Cooperative Effect of Oncogenic MET and PIK3CA in an HGF-Dominant Environment in Breast Cancer

Shuying Liu1,2, Shunqiang Li3, Bailiang Wang4

  • 1Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas. dtripathy@mdanderson.org sliu@mdanderson.org.

Insights

Targeting MET and PIK3CA pathways together shows promise for aggressive breast cancer. Combined therapy overcomes resistance, suggesting phosphorylated MET and PIK3CA mutations as key biomarkers for patient selection.

Area of Science:

  • Oncology
  • Molecular Biology
  • Cancer Therapeutics

Background:

  • Oncogenic MET and PIK3CA signaling pathways are implicated in breast cancer development.
  • Current targeted therapies for these pathways show modest efficacy and resistance mechanisms are unclear.
  • Existing mouse models are insufficient for studying the human HGF-MET axis.

Purpose of the Study:

  • To evaluate the synergistic effects of MET and PIK3CA in a human HGF-MET environment in vivo.
  • To investigate resistance mechanisms and the efficacy of combined MET and PI3K targeting.
  • To explore associated cell signaling changes and validate findings in patient-derived xenografts.

Main Methods:

  • Development of human HGF-MET paired mouse models.
  • In vivo evaluation of oncogenic MET/PIK3CA cooperation and targeted therapy resistance.
  • Functional proteomics to analyze cell signaling pathways.
  • Assessment of breast cancer patient-derived xenografts (PDX) response to HGF stimulation.

Main Results:

  • Oncogenic MET/PIK3CA synergistically promoted aggressive breast cancer behavior and resistance to monotherapy.
  • Combined MET and PI3K targeting effectively overcame resistance and inhibited associated oncogenic pathways.
  • HGF stimulation induced MET phosphorylation in PDX models, but expression levels did not correlate with phosphorylation status.

Conclusions:

  • Combined MET and PI3K targeting represents a potential clinical strategy for breast cancer.
  • Phosphorylated MET and PIK3CA mutation status are proposed as predictive biomarkers for patient selection in clinical trials.

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