A Non-Negative Matrix Tri-Factorization Based Method for Predicting Antitumor Drug Sensitivity

Testa, Carolina; Pidò, Sara; Pinoli, Pietro

doi:10.1007/978-3-031-20837-9_8

Cited by 2 publications

(1 citation statement)

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“…Computational methodologies are progressively being embraced in the scientific community as a valuable adjunct to traditional experimental procedures, with the objective of expediting discovery processes and unveiling novel targets [32]. Several groups have embarked on computational explorations to pinpoint gene pairs demonstrating Synthetic Lethality (SL) [12,33], with a subset harnessing the potential of machine learning to uncover new SL couples [34]. Simultaneously, others have delved into databases in pursuit of drugs amenable to repurposing [19].…”

Section: In Vitro Validation Of Simvastatin As Anticancer Agentmentioning

confidence: 99%

The molecular basis of the anticancer effect of statins

Buccioli,

Testa,

Jacchetti

et al. 2024

Preprint

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Statins, one of the most used class of cardiovascular drugs with the primary function of reducing blood cholesterol levels, exert their effect by inhibiting the enzyme HMG-CoA reductase, the key player in cholesterol biosynthesis. While the primary indication for statins is the prevention of cardiovascular diseases, there has been growing interest in their potential anticancer effects. However, the current evidence on these effects is largely based on epidemiological observations and preclinical research, not yet substantiated by knowledge of the mechanisms behind it. Here we show that statins have an anticancer effect as they exploit the principle of Synthetic Lethality, a concept in which the combination of two non-lethal genetic or molecular events results in cell death or impairment. When either of these events occurs alone, it is not lethal, but when they happen coupled, they create a lethal condition for the cell. In this work we report that statins emerged from a computational data analysis that we performed on approximately 37,000 synthetic lethality couples. We performed this analysis to select repurposable drugs that could target genes involved in Synthetic Lethality couples with metastatic genes. We validated our discoveryin vitroby drug tests performed on cell lines derived from cancers of the breast, ovary, and cervix. Our data-driven drug repurposing strategy allowed us to understand the molecular basis of the anticancer effect of statins, a discovery which can be directly translated into practical clinical applications in oncology.

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