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Updated: Sep 30, 2025
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
A network-based approach to integrate nutrient microenvironment in the prediction of synthetic lethality in cancer
Iñigo Apaolaza1,2,3, Edurne San José-Enériz4,5,6, Luis V Valcarcel1,4,5
1Universidad de Navarra, Tecnun Escuela de Ingeniería, San Sebastián, Spain.
Abstract:
Synthetic Lethality (SL) is currently defined as a type of genetic interaction in which the loss of function of either of two genes individually has limited effect in cell viability but inactivation of both genes simultaneously leads to cell death. Given the profound genomic aberrations acquired by tumor cells, which can be systematically identified with -omics data, SL is a promising concept in cancer research. In particular, SL has received much attention in the area of cancer metabolism, due to the fact that relevant functional alterations concentrate on key metabolic pathways that promote cellular proliferation. With the extensive prior knowledge about human metabolic networks, a number of computational methods have been developed to predict SL in cancer metabolism, including the genetic Minimal Cut Sets (gMCSs) approach. A major challenge in the application of SL approaches to cancer metabolism is to systematically integrate tumor microenvironment, given that genetic interactions and nutritional availability are interconnected to support proliferation. Here, we propose a more general definition of SL for cancer metabolism that combines genetic and environmental interactions, namely loss of gene functions and absence of nutrients in the environment. We extend our gMCSs approach to determine this new family of metabolic synthetic lethal interactions. A computational and experimental proof-of-concept is presented for predicting the lethality of dihydrofolate reductase (DHFR) inhibition in different environments. Finally, our approach is applied to identify extracellular nutrient dependences of tumor cells, elucidating cholesterol and myo-inositol depletion as potential vulnerabilities in different malignancies.
Insights
Synthetic lethality (SL) combines gene and environmental interactions to predict cancer cell death. This approach identifies vulnerabilities like cholesterol and myo-inositol depletion, offering new cancer treatment strategies.
Area of Science:
- Cancer Research
- Metabolic Engineering
- Computational Biology
Background:
- Synthetic lethality (SL) is a genetic interaction where loss of either gene is tolerated, but loss of both is lethal.
- Cancer cells exhibit genomic aberrations, making SL a promising strategy for targeted cancer therapies.
- Current SL approaches in cancer metabolism often overlook the crucial role of the tumor microenvironment and nutrient availability.
Purpose of the Study:
- To propose a generalized definition of synthetic lethality (SL) in cancer metabolism that integrates both genetic and environmental factors.
- To extend the genetic Minimal Cut Sets (gMCSs) approach for predicting metabolic synthetic lethal interactions considering environmental conditions.
- To identify novel extracellular nutrient dependencies and potential therapeutic vulnerabilities in various cancer types.
Main Methods:
- Developed an extended gMCSs computational framework to model combined genetic and environmental interactions.
- Integrated knowledge of human metabolic networks with environmental variables.
- Performed computational and experimental validation using dihydrofolate reductase (DHFR) inhibition as a proof-of-concept.
- Applied the approach to identify specific nutrient depletions as cancer vulnerabilities.
Main Results:
- Successfully extended the gMCSs approach to define and predict metabolic synthetic lethal interactions influenced by environmental factors.
- Demonstrated the computational and experimental validity of the approach using DHFR inhibition.
- Identified extracellular nutrient depletions, specifically cholesterol and myo-inositol, as significant vulnerabilities in different malignancies.
- Revealed interconnectedness between genetic alterations and nutrient availability in supporting tumor proliferation.
Conclusions:
- The proposed generalized SL definition and extended gMCSs approach effectively integrate environmental interactions into metabolic synthetic lethality predictions.
- This framework offers a powerful tool for uncovering novel cancer dependencies and guiding the development of targeted therapies.
- Targeting extracellular nutrient dependencies, such as cholesterol and myo-inositol, presents a promising therapeutic avenue for specific cancer types.
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