PT  - JOURNAL ARTICLE
AU  - Hanna Najgebauer
AU  - Mi Yang
AU  - Hayley E Francies
AU  - Clare Pacini
AU  - Euan A Stronach
AU  - Mathew J Garnett
AU  - Julio Saez-Rodriguez
AU  - Francesco Iorio
TI  - CELLector: Genomics Guided Selection of Cancer <em>in vitro</em> Models
AID  - 10.1101/275032
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 275032
4099  - http://biorxiv.org/content/early/2020/03/07/275032.short
4100  - http://biorxiv.org/content/early/2020/03/07/275032.full
AB  - 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.Graphical Abstract