大规模癌症基因组研究中的单突变:揭开癌症基因组的尾巴。

IF 3.4 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
NAR cancer Pub Date : 2024-03-12 eCollection Date: 2024-03-01 DOI:10.1093/narcan/zcae010
Sanket Desai, Suhail Ahmad, Bhargavi Bawaskar, Sonal Rashmi, Rohit Mishra, Deepika Lakhwani, Amit Dutt
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引用次数: 0

摘要

单个或低频驱动突变的识别具有挑战性。我们提出了一种领域驱动突变估算器(DOME)来识别罕见的候选驱动突变。DOME 结合保守的 PFAM 结构域内由蛋白质结构和体细胞突变倾向确定的功能和生化残基背景,分析与已知统计热点和抗性突变类似的位置,并整合 CADD 评分方案。与其他七种工具相比,除一种工具外,DOME 在预测功能性癌症驱动因素方面的准确性均优于或相当于所有评估工具。DOME 通过对 1331 个基因(包括 1192 个非癌症基因普查基因)域边界内的全蛋白质组错义变异进行分析,确定了一组独特的 32 917 个高置信度预测驱动基因突变,突出了其在癌症基因组分析中的独特地位。此外,对8799份TCGA(癌症基因组图谱)和内部肿瘤样本的分析显示,有847个潜在的驱动基因突变,其中酪氨酸激酶成员的突变占主导地位,突显了其在癌症中的重要地位。总之,DOME 是对目前在个性化治疗中识别新型、低频驱动基因和耐药突变方法的补充。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32 917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA (The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

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CiteScore
6.90
自引率
0.00%
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审稿时长
13 weeks
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