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

Mehmet Eren Ahsen, Todd P. Boren, Nitin K. Singh, Burook Misganaw, Jayanthi S. Lea, David S. Miller, Michael A. White, Mathukumalli Vidyasagar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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. 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.

Original languageEnglish (US)
Title of host publicationACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics
PublisherAssociation for Computing Machinery, Inc
Pages522-524
Number of pages3
ISBN (Electronic)9781450342254
DOIs
StatePublished - Oct 2 2016
Event7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016 - Seattle, United States
Duration: Oct 2 2016Oct 5 2016

Other

Other7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, ACM-BCB 2016
CountryUnited States
CitySeattle
Period10/2/1610/5/16

Keywords

  • Endometrial cancer
  • Machine learning
  • Support vector machines,biomarker discovery

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Biomedical Engineering
  • Computer Science Applications

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  • Cite this

    Ahsen, M. E., Boren, T. P., Singh, N. K., Misganaw, B., Lea, J. S., Miller, D. S., White, M. A., & Vidyasagar, M. (2016). Sparse feature selection for classification and prediction of metastasis in endometrial cancer. In ACM-BCB 2016 - 7th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (pp. 522-524). Association for Computing Machinery, Inc. https://doi.org/10.1145/2975167.2985667