title: A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence.
abstract: BACKGROUND
  Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence.
  
  
  METHODS
  We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients.
  
  
  RESULTS
  We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Over-expression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test).
  
  
  CONCLUSIONS
  Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence.
authors: Div, of Applied Molecular Oncology, Princess Margaret Hospital, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada. juris@ai.utoronto.ca.
authors: Patricia P
authors: PP
authors: Reis
authors: Levi
authors: L
authors: Waldron
authors: Bayardo
authors: B
authors: Perez-Ordonez
authors: Melania
authors: M
authors: Pintilie
authors: Natalie Naranjo
authors: NN
authors: Galloni
authors: Yali
authors: Y
authors: Xuan
authors: Nilva K
authors: NK
authors: Cervigne
authors: Giles C
authors: GC
authors: Warner
authors: Antti A
authors: AA
authors: Makitie
authors: Colleen
authors: C
authors: Simpson
authors: David
authors: D
authors: Goldstein
authors: Dale
authors: D
authors: Brown
authors: Ralph
authors: R
authors: Gilbert
authors: Patrick
authors: P
authors: Gullane
authors: Jonathan
authors: J
authors: Irish
authors: Igor
authors: I
authors: Jurisica
authors: Suzanne
authors: S
authors: Kamel-Reid
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics; D002294:Carcinoma, Squamous Cell / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology*; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D046228:Microarray Analysis; D009062:Mouth Neoplasms / Q000175:diagnosis / Q000235:genetics* / Q000473:pathology*; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis* / Q000235:genetics / Q000473:pathology; D011379:Prognosis
chemical_list: D014408:Biomarkers, Tumor