Program in Systems Biology
Immunology and Infectious Disease | Immunoprophylaxis and Therapy | Molecular Biology | Systems Biology | Therapeutics
Most patients with advanced cancer eventually acquire resistance to targeted therapies, spurring extensive efforts to identify molecular events mediating therapy resistance. Many of these events involve synthetic rescue (SR) interactions, where the reduction in cancer cell viability caused by targeted gene inactivation is rescued by an adaptive alteration of another gene (the rescuer). Here, we perform a genome-wide in silico prediction of SR rescuer genes by analyzing tumor transcriptomics and survival data of 10,000 TCGA cancer patients. Predicted SR interactions are validated in new experimental screens. We show that SR interactions can successfully predict cancer patients' response and emerging resistance. Inhibiting predicted rescuer genes sensitizes resistant cancer cells to therapies synergistically, providing initial leads for developing combinatorial approaches to overcome resistance proactively. Finally, we show that the SR analysis of melanoma patients successfully identifies known mediators of resistance to immunotherapy and predicts novel rescuers.
drug combination, drug resistance, immunotherapy, synergy
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Copyright 2019 The Authors. Published under the terms of the CC BY 4.0 license. License: This is an open access article under the terms of the Creative Commons Attribution 4.0 License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
DOI of Published Version
Mol Syst Biol. 2019 Mar 11;15(3):e8323. doi: 10.15252/msb.20188323. Link to article on publisher's site
Molecular systems biology
Sahu AD, Ponomarova O, Ruppin E. (2019). Genome-wide prediction of synthetic rescue mediators of resistance to targeted and immunotherapy. Open Access Publications by UMass Chan Authors. https://doi.org/10.15252/msb.20188323. Retrieved from https://escholarship.umassmed.edu/oapubs/3804
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.