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Precision medicine review: rare driver mutations and their biophysical classification

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      How can biophysical principles help precision medicine identify rare driver mutations? A major tenet of pragmatic approaches to precision oncology and pharmacology is that driver mutations are very frequent. However, frequency is a statistical attribute, not a mechanistic one. Rare mutations can also act through the same mechanism, and as we discuss below, “latent driver” mutations may also follow the same route, with “helper” mutations. Here, we review how biophysics provides mechanistic guidelines that extend precision medicine. We outline principles and strategies, especially focusing on mutations that drive cancer. Biophysics has contributed profoundly to deciphering biological processes. However, driven by data science, precision medicine has skirted some of its major tenets. Data science embodies genomics, tissue- and cell-specific expression levels, making it capable of defining genome- and systems-wide molecular disease signatures. It classifies cancer driver genes/mutations and affected pathways, and its associated protein structural data guide drug discovery. Biophysics complements data science. It considers structures and their heterogeneous ensembles, explains how mutational variants can signal through distinct pathways, and how allo-network drugs can be harnessed. Biophysics clarifies how one mutation—frequent or rare—can affect multiple phenotypic traits by populating conformations that favor interactions with other network modules. It also suggests how to identify such mutations and their signaling consequences. Biophysics offers principles and strategies that can help precision medicine push the boundaries to transform our insight into biological processes and the practice of personalized medicine. By contrast, “phenotypic drug discovery,” which capitalizes on physiological cellular conditions and first-in-class drug discovery, may not capture the proper molecular variant. This is because variants of the same protein can express more than one phenotype, and a phenotype can be encoded by several variants.

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            Author and article information

            [1 ]ISNI 0000 0004 1936 8075, GRID grid.48336.3a, Cancer and Inflammation Program, Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research, , National Cancer Institute, ; 1050 Boyles St., Frederick, MD 21702 USA
            [2 ]ISNI 0000 0004 1937 0546, GRID grid.12136.37, Department of Human Molecular Genetics and Biochemistry, Sackler School of Medicine, , Tel Aviv University, ; 69978 Tel Aviv, Israel
            [3 ]ISNI 0000 0001 0675 4725, GRID grid.239578.2, Genomic Medicine Institute, Lerner Research Institute, , Cleveland Clinic, ; Cleveland, OH 44106 USA
            [4 ]ISNI 0000 0001 2164 3847, GRID grid.67105.35, Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, , Case Western Reserve University, ; Cleveland, OH 44195 USA
            [5 ]ISNI 0000 0001 2164 3847, GRID grid.67105.35, Case Comprehensive Cancer Center, , Case Western Reserve University School of Medicine, ; Cleveland, OH 44106 USA
            Author notes

            This article is part of a Special Issue on ‘Big Data’

            Guest Editors: Joshua WK Ho and Eleni Giannoulatou

            Biophys Rev
            Biophys Rev
            Biophysical Reviews
            Springer Berlin Heidelberg (Berlin/Heidelberg )
            4 January 2019
            4 January 2019
            February 2019
            : 11
            : 1
            : 5-19
            30610579 6381362 496 10.1007/s12551-018-0496-2
            © The Author(s) 2019

            Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

            Funded by: FundRef, National Heart, Lung, and Blood Institute;
            Award ID: K99HL138272
            Award ID: R00HL138272
            Award Recipient :
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            © International Union for Pure and Applied Biophysics (IUPAB) and Springer-Verlag GmbH Germany, part of Springer Nature 2019


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