Conclusion
By constructing a comprehensive model, we are able to reliably predict recurrence in endometrioid endometrial cancer. We devised a clinically useful scoring system and thresholds to discriminate risk of recurrence. Finally, the data presented here open a window to understanding the mechanisms of recurrence in endometrial cancer.
Methods
A cohort of endometrioid endometrial cancer patients treated at our institution was assembled. Clinical characteristics were extracted from patient charts. Primary tumors from these patients were obtained and total tissue RNA extracted for RNA sequencing. A prediction model was designed containing both clinical characteristics and molecular profiling of the tumors. The same analysis was carried out with data derived from The Cancer Genome Atlas for replication and external validation.
Results
Prediction models derived from our institutional data predicted recurrence with high accuracy as evidenced by areas under the curve approaching 1. Similar trends were observed in the analysis of TCGA data. Further, a scoring system for risk of recurrence was devised that showed specificities as high as 81% and negative predictive value as high as 90%. Lastly, we identify specific molecular characteristics of patient tumors that may contribute to the process of disease recurrence.