Subtype-Specific Dependencies and Drug Vulnerabilities Enable Precision Therapeutics in Head and Neck Cancer

Area of Science:

• Oncology
• Molecular Biology
• Genomics

Background:

• Head and neck squamous cell carcinoma (HNSCC) exhibits significant molecular heterogeneity.
• Current HNSCC subtype classifications lack direct therapeutic application.

Purpose of the Study:

• To develop a mechanistic platform linking transcriptomic diversity to actionable tumor states in HNSCC.
• To identify subtype-specific therapeutic strategies for HPV-negative HNSCC.
• To create a predictive biomarker for EGFR-inhibitor response.

Main Methods:

• Integrated analysis of multi-cohort RNA-seq data from 727 HNSCC tumors.
• Utilized genome-scale CRISPR screens and pharmacologic profiling.
• Developed a machine learning-based transcriptomic predictor for erlotinib response.

Main Results:

• Defined four distinct tumor survival circuits: proliferative, epithelial-differentiated, EMT-like, and metabolic.
• Mapped specific drug liabilities to each identified tumor circuit.
• Developed and validated a 13-gene signature predicting erlotinib response with high accuracy (R = 0.93).

Conclusions:

• Established a framework connecting HNSCC subtypes to dependencies and therapies.
• Demonstrated the potential for precision stratification in HNSCC treatment.
• Proposed a clinically feasible approach for biomarker deployment to guide therapy selection.