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Abstract A064: High-throughput organoid drug screening to identify molecular vulnerabilities in pancreatic cancer

2022· article· en· W4309191349 on OpenAlexaff
Irene Y. Xie, Zhen-Mei Liu, Karen Ng, Eugenia Flores‐Figueroa, Gun Ho Jang, Amy X. Zhang, Stephanie Ramotar, Anna Dodd, Julie M. Wilson, Grainne M. O’Kane, Jennifer J. Knox, Steven Gallinger, Faiyaz Notta

Bibliographic record

VenueCancer Research · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsPrincess Margaret Cancer CentreOntario Institute for Cancer ResearchUniversity of Toronto
Fundersnot available
KeywordsOrganoidKRASPancreatic cancerDrug repositioningDrug discoveryCancerCDKN2AMedicineDrugTranscriptomeTargeted therapyBiologyComputational biologyBioinformaticsOncologyCancer researchInternal medicinePharmacologyGeneColorectal cancerGenetics

Abstract

fetched live from OpenAlex

Abstract Pancreatic ductal adenocarcinoma (PDAC) is a lethal cancer that typically presents at the advanced or metastatic stage. Although some patients achieve partial responses with chemotherapy, most tumors progress rapidly and become resistant to therapy. Finding targeted therapy for PDAC remains a major challenge due to the therapeutic intractability of the four major drivers (KRAS, TP53, CDKN2A, SMAD4), and the diversity in secondary alterations driven by genomic instability. We hypothesize that this genomic complexity exposes unique molecular vulnerabilities in each patient which may be uncovered by phenotypic screening. Patient derived organoids are a novel 3-D cell culture model which can reliably expand tumors ex vivo and recapitulate genomic, transcriptomic, and drug response characteristics of the patient. Most organoid work has focused on predicting patient response to standard chemotherapy or a small number of targeted therapies, but to date there has been no effort to systematically profile a large number of agents with integrated genomic and transcriptomic data. This will allow us to define the molecular basis of patient-specific drug vulnerabilities and identify biomarkers for drug repurposing and discovery specific to PDAC. To define the functional genomic landscape of drug response in PDAC, we optimized a high-throughput drug screening platform for rapid drug profiling in organoid models with matched WGS and RNAseq. Retrospective screening of 2935 FDA-approved and investigational drugs in a cohort of 58 primary and metastatic organoids identified 799 drugs with cytotoxic activity in at least one organoid. 600 drugs were cherry-picked to enrich for diverse mechanisms of action and dose-response curve validation was performed. Drug sensitivity profiles were highly organoid-specific, and all organoids had unique sensitivities when responses were compared across the cohort. Integration of functional and molecular profiling revealed known gene-drug associations such as MDM2 inhibitor activity in TP53 wildtype organoids, but also identified novel associations such as anagrelide sensitivity in an organoid with high PDE3A expression, which has recently been linked to interactions between PDE3A and the poorly characterized Schlafen 12 protein. Anti-tumor activity of anagrelide validated in a xenograft model. Our work highlights the utility of patient-derived organoids for phenotypic drug screening and biomarker discovery, which we plan to investigate further in an upcoming prospective trial. Citation Format: Irene Y Xie, Zhen-Mei Liu, Karen Ng, Eugenia Flores-Figueroa, Gun Ho Jang, Amy X. Zhang, Stephanie Ramotar, Anna Dodd, Julie Wilson, Grainne M. O'Kane, Jennifer J. Knox, Steven Gallinger, Faiyaz Notta. High-throughput organoid drug screening to identify molecular vulnerabilities in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A064.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.380
Teacher spread0.345 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2022
Admission routes1
Has abstractyes

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