Landau Jakob, Tsaban Linoy, Yaacov Adar, Ben Cohen Gil, Rosenberg Shai
Gaffin Center for Neuro-Oncology, Sharett Institute for Oncology, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
The Wohl Institute for Translational Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
Cancer Res. 2023 Jan 4;83(1):74-88. doi: 10.1158/0008-5472.CAN-22-1038.
Driver mutations endow tumors with selective advantages and produce an array of pathogenic effects. Determining the function of somatic variants is important for understanding cancer biology and identifying optimal therapies. Here, we compiled a shared dataset from several cancer genomic databases. Two measures were applied to 535 cancer genes based on observed and expected frequencies of driver variants as derived from cancer-specific rates of somatic mutagenesis. The first measure comprised a binary classifier based on a binomial test; the second was tumor variant amplitude (TVA), a continuous measure representing the selective advantage of individual variants. TVA outperformed all other computational tools in terms of its correlation with experimentally derived functional scores of cancer mutations. TVA also highly correlated with drug response, overall survival, and other clinical implications in relevant cancer genes. This study demonstrates how a selective advantage measure based on a large cancer dataset significantly impacts our understanding of the spectral effect of driver variants in cancer. The impact of this information will increase as cancer treatment becomes more precise and personalized to tumor-specific mutations.
A new selective advantage estimation assists in oncogenic driver identification and relative effect measurements, enabling better prognostication, therapy selection, and prioritization.
驱动突变赋予肿瘤选择性优势并产生一系列致病效应。确定体细胞变异的功能对于理解癌症生物学和确定最佳治疗方法至关重要。在此，我们从多个癌症基因组数据库汇编了一个共享数据集。基于从癌症特异性体细胞突变率得出的驱动变异的观察频率和预期频率，对535个癌症基因应用了两种测量方法。第一种测量方法包括基于二项式检验的二元分类器；第二种是肿瘤变异幅度（TVA），这是一种连续测量方法，代表个体变异的选择性优势。就与癌症突变的实验得出的功能评分的相关性而言，TVA优于所有其他计算工具。TVA还与相关癌症基因中的药物反应、总生存期及其他临床意义高度相关。这项研究证明了基于大型癌症数据集的选择性优势测量方法如何显著影响我们对癌症中驱动变异的光谱效应的理解。随着癌症治疗变得更加精确并针对肿瘤特异性突变进行个性化，此信息的影响将会增加。
一种新的选择性优势估计有助于致癌驱动因子的识别和相对效应测量，从而实现更好的预后、治疗选择和优先级排序。
