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Assessing therapeutic responses in Kras mutant cancers using genetically engineered mouse models
Mallika Singh1, Anthony Lima, Rafael Molina
1Department of Molecular Biology, Genentech, Inc., South San Francisco, California, USA. msingh@gene.com
Nature Biotechnology
|May 25, 2010
Summary
New genetically engineered mouse models (GEMMs) show promise for predicting anti-cancer drug efficacy. These advanced models accurately reflect human responses, aiding in the development of novel cancer therapies.
Area of Science:
- Oncology
- Translational Medicine
- Preclinical Research
Background:
- Current preclinical models like xenografts and early genetically engineered mouse models (GEMMs) have limitations in predicting human clinical outcomes for novel anti-cancer agents.
- Recent advancements in GEMMs offer closer emulation of human disease, but their predictive capability for therapeutic responses requires systematic evaluation.
Purpose of the Study:
- To systematically evaluate the utility of two state-of-the-art mutant Kras-driven GEMMs in predicting clinical therapeutic responses.
- To assess the efficacy of standard-of-care chemotherapeutics and combination therapies (EGFR and VEGF inhibitors) in these GEMMs.
Main Methods:
- Utilized two Kras-driven GEMMs: one for non-small-cell lung carcinoma and one for pancreatic adenocarcinoma.
- Modeled standard clinical endpoints, including overall survival and progression-free survival, using noninvasive imaging.
- Assessed responses to standard chemotherapeutics and combination therapies involving EGFR and VEGF inhibitors.
Main Results:
- The evaluated GEMMs demonstrated a strong correlation with human responses in corresponding clinical trials.
- Noninvasive imaging modalities effectively modeled clinical endpoints like overall and progression-free survival.
- The models provide a foundation for predicting therapeutic outcomes in cancer treatment.
Conclusions:
- Validated GEMMs can accurately model human anti-cancer therapeutic responses, outperforming traditional preclinical models.
- These advanced GEMMs are valuable tools for predicting treatment outcomes and investigating mechanisms of therapeutic response and resistance.
- The findings support the use of these validated GEMMs in accelerating the development of effective cancer therapies.
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