Back

Immune cell regulation in stem cell niche contacting glioblastomas.

Bartkowiak, T.; Lima, S. M.; Hayes, M. J.; Mistry, A. M.; Sinnaeve, J.; Leelatian, N.; Roe, C. E.; Mobley, B. C.; Weaver, K. D.; Thompson, R. C.; Chambless, L. B.; Ihrie, R. A.; Irish, J. M.

2022-02-16 immunology
10.1101/2022.02.16.480731 bioRxiv
Show abstract

Glioblastomas (GBM) are tumors for which immune-targeted therapies have failed to show clinical benefit and for which few biomarkers provide context for meaningful therapeutic stratification. Radiographic contact of GBM tumors with the lateral ventricle stem cell niche correlates with worse patient prognosis; however, the extent to which proximity to the ventricle impacts antitumor immunity remains unknown. We demonstrate that T cell checkpoint receptor expression is elevated in ventricle-contacting GBM as is the abundance of a specific, suppressive CD32+CD44+HLADhigh myeloid population suggesting a distinct immunoregulatory influence on antitumor immunity in proximity to the lateral ventricle. Phospho-specific mass cytometric profiling revealed extensively impaired immune signaling in ventricle-contacting GBM in response to inflammatory cytokine stimulation, further supporting a suppressive milieu influencing immunity at the lateral ventricle. Collectively, we identify a regulatory impact of ventricle contact on antitumor immunity in the brain, and reveal novel clinically targetable mechanisms of immunomodulation in patients with glioblastoma. Significance StatementWe demonstrate that the immune microenvironment of glioblastoma tumors contacting the lateral ventricle differs from non-contacting tumors. This work connects immune-biology to a radiographically detectable feature, the lateral ventricle, and highlights non-invasive imaging as a means to identify targetable immune features in glioblastoma tumors.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.