ROBO1 CAR-T CELL THERAPY FOR TREATMENT REFRACTORY BRAIN CANCER
Bibliographic record
Abstract
Abstract BTFC travel award recipient Glioblastoma (GBM) is the most common malignant adult brain tumor, with a dismal prognosis. Despite the multimodal treatment regimen, tumor recurrence is inevitable, and no standardized treatment exists for recurrent glioblastoma (rGBM). Neurodevelopment signaling pathways are often hijacked during tumor progression. Roundabout guidance receptor 1 (ROBO1) protein is involved in axonal guidance during neurodevelopment. Our preliminary findings implicated aberrant ROBO1 signaling axis to be associated with higher tumorigenecity in GBM, making it a functionally relevant therapeutic target. Moreover, ROBO1 was highly expressed on the surface of malignant and treatment-refractory brain tumor initiating cells (BTICs) in rGBM, brain metastasis (BM) and medulloblastoma (MB), prompting the development of an anti-ROBO1 CAR-T cell therapy. Here we report the development and validation of second-generation CAR-T cells using a single-domain antibody targeting ROBO1 expressing BTICs across three different brain tumors. We validated anti-ROBO1 CAR-T cells in-vitro and in-vivo using our established patient derived tumor models. In-vitro studies demonstrated upregulation of activation markers, enhanced cytokine release, markedly increased proliferation, and induction of potent and specific tumor cell death with ROBO1 CAR-T cells as compared to untransduced cells (UT) in all the three cancers. These findings were further validated in-vivo using 3 different BTIC lines one each from rGBM, MB and BM. ROBO1 CAR-T showed significant reduction in tumor burden and significant increase in survival of the mice treated with ROBO1 CAR-T cells in all the three cohorts. Thus, targeting ROBO1 could be a therapeutically tractable strategy for treating rGBM as well as other brain malignancies.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".