Abstract A015: GPNMB CAR T cells target both Glioblastoma and its immunosuppressive niche to relieve immune evasion
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) remains a formidable challenge in neuro-oncology, characterized by heterogeneity and aggressive tumor growth. The identification of novel biomarkers and therapeutic targets is crucial for advancing promising therapies. This study profiled patient-derived tumor samples utilizing single-cell RNA sequencing and proteomics platforms to uncover the upregulation of glycoprotein non-metastatic melanoma protein B (GPNMB) in tumor cells, particularly in post-treatment recurrent tumor cells, as well as in tumor-associated macrophages (TAMs), which constitute a major population of immune cells in treatment-refractory recurrent GBM. By exploring its expression patterns against normal tissue specimens and utilizing a series of patient-derived xenograft and humanized mouse models, we explored GPNMB as a target for Chimeric Antigen Receptor T cells (CAR-Ts) as a monotherapy and in rationally designed combination treatment regimens. We also demonstrated the utility of the GPNMB CAR-T cell in eliminating TAMs, a major contributor to immunosuppression in solid tumors. Our study reveals the development of GPNMB-targeting therapies as part of a promising and effective combinatorial treatment strategy for GBM. Citation Format: Sheila K Singh, Neil Savage, Franz J Zemp, Vaseem Shaikh, Shan Grewal, Nicholas Mikolajewicz, Hinda Najem, Chitra Venugopal, Thomas Kislinger, Amy Heimberger, Douglas Mahoney, Jason Moffat. GPNMB CAR T cells target both Glioblastoma and its immunosuppressive niche to relieve immune evasion [abstract]. In: Proceedings of the AACR IO Conference: Discovery and Innovation in Cancer Immunology: Revolutionizing Treatment through Immunotherapy; 2025 Feb 23-26; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Immunol Res 2025;13(2 Suppl):Abstract nr A015.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".