OBJECTIVES: This is a pilot study designed to identify gene expression profiles able to stratify head and neck squamous cell carcinoma (HNSCC) tumors that may or may not respond to chemoradiation or radiation therapy. STUDY DESIGN: We prospectively evaluated 14 HNSCC specimens, arising from patients undergoing chemoradiotherapy or radiotherapy alone with curative intent. A complete response was assessed by clinical evaluation with no evidence of gross tumor after a 2-year follow-up period. METHODS: Residual biopsy samples from eight complete responders (CR) and six nonresponders (NR) were evaluated by genome-wide gene expression profiling using HG-U133A 2.0 arrays. Univariate parametric t-tests with proportion of false discoveries controlled by multivariate permutation tests were used to identify genes with significantly different gene expression levels between CR and NR cases. Six different prediction algorithms were used to build gene predictor lists. Three representative genes showing 100% crossvalidation support after leave-one-out crossvalidation (LOOCV) were further validated using real-time QRT-PCR. RESULTS: We identified 167 significant probe sets that discriminate between the two classes, which were used to build gene predictor lists. Thus, 142 probe sets showed an accuracy of prediction ranging from 93% to 100% across all six prediction algorithms. The genes represented by these 142 probe sets were further classified into different functional networks that included cellular development, cellular movement, and cancer. CONCLUSIONS: The results presented herein offer encouraging preliminary data that may provide a basis for a more precise prognosis of HNSCC, as well as a molecular-based therapy decision for the management of these cancers.
OBJECTIVES: This is a pilot study designed to identify gene expression profiles able to stratify head and neck squamous cell carcinoma (HNSCC) tumors that may or may not respond to chemoradiation or radiation therapy. STUDY DESIGN: We prospectively evaluated 14 HNSCC specimens, arising from patients undergoing chemoradiotherapy or radiotherapy alone with curative intent. A complete response was assessed by clinical evaluation with no evidence of gross tumor after a 2-year follow-up period. METHODS: Residual biopsy samples from eight complete responders (CR) and six nonresponders (NR) were evaluated by genome-wide gene expression profiling using HG-U133A 2.0 arrays. Univariate parametric t-tests with proportion of false discoveries controlled by multivariate permutation tests were used to identify genes with significantly different gene expression levels between CR and NR cases. Six different prediction algorithms were used to build gene predictor lists. Three representative genes showing 100% crossvalidation support after leave-one-out crossvalidation (LOOCV) were further validated using real-time QRT-PCR. RESULTS: We identified 167 significant probe sets that discriminate between the two classes, which were used to build gene predictor lists. Thus, 142 probe sets showed an accuracy of prediction ranging from 93% to 100% across all six prediction algorithms. The genes represented by these 142 probe sets were further classified into different functional networks that included cellular development, cellular movement, and cancer. CONCLUSIONS: The results presented herein offer encouraging preliminary data that may provide a basis for a more precise prognosis of HNSCC, as well as a molecular-based therapy decision for the management of these cancers.
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