Examining methotrexate’s safety and efficacy in combination with immune checkpoint inhibitors to control immune related inflammatory arthritis in cancer
Bibliographic record
Abstract
Abstract Immune checkpoint inhibitors (ICIs) are associated with high rates of toxic side effects known as immune related adverse events such as inflammatory arthritis (ir-RA). ir-RA is empirically treated with drugs for rheumatoid arthritis such as methotrexate (MTX) and hydroxychloroquine which was recently shown to reduce the anti-tumor benefit of ICIs in mouse models of cancer. We have set out to determine the safety of MTX use in combination with ICIs, its effect on the anti-tumoral immune response and its efficacy in treating ir-RA in a novel arthritis-B16 PD-L1 tumor mouse model. We observed that MTX did not diminish the anti-tumor benefit of ICIs in our melanoma model and significantly altered the intra-tumoral CD8+T cell population by promoting the formation of central memory cells and diminishing PD1 expression. We have identified that MTX acts on neuropilin-1 (NRP1), a receptor identified as a CD8+T cell exhaustion marker promoting terminal exhaustion and limiting memory cell formation in tumors. We have shown that MTX treatment in vitro reduces the expression of NRP1 on stimulated CD8+T cells. We have also advanced in the development of a novel arthritis-tumor model showing that MTX can control and diminish inflammation in the delayed type hypersensitivity arthritis (DTHA) and reduced the frequency of Th1 and Th17 cells in the popliteal lymph nodes of mice, with Th17 cells known to have a crucial role in ir-RA. Together these results show that MTX is safe for use in combination with ICIs in our tumor model and is effective in our DTHA model that mimics the immune profile of ir-RA. With the development of our combined arthritis-tumor model we will be able to assess MTX’s efficacy in controlling arthritis in ICIs treated tumor bearing mice.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".