Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA

Validating reference-based algorithms to determine cell-type heterogeneity in ovarian cancer DNA methylation studies

Information about cell composition in tissue samples is crucial for biomarker discovery and prognosis. Specifically, cancer tissue samples present challenges in deconvolution studies due to mutations and genetic rearrangements. Here, we optimized a robust, DNA methylation-based protocol, to be used for deconvolution of ovarian cancer samples. We compared several state-of-the-art methods (HEpiDISH, MethylCIBERSORT and ARIC) and validated the proposed protocol in an in-silico mixture and in an external dataset containing samples from ovarian cancer patients and controls. The deconvolution protocol we eventually implemented is based on MethylCIBERSORT. Comparing deconvolution methods, we paid close attention to the role of a reference panel. We postulate that a possibly high number of samples (in our case: 247) should be used when building a reference panel to ensure robustness and to compensate for biological and technical variation between samples. Subsequently, we tested the performance of the validated protocol in our own study cohort, consisting of 72 patients with malignant and benign ovarian disease as well as in five external cohorts. In conclusion, we refined and validated a reference-based algorithm to determine cell type composition of ovarian cancer tissue samples to be used in cancer biology studies in larger cohorts.

CimpleG: finding simple CpG methylation signatures

DNA methylation signatures are usually based on multivariate approaches that require hundreds of sites for predictions. Here, we propose a computational framework named CimpleG for the detection of small CpG methylation signatures used for cell-type classification and deconvolution. We show that CimpleG is both time efficient and performs as well as top performing methods for cell-type classification of blood cells and other somatic cells, while basing its prediction on a single DNA methylation site per cell type. Altogether, CimpleG provides a complete computational framework for the delineation of DNAm signatures and cellular deconvolution.

Multiregional single-cell proteogenomic analysis of ccRCC reveals cytokine drivers of intratumor spatial heterogeneity

Intratumor heterogeneity (ITH) represents a major challenge for anticancer therapies. An integrated, multidimensional, multiregional approach dissecting ITH of the clear cell renal cell carcinoma (ccRCC) tumor microenvironment (TME) is employed at the single-cell level with mass cytometry (CyTOF), multiplex immunofluorescence (MxIF), and single-nucleus RNA sequencing (snRNA-seq) and at the bulk level with whole-exome sequencing (WES), RNA-seq, and methylation profiling. Multiregional analyses reveal unexpected conservation of immune composition within each individual patient, with profound differences among patients, presenting patient-specific tumor immune microenvironment signatures despite underlying genetic heterogeneity from clonal evolution. Spatial proteogenomic TME analysis using MxIF identifies 14 distinct cellular neighborhoods and, conversely, demonstrated architectural heterogeneity among different tumor regions. Tumor-expressed cytokines are identified as key determinants of the TME and correlate with clinical outcome. Overall, this work signifies that spatial ITH occurs in ccRCC, which may drive clinical heterogeneity and warrants further interrogation to improve patient outcomes.

Effect of menopausal hormone therapy on methylation levels in early and late postmenopausal women

BACKGROUND

Cardiovascular disease (CVD) remains the leading cause of death among postmenopausal women but standard primary prevention strategies in women are not as effective as in men. By comparison, the Early versus Late Intervention Trial with Estradiol (ELITE) study demonstrated that hormone therapy (HT) was associated with significant reduction in atherosclerosis progression in women who were within six years of menopause compared to those who were 10 or more years from menopause. These findings are consistent with other studies showing significant reductions in all-cause mortality and CVD with HT, particularly when initiated in women younger than 60 years of age or within 10 years since menopause. To explore the biological mechanisms underlying the age-related atheroprotective effects of HT, we investigated changes in methylation of blood cells of postmenopausal women who participated in ELITE.

RESULTS

We first validated the epigenetic data generated from blood leukocytes of ELITE participants by replicating previously known associations between smoking and methylation levels at previously identified CpG sites, such as cg05575921 at the AHRR locus. An epigenome-wide association study (EWAS) evaluating changes in methylation through interactions with time-since-menopause and HT revealed two significantly associated CpG sites on chromosomes 12 (cg19552895; p = 1.1 × 10^-9) and 19 (cg18515510; p = 2.4 × 10^-8). Specifically, HT resulted in modest, but significant, increases in methylation levels at both CpGs but only in women who were 10 or more years since menopause and randomized to HT. Changes in carotid artery intima-media thickness (CIMT) from baseline to 36 months after HT were not significantly correlated with changes in methylation levels at either cg19552895 or cg18515510. Evaluation of other previously identified CpG sites at which methylation levels in either blood or vascular tissue were associated with atherosclerosis also did not reveal any differences in methylation as a function of HT and time-since-menopause or with changes in CIMT.

CONCLUSIONS

We identified specific methylation differences in blood in response to HT among women who were 10 or more years since menopause. The functional consequence of these change with respect to atherosclerosis progression and protective effects of HT remains to be determined and will require additional studies.

Immune cell deconvolution of bulk DNA methylation data reveals an association with methylation class, key somatic alterations, and cell state in glial/glioneuronal tumors

It is recognized that the tumor microenvironment (TME) plays a critical role in the biology of cancer. To better understand the role of immune cell components in CNS tumors, we applied a deconvolution approach to bulk DNA methylation array data in a large set of newly profiled samples (n = 741) as well as samples from external data sources (n = 3311) of methylation-defined glial and glioneuronal tumors. Using the cell-type proportion data as input, we used dimensionality reduction to visualize sample-wise patterns that emerge from the cell type proportion estimations. In IDH-wildtype glioblastomas (n = 2,072), we identified distinct tumor clusters based on immune cell proportion and demonstrated an association with oncogenic alterations such as EGFR amplification and CDKN2A/B homozygous deletion. We also investigated the immune cluster-specific distribution of four malignant cellular states (AC-like, OPC-like, MES-like and NPC-like) in the IDH-wildtype cohort. We identified two major immune-based subgroups of IDH-mutant gliomas, which largely aligned with 1p/19q co-deletion status. Non-codeleted gliomas showed distinct proportions of a key genomic aberration (CDKN2A/B loss) among immune cell-based groups. We also observed significant positive correlations between monocyte proportion and expression of PD-L1 and PD-L2 (R = 0.54 and 0.68, respectively). Overall, the findings highlight specific roles of the TME in biology and classification of CNS tumors, where specific immune cell admixtures correlate with tumor types and genomic alterations.

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

Background

The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand—methylated or non-methylated—and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing.

Results

We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a “FibroScore” correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types.

Conclusion

This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.

Frameshift mutations at the C-terminus of HIST1H1E result in a specific DNA hypomethylation signature

BACKGROUND

We previously associated HIST1H1E mutations causing Rahman syndrome with a specific genome-wide methylation pattern.

RESULTS

Methylome analysis from peripheral blood samples of six affected subjects led us to identify a specific hypomethylated profile. This "episignature" was enriched for genes involved in neuronal system development and function. A computational classifier yielded full sensitivity and specificity in detecting subjects with Rahman syndrome. Applying this model to a cohort of undiagnosed probands allowed us to reach diagnosis in one subject.

CONCLUSIONS

We demonstrate an epigenetic signature in subjects with Rahman syndrome that can be used to reach molecular diagnosis.