Abstract 5162: Decreases in blood pressure in a patient-derived xenograft model of pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Introduction: Chemotherapy and radiation treatment cause cardiac injury and dysfunction. It remains unclear how cancer affects cardiac structure and function in therapy-naïve mice. Emerging data shows that epithelial ovarian cancer affects hemodynamics by causing cardiac atrophy, intrinsic cardiac dysfunction and arterial hypotension in mice. As pancreatic cancer is associated with heart disease risk, we wanted to explore if impaired hemodynamics occurs in other types of cancer. Methods: We utilized a patient-derived xenograft (PDX) model of pancreatic ductal adenocarcinoma (PDAC), established by implanting fresh tumor fragments from a human PDAC patient into NSG mice. Cardiovascular hemodynamic parameters were analyzed at 28-weeks post-tumor implantation, which coincided with an advanced stage of tumor development. Results: Invasive hemodynamic assessments showed a decrease in systolic and diastolic blood pressure. Conclusion: Independent of chemotherapy, pancreatic cancer causes arterial hypotension, impairing perfusion and overall recovery. Monitoring cardiovascular health is crucial to prevent further deterioration, optimize outcomes, and improve the quality of life for pancreatic cancer patients throughout their journey. Citation Format: Amelia R. Malicki, Leslie M. Ogilvie, Alexa N. King, Patrick Sanosa, Jana Michaud, Bridget Coyle-Asbil, Praveen Bhoopathi, Vignesh Vudatha, Jose G. Trevino, Keith R. Brunt, Jeremy A. Simpson. Decreases in blood pressure in a patient-derived xenograft model of pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5162.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".