Biology subjects

Kopetz, S.

Publications and source records attributed to Kopetz, S..

25 records · Page 2Linked to original sources

bioRxiv2022

Resolving clonal substructure from single cell genomic data using CopyKit

High-throughput methods for single cell copy number sequencing have enabled the profiling of thousands of cells in parallel, yet there remains a significant bottleneck for data analysis. Here we present CopyKit, a comprehensive set of computational methods for the pre-processing and analysis of single cell copy number data to resolve clonal substructure and reconstruct genetic lineages in tumors. We performed single cell DNA sequencing of 2977 cells from multiple spatial regions in two liver metastasis and 7365 cells from three primary tumors with matched metastatic tissues. In the liver metastases, CopyKit resolved clonal substructure in different spatial regions, which revealed both clonal intermixing and spatial segregation in the tumor mass. In the matched metastatic colorectal and breast cancers, CopyKit resolved metastatic lineages and identified subclones and genomic events that were associated with metastases. These applications show that CopyKit is comprehensive tool for resolving copy number substructure in tumors.

genomics↗

bioRxiv2021

CliP: subclonal architecture reconstruction of cancer cells in DNA sequencing data using a penalized likelihood model

Tumor subclonal architecture shapes cancer evolution, yet subclonal reconstruction from bulk sequencing remains difficult to scale due to computational cost and model complexity. We present CliPP, a penalized-likelihood framework that jointly estimates cellular prevalence with pairwise fusion penalties, automatically identifying subclones without requiring extensive priors. Across simulations and 2,778 whole-genome tumors with external consensus reconstructions, CliPP achieves consistently good performances when compared to state-of-the-art approaches while providing substantial runtime reductions. Applied to 7,000+ tumors across >30 cancer types, CliPP quantifies pervasive subclonality and delineates cohort-level subclone landscapes. CliPP enables fast, reproducible large-scale subclonal analysis and is freely available to the community through GitHub and a shiny app.

bioinformatics↗

bioRxiv2020

Differing total mRNA expression shapes the molecular and clinical phenotype of cancer

Cancers can vary greatly in their transcriptomes. In contrast to alterations in specific genes or pathways, differences in tumor cell total mRNA content have not been comprehensively assessed. Technical and analytical challenges have impeded examination of total mRNA expression at scale across cancers. To address this, we developed a model for quantifying tumor-specific total mRNA expression (TmS) from bulk sequencing data, which performs transcriptomic deconvolution while adjusting for mixed genomes. We used single-cell RNA sequencing data to demonstrate total mRNA expression as a feature of tumor phenotype. We estimated and validated TmS in 5,015 patients across 15 cancer types identifying significant inter-individual variability. At a pan-cancer level, high TmS is associated with increased risk of disease progression and death. Cancer type-specific patterns of genetic alterations, intra-tumor genetic heterogeneity, as well as pan-cancer trends in metabolic dysregulation and hypoxia contribute to TmS. Taken together, our results suggest that measuring cell-type specific total mRNA expression offers a broader perspective of tracking cancer transcriptomes, which has important biological and clinical implications.

genomics↗

bioRxiv2020

Chromatin State Dynamics Confers Specific Therapeutic Strategies in Enhancer Subtypes of Colorectal Cancer

The extent and function of chromatin state aberrations during colorectal cancer (CRC) progression is not completely understood. Here, by comprehensive epigenomic characterization of 56 tumors, adenomas, and their matched normal tissues, we define the dynamics of chromatin states during the progression of colorectal cancer. H3K27ac-marked active enhancer state could distinguish between different stages of CRC progression. By epigenomic editing, we present evidence that gains of tumor-specific enhancers for crucial oncogenes, such as ASCL2 and FZD10, was crucial for excessive proliferation. Consistently, combination of MEK plus bromodomain (BET) inhibition was found to have synergistic effects in CRC patient-derived xenograft (PDX) models. Probing inter-tumor heterogeneity, we identified four distinct enhancer subtypes (EpiC), three of which correlate well with previously defined transcriptomic subtypes (CMSs). Importantly, CMS2 can be divided into two EpiC subgroups with significant survival differences. Leveraging such correlation, we devised a combinatorial therapeutic strategy of enhancer-blocking bromodomain inhibitors with pathway-specific inhibitors (PARPi, EGFRi, and TGF{beta}i) for three EPIC groups. Our data suggest that the dynamics of active enhancer underlies colorectal cancer progression and the patient-specific active enhancer patterns govern their unique gene expression patterns which can be leveraged for precision combination therapy.

cancer biology↗

bioRxiv2020

Relative Abundance of SARS-CoV-2 Entry Genes in the Enterocytes of the Lower Gastrointestinal Tract

COVID-19, the disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has rapidly spread throughout the world and was declared a pandemic by the World Health Organization, thus leading to a rapid surge in the efforts to understand the mechanisms of transmission, methods of prevention, and potential therapies. While COVID-19 frequently manifests as a respiratory infection,1 there is evidence for infection of the gastrointestinal (GI) tract1-4 with documented viral RNA shedding in the stool of infected patients.2,4 In this study, we aimed to investigate the expression of ACE2 and TMPRSS2, which are required for SARS-CoV-2 entry into mammalian cells,5 from single-cell RNA sequencing (scRNA-seq) datasets of five different parts of the GI tract: esophagus, stomach, pancreas, small intestine, and colon/rectum.

bioinformatics↗

bioRxiv2019

Gene-specific methylation profiles for integrative methylation-expression analysis in cancer research

DNA methylation is a key epigenetic factor regulating gene expression. While promoter-associated methylation has been extensively studied, recent publications have revealed that functionally important methylation also occurs in intergenic and distal regions, and varies across genes and tissue types. Given the growing importance of inter-platform integrative genomic analyses, there is an urgent need to develop methods to construct gene-level methylation summaries that account for the potentially complex relationships between methylation and expression. We introduce a novel sequential penalized regression approach to construct gene-specific methylation profiles (GSMPs) which find for each gene and tissue type a sparse set of CpGs best explaining gene expression and weights indicating direction and strength of association. Using TCGA and MD Anderson colorectal cohorts to build and validate our models, we demonstrate our strategy better explains expression variability than standard approaches and produces gene-level scores showing key methylation differences across recently discovered colorectal cancer subtypes. We share an R Shiny app that presents GSMP results for colorectal, breast, and pancreatic cancer with plans to extend it to all TCGA cancer types. Our approach yields tissue-specific, gene-specific sparse lists of functionally important CpGs that can be used to construct gene-level methylation scores that are maximally correlated with gene expression for use in integrative models, and produce a tissue-specific summary of which genes appear to be strongly regulated by methylation. Our results introduce an important resource to the biomedical community for integrative genomics analyses involving DNA methylation.

genomics↗

bioRxiv2019

An R Implementation of Tumor-Stroma-Immune Transcriptome Deconvolution Pipeline using DeMixT

The deconvolution of transcriptomic data from heterogeneous tissues in cancer studies remains challenging. Available software faces difficulties for accurately estimating both component-specific proportions and expression profiles for individual samples. To address these challenges, we present a new R-implementation pipeline for the more accurate and efficient transcriptome deconvolution of high dimensional data from mixtures of more than two components. The pipeline utilizes the computationally efficient DeMixT R-package with OpenMP and additional cancer-specific biological information to perform three-component deconvolution without requiring data from the immune profiles. It enables a wide application of DeMixT to gene expression datasets available from cancer consortium such as the Cancer Genome Atlas (TCGA) projects, where, other than the mixed tumor samples, a handful of normal samples are profiled in multiple cancer types. We have applied this pipeline to two TCGA datasets in colorectal adenocarcinoma (COAD) and prostate adenocarcinoma (PRAD). In COAD, we found varying distributions of immune proportions across the Consensus Molecular Subtypes, from the highest to the lowest being CMS1, CMS3, CMS4 and CMS2. In PRAD, we found the immune proportions are associated with progression-free survival (p<0.01) and negatively correlated with Gleason scores (p<0.001). Our DeMixT-centered analysis protocol opens up new opportunities to investigate the tumor-stroma-immune microenvironment, by providing both proportions and component-specific expressions, and thus better define the underlying biology of cancer progression.\n\nAvailability and implementation: An R package, scripts and data are available: https://github.com/wwylab/DeMixTallmaterials.

bioinformatics↗