患者由来オルガノイドを用いた膵癌サブタイプの判別と臨床予後予測
Bibliographic record
Abstract
Background Recently, two molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) have been proposed: the "Classical" and "Basal-like" subtypes, with the former showing better clinical outcomes than the latter.However, the "molecular" classification has not been applied in realworld clinical practice.This study aimed to establish patientderived organoids (PDOs) for PDAC and evaluate their application in subtype classification and clinical outcome prediction.Methods We utilized tumor samples acquired through endoscopic ultrasound-guided fine-needle biopsy and established a PDO library for subsequent use in morphological assessments, RNA-seq analyses, and in vitro drug response assays.We also conducted a prospective clinical study to evaluate whether analysis using PDOs can predict treatment response and prognosis.Results PDOs of PDAC were established at a high efficiency (> 70%) with at least 100,000 live cells.Morphologically, PDOs were classified as gland-like structures (GL type) and densely proliferating inside (DP type) less than 2 weeks after tissue sampling.RNA-seq analysis revealed that the "morphological" subtype (GL vs. DP) corresponded to the "molecular" subtype ("Classical" vs. "Basal-like").The "morphological" classification predicted the clinical treatment response and prognosis; the median overall survival of patients with GL type was significantly longer than that with DP type (P < 0.005).The GL type showed a better response to gemcitabine than the DP type in vitro, whereas the drug response of the DP type was improved by the combination of ERK inhibitor and chloroquine.Conclusions PDAC PDOs help in subtype determination and clinical outcome prediction, thereby facilitating the bench-to-bedside precision medicine for PDAC.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.011 |
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".