Immunotherapy using IgE or CAR T cells for cancers expressing the tumor antigen SLC3A2
Bibliographic record
Abstract
BACKGROUND: Cancer immunotherapy with monoclonal antibodies and chimeric antigen receptor (CAR) T cell therapies can benefit from selection of new targets with high levels of tumor specificity and from early assessments of efficacy and safety to derisk potential therapies. METHODS: Employing mass spectrometry, bioinformatics, immuno-mass spectrometry and CRISPR/Cas9 we identified the target of the tumor-specific SF-25 antibody. We engineered IgE and CAR T cell immunotherapies derived from the SF-25 clone and evaluated potential for cancer therapy. RESULTS: We identified the target of the SF-25 clone as the tumor-associated antigen SLC3A2, a cell surface protein with key roles in cancer metabolism. We generated IgE monoclonal antibody, and CAR T cell immunotherapies each recognizing SLC3A2. In concordance with preclinical and, more recently, clinical findings with the first-in-class IgE antibody MOv18 (recognizing the tumor-associated antigen Folate Receptor alpha), SF-25 IgE potentiated Fc-mediated effector functions against cancer cells in vitro and restricted human tumor xenograft growth in mice engrafted with human effector cells. The antibody did not trigger basophil activation in cancer patient blood ex vivo, suggesting failure to induce type I hypersensitivity, and supporting safe therapeutic administration. SLC3A2-specific CAR T cells demonstrated cytotoxicity against tumor cells, stimulated interferon-γ and interleukin-2 production in vitro. In vivo SLC3A2-specific CAR T cells significantly increased overall survival and reduced growth of subcutaneous PC3-LN3-luciferase xenografts. No weight loss, manifestations of cytokine release syndrome or graft-versus-host disease, were detected. CONCLUSIONS: These findings identify efficacious and potentially safe tumor-targeting of SLC3A2 with novel immune-activating antibody and genetically modified cell therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 teacher head, 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".