Author(s): Reis, Patricia P. [UNESP] ; Tokar, Tomas ; Goswami, Rashmi S. ; Xuan, Yali ; Sukhai, Mahadeo ; Seneda, Ana Laura [UNESP] ; Móz, Luis E. S. ; Perez-Ordonez, Bayardo ; Simpson, Colleen ; Goldstein, David ; Brown, Dale ; Gilbert, Ralph ; Gullane, Patrick ; Irish, Jonathan ; Jurisica, Igor ; Kamel-Reid, Suzanne
Date: 2020
Persistent ID: http://hdl.handle.net/11449/200026
Origin: Oasisbr
Made available in DSpace on 2020-12-12T01:55:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-12-01
Canada Foundation for Innovation
Ontario Institute for Cancer Research
Canada Research Chairs
Prognostic biomarkers for recurrence of Oral Squamous Cell Carcinoma (OSCC) are urgently needed. We aimed to independently validate a 4-gene expression signature (MMP1, COL4A1, P4HA2, THBS2) predictive of OSCC recurrence risk. Gene expression was measured using Nanostring nCounter® in 245 histologically normal surgical resection margins from 62 patients. Association between risk scores for individual patients and recurrence was assessed by Kaplan-Meier analysis. Signature performance was quantified by concordance index (CI), hazard ratio (HR) and the area under receiver operating characteristics (AUC). Risk scores for recurrence were significantly higher than recurrence-free patients (p = 9.58e-7, Welch’s t-test). A solid performance of the 4-gene signature was determined: CI = 0.64, HR = 3.38 (p = 1.4E-4; log-rank test), AUC = 0.71. We showed that three margins per patient are sufficient to preserve predictive performance (CI = 0.65; HR = 2.92; p = 2.94e-3; AUC = 0.71). Association between the predicted risk scores and recurrence was assessed and showed HR = 2.44 (p = 9.6E-3; log-rank test, N = 62). Signature performance analysis was repeated using an optimized threshold (70th percentile of risks), resulting in HR = 3.38 (p = 1.4E-4; log-rank test, N = 62). The 4-gene signature was validated as predictive of recurrence risk in an independent cohort of patients with resected OSCC and histologically negative margins, and is potentially applicable for clinical decision making on adjuvant treatment and disease monitoring.
São Paulo State University UNESP Faculty of Medicine Department of Surgery and Orthopedics
Krembil Research Institute University Health Network
Department of Clinical Pathology Sunnybrook Health Sciences Centre
Princess Margaret Cancer Centre University Health Network
Brazilian Institute for Cancer Control
Department of Pathology Toronto General Hospital University Health Network
Departments of Medical Biophysics University of Toronto
Department of Computer Science University of Toronto
Institute of Neuroimmunology Slovak Academy of Sciences
Clinical Laboratory Genetics Genome Diagnostics University Health Network
Department of Laboratory Medicine and Pathobiology The University of Toronto
São Paulo State University UNESP Faculty of Medicine Department of Surgery and Orthopedics
