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

Sep 21, 2026

Cancer metabolism involves pathway reprogramming that enables malignant cells to self-sustain, offering opportunities for targeted therapies that disrupt proliferation while limiting damage to healthy tissue. Genome-scale metabolic models, including Human1, support the genetic Minimal Cut Set (gMCS) strategy, which leverages synthetic lethality to identify metabolic vulnerabilities in cancer. The review summarizes the gMCS framework and performs a systematic meta-analysis using two cancer gene-essentiality datasets, evaluating thresholds and gene-expression handling, examining whether gMCS length affects predictive power, assessing the role of genes shared across gMCSs, and cataloging essential metabolic tasks in Human1. Results refine parameters to boost prediction and reduce computation time by selecting key gMCS lengths.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Abstract

Introduction

Synthetic lethality and genetic minimal cut sets

Precision and targeted medicine

Network-based approaches and constraint-based modelling

gMCS approach and its link to synthetic lethality

Briefings in Bioinformatics, 2024, 25(3), bbae115 [https://doi.org/10.1093/bib/bbae115](https://doi.org/10.1093/bib/bbae115) Review Review and meta-analysis of the genetic Minimal Cut Set approach for gene essentiality prediction in cancer metabolism Danel Olaverri-Mendizabal , Luis V. Valcárcel , Naroa Barrena , Carlos J. Rodríguez and Francisco J. Planes Corresponding author. Francisco J. Planes, Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI), University of Navarra, Campus Universitario, Pamplona 31080, Spain. Tel.: +34-943-219877; Fax: +34-943-311442; E-mail: fplanes@tecnun.es Abstract Cancer metabolism is a marvellously complex topic, in part, due to the reprogramming of its pathways to self-sustain the malignant phenotype in the disease, to the detriment of its healthy counterpart. Understanding these adjustments can provide novel targeted therapies that could disrupt and impair proliferation of cancerous cells. For this very purpose, genome-scale metabolic models (GEMs) have been developed, with Human1 being the most recent reconstruction of the human metabolism. Based on GEMs, we introduced the genetic Minimal Cut Set (gMCS) approach, an uncontextualized methodology that exploits the concepts of synthetic lethality to predict metabolic vulnerabilities in cancer. gMCSs define a set of genes whose knockout would render the cell unviable by disrupting an essential metabolic task in GEMs, thus, making cellular proliferation impossible. Here, we summarize the gMCS approach and review the current state of the methodology by performing a systematic meta-analysis based on two datasets of gene essentiality in cancer. First, we assess several thresholds and distinct methodologies for discerning highly and lowly expressed genes. Then, we address the premise that gMCSs of distinct length should have the same predictive power. Finally, we question the importance of a gene partaking in multiple gMCSs and analyze the importance of all the essential metabolic tasks defined in Human1 . Our meta-analysis resulted in parameter evaluation to increase the predictive power for the gMCS approach, as well as a significant reduction of computation times by only selecting the crucial gMCS lengths, proposing the pertinency of particular parameters for the peak processing of gMCS. Keywords: synthetic lethality; genetic Minimal Cut Sets; genome-scale metabolic models; constraint based modelling; gene essentiality analysis INTRODUCTION Synthetic lethality and genetic minimal cut sets Precision and targeted medicine have emerged as compelling topics in cancer research. Their main challenge is the development of novel treatments that selectively target malignant cells while sparing healthy ones in order to increase therapy sensitivity and decrease side-effects, leading to a better quality of life for patients. A promising approach to achieving this goal is synthetic lethality (SL), which refers to the interaction between two genes in which a perturbation, such as a mutation, RNA interference knockdown, or inhibition, affecting either gene alone does not imply a loss in cell viability; however, the perturbation of both genes simultaneously results in lethality [1] . SL provides a promising avenue for developing targeted therapies for cancer, and several approaches have been used to detect SL in different cancer types, including in vitro studies [2] and computational methods [3 , 4] . Network-based approaches to predict SL in cancer cells have received much attention in the field of systems biology [5] . In particular, constraint-based modelling, a computational framework for the analysis of genome-scale metabolic networks, has experienced great advance in the last decade to study cancer metabolism [6–10] . Among existing approaches, the genetic Minimal Cut Sets (gMCSs) approach constitutes a unique strategy that directly connects with the concept of SL [11] . The gMCS approach extends the concept of SL t

FAQs

What is the genetic Minimal Cut Set (gMCS) approach used for?

gMCSs identify metabolic vulnerabilities by defining gene sets whose knockout renders cancer cells unviable in genome-scale metabolic models, thereby preventing proliferation.

How does the study evaluate the impact of different gMCS lengths?

The meta-analysis tests the premise that gMCSs of distinct length should show the same predictive power for gene essentiality in cancer.

What outcomes does the meta-analysis report for improving the gMCS method?

It provides parameter evaluations to increase predictive power and reduces computation time by selecting only crucial gMCS lengths, supporting targeted parameter choices for efficient gMCS processing.

Sep 21, 2026

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