Amplifying cancer vaccine responses by modifying pathogenic gene programs in tumor cells
Bibliographic record
Abstract
Immunosuppressive factors, such as vascular endothelial growth factor, transforming growth factor-beta, prostaglandin E2, interleukin (IL)-10, and IL-6, are made frequently by cancer cells. These factors, along with others, can inhibit the development and function of tumor-reactive effector T cells and the clinical results of cancer vaccines. Production of these factors by tumor cells is associated with disease progression and may represent an active immune surveillance escape mechanism. However, a number of factors appear to be made directly in response to signaling molecules, such as RAS, AKT, and signal transducer and activator of transcription 3, which are activated as a result of genetic events that occur during oncogenesis. Methods to overcome the negative effects of immunosuppressive factors, which are "hard wired" into gene programs of cancer cells, might then improve the results of cancer vaccines. For example, specific blocking antibodies, which recognize such factors, or kinase inhibitors, which block the signaling pathways that lead to their production, could potentially be used as vaccine adjuvants. The effects of immunosuppressive factors may also be "turned off" by cytokines with tumor suppressor properties. The enhanced clinical and immunological effects of melanoma vaccines observed after the administration of high doses of interferon-alpha2b provide a "proof of principle" in human patients, that agents which counter the gene programs of cancer cells, causing them to intrinsically resist tumor-reactive T cells, may improve significantly the efficacy of cancer vaccines.
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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) | 0.002 | 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".