Combination therapy for cancer with IL-27 and anti-PD-1: A simplified mathematical model
Bibliographic record
Abstract
Many experiential and clinical trials in cancer treatment show that a combination of immune checkpoint inhibitor with another agent can improve the tumor reduction. Anti Programmed death 1 (Anti-PD-1) is one of these immune checkpoint inhibitors that re-activate immune cells to inhibit tumor growth. In this work, we consider a combination treatment of anti-PD-1 and Interleukin-27 (IL-27). IL-27 has anti-cancer functions to promote the development of Th1 and CD8$^+$ T cells, but it also upregulates the expression of PD-1 and Programmed death ligand 1 (PD-L1) to inactivate these T cells. Thus, the functions of IL-27 in tumor growth is controversial. Hence, we create a simplified mathematical model to investigate whether IL-27 is pro-cancer or anti-cancer in the combination with anti-PD-1 and to what degree anti-PD-1 improves the efficacy of IL-27. Our synergy analysis for the combination treatment of IL-27 and anti-PD-1 shows that (i) ant-PD-1 can efficiently improve the treatment efficacy of IL-27; and (ii) there exists a monotone increasing function $F_c(G)$ depending on the treatment efficacy of anti-PD-1 $G$ such that IL-27 is an efficient anti-cancer agent when its dose is smaller than $F_c(G)$, whereas IL-27 is a pro-cancer agent when its dose is higher than $F_c(G)$. Our analysis also provides the existence and the local stability of the trivial, non-negative, and positive equilibria of the model. Combining with simulation, we discuss the effect of the IL-27 dosage on the equilibria and find that the T cells and IFN-$\gamma$ could vanish and tumor cells preserve, when the production rate of T cells by IL-27 is low or the dosage of IL-27 is low.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".