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Assessment of FBA Based Gene Essentiality Analysis in Cancer with a Fast Context-Specific Network Reconstruction
Luis Tobalina1, Jon Pey1, Alberto Rezola1
1CEIT and Tecnun (University of Navarra), Manuel de Lardizábal 15, 20018, San Sebastian, Spain.
Motivation:
Gene 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.
Results:
In 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.
Insights
Flux Balance Analysis-based Gene Essentiality Analysis (FBA-based GEA) identifies cancer-specific metabolic targets. This study assesses network reconstruction
Area of Science:
- Computational Biology
- Systems Biology
- Cancer Metabolism
Background:
- Flux Balance Analysis-based Gene Essentiality Analysis (FBA-based GEA) is a key method for identifying metabolic therapeutic targets in cancer.
- Reconstructing cancer-specific metabolic networks using gene expression data is a crucial step in FBA-based GEA.
- The impact of the reconstruction process on FBA-based GEA outcomes remains largely unassessed.
Purpose of the Study:
- To investigate the influence of context-specific network reconstruction on FBA-based GEA results for identifying cancer-specific essential genes.
- To evaluate the reliability of FBA-based GEA by comparing in-silico predictions with experimental gene silencing data.
Main Methods:
- Reconstructed context-specific metabolic networks for 174 cancer cell lines using gene expression data from the Cancer Cell Line Encyclopedia (CCLE).
- Performed FBA-based GEA on these networks and compared results with analyses using randomly generated expression patterns.
- Validated predicted essential genes against high-throughput gene silencing data from Project Achilles and literature-based evidence in specific cancer types.
Main Results:
- Identified a subset of essential genes consistently predicted from gene expression data but rare in random networks, suggesting potential cancer-specific drug targets.
- Observed discrepancies between in-silico predictions and experimental gene silencing data, highlighting the significant influence of biomass reaction selection.
- Validated a portion of the predicted targets in Glioblastoma Multiforme and Non-Small Cell Lung Cancer cell lines, indicating promising leads for further research.
Conclusions:
- Context-specific network reconstruction is vital for FBA-based GEA in cancer, yielding a distinct set of potential therapeutic targets.
- The choice of biomass reaction significantly impacts FBA-based GEA outcomes, necessitating careful consideration and standardization.
- Despite challenges, FBA-based GEA remains a valuable approach for discovering novel cancer-specific metabolic targets, requiring further experimental validation.
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