Generation and Characterization of Patient‐Derived Head and Neck, Oral, and Esophageal Cancer Organoids
Bibliographic record
Abstract
Abstract Esophageal cancers comprise adenocarcinoma and squamous cell carcinoma, two distinct histologic subtypes. Both are difficult to treat and among the deadliest human malignancies. We describe protocols to initiate, grow, passage, and characterize patient‐derived organoids (PDO) of esophageal cancers, as well as squamous cell carcinomas of oral/head‐and‐neck and anal origin. Formed rapidly (<14 days) from a single‐cell suspension embedded in basement membrane matrix, esophageal cancer PDO recapitulate the histology of the original tumors. Additionally, we provide guidelines for morphological analyses and drug testing coupled with functional assessment of cell response to conventional chemotherapeutics and other pharmacological agents in concert with emerging automated imaging platforms. Predicting drug sensitivity and potential therapy resistance mechanisms in a moderate‐to‐high throughput manner, esophageal cancer PDO are highly translatable in personalized medicine for customized esophageal cancer treatments. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1 : Generation of esophageal cancer PDO Basic Protocol 2 : Propagation and cryopreservation of esophageal cancer PDO Basic Protocol 3 : Imaged‐based monitoring of organoid size and growth kinetics Basic Protocol 4 : Harvesting esophageal cancer PDO for histological analyses Basic Protocol 5 : PDO content analysis by flow cytometry Basic Protocol 6 : Evaluation of drug response with determination of the half‐inhibitory concentration (IC 50 ) Support Protocol : Production of RN in HEK293T cell conditioned medium
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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 | 0.002 |
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".