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.
摘要：
切除修复交叉互补组2（ERCC2）编码DNA解旋酶着色性干皮病D组，在转录和核苷酸切除修复中起作用。ERCC2中的点突变是约10％的膀胱癌（BLCA）的推定驱动因素，并且是顺铂治疗反应的潜在阳性生物标志物。然而，直接归因于ERCC2突变的预后意义及其在基因组不稳定中的致病作用仍然知之甚少.我们首次证明突变型ERCC2是BLCA预后的独立预测因子。然后，我们使用一组ERCC2野生型（n=343）和突变体（n=39）BLCA全基因组检查了其对体细胞突变景观的影响。在ERCC2突变体中，体细胞突变的全基因组分布显着改变，包括T[C>T]N富集，改变了复制时间相关性，和CTCF-粘附素结合位点突变热点。我们利用这些改变来开发用于预测致病性ERCC2突变的机器学习模型。这可能有助于告知BLCA患者的治疗。
