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Patient-derived models of acquired resistance can identify effective drug combinations for cancer
Adam S Crystal1, Alice T Shaw1, Lecia V Sequist1
1Massachusetts General Hospital Cancer Center, Department of Medicine and Harvard Medical School, Boston, MA 02114, USA.
Abstract:
Targeted cancer therapies have produced substantial clinical responses, but most tumors develop resistance to these drugs. Here, we describe a pharmacogenomic platform that facilitates rapid discovery of drug combinations that can overcome resistance. We established cell culture models derived from biopsy samples of lung cancer patients whose disease had progressed while on treatment with epidermal growth factor receptor (EGFR) or anaplastic lymphoma kinase (ALK) tyrosine kinase inhibitors and then subjected these cells to genetic analyses and a pharmacological screen. Multiple effective drug combinations were identified. For example, the combination of ALK and MAPK kinase (MEK) inhibitors was active in an ALK-positive resistant tumor that had developed a MAP2K1 activating mutation, and the combination of EGFR and fibroblast growth factor receptor (FGFR) inhibitors was active in an EGFR mutant resistant cancer with a mutation in FGFR3. Combined ALK and SRC (pp60c-src) inhibition was effective in several ALK-driven patient-derived models, a result not predicted by genetic analysis alone. With further refinements, this strategy could help direct therapeutic choices for individual patients.
Insights
Researchers developed a pharmacogenomic platform to discover drug combinations that overcome cancer treatment resistance. This approach identified effective combinations for lung cancer patients resistant to targeted therapies.
Area of Science:
- Oncology
- Pharmacogenomics
- Drug Discovery
Background:
- Targeted cancer therapies show clinical responses but often face tumor resistance.
- Resistance to epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) inhibitors is a significant clinical challenge.
Purpose of the Study:
- To establish a pharmacogenomic platform for rapid discovery of drug combinations overcoming acquired resistance.
- To identify effective drug combinations for patient-derived lung cancer models with acquired resistance.
Main Methods:
- Established cell culture models from lung cancer patients resistant to EGFR or ALK inhibitors.
- Performed genetic analyses and extensive pharmacological screening on these models.
- Validated drug combinations in patient-derived xenograft models.
Main Results:
- Identified multiple effective drug combinations, including ALK and MEK inhibitors for MAP2K1-mutant resistant tumors.
- Discovered EGFR and FGFR inhibitor combinations effective in FGFR3-mutant resistant lung cancer.
- Found combined ALK and SRC inhibition effective in ALK-driven models, independent of genetic prediction.
Conclusions:
- The pharmacogenomic platform enables rapid identification of resistance-overcoming drug combinations.
- This strategy holds promise for guiding therapeutic choices in individual cancer patients.
- Combined inhibition strategies may be crucial for overcoming complex resistance mechanisms.
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