Synergistic Effect of Thermoneutral Housing and Chronotherapeutic PD-1 Blockade Overcomes Melanoma Resistance
Bibliographic record
Abstract
Abstract Background Immune-checkpoint blockade transformed melanoma therapy, yet the B16-F10 model resists PD-1 inhibition. Sub-thermoneutral housing (22°C) causes cold stress, and dosing outside circadian immune peaks dampens anti-tumour immunity. We hypothesised eliminating both would render B16-F10 curable. Methods C57BL/6 mice (n=40) with B16-F10 tumours were randomised to conventional (22°C; ZT22 PD-1 timing), chronotherapy only (22°C; ZT4), thermoneutrality only (30°C; ZT22), or “double-hit” (30°C; ZT4). Anti-PD-1 was given on days 3, 6, 9, 12. Tumour volume, weight, survival, and day-14 serum IFN-γ were recorded. Synergy threshold Δ ≥ 20% was prespecified. Results Day 14 control tumours were 1207±48 mm 3 . Chronotherapy and thermoneutrality slowed growth (737±55 mm 3 and 565±28 mm 3 respectively; p<0.0001). The double-hit nearly halted progression (43±12 mm 3 , –96%; p<10□ 1 □), yielding seven complete responses and 100% survival. IFN-γ increased 8.8-fold versus control (430±24 vs 48±7 pg mL□ 1 ; p<10□ 1 □). Bliss Δ was 25%, confirming synergy. Conclusions Removing cold stress and circadian mistiming unmasks curative PD-1 response in refractory melanoma. Available warming blankets and morning infusions suggest a practical tactic to rescue PD-1 efficacy in “cold” tumours. A factorial multicentre trial is warranted.
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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) | 0.002 | 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".