Immune Modulation by Chemotherapy or Immunotherapy to Enhance Cancer Vaccines
Bibliographic record
Abstract
Chemotherapy has been a mainstay in cancer treatment for many years. Despite some success, the cure rate with chemotherapy remains unsatisfactory in some types of cancers, and severe side effects from these treatments are a concern. Recently, understanding of the dynamic interplay between the tumor and immune system has led to the development of novel immunotherapies, including cancer vaccines. Cancer vaccines have many advantageous features, but their use has been hampered by poor immunogenicity. Many developments have increased their potency in pre-clinical models, but cancer vaccines continue to have a poor clinical track record. In part, this could be due to an inability to effectively overcome tumor-induced immune suppression. It had been generally assumed that immune-stimulatory cancer vaccines could not be used in combination with immunosuppressive chemotherapies, but recent evidence has challenged this dogma. Chemotherapies could be used to condition the immune system and tumor to create an environment where cancer vaccines have a better chance of success. Other types of immunotherapies could also be used to modulate the immune system. This review will discuss how immune modulation by chemotherapy or immunotherapy could be used to bolster the effects of cancer vaccines and discuss the advantages and disadvantages of these treatments.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".