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Rational design of non-resistant targeted cancer therapies

Francisco Martínez-Jiménez1,2,3, John P Overington4, Bissan Al-Lazikani5

  • 1CNAG-CRG, Centre for Genomic Regulation (CRG), Barcelona Institute of Science and Technology (BIST), Baldiri i Reixac 4, 08028 Barcelona, Spain.

Scientific Reports
|April 25, 2017
PubMed

Insights

This study introduces a computational framework to predict cancer drug resistance mutations. It identifies key mutations and suggests alternative therapies to overcome resistance, improving targeted cancer treatment strategies.

Area of Science:

  • Oncology
  • Computational Biology
  • Genomics

Background:

  • Drug resistance is a significant challenge in targeted cancer therapy.
  • Mutations in drug-target binding sites are a primary cause of resistance.
  • A comprehensive analysis of the mutational landscape for predicting resistance is lacking.

Purpose of the Study:

  • To develop and validate a computational framework for detecting cancer drug resistance mutations.
  • To identify single point mutations with a high probability of causing resistance to targeted therapies.
  • To propose alternative therapies that can overcome identified drug resistance.

Main Methods:

  • A computational framework combining cancer-specific likelihood and resistance impact was developed.
  • The model was validated using EGFR-gefitinib treatment in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Cancer (LSCC).
  • Applicability was further exemplified with ERK2-VTX11e treatment for melanoma and colorectal cancer.

Main Results:

  • The model successfully identified known resistance mutations, such as EGFR-T790M and ERK2-P58L/S/T.
  • New, previously undescribed mutations potentially causing drug resistance were predicted.
  • A sensitivity map of alternative ERK2 and EGFR inhibitors was generated, highlighting low-impact molecules.

Conclusions:

  • The developed framework effectively predicts drug resistance mutations in targeted cancer therapy.
  • It offers a prospective approach to identify treatment-threatening mutations and suggests alternative therapeutic strategies.
  • The findings advance the understanding of resistance mechanisms and inform the development of more effective cancer treatments.

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