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Regional motif diversity score in cfDNA predicts pembrolizumab response in head and neck cancerCan a DNA pattern predict who will heal from head and neck cancer immunotherapy?

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Key Takeaway
Consider rMDS in cfDNA as an investigational biomarker for immunotherapy response in HNSCC.

This prospective, multi-institutional phase II clinical trial enrolled 68 patients with locally advanced, surgically resectable head and neck squamous cell carcinoma (HNSCC). All patients received neoadjuvant and adjuvant pembrolizumab treatment. The study aimed to distinguish immunotherapy responders from non-responders using a novel biomarker called the regional motif diversity score (rMDS), which measures genome-wide variations in cell-free DNA fragment end motifs.

The primary finding was that rMDS robustly distinguished responders from non-responders, outperforming established cfDNA fragmentomic metrics and copy number alterations. A machine learning classifier based on rMDS demonstrated robust predictive performance across multiple validation settings, with the highest accuracy at post-treatment timepoints (AUC 0.89-0.99). This performance was superior to using PD-L1 expression or tumor fraction. Patients predicted to be responders by the classifier showed significant trends toward improved disease-free survival (hazard ratio: 2.67; 95% CI: 1.03-6.92; log rank test p=0.035).

Safety and tolerability data were not reported. The study's key limitations include its phase II design, relatively small sample size, and the lack of reported adverse event data. The findings support the potential integration of rMDS into future risk assessment frameworks, but its clinical utility remains investigational and requires confirmation in larger, randomized trials.

Imagine waiting months for cancer treatment to work, only to find out at the end that it simply did not help. This is the painful reality for many patients with head and neck squamous cell carcinoma. In this study, researchers looked at the DNA floating freely in the blood of 68 patients receiving pembrolizumab, a powerful immunotherapy drug. They searched for a specific pattern called the regional motif diversity score, or rMDS, which measures how varied the DNA fragments are in different parts of the genome.

The results were striking. This new DNA pattern reliably told the difference between patients who responded well to the drug and those who did not. It worked better than established methods that look at tumor size or immune protein levels. Even more importantly, patients predicted to respond based on this DNA signal showed a significant trend toward living longer without the cancer returning.

However, we must be careful not to get ahead of the science. This study involved a small group of patients across multiple hospitals, and the researchers did not report specific safety issues or long-term follow-up data. While the findings are exciting and support future use in risk assessment, this is still early research. We need larger studies to confirm these results before they become standard tools for doctors.

What this means for you:
A new DNA pattern predicted who would respond to cancer immunotherapy better than current tests, but this is early research.

Study Details

Study typePhase2
Sample sizen = 68
EvidenceLevel 3
PublishedMar 2026
View Original Abstract ↓
Reliable, minimally invasive biomarkers for predicting immunotherapy response in head and neck squamous cell carcinoma (HNSCC) remain an unmet clinical need. Here, using patients from a prospective, multi-institutional phase II clinical trial (NCT02641093), we performed whole-genome sequencing of 185 plasma cell-free DNA (cfDNA) samples collected longitudinally from 68 patients with locally advanced, surgically resectable HNSCC undergoing neoadjuvant and adjuvant pembrolizumab treatment. We developed the regional motif diversity score (rMDS), a novel fragmentomic metric quantifying the entropy of cfDNA 5' end motifs across genomic regions. Remarkably, unsupervised analysis revealed that rMDS robustly distinguished immunotherapy responders from non-responders, outperforming established cfDNA fragmentomic metrics and copy number alterations, while demonstrating independence from technical confounders. Longitudinal analysis revealed dynamic rMDS changes in genomic regions enriched for immune-, lectin-, and keratinization-related genes-hallmarks of squamous cell carcinoma-reflecting the interplay between tumor and peripheral immunity during the immunotherapy treatment. Interestingly, the regions with the most dynamic rMDS changes were highly enriched in telomere-proximal loci, suggesting a novel link between telomere biology and cfDNA fragmentation. A machine learning classifier based on rMDS achieved robust predictive performance across multiple validation settings (AUC 0.89-0.99), with the highest accuracy at post-treatment timepoints and superior to PD-L1 expression and tumor fraction in the same sample. Predicted responders demonstrated significant trends toward improved disease-free survival (log rank test p=0.035, hazard ratio: 2.67, 95% confidence interval: 1.03-6.92), underscoring the clinical utility of rMDS-based stratification. These findings position rMDS as a biologically meaningful and clinically actionable biomarker for immunotherapy response in HNSCC, supporting its integration into future risk assessment frameworks and broader cancer care.
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