Depletion of Fibrinogen Suppresses Growth of Primary Tumors and Metastasis of Pancreatic Ductal Adenocarcinoma
Bibliographic record
Abstract
BACKGROUND & AIMS: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, highly metastatic disease that provokes dysregulation of the coagulation system. Patients exhibit significantly elevated circulating levels of blood clotting protein fibrin(ogen). Extravascular fibrin deposits contribute to the complex tumor microenvironment in PDAC. METHODS: We depleted fibrinogen in 3 PDAC patient-derived xenograft models using technology platforms that are currently being tested clinically (antisense oligonucleotide or lipid nanoparticles containing small interfering RNAs) and monitored tumor growth and metastasis. Proteomics and spatial transcriptomics were used to interrogate the mechanisms behind the in vivo work. RESULTS: The role of fibrin on tumor progression was evaluated in vitro and in vivo and reduction of fibrin led to decreased tumor cell proliferation in vitro and significantly suppressed primary orthotopic tumor growth. Fibrin depletion provoked a significant shift in extracellular matrix-associated proteins and serine protease inhibitors, suggesting a decrease in the activity of serine proteases known to be responsible for extracellular matrix remodeling and metastatic dissemination. Spatial transcriptomics revealed that tumors from fibrinogen-depleted mice exhibit significantly increased presence of stromal components, including tumor-restraining cancer-associated fibroblasts. Congruently, fibrinogen knockdown in a metastatic orthotopic model markedly impaired spontaneous metastasis to the liver. However, fibrinogen knockdown did not affect liver colonization in an intrasplenic injection model, which recapitulates the late stages of metastasis. CONCLUSIONS: These data suggest that fibrin(ogen) reprograms the primary tumor microenvironment to support growth and promote early, but not late, metastatic steps. Our findings support prospective evaluation of a novel clinical approach involving the integration of fibrin(ogen)-targeting or depleting agents into chemotherapy regimens to control the spread of pancreatic cancer.
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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) | 0.001 | 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".