Network-Based Inference of Cancer Progression from Microarray Data

Cancer cells exhibit a common phenotype of uncontrolled cell growth, but this phenotype may arise from many different combinations of mutations. By inferring how cells evolve in individual tumors, a process called cancer progression, we may be able to identify important mutational events for different tumor types, potentially leading to new therapeutics and diagnostics. Prior work has shown that it is possible to infer frequent progression pathways by using gene expression profiles to estimate “distances” between tumors. Individual mutations can, however, result in large shifts in expression levels, making it difficult to accurately identify evolutionary distance from differences in expression. Here, we apply gene network models in order to improve our ability to estimate evolutionary distances from expression data by controlling for correlations among co-regulated genes. We test two variants of this approach, one using full regulatory networks inferred from a candidate gene set and the other using simplified modular networks inferred from clusters of similarly expressed genes. Application to a set of E2F-responsive genes from a lung cancer microarray data set shows a small improvement in phylogenies when correcting from the full network but a more substantial improvement when correcting from the modular network. These results suggest that a network correction approach can lead to better identification of tumor similarity, but that sophisticated network models are needed to control for the large hypothesis space and sparse data currently available.