Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients.
Integrative analysis of somatic mutations and transcriptomic data to functionally stratify breast cancer patients.
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DOI：
10.1186/s12864-016-2902-0
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发表时间：
2016-08-22
期刊：
影响因子：
4.4
通讯作者：
Huang K
中科院分区：
文献类型：
--
作者：
Zhang J;Abrams Z;Parvin JD;Huang K
体细胞突变可以作为潜在的生物标志物用于癌症患者的亚型分型和预测结果。然而，癌症患者通常携带许多体细胞突变，这些突变并不总是集中在特定的基因组位点，这表明这些突变可能影响共同的途径或基因相互作用网络，而不是共同的基因。因此，挑战是使用多模态数据来识别突变之间的功能关系。我们开发了一种新的方法，用于整合患者体细胞突变，转录组和临床数据，以挖掘潜在的功能基因组，这些基因组可用于将癌症患者分为具有不同临床结果的组。具体来说，我们使用距离相关性度量挖掘来自不同患者的突变基因表达谱之间的相关性。通过这种方法，我们能够根据受影响基因之间的功能关系使用其表达谱对患者进行聚类，并使用多维缩放来可视化结果。有趣的是，我们确定了一个稳定的乳腺癌患者亚组，这些患者高度富集ER阴性和三阴性亚型，并且它们所携带的体细胞突变基因能够作为潜在的生物标志物来预测几种不同乳腺癌数据集中的患者生存率，特别是在缺乏可靠生物标志物的ER阴性队列中。我们的方法提供了一种新的和有前途的方法，整合基因分型和基因表达数据在复杂疾病的患者分层。本文的在线版本（doi：10.1186/s12864-016-2902-0）包含补充材料，可供授权用户使用。
Somatic mutations can be used as potential biomarkers for subtyping and predicting outcomes for cancer patients. However, cancer patients often carry many somatic mutations, which do not always concentrate on specific genomic loci, suggesting that the mutations may affect common pathways or gene interaction networks instead of common genes. The challenge is thus to identify the functional relationships among the mutations using multi-modal data. We developed a novel approach for integrating patient somatic mutation, transcriptome and clinical data to mine underlying functional gene groups that can be used to stratify cancer patients into groups with different clinical outcomes. Specifically, we use distance correlation metric to mine the correlations between expression profiles of mutated genes from different patients. With this approach, we were able to cluster patients based on the functional relationships between the affected genes using their expression profiles, and to visualize the results using multi-dimensional scaling. Interestingly, we identified a stable subgroup of breast cancer patients that are highly enriched with ER-negative and triple-negative subtypes, and the somatic mutation genes they harbor were capable of acting as potential biomarkers to predict patient survival in several different breast cancer datasets, especially in ER-negative cohorts which has lacked reliable biomarkers. Our method provides a novel and promising approach for integrating genotyping and gene expression data in patient stratification in complex diseases. The online version of this article (doi:10.1186/s12864-016-2902-0) contains supplementary material, which is available to authorized users.
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