Predicting cancer development in oral leukoplakia: ten years of translational research

Our 10-year translational study of the oral premalignant lesion (OPL) model has advanced the basic understanding of carcinogenesis. Although retinoids have established activity in this model, a substantial percentage of our OPL patients progress to cancer, especially after treatment is stopped. On the basis of our 10-year OPL study, we have developed the first comprehensive tool for assessing cancer risk of OPL patients. This cancer risk assessment tool incorporates medical/demographic variables, epidemiological factors, and cellular and molecular biomarkers. Between 1988 and 1991, 70 advanced OPL patients were enrolled in a chemoprevention trial of induction with high dose isotretinoin (1.5 mg/kg/day for 3 months) followed by 9 months of maintenance treatment with either low dose isotretinoin (0.5 mg/kg/day) or beta-carotene (30 mg/d; total treatment duration, 1 year). We assessed the relationship between cancer risk factors and time to cancer development by means of exploratory data analysis, logrank test, Cox proportional hazard model, and recursive partitioning. With a median follow-up of 7 years, 22 of our 70 patients (31.4%) developed cancers in the upper aerodigestive tract following treatment. The overall cancer incidence was 5.7% per year. The most predictive factors of cancer risk are OPL histology, cancer history, and three of the five biomarkers we assessed (chromosomal polysomy, p53 protein expression, and loss of heterozygosity at chromosome 3p or 9p). In the multivariable Cox model, histology (P = 0.0003) and the combined biomarker score of chromosomal polysomy, p53, and loss of heterozygosity (P = 0.0008) are the strongest predictors for cancer development. Retinoic acid receptor beta and micronuclei were not associated with increased cancer risk. We have demonstrated a successful strategy of comprehensive cancer risk assessment in OPL patients. Combining conventional medical/demographic variables and a panel of three biomarkers can identify high risk patients in our sample. This result will need to be validated by future studies. With the identification of high risk individuals, more efficient chemoprevention trials and molecular targeting studies can be designed.