We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Sparse feature selection for classification and prediction of metastasis in endometrial cancer.
- Authors
Ahsen, Mehmet Eren; Boren, Todd P.; Singh, Nitin K.; Misganaw, Burook; Mutch, David G.; Moore, Kathleen N.; Backes, Floor J.; McCourt, Carolyn K.; Lea, Jayanthi S.; Miller, David S.; White, Michael A.; Vidyasagar, Mathukumalli
- Abstract
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.
- Subjects
METASTASIS; ENDOMETRIAL cancer risk factors; FEATURE selection; CLASSIFICATION algorithms; MICRORNA; PROGNOSIS
- Publication
BMC Genomics, 2017, Vol 18, p1
- ISSN
1471-2164
- Publication type
Article
- DOI
10.1186/s12864-017-3604-y