Sensitive detection of pathway perturbations in cancers

Corban G Rivera,Tm Murali,Brett M Tyler

doi:10.1186/1471-2105-13-s3-s9

Abstract

BackgroundThe 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.ResultsIn 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.ConclusionsOur 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.

Highlights

Complex diseases such as cancer are associated with the alteration or dis-regulation of multiple pathways and processes in the cell
Given genome-wide gene expression measurements in multiple patients diagnosed with a disease in a tissue and from normal samples of that tissue, our goal is to determine whether the pathway P = (G, I) is perturbed in the disease and to compute the subgraph of P that is most perturbed in the disease
After describing the pathway and gene expression datasets we used, we present our results in five stages

Summary

Results

After describing the pathway and gene expression datasets we used, we present our results in five stages. GSEA measures the statistical significance of Figure 2 A comparison of the p-values of the most-perturbed sub-pathways computed by ActiveModules to those computed by our algorithm. Sub-GSE failed to identify any cancer associations for the ID or the alpha 6 beta 4 integrin signaling pathways These pathways are known to be perturbed in multiple tumor types. The use of Stouffer’s z-score to combine multiple p-values provides an important advantage over methods that consider pathway membership alone: in many perturbed pathways, we noticed that the receptor protein at the head of the pathway was very slightly differentially expressed, often not to a statistically significant extent, whereas many genes with products downstream of the receptor were differentially expressed (data not shown). It would be interesting to use universal protein interaction networks in order to expand curated pathways

Conclusions

Introduction

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