Abstract
Excision repair cross-complementation group 2 (ERCC2) encodes the DNA helicase xeroderma pigmentosum group D, which functions in transcription and nucleotide excision repair. Point mutations in ERCC2 are putative drivers in around 10% of bladder cancers (BLCAs) and a potential positive biomarker for cisplatin therapy response. Nevertheless, the prognostic significance directly attributed to ERCC2 mutations and its pathogenic role in genome instability remain poorly understood. We first demonstrated that mutant ERCC2 is an independent predictor of prognosis in BLCA. We then examined its impact on the somatic mutational landscape using a cohort of ERCC2 wild-type (n = 343) and mutant (n = 39) BLCA whole genomes. The genome-wide distribution of somatic mutations is significantly altered in ERCC2 mutants, including T[C>T]N enrichment, altered replication time correlations, and CTCF-cohesin binding site mutation hotspots. We leverage these alterations to develop a machine learning model for predicting pathogenic ERCC2 mutations, which may be useful to inform treatment of patients with BLCA.