c-CBL Mutations Predict Lung Cancer Sensitivity to Chemotherapeutic Agents that Target EGFR and MET
SUMMARY
A diagnostic technology that enables prediction of lung cancer sensitivity to targeted chemotherapeutic agents by identifying specific c-CBL gene mutations, thus facilitating more effective personalized treatment strategies for non-small cell lung cancer and potentially other cancers.
The Unmet Need: Predictive biomarkers to determine cancer responsiveness to c-MET and EGFR therapies
- Lung cancer remains a leading cause of cancer incidence and mortality, with approximately 220,000new cases and 160,000 deaths annually in the United States alone, reflecting substantial clinical challenge; however, current treatments targeting receptor tyrosine kinases such as c-MET and EGFR often encounter resistance, limiting therapeutic efficacy and contributing to poor patient outcomes.
- The oncology diagnostics field is increasingly focused on molecular biomarkers that can stratify patient populations based on predicted drug sensitivity, enabling precision medicine approaches that improve trial design efficiency and treatment success rates in solid tumors.
The Proposed Solution: Genetic testing for 14 somatic mutations in the c-CBL gene to predict tumor susceptibility to c-MET inhibitor therapies
- The faculty inventor developed a diagnostic assay detecting 14 identified somatic mutations within the c-CBL gene locus present in lung and head and neck cancers, which correlate with tumor responsiveness to c-MET inhibitors such as SU11274; this genetic test enables stratification of patients based on mutation status to guide therapeutic decisions. Unlike existing approaches that do not incorporate c-CBL mutation status, this method provides insight into resistance mechanisms linked to altered receptor endocytosis and downstream signaling pathways. Preliminary validation has been demonstrated through in vitro NSCLC cell viability assays and knockdown models, with ongoing evaluations including mouse xenograft studies and retrospective clinical sample analyses to establish correlation with drug response.
ADVANTAGES
- Predictive biomarker for personalized oncology
- Improved patient stratification for clinical trials
- Correlates genetic mutation with drug sensitivity
- Supports targeted therapy decision-making
- Validated in non-small cell lung cancer cell models
- Potential applicability across multiple cancer types
APPLICATIONS
- Non-small cell lung cancer treatment stratification
- Head and neck cancer therapeutic decision support
- Clinical trial enrichment for c-MET inhibitor therapies
PUBLICATIONS