Patient-derived esophageal adenocarcinoma organ chip: a physiologically relevant platform for functional precision oncology
Bibliographic record
Abstract
BACKGROUND: Esophageal adenocarcinoma (EAC) is the sixth most deadly cancer worldwide, with increasing incidence in North America. As no targeted therapy or immunotherapy has revolutionized the management of EAC, chemotherapy is the only standard of care. Most patients with EAC experience poor outcomes because of the inherent or acquired resistance to chemotherapy. METHODS: Adapting a patient-centered approach, we leveraged a microfluidic cell culture technology platform (Emulate), organoids derived from treatment-naive patient tumors or adjacent normal tissues, and patient-matched cancer-associated or normal fibroblasts respectively, to develop a novel, physiologically relevant, high-fidelity preclinical esophagus-on-a-chip model. H&E, immunofluorescence staining, live/dead assay, LDH assay, and ELISA-based detection of tumor biomarkers were used to assess treatment responses. RESULTS: Each patient-specific stroma-inclusive microfluidic esophageal adenocarcinoma on-a-chip (EAC chip) faithfully recreates the tumor-stroma interface while preserving the full diversity of two cell types (epithelia and fibroblasts), genetic landscapes and histological architecture of the source tumors. EAC chips also accurately predict the response to neoadjuvant chemotherapy (NACT) within a clinically useful timeframe (approx. 12 days). A docetaxel-based triplet chemotherapy regimen matched with the treatment of the source patient was successfully perfused through the interstitial space within this model. Therefore, EAC chips more accurately recapitulate inpatient pathological and objective responses than the corresponding static 3D-organoid-only cultures. CONCLUSIONS: Overall, this model is an effective tool for predicting patients' responses to chemotherapy and testing tumor- or stroma-targeted alternative therapies. Moreover, these high-fidelity, low-throughput EAC chips effectively complement high-throughput PDO culture-based drug testing and provide improved insights into drug efficacy before human studies.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".