Altering the dendritic cell signature in the lung: a new adjuvant to immune checkpoint inhibitor therapies?
Bibliographic record
Abstract
Abstract Lung cancer is the leading cause of cancer-related death. While the recent introduction of immune checkpoint immunotherapies significantly improves patient outcome, many do not respond to this treatment. Dendritic cells (DCs) activate CD8+ T cells to promote the anticancer immune response. Also, in mice, pulmonary CD103+ DC1s specialize in tumour antigen presentation. However, cancer induces an immunosuppressive microenvironment that alters immune function to promote tumor development. Yet, the impact of lung tumor development on DCs remains misunderstood. To study the impact of lung tumour development on the lung dendritic cell signature, Lewis lung carcinoma (LLC) and B16F10 cells (melanoma lung metastasis) were injected intravenously, and lung DC populations analysed by flow cytometry. We observed that in cancer, the proportions of CD103+ DC1 are largely reduced, while an uncharacterized lung CD103+CD11b+ DC population is induced. The latest express surface markers and transcription factors associated with the DC2 population and high levels of PD-L1 and PD-L2 regulatory molecules. This is of crucial importance to immune checkpoint inhibitor therapies, as they rely on the efficient presentation of tumour antigen by DCs to induce T cell responses. In order to promote the anti-tumor capacity of the DC signature, CD103+ DC1s were injected in combination with an immune checkpoint inhibitor (anti-PD-1) in the B16F10 model of resistance to anti-PD1 therapy. The co-injection led to improved sensitivity to immunotherapy. Thus, promoting the replenishment of an anticancer DC environment could be an interesting therapeutic avenue to increase the efficiency of existing immune checkpoint inhibitor therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".