Novel Methods of Targeting IL-1 Signalling for the Treatment of Breast Cancer Bone Metastasis
Bibliographic record
Abstract
Breast cancer bone metastasis is currently incurable. Evidence suggests that inhibiting IL-1 signalling with the IL1R antagonist, Anakinra, or the IL1β antibody, Canakinumab, prevents metastasis and almost eliminates breast cancer growth in the bone. However, these drugs increase primary tumour growth. We, therefore, investigated whether targeting other members of the IL-1 pathway (Caspase-1, IL1β or IRAK1) could reduce bone metastases without increasing tumour growth outside of the bone. Inhibition of IL-1 via MLX01 (IL1β secretion inhibitor), VRT043198/VX765 (Caspase-1 inhibitor), Pacritinib (IRAK1 inhibitor) or Anakinra (IL1R antagonist) on tumour cell viability, migration and invasion were assessed in mouse mammary E0771 and Py8119 cells in vitro and on primary tumour growth, spontaneous metastasis and metastatic outgrowth in vivo. In vitro, Inhibition of IL-1 signalling by MLX01, VRT043198 and Anakinra reduced migration of E0771 and Py8119 cells and reversed tumour-derived IL1β induced-increased invasion and migration towards bone cells. In vivo, VX765 and Anakinra significantly reduced spontaneous metastasis and metastatic outgrowth in the bone, whereas MLX01 reduced primary tumour growth and bone metastasis. Pacritinib had no effect on metastasis in vitro or in vivo. Targeting IL-1 signalling with small molecule inhibitors may provide a new therapeutic strategy for breast cancer bone metastasis.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".