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首页> 外文期刊>BMC Genomics >A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles
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A computational method for clinically relevant cancer stratification and driver mutation module discovery using personal genomics profiles

机译：使用个人基因组学资料进行临床相关癌症分层和驾驶员突变模块发现的计算方法

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Background Personalized genomics instability, e.g., somatic mutations, is believed to contribute to the heterogeneous drug responses in patient cohorts. However, it is difficult to discover personalized driver mutations that are predictive of drug sensitivity owing to diverse and complex mutations of individual patients. To circumvent this problem, a novel computational method is presented to discover potential drug sensitivity relevant cancer subtypes and identify driver mutation modules of individual subtypes by coupling differentially expressed genes (DEGs) based subtyping analysis with the driver mutation network analysis. Results The proposed method was applied to breast cancer and lung cancer samples available from The Cancer Genome Atlas (TCGA). Cancer subtypes were uncovered with significantly different survival rates, and more interestingly, distinct driver mutation modules were also discovered among different subtypes, indicating the potential mechanism of heterogeneous drug sensitivity. Conclusions The research findings can be used to help guide the repurposing of known drugs and their combinations in order to target these dysfunctional modules and their downstream signaling effectively for achieving personalized or precision medicine treatment.
机译：背景技术据信，个性化的基因组不稳定性，例如体细胞突变，导致了患者队列中的异种药物反应。然而，由于个体患者的多样和复杂的突变，很难发现可预测药物敏感性的个性化驱动因子突变。为了解决这个问题，提出了一种新颖的计算方法，以发现潜在的与药物敏感性相关的癌症亚型，并通过将基于差异表达基因（DEG）的亚型分析与驱动程序突变网络分析相结合，识别单个亚型的驱动程序突变模块。结果所提出的方法适用于可从The Cancer Genome Atlas（TCGA）获得的乳腺癌和肺癌样品。发现了具有显着不同的存活率的癌症亚型，更有趣的是，在不同的亚型中还发现了不同的驱动程序突变模块，这表明了异构药物敏感性的潜在机制。结论研究结果可用于指导已知药物及其组合的再利用，从而有效地靶向这些功能障碍的模块及其下游信号，从而实现个性化或精确的药物治疗。

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