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Immune landscape of a genetically engineered murine model of glioma compared with human glioma
Daniel B. Zamler, … , Giulio F. Draetta, Jian Hu
Daniel B. Zamler, … , Giulio F. Draetta, Jian Hu
Published June 2, 2022
Citation Information: JCI Insight. 2022;7(12):e148990. https://doi.org/10.1172/jci.insight.148990.
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Research Article Neuroscience Oncology

Immune landscape of a genetically engineered murine model of glioma compared with human glioma

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Abstract

Novel therapeutic strategies targeting glioblastoma (GBM) often fail in the clinic, partly because preclinical models in which hypotheses are being tested do not recapitulate human disease. To address this challenge, we took advantage of our previously developed spontaneous Qk/Trp53/Pten (QPP) triple-knockout model of human GBM, comparing the immune microenvironment of QPP mice with that of patient-derived tumors to determine whether this model provides opportunity for gaining insights into tumor physiopathology and preclinical evaluation of therapeutic agents. Immune profiling analyses and single-cell sequencing of implanted and spontaneous tumors from QPP mice and from patients with glioma revealed intratumoral immune components that were predominantly myeloid cells (e.g., monocytes, macrophages, and microglia), with minor populations of T, B, and NK cells. When comparing spontaneous and implanted mouse samples, we found more neutrophils and T and NK cells in the implanted model. Neutrophils and T and NK cells were increased in abundance in samples derived from human high-grade glioma compared with those derived from low-grade glioma. Overall, our data demonstrate that our implanted and spontaneous QPP models recapitulate the immunosuppressive myeloid-dominant nature of the tumor microenvironment of human gliomas. Our model provides a suitable tool for investigating the complex immune compartment of gliomas.

Authors

Daniel B. Zamler, Takashi Shingu, Laura M. Kahn, Kristin Huntoon, Cynthia Kassab, Martina Ott, Katarzyna Tomczak, Jintan Liu, Yating Li, Ivy Lai, Rocio Zorilla-Veloz, Cassian Yee, Kunal Rai, Betty Y.S. Kim, Stephanie S. Watowich, Amy B. Heimberger, Giulio F. Draetta, Jian Hu

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Figure 3

Clustering to reveal subtypes.

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Clustering to reveal subtypes.
(A) Subtype clustering at resolutions of ...
(A) Subtype clustering at resolutions of 0.65 and 0.8 combined mouse spontaneous and implanted QPP tumor data set with 9 PCs determined by elbow plot. (B) Aggregated human GBM data set with 20 PCs determined by elbow plot.

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