DNA Methylation-Derived Immune Cell Proportions and Cancer Risk, Including Lung Cancer, in Black Participants.

Prior cohort studies assessing cancer risk based on immune cell subtype profiles have predominantly focused on White populations. This limitation obscures vital insights into how cancer risk varies across race. Immune cell subtype proportions were estimated using deconvolution based on leukocyte DNA methylation markers from blood samples collected at baseline on participants without cancer in the Atherosclerosis Risk in Communities (ARIC) Study. Over a mean of 17.5 years of follow-up, 668 incident cancers were diagnosed in 2,467 Black participants. Cox proportional hazards regression was used to examine immune cell subtype proportions and overall cancer incidence and site-specific incidence (lung, breast, and prostate cancers). Higher T regulatory cell proportions were associated with statistically significantly higher lung cancer risk (hazard ratio = 1.22, 95% confidence interval = 1.06-1.41 per percent increase). Increased memory B cell proportions were associated with significantly higher risk of prostate cancer (1.17, 1.04-1.33) and all cancers (1.13, 1.05-1.22). Increased CD8+ naïve cell proportions were associated with significantly lower risk of all cancers in participants ≥55 years (0.91, 0.83-0.98). Other immune cell subtypes did not display statistically significant associations with cancer risk. These results in Black participants align closely with prior findings in largely White populations. Findings from this study could help identify those at high cancer risk and outline risk stratifying to target patients for cancer screening, prevention, and other interventions. Further studies should assess these relationships in other cancer types, better elucidate the interplay of B cells in cancer risk, and identify biomarkers for personalized risk stratification.

Blood Leukocyte DNA Methylation Markers of Periodontal Disease and Risk of Lung Cancer in a Case-Control Study Nested in the CLUE II Cohort.

BACKGROUND: Periodontal disease and DNA methylation markers have separately been associated with lung cancer risk. Examining methylation levels at genomic regions previously linked to periodontal disease may provide insight on the link between periodontal disease and lung cancer. METHODS: In a nested case-control study drawn from the CLUE II cohort, we measured DNA methylation levels in 208 lung cancer cases and 208 controls. We examined the association between 37 DNA methylated CpG sites at three genomic regions, Homeobox 4 (HOXA4), Zinc finger protein (ZFP57) and a lcRNA gene region located in Chr10 (ENSG00000231601), and lung cancer risk. RESULTS: Statistically significant associations with lung cancer risk were observed for all 14 CpG sites from HOXA4 (odds ratio [OR] ranging 1.41-1.62 for 1 SD increase in methylation level, especially within 15 years) and one CpG site on gene ENSG00000231601 (OR=1.34 for 1 SD increase in DNA methylation level). While CpG sites on gene ZFP57 were not associated with lung cancer risk overall, statistically significant inverse associations were noted for six CpGs when restricting follow-up to 15 years (OR=0.73-0.77 for 1 SD increase in methylation level). CONCLUSION: Key methylation changes associated with periodontal disease are also associated with lung cancer risk. For both HOXA4 and ZFP57, that these associations were stronger within 15 years of follow up suggests that they act late in the natural history of lung cancer. IMPACT: Identifying biological pathways that link periodontal disease and lung cancer could provide new opportunities for lung cancer detection and prevention.

Immunomethylomic profiles of long-term head and neck squamous cell carcinoma survivors on immune checkpoint inhibitors.

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.

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Matched analysis of detailed peripheral blood and tumor immune microenvironment profiles in bladder cancer.

Background: Bladder cancer and therapy responses hinge on immune profiles in the tumor microenvironment (TME) and blood, yet studies linking tumor-infiltrating immune cells to peripheral immune profiles are limited. Methods: DNA methylation cytometry quantified TME and matched peripheral blood immune cell proportions. With tumor immune profile data as the input, subjects were grouped by immune infiltration status and consensus clustering. Results: Immune hot and cold groups had different immune compositions in the TME but not in circulating blood. Two clusters of patients identified with consensus clustering had different immune compositions not only in the TME but also in blood. Conclusion: Detailed immune profiling via methylation cytometry reveals the significance of understanding tumor and systemic immune relationships in cancer patients.

Bladder cancer and treatment outcomes depend on the immune profiles in the tumor and blood. Our study, using DNA methylation cytometry, measured immune cell proportions in both areas. Patients were grouped based on immune status and consensus clustering. Results showed distinct immune compositions in the tumor, but not in blood, for hot and cold groups. Consensus clustering revealed two patient clusters with differing immune compositions in both tumor and blood. This detailed immune profiling highlights the importance of understanding the complex interplay between tumor and systemic immunity in bladder cancer patients.

An assessment of compositional methods for the analysis of DNA methylation-based deconvolution estimates.

DNA methylation (DNAm)-based deconvolution estimates contain relative data, forming a composition, that standard methods (testing directly on cell proportions) are ill-suited to handle. In this study we examined the performance of an alternative method, analysis of compositions of microbiomes (ANCOM), for the analysis of DNAm-based deconvolution estimates. We performed two different simulation studies comparing ANCOM to a standard approach (two sample t-test performed directly on cell proportions) and analyzed a real-world data from the Women's Health Initiative to evaluate the applicability of ANCOM to DNAm-based deconvolution estimates. Our findings indicate that ANCOM can effectively account for the compositional nature of DNAm-based deconvolution estimates. ANCOM adequately controls the false discovery rate while maintaining statistical power comparable to that of standard methods.

DNA methylation (DNAm)-based deconvolution provides highly accurate estimates of the proportion of each cell type in a mixed-cell type biological sample (e.g., whole-blood). These estimates can be used for examining the association between cell type proportions and biological or clinical end points; for example, comparing the estimated neutrophil proportion in whole blood between smokers and non-smokers. Cell proportion data has unique features which present challenges for traditional and widely used statistical methods. In response to this issue, our work presents two simulation studies and a real-world analysis that benchmark the performance of current standard statistical methods against an alternative method called analysis composition of microbes (ANCOM), which was originally developed for the analysis of microbiome data. In our real-world analysis we used DNAm data collected from Women's Health Initiative Long Life Study I and compared the results of each method against a gold-standard that is typically not available for these analyses. In each of our simulation studies, ANCOM was able to detect true differences in cell proportions between the groups being compared but had a much lower rate of false discovery compared with the standard statistical methods. Our real-world analysis demonstrated similar findings. Overall, our study highlights the potential of ANCOM as a powerful and robust method for analyzing DNAm-derived deconvolution estimates when the interest is comparisons of cell type proportions and biological or clinical end points. ANCOM's ability to minimize false discovery while maintaining robust statistical power positions it as a valuable addition to the epigenomic analysis toolkit.

Enrichment of a neutrophil-like monocyte transcriptional state in glioblastoma myeloid suppressor cells.

Glioblastomas (GBM) are lethal central nervous system cancers associated with tumor and systemic immunosuppression. Heterogeneous monocyte myeloid-derived suppressor cells (M-MDSC) are implicated in the altered immune response in GBM, but M-MDSC ontogeny and definitive phenotypic markers are unknown. Using single-cell transcriptomics, we revealed heterogeneity in blood M-MDSC from GBM subjects and an enrichment in a transcriptional state reminiscent of neutrophil-like monocytes (NeuMo), a newly described pathway of monopoiesis in mice. Human NeuMo gene expression and Neu-like deconvolution fraction algorithms were created to quantitate the enrichment of this transcriptional state in GBM subjects. NeuMo populations were also observed in M-MDSCs from lung and head and neck cancer subjects. Dexamethasone (DEX) and prednisone exposures increased the usage of Neu-like states, which were inversely associated with tumor purity and survival in isocitrate dehydrogenase wildtype (IDH WT) gliomas. Anti-inflammatory ZC3HA12/Regnase-1 transcripts were highly correlated with NeuMo expression in tumors and in blood M-MDSC from GBM, lung, and head and neck cancer subjects. Additional novel transcripts of immune-modulating proteins were identified. Collectively, these findings provide a framework for understanding the heterogeneity of M-MDSCs in GBM as cells with different clonal histories and may reshape approaches to study and therapeutically target these cells.