《英文文献/摘要翻译》站在时间尽头2012

19、Driver mutation catalogue

　　We created a comprehensive catalogue of mutations in known and newly discovered cancer genes across all samples and variant classes, similar to that previously described for primary tumours.
　　我们创建了一个已知的和新发现的癌症基因突变的全面目录，包含所有的样本和变体类别——类似于之前描述的原发肿瘤。

　　We used a prioritization scheme to give a likelihood score for each mutation being a potential driver event.
　　我们使用一个优化方案给【每次突变是潜在驱动】的可能性评分。

　　By taking into account the proportion of SNVs and indels estimated to be passengers using the dNdScv R package, we found 13,384 somatic candidate driver events among the 20,071 identified mutations in the combined gene panel, together with 189 germline predisposition variants.
　　通过使用 dNdScv R package，衡量估计是乘客基因突变的【单核苷酸变异和插入】的比例，我们在联合基因组里的20071个经鉴定的突变中，发现了13384个体细胞候选驱动，连同189种种系倾向变异。

　　（dNdScv R package是一组可能性最大的dN/dS方法，用来量化癌症和体细胞进化中的自然选择。）

　　The somatic candidate drivers include 7,400 coding mutations, 615 non-coding point-mutation drivers, 2,700 homozygous deletions (25% of which are in common fragile sites), 2,392 focal amplifications and 276 fusion events.
　　体细胞候选驱动包括 7400个编码突变、 615 个非编码点突变驱动、 2700 个纯合缺失（其中25%位于常见脆性位点）、2392 个局灶扩增和 276 个融合。

　　For non-coding variants, only essential splice sites and promoter mutations in TERT were included in the study owing to the current lack of robust evidence for other recurrent oncogenic non-coding mutations.
　　对于非编码变体，由于目前缺乏其他周期性致癌非编码突变的可靠证据，研究仅包括TERT基因中的基本剪接位和启动子突变。

　　A total of 257 variants were found at 5 known recurrent variant hotspots and included in the candidate driver catalogue.
　　共257个变异，在5个已知的周期性变异热点中被发现，并列入候选驱动一览表。

　　For the cohort as a whole, 55% of point mutations in the gene panel candidate driver catalogue were predicted to be genuine driver events, using our prioritization scheme.
　　对于整个队列，我们使用优化方案，预计【多组基因候选驱动一览表】中55%的点突变是真正的驱动。

　　To facilitate the analysis of variants of unknown significance at a per-patient level, we calculated a sample-specific likelihood score for each point mutation being a driver event by taking into account the mutational burden of the sample, the biallelic inactivation status for TSGs, and hotspot positions for oncogenes.
　　为促进每例患者未知重要性的变异的分析，我们通过衡量样本的突变负荷、肿瘤抑制基因双等位基因失活的情形、肿瘤基因的热点位置，为【每个点突变是驱动】的可能性计算了每一样本单独的评分。