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A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein
Anubha Dey1, Suresh Mudunuri2, Manjari Kiran1
1Department of Systems and Computational Biology, School of Life Sciences, University of Hyderabad, Hyderabad, India.
Abstract:
Synthetic lethality (SL) and synthetic viability (SV) are commonly studied genetic interactions in the targeted therapy approach in cancer. In SL, inhibiting either of the genes does not affect the cancer cell survival, but inhibiting both leads to a lethal phenotype. In SV, inhibiting the vulnerable gene makes the cancer cell sick; inhibiting the partner gene rescues and promotes cell viability. Many low and high-throughput experimental approaches have been employed to identify SLs and SVs, but they are time-consuming and expensive. The computational tools for SL prediction involve statistical and machine-learning approaches. Almost all machine learning tools are binary classifiers and involve only identifying SL pairs. Most importantly, there are limited properties known that best describe and discriminate SL from SV. We developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest based machine learning model for genetic interaction prediction. Network properties of protein derived from physical protein-protein interactions are used as features to classify SL and SV. The model results in an accuracy of ~80% for the training dataset (CGIdb, BioGRID, and SynLethDB) and performs well on DepMap and other experimentally derived reported datasets. Amongst all the network properties, the shortest path, average neighbor2, average betweenness, average triangle, and adhesion have significant discriminatory power. MAGICAL is the first multi-class model to identify discriminatory features of synthetic lethal and viable interactions. MAGICAL can predict SL and SV interactions with better accuracy and precision than any existing binary classifier.
Insights
Synthetic lethality and synthetic viability are key in cancer therapy. MAGICAL, a new multi-class model, accurately predicts these genetic interactions using network properties, outperforming existing binary classifiers.
Area of Science:
- Computational biology
- Genetics
- Bioinformatics
Background:
- Synthetic lethality (SL) and synthetic viability (SV) are crucial genetic interactions for targeted cancer therapy.
- Current experimental methods for identifying SLs and SVs are costly and time-consuming.
- Existing computational tools primarily focus on binary classification of SL pairs, lacking discrimination between SL and SV.
Purpose of the Study:
- To develop a novel computational model for predicting both synthetic lethality and synthetic viability interactions.
- To identify key network properties that effectively discriminate between SL and SV interactions.
- To improve the accuracy and precision of genetic interaction prediction in cancer research.
Main Methods:
- Developed MAGICAL (Multi-class Approach for Genetic Interaction in Cancer via Algorithm Learning), a multi-class random forest machine learning model.
- Utilized network properties derived from protein-protein interactions as features for classification.
- Trained and validated the model on established genetic interaction datasets (CGIdb, BioGRID, SynLethDB) and external datasets (DepMap).
Main Results:
- MAGICAL achieved approximately 80% accuracy on the training dataset and demonstrated strong performance on independent datasets.
- Identified specific network properties, including shortest path, average neighbor2, average betweenness, average triangle, and adhesion, as significant discriminators between SL and SV.
- MAGICAL is the first multi-class model capable of identifying discriminatory features for both synthetic lethal and viable interactions.
Conclusions:
- MAGICAL provides a more accurate and precise method for predicting synthetic lethality and synthetic viability compared to existing binary classifiers.
- The model's ability to differentiate between SL and SV using network properties offers valuable insights into cancer genetics.
- This approach has the potential to accelerate the discovery of novel therapeutic targets in oncology.
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