Hydroxychloroquine (HCQ) reverses anti-PD-1 immune murine checkpoint blockade: TCF1 as a marker in humans for COVID-19 and HCQ therapy
Bibliographic record
Abstract
Abstract Coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a serious threat to global public health. Hydroxychloroquine (HCQ) and the antibiotic azithromycin (AZ) are still being used by thousands and numerous hospitals to treat COVID-19. In a related context, immunotherapy using checkpoint blockade (ICB) with antibodies such as anti-PD-1 has revolutionised cancer therapy. Given that cancer patients on ICB continue to be infected with SARS-CoV-2, an understanding of the effects of HCQ and AZ on the elimination of tumors by anti-PD-1 ICB is urgently needed. In this study, we report that HCQ alone, or in combination with AZ, at doses used to treat COVID-19 patients, reverses the therapeutic benefit of anti-PD-1 in controlling B16 melanoma tumor growth in mice. No deleterious effect was seen on untreated tumors, or in using AZ alone in anti-PD-1 immunotherapy. 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 blocked the appearance of a subset of terminally exhausted CD8+ TILs. No effect was seen on the presence of CD4+ T-cells, FoxP3+ Tregs, thymic subsets, B-cells, antibody production, myeloid cells, or the vasculature of mice. Lastly, we identified TCF-1 expression in peripheral CD8+ T-cells from cancer or non-cancer human patients infected with SARs CoV2 as a marker for the effects of COVID-19 and HCQ on the immune system. 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. Graphic Abstract
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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) | 0.001 | 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".