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CELLector: Genomics Guided Selection of Cancer in vitro Models

View ORCID ProfileHanna Najgebauer, Mi Yang, Hayley E Francies, Clare Pacini, Euan A Stronach, Mathew J Garnett, View ORCID ProfileJulio Saez-Rodriguez, View ORCID ProfileFrancesco Iorio
doi: https://doi.org/10.1101/275032
Hanna Najgebauer
1Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
2European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
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  • ORCID record for Hanna Najgebauer
Mi Yang
4Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany
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Hayley E Francies
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
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Clare Pacini
1Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
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Euan A Stronach
1Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
6Functional Genomics GlaxoSmithKline, Stevenage, UK
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Mathew J Garnett
1Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
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Julio Saez-Rodriguez
2European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
4Faculty of Medicine, Joint Research Centre for Computational Biomedicine, RWTH Aachen University, Aachen 52057, Germany
5Institute for Computational Biomedicine, Faculty of Medicine, BIOQUANT-Center, Heidelberg University, Heidelberg, Germany
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Francesco Iorio
1Open Targets, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
3Wellcome Trust Sanger Institute, Wellcome Genome Campus, Cambridge CB10 1SA, UK
7Human Technopole, 20157, Milano, Italy
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  • For correspondence: fi1@sanger.ac.uk
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Summary

The selection of appropriate cancer models is a key prerequisite for maximising translational potential and clinical relevance of in-vitro oncology studies.

We developed CELLector: a computational method (implemented in an open source R Shiny application and R package) allowing researchers to select the most relevant cancer cell lines in a patient-genomic guided fashion. CELLector leverages tumour genomics data to identify recurrent sub-types with associated genomic signatures. It then evaluates these signatures in cancer cell lines to rank them and prioritise their selection. This enables users to choose appropriate models for inclusion/exclusion in retrospective analyses and future studies. Moreover this allows bridging data from cancer cell line screens to precisely defined sub-cohorts of primary tumours. Here, we demonstrate usefulness and applicability of our method through example use cases, showing how it can be used to prioritise the development of new in-vitro models and to effectively unveil patient-derived multivariate prognostic and therapeutic markers.

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Footnotes

  • ↵8 Lead Contact

  • https://ot-cellector.shinyapps.io/CELLector_App/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted March 07, 2020.
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CELLector: Genomics Guided Selection of Cancer in vitro Models
Hanna Najgebauer, Mi Yang, Hayley E Francies, Clare Pacini, Euan A Stronach, Mathew J Garnett, Julio Saez-Rodriguez, Francesco Iorio
bioRxiv 275032; doi: https://doi.org/10.1101/275032
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CELLector: Genomics Guided Selection of Cancer in vitro Models
Hanna Najgebauer, Mi Yang, Hayley E Francies, Clare Pacini, Euan A Stronach, Mathew J Garnett, Julio Saez-Rodriguez, Francesco Iorio
bioRxiv 275032; doi: https://doi.org/10.1101/275032

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