Abstract IA015: GD2-specific CAR T cell therapies for pediatric solid cancers
Bibliographic record
Abstract
Abstract Glycolipid GD2 is an attractive immune target in neuroblastoma. Monoclonal antibodies against GD2 are approved agents for the consolidation of remissions but can not eradicate measurable disease. GD2 was the first target for CAR-engineered T cells addressed in a pediatric cancer, with a reassuring safety profile and individual remissions in first-in-human clinical investigation and durable complete responses with advanced CAR designs. However, the activity of GD2 CAR T cell therapy remains limited to proportions of patients with low burden disease. Major hurdles are local barriers in the tumor microenvironment that impede therapeutic immune responses. One potential solution are CAR T cell designs with engineered secretion of activating cytokines that increase numbers of tumor-infiltrating T cells, enhance their potency within an immunosuppressive tumor milieu and activate and/or reprogram bystander cells for a concerted antitumor immune response. Our group has developed a novel investigational medicinal product, GD2-specific autologous CAR T cells with CAR-inducible secretion of cytokine IL-18, a potent danger signal with an important physiological role in antiviral immune defense. This product had superior in vivo antitumor activity compared to a control construct without IL-18 in preclinical work and has now entered clinical investigation in pediatric cancer patients (EU CT 2022-501725-21-00). Besides neuroblastoma, further cancers can express GD2 at antigen densities sufficient to trigger CAR-induced effector cell responses. In the pediatric population, this includes bone sarcomas (Ewing sarcoma, osteosarcoma) and various CNS cancers such as diffuse midline gliomas and medulloblastoma. To enable selection of patients with adequate GD2 expression for inclusion into GD2 CAR T cell trials, we have established an immunofluorescence-based assay that allows reproducible and robust detection of the glycolipid in paraffine-embedded tissue sections. Moreover, we have found that epigenetic modulation with a clinically approved inhibitor of methyltransferase EZH2 can selectively upregulate GD2 in Ewing sarcoma to high, targetable densities both in vitro and in vivo, potentially extending the applicability of GD2-targeted immunotherapies to patients with initially GD2-negative or low disease. Still, to advance GD2-targeted CAR T cell therapies to their full potential, advanced designs and combination strategies are needed to enable higher and prolonged performance. Early clinical trials with insights into the pre- and posttherapeutic tumor microenvironment will allow to gain knowledge on the biological effects of novel CAR T cell designs to inform future improvements. While the safety and preliminary efficacy of innovative cell-therapeutic agents are being evaluated in early-phase trials, investigators in the well-established international Pediatric Oncology networks must begin to consider academia-driven solutions for the subsequent development and distribution of these complex cell-based therapeutics for the benefit of all patients. Citation Format: Claudia Rossig. GD2-specific CAR T cell therapies for pediatric solid cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pediatric Cancer Research; 2024 Sep 5-8; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl):Abstract nr IA015.
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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) | 0.004 | 0.002 |
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".