Human Autologous In Vitro Models of Glioma Immunogene Therapy Using B7-2, GM-CSF, and IL12
Bibliographic record
Abstract
BACKGROUND: Cancer immunogene therapy is based on vaccination with radiated, autologous tumor cells transduced with immunostimulatory genes. To help determine an optimal glioma immunogene therapy strategy, we stimulated lymphocytes with autologous human glioma cells transduced with B7-2 (CD86), granulocyte-macrophage colony-stimulating factor (GM-CSF), and/or interleukin-12 (IL12). METHODS: A human glioma-derived cell culture (Ed147.BT) was transduced with B7-2, GM-CSF, and/or IL12 using retroviral vectors. Autologous peripheral blood mononuclear cells (PBMC) were co-cultured with irradiated gene-transduced tumor alone or a combination of radiated wild type and gene-transduced cells. Peripheral blood mononuclear cells proliferation was determined by serial cell counts. Peripheral blood mononuclear cells phenotype was assessed by flow cytometry for CD4, CD8, and CD16. Anti-tumor cytotoxicity was determined by chromium-51 (51Cr) release assay. RESULTS: Peripheral blood mononuclear cells cell numbers all decreased during primary stimulation but tumor cells expressing B7-2 or GM-CSF consistently caused secondary proliferation. Tumors expressing B7-2 and GM-CSF or B7-2, GM-CSF, and IL12 consistently increased PBMC CD8+ (cytotoxic T) and CD16+ (natural killer) percentages. Interestingly, anti-tumor cytotoxicity only exceeded that of PBMC stimulated with wild type tumor alone when peripheral blood mononuclear cells were stimulated with both wild type tumor and B7-2/GM-CSF- (but not IL12) transduced cells. CONCLUSIONS: PBMC proliferation and phenotype is altered as expected by exposure to immunostimulatory gene-transduced tumor. However, transduced tumor cells alone do not stimulate greater anti-tumor cytotoxicity than wild type tumor. Only B7-2/GM-CSF-transduced cells combined with wild type produced increased cytotoxicity. This may reflect selection of tumor subclones with limited antigenic spectra during retrovirus-mediated gene transfer.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".