Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables
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Steven W. Mes1,*, Dennis te Beest2,*, Tito Poli3, Silvia Rossi4, Kathrin Scheckenbach5, Wessel N. van Wieringen2,6, Arjen Brink1, Nicoletta Bertani4, Davide Lanfranco3, Enrico M. Silini7, Paul J. van Diest8, Elisabeth Bloemena9,10, C. René Leemans1, Mark A. van de Wiel2,6,* and Ruud H. Brakenhoff1,*
1Department of Otolaryngology – Head and Neck Surgery, VU University Medical Center, Amsterdam, The Netherlands
2Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands
3Unit of Maxillo-Facial Surgery, Department of Biomedical, Biotechnological and Translational Sciences (S.Bi.Bi.T.), University of Parma, Parma, Italy
4COMT & Department of Life Science, University of Parma, Parma, Italy
5Department of Otorhinolaryngology, Head and Neck Surgery, Medical Faculty Heinrich Heine University, Düsseldorf, Germany
6Department of Mathematics, VU University Amsterdam, Amsterdam, The Netherlands
7Department of Pathology and Laboratory Medicine, University of Parma, Parma, Italy
8Department of Pathology, University Medical Center Utrecht, Utrecht, The Netherlands
9Department of Pathology, VU University Medical Center, Amsterdam, The Netherlands
10Department of Maxillofacial Surgery/Oral Pathology, Academic Medical Centre for Dentistry, Amsterdam, The Netherlands
*These authors have contributed equally to this work
Mark A. van de Wiel, email: firstname.lastname@example.org
Ruud H. Brakenhoff, email: email@example.com
Keywords: head and neck cancer, oral cancer, lymph node metastasis, prognostic modeling, expression profiling
Received: February 21, 2017 Accepted: June 12, 2017 Published: July 26, 2017
Accurate staging and outcome prediction is a major problem in clinical management of oral cancer patients, hampering high precision treatment and adjuvant therapy planning. Here, we have built and validated multivariable models that integrate gene signatures with clinical and pathological variables to improve staging and survival prediction of patients with oral squamous cell carcinoma (OSCC). Gene expression profiles from 249 human papillomavirus (HPV)-negative OSCCs were explored to identify a 22-gene lymph node metastasis signature (LNMsig) and a 40-gene overall survival signature (OSsig). To facilitate future clinical implementation and increase performance, these signatures were transferred to quantitative polymerase chain reaction (qPCR) assays and validated in an independent cohort of 125 HPV-negative tumors. When applied in the clinically relevant subgroup of early-stage (cT1-2N0) OSCC, the LNMsig could prevent overtreatment in two-third of the patients. Additionally, the integration of RT-qPCR gene signatures with clinical and pathological variables provided accurate prognostic models for oral cancer, strongly outperforming TNM. Finally, the OSsig gene signature identified a subpopulation of patients, currently considered at low-risk for disease-related survival, who showed an unexpected poor prognosis. These well-validated models will assist in personalizing primary treatment with respect to neck dissection and adjuvant therapies.
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