METHODS: Total RNA was extracted from formalin-fixed paraffin-embedded tissue biopsies of 20 OPMD cases with known clinical outcomes (10 MT vs. 10 NT). Samples were assessed for quantity, quality and integrity of RNA prior to sequencing. Analysis for differential gene expression between MT and NT was performed using statistical packages in R. Genes were considered to be significantly differentially expressed if the False Discovery Rate corrected P-value was 1.90). Analysis of RNA-Sequencing outputs revealed 41 genes (34 protein-coding; 7 non-coding) that were significantly differentially expressed between MT and NT cases. The log2 fold change for the statistically significant differentially expressed genes ranged from -2.63 to 2.48, with 23 protein-coding genes being downregulated and 11 protein-coding genes being upregulated in MT cases compared to NT cases.
CONCLUSION: Several candidate genes that may play a role in malignant transformation of OPMD have been identified. Experiments to validate these candidates are underway. It is anticipated that this work will contribute to better understanding of the etiopathogenesis of OPMD and development of novel biomarkers.
METHODS: Patients with oral epithelial dysplasia at one hospital were selected as the 'training set' (n = 56) whilst those at another hospital were selected for the 'test set' (n = 66). RNA was extracted from formalin-fixed paraffin-embedded (FFPE) diagnostic biopsies and analysed using the NanoString nCounter platform. A targeted panel of 42 genes selected on their association with oral carcinogenesis was used to develop a prognostic gene signature. Following data normalisation, uni- and multivariable analysis, as well as prognostic modelling, were employed to develop and validate the gene signature.
RESULTS: A prognostic classifier composed of 11 genes was developed using the training set. The multivariable prognostic model was used to predict patient risk scores in the test set. The prognostic gene signature was an independent predictor of malignant transformation when assessed in the test set, with the high-risk group showing worse prognosis [Hazard ratio = 12.65, p = 0.0003].
CONCLUSIONS: This study demonstrates proof of principle that RNA extracted from FFPE diagnostic biopsies of OPMD, when analysed on the NanoString nCounter platform, can be used to generate a molecular classifier that stratifies the risk of malignant transformation with promising clinical utility.
METHODS: EGFR GCN was examined by in situ hybridization (ISH) in biopsies from 78 patients with OPMD and 92 patients with early-stage (stages I and II) OSCC. EGFR ISH signals were scored by two pathologists and a category assigned by consensus. The data were correlated with patient demographics and clinical outcomes.
RESULTS: OPMD with abnormal EGFR GCN were more likely to undergo malignant transformation than diploid cases. EGFR genomic gain was detected in a quarter of early-stage OSCC, but did not correlate with clinical outcomes.
CONCLUSION: These data suggest that abnormal EGFR GCN has clinical utility as a biomarker for the detection of OPMD destined to undergo malignant transformation. Prospective studies are required to verify this finding. It remains to be determined if EGFR GCN could be used to select patients for EGFR-targeted therapies.
IMPACT: Abnormal EGFR GCN is a potential biomarker for identifying OPMD that are at risk of malignant transformation. Cancer Epidemiol Biomarkers Prev; 25(6); 927-35. ©2016 AACR.