Abstract
BackgroundPathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms.MethodsWe combine data from two studies that propose the existence of the Molecular Apocrine phenotype. We use quantile normalization and XPN to minimize institutional bias in the data. We use hierarchical clustering, principal components analysis, and comparison of gene signatures derived from Significance Analysis of Microarrays to establish the existence of the Molecular Apocrine subtype and the equivalence of its molecular phenotype across both institutions. Statistical significance was computed using the Fasano & Franceschini test for separation of principal components and the hypergeometric probability formula for significance of overlap in gene signatures. We perform pathway analysis using LeFEminer and Backward Chaining Rule Induction to identify a signaling network that differentiates the subset. We identify a larger cohort of samples in the public domain, and use Gene Shaving and Robust Bayesian Network Analysis to detect pathways that interact with the defining signal.ResultsWe demonstrate that the two separately introduced ER- breast cancer subsets represent the same tumor type, called Molecular Apocrine breast cancer. LeFEminer and Backward Chaining Rule Induction support a role for AR signaling as a pathway that differentiates this subset from others. Gene Shaving and Robust Bayesian Network Analysis detect interactions between the AR pathway, EGFR trafficking signals, and ErbB2.ConclusionWe propose criteria for meta-analysis that are able to demonstrate statistical significance in establishing molecular equivalence of subsets across institutions. Data mining strategies used here provide an alternative method to comparison with cell lines for discovering seminal pathways and interactions between signaling networks. Analysis of Molecular Apocrine breast cancer implies that therapies targeting AR might be hampered if interactions with ErbB family members are not addressed.
Highlights
Pathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology
Since there has not been a meta-analysis of both studies to confirm that the individual tumor clusters represent the same breast cancer subset as defined by gene expression, we start by performing a comparative study. We call this a test of "molecular equivalence," and we propose a set of criteria for establishing molecular equivalence cancer subsets defined by gene expression data: 1) the majority of the molecular phenotype should cluster together and their combined profile should be distinct from the remaining samples in unsupervised clustering of the combined data; 2) there should be significant overlap of the gene signatures used to classify the phenotype from each institution; and 3) a classifier trained on data from one institution should be able to predict the phenotype correctly in the other institution's data, and vice versa
Data Normalization We combine the index cohorts into a single, homogeneous dataset with quantile normalization (QN) performed using the DNA-Chip Analyzer (dChip) software package [19,20] followed by a recently published cross-study normalization scheme (XPN) that results in removal of persistent systematic bias and noise [21]
Summary
Pathway discovery from gene expression data can provide important insight into the relationship between signaling networks and cancer biology. Oncogenic signaling pathways are commonly inferred by comparison with signatures derived from cell lines. We use the Molecular Apocrine subtype of breast cancer to demonstrate our ability to infer pathways directly from patients' gene expression data with pattern analysis algorithms. Gene expression array data can be mined to provide critical insight into our understanding of the relationship between signaling networks and the biology of cancer [13]. The conventional method of identifying oncogenic pathways and their interactions has been through studying cell lines [1,2,7,8]. Our goal is to be able to identify dominant pathways using data mining methods that do not require direct comparison with cell lines. Our results contribute to both bioinformatics and to breast cancer biology
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