Precision medicine review: rare driver mutations and their biophysical classification

Ruth Nussinov*, Hyunbum Jang, Chung Jung Tsai, Feixiong Cheng

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

39 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)5-19
Number of pages15
JournalBiophysical Reviews
Volume11
Issue number1
DOIs
StatePublished - 7 Feb 2019

Funding

FundersFunder number
National Institutes of HealthHHSN261200800001E
National Heart, Lung, and Blood InstituteK99HL138272
Frederick National Laboratory for Cancer Research

    Keywords

    • Conformational ensembles
    • Deep sequencing
    • Drug discovery
    • Genomics
    • KRas
    • Machine learning
    • Pharmacology
    • Phenotypic drug discovery
    • Proteomics
    • Ras
    • Signaling pathways

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