Evaluating the transcriptional fidelity of cancer models

Genome Medicine

Stephanie Cai¹  Da Peng¹  Qin Bian²  Pavithra Kumar²  Patrick Cahan³  Kathleen DiNapoli⁴  Edroaldo Lummertz da Rocha⁵  Franklin W. Huang⁶  Bradley Isaacs⁷  Wen-Hsin Tai⁷  Rachel Gleyzer⁷ 

[1] Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Institute for Cell Engineering, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Department of Biomedical Engineering, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Institute for Cell Engineering, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Department of Cell Biology, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;Department of Electrical and Computer Engineering, Johns Hopkins University, 21218, Baltimore, MD, USA;Department of Microbiology, Immunology and Parasitology, Federal University of Santa Catarina, Florianópolis, SC, Brazil;Division of Hematology/Oncology, Department of Medicine; Helen Diller Family Cancer Center; Bakar Computational Health Sciences Institute; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA, USA;Institute for Cell Engineering, Johns Hopkins University School of Medicine, 21205, Baltimore, MD, USA;

关键词: Cancer models;    Machine learning;    Cancer cell lines;    PDX;    GEMM;    Tumoroid;    Tumor classification;   

DOI  :  10.1186/s13073-021-00888-w

来源: Springer

【 摘 要 】

BackgroundCancer researchers use cell lines, patient-derived xenografts, engineered mice, and tumoroids as models to investigate tumor biology and to identify therapies. The generalizability and power of a model derive from the fidelity with which it represents the tumor type under 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.MethodsWe developed a machine learning-based 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, 26 distinct genetically engineered mouse models, and 131 tumoroids. We validated CancerCellNet by application to independent data, and we tested several predictions with immunofluorescence.ResultsWe have documented the cancer models with the greatest transcriptional fidelity to natural tumors, we have identified cancers underserved by adequate models, and we have found models with annotations that do not match their classification. By comparing models across modalities, we report that, on average, genetically engineered mice and tumoroids have higher transcriptional fidelity than patient-derived xenografts and cell lines in four out of five tumor types. However, several patient-derived xenografts and tumoroids have classification scores that are on par with native tumors, highlighting both their potential as faithful model classes and their heterogeneity.ConclusionsCancerCellNet enables the rapid assessment of transcriptional fidelity of tumor models. We have made CancerCellNet available as a freely downloadable R package (https://github.com/pcahan1/cancerCellNet) and as a web application (http://www.cahanlab.org/resources/cancerCellNet_web) that can be applied to new cancer models that allows for direct comparison to the cancer models evaluated here.

【 授权许可】

CC BY