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This article is part of the supplement: ACM Conference on Bioinformatics, Computational Biology and Biomedicine 2011

Open Access Open Badges Proceedings

Sensitive detection of pathway perturbations in cancers

Corban G Rivera14, Brett M Tyler2 and TM Murali13*

Author Affiliations

1 Department of Computer Science, Virginia Tech, Blacksburg, VA, USA

2 Virginia Bioinformatics Institute, Virginia Tech, Blacksburg, VA, USA

3 ICTAS Center for Systems Biology of Engineered Tissues, Virginia Tech, Blacksburg, VA, USA

4 Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA

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BMC Bioinformatics 2012, 13(Suppl 3):S9  doi:10.1186/1471-2105-13-S3-S9

Published: 21 March 2012



The normal functioning of a living cell is characterized by complex interaction networks involving many different types of molecules. Associations detected between diseases and perturbations in well-defined pathways within such interaction networks have the potential to illuminate the molecular mechanisms underlying disease progression and response to treatment.


In this paper, we present a computational method that compares expression profiles of genes in cancer samples to samples from normal tissues in order to detect perturbations of pre-defined pathways in the cancer. In contrast to many previous methods, our scoring function approach explicitly takes into account the interactions between the gene products in a pathway. Moreover, we compute the sub-pathway that has the highest score, as opposed to merely computing the score for the entire pathway. We use a permutation test to assess the statistical significance of the most perturbed sub-pathway. We apply our method to 20 pathways in the Netpath database and to the Global Cancer Map of gene expression in 18 cancers. We demonstrate that our method yields more sensitive results than alternatives that do not consider interactions or measure the perturbation of a pathway as a whole. We perform a sensitivity analysis to show that our approach is robust to modest changes in the input data. Our method confirms numerous well-known connections between pathways and cancers.


Our results indicate that integrating differential gene expression with the interaction structure in a pathway is a powerful approach for detecting links between a cancer and the pathways perturbed in it. Our results also suggest that even well-studied pathways may be perturbed only partially in any given cancer. Further analysis of cancer-specific sub-pathways may shed new light on the similarities and differences between cancers.