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ELISL: early-late integrated synthetic lethality prediction in cancer.
Tepeli, Yasin I; Seale, Colm; Gonçalves, Joana P.
Afiliación
  • Tepeli YI; Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands.
  • Seale C; Pattern Recognition & Bioinformatics, Department of Intelligent Systems, Faculty EEMCS, Delft University of Technology, Delft, The Netherlands.
  • Gonçalves JP; Holland Proton Therapy Center (HollandPTC), Delft, The Netherlands.
Bioinformatics ; 40(1)2024 01 02.
Article en En | MEDLINE | ID: mdl-38113447
ABSTRACT
MOTIVATION Anti-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.

RESULTS:

We 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Mutaciones Letales Sintéticas / Neoplasias Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Mutaciones Letales Sintéticas / Neoplasias Límite: Humans Idioma: En Revista: Bioinformatics Asunto de la revista: INFORMATICA MEDICA Año: 2024 Tipo del documento: Article País de afiliación: Países Bajos Pais de publicación: Reino Unido