Abstract

MotivationGene Essentiality Analysis based on Flux Balance Analysis (FBA-based GEA) is a promising tool for the identification of novel metabolic therapeutic targets in cancer. The reconstruction of cancer-specific metabolic networks, typically based on gene expression data, constitutes a sensible step in this approach. However, to our knowledge, no extensive assessment on the influence of the reconstruction process on the obtained results has been carried out to date.ResultsIn this article, we aim to study context-specific networks and their FBA-based GEA results for the identification of cancer-specific metabolic essential genes. To that end, we used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines. In order to more clearly observe the effect of cancer-specific expression data, we did the same analysis using randomly generated expression patterns. Our computational analysis showed some essential genes that are fairly common in the reconstructions derived from both gene expression and randomly generated data. However, though of limited size, we also found a subset of essential genes that are very rare in the randomly generated networks, while recurrent in the sample derived networks, and, thus, would presumably constitute relevant drug targets for further analysis. In addition, we compare the in-silico results to high-throughput gene silencing experiments from Project Achilles with conflicting results, which leads us to raise several questions, particularly the strong influence of the selected biomass reaction on the obtained results. Notwithstanding, using previous literature in cancer research, we evaluated the most relevant of our targets in three different cancer cell lines, two derived from Gliobastoma Multiforme and one from Non-Small Cell Lung Cancer, finding that some of the predictions are in the right track.

Highlights

  • Recent findings show that cancer cells adapt their metabolic processes to enhance proliferation [1,2]

  • We used gene expression datasets from the Cancer Cell Line Encyclopedia (CCLE), evaluating the results obtained in 174 cancer cell lines

  • We have evaluated the accuracy of the results obtained from Flux Balance Analysis based Gene Essentiality Analysis (FBA based gene essentiality analysis (GEA)) when networks contextualized with gene expression are used

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Summary

Introduction

Recent findings show that cancer cells adapt their metabolic processes to enhance proliferation [1,2]. Cancer cells consume additional nutrients and divert those nutrients into macromolecular synthesis pathways. Relevant mutations in metabolic genes and accumulations of key metabolites have been detected in cancer cells [5]. In light of these evidences, the study of cellular metabolism in cancer research has been actively reawakened. Holistic systems biology approaches, based on genome-scale metabolic networks and high-throughput “omics” data, open new avenues to exploit metabolic disorders of tumour cells, for addressing different unmet clinical needs in cancer

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