title: Sparse feature selection for classification and prediction of metastasis in endometrial cancer.
abstract: 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.
  
  
  
  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%).
  
  
  
  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.
authors.affiliation: IBM Research, Yorktown Heights, NY, USA.
authors.forename: Mehmet Eren
authors.initials: ME
authors.lastname: Ahsen
authors.affiliation: The University of Tennessee, College of Medicine, KnoxvilleTN, USA.
authors.forename: Todd P
authors.initials: TP
authors.lastname: Boren
authors.affiliation: Apple R&D, Austin, TX, USA.
authors.forename: Nitin K
authors.initials: NK
authors.lastname: Singh
authors.affiliation: Harvard University, Cambridge, MA, USA.
authors.forename: Burook
authors.initials: B
authors.lastname: Misganaw
authors.affiliation: The Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: David G
authors.initials: DG
authors.lastname: Mutch
authors.affiliation: The University of Oklohoma, Norman, OK, USA.
authors.forename: Kathleen N
authors.initials: KN
authors.lastname: Moore
authors.affiliation: The Ohio State University, Columbus, OH, USA.
authors.forename: Floor J
authors.initials: FJ
authors.lastname: Backes
authors.affiliation: Women and Infants Hospital, Brown University, Providence, RI, USA.
authors.forename: Carolyn K
authors.initials: CK
authors.lastname: McCourt
authors.affiliation: University of Texas Southwestern Medical Center, TX, Dallas, USA.
authors.forename: Jayanthi S
authors.initials: JS
authors.lastname: Lea
authors.affiliation: University of Texas Southwestern Medical Center, TX, Dallas, USA.
authors.forename: David S
authors.initials: DS
authors.lastname: Miller
authors.affiliation: University of Texas Southwestern Medical Center, TX, Dallas, USA. michael.white@utsouthwestern.edu.
authors.forename: Michael A
authors.initials: MA
authors.lastname: White
authors.affiliation: The University of Texas at Dallas, Richardson, TX, USA. m.vidyasagar@utdallas.edu.
authors.forename: Mathukumalli
authors.initials: M
authors.lastname: Vidyasagar
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods; D016889:Endometrial Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D023281:Genomics / Q000379:methods*; D006801:Humans; D035683:MicroRNAs / Q000235:genetics; D009362:Neoplasm Metastasis; D009367:Neoplasm Staging; D011379:Prognosis
chemical_list: D035683:MicroRNAs
keywords: Endometrial cancer; Lymph node metastasis; Machine learning; Sparse classification