Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables
| Autor: | Arjen Brink, Enrico Maria Silini, R. Leemans, Mark A. van de Wiel, Paul J. van Diest, Dennis E. te Beest, Steven W. Mes, Ruud H. Brakenhoff, Tito Poli, Elisabeth Bloemena, N. Bertani, Kathrin Scheckenbach, Wessel N. van Wieringen, Davide Lanfranco, Silvia Rossi |
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| Přispěvatelé: | Mathematics, Amsterdam Neuroscience - Cellular & Molecular Mechanisms, Amsterdam Neuroscience - Complex Trait Genetics, Maxillofacial Surgery (AMC + VUmc), Epidemiology and Data Science, CCA - Cancer biology and immunology, Other Research, AGEM - Digestive immunity, Pathology, Otolaryngology / Head & Neck Surgery, MKA Vumc (OII, ACTA) |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0301 basic medicine
Oncology medicine.medical_specialty Pathology medicine.medical_treatment Prognostic modeling 03 medical and health sciences 0302 clinical medicine SDG 3 - Good Health and Well-being Internal medicine medicine Adjuvant therapy Journal Article Head and neck cancer Pathological Expression profiling Lymph node metastasis business.industry Oral cancer Cancer Neck dissection Gene signature medicine.disease 3. Good health Gene expression profiling stomatognathic diseases 030104 developmental biology Real-time polymerase chain reaction 030220 oncology & carcinogenesis business Research Paper |
| Zdroj: | Oncotarget, 8(35), 59312-59323. Impact Journals Oncotarget, 8(35), 59312. Impact Journals Mes, S W, te Beest, D, Poli, T, Rossi, S, Scheckenbach, K, van Wieringen, W N, Brink, A, Bertani, N, Lanfranco, D, Silini, E M, van Diest, P J, Bloemena, E, René Leemans, C, van de Wiel, M A & Brakenhoff, R H 2017, ' Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables ', Oncotarget, vol. 8, no. 35, pp. 59312-59323 . https://doi.org/10.18632/oncotarget.19576 Oncotarget |
| ISSN: | 1949-2553 |
| Popis: | 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 HPVnegative 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. |
| Databáze: | OpenAIRE |
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