Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
Clinical Medicine Research Institute, Jinan University, Guangzhou, Guangdong, China.
PLoS Comput Biol. 2021 Apr 5;17(4):e1008792. doi: 10.1371/journal.pcbi.1008792. eCollection 2021 Apr.
Pathway level understanding of cancer plays a key role in precision oncology. However, the current amount of high-throughput data cannot support the elucidation of full pathway topology. In this study, instead of directly learning the pathway network, we adapted the probabilistic OR gate to model the modular structure of pathways and regulon. The resulting model, OR-gate Network (ORN), can simultaneously infer pathway modules of somatic alterations, patient-specific pathway dysregulation status, and downstream regulon. In a trained ORN, the differentially expressed genes (DEGs) in each tumour can be explained by somatic mutations perturbing a pathway module. Furthermore, the ORN handles one of the most important properties of pathway perturbation in tumours, the mutual exclusivity. We have applied the ORN to lower-grade glioma (LGG) samples and liver hepatocellular carcinoma (LIHC) samples in TCGA and breast cancer samples from METABRIC. Both datasets have shown abnormal pathway activities related to immune response and cell cycles. In LGG samples, ORN identified pathway modules closely related to glioma development and revealed two pathways closely related to patient survival. We had similar results with LIHC samples. Additional results from the METABRIC datasets showed that ORN could characterize critical mechanisms of cancer and connect them to less studied somatic mutations (e.g., BAP1, MIR604, MICAL3, and telomere activities), which may generate novel hypothesis for targeted therapy.
癌症通路水平的理解在精准肿瘤学中起着关键作用。然而，目前高通量数据的数量还不足以阐明完整的通路拓扑结构。在本研究中，我们没有直接学习通路网络，而是采用概率“或”门来模拟通路和调控网络的模块化结构。由此产生的模型，即“或”门网络（ORN），可以同时推断体细胞改变的通路模块、患者特定的通路失调状态和下游调控网络。在经过训练的 ORN 中，每个肿瘤中的差异表达基因（DEG）可以通过体细胞突变扰乱通路模块来解释。此外，ORN 处理了肿瘤中通路扰动的一个最重要的特性，即互斥性。我们已经将 ORN 应用于 TCGA 中的低级别胶质瘤（LGG）样本和肝肝细胞癌（LIHC）样本，以及 METABRIC 中的乳腺癌样本。这两个数据集都显示了与免疫反应和细胞周期相关的异常通路活性。在 LGG 样本中，ORN 确定了与神经胶质瘤发展密切相关的通路模块，并揭示了与患者生存密切相关的两条通路。我们在 LIHC 样本中也得到了类似的结果。METABRIC 数据集的其他结果表明，ORN 可以描述癌症的关键机制，并将其与研究较少的体细胞突变（例如 BAP1、MIR604、MICAL3 和端粒活性）联系起来，这可能为靶向治疗产生新的假设。
