Biology subjects

Arslan, E.

Publications and source records attributed to Arslan, E..

3 recordsLinked to original sources

bioRxiv2021

KMT2D Loss Promotes Head and Neck Squamous Cell Carcinoma Through Enhancer Reprogramming and Modulation of Immune Microenvironment

Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer worldwide, with 5-year survival of [~]50%. Genomic profiling studies have identified important somatic mutations in this disease which presents an opportunity for precision medicine. We demonstrate that KMT2D, a histone methyltransferase harbors somatic mutations in [~]17% of HNSCC and is associated with 2-year recurrence in TCGA data. Consistent with algorithmic prediction of bring a driver tumor-suppressor event, its loss results in larger oral tumors in immune-proficient orthotopic models. Mechanistically, we find that KMT2D knockdown or KMT2D mutation causes loss of H3K4me1-marked enhancers harboring IRF7/9 binding sites, which is known to regulate interferon signaling. Indeed, KMT2D loss in human and murine cell lines deregulated transcriptional levels of cytokine expression and impacted numerous immune signaling pathways, including interferon signaling. Consistently, Kmt2d knockdown in murine tumors exhibited decrease in IFN{gamma}-producing effector T cells and an increase in T-cells with an exhausted phenotype. Epistasis experiments showed that exogenous treatment with IFN{gamma} abrogated the increased tumor growth in Kmt2d-deficient oral tumors. Together, these results support the role of KMT2D as a tumor suppressor in HNSCC that regulates the tumor microenvironment by modulating H3K4me1-marked enhancers controlling interferon signaling.

cancer biology↗

bioRxiv2021

Population-specific genome graphs improve high-throughput sequencing data analysis: A case study on the Pan-African genome

Graph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference to represent the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based toolkits for NGS read alignment and variant calling, methods to curate genomic variants and subsequently construct genome graphs remains an understudied problem that inevitably determines the effectiveness of the overall bioinformatics pipeline. In this study, we discuss obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and demonstrate this approach on the whole-genome samples of African ancestry. Our results show that population-specific graphs, as more representative alternatives to linear or generic graph references, can achieve significantly lower read mapping errors and enhanced variant calling sensitivity, in addition to providing the improvements of joint variant calling without the need of computationally intensive post-processing steps.

bioinformatics↗

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↗