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Leelatian, N.

Publications and source records attributed to Leelatian, N..

2 recordsLinked to original sources

Immune cell regulation in stem cell niche contacting glioblastomas.

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.

immunology↗

High risk glioblastoma cells revealed by machine learning and single cell signaling profiles

Recent developments in machine learning implemented dimensionality reduction and clustering tools to classify the cellular composition of patient-derived tissue in multi-dimensional, single cell studies. Current approaches, however, require prior knowledge of either categorical clinical outcomes or cell type identities. These algorithms are not well suited for application in tumor biology, where clinical outcomes can be continuous and censored and cell identities may be novel and plastic. Risk Assessment Population IDentification (RAPID) is an unsupervised, machine learning algorithm that identifies single cell phenotypes and assesses clinical risk stratification as a continuous variable. Single cell mass cytometry evaluated 34 different phospho-proteins, transcription factors, and cell identity proteins in tumor tissue resected from patients bearing IDH wild-type glioblastomas. RAPID identified and characterized multiple biologically distinct tumor cell subsets that independently and continuously stratified patient outcome. RAPID is broadly applicable for single cell studies where atypical cancer and immune cells may drive disease biology and treatment responses.

cancer biology↗