Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery.

Abstract

Pathway deregulation has been identified as a key driver of carcinogenesis, with proteins in signaling pathways serving as primary targets for drug development. Deregulation can be driven by a number of molecular events, including gene mutation, epigenetic changes in gene promoters, overexpression, and gene amplifications or deletions. We demonstrate a novel approach that identifies pathways of interest by integrating outlier analysis within and across molecular data types with gene set analysis. We use the results to seed the top-scoring pair algorithm to identify robust biomarkers associated with pathway deregulation. 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 pediatric AML tumors.

• Authors: Michael F Ochs, Jason E Farrar, Michael Considine, Yingying Wei, Soheil Meshinchi, Robert J Arceci
• Journal: IEEE/ACM transactions on computational biology and bioinformatics (IEEE/ACM Trans Comput Biol Bioinform) 2014 May-Jun Vol. 11 Issue 3 Pg. 520-32 ISSN: 1557-9964 [Electronic] United States
• PMID: 26356020 (Publication Type: Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't)

Chemical References

• Biomarkers, Tumor

Topics

• Algorithms
• Biomarkers, Tumor (analysis, genetics, metabolism)
• Child
• Gene Expression Profiling (methods)
• Genomics (methods)
• Humans
• Leukemia, Myeloid (genetics, metabolism)
• Methylation
• Models, Statistical
• Signal Transduction