Hypoxia Predicts Aggressive Growth and Spontaneous Metastasis Formation from Orthotopically Grown Primary Xenografts of Human Pancreatic Cancer
Bibliographic record
Abstract
Hypoxia in solid tumors is associated with treatment resistance and increased metastatic potential. Although hypoxia has been reported in pancreatic cancer patients, there is little direct evidence that this contributes to their overall poor prognosis. To address this, we examined the associations between hypoxia and biological aggression in a series of patient-derived xenografts grown orthotopically. Early passage xenografts were established from 16 patients undergoing surgery for pancreatic cancer and maintained in the pancreas of immune-deprived mice. Hypoxic cells were labeled using the 2-nitroimidazole probe EF5 and stained for immunofluorescence microscopy of tissue sections or as cell suspensions for flow cytometry. Bromodeoxyuridine (BrdUrd) uptake, microvessel density, cleaved caspase-3, and the differentiation markers E-cadherin, cytokeratin 19, and vimentin were analyzed in relation to hypoxia. Orthotopic implants closely resembled the histology of the original surgical samples. The 16 primary xenografts showed a wide range in their growth rates and metastatic potential, reminiscent of the spectrum of behavior seen in the clinic. EF5 labeling, tumor growth rates, and metastatic patterns were highly consistent within replicates, indicating a significant transmissible (genetic or epigenetic) component. Hypoxia was highly correlated with rapid tumor growth, increased BrdUrd uptake, and with spontaneous metastasis formation. mRNA expression analysis showed increased expression of genes involved in cell survival and proliferation in the hypoxic models. The results suggest that hypoxia is a major adverse prognostic factor in pancreatic cancer patients and support the introduction of techniques to measure hypoxia directly in patients and the development of treatment protocols to target hypoxia.
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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".