Comprehensive gene expression meta-analysis of head and neck squamous cell carcinoma microarray data defines a robust survival predictor.
BACKGROUND
Head and neck squamous cell carcinoma refers to a heterogeneous disease frequently aggressive in its biologic behavior. Despite the improvements in the therapeutic modalities, the long-term survival rate remained unchanged over the past decade and patients with this type of cancer are at a high risk of developing recurrence. For this reason, there is a great need to find better ways to foresee outcome, to improve treatment choices, and to enable a more personalized approach.


METHODS
Nine microarray gene expression datasets, reporting survival data of a total of 841 samples, were retrieved from publicly repositories. Three datasets, profiled on the same version of microarray chips, were selected and merged following a meta-analysis approach to build a training set. The remaining six studies were used as independent validation sets.


RESULTS
The training set led us to identify a 172-gene signature able to stratify patients in low or high risk of relapse [log-rank, P = 2.44e-05; hazard ratio (HR) = 2.44, 95% confidence interval (CI) 1.58-3.76]. The model based on the 172 genes was validated on the six independent datasets. The performance of the model was challenged against other proposed prognostic signatures (radiosensitivity index, 13-gene oral squamous cell carcinoma signature, hypoxia metagene, 42-gene high-risk signature) and was compared with a human papillomavirus (HPV) signature: our model resulted independent and even better in prediction.


CONCLUSIONS
We have identified and validated a prognostic model based on the expression of 172 genes, independent from HPV status and able to improve assessment of patient's risk of relapse compared with other molecular signatures. In order to transpose our model into a useful clinical grade assay, additional work is needed following the framework established by the Institute of Medicine and REMARK guidelines.
Functional Genomics and Informatics, Department of Experimental Oncology and Molecular Medicine.
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De Cecco
Head and Neck Medical Oncology Unit, Department of Molecular Oncology.
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Bossi
Head and Neck Medical Oncology Unit, Department of Molecular Oncology.
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Locati
Functional Genomics and Informatics, Department of Experimental Oncology and Molecular Medicine Molecular Therapies, Department of Experimental Oncology and Molecular Medicines, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy silvana.canevari@istitutotumori.mi.it.
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Canevari
Head and Neck Medical Oncology Unit, Department of Molecular Oncology.
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Licitra
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000235:genetics*; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis / Q000235:genetics* / Q000401:mortality*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006258:Head and Neck Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000401:mortality*; D006801:Humans; D008297:Male; D008875:Middle Aged; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D000077195:Squamous Cell Carcinoma of Head and Neck; D016019:Survival Analysis; D059467:Transcriptome; D055815:Young Adult
D014408:Biomarkers, Tumor
HNSCC; gene expression; meta-analysis; microarray; survival prediction