Dataset Information

RNA-seq access dataset for epigenomics landscape of colorectal cancer

ABSTRACT: Enhancer aberrations are beginning to emerge as a key feature of colorectal cancers, however, we have limited understanding of epigenomic patterns that distinguish tumors and underlying heterogeneity between tumors. Here, using epigenomic profiling of colorectal tumors, adenomas and normal colon tissues, we identify unique pattern of regulatory elements in colorectal cancer, which could reliably distinguish tumors from normal colon specimens. We define shared and unique enhancer elements during colorectal cancer progression using normal adjacent colon, adenomas and adenocarcinomas. We validate the functional nature of tumor-specific enhancers for important oncogenes such as ASCL2 and Fzd10. NMF clustering identified 4 epigenetic (EPIC) subtypes in colorectal cancer, which mimics consensus molecular subtypes (CMS)1, with an advantage of introducing a novel epigenetically-identifiable subtype with poor prognosis and survival. Based on this correlation, we defined and validated a combination therapeutic strategy of enhancer-blocking BETi with pathway specific inhibitor for 3 CMS subtypes. In conclusion, by comprehensive characterization of chromatin state patterns in colorectal tumors, we define epigenomic patterns during tumor evolution, heterogeneity of enhancers among patients and a combination therapy strategy for CMS-subgroups.

ORGANISM(S): Homo sapiens

PROVIDER: GSE136630 | GEO | 2021/05/22

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Project description:The Colorectal Cancer Subtyping Consortium established four Consensus Molecular Subtypes (CMS) in colorectal cancer: CMS1 (microsatellite-instability [MSI], Immune), CMS2 (Canonical, epithelial), CMS3 (Metabolic), and CMS4 (Mesenchymal). However, in the clinical practice only patients with MSI tumors have experienced a change in the management of their disease. Proteomics has experimented significant technical advances with the implementation of data-independent acquisition (DIA) as the gold-standard for the analysis of large cohort of patients, providing a dynamic framework of the disease and direct information about the effectors of the biological processes. The aim of this study is the characterization of the proteome of colon cancer CMS by DIA–based proteomics and expand the utility of CMS in the clinical practice. CMS were assigned and differences in the protein profiles between CMS were defined and evaluated using a Systems Biology approach. One hundred fifty-eight paraffin samples from colon cancer patients with stage II-III disease treated with adjuvant chemotherapy were analyzed. After applying quality criteria, 1,940 identified and quantified proteins were used for the analyses. CMS1 had an overexpression of proteins related to immune system, specifically neutrophils, phagocytosis and antimicrobial response, and a glycolytic profile. CMS3 had an overexpression of proteins related to metabolic pathways, while CMS2 showed an intermediate protein profile between CMS1-3, but with a characteristic profile regarding translation, adhesion, and mitochondria and metabolism biological processes. Two proteomics subtypes within CMS2 with different protein characteristics and prognosis were identified. CMS4 was the most different group with an overexpression of proteins related to angiogenesis, extracellular matrix, focal adhesion, and complement activation. In conclusion, DIA LC-MS/MS proteomics characterization of CRC CMS established new features for each CMS, such as a higher mitochondrial activity in CMS2 or an overexpression of proteins related to phagocytosis in CMS1, but also confirmed previous findings established using transcriptomics such as an overexpression of proteins related to angiogenesis in CMS4. In this CMS4 subtype, several adhesion-related proteins suggested a high metastatic profile and a possible chemoresistance that may explain the worse prognosis of this subtype. CMS1 was characterized as immune and glycolitic-active subtype, being the combination of immunotherapy and glycolytic inhibitors a possible therapeutic strategy. Additionally, two different CMS2 proteomics-based subtypes with different prognosis and possible therapeutic targets were identified. These proccesses could be used as therapeutic targets in the future.

Dataset Information

RNA-seq access dataset for epigenomics landscape of colorectal cancer

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets