MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms

A problem in network biology is identification of context-specific networks. Here the authors report Multi-Omics Binary Integration via Lasso Ensembles (MOBILE) to nominate molecular features associated with cellular phenotypes and pathways, and use this to assess interferon-& gamma; regulated PD-L1 expression. Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-& gamma; (IFN & gamma;) regulated PD-L1 expression. Our analyses suggest that IFN & gamma;-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGF & beta;1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.