A genetic expression profile associated with oral cancer identifies a group of patients at high risk of poor survival

Abstract

Purpose: To determine if gene expression signature of invasive oral squamous cell carcinoma (OSCC) can subclassify OSCC based on survival.

Experimental design: We analyzed the expression of 131 genes in 119 OSCC, 35 normal, and 17 dysplastic mucosa to identify cluster-defined subgroups. Multivariate Cox regression was used to estimate the association between gene expression and survival. By stepwise Cox regression, the top predictive models of OSCC-specific survival were determined and compared by receiver operating characteristic analysis.

Results: The 3-year overall mean+/-SE survival for a cluster of 45 OSCC patients was 38.7+/-0.09% compared with 69.1+/-0.08% for the remaining patients. Multivariate analysis adjusted for age, sex, and stage showed that the 45 OSCC patient cluster had worse overall and OSCC-specific survival (hazard ratio, 3.31; 95% confidence interval, 1.66-6.58 and hazard ratio, 5.43; 95% confidence interval, 2.32-12.73, respectively). Stepwise Cox regression on the 131 probe sets revealed that a model with a term for LAMC2 (laminin gamma2) gene expression best identified patients with worst OSCC-specific survival. We fit a Cox model with a term for a principal component analysis-derived risk score marker and two other models that combined stage with either LAMC2 or PCA. The area under the curve for models combining stage with either LAMC2 or PCA was 0.80 or 0.82, respectively, compared with 0.70 for stage alone (P=0.013 and 0.008, respectively).

Conclusions: Gene expression and stage combined predict survival of OSCC patients better than stage alone.

Figure 1

Supervised hierarchical cluster analysis of the gene expression data. The 131 probe sets were clustered as described in the text. The dendogram at the top measures samples’ degree of relatedness in gene expression. The color bar underneath the heat map codes the samples according to tissue phenotype: normal in yellow, dysplasias in cyan and tumors in red. Each column in the heat map represents the expression levels for all genes in a particular sample, whereas each row represents the relative expression of a particular gene across all samples. The expression level of any gene in any given sample (relative to the mean expression level of that gene across all samples) is reported along a color scale in which red represents transcription up-regulation, green represents down-regulation, and the color intensity indicates the magnitude of deviation from the mean. Cluster 1 refers to a group of probe sets which appear to be only fully downregulated in a group of 45 patients labeled with an orange bar at the bottom of the heat map (see text).

Figure 2

Principal component analysis using the 131 probe sets. The first principal component (PC) is plotted on the x-axis and captures 63.28 % of the variance. The second PC is plotted on the y-axis and captures 5.66 % of the variance.

Figure 3

Survival and OSCC-specific mortality estimates in OSCC patients. The two groups were identified with hierarchical clustering analysis using the 131 differentially expressed genes in invasive OSCC as described in the text. A. Kaplan-Meier analysis of all-cause mortality. Vertical marks represent censored events. B. Cumulative incidence of OSCC-specific mortality.

Figure 4

Receiver Operating Characteristic Analysis of 2-year Survival Comparing the Prognostic Ability of Stage with Gene Expression Data. A. ROC Curves for 2-year survival for, ‘stage’, ‘LAMC2’ and ‘PCA’. B. ROC Curves for 2-year survival for models ‘stage’, ‘stage and LAMC2’ and ‘stage and PCA’. C. Area Under the Curve (AUC) and bootstrapped 95% Confidence Intervals for all five models.