Sparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Cancer

Metastasis via pelvic and/or para-aortic lymph nodes is a major risk factor for endometrial cancer. Lymph-node resection ameliorates risk but is associated with significant co-morbidities. Incidence in patients with stage I disease is 4-22% but no mechanism exists to accurately predict it. Therefore, national guidelines for primary staging surgery include pelvic and para-aortic lymph node dissection for all patients whose tumor exceeds 2cm in diameter. We sought to identify a robust molecular signature that can accurately classify risk of lymph node metastasis in endometrial cancer patients. We introduce a new feature selection algorithm, lone star, for applications where the number of samples is far smaller than the number of measured features per sample. We applied lone star to develop a predictive miRNA expression signature on a training. When applied on an independent testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR= 6.25%). Our results indicate that the evaluation of the quantitative sparse-feature classifier proposed here in clinical trials may lead to significant improvement in the prediction of lymphatic metastases in endometrial cancer patients.