The Diverse Roles of Monoclonal Antibodies in Cancer Immunotherapy and Their Relative Effectiveness: A Literature Review
Bibliographic record
Abstract
Introduction: In modern oncology, immunotherapy has emerged as a promising treatment modality for numerous cancers. At the forefront of personalized medicine, immunotherapy utilizes components of a patient's immune system to selectively target cancer cells. Numerous immunotherapy drugs have been developed thus far, including monoclonal antibodies (mAbs). mAbs are genetically-identical protein antibodies created in the laboratory through recombinant DNA technology. They are capable of recognizing molecules that are uniquely present on the surface of cancer cells, such as tumour-specific antigens and/or receptors. This narrative review explores the various uses of mAbs in the treatment of cancer. Methods: A narrative literature review was conducted to analyse and synthesize current and prior research surrounding the various uses of mAbs in the context of cancer treatment. Specific examples and potential shortfalls of various treatment methods were also analysed. Results: mAbs can be used in several distinct ways to target cancerous cells. In their native immunoglobulin G form, mAbs direct immune cells to tumours and induce cytotoxicity via initiating biochemical cascades, leading to effects such as phagocytosis, opsonisation, activation of immune cells, degranulation, and cytokine release, among others. mAbs may also be conjugated with radionuclides, or traditional chemotherapeutic agents for targeted drug delivery, or used to target the immune system via conjugation to cytokines, or other mAbs which directly interact with immune cells for targeted recruitment. mAbs targeting immune checkpoints can also be used to enhance cancer-related immune responses. However, mAbs are not perfect, and are thus prone to a slew of limitations which are still being addressed. Discussion: mAbs are highly useful, primarily as a result of their specific molecular recognition abilities. This property underlies all uses in cancer immunotherapy and can further be exploited in the development of new immunotherapy technologies and methodologies, along with the elucidation of novel antigens and targets in cancers, to further improve the field and address limitations. Conclusions: This literature review aims to synthesize data pertaining to the various potential uses of mAbs in cancer treatment. This approach will provide more insight into the current state of immunotherapeutics, and where additional research must be conducted.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".