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Quantitative In Vivo Analyses Reveal a Complex Pharmacogenomic Landscape in Lung Adenocarcinoma
Chuan Li1, Wen-Yang Lin2, Hira Rizvi3
1Department of Biology, Stanford University, Stanford, California.
Abstract:
The lack of knowledge about the relationship between tumor genotypes and therapeutic responses remains one of the most critical gaps in enabling the effective use of cancer therapies. Here, we couple a multiplexed and quantitative experimental platform with robust statistical methods to enable pharmacogenomic mapping of lung cancer treatment responses in vivo. The complex map of genotype-specific treatment responses uncovered that over 20% of possible interactions show significant resistance or sensitivity. Known and novel interactions were identified, and one of these interactions, the resistance of KEAP1-mutant lung tumors to platinum therapy, was validated using a large patient response data set. These results highlight the broad impact of tumor suppressor genotype on treatment responses and define a strategy to identify the determinants of precision therapies. SIGNIFICANCE: An experimental and analytical framework to generate in vivo pharmacogenomic maps that relate tumor genotypes to therapeutic responses reveals a surprisingly complex map of genotype-specific resistance and sensitivity.
Insights
Understanding tumor genotypes is key for effective cancer therapies. This study maps genotype-specific lung cancer treatment responses, revealing complex interactions and validating KEAP1 mutations’ resistance to platinum therapy.
Area of Science:
- Oncology
- Genetics
- Pharmacology
Background:
- Knowledge gaps exist regarding tumor genotype and cancer therapy response.
- Effective precision cancer therapies require understanding genotype-drug interactions.
- Lung cancer treatment efficacy is hindered by unknown genotype-specific responses.
Purpose of the Study:
- To create a pharmacogenomic map of lung cancer treatment responses in vivo.
- To identify genotype-specific drug sensitivities and resistances.
- To establish a framework for discovering determinants of precision cancer therapies.
Main Methods:
- Coupling a multiplexed, quantitative experimental platform with statistical methods.
- Generating in vivo pharmacogenomic maps.
- Validating identified interactions with patient response data.
Main Results:
- Over 20% of possible genotype-treatment interactions showed significant resistance or sensitivity.
- Identified both known and novel genotype-specific interactions.
- Validated KEAP1-mutant lung tumors' resistance to platinum therapy using patient data.
Conclusions:
- Tumor suppressor genotypes significantly impact treatment responses.
- A complex map of genotype-specific resistance and sensitivity was revealed.
- The developed framework can identify determinants for precision therapies.
