Abstract B45: Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell and patient-derived tumor organoids
Bibliographic record
Abstract
Abstract There are few in vitro models of exocrine pancreas development and primary human pancreatic adenocarcinoma (PDAC). We establish three-dimensional culture conditions to induce the differentiation of human pluripotent stem cells (PSCs) into exocrine progenitor organoids that form ductal and acinar structures in culture and in vivo. Expression of mutant KRAS or TP53 in progenitor organoids induces mutation-specific phenotypes in culture and in vivo. Expression of TP53R175H induced cytosolic SOX9 localization. In patient tumors bearing TP53 mutations, SOX9 was cytoplasmic and associated with mortality. Culture conditions are also defined for clonal generation of tumor organoids from freshly resected PDAC. Tumor organoids maintain the differentiation status, histoarchitecture, phenotypic heterogeneity of the primary tumor, and retain patient-specific physiologic changes including hypoxia, oxygen consumption, epigenetic marks, and differential sensitivity to EZH2 inhibition. Thus, pancreatic progenitor organoids and tumor organoids can be used to model PDAC and for drug screening to identify precision therapy strategies. Citation Format: Ling Huang, Audrey Holtzinger, Ishaan Jagan, Cristina Nostro, Rennian Wang, Lakshmi Muthuswamy, Cheryl Arrowsmith, Sean Cleary, David Schaeffer, Michael Roehrl, Tsao Ming-Sound, Steven Gallinger, Gordon Keller, Senthil Muthuswamy.{Authors}. Ductal pancreatic cancer modeling and drug screening using human pluripotent stem cell and patient-derived tumor organoids. [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2016 May 12-15; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2016;76(24 Suppl):Abstract nr B45.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".