Predicting the clinical outcome of oral potentially malignant disorders using transcriptomic-based molecular pathology

Sathasivam, Hans Prakash, Kist, Ralf, Sloan, Philip, Thomson, Peter, Nugent, Michael, Alexander, John, Haider, Syed, and Robinson, Max (2021) Predicting the clinical outcome of oral potentially malignant disorders using transcriptomic-based molecular pathology. British Journal of Cancer, 125. pp. 413-421.

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Background: This study was undertaken to develop and validate a gene expression signature that characterises oral potentially malignant disorders (OPMD) with a high risk of undergoing malignant transformation.

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.

Item ID: 68422
Item Type: Article (Research - C1)
ISSN: 1532-1827
Copyright Information: © The Author(s), under exclusive licence to Cancer Research UK 2021
Date Deposited: 02 Dec 2021 01:39
FoR Codes: 32 BIOMEDICAL AND CLINICAL SCIENCES > 3211 Oncology and carcinogenesis > 321103 Cancer genetics @ 100%
SEO Codes: 20 HEALTH > 2001 Clinical health > 200105 Treatment of human diseases and conditions @ 100%
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