Dr. Choudhury on The Need for Biomarkers to Predict Benefit With PARP Inhibitors in mCRPC

Atish D. Choudhury, MD, PhD, chair, Gelb Center for Translational Research, senior physician, Dana-Farber Cancer Institute, assistant professor of medicine, Harvard Medical School, discusses the need for expanded genomic profiling and additional biomarkers to better predict which patients with metastatic castration-resistant prostate cancer (mCRPC) could benefit from treatment with PARP inhibitor–based combinations.

To better predict which patients could benefit from the use of PARP inhibitor–based combination, investigators need to identify biomarkers beyond homologous recombination repair mutation status, Choudhury says. Increased genomic profiling could detect additional biomarkers and potentially improve the ability to predict the likelihood of benefit for certain patients, Choudhury adds.

Along with identifying patients who could benefit from treatment, predicting benefit from PARP inhibitor–based combinations could also spare certain patients from unnecessary treatments that would not provide a clinical benefit. For example, when PARP inhibitors are combined with an androgen receptor (AR) inhibitor, these combinations carry a large pill burden, Choudhury continues. Patients receiving this type of combination will often take up to 4 pills a day from each class of drug, and there are additional toxicities associated with the use of PARP inhibitors with AR inhibitors, Choudhury says. He also notes that these combinations can also include a significant financial burden.

As the overall landscape for the treatment of prostate cancer continues to expand with the emergence of additional therapies, findings ways to avoid subjecting patients to therapeutic strategies that they are unlikely to benefit from needs to be explored, Choudhury continues. By utilizing genomic profiling to detect relevant biomarkers, considering a patient’s overall disease status, and the weighing the presence of any other comorbidities, a clinician may be able to better predict whether a given patient may respond to a specific treatment strategy. Doing this would also prevent unnecessary physical and financial adverse effects for a select group of patients, Choudhury concludes.