ArticleBioinformatics (Oxford, England)2024
ELISL: early-late integrated synthetic lethality prediction in cancer.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 8 citations in OpenAlex.
- Context defines precision: rethinking KRAS inhibition in oncology.NPJ precision oncology · 2026Review
- Integrating artificial intelligence across the cancer drug discovery pipeline using a design-test-refine workflow.Frontiers in oncology · 2026Review
- Perspectives on cancer therapy-synthetic lethal precision medicine strategies, molecular mechanisms, therapeutic targets and current technical challenges.Cell death discovery · 2025Review
- Benchmarking machine learning methods for synthetic lethality prediction in cancer.Nature communications · 2024Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationAnti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection bias in SL data and reliant on cancer or tissue type-specific omics, which can be scarce. Notably, sequence similarity remains underexplored as a proxy for related gene function and joint essentiality.
resultsWe propose ELISL, Early-Late Integrated SL prediction with forest ensembles, using context-free protein sequence embeddings and context-specific omics from cell lines and tissue. Across eight cancer types, ELISL showed superior robustness to selection bias and recovery of known SL genes, as well as promising cross-cancer predictions. Co-occurring mutations in a BRCA gene and ELISL-predicted pairs from the HH, FGF, WNT, or NEIL gene families were associated with longer patient survival times, revealing therapeutic potential. AVAILABILITY AND IMPLEMENTATION: Data: 10.6084/m9.figshare.23607558 & Code: github.com/joanagoncalveslab/ELISL.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.