Abstract 168: Esophageal adenocarcinoma-on-a-chip; modeling patient specific disease progression and a step towards functional precision oncology
Bibliographic record
Abstract
Abstract Introduction: Esophageal cancer causes sixth most cancer-related morbidity and mortality worldwide, with a survival rate of <20%. Incidence of esophageal adenocarcinoma (EA) subtype has been rising (>60%) in North America. Systemic docetaxel-based triplet chemotherapy represents the best standard of care, approx. 60% of patients showcasing innate chemo-resistance or developing acquired chemo-resistance. 3D organoids provide a robust and heterogeneous cell source but lack the stromal microenvironment. We established a high-fidelity, stromal-inclusive tumor-on-chip platform expanding on tumor heterogeneity with micro-physiological relevance and flexible complexity. Methods: A total of 8 (4 chemo-sensitive and 4 chemo-resistant) treatment naïve patient-derived organoids (PDOs) and matched fibroblasts were selected to develop syngeneic human esophageal microtissues (tumor and adjacent) on commercially available (Emulate), compartmentalized, porous polydimethylsiloxane (PDMS) membrane-based microfluidic device. Chips accommodate organoids-derived epithelial cells at the upper mucosal channel and matched fibroblasts at the bottom stromal channel. 3D-microtissue development on the chip and treatment-induced cytotoxicity were examined for up to 11 days. We followed the FLOT (docetaxel, oxaliplatin, and 5-fluorouracil; 1:1.7:52) based chemotherapy regime on the chip and evaluated the recapitulation of patient-specific response. Epithelial barrier integrity, adenocarcinoma-associated cellular proteins, and level of cyfra 21-1 (circulating tumor biomarker) were determined on-chip grown microtissues. Results: Real-time brightfield microscopic observations revealed that the EA chips manifest mucin production, adequate tissue tight epithelial barriers, and distinct morphological characteristics that emerged with adenocarcinoma progression. Presence of pleomorphic cells (multiple shapes and sizes), establishment of tight epithelial barriers and formation of mature microvilli was observed via electron microscopy. Patient specific tumor tissue growth were observed for maximum 12 days under a physiologically relevant media flow. Perfusion of pharmacokinetically pertinent doses of triplet chemotherapeutic compounds performed through the stromal channel and each chip recapitulated level of chemosensitivity observed in the patient. Fluorescent probes and LDH assay also demonstrated patient-specific chemo response. Confocal imaging revealed differential expression of proliferation (ki67), epithelial (CK7, E-cadherin), and mesenchymal (Vimentin) markers in each patient-derived chip. Conclusions: Human esophageal adenocarcinoma-on-a-chip is a novel, most human-relevant biomimetic platform built on a microfluidic chip and a potential alternative to inadequate pre-clinical animal models in the future. Citation Format: Sanjima Pal, Elee Shimshoni, Salvador Flores-Torres, Julie Bérubé, Kulsum Tai, Iris Kong, Betty Giannias, Sean Hall, Nicholas Bertos, Veena Sangwan, Donald E. Ingber, Lorenzo Ferri. Esophageal adenocarcinoma-on-a-chip; modeling patient specific disease progression and a step towards functional precision oncology [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 168.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".