Biology subjects

Orouji, E.

Publications and source records attributed to Orouji, E..

9 recordsLinked to original sources

bioRxiv2026

Chromatin Landscape of Cancer Cell Lines Identifies Enhancer Subtypes

Epigenetic aberrations are a hallmark of cancer; however, systematic chromatin state maps of cancer cells are unavailable. We generated and analyzed 803 histone mark profiles in 142 cancer cell lines and 114 human tumors belonging to 9 solid tumor types. Irrespective of their cell-of-origin, cancer cells segregate from normal tissues based on their enhancer patterns, suggesting enhancer deregulation is a fundamental epigenetic feature in cancer. Enhancer based clustering defined 5 distinct subgroups of cancer cells (EpiC1-5) with unique developmental trajectories, molecular features and dependencies. Importantly, we define a set of core TFs that are critical for EpiC-specific enhancer patterns and survival. Notably, EpiC4 represented a predominantly epigenetic, pan-cancer subtype that displays poor survival, activation and dependence on a FN1-CAV1-SRC-PI3K-AKT signaling network. Together, these data uncover enhancer heterogeneity in pan-cancer systems with identification of a novel enhancer-based subtype and identify potential new therapeutic targets associated with unique epigenetic features.

cancer biology↗

bioRxiv2024

Benchmarking Peak Calling Methods for CUT&RUN

Cleavage Under Targets and Release Using Nuclease (CUT&RUN) has rapidly gained prominence as an effective approach for mapping protein-DNA interactions, especially histone modifications, offering substantial improvements over conventional chromatin immunoprecipitation sequencing (ChIP-seq). However, the effectiveness of this technique is contingent upon accurate peak identification, necessitating the use of optimal peak calling methods tailored to the unique characteristics of CUT&RUN data. Here, we benchmark four prominent peak calling tools, MACS2, SEACR, GoPeaks, and LanceOtron, evaluating their performance in identifying peaks from CUT&RUN datasets. Our analysis utilizes in-house data of three histone marks (H3K4me3, H3K27ac, and H3K27me3) from mouse brain tissue, as well as samples from the 4D Nucleome database. We systematically assess these tools based on parameters such as the number of peaks called, peak length distribution, signal enrichment, and reproducibility across biological replicates. Our findings reveal substantial variability in peak calling efficacy, with each method demonstrating distinct strengths in sensitivity, precision, and applicability depending on the histone mark in question. These insights provide a comprehensive evaluation that will assist in selecting the most suitable peak caller for high-confidence identification of regions of interest in CUT&RUN experiments, ultimately enhancing the study of chromatin dynamics and transcriptional regulation.

genomics↗

bioRxiv2023

Single-nucleus multiomic atlas of frontal cortex in amyotrophic lateral sclerosis with a deep learning-based decoding of alternative polyadenylation mechanisms

Amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD) are two age-related and fatal neurodegenerative disorders that lie on a shared disease spectrum. While both disorders involve complex interactions between neuronal and glial cells, the specific cell-type alterations and their contributions to disease pathophysiology remain incompletely understood. Here, we applied single-nucleus RNA sequencing of the orbitofrontal cortex, a region affected in ALS-FTLD, to map cell-type specific transcriptional signatures in C9orf72-related ALS (with and without FTLD) and sporadic ALS cases. Our findings reveal disease- and cell-type-specific transcriptional changes, with neurons exhibiting the most pronounced alterations, primarily affecting mitochondrial function, protein homeostasis, and chromatin remodeling. A comparison with independent datasets from different cortical regions of C9orf72 and sporadic ALS cases showed concordance in several pathways, with neuronal STMN2 and NEFL showing consistent up-regulation between brain regions and disease subtypes. We also interrogated alternative polyadenylation (APA) as an additional layer of transcriptional regulation, demonstrating that APA events are not correlated with identified gene expression changes. To interpret these events, we developed APA-Net, a deep learning model that integrates transcript sequences with RNA-binding protein expression profiles, revealing cell type-specific patterns of APA regulation. Our atlas illuminates cell type-specific pathomechanisms of ALS/FTLD, providing a valuable resource for further investigation.

neuroscience↗

bioRxiv2022

A primary patient-derived model for investigating functional heterogeneity within the human Leukemic Stem Cell Compartment

The ability of leukemic stem cells (LSC) to evade therapy and fuel leukemic progression causing relapse impedes therapeutic success in acute myeloid leukemia (AML). The LSC pool within a patient sample is not homogenous but comprises distinct LSC subsets that vary in self-renewal and propagation properties. The stemness programs that underlie LSC types are poorly understood since human LSC studies require primary patient samples where LSC numbers are low and isolation methods impure. To overcome these challenges, we developed a patient-derived AML model system (OCI-AML22) displaying a functionally, transcriptionally and epigenetically defined cellular hierarchy driven by functional LSCs that can be immunophenotypically identified and isolated. Through single cell and functional approaches, the OCI-AML22 LSC fraction was found to contain distinct LSCs that vary in proliferative and differentiation properties. OCI-AML22 represents a valuable resource to decipher mechanisms driving stemness and the multiple layers of heterogeneity within LSCs.

cancer biology↗

bioRxiv2021

H3K4me1-marked Enhancer Activation in Resistant Prostate Cancers Implicates SOX4 and MENIN Inhibition as Therapeutic Strategies

Chromatin elements and regulators play important roles during progression of prostate cancer, however, their involvement in response to therapy is less well understood. Using comprehensive chromatin profiling of patient-derived tumors, we find that enhancer elements marked by H3K4me1 are highly enriched in aggressive therapy-resistant prostate cancers on important resistance-driving genes, such as those involved in FOXA1, NOTCH and TGF-{beta} signaling. Importantly, by targeting H3K4me1-elements through inhibition of the MLL complex, a H3K4 methyltransferase, we reduced the proliferative capacity and H3K4me1-associated loci in enzalutamide-resistant prostate cancer lines. We identify AR, FOXA1, HOXB13 and SOX4 as a subset of core TFs that are critical for establishing transcriptional networks via active enhancer reprogramming during acquisition of resistance to therapy. Knock-down of SOX4 reduced cell proliferation and disrupted the H3K4me1 enhancer landscape, further suggesting a role for this TF in therapy-resistance. Overall, our data implicate H3K4me1-marked enhancers as a key epigenetic feature of therapy-resistance, implicate SOX4 in enhancer reprogramming and suggest use of MLL/MENIN inhibitors as a potential therapeutic strategy in high-grade and locally advanced prostate cancers that do not respond to traditional therapies.

cancer biology↗

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↗

bioRxiv2019

Bivalent and Broad Chromatin Domains Regulate Pro-metastatic Drivers in Melanoma

Chromatin deregulation is an emerging hallmark of cancer. However, the extent of epigenetic aberrations during tumorigenesis and their relationship with genetic aberrations are poorly understood. Using ChIP-sequencing for enhancers (H3K27ac and H3K4me1), promoters (H3K4me3), active transcription (H3K79me2) and polycomb (H3K27me3) or heterochromatin (H3K9me3) repression we generated chromatin state profiles in metastatic melanoma using 46 tumor samples and cell lines. We identified a strong association of NRAS, but not BRAF mutations, with bivalent states harboring H3K4me3 and H3K27me3 marks. Importantly, the loss and gain of bivalent states occurred on important pro-metastasis regulators including master transcription factor drivers of mesenchymal phenotype including ZEB1, TWIST1, SNAI1 and CDH1. Unexpectedly, a subset of these and additional pro-metastatic drivers (e.g. POU3F2, SOX9 and PDGFRA) as well as melanocyte-specific master regulators (e.g. MITF, ZEB2, and TFAP2A) were regulated by exceptionally wide H3K4me3 domains that can span tens of thousands of kilobases suggesting roles of this new epigenetic element in melanoma metastasis. Overall, we find that BRAF, NRAS and WT melanomas may use bivalent states and broad H3K4me3 domains in a specific manner to regulate pro-metastatic drivers. We propose that specific epigenetic traits - such as bivalent and broad domains - get assimilated in the epigenome of pro-metastatic clones to drive evolution of cancer cells to metastasis.

cancer biology↗

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↗

bioRxiv2018

Enhancer Reprogramming Confers Dependence on Glycolysis and IGF signaling in KMT2D Mutant Melanoma

Epigenetic modifiers have emerged as important regulators of tumor progression. We identified histone methyltransferase KMT2D as a potent tumor-suppressor through an in vivo epigenome-focused pooled RNAi screen in melanoma. KMT2D harbors frequent somatic point mutations in multiple tumor types. How these events contribute to tumorigenesis and whether they impart therapeutic vulnerability are poorly understood. To address these questions, we generated a genetically engineered mouse model of melanoma based on conditional and melanocyte-specific deletion of KMT2D. We demonstrate KMT2D as a bona fide tumor suppressor which cooperates with activated BRAF. KMT2D-deficient tumors showed substantial reprogramming of key metabolic pathways including glycolysis. Glycolysis enzymes, intermediate metabolites and glucose consumption rate were aberrantly upregulated in KMT2D mutant cells. The pharmacological inhibition of glycolysis reduced proliferation and tumorigenesis preferentially in KMT2D mutant cells. Mechanistically, KMT2D loss caused drastic reduction of H3K4me1-marked active enhancer states. Loss of distal enhancer and subsequent reduction in expression of IGFBP5 activated IGF1R-AKT to increase glycolysis in KMT2D-deficient cells. We conclude that KMT2D loss promotes tumorigenesis by facilitating increased usage of glycolysis pathway for enhanced biomass needs via enhancer reprogramming. Our data imply that inhibition of glycolysis or IGFR pathway could be a potential therapeutic strategy in KMT2D mutant tumors.

cancer biology↗