Hydroxychloroquine (HCQ) decreases the benefit of anti-PD-1 immune checkpoint blockade in tumor immunotherapy
Bibliographic record
Abstract
Immunotherapy using checkpoint blockade (ICB) with antibodies such as anti-PD-1 has revolutionised the treatment of many cancers. Despite its use to treat COVID-19 patients and autoimmune diseases such as systemic lupus erythematosus and rheumatoid arthritis, the effect of hydroxychloroquine (HCQ) on cancer immunotherapy has not been examined. In this study, remarkably, we find that HCQ alone, or in combination with azithromycin (AZ), at doses used to treat patients, decreased the therapeutic benefit of anti-PD-1 in cancer immunotherapy. No deleterious effect was seen on untreated tumors. Mechanistically, HCQ and HCQ/AZ inhibited PD-L1 expression on tumor cells, while specifically targeting the anti-PD-1 induced increase in progenitor CD8+CD44+PD-1+TCF1+ tumor infiltrating T cells (TILs) and the generation of CD8+CD44+PD-1+ effectors. Surprisingly, it also impaired the appearance of a subset of terminally exhausted CD8+ TILs. No effect was seen on the presence of CD4+ T cells, FoxP3+ regulatory T cells (Tregs), thymic subsets, B cells, antibody production, myeloid cells, or the vasculature of mice. This study indicates for the first time that HCQ and HCQ/AZ negatively impact the ability of anti-PD-1 checkpoint blockade to promote tumor rejection.
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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 | 0.000 | 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) | 0.002 | 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".