Abstract A064: High-throughput organoid drug screening to identify molecular vulnerabilities in pancreatic cancer
Bibliographic record
Abstract
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.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".