to broadly assess HRD rather than focusing solely on BRCA. For example, genomic scar (large-scale state transitions) analysis identifies abnormal areas of transition in the cancer genome that may be indicative of HRD. High tumor mutational burden has also shown potential as an HRD predictor. Ongoing research continues exploring the ability of HRD biomarkers to predict response to PARP inhibitors beyond BRCA status alone.

Biomarkers of Resistance

Unfortunately, primary and acquired resistance still limits the effectiveness of PARP inhibitors in some patients. Research investigating resistance mechanisms has identified potential predictive resistance biomarkers. For example, reversion mutations that restore BRCA protein function can cause resistance in BRCA-mutated cancers treated with PARP inhibitors. Expression levels of P-glycoprotein, the agent that effluxes drugs from cancer cells, may influence resistance as well. Continued study of resistance biomarkers may help guide sequential or combination therapy approaches to overcome or prevent resistance in the future.

Non-coding Biomarkers

While most predictive biomarkers to date involve protein-coding genes and mutations, non-coding regions of the genome are also emerging areas of investigation. Levels of long non-coding RNA (lncRNA) molecules associated with DNA damage response and homologous recombination are under study. For instance, high SPRY4-IT1 expression may predict better PARP inhibitor response in ovarian cancer. MicroRNAs (miRNAs) that regulate DNA repair and cell cycle pathways are also being profiled in patient tumors. Assessing non-coding biomarkers could provide novel predictors beyond the limitations of traditional protein biomarkers alone.

Liquid Biopsy Opportunities

Obtaining sufficient tumor tissue often requires an invasive biopsy which may not reflect genetic heterogeneity across metastatic sites. Liquid biopsy of blood or other bodily fluids offers a non-invasive method to monitor biomarkers over time and across the entire disease. Circulating tumor DNA (ctDNA) analysis can detect BRCA mutations and assess homologous recombination deficiency status in a blood test. Exosomes containing tumor-specific proteins, RNA and DNA also hold potential as “liquid biopsies.” Development of standardized, high sensitivity liquid biopsy tests could transform PARP inhibitor selection and monitoring in the clinic.

Multigene Panels

No single biomarker perfectly predicts response or resistance to PARP Inhibitor Biomarkers. Combining multiple biomarkers into comprehensive DNA damage response signature panels may give a fuller picture of tumor vulnerabilities and weaknesses. Next-generation multigene panels are being tested for their ability to more precisely classify cancers as homologous recombination proficient versus deficient based on multiple genes involved in DNA repair pathways. Stratifying patients based on multilayered genomic signatures versus single biomarkers may maximize the proportion of patients likely to benefit from PARP inhibition.

Clinical Implementation

As the understanding of PARP inhibitor predictive biomarkers advances, the next challenge is efficiently translating promising biomarkers into clinical practice. Large-scale prospective trials are refining multigene classifiers and validating new biomarkers. Assay standardization is critical so that biomarkers can be reproducibly assessed across laboratories. Once validated, comprehensive genomic profiling using broad next-generation sequencing panels may enable assessment of multiple predictive genomic alterations from a single clinical test. Continued biomarker research holds promise to better personalize PARP inhibitor therapy and optimize outcomes for cancer patients.

PARP inhibitor response is driven by a tumor’s ability to repair DNA damage, particularly through homologous recombination. While initial focus centered on BRCA mutation status, emerging biomarkers aim to more comprehensively capture homologous recombination deficiency beyond single genes. Predictors of acquired resistance are also an active area of investigation. Non-coding biomarkers and liquid biopsies represent new frontiers with potential to enhance precision oncology. Validating robust multiparameter biomarkers through large prospective studies remains a priority to optimize PARP inhibitor selection and monitoring in clinical practice.

1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it.