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Gene Expression Profiling of Colon Cancer by DNA Microarrays And Oncogene (2004) 23, 1377–1391 & 2004 Nature Publishing Group All rights reserved 0950-9232/04 $25.00 www.nature.com/onc Gene expression profiling of colon cancer by DNA microarrays and correlation with histoclinical parameters Franc¸ois Bertucci1,2,3,Se´ bastien Salas1,9,Se´ verine Eysteries1,9, Vale´ ry Nasser1,9, Pascal Finetti1, Christophe Ginestier1, Emmanuelle Charafe-Jauffret1,4,Be´ atrice Loriod5, Loı¨ c Bachelart1,Je´ roˆ me Montfort5, Genevie` ve Victorero5, Fre´ de´ ric Viret2, Vincent Ollendorff6, Vincent Fert7, Marc Giovaninni2, Jean-Robert Delpero8, Catherine Nguyen5,7, Patrice Viens2,3, Genevie` ve Monges1,3,4, Daniel Birnbaum*,1,7 and Re´ mi Houlgatte5,7 1De´partement d’Oncologie Mole´culaire, Institut Paoli-Calmettes (IPC) and U119 Inserm, IFR57, Marseille, France; 2De´partement d’Oncologie Me´dicale, IPC, Marseille, France; 3Faculte´ de Me´decine, Universite´ de la Me´diterrane´e, Marseille, France; 4De´partement de Biopathologie, IPC, Marseille, France; 5Laboratoire TAGC, ERM206 Inserm, Marseille, France; 6Institut Me´diterrane´en de Recherche en Nutrition, Marseille, France; 7Ipsogen SA, Marseille, France; 8De´partement de Chirurgie, IPC, Marseille, France Different diagnostic and prognostic groups of colorectal Introduction carcinoma (CRC) have been defined.However, accurate diagnosis and prediction of survival are sometimes difficult. Despite progresses made during the last decades, color- Gene expression profiling might improve these classifica- ectal cancer (CRC) remains one of the most frequent tions and bring new insights into underlying molecular and deadly neoplasias in the Western countries. Current mechanisms.We profiled 50 cancerous and noncancerous prognostic models based on histoclinical parameters colon tissues using DNA microarrrays consisting of B8000 apprehend only poorly the heterogeneity of disease and spotted human cDNA.Global hierarchical clustering was are not accurate enough for prediction in the individual to some extent able to distinguish clinically relevant patient. Tumours with different genetic alterations, subgroups, normal versus cancer tissues and metastatic developing in different host backgrounds, may have versus nonmetastatic tumours.Supervised analyses im- the same clinical presentation but follow quite different proved these segregations by identifying sets of genes that evolutions. One goal of molecular analysis is to identify, discriminated between normal and tumour tissues, tumours among complex networks of molecules involved in associated or not with lymph node invasion or genetic progression, markers that could differentiate subgroups instability, and tumours from the right or left colon.A of tumours with distinct behaviour and prognosis similar approach identified a gene set that divided patients allowing patients to receive appropriate treatment. with significantly different 5-year survival (100% in one Molecular studies have been largely focused on group and 40% in the other group; P ¼ 0.005). Discrimi- individual candidate genes, contrasting with the mole- nator genes were associated with various cellular processes. cular complexity of disease. The multistep progression An immunohistochemical study on 382 tumour and normal of CRC requires years and possibly decades and is samples deposited onto a tissue microarray subsequently accompanied by a number of genetic alterations (KRAS, validated the upregulation of NM23 in CRC and a APC, P53 and mismatch repair (MMR) genes, WNT downregulation in poor prognosis tumours.These results and TGFb pathways) that accumulate and combine suggest that microarrays may provide means to improve their effects (Vogelstein et al., 1988; Fearon and the classification of CRC, provide new potential targets Vogelstein, 1990). Despite the huge amount of studies, against carcinogenesis and new diagnostic and/or prog- applications remain limited for CRC patients. Little is nostic markers and therapeutic targets. known about molecular alterations associated with the Oncogene (2004) 23, 1377–1391. doi:10.1038/sj.onc.1207262 heterogeneity of disease, and no molecular marker has been validated for use as a new diagnostic or prognostic Keywords: colon cancer; DNA microarray; expression parameter applicable to routine clinical practice. New profiles; microsatellite instability; prognosis models based on a deeper molecular understanding of disease are required to improve screening, diagnosis, treatment and ultimately survival. DNA microarray technology allows the measure of the mRNA expression level of thousands of genes *Correspondence: D Birnbaum, U.119 Inserm, IFR57, 27 Bd. Leı¨ simultaneously in a single assay, thus providing a Roure, 13009 Marseille, France; E-mail: [email protected] molecular definition of a sample adapted to tackle the serm.fr 9These authors have equally contributed combinatory and complex nature of cancers (Bertucci Received 24 June 2003; revised 30 September 2003; accepted 7 October et al., 2001; Mohr et al., 2002). Gene expression 2003 profiling can reveal biologically and/or clinically DNA microarray and colon cancer F Bertucci et al 1378 relevant subgroups of tumours (Alizadeh et al., 2000; microarrays containing B8000 spotted PCR products Garber et al., 2001; Beer et al., 2002; Bertucci et al., fromknown genes and expressed sequence tags (ESTs). 2002; Devilard et al., 2002; Singh et al., 2002) and Analyses of normalized expression levels were both improve our mechanistic understanding of oncogenesis. unsupervised and supervised. Gene expression profiling-based studies of CRC have Unsupervised hierarchical clustering of all samples so far compared normal and tumour tissue samples or was first applied, based on the expression profile of different stages of disease (Alon et al., 1999; Backert genes with a significant variation in expression level et al., 1999; Hegde et al., 2001; Kitahara et al., 2001; across all the experiments. Results are displayed in a Notterman et al., 2001; Agrawal et al., 2002; Birken- colour-coded matrix (Figure 1a) where samples are kamp-Demtroder et al., 2002; Lin et al., 2002; Zou et al., ordered on the horizontal axis and genes on the vertical 2002; Frederiksen et al., 2003; Tureci et al., 2003; axis on the basis of similarity of their expression profiles. Williams et al., 2003), but none has directly addressed The 50 samples were sorted into two large clusters that the issue of prognosis or MSI phenotype. Here we have extensively differed with respect to normal or cancer applied DNA microarray technology to analyse the type (Figure 1b, top): 87% were noncancerous in the left expression of B8000 genes in 50 cancer and noncancer- cluster and 87% were cancerous in the right cluster. As ous colon tissue samples. Unsupervised hierarchical expected, the CRC cell lines represented a branch of the clustering discovered new subgroups of tumours. ‘cancer’ cluster. Hierarchical clustering also allowed Supervised analysis identified several genes differentially identification of gene clusters corresponding to defined expressed between normal and cancer samples and functions or cell types, several of which are indicated by between subgroups of colon cancer defined upon coloured bars on the right of Figure 1a and are zoomed histoclinical parameters, including clinical outcome in Figure 1b. Three are overexpressed in tissue samples and MSI phenotype. Some of the potential markers overall as compared to epithelial cell lines, reflecting the were further studied using immunohistochemistry (IHC) cell heterogeneity of tissues: an ‘immune cluster’ with on sections of tissue microarrays (TMA). different subclusters including an MHC class I sub- cluster that correlated with an interferon-related sub- cluster, and an MHC class II subcluster, a ‘stromal cluster’ rich in genes expressed in stromal cells Results (COL1A1, COL1A2, COL3A1, MMP2, TIMP1, SPARC, CSPG2, PECAM, INHBA), and a ‘smooth Gene expression profiling of CRC and unsupervised muscle cluster’ (CNN1, CALD1, DES, MYH11, SMTN, classification TAGL) that was globally overexpressed in normal The mRNA expression profiles of 50 cancer and tissues as compared to cancer tissues. An ‘early-response noncancerous colon samples (45 clinical tissue samples cluster’ included immediate-early genes (JUNB, FOS, and five cell lines) (Table 1) were determined using DNA EGR1, NR4A1, DUSP1) involved in the human cellular Table 1 Characteristics of cancer samples profiled using DNA microarrays Patient Sex Age Location Grade pT UICC pN UICC AJCC stage MSI status Treatment Outcome (months) 7650 M 74 Descending colon G pT3 pN1 4 (liver) MSI pS+pCT AWC 4 8582 F 80 Ascending colon P pT3 pN3 4 (liver) MSI pS D 1 7442 M 64 Transverse colon G pT3 pN1 4 (liver) MSS pS+pCT D 32 8208 M 40 Transverse colon M pT3 pN2 4 (liver) MSS cS+adj CT D 41 7835 F 72 Transverse colon G pT3 pN3 4 (liver) MSS pS+pCT D 17 8656 F 57 Descending colon G pT3 pN2 4 (liver) MSS cS+adj CT AWC 66 8031 F 46 Descending colon G pT3 pN2 3 MSS cS+adj CT MR 4 - D 7 6927 M 71 Descending colon G pT3 NA NA MSS cS+adj CT NED 10 9118 F 75 Ascending colon G pT3 pN1 2 MSI cS+adj CT NED 56 8904 M 80 Descending colon G pT3 pN1 2 MSI cS NED 18 6974 M 68 Ascending colon P pT3 pN1 2 MSI cS+adj CT NED 97 8646 M 74 Descending colon G pT3 pN1 2 MSS cS NED 63 8458 M 56 Descending colon G pT3 pN1 2 MSS cS+adj CT NED 69 6992 F 65 Ascending colon G pT3 pN1 2 MSS cS+adj CT NED 98 7094 F 87 Descending colon G pT3 pN1 2 MSS cS NED 64 8252 F 54 RectumG pT4 pN1 2 MSS cS+adj CT NED 74 9075 F 45 Ascending colon G pT2 pN1 1 MSI cS MR 23 - D 38 7505 M 71 Ascending colon G pT1 pN1 1 MSI cS NED 88 7043 M 70 Descending colon G pT2 pN1 1 MSS cS NED 97 6952 M 58 Descending colon G pT2 pN1 1 MSS cS NED 65 7597 F 72 RectumG pT2 pN1 1 MSS cS NED 87 7815 M 63 RectumG pT2 pN1 1 MSI cS MR 10 - D 40 Sex: M, male; F, female. Grade: G, good; M, moderate; P, poor. pT, pathological staging of primary tumour; UICC, International Union Against Cancer; pN, pathological staging of regional lymph nodes; AJCC, American Joint Committee on Cancer; NA, not available, but no metastasis; MSI, microsatellite instability.
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