Interleukin-12 initiates an immune response that leads to tumour rejection (B151)
Bibliographic record
Abstract
Abstract Immunotherapies are developed based on the notion that cancer cells can be targeted and eliminated by an appropriately stimulated immune system. Integral to both the innate and acquired immune systems, cytokines are ideal candidates for use in immunotherapy. We have developed two systems of interleukin-12 (IL-12) therapy, in a murine model, that elicit protective immune responses against the acute lymphoblastic leukemia (ALL) cell line, 70Z/3. The first system involves direct injection of IL-12, and the second is a tumour cell-mediated approach. Initially we found that direct delivery of low doses of IL-12 is sufficient to elicit a long-term protective immune response against an established tumour burden, mediated by both CD4+ and CD8+ T cells. Based on this knowledge, we created a lentiviral vector expressing murine IL-12, and transduced 70Z/3 cells to obtain IL-12 secreting tumor cells. We demonstrated that anti-tumour immunity in the second model is long lasting but is primarily dependent on the CD4+ T cell subset alone. Our results highlight that the mode of IL-12 delivery has a distinct impact on the immune response that is initiated and can lead to tumour clearance by way of disparate mechanisms. Furthermore, we demonstrate some of the underlying conditions within the tumour microenvironment that may have resulted in these differential outcomes.
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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) | 0.003 | 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".