Patient-Derived Xenograft Models for Pancreatic Adenocarcinoma Demonstrate Retention of Tumor Morphology through Incorporation of Murine Stromal Elements
Bibliographic record
Abstract
Direct implantation of viable surgical specimens provides a representative preclinical platform in pancreatic adenocarcinoma. Patient-derived xenografts consistently demonstrate retained tumor morphology and genetic stability. However, the evolution of the tumor microenvironment over time remains poorly characterized in these models. This work specifically addresses the recruitment and incorporation of murine stromal elements into expanding patient-derived pancreatic adenocarcinoma xenografts, establishing the integration of murine cells into networks of invading cancer cells. In addition, we provide methods and observations in the establishment and maintenance of a patient-derived pancreatic adenocarcinoma xenograft model. A total of 25 histologically confirmed pancreatic adenocarcinoma specimens were implanted subcutaneously into nonobese diabetic severe combined immunodeficiency mice. Patient demographics, staging, pathological analysis, and outcomes were analyzed. After successful engraftment of tumors, histological and immunofluorescence analyses were performed on explanted tumors. Pancreatic adenocarcinoma specimens were successfully engrafted in 15 (60%) of 25 attempts. Successful engraftment does not appear to correlate with clinicopathologic factors or patient survival. Tumor morphology is conserved through multiple passages, and tumors retain metastatic potential. Interestingly, despite morphological similarity between passages, human stromal elements do not appear to expand with invading cancer cells. Rather, desmoplastic murine stroma dominates the xenograft microenvironment after the initial implantation. Recruitment of stromal elements in this manner to support and maintain tumor growth represents a novel avenue for investigation into tumor-stromal interactions.
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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 | 0.001 | 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) | 0.002 | 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".