Several types of cancer show defects in the DNA repair machinery. DNA-damaging chemotherapy and targeted therapy with the novel PARP inhibitors (PARPi) are effective treatment options in these cases. PARPi drugs have been approved for ovarian cancer and are in advanced clinical development for breast cancer. However, in order to ensure efficacy it is key to accurately discriminate tumours with defective (versus adequate) DNA damage response. Thus, the identification of reliable biomarkers of PARPi response would greatly improve patient’s life, reducing current unnecessary overtreatments, side-effects and healthcare costs.
- To develop a routine clinical test to accurately select patients who could benefit from PARPi treatment.
Problem to Solve
Breast and ovarian cancers are prevalent medical conditions, with more than 1.7M and 0.2M new cases diagnosed each year. Of these, about 10% of breast and 24% of ovarian cancers are hereditary and caused by mutations in genes used by the cell to repair DNA damage. While these tumors are expected to be more sensitive to PARPi drugs, available tests to select patients for PARPi treatment show a limited positive predictive value, with over half of the patients not responding. Therefore, there is a need to develop novel predictive tools to accurately identify which patients are likely to respond to these treatments.
PARPiPRED is a companion diagnostic tool to be used in targeted therapy with PARPi. It can help physicians to select suitable treatments, improving survival and reducing toxicity commonly associated with ineffective cancer therapies. The PARiPRED method combines an in vitro diagnostic test and an imaging tool that automates image analysis, score calculation and creates a patient report with treatment recommendations, facilitating its routine clinical implementation.
Level of InnovationPARPiPRED will allow a more accurate selection of PARPi-sensitive cancers. This innovative method could speed up the clinical approval of PARPi drugs for other cancer types, and perhaps predict responses to other DNA-damaging agents, such as platinum therapy, novel drugs and combination strategies.