title: 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.forename: Michael F
authors.initials: MF
authors.lastname: Ochs
authors.forename: Jason E
authors.initials: JE
authors.lastname: Farrar
authors.forename: Michael
authors.initials: M
authors.lastname: Considine
authors.forename: Yingying
authors.initials: Y
authors.lastname: Wei
authors.forename: Soheil
authors.initials: S
authors.lastname: Meshinchi
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Arceci
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis* / Q000235:genetics / Q000378:metabolism; D002648:Child; D020869:Gene Expression Profiling / Q000379:methods*; D023281:Genomics / Q000379:methods*; D006801:Humans; D007951:Leukemia, Myeloid / Q000235:genetics* / Q000378:metabolism; D008745:Methylation; D015233:Models, Statistical; D015398:Signal Transduction
chemical_list: D014408:Biomarkers, Tumor