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Updated: May 27, 2026
Mapping the Structure-Function Relationships of Disordered Oncogenic Transcription Factors Using Transcriptomic Analysis
Published on: June 27, 2020
Transcriptional states define dependencies and therapeutic vulnerabilities in head and neck cancer
Joel M Vaz1, Songli Zhu1, Mateo Useche2
1Human Biology Division, Fred Hutchinson Cancer Center, Seattle, WA, USA.
Abstract:
Molecular heterogeneity in head and neck squamous cell carcinoma (HNSCC) is well recognized, yet existing subtype frameworks remain largely descriptive and have not translated into therapeutic decision-making. Here, we establish a mechanistic platform that converts transcriptomic diversity into drug-actionable tumor states. Integrating multi-cohort RNA-seq from 727 tumors across five independent datasets, genome-scale CRISPR dependency maps, and pharmacologic screening, we define distinct tumor survival circuits across HPV-negative HNSCC and nominate subtype-matched therapeutic strategies. These circuits encompass a proliferative axis (MYC, MET/FAK, inflammatory and translational programs), an epithelial-differentiated/adhesion program, an EMT-like state with stromal activation, and mitochondrial/oxidative metabolic states, each mapping to selective liabilities (e.g., mitotic/autophagy control, ERBB/PI3K and cadherin signaling, OXPHOS/mitochondrial translation, and G2/M-integrin-Notch pathways, respectively). We then develop a transcriptomic predictor of EGFR-inhibitor response using machine learning and validate it in prospectively collected, fresh patient-derived 3D microtumors. The resulting 13-gene signature identifies erlotinib-responsive tumors (R = 0.93) and maps biologically to an epithelial-differentiated state, outperforming EGFR expression alone. Our study establishes a subtype-to-dependency-to-therapy framework, enabling precision stratification and providing a clinically feasible path for prospective biomarker deployment.
Insights
This study defines actionable tumor states in head and neck squamous cell carcinoma (HNSCC) by linking molecular subtypes to specific drug vulnerabilities. A new 13-gene signature predicts response to EGFR inhibitors, enabling precision therapy for HNSCC patients.
Area of Science:
- Oncology
- Molecular Biology
- Genomics
Background:
- Head and neck squamous cell carcinoma (HNSCC) exhibits molecular heterogeneity, but current subtypes lack therapeutic translation.
- Existing frameworks for HNSCC classification are descriptive and do not guide clinical decision-making.
Purpose of the Study:
- To develop a mechanistic platform converting transcriptomic diversity into drug-actionable tumor states in HNSCC.
- To identify subtype-matched therapeutic strategies for HPV-negative HNSCC.
- To create a transcriptomic predictor for EGFR-inhibitor response.
Main Methods:
- Integration of multi-cohort RNA-sequencing data from 727 tumors across five datasets.
- Application of genome-scale CRISPR screens and pharmacologic profiling to identify tumor survival circuits.
- Development and validation of a machine learning-based 13-gene signature for predicting erlotinib response.
Main Results:
- Defined four distinct tumor survival circuits in HPV-negative HNSCC: proliferative, epithelial-differentiated, EMT-like, and metabolic.
- Identified subtype-specific vulnerabilities, including mitotic/autophagy control, ERBB/PI3K signaling, and OXPHOS pathways.
- Developed a 13-gene signature accurately predicting erlotinib response (R=0.93), linked to an epithelial-differentiated state.
Conclusions:
- Established a framework linking HNSCC subtypes to dependencies and therapeutic strategies for precision stratification.
- Demonstrated a clinically feasible approach for deploying a transcriptomic biomarker to guide EGFR-inhibitor therapy.
- The 13-gene signature offers improved prediction of erlotinib response compared to EGFR expression alone.
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