Exploring immune interactions in triple negative breast cancer: IL-1β inhibition and its therapeutic potential
Bibliographic record
Abstract
Triple negative breast cancer (TNBC) has poor prognosis when compared to other breast cancer subtypes. Despite pre-clinical data supporting an immune targeted approach for TNBCs, immunotherapy has failed to demonstrate the impressive responses seen in other solid tumor malignancies. Additional strategies to modify the tumor immune microenvironment and potentiate response to immunotherapy are needed. In this review, we summarise phase III data supporting the use of immunotherapy for TNBC. We discuss the role of IL-1β in tumorigenesis and summarize pre-clinical data supporting IL-1β inhibition as a potential therapeutic strategy in TNBC. Finally, we present current trials evaluating IL-1β in breast cancer and other solid tumor malignancies and discuss future studies that may provide a strong scientific rationale for the combination of IL-1β and immunotherapy in the neoadjuvant and metastatic setting for people with TNBC.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".