Creation of EGD-Derived Gastric Cancer Organoids to Predict Treatment Responses
Bibliographic record
Abstract
Gastric adenocarcinoma (GAd) is the third leading cause of cancer-related deaths worldwide. Most patients require perioperative chemotherapy, yet methods to accurately predict responses to therapy are lacking. Thus, patients may be unnecessarily exposed to considerable toxicities. Here, we present a novel methodology using patient-derived organoids (PDOs) that rapidly and accurately predicts the chemotherapy efficacy for GAd patients. Methods: Endoscopic GAd biopsies were obtained from 19 patients, shipped overnight, and PDOs were developed within 24 h. Drug sensitivity testing was performed on PDO single-cells with current standard-of-care systemic GAd regimens and cell viability was measured. Whole exome sequencing was used to confirm the consistency of tumor-related gene mutations and copy number alterations between primary tumors, PDOs, and PDO single-cells. Results: Overall, 15 of 19 biopsies (79%) were appropriate for PDO creation and single-cell expansion within 24 h of specimen collection and overnight shipment. With our PDO single-cell technique, PDOs (53%) were successfully developed. Subsequently, two PDO lines were subjected to drug sensitivity testing within 12 days from initial biopsy procurement. Drug sensitivity assays revealed unique treatment response profiles for combination drug regimens in both of the two unique PDOs, which corresponded with the clinical response. Conclusions: The successful creation of PDOs within 24 h of endoscopic biopsy and rapid drug testing within 2 weeks demonstrate the feasibility of our novel approach for future applications in clinical decision making. This proof of concept sets the foundation for future clinical trials using PDOs to predict clinical responses to GAd 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".