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      The Consensus Molecular Subtypes of Colorectal Cancer

      research-article
      1 , 1 , 2 , 3 , 4 , 5 , 6 , 7 , 5 , 8 , 9 , 7 , 1 , 10 , 8 , 7 , 3 , 3 , 7 , 7 , 7 , 11 , 10 , 10 , 10 , 12 , 13 , 1 , 14 , 15 , 11 , 2 , 6 , 1 ,   16 , 3 , 9 , 6 , 7 , 17 , 10 , 3 , 18
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          Abstract

          Colorectal cancer (CRC) is a frequently lethal disease with heterogeneous outcomes and drug responses. To resolve inconsistencies among the reported gene expression–based CRC classifications and facilitate clinical translation, we formed an international consortium dedicated to large-scale data sharing and analytics across expert groups. We show marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMS) with distinguishing features: CMS1 (MSI Immune, 14%), hypermutated, microsatellite unstable, strong immune activation; CMS2 (Canonical, 37%), epithelial, chromosomally unstable, marked WNT and MYC signaling activation; CMS3 (Metabolic, 13%), epithelial, evident metabolic dysregulation; and CMS4 (Mesenchymal, 23%), prominent transforming growth factor β activation, stromal invasion, and angiogenesis. Samples with mixed features (13%) possibly represent a transition phenotype or intra-tumoral heterogeneity. We consider the CMS groups the most robust classification system currently available for CRC – with clear biological interpretability – and the basis for future clinical stratification and subtype–based targeted interventions.

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          Most cited references34

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          Oncogenic Kras Maintains Pancreatic Tumors through Regulation of Anabolic Glucose Metabolism

          Tumor maintenance relies on continued activity of driver oncogenes, although their rate-limiting role is highly context dependent. Oncogenic Kras mutation is the signature event in pancreatic ductal adenocarcinoma (PDAC), serving a critical role in tumor initiation. Here, an inducible Kras(G12D)-driven PDAC mouse model establishes that advanced PDAC remains strictly dependent on Kras(G12D) expression. Transcriptome and metabolomic analyses indicate that Kras(G12D) serves a vital role in controlling tumor metabolism through stimulation of glucose uptake and channeling of glucose intermediates into the hexosamine biosynthesis and pentose phosphate pathways (PPP). These studies also reveal that oncogenic Kras promotes ribose biogenesis. Unlike canonical models, we demonstrate that Kras(G12D) drives glycolysis intermediates into the nonoxidative PPP, thereby decoupling ribose biogenesis from NADP/NADPH-mediated redox control. Together, this work provides in vivo mechanistic insights into how oncogenic Kras promotes metabolic reprogramming in native tumors and illuminates potential metabolic targets that can be exploited for therapeutic benefit in PDAC. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Multiplatform analysis of 12 cancer types reveals molecular classification within and across tissues of origin.

            Recent genomic analyses of pathologically defined tumor types identify "within-a-tissue" disease subtypes. However, the extent to which genomic signatures are shared across tissues is still unclear. We performed an integrative analysis using five genome-wide platforms and one proteomic platform on 3,527 specimens from 12 cancer types, revealing a unified classification into 11 major subtypes. Five subtypes were nearly identical to their tissue-of-origin counterparts, but several distinct cancer types were found to converge into common subtypes. Lung squamous, head and neck, and a subset of bladder cancers coalesced into one subtype typified by TP53 alterations, TP63 amplifications, and high expression of immune and proliferation pathway genes. Of note, bladder cancers split into three pan-cancer subtypes. The multiplatform classification, while correlated with tissue-of-origin, provides independent information for predicting clinical outcomes. All data sets are available for data-mining from a unified resource to support further biological discoveries and insights into novel therapeutic strategies. Copyright © 2014 Elsevier Inc. All rights reserved.
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              CpG island methylator phenotype underlies sporadic microsatellite instability and is tightly associated with BRAF mutation in colorectal cancer.

              Aberrant DNA methylation of CpG islands has been widely observed in human colorectal tumors and is associated with gene silencing when it occurs in promoter areas. A subset of colorectal tumors has an exceptionally high frequency of methylation of some CpG islands, leading to the suggestion of a distinct trait referred to as 'CpG island methylator phenotype', or 'CIMP'. However, the existence of CIMP has been challenged. To resolve this continuing controversy, we conducted a systematic, stepwise screen of 195 CpG island methylation markers using MethyLight technology, involving 295 primary human colorectal tumors and 16,785 separate quantitative analyses. We found that CIMP-positive (CIMP+) tumors convincingly represent a distinct subset, encompassing almost all cases of tumors with BRAF mutation (odds ratio = 203). Sporadic cases of mismatch repair deficiency occur almost exclusively as a consequence of CIMP-associated methylation of MLH1 . We propose a robust new marker panel to classify CIMP+ tumors.
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                Author and article information

                Journal
                9502015
                8791
                Nat Med
                Nat. Med.
                Nature medicine
                1078-8956
                1546-170X
                13 September 2015
                12 October 2015
                November 2015
                01 May 2016
                : 21
                : 11
                : 1350-1356
                Affiliations
                [1 ]Sage Bionetworks, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
                [2 ]Vall d'Hebron Institute of Oncology (VHIO), Universitat Autònoma de Barcelona, Barcelona, Spain
                [3 ]LEXOR, Center for Experimental Molecular Medicine (CEMM), Academic Medical Center (AMC), University of Amsterdam, Amsterdam, Netherlands
                [4 ]Department of Biomedical Sciences, City University of Hong Kong, Hong Kong
                [5 ]Ligue Nationale Contre le Cancer, Paris, France
                [6 ]The Netherlands Cancer Institute (NKI), Amsterdam, Netherlands
                [7 ]SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
                [8 ]Agendia NV, Amsterdam, Netherlands
                [9 ]The Institute of Cancer Research, London, UK
                [10 ]The University of Texas M.D. Anderson Cancer Center, Houston, Texas, USA
                [11 ]Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
                [12 ]Gustave Roussy, Villejuif, France
                [13 ]Laboratorio de Genomica y Microarrays, Instituto de Investigación Sanitaria San Carlos, Hospital Clinico San Carlos, Madrid, Spain
                [14 ]Institut Catala d'Oncologia, L’Institut d'Investigació Biomèdica de Bellvitge, Barcelona, Spain
                [15 ]Oregon Health Sciences University, Portland, OR, USA
                [16 ]Universite Paris Descartes, Paris, France
                [17 ]University of Lausanne, Lausanne, Switzerland
                [18 ]Universitair ziekenhuis Leuven, Leuven, Belgium
                Author notes
                Corresponding authors: Justin Guinney, justin.guinney@ 123456sagebase.org , Louis Vermeulen, l.vermeulen@ 123456amc.uva.nl , Sabine Tejpar, sabine.tejpar@ 123456uzleuven.be
                [*]

                co-first authors

                [§]

                co-senior authors

                Article
                NIHMS722223
                10.1038/nm.3967
                4636487
                26457759
                abeba1dc-31ce-40f3-b906-f21f178d1888

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