The Potential of Combining Tubulin-Targeting Anticancer Therapeutics and Immune Therapy
Bibliographic record
Abstract
Cancer immune therapy has recently shown tremendous promise to combat many different cancers. The microtubule is a well-defined and very effective cancer therapeutic target. Interestingly, several lines of evidence now suggest that microtubules are intimately connected to the body's immune responses. This raises the possibility that the combination of microtubule inhibitors and immune therapy can be a highly effective option for cancer treatments. However, our understanding on this potentially important aspect is still very limited, due in part to the multifaceted nature of microtubule functions. Microtubules are not only involved in maintaining cell morphology, but also a variety of cellular processes, including the movement of secretory vesicles and organelles, intracellular macromolecular assembly, signaling pathways, and cell division. Microtubule inhibitors may be subdivided into two classes: Anti-depolymerization agents such as the taxane family, and anti-polymerization agents such as colchicine and vinka alkaloids. These two different classes may have different effects on immune cell subtypes. Anti-depolymerization agents can not only induce NK cells, but also appear to inhibit T regulatory (Treg) cells. However, different inhibitors may have different functions even among the same class. For example, the doxetaxel anti-depolymerization agent up-regulates cytotoxic T cells, while paclitaxel down-regulates them. Certain anti-polymerization agents such as colchicine appear to down-regulate most immune cell types, while inducing dendritic cell maturation and increasing M1 macrophage population. In contrast, the vinblastine anti-polymerization agent activates many of these cell types, albeit down-regulating Treg cells. In this review, we focus on the various effects of tubulin inhibitors on the activities of the body's immune system, in the hope of paving the way to develop an effective cancer therapy by combining tubulin-targeting anticancer agents and immune therapy.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".