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Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational

Ruth Nussinov1,2, Hyunbum Jang1, Chung-Jung Tsai1

  • 1Computational Structural Biology Section, Basic Science Program, Frederick National Laboratory for Cancer Research, National Cancer Institute, Frederick, Maryland, United States of America.

Plos Computational Biology
|March 29, 2019
PubMed

Insights

Precision oncology uses genomic data to tailor cancer treatments. This review integrates precision pharmacology, examining protein structures and signaling pathways for improved therapeutic strategies.

Area of Science:

  • Oncology
  • Genomics
  • Pharmacology

Background:

  • Precision medicine and oncology aim to improve cancer treatment by aligning therapies with a patient's tumor genomic alterations.
  • Major initiatives involve whole-genome sequencing, large databases, and statistical analysis to identify actionable alterations and molecular targets.

Purpose of the Study:

  • To provide a comprehensive review of precision oncology, uniquely incorporating precision pharmacology.
  • To articulate the protein structural landscape and its impact on cellular signaling pathways within the precision pharmacology framework.

Main Methods:

  • Review of existing literature on precision oncology, genomic sequencing, and statistical analysis of cancer data.
  • Integration of principles from structural biology and pharmacology to analyze protein alterations and their functional consequences.
  • Exploration of the physicochemical basis of molecular interactions in cancer signaling.

Main Results:

  • Statistical analysis of genomic data is crucial but may not fully capture the functional impact of mutations, especially rare ones, by not identifying protein conformational changes.
  • Precision pharmacology offers a deeper understanding by considering the structural and functional consequences of genomic alterations on cellular pathways.
  • The review highlights the importance of understanding protein structure and signaling for identifying effective, actionable mutations.

Conclusions:

  • Integrating precision pharmacology with precision oncology provides a more complete picture of cancer biology and treatment strategies.
  • Understanding the protein structural landscape and its effects on cellular signaling is essential for advancing precision cancer therapy.
  • This approach offers a physicochemical basis for interpreting genomic data, potentially improving the identification of rare but actionable mutations.

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