Head and Neck

This study introduces a stacked ensemble machine learning classifier designed to predict the tissue of origin for cancers using circulating tumor DNA (ctDNA). The model integrates 11 multidimensional ctDNA features, encompassing genomic, fragmentomic, methylation/repeat, and microbial signals, derived from low-pass whole-genome sequencing. It demonstrated robust performance, achieving top-1 and top-2 accuracies of 80% and 90% in an independent validation cohort of 1,221 samples. The classifier proved effective even with low tumor fractions and successfully identified the primary site in 73.3% of cancers of unknown primary (CUP) cases. This non-invasive approach holds significant potential for guiding clinical decision-making.

A novel study developed a non-invasive biomarker, the regional motif diversity score (rMDS), based on whole-genome sequencing of plasma cell-free DNA (cfDNA). This biomarker predicts immunotherapy response in patients with head and neck squamous cell carcinoma (HNSCC). The rMDS demonstrated superior performance compared to existing metrics in distinguishing responders from non-responders to pembrolizumab. Longitudinal rMDS changes were associated with genomic regions linked to immunity and keratinization, suggesting a connection to telomere biology. An rMDS-based classifier achieved a high AUC (0.89-0.99) and was associated with improved disease-free survival.