Synergistic effect of thermoneutral housing and chronotherapeutic PD-1 blockade overcomes melanoma resistance
Bibliographic record
Abstract
BACKGROUND: The clinical efficacy of immune checkpoint inhibitors (ICIs) is profoundly limited by primary and acquired resistance. This challenge is mirrored in preclinical research, where artifactual immunosuppression induced by standard husbandry practices-specifically chronic cold stress and circadian mistiming of drug delivery-confounds the assessment of therapeutic potential. We hypothesized that synergistic host-state optimization, by simultaneously resolving these two physiological stressors, could overcome profound ICI resistance. METHODS: In a prospective, randomized, 6-arm study, we systematically dissected the individual and synergistic impacts of thermoneutral housing (30°C vs. 22°C) and chronobiologically-optimized anti-PD-1 administration (ZT4 vs. ZT22) in the notoriously resistant B16-F10 melanoma model. Rigorous endpoints included tumor eradication, 60-day survival, and functional immunological memory. Mechanistic interrogation involved deep transcriptional profiling of the tumor microenvironment (TME), systemic cytokine quantification, and pharmacokinetic analysis. RESULTS: Single-variable corrections conferred only marginal survival benefit. In stark contrast, the dual intervention of thermoneutrality plus chronotherapy synergistically induced significant tumor eradication, culminating in a 30 % complete response rate and 30 % long-term survival (p < 0.0001) in this otherwise incurable model. This curative effect, confirmed to be independent of drug pharmacokinetics, was driven by a radical reprogramming of the TME into a highly inflamed state, evidenced by an over 6-fold upregulation of key cytotoxic effector genes (e.g., Gzmb) and an almost 3-fold surge in systemic IFN-γ. Critically, a majority of survivors established durable immunity against lethal tumor rechallenge. CONCLUSION: Host physiological state is a dominant and actionable determinant of immunotherapy outcome. By resolving ubiquitous preclinical artifacts, we converted a checkpoint-refractory malignancy into a curable disease using standard anti-PD-1 monotherapy. These findings challenge current research paradigms and mandate immediate clinical investigation of these simple, non-toxic strategies to amplify ICI efficacy in patients with cancer.
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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) | 0.001 | 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".