Hans Prakash Sathasivam1,2, Ralf Kist1,3, Syed Haider4, Max Robinson5,6, Philip Sloan1,7, Peter Thomson8, Michael Nugent9, John Alexander4. 1. School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK. 2. Cancer Research Centre, Institute for Medical Research, National Institutes of Health, Ministry of Health Malaysia, Setia Alam, Malaysia. 3. Newcastle University Biosciences Institute, Newcastle University Centre for Cancer, Newcastle upon Tyne, UK. 4. The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK. 5. School of Dental Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK. max.robinson@nhs.net. 6. Department of Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. max.robinson@nhs.net. 7. Department of Cellular Pathology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK. 8. Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong, Hong Kong SAR. 9. Oral and Maxillofacial Surgery, Sunderland Royal Hospital, Sunderland, UK.
Abstract
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.
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.
Authors: Gary K Geiss; Roger E Bumgarner; Brian Birditt; Timothy Dahl; Naeem Dowidar; Dwayne L Dunaway; H Perry Fell; Sean Ferree; Renee D George; Tammy Grogan; Jeffrey J James; Malini Maysuria; Jeffrey D Mitton; Paola Oliveri; Jennifer L Osborn; Tao Peng; Amber L Ratcliffe; Philippa J Webster; Eric H Davidson; Leroy Hood; Krassen Dimitrov Journal: Nat Biotechnol Date: 2008-02-17 Impact factor: 54.908
Authors: Pierre Saintigny; Adel K El-Naggar; Vali Papadimitrakopoulou; Hening Ren; You-Hong Fan; Lei Feng; J Jack Lee; Edward S Kim; Waun Ki Hong; Scott M Lippman; Li Mao Journal: Clin Cancer Res Date: 2009-09-22 Impact factor: 12.531
Authors: David W Scott; Fong Chun Chan; Fangxin Hong; Sanja Rogic; King L Tan; Barbara Meissner; Susana Ben-Neriah; Merrill Boyle; Robert Kridel; Adele Telenius; Bruce W Woolcock; Pedro Farinha; Richard I Fisher; Lisa M Rimsza; Nancy L Bartlett; Bruce D Cheson; Lois E Shepherd; Ranjana H Advani; Joseph M Connors; Brad S Kahl; Leo I Gordon; Sandra J Horning; Christian Steidl; Randy D Gascoyne Journal: J Clin Oncol Date: 2012-11-26 Impact factor: 44.544
Authors: Maricris Macabeo-Ong; Caroline H Shiboski; Sol Silverman; David G Ginzinger; Nusi Dekker; David T W Wong; Richard C K Jordan Journal: Oral Oncol Date: 2003-10 Impact factor: 5.337
Authors: Chai Phei Gan; Bernard Kok Bang Lee; Shin Hin Lau; Thomas George Kallarakkal; Zuraiza Mohamad Zaini; Bryan Kit Weng Lye; Rosnah Binti Zain; Hans Prakash Sathasivam; Joe Poh Sheng Yeong; Natalia Savelyeva; Gareth Thomas; Christian H Ottensmeier; Hany Ariffin; Sok Ching Cheong; Kue Peng Lim Journal: Front Immunol Date: 2022-09-02 Impact factor: 8.786