IL-1B drives opposing responses in primary tumours and bone metastases; harnessing combination therapies to improve outcome in breast cancer
Why this work is in the frame
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Bibliographic record
Abstract
Breast cancer bone metastasis is currently incurable, ~75% of patients with late-stage breast cancer develop disease recurrence in bone and available treatments are only palliative. We have previously shown that production of the pro-inflammatory cytokine interleukin-1B (IL-1B) by breast cancer cells drives bone metastasis in patients and in preclinical in vivo models. In the current study, we have investigated how IL-1B from tumour cells and the microenvironment interact to affect primary tumour growth and bone metastasis through regulation of the immune system, and whether targeting IL-1 driven changes to the immune response improves standard of care therapy for breast cancer bone metastasis. Using syngeneic IL-1B/IL1R1 knock out mouse models in combination with genetic manipulation of tumour cells to overexpress IL-1B/IL1R1, we found that IL-1B signalling elicited an opposite response in primary tumours compared with bone metastases. In primary tumours, IL-1B inhibited growth, by impairing the infiltration of innate immune cell subsets with potential anti-cancer functions but promoted enhanced tumour cell migration. In bone, IL-1B stimulated the development of osteolytic metastases. In syngeneic models of breast cancer, combining standard of care treatments (Doxorubicin and Zoledronic acid) with the IL-1 receptor antagonist Anakinra inhibited both primary tumour growth and metastasis. Anakinra had opposite effects on the immune response compared to standard of care treatment, and its anti-inflammatory signature was maintained in the combination therapy. These data suggest that targeting IL-1B signalling may provide a useful therapeutic approach to inhibit bone metastasis and improve efficacy of current treatments for breast cancer patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it