Development and Validation of a Gene Signature for Patients with Head and Neck Carcinomas Treated by Postoperative Radio(chemo)therapy

• Published in: Clinical cancer research 2018-03, Vol.24 (6), p.1364-1374
• Main Authors: Schmidt, Stefan, Linge, Annett, Zwanenburg, Alex, Leger, Stefan, Lohaus, Fabian, Krenn, Constanze, Appold, Steffen, Gudziol, Volker, Nowak, Alexander, von Neubeck, Cläre, Tinhofer, Inge, Budach, Volker, Sak, Ali, Stuschke, Martin, Balermpas, Panagiotis, Rödel, Claus, Bunea, Hatice, Grosu, Anca-Ligia, Abdollahi, Amir, Debus, Jürgen, Ganswindt, Ute, Belka, Claus, Pigorsch, Steffi, Combs, Stephanie E, Mönnich, David, Zips, Daniel, Baretton, Gustavo B, Buchholz, Frank, Baumann, Michael, Krause, Mechthild, Löck, Steffen
• Format: Article
• Language: English
• Subjects: Cancer; Chemotherapy; Clinical trials; Data processing; Experimental design; Gene expression; Genes; Head & neck cancer; Head and neck; Head and neck carcinoma; Human papillomavirus; Hypoxia; Learning algorithms; Machine learning; Mathematical models; Medical research; Parameters; Patients; Predictive control; Squamous cell carcinoma; Stem cells; Training
• ISSN: 1078-0432; 1557-3265
• DOI: 10.1158/1078-0432.CCR-17-2345

The aim of this study was to identify and independently validate a novel gene signature predicting locoregional tumor control (LRC) for treatment individualization of patients with locally advanced HPV-negative head and neck squamous cell carcinomas (HNSCC) who are treated with postoperative radio(chemo)therapy (PORT-C). Gene expression analyses were performed using NanoString technology on a multicenter training cohort of 130 patients and an independent validation cohort of 121 patients. The analyzed gene set was composed of genes with a previously reported association with radio(chemo)sensitivity or resistance to radio(chemo)therapy. Gene selection and model building were performed comparing several machine-learning algorithms. We identified a 7-gene signature consisting of the three individual genes , and one metagene combining the highly correlated genes , and The 7-gene signature was used, in combination with clinical parameters, to fit a multivariable Cox model to the training data (concordance index, ci = 0.82), which was successfully validated (ci = 0.71). The signature showed improved performance compared with clinical parameters alone (ci = 0.66) and with a previously published model including hypoxia-associated genes and cancer stem cell markers (ci = 0.65). It was used to stratify patients into groups with low and high risk of recurrence, leading to significant differences in LRC in training and validation ( < 0.001). We have identified and validated the first hypothesis-based gene signature for HPV-negative HNSCC treated by PORT-C including genes related to several radiobiological aspects. A prospective validation is planned in an ongoing prospective clinical trial before potential application in clinical trials for patient stratification. .