Gene Expression Signatures for Head and Neck Cancer Patient Stratification: Are Results Ready for Clinical Application?

Abstract

Head and neck squamous cell carcinoma (HNSCC) is the sixth leading cancer by incidence worldwide and considering the recent EUROCARE-5 population-based study the 5-year survival rate of HNSCC patients in Europe ranges between 69% in localized cases and 34% in patients with regional involvement. The development of high-throughput gene expression assays in the last two decades has provided the invaluable opportunity to improve our knowledge on cancer biology and to identify predictive signatures in the most deeply analyzed malignancies, such as hematological and breast cancers. At variance, till 2010, the number of reliable reports referring gene expression data related to HSNCC biology and prediction was quite limited. A critical revision of the literature reporting gene expression data in HNSCC indicated that in the last 6 years, there were new important studies with a relevant increase in the sample size and a more accurate selection of cases, the publication of a growing number of studies applying a computational integration (meta-analysis) of different microarray datasets addressing similar clinical/biological questions, the increased use of molecular sub-classification of tumors according to their gene expression, and the release of the publicly available largest dataset in HNSCC by The Cancer Genome Atlas (TCGA) consortium. Overall, also for this disease, it become evident that the expression analysis of the entire transcriptome has been enabling to achieve the identification of promising molecular signatures for (i) disclosure of the biology behind carcinogenesis with special focus on the HPV-related one, (ii) prediction of tumor recurrence or metastasis development, (iii) identification of subgroups of tumors with different biology and associated prognosis, and (iv) prediction of outcome and/or response to therapy. The increasing awareness of the relevance of strict collaboration among clinicians and translational researchers would in a near future enable the application of a personalized HNSCCs patients' treatment in the clinical practice based also on gene expression signatures.

Keywords: Gene expression; HNSCC; Meta-analysis-subtype stratification; Microarray; RNA-seq; Therapy prediction.