Outlier gene set analysis combined with top scoring pair provides robust biomarkers of pathway activity

Authors:
Michael F. Ochs;Jason E. Farrar;Michael Considine;Yingying Wei;Soheil Meschinchi;Robert J. Arceci
Affiliations:
The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD;College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR;The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD;The Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD;Fred Hutchinson Cancer Research Center, Seattle, WA;Ronald A. Matricaria Institute of Molecular Medicine, Phoenix Children's Hospital, Phoenix, AZ
Venue:
PRIB'13 Proceedings of the 8th IAPR international conference on Pattern Recognition in Bioinformatics
Year:
2013

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Abstract

Cancer is a disease driven by pathway activity, while useful biomarkers to predict outcome (prognostic markers) or determine treatment (treatment markers) rely on individual genes, proteins, or metabolites. We provide a novel approach that isolates pathways of interest by integrating outlier analysis and gene set analysis and couple it to the top-scoring pair algorithm to identify robust biomarkers. We demonstrate this methodology on pediatric acute myeloid leukemia (AML) data. We develop a biomarker in primary AML tumors, demonstrate robustness with an independent primary tumor data set, and show that the identified biomarkers also function well in relapsed AML tumors.