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AlloDriver: a method for the identification and analysis of cancer driver targets

Kun Song1,2, Qian Li1,3, Wei Gao4

  • 1Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, Clinical and Fundamental Research Center, Department of Pharmacy, Renji Hospital, Shanghai Jiao-Tong University School of Medicine (SJTU-SM), Shanghai 200127, China.

Nucleic Acids Research
|May 10, 2019
PubMed

Insights

AlloDriver identifies cancer driver genes by analyzing mutation effects on protein structures. This method aids in discovering new therapeutic targets for cancer by pinpointing functional mutations.

Area of Science:

  • Genomics
  • Proteomics
  • Cancer Biology

Background:

  • Somatic mutations in cancer can alter protein function, offering potential therapeutic targets.
  • Identifying functional variants is key to understanding tumorigenesis and developing targeted cancer therapies.

Purpose of the Study:

  • To present AlloDriver, a computational strategy for identifying cancer driver genes and proteins from mutation data.
  • To leverage structural and dynamic protein features to prioritize mutations impacting protein function.

Main Methods:

  • AlloDriver maps clinical cancer mutations to allosteric and orthosteric sites on 3D protein structures.
  • The strategy prioritizes potentially functional genes/proteins based on mutation location and predicted impact.

Main Results:

  • AlloDriver successfully identified known cancer driver mutations and genes/proteins from clinical samples.
  • The method discovered a novel cancer driver mutation (L1143F) in PTPRK in head and neck squamous cell carcinoma (HNSC), which was experimentally validated.

Conclusions:

  • AlloDriver is a validated strategy for discovering novel cancer driver proteins and mutations.
  • This approach can uncover new molecular mechanisms of tumorigenesis and identify new therapeutic targets.

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