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Abstract Many signaling and other genes known as “hidden” drivers may not be genetically or epigenetically altered or differentially expressed at the mRNA or protein levels, but, rather, drive a phenotype such as tumorigenesis via post-translational modification or other mechanisms. However, conventional approaches based on genomics or differential expression are limited in exposing such hidden drivers. Here, we present a comprehensive algorithm and toolkit NetBID2 (data-driven network-based Bayesian inference of drivers, version 2), which reverse-engineers context-specific interactomes and integrates network activity inferred from large-scale multi-omics data, empowering the identification of hidden drivers that could not be detected by traditional analyses. NetBID2 has substantially re-engineered the previous prototype version by providing versatile data visualization and sophisticated statistical analyses, which strongly facilitate researchers for result interpretation through end-to-end multi-omics data analysis. We demonstrate the power of NetBID2 using three hidden driver examples. We deploy NetBID2 Viewer, Runner, and Cloud apps with 145 context-specific gene regulatory and signaling networks across normal tissues and paediatric and adult cancers to facilitate end-to-end analysis, real-time interactive visualization and cloud-based data sharing. NetBID2 is freely available at https://jyyulab.github.io/NetBID .

Notch1 is frequently activated promoting T-cell acute lymphoblastic leukaemia (T-ALL). Here, the authors show that Notch1 induces oxidative phosphorylation dependency in T-ALL and synergism when inhibiting both mitochondrial complex I and glutaminolysis in preclinical murine and human xenograft models.

The authors previously showed that mTOR controls the function of regulatory T cells. Here they show how this mTOR signaling orchestrates homeostasis of Treg-cell subsets and prevents fatal autoimmunity.