Abstract 564: Targeting axonal guidance with anti-ROBO1 CAR T cells: A new therapeutic strategy for malignant brain cancer
Bibliographic record
Abstract
Abstract No standardized treatment exists for patients with recurrent glioblastoma (GBM). Given the aggressive nature of the disease and difficulty in modeling tumor recurrence, minimal efforts have been made to design rational therapies against it. The roundabout guidance receptor 1 (ROBO1) protein is involved in axonal guidance during neurodevelopment and is aberrantly upregulated in glioma where it mediates glioma cell migration. Here, we present that ROBO1 is highly expressed on the surface of malignant and treatment-refractory brain tumor initiating cells (BTICs), prompting the development of an anti-ROBO1 CAR-T cell therapy. Using the binding region of a single-domain antibody targeting ROBO1, we developed second-generation anti-ROBO1 CAR-T cells specific and effective against ROBO1-expressing BTICs. Upon antigen exposure, anti-ROBO1 CAR-T cells upregulated markers of activation and degranulation. Additionally, treatment of orthotopic and patient-derived brain tumor xenograft models with anti-ROBO1 CAR-T cells resulted in reduced tumor burden and prolonged survival, demonstrating the therapy’s therapeutic potential for treating neoplastic brain malignancies. Citation Format: Sheila Kumari Singh, Chirayu R. Chokshi, Benjamin Brakel, Martin A. Rossotti, Chitra Venugopal, Sabra Salim, Kevin Henry. Targeting axonal guidance with anti-ROBO1 CAR T cells: A new therapeutic strategy for malignant brain cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 564.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".