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Record W4311708185 · doi:10.1093/bjs/znac404.035

OGC O03 Patient-derived oesophageal adenocarcinoma organoids may predict response to induction therapies in oesophageal cancer

2022· article· en· W4311708185 on OpenAlexaff
Jarlath Bolger, Jonathan Allen, Nikolina Radulovich, Christine Ng, Frances Allison, Yvonne Bach, Premalatha Shathasivam, Ming‐Sound Tsao, Elena Elimova, Gail Darling, Jonathan Yeung

Bibliographic record

VenueBritish journal of surgery · 2022
Typearticle
Languageen
FieldMedicine
TopicEsophageal Cancer Research and Treatment
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineIrinotecanInternal medicinePaclitaxelOncologyOrganoidNeoadjuvant therapyAdenocarcinomaCancerCisplatinTaxaneComplete responseGastroenterologyChemotherapyColorectal cancerBreast cancer

Abstract

fetched live from OpenAlex

Abstract Background The current management of locally-advanced oesophageal adenocarcinoma (OAC) includes neoadjuvant therapy; however, there are no robust markers that predict treatment response. While 25% of patients will have a complete pathological response, up to 40% will have little or no response. Identification of this non-responsive subgroup prior to treatment and robust testing with alternative drug panels may help to generate personalised induction regimens. Patient derived organoids have shown some promise in other cancers as a model for personalised therapy. The aim of this study is to determine the feasibility of utilising PDOs to predict response to induction therapy. Methods PDOs were generated from endoscopic biopsies taken pre-treatment in patients with locally advanced (LA) or metastatic (M) esophageal cancer. For those with LA disease, samples were also taken post-resection. PDOs were established, passaged, then treated with a drug panel of platinum-based drugs, taxane-based drugs, topoisomerase inhibitors and 5-flurouracil. Treatment response curves and growth metrics were mapped back to treatment response, based on pathological tumour regression grade in the LA group and clinical response based on cross-sectional imaging in the M group. Results 19 organoids from 7 LA tumours[YJ1] [JB2] and 10 organoids from 8 M tumours were treated. For LA PDOs, there were significant correlations between cisplatin IC50 (p=0.007), EC50 (p=0.002) and TRG. There was a correlation between paclitaxel AUC and TRG (p=0.02). For PDOs treated with irinotecan, there was an in-vitro response in 45% of organoids which had no clinical response to induction therapy. None of these patients received a topoisomerase inhibitor in their induction therapy. For PDOs treated with 5-FU, 41% of those with no response to induction therapy showed an in vitro response to treatment. Those organoids showing a response had not received 5-FU as part of induction. For M PDOs, there was a correlation between cisplatin and clinical response for AUC (p=0.04), and a trend for IC50 (p=0.07). There were also correlations between paclitaxel and clinical response for IC50 (p=0.04) and AUC (p=0.01). There were no correlations with irinotecan or 5-FU in this subset. These results raise the possibility that the response of PDOs to drug treatments in vitro may in future be used to develop personalised drug induction regimens for patients with esophageal cancer. Conclusions Treatment responses of OAC PDOs treated in vitro with standard chemotherapeutic agents may predict clinical response in the corresponding patient's tumor. A PDO model may form the basis for screening therapeutic agents in the neoadjuvant window, allowing the development of truly personalised neoadjuvant strategies. Further in vitro and in vivo testing is warranted to determine how these models may be applied to a clinical context.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.252
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.031
GPT teacher head0.290
Teacher spread0.259 · 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.

Study designObservational
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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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