A Novel Algorithm for Simplification of Complex Gene Classifiers in Cancer
Year: 2013
Type: article
Abstract: The clinical application of complex molecular classifiers as diagnostic or prognostic tools has been limited by the time and cost needed to apply them to patients. Using an existing 50-gene expression signature known to separate two molecular subtypes of the pediatric cancer rhabdomyosarcoma, we show that an exhaustive iterative search algorithm can distill this complex classifier down to two or three features with equal discrimination. We validated the two-gene signatures using three separate and distinct datasets, including one that uses degraded RNA extracted from formalin-fixed, paraffin-embedded material. Finally, to show the generalizability of our algorithm, we applied it to a lung cancer dataset to find minimal gene signatures that can distinguish survival. Our approach can easily be generalized and coupled to existing technical platforms to facilitate the discovery of simplified signatures that are ready for routine clinical use.
Source: Cancer Research
Institutions: University of Southern California, Seattle Children's Hospital, National Institutes of Health, Nationwide Children's Hospital, Children's Hospital of Los Angeles, University of Washington, National Cancer Institute, University of Oklahoma Health Sciences Center, The Ohio State University, University of Nebraska Medical Center, The University of Texas Southwestern Medical Center
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2130100581