Inhibition of the Stromal p38MAPK/MK2 Pathway Limits Breast Cancer Metastases and Chemotherapy-Induced Bone Loss
Bibliographic record
Abstract
Abstract The role of the stromal compartment in tumor progression is best illustrated in breast cancer bone metastases, where the stromal compartment supports tumor growth, albeit through poorly defined mechanisms. p38MAPKα is frequently expressed in tumor cells and surrounding stromal cells, and its expression levels correlate with poor prognosis. This observation led us to investigate whether inhibition of p38MAPKα could reduce breast cancer metastases in a clinically relevant model. Orally administered, small-molecule inhibitors of p38MAPKα or its downstream kinase MK2 each limited outgrowth of metastatic breast cancer cells in the bone and visceral organs. This effect was primarily mediated by inhibition of the p38MAPKα pathway within the stromal compartment. Beyond effectively limiting metastatic tumor growth, these inhibitors reduced tumor-associated and chemotherapy-induced bone loss, which is a devastating comorbidity that drastically affects quality of life for patients with cancer. These data underscore the vital role played by stromal-derived factors in tumor progression and identify the p38MAPK-MK2 pathway as a promising therapeutic target for metastatic disease and prevention of tumor-induced bone loss. Significance: Pharmacologically targeting the stromal p38MAPK-MK2 pathway limits metastatic breast cancer growth, preserves bone quality, and extends survival. Cancer Res; 78(19); 5618–30. ©2018 AACR.
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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) | 0.003 | 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".