CELLector: Genomics Guided Selection of Cancer in vitro Models

Abstract

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 O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=161 SRC="FIGDIR/small/275032v3_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@11f46b1org.highwire.dtl.DTLVardef@5a207aorg.highwire.dtl.DTLVardef@10a57edorg.highwire.dtl.DTLVardef@12b332_HPS_FORMAT_FIGEXP M_FIG C_FIG