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Record W3015654418 · doi:10.1002/cpsc.109

Generation and Characterization of Patient‐Derived Head and Neck, Oral, and Esophageal Cancer Organoids

2020· article· en· W3015654418 on OpenAlexaff
Tatiana A. Karakasheva, Takashi Kijima, Masataka Shimonosono, Hisatsugu Maekawa, Varun Sahu, Joel Gabre, Ricardo Cruz‐Acuña, Véronique Giroux, Veena Sangwan, Kelly A. Whelan, Shoji Natsugoe, Angela J. Yoon, Elizabeth Philipone, Andres J. Klein–Szanto, Gregory G. Ginsberg, Gary W. Falk, Julian A. Abrams, Jianwen Que, Devraj Basu, Lorenzo Ferri, J. Alan Diehl, Adam J. Bass, Timothy C. Wang, Anil K. Rustgi, Hiroshi Nakagawa

Bibliographic record

VenueCurrent Protocols in Stem Cell Biology · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Cells and Metastasis
Canadian institutionsMcGill UniversityMontreal General HospitalUniversité de Sherbrooke
FundersNational Institute of Dental and Craniofacial ResearchNational Institute of Diabetes and Digestive and Kidney DiseasesNational Institute on Alcohol Abuse and AlcoholismUniversity of PennsylvaniaMinistry of Education, Culture, Sports, Science and TechnologyNational Institute of Environmental Health SciencesAmerican Cancer SocietyNational Cancer InstituteNational Science Foundation
KeywordsEsophageal cancerOrganoidCancerMedicineHead and neck cancerCancer researchAdenocarcinomaOncologyPathologyBiologyInternal medicine

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.080
GPT teacher head0.352
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations94
Published2020
Admission routes1
Has abstractyes

Explore more

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