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Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism

Year: 2024
Type: review
Abstract: Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant \nphenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted \ntherapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) \nhave been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the \ngenetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict \nmetabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an \nessential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review \nthe current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. \nFirst, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the \npremise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking \nin multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1. Our meta-analysis resulted in \nparameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times \nby only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS.
Authors: Olaverri, D. (Danel), Valcárcel-García, L.V. (Luis Vitores), Barrena, N. (Naroa), Rodríguez, C. (Carlos), Planes-Pedreño, F.J. (Francisco Javier)
Language: English
Open Access status: green
OpenAlex ID: W7005646669
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