Open Access Highly Accessed Research article

A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence

Patricia P Reis12, Levi Waldron3, Bayardo Perez-Ordonez4, Melania Pintilie56, Natalie Naranjo Galloni17, Yali Xuan1, Nilva K Cervigne1, Giles C Warner8, Antti A Makitie9, Colleen Simpson10, David Goldstein10, Dale Brown10, Ralph Gilbert10, Patrick Gullane10, Jonathan Irish10, Igor Jurisica113* and Suzanne Kamel-Reid112134*

Author Affiliations

1 Div. of Applied Molecular Oncology, Princess Margaret Hospital, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada

2 Dept. of Surgery and Orthopedics, Faculty of Medicine, São Paulo State University - UNESP, Botucatu, SP, Brazil

3 Ontario Cancer Institute and the Campbell Family Institute for Cancer Research, Toronto, ON, Canada

4 Dept. of Pathology, Toronto General Hospital, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada

5 Dept. of Biostatistics, Princess Margaret Hospital, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada

6 Dalla Lana School of Public Health Sciences, University of Toronto, Toronto, ON, Canada

7 Dept. of Otolaryngology, Hospital Calderon Guardia, San Jose, Costa Rica

8 Dept. of Otolaryngology/Head and Neck Surgery, Worcester Royal Hospital, Worcester, UK

9 Dept. of Otolaryngology/Head and Neck Surgery, Helsinki University Central Hospital, University of Helsinki, Helsinki, Finland

10 Dept. of Otolaryngology/Surgical Oncology, Princess Margaret Hospital, Ontario Cancer Institute, University Health Network, Toronto, ON, Canada

11 Dept. of Computer Science, University of Toronto, Toronto, ON, Canada

12 Dept. of Laboratory Medicine and Pathobiology, University of Toronto, ON, Canada

13 Dept. of Medical Biophysics, University of Toronto, Toronto, ON, Canada

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BMC Cancer 2011, 11:437  doi:10.1186/1471-2407-11-437

Published: 11 October 2011

Additional files

Additional file 1:

Methods S1. Description of methods used for RNA isolation, oligonucleotide array experiments, quantitative real-time reverse-transcription PCR validation and protein-protein interaction network analysis.

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Additional file 2:

Table S1. Results of meta-analysis of the five public data sets identified 667 up-regulated genes in OSCC.

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Additional file 3:

Table S2. List of 138 over-expressed genes, with FDR < 0.01, identified in both the meta-analysis of public datasets and our microarray experiment.

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Additional file 4:

Table S3. Results of Gene Ontology enrichment analysis of the 138 genes identified as over-expressed in both the public datasets and our microarray experiment.

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Additional file 5:

GO biological function. Graphical representation of GO annotation (biological function).

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Additional file 6:

GO cellular component. Graphical representation of GO annotation (cellular component).

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Additional file 7:

GO molecular function. Graphical representation of GO annotation (molecular function).

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Additional file 8:

Figure S1. Distribution of nominal p-values for univariate association of the 138 genes identified as over-expressed in OSCC with recurrence. P-values were determined by Cox regression using the maximum expression in any margin of each patient. The empirical null distribution was determined from association of these same genes with 1,000 permutations of the outcome labels. The observed nominal p-values are significantly enriched for small values (p = 0.001, Kolmogorov-Smirnov test). It is worth noting that recurrence was not used at any stage in the selection of these genes.

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Additional file 9:

Figure S2. Survival Receiver Operating Characteristic curve for recurrence at 36 months in the test set. Using a high threshold to define high-risk patients predicts a majority of recurrences (true positives) at a low false-positive rate (20%). While we maintained the standard median cutoff for this study due to the limited sample size, a larger study in the future may be able to further tune the cutoff threshold to optimize sensitivity and specificity in the context of the relative risks that treatment options informed by this prognostic score entail. The area under the ROC curve (AUC) for recurrence within 36 months is 0.73, which is an improvement over the expected AUC of 0.5 for non-predictive risk scores.

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Additional file 10:

Figure S3. Bootstrap validation of four-gene signature risk score in training and validation sets. Density lines represent the distribution of hazard ratios observed in 1,000 re-samplings of a single margin, randomly chosen, from each patient.

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