An Active Learning Framework Improves Tumor Variant Interpretation

XPA tumor variant leads to defects in NER that sensitize cells to cisplatin

Nucleotide excision repair (NER) reduces efficacy of treatment with platinum (Pt)-based chemotherapy by removing Pt lesions from DNA. Previous study has identified that missense mutation or loss 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 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 protein, Xeroderma Pigmentosum Complementation Group A (XPA), that disrupt repair. In this study, we report in-depth analyses of a subset of the predicted 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. Our findings demonstrate that tumor mutations in XPA impact cell survival after cisplatin treatment and provide valuable mechanistic insights to improve variant effect prediction. Broadly, these findings suggest XPA tumor variants should be considered when predicting chemotherapy response.