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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
An in-silico approach to predict and exploit synthetic lethality in cancer metabolism
Iñigo Apaolaza1, Edurne San José-Eneriz2, Luis Tobalina1,3
1CEIT and Tecnun, University of Navarra, Manuel de Lardizábal 13, 20018, San Sebastián, Spain.
Abstract:
Synthetic lethality is a promising concept in cancer research, potentially opening new possibilities for the development of more effective and selective treatments. Here, we present a computational method to predict and exploit synthetic lethality in cancer metabolism. Our approach relies on the concept of genetic minimal cut sets and gene expression data, demonstrating a superior performance to previous approaches predicting metabolic vulnerabilities in cancer. Our genetic minimal cut set computational framework is applied to evaluate the lethality of ribonucleotide reductase catalytic subunit M1 (RRM1) inhibition in multiple myeloma. We present a computational and experimental study of the effect of RRM1 inhibition in four multiple myeloma cell lines. In addition, using publicly available genome-scale loss-of-function screens, a possible mechanism by which the inhibition of RRM1 is effective in cancer is established. Overall, our approach shows promising results and lays the foundation to build a novel family of algorithms to target metabolism in cancer.Exploiting synthetic lethality is a promising approach for cancer therapy. Here, the authors present an approach to identifying such interactions by finding genetic minimal cut sets (gMCSs) that block cancer proliferation, and apply it to study the lethality of RRM1 inhibition in multiple myeloma.
Insights
This study introduces a computational method to find synthetic lethality in cancer metabolism, identifying vulnerabilities by targeting ribonucleotide reductase catalytic subunit M1 (RRM1) in multiple myeloma.
Area of Science:
- Computational biology
- Cancer metabolism
- Synthetic lethality
Background:
- Synthetic lethality offers a promising avenue for targeted cancer therapies.
- Identifying metabolic vulnerabilities in cancer is crucial for developing selective treatments.
Purpose of the Study:
- To present a novel computational method for predicting and exploiting synthetic lethality in cancer metabolism.
- To evaluate the therapeutic potential of inhibiting ribonucleotide reductase catalytic subunit M1 (RRM1) in multiple myeloma.
Main Methods:
- Utilized genetic minimal cut sets (gMCSs) and gene expression data for computational prediction.
- Applied the gMCS framework to analyze RRM1 inhibition in multiple myeloma cell lines.
- Integrated public genome-scale loss-of-function screens to elucidate mechanisms.
Main Results:
- The gMCS computational framework demonstrated superior performance in predicting metabolic vulnerabilities.
- Computational and experimental studies confirmed the lethality of RRM1 inhibition in multiple myeloma cell lines.
- A potential mechanism for RRM1 inhibition's efficacy in cancer was identified.
Conclusions:
- The developed computational approach effectively predicts and exploits synthetic lethality in cancer metabolism.
- This work lays the groundwork for novel algorithms targeting cancer metabolism.
- Targeting RRM1 presents a promising strategy for multiple myeloma treatment.
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