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Rigshospitalet - a part of Copenhagen University Hospital
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Immunohistochemical and molecular imaging biomarker signature for the prediction of failure site after chemoradiation for head and neck squamous cell carcinoma

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OBJECTIVE: To identify a failure site-specific prognostic model by combining immunohistochemistry (IHC) and molecular imaging information to predict long-term failure type in squamous cell carcinoma of the head and neck.

PATIENT AND METHODS: Tissue microarray blocks of 196 head and neck squamous cell carcinoma cases were stained for a panel of biomarkers using IHC. Gross tumor volume (GTV) from the PET/CT radiation treatment planning CT scan, maximal Standard Uptake Value (SUVmax) of fludeoxyglucose (FDG) and clinical information were included in the model building using Cox proportional hazards models, stratified for p16 status in oropharyngeal carcinomas. Separate models were built for time to locoregional failure and time to distant metastasis.

RESULTS: Higher than median p53 expression on IHC tended toward a risk factor for locoregional failure but was protective for distant metastasis, χ(2) for difference p = .003. The final model for locoregional failure included p53 (HR: 1.9; p: .055), concomitant cisplatin (HR: 0.41; p: .008), β-tubulin-1 (HR: 1.8; p: .08), β-tubulin-2 (HR: 0.49; p: .057) and SUVmax (HR: 2.1; p: .046). The final model for distant metastasis included p53 (HR: 0.23; p: .025), Bcl-2 (HR: 2.6; p: .08), SUVmax (HR: 3.5; p: .095) and GTV (HR: 1.7; p: .063).

CONCLUSIONS: The models successfully distinguished between risk of locoregional failure and risk of distant metastasis, which is important information for clinical decision-making. High p53 expression has opposite prognostic effects for the two endpoints; increasing risk of locoregional failure, but decreasing the risk of metastatic failure, but external validation of this finding is needed.

Original languageEnglish
JournalActa oncologica
Volume56
Issue number11
Pages (from-to)1562-1570
ISSN0284-186X
DOIs
Publication statusPublished - 2017

    Research areas

  • Journal Article

ID: 51769840