KRAS mutant tumor subpopulations can subvert durable responses to personalized cancer treatments

Barbara L Parsons1, Meagan B Myers1

  • 1US FDA, National Center for Toxicological Research, Division of Genetic & Molecular Toxicology, HFT-120, 3900 NCTR Road, Jefferson, AR 72079, USA.

Personalized Medicine
|November 22, 2016
PubMed

Insights

KRAS mutations and undetected subpopulations in colorectal and lung cancers limit anti-EGFR therapy effectiveness. Combining KRAS-targeting therapies with EGFR inhibitors may improve patient outcomes.

Area of Science:

  • Oncology
  • Molecular Biology
  • Cancer Genetics

Background:

  • KRAS mutations in colorectal and lung cancers are associated with resistance to EGFR-targeted therapies.
  • A significant proportion of patients with KRAS wild-type tumors also exhibit resistance to these treatments.
  • Tumor relapse occurs in both KRAS wild-type and mutant cancer patients, with slightly longer progression-free survival observed in wild-type cases.

Purpose of the Study:

  • To investigate the role of KRAS mutations, including undetected subpopulations, in anti-EGFR therapy resistance.
  • To explore the potential of combination therapies targeting both KRAS mutant cells and EGFR signaling.

Main Methods:

  • Analysis of KRAS mutation status in colorectal and lung cancer patients treated with anti-EGFR therapies.
  • Assessment of tumor relapse patterns and progression-free survival based on KRAS mutational status.
  • Evaluation of the impact of detected and undetected KRAS mutant tumor subpopulations on therapeutic efficacy.

Main Results:

  • KRAS mutations predict primary resistance to EGFR-targeted therapies.
  • Undetected KRAS mutant subpopulations within tumors undermine the efficacy of anti-EGFR treatments.
  • Tumor heterogeneity, including polyclonal origins and clonal populations, contributes to therapeutic failure.

Conclusions:

  • Combination therapies targeting KRAS mutant cells and downregulating EGFR signaling should be considered.
  • Preclinical models assessing combination therapies in heterogeneous tumor populations are crucial for advancing treatment strategies.

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