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Tissue biomarkers for immune checkpoint inhibitor (ICI) response are limited by tumor sample heterogeneity and availability. This study identifies clinically actionable pretreatment blood biomarkers that are associated with ICI treatment response and survival in recurrent/metastatic head and neck squamous cell carcinoma. A prospective multi-center study enrolled 100 patients before standard-of-care immunotherapy. Blood immune profiles, measured by methylation cytometry, were assessed alongside tumor mutational burden (TMB) and PD-L1 combined proportion score (CPS). TMB and PD-L1 CPS were available for 56 and 91 patients, respectively. High neutrophils, monocytes, and neutrophil-to-lymphocyte ratio were associated with worse survival, while high CD4T cells, especially naïve CD4T cells, and lymphocyte-to-monocyte ratio were associated with better survival. Significant interactions between TMB and peripheral immune profiles for both progression-free and overall survival were found. Clinically relevant pretreatment peripheral immune biomarkers were identified, demonstrating the potential of DNA-based immune profiling to predict ICI response before treatment.

Aim: This study addresses the challenge of predicting the response of head and neck squamous cell carcinoma (HNSCC) patients to immunotherapy.Methods: Using DNA methylation cytometry, we analyzed the immune profiles of six HNSCC patients who showed a positive response to immunotherapy over a year without disease progression.Results: There was an initial increase in CD8 T memory cells and natural killer cells during the first four cycles of immunotherapy, which then returned to baseline levels after a year. Baseline CD8 T cell levels were lower in HNSCC immunotherapy responders but became similar to those in healthy subjects after immunotherapy.Conclusion: These findings suggest that monitoring fluctuations in immune profiles could potentially identify biomarkers for immunotherapy response in HNSCC patients.

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health‐related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell‐type deconvolution with EAA predicted by six widely‐used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging‐related diseases, precision medicine, and therapeutic interventions. [ABSTRACT FROM AUTHOR]

Menstrual effluent cell profiles have potential as noninvasive biomarkers of female reproductive and gynecological health and disease. We used DNA methylation-based cell type deconvolution (methylation cytometry) to identify cell type profiles in self-collected menstrual effluent. During the second day of their menstrual cycle, healthy participants collected menstrual effluent using a vaginal swab, menstrual cup, and pad. Immune cell proportions were highest in menstrual cup samples, and epithelial cells were highest in swab samples. Our work demonstrates the feasibility and utility of menstrual effluent cell profiling in population-level research using remotely collected samples and DNA methylation.