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.
Original language | English (US) |
---|---|
Article number | 6674300 |
Pages (from-to) | 520-532 |
Number of pages | 13 |
Journal | IEEE/ACM Transactions on Computational Biology and Bioinformatics |
Volume | 11 |
Issue number | 3 |
DOIs | |
State | Published - 2014 |
Keywords
- Biomarker
- Data integration
- Genomics
- Statistical analysis
ASJC Scopus subject areas
- Biotechnology
- Genetics
- Applied Mathematics