Poor-prognosis colon cancer is defined by a molecularly distinct subtype and develops from serrated precursor lesions
TL;DR: This work demonstrates, using an unsupervised classification strategy involving over 1,100 individuals with colon cancer, that three main molecularly distinct subtypes can be recognized, and provides evidence that this subtype relates to sessile-serrated adenomas.
Abstract: Colon cancer is a clinically diverse disease. This heterogeneity makes it difficult to determine which patients will benefit most from adjuvant therapy and impedes the development of new targeted agents. More insight into the biological diversity of colon cancers, especially in relation to clinical features, is therefore needed. We demonstrate, using an unsupervised classification strategy involving over 1,100 individuals with colon cancer, that three main molecularly distinct subtypes can be recognized. Two subtypes have been previously identified and are well characterized (chromosomal-instable and microsatellite-instable cancers). The third subtype is largely microsatellite stable and contains relatively more CpG island methylator phenotype-positive carcinomas but cannot be identified on the basis of characteristic mutations. We provide evidence that this subtype relates to sessile-serrated adenomas, which show highly similar gene expression profiles, including upregulation of genes involved in matrix remodeling and epithelial-mesenchymal transition. The identification of this subtype is crucial, as it has a very unfavorable prognosis and, moreover, is refractory to epidermal growth factor receptor-targeted therapy.
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Sage Bionetworks1, Autonomous University of Barcelona2, University of Amsterdam3, City University of Hong Kong4, Netherlands Cancer Institute5, Swiss Institute of Bioinformatics6, University of Texas MD Anderson Cancer Center7, École Polytechnique Fédérale de Lausanne8, Institut Gustave Roussy9, Hospital Clínico San Carlos10, Oregon Health & Science University11, Paris Descartes University12, University of Lausanne13, Katholieke Universiteit Leuven14
TL;DR: An international consortium dedicated to large-scale data sharing and analytics across expert groups is formed, showing marked interconnectivity between six independent classification systems coalescing into four consensus molecular subtypes (CMSs) with distinguishing features.
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 (CMSs) with distinguishing features: CMS1 (microsatellite instability immune, 14%), hypermutated, microsatellite unstable and strong immune activation; CMS2 (canonical, 37%), epithelial, marked WNT and MYC signaling activation; CMS3 (metabolic, 13%), epithelial and 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 intratumoral 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.
3,351 citations
Vanderbilt University1, Pacific Northwest National Laboratory2, Washington University in St. Louis3, Fred Hutchinson Cancer Research Center4, Icahn School of Medicine at Mount Sinai5, National Institutes of Health6, Harvard University7, Massachusetts Institute of Technology8, Georgetown University9, Johns Hopkins University10, Leidos11, Memorial Sloan Kettering Cancer Center12, National Institute of Standards and Technology13, New York University14, Stanford University15, University of Chicago16, University of North Carolina at Chapel Hill17, University of Washington18, Virginia Tech19
TL;DR: Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.
Abstract: Extensive genomic characterization of human cancers presents the problem of inference from genomic abnormalities to cancer phenotypes. To address this problem, we analysed proteomes of colon and rectal tumours characterized previously by The Cancer Genome Atlas (TCGA) and perform integrated proteogenomic analyses. Somatic variants displayed reduced protein abundance compared to germline variants. Messenger RNA transcript abundance did not reliably predict protein abundance differences between tumours. Proteomics identified five proteomic subtypes in the TCGA cohort, two of which overlapped with the TCGA 'microsatellite instability/CpG island methylation phenotype' transcriptomic subtype, but had distinct mutation, methylation and protein expression patterns associated with different clinical outcomes. Although copy number alterations showed strong cis- and trans-effects on mRNA abundance, relatively few of these extend to the protein level. Thus, proteomics data enabled prioritization of candidate driver genes. The chromosome 20q amplicon was associated with the largest global changes at both mRNA and protein levels; proteomics data highlighted potential 20q candidates, including HNF4A (hepatocyte nuclear factor 4, alpha), TOMM34 (translocase of outer mitochondrial membrane 34) and SRC (SRC proto-oncogene, non-receptor tyrosine kinase). Integrated proteogenomic analysis provides functional context to interpret genomic abnormalities and affords a new paradigm for understanding cancer biology.
1,183 citations
French Institute of Health and Medical Research1, Pierre-and-Marie-Curie University2, Paris Descartes University3, University of Bern4, University of Erlangen-Nuremberg5, Providence Portland Medical Center6, Radboud University Nijmegen7, Umeå University8, Karolinska Institutet9, Humanitas University10, Medical University of Graz11, Dorset County Hospital NHS Foundation Trust12, Charles University in Prague13, Cornell University14, University Health Network15, University of Toronto16, Keio University17, Yamaguchi University18, Kindai University19, Harvard University20, Royal Melbourne Hospital21, University of Melbourne22, Sapporo Medical University23, Kurume University24, Xi'an Jiaotong University25, University of Texas MD Anderson Cancer Center26, Mayo Clinic27, Grigore T. Popa University of Medicine and Pharmacy28, Oregon Health & Science University29
TL;DR: In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC or UICCTNM classification.
Abstract: The American Joint Committee on Cancer/Union Internationale Contre le Cancer (AJCC/UICC) TNM staging system provides the most reliable guidelines for the routine prognostication and treatment of colorectal carcinoma. This traditional tumour staging summarizes data on tumour burden (T), the presence of cancer cells in draining and regional lymph nodes (N) and evidence for distant metastases (M). However, it is now recognized that the clinical outcome can vary significantly among patients within the same stage. The current classification provides limited prognostic information and does not predict response to therapy. Multiple ways to classify cancer and to distinguish different subtypes of colorectal cancer have been proposed, including morphology, cell origin, molecular pathways, mutation status and gene expression-based stratification. These parameters rely on tumour-cell characteristics. Extensive literature has investigated the host immune response against cancer and demonstrated the prognostic impact of the in situ immune cell infiltrate in tumours. A methodology named 'Immunoscore' has been defined to quantify the in situ immune infiltrate. In colorectal cancer, the Immunoscore may add to the significance of the current AJCC/UICC TNM classification, since it has been demonstrated to be a prognostic factor superior to the AJCC/UICC TNM classification. An international consortium has been initiated to validate and promote the Immunoscore in routine clinical settings. The results of this international consortium may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).
1,128 citations
Cites background or result from "Poor-prognosis colon cancer is defi..."
...Other gene expression-based analysis described three groups of CRC patients [23], which correlated with two of the known molecularly defined groups, namely the MSI (renamed CCS2) and the CIN (CCS1) groups, whereas the third group corresponded partly with the CIMP group (CCS3)....
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...These issues were not addressed in either study [22,23]....
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...Unfortunately, half of the patients turned out to be unclassifiable [22] and the absence of CDX2 positivity in all CCS3 samples (25% of CRCs) [23] does not correspond with extensive literature data (98% positivity in CRCs) [27]....
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TL;DR: It is shown that the use of TGF-β signaling inhibitors to block the cross-talk between cancer cells and the microenvironment halts disease progression, and all poor-prognosis CRC subtypes share a gene program induced by T GF-β in tumor stromal cells.
Abstract: Recent molecular classifications of colorectal cancer (CRC) based on global gene expression profiles have defined subtypes displaying resistance to therapy and poor prognosis. Upon evaluation of these classification systems, we discovered that their predictive power arises from genes expressed by stromal cells rather than epithelial tumor cells. Bioinformatic and immunohistochemical analyses identify stromal markers that associate robustly with disease relapse across the various classifications. Functional studies indicate that cancer-associated fibroblasts (CAFs) increase the frequency of tumor-initiating cells, an effect that is dramatically enhanced by transforming growth factor (TGF)-β signaling. Likewise, we find that all poor-prognosis CRC subtypes share a gene program induced by TGF-β in tumor stromal cells. Using patient-derived tumor organoids and xenografts, we show that the use of TGF-β signaling inhibitors to block the cross-talk between cancer cells and the microenvironment halts disease progression.
857 citations
TL;DR: The results demonstrate that unbiased single-cell RNA–seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.
Abstract: Intratumoral heterogeneity is a major obstacle to cancer treatment and a significant confounding factor in bulk-tumor profiling. We performed an unbiased analysis of transcriptional heterogeneity in colorectal tumors and their microenvironments using single-cell RNA-seq from 11 primary colorectal tumors and matched normal mucosa. To robustly cluster single-cell transcriptomes, we developed reference component analysis (RCA), an algorithm that substantially improves clustering accuracy. Using RCA, we identified two distinct subtypes of cancer-associated fibroblasts (CAFs). Additionally, epithelial-mesenchymal transition (EMT)-related genes were found to be upregulated only in the CAF subpopulation of tumor samples. Notably, colorectal tumors previously assigned to a single subtype on the basis of bulk transcriptomics could be divided into subgroups with divergent survival probability by using single-cell signatures, thus underscoring the prognostic value of our approach. Overall, our results demonstrate that unbiased single-cell RNA-seq profiling of tumor and matched normal samples provides a unique opportunity to characterize aberrant cell states within a tumor.
777 citations
References
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TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Abstract: Random forests are a combination of tree predictors such that each tree depends on the values of a random vector sampled independently and with the same distribution for all trees in the forest. The generalization error for forests converges a.s. to a limit as the number of trees in the forest becomes large. The generalization error of a forest of tree classifiers depends on the strength of the individual trees in the forest and the correlation between them. Using a random selection of features to split each node yields error rates that compare favorably to Adaboost (Y. Freund & R. Schapire, Machine Learning: Proceedings of the Thirteenth International conference, aaa, 148–156), but are more robust with respect to noise. Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the splitting. Internal estimates are also used to measure variable importance. These ideas are also applicable to regression.
79,257 citations
37,017 citations
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
Abstract: Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
34,830 citations
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.
14,144 citations
TL;DR: A method that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements is described, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
Abstract: Microarrays can measure the expression of thousands of genes to identify changes in expression between different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. We describe a method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to estimate the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estimated FDR of 12%, compared with FDRs of 60 and 84% by using conventional methods of analysis. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.
12,102 citations