Source: cancerpreventionresearch.aacrjournals.org
Authors: Pierre Saintigny et al.

Patients with oral premalignant lesion (OPL) have a high risk of developing oral cancer. Although certain risk factors, such as smoking status and histology, are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development.

Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course.

Gene expression profiles were associated with oral cancer–free survival and used to develop multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinicopathologic risk factors. On the basis of the gene expression profile data, we also identified 2,182 transcripts significantly associated with oral cancer risk–associated genes (P value < 0.01; univariate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomal components as the top gene sets associated with oral cancer risk. In multiple independent data sets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. Source: Cancer Prev Res; 4(2); 218–29. ©2011 AACR.

Authors:
1. Pierre Saintigny1,
2. Li Zhang2,
3. You-Hong Fan1,
4. Adel K. El-Naggar3,
5. Vassiliki A. Papadimitrakopoulou1,
6. Lei Feng4,
7. J. Jack Lee4,
8. Edward S. Kim1,
9. Waun Ki Hong1 and
10. Li Mao1,5

Authors’ affiliations:
Departments of 1Thoracic/Head and Neck Medical Oncology, 2Bioinformatics and Computational Biology, 3Pathology, and 4Biostatistics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas; and 5University of Maryland Dental School, Baltimore, Maryland