A panel of DNA methylation markers for the classification of consensus molecular subtypes 2 and 3 in patients with colorectal cancer
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PROVIDER: PRJNA692117 | ENA |
REPOSITORIES: ENA
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Project description:INTRODUCTION. Consensus molecular subtypes (CMSs) can guide precision treatment of colorectal cancer (CRC). Currently available dedicated assays can identify CMS1 and CMS4 cases very well while a dedicated test to distinguish CMS2 and 3 is lacking. This study aimed to identify a panel of methylation markers to distinguish between CMS2 and 3 in patients with CRC. METHODS. Fresh-frozen tumor tissue of 239 patients with stage I-III CRC who underwent surgery between 2007 and 2014 was included. CMS classification was performed on RNA-seq data using the single-sample prediction parameter from the “CMSclassifier” package. Methylation profiles were gathered/obtained using the Infinium HumanMethylation450 BeadChip for 146 samples (124 CMS2 and 22 CMS3) . We performed adaptive group-regularised logistic ridge regression with post-hoc group-weighted elastic net marker selection to build prediction models for classification of CMS2 and CMS3 based on 15, 10 or 5 methylation markers. Data from the Cancer Genome Atlas project was used for independent validation of our obtained models. RESULTS. Overall methylation profiles differed between CMS2 and CMS3 tumors. Group-regularisation of the methylation probes was done based on their location either relative to a CpG island or relative to a gene present in the CMS classifier. This resulted in two different prediction models and subsequently different marker panels. For both panels, even when using only 5 markers, the sensitivity, specificity, and accuracy were >90%. Independent validation of the fixed models in TCGA data showed comparable performances. CONCLUSION. Our highly sensitive and specific methylation marker panel can be used to distinguish CMS2 and 3. This enables future development of a qPCR DNA methylation assay in patients with CRC to provide a specific and non-invasive classification tool.
2021-12-08 | GSE164811 | GEO
Project description:A consensus molecular subtypes classification strategy for clinical colorectal cancer tissues
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Project description:Analyses of large transcriptomics datasets of muscle-invasive bladder cancer (MIBC) has led to a consensus classification. Molecular subtypes of upper tract urothelial carcinomas (UTUC) are less known. Our objective was to determine the relevance of consensus classification in UTUCs by characterizing a novel cohort of surgically treated ≥pT1 tumors.Subtype IHC markers GATA3-CK5/6-TUBB2B in multiplex, CK20, p16 and Ki67, MMR proteins, PD-L1 IHC were evaluated. Heterogeneity was assessed morphologically and/or with subtype IHC. FGFR3 mutations were identified by pyrosequencing. We performed 3’RNA-seq, including with multisampling in heterogeneous cases. Consensus classes, unsupervised groups, and microenvironment cell abundance were determined using gene expression.Most of the 66 patients were men (77.3%), with pT1 (n=23, 34.8%) or pT2-4 stage UTUC (n=43, 65.2%). FGFR3 mutations and dMMR status were identified in 40% and 4.7% of cases, respectively. Consensus subtypes robustly classified UTUCs and reflected intrinsic subgroups. All pT1 tumors were classified as Luminal papillary (LumP). Combining our consensus classification results to that of previously published UTUC cohorts, LumP tumors represented 57.2% of ≥pT2 UTUC, which was significantly higher than in MIBC. Ten patients (15.2%) harbored areas of distinct subtypes. Consensus classes were associated with FGFR3 mutations, stage, morphology and IHC. The majority of LumP tumors were characterized by low immune infiltration and PD-L1 expression, in particular if FGFR3 mutated.
2023-10-10 | GSE244957 | GEO
Project description:This work presents a novel Consensus Molecular Subtypes (CMS) classifier for colorectal cancer (CRC), optimized for degraded RNA stemming from clinical formalin-fixed paraffin-embedded tissue samples.
2024-05-22 | GSE267010 | GEO
Project description:Consensus molecular subtypes classification of colorectal cancer as a predictive factor for chemotherapeutic efficacy against metastatic colorectal cancer
| PRJNA413394 | ENA
Project description:Colorectal cancer (CRC) is a highly heterogeneous disease both from a molecular and clinical perspective. Several distinct molecular entities, such as microsatellite instability (MSI), have been defined that make up biologically distinct subgroups with their own clinical course. Recent data indicated that CRC can be best segregated into four groups called Consensus Molecular Subtypes (CMS1-4), which each have a unique biology and gene expression pattern. In order to develop improved, subtype-specific therapies and to gain insight into the molecular wiring and origin of these subtypes, reliable models are needed. This study was designed to determine the heterogeneity and identify the presence of CMSs in a large panel of CRC cell lines, primary cultures and patient-derived xenografts (PDX). We provide a repository encompassing this heterogeneity and moreover describe that a large part of the models can be robustly assigned to one of the four CMSs, independent of the stromal contribution. We subsequently validate our CMS stratification by functional analysis which for instance shows mesenchymal enrichment in CMS4 and metabolic dysregulation in CMS3. Finally, we observe a clear difference in sensitivity to chemotherapy-induced apoptosis, specifically between CMS2 and CMS4. This relates to the in vivo efficacy of chemotherapy, which delays outgrowth of CMS2, but not CMS4 xenografts. This indicates that molecular subtypes are faithfully modelled in the CRC cell cultures and PDXs, representing tumour cell intrinsic and stable features. This repository provides researchers with a platform to study CRC using the existing heterogeneity.
2017-12-01 | GSE100478 | GEO
Project description:Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Currently this classification relies mainly on histological assessment, but gene expression analysis bymicroarrays has shown great promise. Here we show that high accuracy, quantitative proteomics can robustly segregate cancer subtypes directly at the level of expressed proteins. We investigated two histologically indistinguishable subtypes of diffuse large B-cell lymphoma (DLBCL): activated Bcell- like (ABC) and germinal-center B-cell-like (GCB) subtypes, by first developing a general lymphoma stable isotope labeling with amino acids in cell culture (SILAC) mix from heavy stable isotope-labeled cell lines. This super- SILAC mix was combined with cell lysates from five ABCDLBCL and five GCB-DLBCL cell lines. Shotgun proteomic analysis on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels yielded a proteome of more than 7,500 identified proteins. High accuracy of quantification allowed robust separation of subtypes by principal component analysis. The main contributors to the classification included proteins known to be differentially expressed between the subtypes such as the transcription factors IRF4 and SPI1/ PU.1, cell surface markers CD44 and CD27, as well as novel candidates. We extracted a signature of 55 proteins that segregated subtypes and contained proteins connected to functional differences between the ABC and GCB-DLBCL subtypes, including many NF-kappa B-regulated genes. Shortening the analysis time to single-shot analysis combined with use of the new linear quadrupole Orbitrap analyzer (Q Exactive) also clearly differentiated between the subtypes. These results show that high resolution shotgun proteomics combined with super-SILAC-based quantification is a promising new technology for tumor characterization and classification.
2015-04-24 | PXD002098 | Pride
Project description:Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Currently this classification relies mainly on histological assessment, but gene expression analysis bymicroarrays has shown great promise. Here we show that high accuracy, quantitative proteomics can robustly segregate cancer subtypes directly at the level of expressed proteins. We investigated two histologically indistinguishable subtypes of diffuse large B-cell lymphoma (DLBCL): activated Bcell- like (ABC) and germinal-center B-cell-like (GCB) subtypes, by first developing a general lymphoma stable isotope labeling with amino acids in cell culture (SILAC) mix from heavy stable isotope-labeled cell lines. This super- SILAC mix was combined with cell lysates from five ABCDLBCL and five GCB-DLBCL cell lines. Shotgun proteomic analysis on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels yielded a proteome of more than 7,500 identified proteins. High accuracy of quantification allowed robust separation of subtypes by principal component analysis. The main contributors to the classification included proteins known to be differentially expressed between the subtypes such as the transcription factors IRF4 and SPI1/ PU.1, cell surface markers CD44 and CD27, as well as novel candidates. We extracted a signature of 55 proteins that segregated subtypes and contained proteins connected to functional differences between the ABC and GCB-DLBCL subtypes, including many NF-kappa B-regulated genes. Shortening the analysis time to single-shot analysis combined with use of the new linear quadrupole Orbitrap analyzer (Q Exactive) also clearly differentiated between the subtypes. These results show that high resolution shotgun proteomics combined with super-SILAC-based quantification is a promising new technology for tumor characterization and classification.
2016-03-10 | MSV000079572 | MassIVE
Project description:Tumor heterogeneity is a major challenge to the treatment of colorectal cancer (CRC). Recently, a transcriptome-based classification was developed, segregating CRC into four consensus molecular subtypes (CMS) with distinct biological and clinical characteristics. Here, we applied the CMS classification on CRC cell lines to identify novel subtype-specific drug vulnerabilities. We combined publicly available transcriptome data from multiple resources to assign 159 CRC cell lines to CMS. By integrating results from large scale drug screens, we discovered that CMS1 cancer is highly vulnerable to the survivin suppressor YM155. We confirmed our results using an independent panel of CRC cell lines and demonstrate a 100-fold higher sensitivity of CMS1 lines. This vulnerability was specific to YM155 and not observed for commonly used chemotherapeutic agents. In CMS1 cancer, low concentrations of YM155 induced apoptosis and expression signatures associated with NFkappaB and ER stress mediated apoptosis signaling. Using a genome-wide CRISPR/Cas9 screen, we discovered a novel role of genes involved in LDL-receptor recycling as modulators of YM155 response in CMS1 CRC. Our work shows that combining drug response data with CMS classification in cell lines can reveal specific vulnerabilities and propose YM155 as novel CMS1 specific drug. We performed expression profiling experiments in order to validate molecular subtypes for selected cell lines
2018-07-03 | GSE116528 | GEO
Project description:Matrisome-focused integrative omics analysis reveals stromal phenotypes associated with consensus molecular subtypes in colorectal cancer
This reseaerch is conducted using colorectal cancer.
TMT 11plex experiment
2022-10-27 | MSV000090604 | MassIVE