Current Immunotherapy Techniques for Cancer Treatment: A Scoping Review
Bibliographic record
Abstract
Introduction: Immunotherapy, or the utilization of the immune system in fighting cancer, has been of interest as of late. Many different immunotherapy strategies exist, such as modifying T cells, generating cancer vaccines, as well as using chemokines, to elicit a strong anti-tumor response. As a strong emerging field in cancer research, this paper aims to conduct a scoping review to investigate the current research in immunotherapy targeting cancer and to summarize popular methods versus under-researched topics in the field. Methods: This scoping review follows PRISMA. Articles were found using MEDLINE, Scopus, and EMBASE, and were then screened using inclusion and exclusion criteria using the title and abstracts and then the full text. After the screening stage, papers chosen were categorized depending upon the authors’ main method of adapting the immune system to target cancer. Results: A total of 194 articles were included in this review. From the 194 articles, the method with the greatest amount of research in adapting the immune system to attack cancer are CAR-T cells, with 31 articles (16.0%). The second greatest category was cancer vaccines (28 articles; 14.4%), the third largest was other T cell-based immunotherapy strategies (25 articles; 12.9%) and the fourth largest was generating antibodies (24 articles; 12.4%). Other notable categories include cytokines and immune checkpoint inhibitors, while the smallest categories include bacteria, natural medicine, and nanoparticles. Discussion: The main fields of CAR-T cells, cancer vaccines, and antibodies commonly target tumor antigens involved in either tumor proliferation and progression or cancer invasion and metastasis. Further research is needed to demonstrate the strengths or limitations of using one immunotherapy technique over the other when it comes to inhibiting both of these cancer hallmarks. Furthermore, the review identifies multiple promising future avenues of immunotherapy that are currently less extensively investigated, such as adapting other immune cells, coupling immunotherapy techniques with nanoparticles, or using bacteria proteins to elicit a stronger immune response. Conclusion: This review aids in summarizing current focuses in the field of immunotherapy and provides future avenues and next steps for cancer research for new scientists pursuing a career in cancer research.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".