Evaluating the transcriptional fidelity of cancer models

Cancer researchers use cell lines, patient derived xenografts, and genetically engineered mice as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derives from the fidelity with which it represents the tumor type of investigation, however, the extent to which this is true is often unclear. The preponderance of models and the ability to readily generate new ones has created a demand for tools that can measure the extent and ways in which cancer models resemble or diverge from native tumors. Here, we present a computational tool, CancerCellNet, that measures the similarity of cancer models to 22 naturally occurring tumor types and 36 subtypes, in a platform and species agnostic manner. We applied this tool to 657 cancer cell lines, 415 patient derived xenografts, and 26 distinct genetically engineered mouse models, documenting the most faithful models, identifying cancers underserved by adequate models, and finding models with annotations that do not match their classification. By comparing models across modalities, we find that genetically engineered mice have higher transcriptional fidelity than patient derived xenografts and cell lines in four out of five tumor types. We have made CancerCellNet available as freely downloadable software and as a web application that can be applied to new cancer models.