Andrews, J. M. | Search bioRxiv

bioRxiv2025

Single Cell Resolution Tracking of Cutaneous T-Cell Lymphoma Reveals Clonal Evolution in Disease Progression

Cutaneous T-cell lymphoma (CTCL) remains a challenging disease due to its significant heterogeneity, therapy resistance, and relentless progression. Multi-omics technologies offer the potential to provide uniquely precise views of disease progression and response to therapy. We present here a comprehensive multi-omics view of CTCL clonal evolution, incorporating exome, whole genome, epigenome, bulk-, single cell (sc) VDJ-, and scRNA-sequencing of 114 clinically annotated serial skin, peripheral blood, and lymph node samples from 35 CTCL patients. We leveraged this extensive dataset to define the molecular underpinnings of CTCL progression in individual patients at single cell resolution with the goal of identifying clinically useful biomarkers and therapeutic targets. Our studies identified a large number of recurrent progression-associated clonal genomic alterations; we highlight mutation of CCR4, PI3K signaling, and PD-1 checkpoint pathways as evasion tactics deployed by malignant T cells. We also identified a gain of function mutation in STAT3 (D661Y) and demonstrated by CUT&RUN-seq that it enhances binding to transcription start sites of genes in Rho GTPase pathways, which we previously reported to have activated chromatin and increased expression in HDACi-resistant CTCL. These data provide further support for a previously unrecognized role for Rho GTPase pathway dysregulation in CTCL progression. A striking number of progression-associated mutations occurred in chromatin methylation modifiers, including EZH2, suggesting that EZH1/2 inhibition may also benefit patients with CTCL. Knowledge of these molecular changes should be leveraged for improved disease monitoring, biomarker-informed clinical trial design, and new therapeutic strategies in this challenging and incurable cancer.

cancer biology↗

bioRxiv2021

An extremes of phenotype approach confirms significant genetic heterogeneity in patients with ulcerative colitis.

Ulcerative colitis (UC) is a major form of inflammatory bowel disease with increasing global incidence. There is significant phenotypic heterogeneity defined by a range of clinical variables including age of onset and disease extent. Clinical outcomes range from long-term remission on minimal therapy to surgical resection. Close to 70% of UC risk can be attributed to genetics and understanding the genetic mechanisms contributing to this risk and disease heterogeneity is vital for understanding disease pathogenesis and improving patient outcomes through targeted screening and therapies. This study aims to characterise the genetic heterogeneity of UC by identifying genomic risk variants specific to mild and/or severe forms of UC, exploring variations in the effect size of known risk variants and assessing the clinical value of a genetic risk score (GRS). We conducted genome-wide association (GWA) analyses in 287 patients with mild UC, 311 patients with severe UC and 583 age- and gender-matched controls. Odds ratios (OR) for mild vs control, severe vs control and combined mild and severe UC vs control were calculated. Using the combined UC data, two independent loci in the HLA region reached genome-wide significance. An additional genome-wide significant signal on chromosome 1 was identified in severe cases only. OR for known risk loci varied between mild and severe patients and were similar to previously published results. Effect estimates from the most recent UC GWA meta-analysis were used to calculate a GRS for each individual. A higher mean GRS was observed in both mild and severe UC cases compared to controls however, there was no difference between the mean GRS for mild and severe UC. Heterogeneity in effect sizes of UC associated variants between mild and severe disease burden suggests the presence of genetically distinct signatures. While large consortium data are needed to identify genome-wide significant variants, additional risk loci may be identified by targeted recruitment of individuals with a history of severe disease. Author SummaryUlcerative colitis (UC) is a chronic and often debilitating form of inflammatory bowel disease affecting approximately 0.3% of the population in industrialized economies. The disease displays significant clinical heterogeneity including age at presentation, disease severity, and the propensity to develop disease-related complications. Several previous studies have demonstrated the heritability of UC, identifying over 30 loci specific to the disease. The majority of these loci have small to modest effect sizes other than those within the Human Leucocyte Antigen (HLA) region on chromosome 6. Using stringent clinical criteria for defining mild and severe forms of UC in an extremes of phenotype approach, we undertook a genome wide association study in a dataset of 1222 participants to investigate genetic heterogeneity in this disease. We demonstrated substantial differences in genetic associations in severe UC as compared to mild UC. While over 2,000 SNPs achieved genome-wide significance in the severe UC analysis, none reached significance for mild UC. These results were reflected in significant differences in odds ratios. We identified Complement Factor B (CFB) as a potential susceptibility gene for severe UC in the Caucasian population with additional tissue gene expression demonstrating a positive correlation with disease severity.

genetics↗

bioRxiv2020

Determining subpopulation methylation profiles from bisulfite sequencing data of heterogeneous samples using DXM

Epigenetic changes, such as aberrant DNA methylation, contribute to cancer clonal expansion and disease progression. However, identifying subpopulation-level changes in a heterogeneous sample remains challenging. Thus, we have developed a computational approach, DXM, to deconvolve the methylation profiles of major allelic subpopulations from the bisulfite sequencing data of a heterogeneous sample. DXM does not require prior knowledge of the number of subpopulations or types of cells to expect. We benchmark DXMs performance and demonstrate improvement over existing methods. We further experimentally validate DXM predicted allelic subpopulation-methylation profiles in four Diffuse Large B-Cell Lymphomas (DLBCLs). Lastly, as proof-of-concept, we apply DXM to a cohort of 31 DLBCLs and relate allelic subpopulation methylation profiles to relapse. We thus demonstrate that DXM can robustly find allelic subpopulation methylation profiles that may contribute to disease progression using bisulfite sequencing data of any heterogeneous sample.

bioinformatics↗