Lim S, Park Y, Hur B, Kim M, Han W, Kim S
Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
Department of Surgery, Seoul National University College of Medicine and Hospital, Seoul, Republic of Korea; Cancer Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea.
Methods. 2016 Nov 1;110:81-89. doi: 10.1016/j.ymeth.2016.06.015. Epub 2016 Jun 18.
Genome-wide gene expression information has been very useful for understanding cancer at the molecular level. In particular, breast cancer has been widely studied by utilizing a large amount of transcriptome data. Although statistical selection of differentially expressed genes, e.g., PAM50, has been successful to classify breast cancer subtypes, understanding breast cancer in terms of biological functions or pathways is still limited. Thus, it is essential to develop a tailored model that unravels breast cancer mechanisms by identifying disease-specific functional units of biological pathways and apply the model for breast cancer prognosis. In this paper, a systematic characterization of breast cancer functional units or 'subsystems' is presented. We propose a novel concept of decomposing biological pathways into subsystems by utilizing protein interaction network, pathway information, and RNA-seq data. Subsystem activation score (SAS) was developed to measure the degree of activation for each subsystem and each patient. This method revealed distinctive genome-wide activation patterns or landscape of subsystems that are differentially activated among samples and among breast cancer subtypes. Then, we used SAS information for prognostic modeling by performing the classification and regression tree (CART) analysis. Eleven subgroups of patients, defined by 10 most significant subsystems, were identified with the maximal discrepancy in survival outcome. Our model not only defined patient subgroups with similar survival outcomes, but also provided patient-specific decision paths determined by subsystem activation status, suggesting functionally informative gene sets of breast cancer.
全基因组基因表达信息对于在分子水平理解癌症非常有用。特别是，通过利用大量转录组数据，乳腺癌已得到广泛研究。尽管对差异表达基因进行统计选择，例如PAM50，已成功用于对乳腺癌亚型进行分类，但从生物学功能或通路方面理解乳腺癌仍然有限。因此，开发一个定制模型至关重要，该模型通过识别生物通路的疾病特异性功能单元来揭示乳腺癌机制，并将该模型应用于乳腺癌预后评估。本文介绍了对乳腺癌功能单元或“子系统”的系统表征。我们提出了一个利用蛋白质相互作用网络、通路信息和RNA测序数据将生物通路分解为子系统的新概念。开发了子系统激活评分（SAS）来衡量每个子系统和每个患者的激活程度。该方法揭示了在样本之间和乳腺癌亚型之间差异激活的独特全基因组激活模式或子系统格局。然后，我们通过进行分类与回归树（CART）分析，将SAS信息用于预后建模。由10个最显著的子系统定义的11个患者亚组被识别出来，其生存结果差异最大。我们的模型不仅定义了具有相似生存结果的患者亚组，还提供了由子系统激活状态决定的患者特异性决策路径，提示了乳腺癌的功能信息基因集。
