{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:17:56Z","timestamp":1772554676437,"version":"3.50.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1008538","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000}}],"reference-count":36,"publisher":"Public Library of Science (PLoS)","issue":"12","license":[{"start":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T00:00:00Z","timestamp":1609113600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>Combinatorial therapies are required to treat patients with advanced cancers that have become resistant to monotherapies through rewiring of redundant pathways. Due to a massive number of potential drug combinations, there is a need for systematic approaches to identify safe and effective combinations for each patient, using cost-effective methods. Here, we developed an exact multiobjective optimization method for identifying pairwise or higher-order combinations that show maximal cancer-selectivity. The prioritization of patient-specific combinations is based on Pareto-optimization in the search space spanned by the therapeutic and nonselective effects of combinations. We demonstrate the performance of the method in the context of BRAF-V600E melanoma treatment, where the optimal solutions predicted a number of co-inhibition partners for vemurafenib, a selective BRAF-V600E inhibitor, approved for advanced melanoma. We experimentally validated many of the predictions in BRAF-V600E melanoma cell line, and the results suggest that one can improve selective inhibition of BRAF-V600E melanoma cells by combinatorial targeting of MAPK\/ERK and other compensatory pathways using pairwise and third-order drug combinations. Our mechanism-agnostic optimization method is widely applicable to various cancer types, and it takes as input only measurements of a subset of pairwise drug combinations, without requiring target information or genomic profiles. Such data-driven approaches may become useful for functional precision oncology applications that go beyond the cancer genetic dependency paradigm to optimize cancer-selective combinatorial treatments.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1008538","type":"journal-article","created":{"date-parts":[[2020,12,28]],"date-time":"2020-12-28T15:38:49Z","timestamp":1609169929000},"page":"e1008538","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":7,"title":["Multiobjective optimization identifies cancer-selective combination therapies"],"prefix":"10.1371","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5739-6278","authenticated-orcid":true,"given":"Otto I.","family":"Pulkkinen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1154-8501","authenticated-orcid":true,"given":"Prson","family":"Gautam","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7270-1792","authenticated-orcid":true,"given":"Ville","family":"Mustonen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0886-9769","authenticated-orcid":true,"given":"Tero","family":"Aittokallio","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2020,12,28]]},"reference":[{"key":"pcbi.1008538.ref001","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/978-1-4939-7493-1_17","article-title":"Methods for High-throughput Drug Combination Screening and Synergy Scoring","volume":"1711","author":"L He","year":"2018","journal-title":"Methods in molecular biology (Clifton, NJ)"},{"issue":"5","key":"pcbi.1008538.ref002","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pcbi.1006752","article-title":"Drug combination sensitivity scoring facilitates the discovery of synergistic and efficacious drug combinations in cancer","volume":"15","author":"A Malyutina","year":"2019","journal-title":"PLOS Computational Biology"},{"issue":"7","key":"pcbi.1008538.ref003","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1038\/nbt.2284","article-title":"Combinatorial drug therapy for cancer in the post-genomic era","volume":"30","author":"B Al-Lazikani","year":"2012","journal-title":"Nature Biotechnology"},{"issue":"12","key":"pcbi.1008538.ref004","doi-asserted-by":"crossref","first-page":"2286","DOI":"10.1016\/j.drudis.2019.09.002","article-title":"Applying synergy metrics to combination screening data: agreements, disagreements and pitfalls","volume":"24","author":"AHC Vlot","year":"2019","journal-title":"Drug Discovery Today"},{"key":"pcbi.1008538.ref005","doi-asserted-by":"crossref","first-page":"181","DOI":"10.3389\/fphar.2015.00181","article-title":"What is synergy? 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