Review Article

Response prediction biomarkers and drug combinations of PARP inhibitors in prostate cancer

Yi-xin Chen1,2, Li-ming Tan3, Jian-ping Gong3, Ma-sha Huang1,2, Ji-ye Yin1,2, Wei Zhang1,2, Hong-hao Zhou1,2, Zhao-qian Liu1,2
1 Department of Clinical Pharmacology, Hunan Key Laboratory of Pharmacogenetics, and National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, China
2 Institute of Clinical Pharmacology, Engineering Research Center for Applied Technology of Pharmacogenomics of Ministry of Education, Central South University, Changsha 410078, China
3 Department of Pharmacy, The Second People’s Hospital of Huaihua City, Huaihua 418000, China
Correspondence to: Zhao-qian Liu: zqliu@csu.edu.cn,
DOI: 10.1038/s41401-020-00604-1
Received: 10 July 2020
Accepted: 20 December 2020
Advance online: 15 February 2021

Abstract

PARP inhibitors are a group of inhibitors targeting poly(ADP-ribose) polymerases (PARP1 or PARP2) involved in DNA repair and transcriptional regulation, which may induce synthetic lethality in BRCAness tumors. Systematic analyzes of genomic sequencing in prostate cancer show that ~10%–19% of patients with primary prostate cancer have inactivated DNA repair genes, with a notably higher proportion of 23%–27% in patients with metastatic castration-resistant prostate cancer (mCRPC). These characteristic genomic alterations confer possible vulnerability to PARP inhibitors in patients with mCRPC who benefit only modestly from other therapies. However, only a small proportion of patients with mCRPC shows sensitivity to PARP inhibitors, and these sensitive patients cannot be fully identified by existing response prediction biomarkers. In this review, we provide an overview of the potential response prediction biomarkers and synergistic combinations studied in the preclinical and clinical stages, which may expand the population of patients with prostate cancer who may benefit from PARP inhibitors.
Keywords: prostate cancer; PARP inhibitors; response prediction biomarkers; synergistic combination strategies

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