Abstract 3161: High throughput drug screening to uncover molecular vulnerabilities in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Abstract Introduction: High-throughput drug screening enables rapid testing of numerous drugs on tumor samples and has achieved breakthroughs in treatment selection for fatal diseases such as acute myeloid leukemia. This study leverages high-throughput screening to identify molecular vulnerabilities in pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer with a 5-year survival rate of just 13%. Methods: Using PDAC patient-derived organoids (PDOs), we developed a platform to screen approximately 3, 000 compounds at a single dose and 600 compounds across six doses on 120 PDOs. Additionally, we designed a tailored focused screening of 22 drugs at 20 doses, prioritizing Health Canada-approved drugs identified as promising in earlier screenings. To ensure robust data analysis, we developed an optimized bioinformatics pipeline incorporating a three-tier evaluation: Area Under the Curve (AUC), Drug Sensitivity Score (DSS), and maximum efficacy (Emax). Results from each PDO were systematically compared against the entire cohort, ensuring that sensitivity profiles reflect reliable outcomes. Additionally, we integrated drug response data with genomics and transcriptomics obtained through whole-genome and whole-exome sequencing of PDOs. Results: We identified highly potent compounds capable of suppressing tumor growth in over 55% of PDOs, prioritizing clinically approved drugs to facilitate rapid translation to patient care. Drug sensitivity patterns from single-dose screenings were corroborated through six-dose validation and a focused screening platform. By integrating drug response data with multi-omics analyses, we uncovered patient-specific drug response profiles linked to molecular vulnerabilities. Notably, we validated a gene-drug association involving anagrelide, a selective agent that induces cytotoxicity in cancer cells with elevated phosphodiesterase PDE3A levels, highlighting the robustness of our approach. Preliminary findings demonstrate a strong concordance between PDO-derived and patient drug responses, establishing a foundation for actionable clinical insights in the ADOPT trial to guide personalized treatment strategies. Conclusion: In conclusion, our high-throughput drug screening platform, integrated with multi-omics analysis and robust bioinformatics, enables the identification of patient-specific molecular vulnerabilities in PDAC. This approach advances the potential for precision oncology, providing a foundation for tailored therapeutic strategies in this highly lethal cancer. Citation Format: Nikta Feizi, Eugenia Flores-Figueroa, Karen Ng, Zhen-Mei Liu, Farnoosh Abbas Aghababazadeh, Gun Ho Jang, Daniela Bevacqua, Stephanie Ramotar, Shawn Hutchinson, Anna Dodd, Julie Wilson, Robert Grant, Ronan Mclaughlin, Erica Tsang, Jennifer Knox, Steven Gallinger, Benjamin Haibe-Kains, Faiyaz Notta. High throughput drug screening to uncover molecular vulnerabilities in pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3161.
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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".