Patient-derived tumor xenograft and organoid models established from resected pancreatic, duodenal and biliary cancers
Bibliographic record
Abstract
Patient-derived xenograft (PDX) and their xenograft-derived organoid (XDO) models that recapitulate the genotypic and phenotypic landscape of patient cancers could help to advance research and lead to improved clinical management. PDX models were established from 276 pancreato-duodenal and biliary cancer resections. Initial, passage 0 (P0) engraftment rates were 59% (118/199) for pancreatic, 86% (25/29) for duodenal, and 35% (17/48) for biliary ductal tumors. Pancreatic ductal adenocarcinoma (PDAC), had a P0 engraftment rate of 62% (105/169). KRAS mutant and wild-type PDAC models were molecularly profiled, and XDO models were generated to perform initial drug response evaluations. Subsets of PDAC PDX models showed global copy number variants and gene expression profiles that were retained with serial passaging, and they showed a spectrum of somatic mutations represented in patient tumors. PDAC XDO models were established, with a success rate of 71% (10/14). Pathway activation of KRAS-MAPK in PDXs was independent of KRAS mutational status. Four wild-type KRAS models were characterized by one with EGFR (L747-P753 del), two with BRAF alterations (N486_P490del or V600E), and one with triple negative KRAS/EGFR/BRAF. Model OCIP256, characterized by BRAF (N486-P490 del), had activated phospho-ERK. A combination treatment of a pan-RAF inhibitor (LY3009120) and a MEK inhibitor (trametinib) effectively suppressed phospho-ERK and inhibited growth of OCIP256 XDO and PDX models. PDAC/duodenal adenocarcinoma have high success rates forming PDX/organoid and retaining their phenotypic and genotypic features. These models may be effective tools to evaluate novel drug combination therapies.
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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".