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      Gene expression patterns unveil a new level of molecular heterogeneity in colorectal cancer

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          Abstract

          The recognition that colorectal cancer (CRC) is a heterogeneous disease in terms of clinical behaviour and response to therapy translates into an urgent need for robust molecular disease subclassifiers that can explain this heterogeneity beyond current parameters (MSI, KRAS, BRAF). Attempts to fill this gap are emerging. The Cancer Genome Atlas (TGCA) reported two main CRC groups, based on the incidence and spectrum of mutated genes, and another paper reported an EMT expression signature defined subgroup. We performed a prior free analysis of CRC heterogeneity on 1113 CRC gene expression profiles and confronted our findings to established molecular determinants and clinical, histopathological and survival data. Unsupervised clustering based on gene modules allowed us to distinguish at least five different gene expression CRC subtypes, which we call surface crypt-like, lower crypt-like, CIMP-H-like, mesenchymal and mixed. A gene set enrichment analysis combined with literature search of gene module members identified distinct biological motifs in different subtypes. The subtypes, which were not derived based on outcome, nonetheless showed differences in prognosis. Known gene copy number variations and mutations in key cancer-associated genes differed between subtypes, but the subtypes provided molecular information beyond that contained in these variables. Morphological features significantly differed between subtypes. The objective existence of the subtypes and their clinical and molecular characteristics were validated in an independent set of 720 CRC expression profiles. Our subtypes provide a novel perspective on the heterogeneity of CRC. The proposed subtypes should be further explored retrospectively on existing clinical trial datasets and, when sufficiently robust, be prospectively assessed for clinical relevance in terms of prognosis and treatment response predictive capacity. Original microarray data were uploaded to the ArrayExpress database ( http://www.ebi.ac.uk/arrayexpress/) under Accession Nos E-MTAB-990 and E-MTAB-1026.

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          Most cited references66

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          Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

          Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.
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            Experimentally derived metastasis gene expression profile predicts recurrence and death in patients with colon cancer.

            Staging inadequately predicts metastatic risk in patients with colon cancer. We used a gene expression profile derived from invasive, murine colon cancer cells that were highly metastatic in an immunocompetent mouse model to identify patients with colon cancer at risk of recurrence. This phase 1, exploratory biomarker study used 55 patients with colorectal cancer from Vanderbilt Medical Center (VMC) as the training dataset and 177 patients from the Moffitt Cancer Center as the independent dataset. The metastasis-associated gene expression profile developed from the mouse model was refined with comparative functional genomics in the VMC gene expression profiles to identify a 34-gene classifier associated with high risk of metastasis and death from colon cancer. A metastasis score derived from the biologically based classifier was tested in the Moffitt dataset. A high score was significantly associated with increased risk of metastasis and death from colon cancer across all pathologic stages and specifically in stage II and stage III patients. The metastasis score was shown to independently predict risk of cancer recurrence and death in univariate and multivariate models. For example, among stage III patients, a high score translated to increased relative risk of cancer recurrence (hazard ratio, 4.7; 95% confidence interval, 1.566-14.05). Furthermore, the metastasis score identified patients with stage III disease whose 5-year recurrence-free survival was >88% and for whom adjuvant chemotherapy did not increase survival time. A gene expression profile identified from an experimental model of colon cancer metastasis predicted cancer recurrence and death, independently of conventional measures, in patients with colon cancer. Copyright 2010 AGA Institute. Published by Elsevier Inc. All rights reserved.
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              Single-cell dissection of transcriptional heterogeneity in human colon tumors

              Cancer is often viewed as a caricature of normal developmental processes, but the extent by which its cellular heterogeneity truly recapitulates multi-lineage differentiation processes of normal tissues remains unknown. Here, we implement “single-cell PCR gene-expression analysis” (SINCE-PCR) to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single-cell (n = 1), we show that transcriptional diversity of cancer tissues is largely explained by in vivo multi-lineage differentiation, not only by clonal genetic heterogeneity. Finally, we show that perturbations in gene-expression programs linked to multi-lineage differentiation strongly associate with patient survival. Guided by SINCE-PCR data, we develop two-gene classifier systems (KRT20 vs CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard-ratios superior to pathological grade and comparable to microarray-derived multi-gene expression signatures.
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                Author and article information

                Journal
                J Pathol
                J. Pathol
                path
                The Journal of Pathology
                John Wiley & Sons, Ltd (Chichester, UK )
                0022-3417
                1096-9896
                September 2013
                08 July 2013
                : 231
                : 1
                : 63-76
                Affiliations
                [1 ]Bioinformatics Core Facility, Swiss Institute of Bioinformatics (SIB) Lausanne, 1015, Switzerland
                [2 ]Institute of Biostatistics and Analyses, Masaryk University Brno, Czech Republic
                [3 ]Department of Oncology, University Hospital Gasthuisberg, Katholik Universiteit Leuven Belgium
                [4 ]University Institute of Pathology, Lausanne University Medical Centre Switzerland
                [5 ]Pfizer Inc., Worldwide Research and Development, Oncology Research Unit La Jolla, CA, USA
                [6 ]Oncosurgery, Geneva University Hospital Switzerland
                [7 ]Swiss Group for Clinical Cancer Research (SAKK) Bern, Switzerland
                [8 ]Département de Formation et Recherche, Lausanne University Medical Centre Switzerland
                Author notes
                Correspondence to: Eva Budinska, Institute of Biostatistics and Analyses, Masaryk University, Kotlarska 2, 611 37 Brno, Czech Republic e-mail: budinska@ 123456iba.muni.cz
                Article
                10.1002/path.4212
                3840702
                23836465
                c5c6c4ed-f7d5-462a-b1f7-7e806a9d94e1
                Copyright © 2013 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

                Re-use of this article is permitted in accordance with the Creative Commons Deed, Attribution 2.5, which does not permit commercial exploitation.

                History
                : 03 February 2013
                : 10 May 2013
                : 14 May 2013
                Categories
                Original Papers

                Pathology
                colorectal cancer,histopathology,gene expression,molecular heterogeneity
                Pathology
                colorectal cancer, histopathology, gene expression, molecular heterogeneity

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