TMIC-41. Anti-Robo1/2 monoclonal antibodies as a novel macrophage-based immunotherapy for glioblastoma
Bibliographic record
Abstract
Abstract Despite groundbreaking advances in Oncology over the past decade including the advent of immunotherapy, Glioblastoma (GBM), is still a therapeutic challenge with poor patient outcomes and an urgent need for effective therapies. SLIT2 is a secreted polypeptide that guides migration of cells expressing ROBO1&2 receptors and is key for axonal guidance and angiogenesis. In primary brain tumors, SLIT2 expression increases with malignant progression with highest levels observed in Grade IV GBM patients when compared to Low Grade Gliomas, where it also correlates with poor prognosis. Mechanistically, SLIT2-ROBO1/2 signaling promotes the recruitment and polarization of tumor-associated microglia/macrophages (TAMs) to the tumor microenvironment (TME) via PI3Kgamma, leading to dysmorphic angiogenesis, T cell exclusion and immunosuppression. Despite key functions in the TME, targeting SLIT2 in Oncology has been limited by the lack of validated blocking reagents with proper penetration and signaling inhibition potential. We have developed high-affinity human monoclonal antibodies recognizing both human and murine ROBO1&2 (Anti-Robo1/2 mAbs) which are capable of inhibiting microglia and macrophage migration and polarization in vitro. Treatment of immunocompetent preclinical GBM models with Anti-Robo1/2 mAbs leads to reduced TAM infiltration and tumor-supportive polarization, with profound changes in the GBM microenvironment. We observed vascular normalization, reduced hypoxia and increased infiltration and anti-tumor effective function of CD8+ T cells. Furthermore, inhibiting SLIT2-ROBO1/2 signaling also acted in the meningeal compartment, normalizing the meningeal lymphatic vasculature of GBM-bearing mice, improving antigen and immune cell drainage to deep cervical lymph nodes and improving anti-tumor immune responses. By modifying the tumor immune micro- and macroenvironment, Anti-Robo1/2 mAbs prolong tumor bearing-mice survival and renders immunotherapy-resistant tumors sensitive to checkpoint inhibition therapy. Altogether, our data suggest that Anti-Robo1/2 mAbs are a potential novel immunotherapeutic agent for GBM by simultaneously targeting TAMs and meningeal lymphatics, allowing for improved efficacy of currently available T cell-based immunotherapies in GBM.
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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".