Combined Blockade of Integrin-α4β1 Plus Cytokines SDF-1α or IL-1β Potently Inhibits Tumor Inflammation and Growth
Bibliographic record
Abstract
Tumor-associated macrophages promote tumor growth by stimulating angiogenesis and suppressing antitumor immunity. Thus, therapeutics that inhibit macrophage recruitment to tumors may provide new avenues for cancer therapy. In this study, we showed how chemoattractants stromal cell-derived growth factor 1 alpha (SDF-1α) and interleukin 1 beta (IL-1β) collaborate with myeloid cell integrin-α4β1 to promote tumor inflammation and growth. We found that SDF-1α and IL-1β are highly expressed in the microenvironments of murine lung, pancreatic, and breast tumors; surprisingly, SDF-1α was expressed only by tumor cells, whereas IL-1β was produced only by tumor-derived granulocytes and macrophages. In vivo, both factors directly recruited proangiogenic macrophages to tissues, whereas antagonists of both factors suppressed tumor inflammation, angiogenesis, and growth. Signals induced by IL-1β and SDF-1α promoted the interaction of talin and paxillin with the cytoplasmic tails of integrin-α4β1, thereby stimulating myeloid cell adhesion to endothelium in vitro and in vivo. Inhibition of integrin-α4β1, SDF-1α, or IL-1β was sufficient to block tumor inflammation and growth, and the combined blockade of these molecules greatly accentuated these effects. Furthermore, antagonists of integrin-α4β1 inhibited chemotherapy-induced tumor inflammation and acted synergistically with chemotherapeutic agents to suppress tumor inflammation and growth. These results show that targeting myeloid cell recruitment mechanisms can be an effective approach to suppress tumor progression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".