Program in Computational Biology and Bioinformatics, Yale University, New Haven, Connecticut.
Department of Biology, Emmanuel College, Boston, Massachusetts.
Cancer Res. 2023 Feb 15;83(4):500-505. doi: 10.1158/0008-5472.CAN-22-1508.
Somatic nucleotide mutations can contribute to cancer cell survival, proliferation, and pathogenesis. Although research has focused on identifying which mutations are "drivers" versus "passengers," quantifying the proliferative effects of specific variants within clinically relevant contexts could reveal novel aspects of cancer biology. To enable researchers to estimate these cancer effects, we developed cancereffectsizeR, an R package that organizes somatic variant data, facilitates mutational signature analysis, calculates site-specific mutation rates, and tests models of selection. Built-in models support effect estimation from single nucleotides to genes. Users can also estimate epistatic effects between paired sets of variants, or design and test custom models. The utility of cancer effect was validated by showing in a pan-cancer dataset that somatic variants classified as likely pathogenic or pathogenic in ClinVar exhibit substantially higher effects than most other variants. Indeed, cancer effect was a better predictor of pathogenic status than variant prevalence or functional impact scores. In addition, the application of this approach toward pairwise epistasis in lung adenocarcinoma showed that driver mutations in BRAF, EGFR, or KRAS typically reduce selection for alterations in the other two genes. Companion reference data packages support analyses using the hg19 or hg38 human genome builds, and a reference data builder enables use with any species or custom genome build with available genomic and transcriptomic data. A reference manual, tutorial, and public source code repository are available at https://townsend-lab-yale.github.io/cancereffectsizeR. Comprehensive estimation of cancer effects of somatic mutations can provide insights into oncogenic trajectories, with implications for cancer prognosis and treatment.
An R package provides streamlined, customizable estimation of underlying nucleotide mutation rates and of the oncogenic and epistatic effects of mutations in cancer cohorts.
体细胞核苷酸突变可促进癌细胞的存活、增殖和发病机制。尽管研究的重点是确定哪些突变是“驱动因素”，哪些是“乘客”，但在临床相关背景下量化特定变体的增殖效应可以揭示癌症生物学的新方面。为了使研究人员能够估计这些癌症效应，我们开发了 cancereffectsizeR，这是一个 R 包，它组织体细胞变异数据，促进突变特征分析，计算特定位置的突变率，并测试选择模型。内置模型支持从单个核苷酸到基因的效应估计。用户还可以估计配对变体集之间的上位效应，或设计和测试自定义模型。通过在泛癌数据集上验证了癌症效应的有效性，表明 ClinVar 中分类为可能致病性或致病性的体细胞变异比大多数其他变异具有更高的效应。事实上，癌症效应比变异流行率或功能影响评分更能预测致病性状态。此外，该方法在肺腺癌中的成对上位效应的应用表明，BRAF、EGFR 或 KRAS 中的驱动突变通常会降低对其他两种基因改变的选择。配套参考数据包支持使用 hg19 或 hg38 人类基因组构建进行分析，并且参考数据生成器可用于具有可用基因组和转录组数据的任何物种或自定义基因组构建。参考手册、教程和公共源代码存储库可在 https://townsend-lab-yale.github.io/cancereffectsizeR 上获得。体细胞突变的癌症效应的综合估计可以深入了解致癌轨迹，对癌症预后和治疗具有重要意义。
一个 R 包提供了一种简化的、可定制的方法，用于估计癌症队列中潜在的核苷酸突变率，以及突变的致癌和上位效应。
