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Prediction of Future Metastasis and Molecular Characterization of Head and Neck Squamous-Cell Carcinoma Based on Transcriptome and Genome Analysis Bymicroarrays

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Prediction of Future Metastasis and Molecular Characterization of Head and Neck Squamous-Cell Carcinoma Based on Transcriptome and Genome Analysis Bymicroarrays Oncogene (2008) 27, 6607–6622 & 2008 Macmillan Publishers Limited All rights reserved 0950-9232/08 $32.00 www.nature.com/onc ONCOGENOMICS Prediction of future metastasis and molecular characterization of head and neck squamous-cell carcinoma based on transcriptome and genome analysis bymicroarrays DS Rickman1,4, R Millon2, A De Reynies1, E Thomas1, C Wasylyk3, D Muller2, J Abecassis2 and BWasylyk 3 1Ligue Nationale Contre le Cancer, Paris, France; 2Centre Re´gional de Lutte Contre le Cancer Paul Strauss, 3 rue Porte de l’Ho¨pital, Strasbourg, France and 3IGBMC CNRS/INSERM/ULP, Illkirch, France Propensityfor subsequent distant metastasis in head and Introduction neck squamous-cell carcinoma (HNSCC) was analysed using 186 primarytumours from patients initiallytreated There are various aetiological factors for head and neck bysurgerythat developed (M) or did not develop (NM) squamous-cell carcinoma (HNSCC), including alcohol metastases as the first recurrent event. Transcriptome consumption with smoking and human papilloma- (Affymetrix HGU133_Plus2, QRT–PCR) and array- virus (HPV) infection, for our patient population comparative genomic hybridization data were collected. (Applebaum et al., 2007; Hashibe et al., 2007). Survival Non-supervised hierarchical clustering based on Affyme- is still poor (5-year survival 30–50%), mainly due to trix data distinguished tumours differing in pathological relapse, metastasis or second cancer (Forastiere et al., differentiation, and identified associated functional 2001; Le Tourneau et al., 2005). The anatomic location changes. Propensityfor metastasis was not associated and TNM staging guide treatment selection, but patients with these subgroups. Using QRT–PCR data we identified with similar tumour characteristics differ in their clinical a four-gene model (PSMD10, HSD17B12, FLOT2 and outcome. Our aim is to identify, in primary tumours, KRT17) that predicts M/NM status with 77% success in molecular signatures that predict the subsequent devel- a separate 79-sample validation group of HNSCC opment of distant metastases in patients treated with samples. This prediction is independent of clinical criteria complete surgical resection and adjuvant therapy. (age, lymph node status, stage, differentiation and Furthermore, we want to study the still poorly under- localization). The most significantlyaltered transcripts stood biological processes that predispose human in M versus NM were significantlyassociated to tumours to the development of metastases, and to metastasis-related functions, including adhesion, mobility define targets for therapy. and cell survival. Several genomic modifications were Our previous study, using large-scale validated significantlyassociated with M/NM status (most notably differential display, identified 820 transcripts that were gains at 4q11–22 and Xq12–28; losses at 11q14–24 and differentially expressed between tumours and normal 17q11 losses) and partlylinked to transcription modifica- tissue, about 10% of which differed between tumours tions. This work yields a basis for the development of that did (M) or did not (NM) develop metastasis as the prognostic molecular signatures, markers and therapeutic first recurrence event (Carles et al., 2006). A subsequent targets for HNSCC metastasis. study, with 28 tumours and Affymetrix U95A arrays Oncogene (2008) 27, 6607–6622; doi:10.1038/onc.2008.251; (Affymetrix, Santa Clara, CA, USA) detected 164 published online 4 August 2008 transcripts whose levels differed significantly between N and NM tumours, but we did not find changes that Keywords: HNSCC; distant metastasis; prognosis; could be used to predict N/NM status in independent intrinsic groups; differentiation samples (Cromer et al., 2004). Signatures of poor ONCOGENOMICS prognosis and metastasis have been identified in other larger scale studies (review: Nguyen and Massague, 2007). We now report a larger study, using more samples (186) and RNA variables (Affymetrix HG- U133 plus 2.0 GeneChips), and we have included an Correspondence: Dr BWasylyk, IGBMC,CNRS/INSERM/ULP, analysis of genomic changes (IntegraChip 4.4K bacterial 1 rue Laurent Fries, BP 10142, Illkirch 67404, France. artificial chromosomes (BAC) comparative genomic E-mail: [email protected] hybridisation (CGH) arrays, array-CGH (aCGH)). 4Current address: Department of Pathology and Laboratory Medicine, Using unsupervised analysis, we defined intrinsic groups Weill Cornell Medical Center, 1300 York Avenue, Room C-458D, New York, NY 10021, USA. that correspond to pathological differentiation. Using Received 6 February 2008; revised 21 May 2008; accepted 27 June 2008; QRT–PCR-validated RNA levels and a training group, published online 4 August 2008 we have defined a four-gene model that predicts future HNSCC, distant metastasis signatures and classifiers DS Rickman et al 6608 distant metastasis with 77% success in an independent associated GO terms and pathways. For example, validation group. We have identified genomic and poorly differentiated tumours (C3) are characterized transcriptomic changes that are significantly different by upregulation in gene cluster a, which is significantly in primary tumours with dissimilar M/NM status. These associated with cell motility. Sample group C4 is findings are useful for the development of prospective associated with upregulation of genes encoding for signatures of metastasis, and for the understanding of proteins related to muscle structure and function biological processes that predispose to the development (approximately half of the 59 significant GO terms that of metastases in patients with HNSCC. are enriched in the gene cluster f, Figure 1; see the GO analysis Supplementary Table 2 sheet 2, and the genes in cluster f in Supplementary Table 2 sheet 1). This is a clue to the origin of cluster C4. Results To further analyse the biological functions associated with pathologically defined differentiation (as opposed Classification of tumours to the intrinsic groups defined by unsupervised classifi- The global differences between the transcriptomes of the cation) we performed a supervised analysis between the HNSCC tumours may result from metastatic propen- tumours classified according to pathological differentia- sity, or other clinical or biological features. To detect tion (Table 1; Supplementary Table 3). Using 1-way these ‘intrinsic’ features, we performed unsupervised ANOVA (Po0.01) and pairwise Wilcoxon tests hierarchical classification as already described (Boyault (Po0.001) we established three lists of genes (a total et al., 2007). We studied extensively the robustness of of 835 genes) that were significantly differentially the topologies (series of dendrograms) obtained under expressed between at least two of the three clinically different conditions: three distinct agglomerative clus- defined groups (Supplementary Table 4). As expected, a tering methods, various thresholds for variance based number of the genes (50) involved in the unsupervised unsupervised gene selection, resampling and addition of clustering (Figure 1) were among the 835 significant Gaussian noise to the data. We found that the sample genes obtained from the supervised analysis. QRT–PCR partitions that yielded between two to four sample validation of seven genes confirmed that there is a groups were very reproducible (Supplementary Table 1) gradient of expression between poorly to well-differ- and identified a consensus partition of four groups entiated tumours (Figure 2; in general there was a very (Supplementary Figure 1). For illustrative purposes, high correlation between the Affymetrix and QRT– we selected a representative sample dendrogram that PCR, data not shown). These genes were chosen as was most related to the consensus partition (Figure 1). representative genes that are differentially expressed in Using Fisher’s exact tests we found that these four tumours according to their pathologically defined major clusters (C1–C4) were not significantly associated differentiation status, and due to the potential impor- with stage, localization, metastasis-free survival tance of the encoded proteins for cell differentiation (see (Figure 1), or any of the other characteristics of the Supplementary Table 5, column H, for a brief descrip- tumours analysed (data not shown). However, C1–C3 tion). Further analysis of the genes that are differentially were significantly associated with degree of differentia- expressed between the tumours could provide insights tion, corresponding to well (C1), moderately (C2) and into the molecular events that are related to pathological poorly (C3) differentiated tumours. C4 was not associa- differentiation of HNSCC. tion with any of the characteristics analysed. These results suggest that pathological differentiation has the biggest effect on the overall differences in transcription Four-gene model predicting future metastasis in HNSCC of the tumours. that is independent of clinical variables To get a better understanding of the molecular Given that metastasis was not associated with any of the determinants of the intrinsic groups, we analysed the intrinsic HNSCC subgroups, we set out to identify a 449-gene list (548 probe sets) that was used to generate molecular signature that is predictive of developing the four clusters in the representative dendrogram future metastasis irrespective of differentiation status shown in Figure 1. Unsupervised cluster analysis was and/or other clinical variables, and that could be used to segregate the genes into six gene groups (a–f; employed in a clinical
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