TY - JOUR T1 - CELLector: Genomics Guided Selection of Cancer <em>in vitro</em> Models JF - bioRxiv DO - 10.1101/275032 SP - 275032 AU - Hanna Najgebauer AU - Mi Yang AU - Hayley Francies AU - Euan A Stronach AU - Mathew J Garnett AU - Julio Saez-Rodriguez AU - Francesco Iorio Y1 - 2018/01/01 UR - http://biorxiv.org/content/early/2018/04/16/275032.abstract N2 - The selection of appropriate cancer models is a key prerequisite for maximising translational potential and clinical relevance of in vitro studies. An important criterion for this selection is the molecular resemblance of available models to the primary disease they represent. While studies are being increasingly conducted to comprehensively compare genomic profiles of cell lines and matched primary tumours, there is no data-driven, robust and user-friendly tool assisting scientists in such selection, by adequately estimating the molecular heterogeneity of a primary disease that is captured by existing models. We developed CELLector: a computational tool implemented in an open source R Shiny application and R package that allows researchers to select the most relevant cancer cell lines in a genomic-guided fashion. CELLector combines methods from graph theory and market basket analysis; it leverages tumour genomics data to explore, rank, and select optimal cell line models in a user-friendly way, enabling scientists to make appropriate and informed choices about model inclusion/exclusion in retrospective analyses and future studies. Additionally, it allows the selection of models within user-defined contexts, for example, by focusing on genomic alterations occurring in biological pathways of interest or considering only predetermined sub-cohorts of cancer patients. Finally, CELLector identifies combinations of molecular alterations underlying disease subtypes currently lacking representative cell lines, providing guidance for the future development of new cancer models. To demonstrate usefulness and applicability of our tool, we present example case studies, where it is used to select representative cell lines for user-defined populations of colorectal cancer patients of current clinical interest. ER -