Genetic profiling to predict recurrence of early cervical cancer

• We developed prediction models for recurrence in early cervical cancer.
• Models using gene set or clinical variables were developed.
• Gene set model showed higher predictive performance than clinical models.

ObjectiveRecurrence is the major cause of death in early cervical cancer. We compared the prediction powers for disease recurrence between the gene set prognostic model and the clinical prognostic model.Materials and methodsA gene set model to predict disease free survival was developed using the cDNA-mediated annealing, selection, extension, and ligation (DASL) assay data set from a cohort of early cervical cancer patients who had been treated with radical surgery with or without adjuvant therapy. A clinical prediction model was also developed using the same cohort, and the ability of predicting recurrence from each model was compared.ResultsAdequate DASL assay profiles were obtained from 300 patients, and we selected 12 genes for the gene set model. When patients were categorized as having a low or high risk by the prognostic score, the Kaplan–Meier curve showed significantly different recurrence rates between the two groups. The clinical model was developed using FIGO stage and post-surgical pathological findings. In multivariate Cox regression analysis of prognostic models, the gene set prognostic model showed a higher hazard ratio than that of the clinical prognostic model.ConclusionsThe genetic quantitative approach may be better in predicting recurrence in early cervical cancer patients.