Perivascular M2 Macrophages Stimulate Tumor Relapse after Chemotherapy
Bibliographic record
Abstract
Tumor relapse after chemotherapy-induced regression is a major clinical problem, because it often involves inoperable metastatic disease. Tumor-associated macrophages (TAM) are known to limit the cytotoxic effects of chemotherapy in preclinical models of cancer. Here, we report that an alternatively activated (M2) subpopulation of TAMs (MRC1(+)TIE2(Hi)CXCR4(Hi)) accumulate around blood vessels in tumors after chemotherapy, where they promote tumor revascularization and relapse, in part, via VEGF-A release. A similar perivascular, M2-related TAM subset was present in human breast carcinomas and bone metastases after chemotherapy. Although a small proportion of M2 TAMs were also present in hypoxic tumor areas, when we genetically ablated their ability to respond to hypoxia via hypoxia-inducible factors 1 and 2, tumor relapse was unaffected. TAMs were the predominant cells expressing immunoreactive CXCR4 in chemotherapy-treated mouse tumors, with the highest levels expressed by MRC1(+) TAMs clustering around the tumor vasculature. Furthermore, the primary CXCR4 ligand, CXCL12, was upregulated in these perivascular sites after chemotherapy, where it was selectively chemotactic for MRC1(+) TAMs. Interestingly, HMOX-1, a marker of oxidative stress, was also upregulated in perivascular areas after chemotherapy. This enzyme generates carbon monoxide from the breakdown of heme, a gas known to upregulate CXCL12. Finally, pharmacologic blockade of CXCR4 selectively reduced M2-related TAMs after chemotherapy, especially those in direct contact with blood vessels, thereby reducing tumor revascularization and regrowth. Our studies rationalize a strategy to leverage chemotherapeutic efficacy by selectively targeting this perivascular, relapse-promoting M2-related TAM cell population.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".