SUMMARY The diverse malignant, stromal, and immune cells in tumors affect growth, metastasis and response to therapy. We profiled transcriptomes of ~6,000 single cells from 18 head and neck squamous cell carcinoma (HNSCC) patients, including five matched pairs of primary tumors and lymph node metastases. Stromal and immune cells had consistent expression programs across patients. Conversely, malignant cells varied within and between tumors in their expression of signatures related to cell cycle, stress, hypoxia, epithelial differentiation, and partial epithelial-to-mesenchymal transition (p-EMT). Cells expressing the p-EMT program spatially localized to the leading edge of primary tumors. By integrating single-cell transcriptomes with bulk expression profiles for hundreds of tumors, we refined HNSCC subtypes by their malignant and stromal composition, and established p-EMT as an independent predictor of nodal metastasis, grade, and adverse pathologic features. Our results provide insight into the HNSCC ecosystem and define stromal interactions and a p-EMT program associated with metastasis.

Objectives Differences among cancer cells within a tumor are important in tumorigenesis and treatment resistance, yet no measure of intratumor heterogeneity is suitable for routine application. We developed a quantitative measure of intratumor genetic heterogeneity, based on differences among mutated loci in the mutant-allele fractions determined by next-generation sequencing (NGS) of tumor DNA. We then evaluated the application of this measure to head and neck squamous cell carcinoma (HNSCC). Materials and Methods We analyzed published electronically available NGS results for 74 HNSCC. For each tumor we calculated mutant-allele tumor heterogeneity (MATH) as the ratio of the width to the center of its distribution of mutant-allele fractions among tumor-specific mutated loci. Results Intratumor heterogeneity assessed by MATH was higher in 3 poor-outcome classes of HNSCC: tumors with disruptive mutations in the TP53 gene (versus wild-type TP53 or non-disruptive mutations), tumors negative versus positive for human papillomavirus (even when restricted to tumors having wild-type TP53), and HPV-negative tumors from smokers with more pack-years of cigarette exposure (with TP53 status taken into account). Conclusion The relation of this type of intratumor heterogeneity to HNSCC outcome classes supports its further evaluation as a prognostic biomarker. As NGS of tumor DNA becomes widespread in clinical research and practice, MATH should provide a simple, quantitative, and clinically practical biomarker to help evaluate relations of intratumor genetic heterogeneity to outcome in any type of cancer.