CELLector: Genomics Guided Selection of Cancer in vitro Models

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.