Countering the advert effects of lung cancer on the anticancer potential of dendritic cell populations reinstates sensitivity to anti-PD-1 therapy
Bibliographic record
Abstract
Lung cancer is the leading cause of cancer-related deaths. While the recent use of immune checkpoint inhibitors significantly improves patient outcomes, responsiveness remains restricted to a small proportion of patients. Conventional dendritic cells (DCs) play a major role in anticancer immunity. In mice, two subpopulations of DCs are found in the lung: DC2s (CD11b+Sirpα+) and DC1s (CD103+XCR1+), the latest specializing in the promotion of anticancer immune responses. However, the impact of lung cancer on DC populations and the consequent influence on the anticancer immune response remain poorly understood. To address this, DC populations were studied in murine models of Lewis Lung Carcinoma (LLC) and melanoma-induced lung metastasis (B16F10). We report that direct exposure to live or dead cancer cells impacts the capacity of DCs to differentiate into CD103+ DC1s, leading to profound alterations in CD103+ DC1 proportions in the lung. In addition, we observed the accumulation of CD103loCD11b+ DCs, which express DC2 markers IRF4 and Sirpα, high levels of T-cell inhibitory molecules PD-L1/2 and the regulatory molecule CD200. Finally, DC1s were injected in combination with an immune checkpoint inhibitor (anti-PD-1) in the B16F10 model of resistance to the anti-PD-1 immune checkpoint therapy; the co-injection restored sensitivity to immunotherapy. Thus, we demonstrate that lung tumor development leads to the accumulation of CD103loCD11b+ DCs with a regulatory potential combined with a reduced proportion of highly-specialized antitumor CD103+ DC1s, which could promote cancer growth. Additionally, promoting an anticancer DC signature could be an interesting therapeutic avenue to increase the efficacy of existing immune checkpoint inhibitors.
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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".