Abstract
BackgroundWith the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis. Here the authors present a pipeline for identifying disease specific gene-drug interactions using CNV (Copy Number Variation) and clinical data from the TCGA (The Cancer Genome Atlas) project. Two cancer types were selected for analysis, LGG (Brain lower grade glioma) and GBM (Glioblastoma multiforme), due to the possible progression from LGG to GBM in some cases. The copy number and clinical data were then used to preform survival analysis on a gene by gene basis on sub-populations of patients exposed to a given drug.ResultsSeveral gene-drug interactions are identified, where the copy number of a gene is associated to survival of a patient exposed to a certain drug. Both Irinotecan/HAS2 (Hyaluronan synthase 2) and Bevacizumab/PGAM1 (Phosphoglycerate mutase 1) are interactions found in this study with independent confirmation. Independent work in colon, breast cancer and leukemia (Györffy, Breast Cancer Res Treat 123:725-731, 2010; Mueller, Mol Cancer Ther 11:3024–3032, 2010; Hitosugi, Cancer Cell 13:585-600, 2012) showed these two interactions can lead to increased survival.ConclusionWhile the pipeline produced several possible interactions where increased survival is linked to normal or increased copy number of a given gene for patients treated with a given drug, no instance of low copy number or full deletion was linked to increased survival. The development of this pipeline shows a promising utility to identify possible beneficial gene-drug interactions that could improve patient survival and may illustrate some of the problems inherent in this kind of analysis on these data.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1255-7) contains supplementary material, which is available to authorized users.
Highlights
With the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis
A pipeline is implemented for integrative analysis of Copy number variation (CNV) data, drug treatment and survival data, for the purpose of identifying beneficial gene-drug interactions, where the copy number of a gene is associated to survival of patients exposed to a certain drug
We only considered drugs with more than 30 patients exposed in the Brain lower grade glioma (LGG) and Glioblastoma multiforme (GBM) data in The Cancer Genome Atlas (TCGA)
Summary
With the advent of large scale biological data collection for various diseases, data analysis pipelines and workflows need to be established to build frameworks for integrative analysis. The authors present a pipeline for identifying disease specific gene-drug interactions using CNV (Copy Number Variation) and clinical data from the TCGA (The Cancer Genome Atlas) project. The copy number and clinical data were used to preform survival analysis on a gene by gene basis on sub-populations of patients exposed to a given drug. The analysis of large scale biological data has multiple challenges including noise filtering and data integration [1, 2] but can provide fruitful queries into questions about various diseases. A pipeline is implemented for integrative analysis of CNV data, drug treatment and survival data, for the purpose of identifying beneficial gene-drug interactions, where the copy number of a gene is associated to survival of patients exposed to a certain drug. Variation in levels of gene expression between patient profiles and obtaining the necessary number of patients for sufficient statistical power are just a few of the hurdles that need to be addressed with any study of this nature
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