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Updated: Sep 3, 2025
A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
Published on: May 27, 2021
Overcoming selection bias in synthetic lethality prediction
Colm Seale1,2, Yasin Tepeli1, Joana P Gonçalves1
1Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft 2628 XE, The Netherlands.
Motivation:
Synthetic lethality (SL) between two genes occurs when simultaneous loss of function leads to cell death. This holds great promise for developing anti-cancer therapeutics that target synthetic lethal pairs of endogenously disrupted genes. Identifying novel SL relationships through exhaustive experimental screens is challenging, due to the vast number of candidate pairs. Computational SL prediction is therefore sought to identify promising SL gene pairs for further experimentation. However, current SL prediction methods lack consideration for generalizability in the presence of selection bias in SL data.
Results:
We show that SL data exhibit considerable gene selection bias. Our experiments designed to assess the robustness of SL prediction reveal that models driven by the topology of known SL interactions (e.g. graph, matrix factorization) are especially sensitive to selection bias. We introduce selection bias-resilient synthetic lethality (SBSL) prediction using regularized logistic regression or random forests. Each gene pair is described by 27 molecular features derived from cancer cell line, cancer patient tissue and healthy donor tissue samples. SBSL models are built and tested using approximately 8000 experimentally derived SL pairs across breast, colon, lung and ovarian cancers. Compared to other SL prediction methods, SBSL showed higher predictive performance, better generalizability and robustness to selection bias. Gene dependency, quantifying the essentiality of a gene for cell survival, contributed most to SBSL predictions. Random forests were superior to linear models in the absence of dependency features, highlighting the relevance of mutual exclusivity of somatic mutations, co-expression in healthy tissue and differential expression in tumour samples.
Availability And Implementation:
https://github.com/joanagoncalveslab/sbsl.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
Synthetic lethality (SL) prediction identifies gene pairs for cancer therapy. A new method, selection bias-resilient synthetic lethality (SBSL), improves prediction accuracy and generalizability by accounting for biases in existing SL data.
Area of Science:
- Genomics
- Computational Biology
- Cancer Therapeutics
Background:
- Synthetic lethality (SL) is a promising strategy for cancer therapy, targeting genes with simultaneous loss of function.
- Identifying novel SL pairs is challenging due to the vast number of potential gene combinations.
- Existing computational methods for SL prediction often overlook selection bias in experimental data, limiting their generalizability.
Purpose of the Study:
- To develop a computational method for predicting synthetic lethal gene pairs that is robust to selection bias.
- To improve the accuracy and generalizability of synthetic lethality prediction for potential anti-cancer drug development.
Main Methods:
- Developed selection bias-resilient synthetic lethality (SBSL) prediction models using regularized logistic regression and random forests.
- Utilized 27 molecular features derived from cancer cell lines, patient tissues, and healthy donors to describe gene pairs.
- Trained and validated models on approximately 8000 experimentally derived SL pairs across breast, colon, lung, and ovarian cancers.
Main Results:
- SBSL models demonstrated superior predictive performance, generalizability, and robustness to selection bias compared to existing methods.
- Gene dependency, a measure of a gene's essentiality for cell survival, was the most significant predictor in SBSL models.
- Random forests outperformed linear models when dependency features were excluded, emphasizing the importance of mutation mutual exclusivity, healthy co-expression, and tumor differential expression.
Conclusions:
- The developed SBSL method offers a more reliable approach to predicting synthetic lethal gene pairs.
- Addressing selection bias is crucial for building robust and generalizable computational models for synthetic lethality.
- The findings highlight the potential of SBSL for accelerating the discovery of targeted cancer therapeutics.
