Issue No. 02 - April-June (2009 vol. 6)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TCBB.2008.126
Yongjin Park , Carnegie Mellon University, Pittsburgh
Stanley Shackney , Drexel University, Pittsburgh
Russell Schwartz , Carnegie Mellon University, Pittsburgh
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. Here, we apply gene network models to improve these estimates of evolutionary distance by controlling for correlations among coregulated genes. We test three variants of this approach: one using an optimized best-fit network, another using sampling to infer a high-confidence subnetwork, and one using a modular network inferred from clusters of similarly expressed genes. Application to lung cancer and breast cancer microarray data sets shows small improvements in phylogenies when correcting from the optimized network and more substantial improvements when correcting from the sampled or modular networks. Our results suggest that a network correction approach improves estimates of tumor similarity, but sophisticated network models are needed to control for the large hypothesis space and sparse data currently available.
Biology and genetics, graphs and networks, trees, machine learning.
S. Shackney, Y. Park and R. Schwartz, "Network-Based Inference of Cancer Progression from Microarray Data," in IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 6, no. , pp. 200-212, 2008.