What question did this study set out to answer?

This research aims to identify biomarkers that predict immunotherapy response in head and neck squamous cell carcinoma through analysis of cell-free DNA.

May 21, 2026Open Access

Genome-wide variation in cell-free DNA end motif entropy predicts immunotherapy response in head and neck cancer

Key Points

This research aims to identify biomarkers that predict immunotherapy response in head and neck squamous cell carcinoma through analysis of cell-free DNA.
Analyzed 185 plasma cell-free DNA samples from 68 patients with HNSCC in a phase II trial.
Developed the regional motif diversity score (rMDS) to measure entropy of cfDNA end motifs.
Used machine learning classification to predict patient responses to pembrolizumab treatment.
rMDS distinguished responders from non-responders with AUC 0.89–0.99.
Predicted responders had improved disease-free survival (log-rank P = 0.035; HR 2.67, 95% CI 1.03–6.92).
Outperformed PD-L1 expression and tumor fraction in predicting treatment outcomes.

Abstract

BACKGROUND. Minimally invasive biomarkers predicting immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinical need. METHODS. Using patients from a prospective, multi-institutional phase II trial, we performed whole-genome sequencing of 185 longitudinal plasma cell-free DNA (cfDNA) samples from 68 patients with locally advanced, surgically resectable HNSCC who received neoadjuvant and adjuvant pembrolizumab. We developed the regional motif diversity score (rMDS), a fragmentomic metric that quantifies the entropy of cfDNA 5′-end motifs across genomic regions. RESULTS. Unsupervised analysis showed rMDS robustly distinguished responders from non-responders, outperforming established fragmentomic metrics and copy number alterations while remaining independent of technical confounders. Longitudinal rMDS changes localized to regions enriched for immune-, lectin-, and keratinization-related genes — hallmarks of squamous cell carcinoma — reflecting tumor–peripheral immunity interplay during treatment. The most dynamic regions clustered at telomere-proximal loci, suggesting a link between telomere biology and cfDNA fragmentation. An rMDS-based machine learning classifier achieved AUC 0.89–0.99 across validation settings, with the highest accuracy post-treatment, outperforming PD-L1 expression and tumor fraction in matched samples. Predicted responders showed improved disease-free survival (log-rank P = 0.035; HR 2.67, 95% CI 1.03–6.92). CONCLUSION. rMDS represents a biologically meaningful, clinically actionable biomarker for immunotherapy response in HNSCC, supporting integration into future risk assessment frameworks. TRIAL REGISTRATION. ClinicalTrials.gov NCT02641093. FUNDING. NHGRI R56HG012360 and startup funds from Cincinnati Children’s Hospital Medical Center, Northwestern University, and Robert H. Lurie Comprehensive Cancer Center (Y.L.); Science Olympiad Alumni Research Grant, Science Olympiad USA Foundation (R.B.); Merck Sharp & Dohme Corp. (T.W.D.).

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