Computational pharmacogenomics screen identifies synergistic statin-compound combinations as anti-breast cancer therapies

981 843 Penn Lab
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Journal:

bioRxiv (September 9, 2020);

DOI: 10.1002/1878-0261.12775

Authors:

Jenna van Leeuwen, Wail Ba-Alawi, Emily Branchard, Joseph Longo, Jennifer Silvester, David W. Cescon, Benjamin Haibe-Kains, Linda Z. Penn, Deena M.A. Gendoo

Abstract

Statins are a family of FDA-approved cholesterol-lowering drugs that inhibit the rate-limiting enzyme of the metabolic mevalonate pathway, which have been shown to have anti-cancer activity. As therapeutic efficacy is increased when drugs are used in combination, we sought to identify agents, like dipyridamole, that potentiate statin-induced tumor cell death. As an antiplatelet agent dipyridamole will not be suitable for all cancer patients. Thus, we developed an integrative pharmacogenomics pipeline to identify agents that were similar to dipyridamole at the level of drug structure, in vitro sensitivity and molecular perturbation. To enrich for compounds expected to target the mevalonate pathway, we took a pathway-centric approach towards computational selection, which we called mevalonate drug network fusion (MVA-DNF). We validated two of the top ranked compounds, nelfinavir and honokiol and demonstrated that, like dipyridamole, they synergize with fluvastatin to potentiate tumor cell death by blocking the restorative feedback loop. This is achieved by inhibiting activation of the key transcription factor that induces mevalonate pathway gene transcription, sterol regulatory element-binding protein 2 (SREBP2). Mechanistically, the synergistic response of fluvastatin+nelfinavir and fluvastatin+honokiol was associated with similar transcriptomic and proteomic pathways, indicating a similar mechanism of action between nelfinavir and honokiol when combined with fluvastatin. Further analysis identified the canonical epithelial-mesenchymal transition (EMT) gene, E-cadherin as a biomarker of these synergistic responses across a large panel of breast cancer cell lines. Thus, our computational pharmacogenomic approach can identify novel compounds that phenocopy a compound of interest in a pathway-specific manner.