Longitudinal PET Imaging to Monitor Treatment Efficacy by Liposomal Irinotecan in Orthotopic Patient-Derived Pancreatic Tumor Models of High and Low Hypoxia
Bibliographic record
Abstract
Abstract Purpose Hypoxia is linked to aggressiveness, resistance to therapy, and poor prognosis of pancreatic tumors. Liposomal irinotecan (nal-IRI, ONIVYDE®) has shown potential in reducing hypoxia in the HT29 colorectal cancer model, and here, we investigate its therapeutic activity and ability to modulate hypoxia in patient-derived orthotopic tumor models of pancreatic cancer. Procedures Mice were randomized into nal-IRI treated and untreated controls. Magnetic resonance imaging was used for monitoring treatment efficacy, positron emission tomography (PET) imaging with F-18-labelled fluoroazomycinarabinoside ([ 18 F]FAZA) for tumor hypoxia quantification, and F-18-labelled fluorothymidine ([ 18 F]FLT) for tumor cell proliferation. Results The highly hypoxic OCIP51 tumors showed significant response following nal-IRI treatment compared with the less hypoxic OCIP19 tumors. [ 18 F]FAZA-PET detected significant hypoxia reduction in treated OCIP51 tumors, 8 days before significant changes in tumor volume. OCIP19 tumors also responded to therapy, although tumor volume control was not accompanied by any reduction in [ 18 F]FAZA uptake. In both models, no differences were observable in [ 18 F]FLT uptake in treated tumors compared with control mice. Conclusions Hypoxia modulation may play a role in nal-IRI’s mechanism of action. Nal-IRI demonstrated greater anti-tumor activity in the more aggressive and hypoxic tumor model. Furthermore, hypoxia imaging provided early prediction of treatment response.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".