Identification of differentially distributed gene expression and distinct sets of cancer-related genes identified by changes in mean and variability

ABSTRACT There is increasing evidence that changes in the variability or overall distribution of gene expression are important both in normal biology and in diseases, particularly cancer. Genes whose expression differs in variability or distribution without a difference in mean are ignored by traditional differential expression-based analyses. Using a Bayesian hierarchical model that provides tests for both differential variability and differential distribution for bulk RNA-seq data, we report here an investigation into differential variability and distribution in cancer. Analysis of eight paired tumour–normal datasets from The Cancer Genome Atlas confirms that differential variability and distribution analyses are able to identify cancer-related genes. We further demonstrate that differential variability identifies cancer-related genes that are missed by differential expression analysis, and that differential expression and differential variability identify functionally distinct sets of potentially cancer-related genes. These results suggest that differential variability analysis may provide insights into genetic aspects of cancer that would not be revealed by differential expression, and that differential distribution analysis may allow for more comprehensive identification of cancer-related genes than analyses based on changes in mean or variability alone.

DNA methylation and cancer incidence: lymphatic–hematopoietic versus solid cancers in the Strong Heart Study

Background Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic–hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic–hematopoietic cancers. Methods Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic–hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms. Results Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic–hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic–hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic–hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic–hematopoietic cancers. Conclusions This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic–hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic–hematopoietic cancers.

Epigenetic changes with age primes mammary luminal epithelia for cancer initiation

SummaryAging causes molecular changes that manifest as stereotypical phenotypes yet aging-associated diseases progress only in certain individuals. At lineage-specific resolution, we show how stereotyped and variant responses are integrated in mammary epithelia. Age-dependent directional changes in gene expression and DNA methylation (DNAm) occurred almost exclusively in luminal cells and implicated genome organizers SATB1 and CTCF. DNAm changes were robust indicators of aging luminal cells, and were either directly (anti-)correlated with expression changes or served as priming events for subsequent dysregulation, such as demethylation of ESR1-binding regions in DNAm-regulatory CXXC5 in older luminal cells and luminal-subtype cancers. Variance-driven changes in the transcriptome of both luminal and myoepithelial lineages further contributed to age-dependent loss of lineage fidelity. The pathways affected by transcriptomic and DNAm changes during aging are commonly linked with breast cancer, and together with the differential variability found across individuals, influence aging-associated cancer susceptibility in a subtype-specific manner.

scMET: Bayesian modelling of DNA methylation heterogeneity at single-cell resolution

High throughput measurements of DNA methylomes at single-cell resolution are a promising resource to quantify the heterogeneity of DNA methylation and uncover its role in gene regulation. However, limitations of the technology result in sparse CpG coverage, effectively posing challenges to robustly quantify genuine DNA methylation heterogeneity. Here we tackle these issues by introducing scMET, a hierarchical Bayesian model which overcomes data sparsity by sharing information across cells and genomic features, resulting in a robust and biologically interpretable quantification of variability. scMET can be used to both identify highly variable features that drive epigenetic heterogeneity and perform differential methylation and differential variability analysis between pre-specified groups of cells. We demonstrate scMET’s effectiveness on some recent large scale single cell methylation datasets, showing that the scMET feature selection approach facilitates the characterisation of epigenetically distinct cell populations. Moreover, we illustrate how scMET variability estimates enable the formulation of novel biological hypotheses on the epigenetic regulation of gene expression in early development. An R package implementation of scMET is publicly available at https://github.com/andreaskapou/scMET.