IL-27, IL-30, and IL-35: A Cytokine Triumvirate in Cancer
Bibliographic record
Abstract
The role of the immune system in anti-tumor immunity cannot be overstated, as it holds the potential to promote tumor eradication or prevent tumor cell escape. Cytokines are critical to influencing the immune responses and interactions with non-immune cells. Recently, the IL-12 and IL-6 family of cytokines have accumulated newly defined members each with specific immune functions related to various cancers and tumorigenesis. There is a need to better understand how cytokines like IL-27, IL-30, and IL-35 interact with one another, and how a developing tumor can exploit these interactions to enhance immune suppression. Current cytokine-based immunotherapies are associated with cytotoxic side effects which limits the success of treatment. In addition to this toxicity, understanding the complex interactions between immune and cancer cells may be one of the greatest challenges to developing a successful immunotherapy. In this review, we bring forth IL-27, IL-30, and IL-35, "sister cytokines," along with more recent additions to the IL-12 family, which serve distinct purposes despite sharing structural similarities. We highlight how these cytokines function in the tumor microenvironment by examining their direct effects on cancer cells as well their indirect actions via regulatory functions of immune cells that act to either instigate or inhibit tumor progression. Understanding the context dependent immunomodulatory outcomes of these sister cytokines, as well as their regulation within the tumor microenvironment, may shed light onto novel cancer therapeutic treatments or targets.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".