EXTH-142. Quantitative surfaceome profiling of high-risk medulloblastoma prioritizes the oncofetal antigen GPC2 for potent CAR-T cell therapy
Bibliographic record
Abstract
Abstract Recurrent medulloblastoma (MB) remains a devastating pediatric brain tumor with <10% survival. CAR-T cells have shown promise in aggressive childhood brain tumors, but success in MB is limited by suitable cell-surface targets. Requirements for efficacious CAR-T antigens include that levels in clinical specimens exceed CAR-T detection thresholds and absence in vital tissues. We profiled the CAR-T antigen landscape of MB using unbiased surfaceome analysis from tumor and normal tissue transcriptomes and precisely quantified antigen densities of selected candidates on tumor biopsies. We found that MB closely resembles prenatal brain and expresses several oncofetal proteins, ranking Glypican-2 (GPC2) as the top candidate. GPC2 antigen densities ranged from 0-15944 molecules/cell on GR3/GR4-MB subgroups (mean: 5019 +/-1595), which often exhibit diminished cure rates. Antigen density determines CAR-T potency and we previously engineered GPC2-CAR-Ts tuned towards clinical densities on neuroblastoma and demonstrated that cJUN-overexpression (OE) augments their potency by lowering antigen detection thresholds (Heitzeneder et. al, 2022). Here, we employ orthotopic xenograft models and locoregional CAR-T delivery to study the interplay between GPC2 antigen density and CAR potency and inform a phase-1 study design. We found that both GPC2-CAR-Ts (+/-cJUN) mediate durable disease control against GPC2-intermediate GR4-MB (ICB1299: 8979 mol/cell and MBT375, newly established PDX: 6270 mol/cell). In a highly aggressive, MYC-amplified, GPC2-high GR3-MB model (SU_MB002: 18493 mol/cell), recurrence was observed in 3/5 mice post GPC2-CAR, while repeated dosing or cJUN-OE significantly improved persistence and long-term anti-tumor control. In a GPC2-low, GR3-MB model (HDMB03: 2750 mol/cell) used to further dissect antigen detection limits, only cJUN.GPC2-CAR maintained anti-tumor responses. These data support an upcoming phase-1 clinical trial at Stanford, testing repeated, locoregional GPC2-CAR-T infusions in patients with recurrent/refractory MB and emphasize needing to enroll subjects with GPC2+ disease, that is predicted to be above CAR detection thresholds, aiming to provide targeted treatment options for this devastating malignancy.
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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) | 0.002 | 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".