Tumor-educated monocyte-dendritic progenitors promote a metastatic switch
Bibliographic record
Abstract
Abstract Myeloid skewing of hematopoietic cells is a prominent promoter of metastasis. However, little is known about their education and differentiation pattern from hematopoietic stem and progenitor cells (HSPCs) during tumor progression and metastasis. Here we show that metastatic tumors dictate a unique differentiation pattern of HSPCs towards a specific myeloid progeny. Using single cell RNA-sequencing analysis integrated with proteomic screen of tumor secretome, we demonstrate that highly metastatic tumors dictate a long-lived differentiation of HSPCs towards monocyte-dendritic progenitors (MDPs) while low-metastatic tumors promote their differentiation into granulocyte-monocyte progenitors (GMPs). This effect is driven by IL-6 axis that is highly active in metastatic tumors. Consequently, loss and gain of function of IL-6 in tumor cells resulted in decreased and increased metastasis and corresponding MDP levels, respectively. Consistently, IL-6-educated MDPs but not GMPs obtained from highly metastatic tumors, adoptively transferred into mice bearing low metastatic tumors resulted in increased metastasis due to their further differentiation into immunosuppressive (M2) macrophages. Overall, our study reveals a new role for tumor-derived IL-6 that hijacks HSPC differentiation program towards myeloid cells that contribute to 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".