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

dc.contributor.VIAF27150194 (Vidyasagar, M)en_US
dc.contributor.authorAhsen, Mehmet Erenen_US
dc.contributor.authorBoren, Todd P.en_US
dc.contributor.authorSingh, Nitin K.en_US
dc.contributor.authorMisganaw, Burooken_US
dc.contributor.authorMutch, David G.en_US
dc.contributor.authorMoore, Kathleen N.en_US
dc.contributor.authorBackes, Floor J.en_US
dc.contributor.authorMcCourt, Carolyn K.en_US
dc.contributor.authorLea, Jayanthi S.en_US
dc.contributor.authorMiller, David S.en_US
dc.contributor.authorWhite, Michael A.en_US
dc.contributor.authorVidyasagar, Mathukumallien_US
dc.contributor.utdAuthorVidyasagar, Mathukumallien_US
dc.date.accessioned2018-06-01T16:45:08Z
dc.date.available2018-06-01T16:45:08Z
dc.date.created2017-03-27
dc.date.issued2017-03-27en_US
dc.descriptionIncludes supplementary materialen_US
dc.description.abstractBackground: 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.en_US
dc.description.departmentErik Jonsson School of Engineering and Computer Scienceen_US
dc.description.sponsorshipNational Science Foundation under Awards 1001643 and 1306630, CPRIT under grant No. RP140517; Welch Foundation grant No. I-1414 and CPRIT Award No. RO110595.en_US
dc.identifier.bibliographicCitationAhsen, Mehmet Eren, Todd P. Boren, Nitin K. Singh, Burook Misganaw, et al. 2017. "Sparse feature selection for classification and prediction of metastasis in endometrial cancer." BMC Genomics 18(suppl. 3), doi:10.1186/s12864-017-3604-yen_US
dc.identifier.issn1471-2164en_US
dc.identifier.issuesuppl. 3en_US
dc.identifier.urihttp://hdl.handle.net/10735.1/5828
dc.identifier.volume18en_US
dc.language.isoenen_US
dc.relation.urihttp://dx.doi.org/10.1186/s12864-017-3604-yen_US
dc.rightsCC BY 4.0 (Attribution) (CC0 1.0 for data unless otherwise stated)en_US
dc.rights©2017 The Authorsen_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.source.journalBMC Genomicsen_US
dc.subjectCanceren_US
dc.subjectEndometrium—Canceren_US
dc.subjectLymph nodesen_US
dc.subjectLymphatic metastasisen_US
dc.subjectMachine learningen_US
dc.subjectMicroRNAen_US
dc.subjectSmall interfering RNAen_US
dc.subjectClinical trialsen_US
dc.titleSparse Feature Selection for Classification and Prediction of Metastasis in Endometrial Canceren_US
dc.type.genrearticleen_US

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