PARP inhibitor olaparib induced differential protein expression in cervical cancer cells

PARP inhibitors are a potential class of chemotherapeutic drugs but PARP inhibitor response has not been explored systematically. We lack a specific understanding of the subset of the proteome preferentially modified in various cancers by PARP inhibitors. Implications of PARP inhibitor and PARP1 in cervical cancer treatment and resistance are not fully elucidated. We conducted a mass spectrometry-based proteomic analysis of cervical cancer Hela cells treated with olaparib. We aimed to identify the alteration in the protein signaling pathway induced by PARP inhibitors beyond the DNA damage response pathway. Our data demonstrate a significant reduction in PARP activity and enhanced cell death after olaparib treatment. We further observed articulated proteomic changes with a significant enrichment of proteins in diverse cellular processes. The differentially expressed proteins were predominantly associated with RNA metabolism, mRNA splicing, processing, and RNA binding. Our data also identified proteins that could probably contribute to survival mechanisms resulting in resistance to PARP inhibitors. Hence, we put forth the overview of proteomic changes induced by PARP inhibitor olaparib in cervical cancer cells. This study highlights the significant proteins modified during PARP inhibition and thus could be a probable target for combination therapies with PARP inhibitors in cervical cancer. This study provides the overview of proteomic changes induced by PARP inhibitor olaparib in cervical cancer Hela cells. We demonstrated that Olaparib inhibited PARP1 activity in Hela cells in a dose-dependent manner while no change was observed in the expression of PARP1. PARP inhibition potentially regulated the RNA metabolism, RNA binding proteins, metastasis-related genes, mitochondrial proteins, transcription factors and regulators, and ubiquitination proteins. We also identified increased expression of tumor-promoting and drug resistance proteins that could contribute to the resistance mechanism to PARPi therapy. This approach will be helpful to generate insights into precision oncology for personalized treatment and generate data repositories for future artificial intelligence-based research.