TY - JOUR
T1 - Prognostic modeling of oral cancer by gene profiles and clinicopathological co-variables
AU - Mes, Steven W.
AU - te Beest, Dennis
AU - Poli, Tito
AU - Rossi, Silvia
AU - Scheckenbach, Kathrin
AU - van Wieringen, Wessel N.
AU - Brink, Arjen
AU - Bertani, Nicoletta
AU - Lanfranco, Davide
AU - Silini, Enrico M.
AU - van Diest, Paul J.
AU - Bloemena, Elisabeth
AU - René Leemans, C.
AU - van de Wiel, Mark A.
AU - Brakenhoff, Ruud H.
PY - 2017/8/29
Y1 - 2017/8/29
N2 - 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.
AB - 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.
KW - Expression profiling
KW - Head and neck cancer
KW - Lymph node metastasis
KW - Oral cancer
KW - Prognostic modeling
UR - http://www.scopus.com/inward/record.url?scp=85029052370&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029052370&partnerID=8YFLogxK
U2 - https://doi.org/10.18632/oncotarget.19576
DO - https://doi.org/10.18632/oncotarget.19576
M3 - Article
C2 - 28938638
SN - 1949-2553
VL - 8
SP - 59312
EP - 59323
JO - Oncotarget
JF - Oncotarget
IS - 35
ER -