Outlier Analysis and Top Scoring Pairfor Integrated Data Analysisand Biomarker Discovery

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,...

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Published in:IEEE/ACM transactions on computational biology and bioinformatics Vol. 11; no. 3; pp. 520 - 532
Main Authors: Ochs, Michael F, Farrar, Jason E, Considine, Michael, Wei, Yingying, Meshinchi, Soheil, Arceci, Robert J
Format: Journal Article
Language:English
Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 01.05.2014
Subjects:
Algorithms
Biomarkers
Carcinogens
Deregulation
Drugs
Genes
Medical treatment
Pathways
Pediatrics
Tumors
ISSN:1545-5963, 1557-9964
Online Access:Get full text
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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.
AbstractList 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.
Author Considine, Michael
Wei, Yingying
Ochs, Michael F
Farrar, Jason E
Meshinchi, Soheil
Arceci, Robert J
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SubjectTerms Algorithms
Biomarkers
Carcinogens
Deregulation
Drugs
Genes
Medical treatment
Pathways
Pediatrics
Tumors
Title Outlier Analysis and Top Scoring Pairfor Integrated Data Analysisand Biomarker Discovery
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