Distinguishing genomic alterations in cancer-associated genes that have functional impact on tumor growth and disease progression from the ones that are passengers and confer no fitness advantage have important clinical implications. Evidence-based methods for nominating drivers are limited by existing knowledge on the oncogenic effects and therapeutic benefits of specific variants from clinical trials or experimental settings. As clinical sequencing becomes a mainstay of patient care, applying computational methods to mine the rapidly growing clinical genomic data holds promise in uncovering functional candidates beyond the existing knowledge base and expanding the patient population that could potentially benefit from genetically targeted therapies. We propose a statistical and computational method (MAGPIE) that builds on a likelihood approach leveraging the mutual exclusivity pattern within an oncogenic pathway for identifying probabilistically both the specific genes within a pathway and the individual mutations within such genes that are truly the drivers. Alterations in a cancer-associated gene are assumed to be a mixture of driver and passenger mutations with the passenger rates modeled in relationship to tumor mutational burden. We use simulations to study the operating characteristics of the method and assess false-positive and false-negative rates in driver nomination. When applied to a large study of primary melanomas, the method accurately identifies the known driver genes within the RTK-RAS pathway and nominates several rare variants as prime candidates for functional validation. A comprehensive evaluation of MAGPIE against existing tools has also been conducted leveraging the Cancer Genome Atlas data.

区分对肿瘤生长和疾病进展有功能影响的癌症相关基因的基因组改变与那些作为乘客且不赋予健康优势的基因组改变具有重要的临床意义。基于证据的驱动因素提名方法受到临床试验或实验环境中特定变异的致癌作用和治疗益处的现有知识的限制。随着临床测序成为患者护理的支柱，应用计算方法来挖掘快速增长的临床基因组数据有望发现现有知识库之外的功能候选者，并扩大可能受益于基因靶向治疗的患者群体。我们提出了一种统计和计算方法（MAGPIE），该方法建立在可能性方法的基础上，利用致癌途径中的相互排斥模式，从概率上识别途径中的特定基因以及这些基因中真正驱动因素的个体突变。癌症相关基因的改变被认为是驱动突变和乘客突变的混合，乘客率与肿瘤突变负荷相关。我们使用模拟来研究该方法的操作特性，并评估驾驶员提名中的假阳性和假阴性率。当应用于原发性黑色素瘤的大型研究时，该方法可以准确识别 RTK-RAS 通路中的已知驱动基因，并指定几种罕见的变异作为功能验证的主要候选者。还利用癌症基因组图谱数据对 MAGPIE 对照现有工具进行了全面评估。