Myeloid-derived suppressor cell depletion therapy targets IL-17A-expressing mammary carcinomas
Bibliographic record
Abstract
Triple-negative breast cancer (TNBC) is an invasive subtype of breast cancer but paradoxically associated with increased tumor-infiltrating leukocytes. The molecular and cellular mechanisms underlying TNBC immunobiology are incompletely understood. Interleukin (IL)-17A is a pro-inflammatory cytokine that has both pro- and anti-tumor effects and found in 40-80% of TNBC samples. We report here that IL-17A mRNA and protein are detectable in some human TNBC cell lines and further upregulated by IL-23 and LPS stimulation. Furthermore, the impact of tumor-derived IL-17A in host immune response and tumor growth was examined using murine TNBC 4T1 mammary carcinoma cells transduced with an adenoviral vector expressing IL-17A (AdIL-17A) or control vector (Addl). Compared to Addl-transduction, AdIL-17A-transduction enhanced 4T1 tumor growth and lung metastasis in vivo, which was associated with a marked expansion of myeloid-derived suppressor cells (MDSCs). However, AdIL-17A-transduction also induced strong organ-specific and time-dependent immune activation indicated by dynamic changes of NK cells, B cells, CD4, and CD8 T cells in peripheral blood, lung, and tumor site, as well as the plasma levels of IFNγ. Such findings highlight that tumor-associated IL-17A induces concurrent immune activation and immune suppression. Administration of anti-Gr1 or anti-G-CSF antibody effectively depleted MDSCs in vivo, markedly reducing the growth of AdIL-17A-transduced 4T1 tumors, and eliminating lung metastasis. Collectively, our study demonstrates that MDSC depletion is an effective and practical approach for treating IL-17A-enriched mammary carcinomas.
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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".