Biology subjects

Edmiston, S. N.

Publications and source records attributed to Edmiston, S. N..

3 recordsLinked to original sources

bioRxiv2026

Chromatin architecture and physical constriction cooperate in phenotype switching and cancer cell dissemination

Phenotypic plasticity is a prominent cancer feature that contributes to metastatic potential and resistance to therapy across multiple cancer types. Cancer cell state transitions have been attributed to transcriptional programs, such as the AP1/TEAD-regulated gene network driving the mesenchymal-like (MES) phenotype. In addition, during dissemination, tumor cells are subjected to variable loads of physical mechanical pressure and constriction across transited tissue, which are thought to impact nuclear molecular crowding. How the interplay between mechanical pressure, global 3D nuclear architecture and transcriptional programs contributes to MES identity and metastatic adaptation remains unclear. Using cutaneous melanoma as a model for early dissemination, we integrate in vitro and in vivo epigenomic profiling with nanoscale imaging of cell lines and patient samples to investigate chromatin organization features underlying the MES phenotype. We find that in MES cells, CTCF is relocated from domain boundaries to regulatory regions of EMT-like genes, leading to reduced insulation, extended topological associated domains (TADs) and increased inter-domain contacts, and de novo formation of chromatin hubs. This conformational rewiring, along with loss of heterochromatin, supports nuclear deformability during invasion and dissemination. Conversely, physical constriction of melanocytic cells induces MES-like chromatin features--including CTCF repositioning and heterochromatin loss-- and promotes metastasis in vivo. Similarly, pharmacological inhibition of the heterochromatin mark H3K9me3 triggers MES characteristics and increases invasiveness. These results demonstrate that metastatic competency involves both epigenetic and structural nuclear reprogramming, enabling shifts in gene networks and physical adaptability. Our findings reveal mechanistic links between nuclear architecture and aggressive tumor behavior, identifying potential biomarkers and therapeutic targets to intercept metastatic progression.

cancer biology↗

bioRxiv2024

Improving Accuracy of Somatic Mutation Profiling in Large Epidemiologic Studies: Addressing Cases without Matched Normal Samples

Ideally, detection of somatic mutations in a tumor is accomplished using a patient-matched sample of normal cells as the benchmark. In this way somatic mutations can be distinguished from rare germline mutations. In large retrospective studies, archival tissue collection can pose challenges in obtaining samples of normal DNA. In this article we propose a protocol that improves somatic mutation analysis in the absence of a matched normal sample. The method was motivated by the InterMEL study, a large-scale epidemiologic investigation involving multiomic, multi-institutional genomic profiling of 1000 primary melanoma samples. The key insight for accomplishing improved mutation calling is the fact that germline mutations should produce a variant allele frequency (VAF) of around 50%. While a similar VAF of 50% would also be expected for somatic mutations in pure tumor samples, typically the tumor purity is much less than 50%, resulting in a considerably lower VAF. Making use of a technique that can simultaneously estimate both tumor purity and VAF from tumor-only samples we have developed a method for better distinguishing somatic versus germline variants. Based on 137 melanomas from the InterMEL Study with matched normal tissue to provide a gold standard we show that the conventional pipeline using a panel of (unmatched) normal samples has a false positive rate of 15.6% and a false negative rate of 3.5%. Our new technique improves these error rates to 6.4% and 2.1%, respectively.

genomics↗

bioRxiv2024

Spatial Immunophenotyping from Whole-Slide Multiplexed Tissue Imaging Using Convolutional Neural Networks

The multiplexed immunofluorescence (mIF) platform enables biomarker discovery through the simultaneous detection of multiple markers on a single tissue slide, offering detailed insights into intratumor heterogeneity and the tumor-immune microenvironment at spatially resolved single cell resolution. However, current mIF image analyses are labor-intensive, requiring specialized pathology expertise which limits their scalability and clinical application. To address this challenge, we developed CellGate, a deep-learning (DL) computational pipeline that provides streamlined, end-to-end whole-slide mIF image analysis including nuclei detection, cell segmentation, cell classification, and combined immuno-phenotyping across stacked images. The model was trained on over 750,000 single cell images from 34 melanomas in a retrospective cohort of patients using whole tissue sections stained for CD3, CD8, CD68, CK-SOX10, PD-1, PD-L1, and FOXP3 with manual gating and extensive pathology review. When tested on new whole mIF slides, the model demonstrated high precision-recall AUC. Further validation on whole-slide mIF images of 9 primary melanomas from an independent cohort confirmed that CellGate can reproduce expert pathology analysis with high accuracy. We show that spatial immuno-phenotyping results using CellGate provide deep insights into the immune cell topography and differences in T cell functional states and interactions with tumor cells in patients with distinct histopathology and clinical characteristics. This pipeline offers a fully automated and parallelizable computing process with substantially improved consistency for cell type classification across images, potentially enabling high throughput whole-slide mIF tissue image analysis for large-scale clinical and research applications.

bioinformatics↗