May 22, 2024

PARP Inhibitor Biomarkers: The Key to Precision Cancer Therapy

One of the most promising areas has been in the development of PARP inhibitors – a class of targeted drugs that block the repair of damaged DNA in cancer cells with certain mutations. However, identifying the right patients who will respond best to these therapies remains a challenge. This is where biomarkers come in – molecular indicators that can help predict which tumors will be sensitive to PARP inhibition. As research continues, several key PARP inhibitor biomarkers are emerging that may play an important role in guiding clinical decisions.

Homologous Recombination Deficiency (HRD)

One of the most important biomarkers for PARP inhibitor response is homologous recombination deficiency (HRD). Cancers with defects in DNA repair pathways that rely on homologous recombination, such as those caused by mutations in BRCA1 and BRCA2 genes, are particularly sensitive to PARP inhibition since it prevents any DNA repair from occurring. Several approaches have been developed to assess HRD status in tumors through techniques like genomic scar analysis, loss of heterozygosity, and telomeric allelic imbalance. Determining a tumor’s HRD score through comprehensive testing has become a standard part of clinical decision making for PARP inhibitor eligibility.

Mutational Load

In addition to HRD, another genomic biomarker showing promise is mutational tumor load. Cancers with elevated numbers of mutations throughout the genome tend to rely more heavily on PARP-dependent repair pathways making them ideal targets. Next-generation sequencing can quantify mutational signatures within tumors and higher mutational burden has correlated with improved outcomes from PARP inhibitors. Cutoff thresholds for mutational load are still being defined but it holds potential as a complementary biomarker to HRD for predicting response, especially in non-BRCA mutated cancers.

Protein Biomarkers

At the protein level, biomarkers of PARP Inhibitors Biomarkers enzyme levels and activity are also being investigated. Elevated PARP1 and PARP2 expression as well as phosphorylated PARylated proteins involved in the DNA damage response have linked to PARP inhibitor sensitivity in preclinical models. Quantitative immunoassays to measure these proteins directly from tumor tissue samples are in development. Protein biomarkers offer the advantage of being detectable through relatively non-invasive liquid biopsies using techniques like mass spectrometry to analyze circulating tumor DNA, cells, and exosomes. This could enable dynamic monitoring of treatment response over time.

Alternative Splicing Variants

Alternative splicing of mRNA contributes another layer of complexity, with emerging evidence that certain PARP1 splice variants may influence outcomes from PARP inhibitors. For instance, one short variant known as PARP1-310Δex11 lacking a caspase cleavage site showed association with resistance in ovarian and breast cancer cell line studies. Beyond PARP1, the full splice profiles of other DDR genes are being explored as potential predictive biomarkers. As RNA sequencing becomes more widespread, accounting for alternative splicing could enhance our precision when selecting optimal patients.

Tissue-Specific Biomarkers

There is also a need to better understand biomarker differences across tumor types. For instance, prostate cancer appears to respond to PARP inhibition more through mutational load than HRD status alone. Biomarkers identified in breast and ovarian tumors may not directly translate to other epithelial cancers or less common histologies. Tissue-specific biomarker discovery remains an active area of ongoing investigation. Multi-omics datasets that integrate genomic, proteomic and other “OMICS” information promise to unravel novel predictive signatures tuned for each cancer indication.

 Integrating Biomarkers

Moving forward, the greatest potential will come from developing robust biomarker panels that incorporate multiple complementary factors rather than relying on any single marker. Composite scores assessing HRD, mutational load, PARP expression levels, alternative splicing patterns and other genome/proteome features hold the most promise for robustly segregating responses versus non-responses. Models are also needed to optimize biomarker cutoffs based on clinical trial outcomes. With further validation, carefully selected biomarker combinations have the power to transform PARP inhibition into a true example of precision oncology by ensuring the right treatment is delivered to the right patient every time.

PARP inhibitor biomarkers represent a dynamic area of research that is key to realizing the full clinical benefit of these promising targeted therapies. As our understanding of the predictive factors continues to expand through multi-omic analyses, it will drive more rational patient selection and individualized management of different cancer types. With further refinement and validation, biomarker-guided PARP inhibition has the potential to transform cancer treatment by making precision oncology a clinical reality.

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