Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA; Department of Pathology and Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Blavatnik School of Computer Science, Tel Aviv University, 69978 Tel Aviv, Israel.
Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA 02138, USA.
Cell Syst. 2019 May 22;8(5):456-466.e5. doi: 10.1016/j.cels.2019.04.005. Epub 2019 May 15.
The identification of molecular pathways driving cancer progression is a fundamental challenge in cancer research. Most approaches to address it are limited in the number of data types they employ and perform data integration in a sequential manner. Here, we describe ModulOmics, a method to de novo identify cancer driver pathways, or modules, by integrating protein-protein interactions, mutual exclusivity of mutations and copy number alterations, transcriptional coregulation, and RNA coexpression into a single probabilistic model. To efficiently search and score the large space of candidate modules, ModulOmics employs a two-step optimization procedure that combines integer linear programming with stochastic search. Applied across several cancer types, ModulOmics identifies highly functionally connected modules enriched with cancer driver genes, outperforming state-of-the-art methods and demonstrating the power of using multiple omics data types simultaneously. On breast cancer subtypes, ModulOmics proposes unexplored connections supported by an independent patient cohort and independent proteomic and phosphoproteomic datasets.
鉴定驱动癌症进展的分子途径是癌症研究中的一个基本挑战。大多数解决该问题的方法在其使用的数据类型数量上都存在局限性，并且以顺序方式执行数据集成。在这里，我们描述了一种新的方法 ModulOmics，该方法通过将蛋白质-蛋白质相互作用、突变和拷贝数改变的互斥性、转录核心调控以及 RNA 共表达整合到一个单一的概率模型中，从头鉴定癌症驱动途径或模块。为了有效地搜索和评分候选模块的大型空间，ModulOmics 采用了两步优化过程，将整数线性规划与随机搜索相结合。在几种癌症类型中的应用表明，ModulOmics 鉴定出了高度功能连接的模块，其中富含癌症驱动基因，优于最先进的方法，并证明了同时使用多种组学数据类型的强大功能。在乳腺癌亚型中，ModulOmics 提出了具有独立患者队列以及独立蛋白质组学和磷酸化蛋白质组学数据集支持的探索性连接。
