DNA methylation microarrays can be employed to interrogate cell-type composition in complex tissues. Here, we expand reference-based deconvolution of blood DNA methylation to include 12 leukocyte subtypes (neutrophils, eosinophils, basophils, monocytes, naïve and memory B cells, naïve and memory CD4 + and CD8 + T cells, natural killer, and T regulatory cells). Including derived variables, our method provides 56 immune profile variables. The IDOL (IDentifying Optimal Libraries) algorithm was used to identify libraries for deconvolution of DNA methylation data for current and previous platforms. The accuracy of deconvolution estimates obtained using our enhanced libraries was validated using artificial mixtures and whole-blood DNA methylation with known cellular composition from flow cytometry. We applied our libraries to deconvolve cancer, aging, and autoimmune disease datasets. In conclusion, these libraries enable a detailed representation of immune-cell profiles in blood using only DNA and facilitate a standardized, thorough investigation of immune profiles in human health and disease.

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.

Background: Cellular compositions of solid tumor microenvironments are heterogeneous, varying across patients and tumor types. High-resolution profiling of the tumor microenvironment cell composition is crucial to understanding its biological and clinical implications. Previously, tumor microenvironment gene expression and DNA methylation-based deconvolution approaches have been shown to deconvolve major cell types. However, existing methods lack accuracy and specificity to tumor type and include limited identification of individual cell types.Results: We employed a novel tumor-type-specific hierarchical model using DNA methylation data to deconvolve the tumor microenvironment with high resolution, accuracy, and specificity. The deconvolution algorithm is namedHiTIMED. Seventeen cell types from three major tumor microenvironment components can be profiled (tumor, immune, angiogenic) byHiTIMED, and it provides tumor-type-specific models for twenty carcinoma types. We demonstrate the prognostic significance of cell types that other tumor microenvironment deconvolution methods do not capture.Conclusion: We developedHiTIMED, a DNA methylation-based algorithm, to estimate cell proportions in the tumor microenvironment with high resolution and accuracy.HiTIMEDdeconvolution is amenable to archival biospecimens providing high-resolution profiles enabling to study of clinical and biological implications of variation and composition of the tumor microenvironment.

Human cancers are heterogenous by their cell composition and origination site. Cancer metastasis generates the conundrum of the unknown origin of migrated tumor cells. Tracing tissue of origin and tumor type in primary and metastasized cancer is vital for clinical significance. DNA methylation alterations play a crucial role in carcinogenesis and mark cell fate differentiation, thus can be used to trace tumor tissue of origin. In this study, we employed a novel tumor-type-specific hierarchical model using genome-scale DNA methylation data to develop a multilayer perceptron model, HiTAIC, to trace tissue of origin and tumor type in 27 cancers from 23 tissue sites in data from 7735 tumors with high resolution, accuracy, and specificity. In tracing primary cancer origin, HiTAIC accuracy was 99% in the test set and 93% in the external validation data set. Metastatic cancers were identified with a 96% accuracy in the external data set. HiTAIC is a user-friendly web-based application through https://sites.dartmouth.edu/salaslabhitaic/. In conclusion, we developed HiTAIC, a DNA methylation-based algorithm, to trace tumor tissue of origin in primary and metastasized cancers. The high accuracy and resolution of tumor tracing using HiTAIC holds promise for clinical assistance in identifying cancer of unknown origin.

Assessing individual responses to glucocorticoid drug therapies that compromise immune status and affect survival outcomes in neuro-oncology is a great challenge. Here we introduce a blood-based neutrophil dexamethasone methylation index (NDMI) that provides a measure of the epigenetic response of subjects to dexamethasone. This marker outperforms conventional approaches based on leukocyte composition as a marker of glucocorticoid response. The NDMI is associated with low CD4 T cells and the accumulation of monocytic myeloid-derived suppressor cells and also serves as prognostic factor in glioma survival. In a non-glioma population, the NDMI increases with a history of prednisone use. Therefore, it may also be informative in other conditions where glucocorticoids are employed. We conclude that DNA methylation remodeling within the peripheral immune compartment is a rich source of clinically relevant markers of glucocorticoid response.

Platinum-based chemotherapy is a widely used strategy for bladder cancer (BCa) treatment. However, its clinical efficacy is affected by chemotherapy resistance via complex molecular mechanisms. Therefore, there is an urgent need to explore new targets for BCa therapy. Here, we showed that bromodomain-4 protein (BRD4) expression is upregulated in BCa tissues and cells. Inhibition of BRD4 attenuated the migration and invasion of BCa cells, which was rescued by the Sonic hedgehog (SHH) pathway activator recombinant human Sonic hedgehog peptide (rhSHH). We further found that cisplatin (DDP) suppressed the migration and invasion of BCa cells in vitro and inhibited tumor growth in vivo. However, overexpression of BRD4 weakened the pharmacological effects of DDP. In brief, our research revealed that BRD4 promotes migration and invasion by positively regulating the SHH pathway, drives DDP resistance in BCa, and is a novel therapeutic target for the treatment of BCa.