Myeloid HIF1α drives pancreatic ductal adenocarcinoma growth and sustains tumour associated macrophage abundance and phenotype via MIF 3664
Bibliographic record
Abstract
Abstract Description Pancreatic ductal adenocarcinoma (PDAC) has the lowest 5-year survival rate of commonly occurring cancers. Since both hypoxia and tumour associated macrophage (TAM) infiltration are key detrimental traits of the disease, we aimed to determine if tumour-infiltrating myeloid cells rely on tumour hypoxia and Hypoxia Inducible Factor 1-alpha (HIF1α) signalling to drive disease progression. Using the hypoxic tracer molecule pimonidazole, we observed that TAMs were enriched in hypoxic tumour areas in an orthotopic, syngeneic model of PDAC in mice. Single-cell RNA-sequencing of these tumours revealed unique, hypoxic, myeloid clusters, including a tissue-reparative, ARG1+ SPP1+ macrophage population. These hypoxic clusters were significantly altered when HIF1α was specifically deleted in myeloid cells (HIF1αf/f LysMCre) and critically, myeloid deletion of HIF1α significantly reduced TAM numbers and slowed PDAC tumour growth. One of the most downregulated mRNAs in both TAMs and tumour associated neutrophils lacking HIF1α was the myeloid-signalling cytokine MIF, and pharmacological inhibition of MIF significantly reduced PDAC tumour burden. We conclude that hypoxic signalling via myeloid HIF1α is a critical factor driving the pro-tumour TAM phenotype in PDAC. Our data identify a mechanistically key role of hypoxia and highlight a previously undescribed mechanism of immune-derived MIF in promoting the progression of this deadly disease. Funding Sources NIH/NCI 1R01CA255670; Medicine by design 406694; TFRI 436605; CIHR 518004. Topic Categories Tumor Immunology: Cellular Responses and Tumor Microevironment (TIME)
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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 | 0.001 |
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".