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Cancer is the second leading cause of death in the United States. A cancer cell is genetically unstable and hence can mutate faster. They can also modify their microenvironment and escape immune surveillance. The difficulties in treating cancer are further compounded by the emergence of rapid resistance to anticancer drugs. The most common ways to attain resistance in cancer cells include alteration in drug transport and metabolism, modification of drug target, elevated DNA damage response, or...
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A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors
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KinaseMD: kinase mutations and drug response database.

Ruifeng Hu1, Haodong Xu1, Peilin Jia1

  • 1Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston TX 77030, USA.

Nucleic Acids Research
|November 2, 2020
PubMed
Summary

KinaseMD is a new database that structurally and functionally annotates kinase mutations and their impact on drug response. It provides critical insights into kinase inhibitor resistance and rewiring events in cancer.

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Area of Science:

  • Biochemistry and Molecular Biology
  • Genomics and Bioinformatics
  • Pharmacology and Drug Discovery

Background:

  • Somatic mutations in kinase genes are prevalent in human diseases, particularly cancer, influencing signaling pathways and drug responses.
  • Understanding kinase mutations is crucial for predicting drug sensitivity and resistance to kinase inhibitors.
  • Existing resources lack comprehensive structural and functional annotations linking kinase mutations to drug response.

Purpose of the Study:

  • To introduce KinaseMD, a novel database for the structural and functional annotation of kinase mutations and their associated drug responses.
  • To provide an integrated resource for exploring somatic mutations, network rewiring, and drug response data for 547 kinases.
  • To uniquely annotate kinase mutations in key structural regions (gatekeeper, A-loop, G-loop, αC-helix) linked to drug resistance.

Main Methods:

  • Curated and integrated data from diverse sources, including 679,374 somatic mutations, 251,222 network-rewiring events, and 390,460 drug response records.
  • Developed unique annotation schemes for mutations within critical kinase protein substructures.
  • Annotated functional mutations, including four types of phosphorylation signal changes (gain, loss, up-regulation, down-regulation).

Main Results:

  • KinaseMD integrates extensive data on kinase mutations, network rewiring, and drug response for 547 kinases.
  • The database provides unique annotations of mutations in key structural domains associated with kinase inhibitor resistance.
  • Functional mutations and phosphorylation signal alterations are also annotated, offering insights into kinase regulatory networks.

Conclusions:

  • KinaseMD serves as a centralized, comprehensive resource for kinase mutations and drug response data.
  • The database offers unique annotations, particularly regarding drug resistance mechanisms linked to specific structural mutations.
  • KinaseMD facilitates research into kinase signaling, disease mechanisms, and the development of targeted therapies.