Targeting Dectin-1 and or VISTA enhances anti-tumor immunity in melanoma but not colorectal cancer model
Bibliographic record
Abstract
PURPOSE: Acquired resistance to immune checkpoint blockers (ICBs) is a major barrier in cancer treatment, emphasizing the need for innovative strategies. Dectin-1 (gene Clec7a) is a C-type lectin receptor best known for its ability to recognize β-glucan-rich structures in fungal cell walls. While Dectin-1 is expressed in myeloid cells and tumor cells, its significance in cancer remains the subject of controversy. METHODS: Using Celc7a-/- mice and curdlan administration to stimulate Dectin-1 signaling, we explored its impact. VISTA KO mice were employed to assess VISTA's role, and bulk RNAseq analyzed curdlan effects on neutrophils. RESULTS: Our findings reveal myeloid cells as primary Dectin-1 expressing cells in the tumor microenvironment (TME), displaying an activated phenotype. Strong Dectin-1 co-expression/co-localization with VISTA and PD-L1 in TME myeloid cells was observed. While Dectin-1 deletion lacked protective effects, curdlan stimulation significantly curtailed B16-F10 tumor progression. RNAseq and pathway analyses supported curdlan's role in triggering a cascade of events leading to increased production of pro-inflammatory mediators, potentially resulting in the recruitment and activation of immune cells. Moreover, we identified a heterogeneous subset of Dectin-1+ effector T cells in the TME. Similar to mice, human myeloid cells are the prominent cells expressing Dectin-1 in cancer patients. CONCLUSION: Our study proposes Dectin-1 as a potential adjunctive target with ICBs, orchestrating a comprehensive engagement of innate and adaptive immune responses in melanoma. This innovative approach holds promise for overcoming acquired resistance to ICBs in cancer treatment, offering avenues for further exploration and development.
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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) | 0.002 | 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".