gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

Valcarcel, L. V.; San Jose-Eneriz, E.; Ordonez, R.; Apaolaza, I.; Valcarcel, A.; Garate, L.; San Miguel, J.; Pineda-Lucena, A.; Agirre, X.; Prosper, F.; Planes, F. J.

doi:10.1101/2022.11.03.514827

bioRxiv · 10.1101/2022.11.03.514827

gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer

Valcarcel, L. V.·San Jose-Eneriz, E.·Ordonez, R.·Apaolaza, I.·Valcarcel, A.·Garate, L.·San Miguel, J.·Pineda-Lucena, A.·Agirre, X.·Prosper, F.·Planes, F. J.

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Abstract

The development of computational tools for the systematic prediction of metabolic vulnerabilities of cancer cells constitutes a central question in systems biology. Here, we present gMCStool, a freely accessible and online tool that allows us to carry out this task in a simple, efficient and intuitive environment. gMCStool exploits the concept of genetic Minimal Cut Sets (gMCSs), a theoretical approach to synthetic lethality based on genome-scale metabolic networks, including a unique database of thousands of synthetic lethals computed from Human1, the most recent metabolic reconstruction of human cells. Based on RNA-seq data, gMCStool extends and improves our previously developed algorithms to predict, visualize and analyze metabolic essential genes in cancer, demonstrating a superior performance than competing algorithms in both accuracy and computational performance. A detailed illustration of gMCStool is presented for multiple myeloma (MM), an incurable hematological malignancy. gMCStool could identify a synthetic lethal that explains the dependency on CTP Synthase 1 (CTPS1) in a sub-group of MM patients. We provide in vitro experimental evidence that supports this hypothesis, which opens a new research area to treat MM.

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Valcarcel, L. V., San Jose-Eneriz, E., Ordonez, R., Apaolaza, I., Valcarcel, A., Garate, L., San Miguel, J., Pineda-Lucena, A., Agirre, X., Prosper, F., Planes, F. J.. 2022-11-04. gMCStool: automated network-based tool to search for metabolic vulnerabilities in cancer. https://doi.org/10.1101/2022.11.03.514827

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