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Outlier Gene Set Analysis Combined with Top Scoring Pair Provides Robust Biomarkers of Pathway Activity

  • Michael F. Ochs
  • Jason E. Farrar
  • Michael Considine
  • Yingying Wei
  • Soheil Meschinchi
  • Robert J. Arceci
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7986)

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.

Keywords

Acute Myeloid Leukemia Outlier Analysis Diagnostic Sample Pediatric Acute Myeloid Leukemia Relapse Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Michael F. Ochs
    • 1
  • Jason E. Farrar
    • 2
  • Michael Considine
    • 1
  • Yingying Wei
    • 3
  • Soheil Meschinchi
    • 4
  • Robert J. Arceci
    • 5
  1. 1.The Sidney Kimmel Comprehensive Cancer CenterJohns Hopkins UniversityBaltimoreUSA
  2. 2.College of MedicineUniversity of Arkansas for Medical SciencesLittle RockUSA
  3. 3.The Bloomberg School of Public HealthJohns Hopkins UniversityBaltimoreUSA
  4. 4.Fred Hutchinson Cancer Research CenterSeattleUSA
  5. 5.Ronald A. Matricaria Institute of Molecular MedicinePhoenix Children’s HospitalPhoenixUSA

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