Strategies to Enhance the Therapeutic Activity of Cancer Vaccines: Using Melanoma as a Model
Bibliographic record
Abstract
Although there has been initial success with some types of immunotherapy, such as adoptive cellular therapy and monoclonal antibody therapy for cancer, the experience with therapeutic cancer vaccines has been much less encouraging. Almost all randomized phase III trials testing therapeutic cancer vaccines have failed to meet their end points. There are several potential explanations for this, ranging from factors related to the clinical trial design and the vaccine itself. Perhaps the most important are host-related factors. Specifically, progression and metastases of many cancers are associated with induction of multiple cancer-specific immune-inhibitory pathways. These inhibitory pathways include induction of T-cell anergy through dendritic cell dysfunction, release of immunosuppressive cytokines, T-cell exhaustion through inhibitory T-cell signaling and T regulatory cell-mediated tumor-specific immune suppression. All of these pathways have been shown to be operational in patients with melanoma. To enhance the activity of therapeutic cancer vaccines, these immunosupressive pathways need to be addressed and reversed. A number of new immunomodulatory reagents that are able to interfere with some of these pathways are now being assessed in the clinic. Sanofi Pasteur designed a clinical trial in patients with advanced or metastatic melanoma that is intended to both induce tumor-specific T-cell responses and modulate or reverse some of the immune suppression pathways that the melanoma has induced. To accomplish this, the recently optimized ALVAC melanoma multi-antigen vaccine is administered with high doses of IFN-alpha. Clinical trial parameters have also been optimized to enhance the likelihood of inducing and documenting antitumor activity. Success with other therapeutic cancer vaccine approaches will likely require similar approaches in which promising immunogenic vaccines are integrated with biologically and clinically active immunomodulatory reagents.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".