Sparse feature selection for classification and prediction of metastasis in endometrial cancer

Mehmet Eren Ahsen, Todd P. Boren, Nitin K. Singh, Burook Misganaw, David G. Mutch, Kathleen N. Moore, Floor J. Backes, Carolyn K. McCourt, Jayanthi S. Lea, David S. Miller, Michael A. White, Mathukumalli Vidyasagar

Research output: Contribution to journalArticlepeer-review


Background: 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. 86 tumors matched for age and race, and evenly distributed between lymph node-positive and lymph node-negative cases, were selected as a training cohort. Genomic micro-RNA expression was profiled for each sample to serve as the predictive feature matrix. An independent set of 28 tumor samples was collected and similarly characterized to serve as a test cohort. Results: A feature selection algorithm was designed for applications where the number of samples is far smaller than the number of measured features per sample. A predictive miRNA expression signature was developed using this algorithm, which was then used to predict the metastatic status of the independent test cohort. A weighted classifier, using 18 micro-RNAs, achieved 100% accuracy on the training cohort. When applied to the testing cohort, the classifier correctly predicted 90% of node-positive cases, and 80% of node-negative cases (FDR = 6.25%). Conclusion: 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.

Original languageEnglish (US)
Article number233
JournalBMC genomics
StatePublished - Mar 27 2017
Externally publishedYes


  • Endometrial cancer
  • Lymph node metastasis
  • Machine learning
  • Sparse classification

ASJC Scopus subject areas

  • Biotechnology
  • Genetics


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