Gene profiling in squamous cell carcinoma of the head and neck

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Abstract
Survival for patients with squamous cell carcinoma of the head and neck (SCCHN) is still poor, despite great technical improvements in radiotherapy and surgery. A possible explanation for this is the lack of individualization in treatment based on biological properties of the tumors, resulting in over- as well as under treatment. Management of SCCHN has mainly been based on TNM classification over the last decades. However, a large amount of studies have shown that biomarkers may add prognostic information, independently of the TNM system, indicating that biological aggressiveness is not entirely reflected by the T- and N-status of the tumor. A conclusion to draw from this is that the present standardized treatment based on macroscopic features of the tumor in many cases will result in suboptimal treatment since important underlaying genetic properties of the tumors are not taken into consideration. A variety of laboratory techniques have been used in studies that investigate the individual biological features, spanning from methods that screen the genome for chromosomal and genetic abnormalities, e.g. cytogenetics, CGH, SKY, cDNA micro array to detailed studies of specific aberrations, e.g. southern, northern and western blotting, PCR based analysis and immunohistochemistry. Dysregulation of genes involved in e.g. cell cycle control, proliferation, drug resistence, and metastasis have been linked to outcome of treatment and survival. The purpose of this review of the literature was to summarize what has been studied so far by cDNA micro array techniques with regards to genetic screening in general and biomarkers that relate to response to therapy and prediction of clinical outcome in particular. We conclude that the majority of investigations that focus on gene profiling have a descriptive character, e.g. comparisons of tumor and normal cells, metastatic and non-metastatic properties, and differences between sub-sites and grades of differentiation. There are just a handful studies that so far have investigated how gene profiling can be used to predict clinical course.