Genome-wide variation in cell-free DNA end-motif entropy predicts immunotherapy response in head and neck cancer.
In a prospective phase II study of 68 patients with resectable head and neck squamous cell carcinoma receiving perioperative pembrolizumab, a genome-wide cfDNA end-motif entropy metric distinguished immunotherapy responders from nonresponders and was associated with disease-free survival.
Open original publication →What the AI sees
In a prospective phase II study of 68 patients with resectable head and neck squamous cell carcinoma receiving perioperative pembrolizumab, a genome-wide cfDNA end-motif entropy metric distinguished immunotherapy responders from nonresponders and was associated with disease-free survival.
Research significance
The study provides evidence that longitudinal regional cfDNA motif diversity correlates with pembrolizumab response; it supports, but does not establish, the hypothesis that rMDS could enable minimally invasive response monitoring or risk stratification after prospective external validation.
Source abstract
BACKGROUNDMinimally invasive biomarkers predicting the immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinical need.METHODSIn 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.RESULTSUnsupervised analysis showed that rMDS robustly distinguished responders from nonresponders, 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 after 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).CONCLUSIONrMDS represents a biologically meaningful and clinically actionable biomarker for the immunotherapy response in HNSCC, and merits integration into future risk assessment frameworks.TRIAL REGISTRATIONClinicalTrials.gov NCT02641093.FUNDINGNational Human Genome Research Institute (NHGRI), NIH grant R56HG012360; startup funds from Cincinnati Children's Hospital Medical Center, Northwestern University, and Robert H. Lurie Comprehensive Cancer Center; Science Olympiad Alumni Research Grant, Science Olympiad USA Foundation; Merck Sharp & Dohme Corp.