Tumor-supportive and Osteoclastogenic Changes Induced by Breast Cancer-derived Factors Are Reversed by Inhibition of γ-Secretase
Bibliographic record
Abstract
During breast cancer metastasis to bone, tumor cells home to bone marrow, likely targeting the stem cell niche, and stimulate osteoclasts, which mediate osteolysis required for tumor expansion. Although osteoblasts contribute to the regulation of the hematopoietic stem cell niche and control osteoclastogenesis through production of proresorptive cytokine RANKL (receptor activator of NF-κB ligand), their role in cancer metastases to bone is not fully understood. C57BL/6J mouse bone marrow cells were treated for 3-12 days with ascorbic acid (50 μg/ml) in the presence or absence of 10% medium conditioned by breast carcinoma cells MDA-MB-231, 4T1, or MCF7. Treatment with cancer-derived factors resulted in a sustained 40-60% decrease in osteoblast differentiation markers, compared with treatment with ascorbic acid alone, and induced an osteoclastogenic change in the RANKL/osteoprotegerin ratio. Importantly, exposure of bone cells to breast cancer-derived factors stimulated the subsequent attachment of cancer cells to immature osteoblasts. Inhibition of γ-secretase using pharmacological inhibitors DAPT and Compound E completely reversed cancer-induced osteoclastogenesis as well as cancer-induced enhancement of cancer cell attachment, identifying γ-secretase activity as a key mediator of these effects. Thus, we have uncovered osteoblasts as critical intermediary of premetastatic signaling by breast cancer cells and pinpointed γ-secretase as a robust target for developing therapeutics potentially capable of reducing both homing and progression of cancer metastases to bone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".