Scholarlink

<p>Existing approaches to identifying cancer genes rely overwhelmingly on DNA sequencing data. Here, we introduce <i>RVdriver</i>, a computational tool that leverages paired bulk genomic&#xa0;and transcriptomic data to classify RNA variant allele frequencies (VAFs) of non-synonymous mutations relative to a synonymous mutation background. We analyze 7882 paired exomes and transcriptomes from 31 cancer types and identify novel, as well as known, cancer genes, complementing other DNA-based approaches. Furthermore, RNA VAFs of individual mutations are able to distinguish “driver” from “passenger” mutations within established cancer genes. This approach highlights the value of multi-omic approaches for cancer gene discovery.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Cancer gene identification from RNA variant allelic frequencies using RVdriver

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月13日

查看全文
  • James R. M. Black,
  • Thomas P. Jones,
  • Carlos Martínez-Ruiz,
  • Maria Litovchenko,
  • Clare Puttick,
  • Charles Swanton,
  • Nicholas McGranahan

摘要

Existing approaches to identifying cancer genes rely overwhelmingly on DNA sequencing data. Here, we introduce RVdriver, a computational tool that leverages paired bulk genomic and transcriptomic data to classify RNA variant allele frequencies (VAFs) of non-synonymous mutations relative to a synonymous mutation background. We analyze 7882 paired exomes and transcriptomes from 31 cancer types and identify novel, as well as known, cancer genes, complementing other DNA-based approaches. Furthermore, RNA VAFs of individual mutations are able to distinguish “driver” from “passenger” mutations within established cancer genes. This approach highlights the value of multi-omic approaches for cancer gene discovery.