cDC1 and interferons promote spontaneous CD4 <sup>+</sup> and CD8 <sup>+</sup> T cell protective responses to breast cancer
Bibliographic record
Abstract
Abstract Here we show that efficient breast cancer immunosurveillance relies on cDC1, conventional CD4 + T cells, CD8 + cytotoxic T lymphocytes (CTL) and later NK/NK T cells. For this process, cDC1 were required constitutively, but especially during the T cell priming phase. In the tumor microenvironment, cDC1 interacted physically and jointly with both CD4 + T cells and tumorspecific CD8 + T cells. We found that interferon (IFN) responses were necessary for the rejection of breast cancer, including cDC1-intrinsic signaling by IFN-γ and STAT1. Surprisingly, cell-intrinsic IFN-I signaling in cDC1 was not required. cDC1 and IFNs shaped the tumor immune landscape, notably by promoting CD4 + and CD8 + T cell infiltration, terminal differentiation and effector functions. XCR1, CXCL9, IL-12 and IL-15 were individually dispensable for breast cancer immunosurveillance. Consistent with our experimental results in mice, high expression in the tumor microenvironment of genes specific to cDC1, CTL, helper T cells or interferon responses are associated with a better prognosis in human breast cancer patients. Our results show that immune control of breast cancer depends on cDC1 and IFNs as previously reported for immunogenic melanoma or fibrosarcoma tumor models, but that the underlying mechanism differ. Revisiting cDC1 functions in the context of spontaneous immunity to cancer should help defining new ways to mobilize cDC1 functions to improve already existing immunotherapies for the benefits of patients. Synopsis Type 1 conventional dendritic cells cross-present tumor antigens to CD8 + T cells. Understanding the regulation of their antitumor functions is important. Cell-intrinsic STAT1/IFN-γ signaling licenses them for efficient CD4 + and CD8 + T cell activation during breast cancer immunosurveillance.
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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 | 0.000 |
| 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".