Biology subjects

Easwaran, H.

Publications and source records attributed to Easwaran, H..

4 recordsLinked to original sources

bioRxiv2026

Polycomb Marked Promoter Methylation Gains in Aging and Cancer are Independent of Replication

DNA methylation alterations are hallmark features of aging and cancer, occurring early during tumor initiation. Yet how these changes evolve during early tumorigenesis, and whether oncogenic signaling actively drives them, remains poorly understood due to limitations in resolving this at the longitudinal and clonal level. Here we developed a genome-wide DNA methylation profiling method for low DNA amounts and applied it to single stem cell-derived clones from a BRAFV600E -inducible mouse colon organoid model, tracking nucleotide-level methylation heterogeneity over time. BRAFV600E induction produced stepwise methylation divergence consistent with progressive clonal takeover, in contrast to gradual epigenetic drift in uninduced controls. Strikingly, while the specific CpG sites altered differed between BRAFV600E and control lineages, they converged on the same genomic elements. Particularly, Polycomb-marked promoter CpG-island methylation gains were not induced or accelerated by BRAFV600E, challenging models in which oncogenic signaling or cell proliferation actively instructs de novo methylation of Polycomb-targeted promoters. Instead, these gains tracked with "biological aging" independent of replication, which was validated across independent aging, cancer, and cell-line datasets. BRAFV600E -induced clones additionally showed progressive loss of intestinal lineage-specification transcription factor programs, consistent with selection for cells adapted to oncogenic stress. Together, these findings show that epigenetic trajectories during early tumor development, are rooted in baseline, age-dependent drivers of epigenetic alterations, and that oncogenic signaling acts primarily by selecting and expanding clones bearing pre-existing, age-associated epigenetic drift rather than instructing new methylation. Our findings have implications for how promoter methylation is interpreted as a cancer biomarker and for identifying upstream aging-associated processes as targets for early intervention.

cancer biology↗

bioRxiv2020

Aging interacts with tumor biology to produce major changes in the immune tumor microenvironment

Advanced age is strongly correlated with both increased cancer incidence and general immune decline. The immune tumor microenvironment (ITME) has been established as an important prognostic of both therapeutic efficacy and overall patient survival. Thus, age-related immune decline is an important consideration for the treatment of a large subset of cancer patients. Current studies of aging-related immune alterations are predominantly performed on non-cancerous tissue, requiring additional study into the effects of age on tumor immune infiltration. We leverage large scale transcriptional data sets from The Cancer Genome Atlas and the Genotype-Tissue Expression project to distinguish normal age-related immune alterations from age-related changes in tumor immune infiltration. We demonstrate that while there is overlap between the normal immune aging phenotype and that of the ITME, there are several changes in immune cell abundance that are specific to the ITME, particularly in T cell, NK cell, and Macrophage populations. These results suggest that aged immune cells are more susceptible to tumor suppression of cytotoxic immune cell infiltration and activity than normal tissues, which creates an unfavorable ITME in older patients in excess of normal immune decline with age and may inform the application of existing and emerging immunotherapies for this large population of patients. We additionally identify that age-related increases in tumor mutational burden are associated with decreased DNA methylation and increased expression of the immune checkpoint genes PDL1, CD80, and LAG3 which may have implications for therapeutic application of immune checkpoint blockade in older patients.

cancer biology↗

bioRxiv2020

Matrix factorization and transfer learning uncover regulatory biology across multiple single-cell ATAC-seq data sets

While single-cell ATAC-seq analysis methods allow for robust clustering of cell types, the question of how to integrate multiple scATAC-seq data sets and/or sequencing modalities is still open. We present an analysis framework that enables such integration by applying the CoGAPS Matrix Factorization algorithm and the projectR transfer learning program to identify common regulatory patterns across scATAC-seq data sets. Using publicly available scATAC-seq data, we find patterns that accurately characterize cell types both within and across data sets. Furthermore, we demonstrate that these patterns are both consistent with current biological understanding and reflective of novel regulatory biology.

bioinformatics↗

bioRxiv2017

Epigenetic Regulation of Gene Expression in Cancer: Techniques, Resources, and Analysis

Cancer is a complex disease, driven by aberrant activity in numerous signaling pathways in even individual malignant cells. Epigenetic changes are critical mediators of these functional changes that drive and maintain the malignant phenotype. Changes in DNA methylation, histone acetylation and methylation, non-coding RNAs, post-translational modifications are all epigenetic drivers in cancer, independent of changes in the DNA sequence. These epigenetic alterations, once thought to be crucial only for the malignant phenotype maintenance, are now recognized as critical also for disrupting essential pathways that protect the cells from uncontrolled growth, longer survival and establishment in distant sites from the original tissue. In this review, we focus on DNA methylation and chromatin structure in cancer. While associated with cancer, the precise functional role of these alterations is an area of active research using emerging high-throughput approaches and bioinformatics analysis tools. Therefore, this review describes these high-throughput measurement technologies, public domain databases for high-throughput epigenetic data in tumors and model systems, and bioinformatics algorithms for their analysis. Advances in bioinformatics data integration techniques that combine these epigenetic data with genomics data are essential to infer the function of specific epigenetic alterations in cancer, and are therefore also a focus of this review. Future studies using these emerging technologies will elucidate how alterations in the cancer epigenome cooperate with genetic aberrations to cause tumorigenesis initiation and progression. This deeper understanding is essential to future studies that will precisely infer patients prognosis and select patients who will be responsive to emerging epigenetic therapies.

cancer biology↗