bioRxivCancer BiologyPreprint
Data-driven discovery of targets for bipotent anticancer drugs identifies Estrogen Related Receptor Alpha
A. Sahu, X. Wang, P. Munson, J. Klomp, X. Wang, S. Gu, G. Qian, P. Nicol, Z. Zeng, C. Wang, C. Tokheim, W. Zhang, J. Fu, J. Wang, N. Nair, J. Rens, M. Bourajjaj, B. Jansen, I. Leenders, J. Lemmers, M. Musters, S. Zanten, L. Zelst, J. Worthington, M. Brown, , K. Flaherty
Abstract
Drugs that kill tumors through multiple mechanisms have potential for broad clinical benefits, with a reduced propensity to resistance. We developed BipotentR, a computational approach to find cancer-cell-specific regulators that simultaneously modulate tumor immunity and another oncogenic pathway. Using tumor metabolism as proof-of-principle, BipotentR identified 38 candidate immune-metabolic regulators by combining epigenomes with bulk and single-cell tumor transcriptomes from patients. Inhibition of top candidate ESRRA (Estrogen Related Receptor Alpha) killed tumors by direct effects on energy metabolism and two immune mechanisms: (i) cytokine induction, causing proinflammatory macrophage polarization (ii) antigen-presentation stimulation, recruiting CD8+T cells into tumors. ESRRA is activated in immune-suppressive and immunotherapy-resistant tumors of many types, suggesting broad clinical relevance. We also applied BipotentR to angiogenesis and growth-suppressor pathways, demonstrating a widely applicable approach to identify drug targets that act simultaneously through multiple mechanisms. BipotentR is publicly available at http://bipotentr.dfci.harvard.edu/. One-Sentence SummaryBipotentR identifies targets for bipotent anticancer drugs, as shown by the energy and immune effects of ESRRA inhibition.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: drug response / drug discovery
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
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