Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor’s genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses-all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Original languageEnglish
Article numbere1006658
JournalPLoS Computational Biology
Volume15
Issue number3
DOIs
StatePublished - Mar 2019

Fingerprint

Dive into the research topics of 'Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers'. Together they form a unique fingerprint.

Cite this