Blee, A. M. | Search bioRxiv

bioRxiv2023

XPA tumor variants lead to defects in NER that sensitize cells to cisplatin

Nucleotide excision repair (NER) neutralizes treatment with platinum (Pt)-based chemotherapy by removing Pt lesions from DNA. Previous study has identified that missense mutation or loss of either of the NER genes Excision Repair Cross Complementation Group 1 and 2 (ERCC1 and ERCC2) leads to improved patient outcomes after treatment with Pt-based chemotherapies. Although most NER gene alterations found in patient tumors are missense mutations, the impact of such mutations in the remaining nearly 20 NER genes is unknown. Towards this goal, we previously developed a machine learning strategy to predict genetic variants in an essential NER scaffold protein, Xeroderma Pigmentosum Complementation Group A (XPA), that disrupt repair activity on a UV-damaged substrate. In this study, we report in-depth analyses of a subset of the predicted NER-deficient XPA variants, including in vitro analyses of purified recombinant protein and cell-based assays to test Pt agent sensitivity in cells and determine mechanisms of NER dysfunction. The most NER deficient variant Y148D had reduced protein stability, weaker DNA binding, disrupted recruitment to damage, and degradation resulting from tumor missense mutation. Our findings demonstrate that tumor mutations in XPA impact cell survival after cisplatin treatment and provide valuable mechanistic insights to further improve variant effect prediction efforts. More broadly, these findings suggest XPA tumor variants should be considered when predicting patient response to Pt-based chemotherapy. SignificanceA destabilized, readily degraded tumor variant identified in the NER scaffold protein XPA sensitizes cells to cisplatin, suggesting that XPA variants can be used to predict response to chemotherapy.

cancer biology↗

bioRxiv2021

An active learning framework improves tumor variant interpretation

For precision medicine to reach its full potential for treatment of cancer and other diseases, protein variant effect prediction tools are needed that characterize variants of unknown significance (VUS) in a patients genome with respect to their likelihood to influence treatment response and outcomes. However, the performance of most variant prediction tools is limited by the difficulty of acquiring sufficient training and validation data. To overcome these limitations, we applied an iterative active learning approach starting from available biochemical, evolutionary, and functional annotations. The potential of active learning to improve variant interpretation was first demonstrated by applying it to synthetic and deep mutational scanning (DMS) datasets for four cancer-relevant proteins. We then probed its utility to guide interpretation and functional validation of tumor VUS in a potential biomarker for cancer therapy sensitivity, the nucleotide excision repair (NER) protein Xeroderma Pigmentosum Complementation Group A (XPA). A quantitative high-throughput cell-based NER activity assay, fluorescence-based multiplex flow-cytometric host cell reactivation (FM-HCR), was used to validate XPA VUS selected by the active learning strategy. In all cases, selecting VUS for validation by active learning yielded an improvement in performance over traditional learning. These analyses suggest that active learning is well-suited to significantly improve interpretation of VUS and cancer patient genomes.

cancer biology↗