Pancreatic cancer growth promoted by bone marrow mesenchymal stromal cell–derived IL-6 is reversed predominantly by IL-6 blockade
Bibliographic record
Abstract
Pancreatic cancer is a highly lethal cancer characterized by local invasiveness, early metastasis, recurrence and high resistance to current therapies. Extensive stroma or desmoplasia is a key histological feature of the disease, and interactions between cancer and stromal cells are critical for pancreatic cancer development and progression. Mesenchymal stromal cells [MSCs] exhibit preferential tropism to primary and metastatic tumor sites and may either suppress or support tumor growth. Although MSCs represent a potential source of pancreatic cancer stroma, their contribution to pancreatic tumor growth remains poorly known. Here, we show that bone marrow MSCs significantly contribute to pancreatic cancer growth in vitro and in vivo. Furthermore, MSCs create a pro-carcinogenic microenvironment through the release of key factors mediating growth and angiogenesis, including interleukin (IL)-6, IL-8, vascular endothelial growth factor and activation of STAT3 signaling in tumor cells. IL-6 released by MSCs was largely responsible for the pro-tumorigenic effects of MSCs. Knockdown of IL-6 expression in MSCs by small interfering RNA (siRNA) abolished the MSC growth-promoting effect in vitro, reducing tumor cell proliferation and clonogenic potential. In addition, in a heterotopic nude mouse model of human pancreatic tumor xenografts, blockade of IL-6 with the anti-IL-6 receptor antibody, tocilizumab, or of its downstream effector STAT3 with the small molecule STAT3 inhibitor S3I-201, abrogated MSC-mediated tumor promotion and delayed tumor formation significantly. Our data demonstrate that MSCs promote pancreatic cancer growth, with IL-6 produced by MSCs playing a pivotal role.
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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 | 0.002 |
| 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".