Cancer and Immune Response: Old and New Evidence for Future Challenges
Bibliographic record
Abstract
Abstract Learning Objectives After completing this course, the reader should be able to: Discuss the current scientific background of immunotherapy applied to cancer treatment.Suggest lines of future investigation in the immunotherapy field.Explain the rationale for developing and discuss the current status of new immunotherapeutic approaches in solid tumors. CME This article is available for continuing medical education credit at http://CME.TheOncologist.com Cancer may occur as a result of abnormal host immune system tolerance. Recent studies have confirmed the occurrence of spontaneous and induced antitumor immune responses expressed as the presence of tumor-infiltrating T cells in the tumor microenvironment in some cancer models. This finding has been recognized as a good prognostic factor in several types of tumors. Some chemotherapy agents, such as anthracyclines and gemcitabine, are effective boosters of the immune response through tumor-specific antigen overexpression after apoptotic tumor cell destruction. Other strategies, such as GM-CSF or interleukin-2, are pursued to increase immune cell availability in the tumor vicinity, and thus improve both antigen presentation and T-cell activation and proliferation. In addition, cytotoxic T lymphocyte antigen 4–blocking monoclonal antibodies enhance immune activity by prolonging T-cell activation. Strategies to stimulate the dormant immune system against tumors are varied and warrant further investigation of their applications to cancer therapy in the future.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".