IMMU-65. TARGETING AXONAL GUIDANCE DEPENDENCIES IN GLIOBLASTOMA WITH ROBO1 CAR T CELLS
Bibliographic record
Abstract
Abstract Resistance to genotoxic therapies and subsequent disease recurrence are hallmarks of aggressive cancers, including glioblastoma (GBM). Here, we uncover functional drivers of post-treatment recurrent GBM using genome scale CRISPR-Cas9 screens accompanied by integrative genomic analysis. Using patient-matched pre- and post-treatment GBM models, our findings uncover large-scale reorganization of functional dependencies at tumor recurrence and implicate protein tyrosine phosphatases 4A2 (PTP4A2) as a novel driver of tumorigenicity in recurrent GBM. Small molecule inhibition and phospho-proteomic analyses reveal a novel PTP4A-ROBO1 signaling axis that modulates tumor invasion, self-renewal and proliferation in recurrent GBM. Since a pan-PTP4A targeting small molecule suffers from poor blood-brain-barrier penetrance, we engineered a novel second generation chimeric antigen receptor (CAR) targeting human ROBO1, a cell surface receptor enriched across GBM specimens. Not only do ROBO1 CAR T cells exhibit a potent and specific anti-tumor effect in vitro, a single dose of ROBO1 CAR T cells doubles median survival in a patient-derived xenograft (PDX) model of recurrent GBM. Expansion of ROBO1 CAR T cells to other invasive brain cancers leads to tumor eradication in ~50% of mice in PDX models of pediatric medulloblastoma and adult lung-to-brain metastases. Together, we provide insights into functional remodeling of GBM at recurrence and present a multi-targetable PTP4A-ROBO1 signaling axis with potential across multiple malignant brain tumors.
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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".