Improved Therapeutic Outcome Following Combination Immunogene Vaccination Therapy in Murine Myeloma
Bibliographic record
Abstract
Increasing evidence suggests a role for immunologic vaccination and therapy in the management of minimal residual myeloma. We have previously demonstrated a synergistic effect of combining the Th1 stimulating cytokine IL-12 with the co-stimulatory molecule CD80 in murine myeloma vaccination therapy. We reasoned that the efficacy of such treatment might be further improved by incorporating additional gene products which enhance the function of antigen presenting cells. Studies were therefore conducted with murine myeloma BM1 cells expressing Flt3L (membrane bound or soluble forms) or GM-CSF and the IL-12 x CD80 combination. Single agent and combined therapeutic approaches were explored. All gene-modified BM1 cells, except BM1/IL-12 x CD80, developed tumors when subcutaneously injected into BALB/c mice. As prophylactic tumor vaccines, the combined use of gene-modified BM1/sFlt3L+GM-CSF+IL-12 x CD80 was most effective, providing 100% protection against subsequent parental BM1 tumor challenge. By comparison, only partial protection was observed with any single gene-engineered tumor vaccine. Notably, IL-12 x CD80 coexpressing BM1 cell vaccines were the most effective therapeutic vaccine in a minimal disease model. Such protective vaccination was achieved by stimulation of lymphocyte proliferation and enhancement of cytotoxic lymphocyte activity.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".