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

  • Head and Neck Cancer (L Licitra, Section Editor)
  • Published:
Current Treatment Options in Oncology Aims and scope Submit manuscript

Opinion statement

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.

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Acknowledgements

We are grateful to Drs Paolo Bossi e MariaLuisa Sensi for their critical reading of the manuscript and their helpful comments. We would like also to thank all the cited authors for their informative papers that enabled us to organize this review and we apologize for potential missing citations. This work was partially supported by Associazione Italiana Ricerca Cancro (AIRC IG 14750 to SC and AIRC IG 18519 to LDC) and by H2020-EU.3.1. - SOCIETAL CHALLENGES - Health, demographic change and well-being (“BD2Decide; Big Data and models for personalized Head and Neck Cancer Decision support” Project ID: 689715).

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Authors and Affiliations

  1. Functional Genomics and Bioinformatics, Department of Experimental Oncology and Molecular Medicine, Fondazione IRCCS Istituto Nazionale dei Tumori, via Amadeo 42, 20133, Milan, Italy

    Luca Tonella Dr, Marco Giannoccaro Dr, Silvana Canevari PhD & Loris De Cecco PhD

  2. Head and Neck Medical Oncology Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, via Venezian 1, 20133, Milan, Italy

    Salvatore Alfieri MD

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  1. Luca Tonella Dr
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  2. Marco Giannoccaro Dr
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  3. Salvatore Alfieri MD
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  4. Silvana Canevari PhD
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  5. Loris De Cecco PhD
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Correspondence to Silvana Canevari PhD or Loris De Cecco PhD.

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This article is part of the Topical Collection on Head and Neck Cancer

Luca Tonella and Marco Giannoccaro equally contributing first author

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Tonella, L., Giannoccaro, M., Alfieri, S. et al. Gene Expression Signatures for Head and Neck Cancer Patient Stratification: Are Results Ready for Clinical Application?. Curr. Treat. Options in Oncol. 18, 32 (2017). https://doi.org/10.1007/s11864-017-0472-2

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  • DOI: https://doi.org/10.1007/s11864-017-0472-2

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