Regression of Experimental Medulloblastoma following Transfer of HER2-Specific T Cells
Bibliographic record
Abstract
Medulloblastoma is a common malignant brain tumor of childhood. Human epidermal growth factor receptor 2 (HER2) is expressed by 40% of medulloblastomas and is a risk factor for poor outcome with current aggressive multimodal therapy. In contrast to breast cancer, HER2 is expressed only at low levels in medulloblastomas, rendering monoclonal antibodies ineffective. We determined if T cells grafted with a HER2-specific chimeric antigen receptor (CAR; HER2-specific T cells) recognized and killed HER2-positive medulloblastomas. Ex vivo, stimulation of HER2-specific T cells with HER2-positive medulloblastomas resulted in T-cell proliferation and secretion of IFN-gamma and interleukin 2 (IL-2) in a HER2-dependent manner. HER2-specific T cells killed autologous HER2-positive primary medulloblastoma cells and medulloblastoma cell lines in cytotoxicity assays, whereas HER2-negative tumor cells were not killed. No functional difference was observed between HER2-specific T cells generated from medulloblastoma patients and healthy donors. In vivo, the adoptive transfer of HER2-specific T cells resulted in sustained regression of established medulloblastomas in an orthotopic, xenogenic severe combined immunodeficiency model. In contrast, delivery of nontransduced T cells did not change the tumor growth pattern. Adoptive transfer of HER2-specific T cells may represent a promising immunotherapeutic approach for medulloblastoma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".