Abstract SY36-02: Human cancer models: From patient-derived systems to clinical translation

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains one of the most lethal malignancies, with most patients diagnosed at advanced stages and limited therapeutic options. The aggressive biology and substantial molecular heterogeneity of PDAC underscore the need for approaches that enable more personalized treatment selection. Patient-derived organoids (PDOs) have emerged as powerful in vitro tumor model that retains key genetic, transcriptional, and phenotypic features of the tumors from which they are derived. As such, PDOs provide an opportunity to bridge fundamental cancer biology with translational and clinical applications, including functional precision medicine. Our work focuses on integrating PDO models into prospective clinical studies of PDAC, most prominently through the PASS-01 clinical trial, a multi-institutional study of patients with stage IV disease. Within this trial, biopsies were collected for molecular correlatives including PDO establishment across six institutions in the United States and Canada. A total of 186 biopsies from 183 enrolled patients were shipped to Cold Spring Harbor Laboratory for PDO generation and characterization. Biopsy samples originated primarily from liver metastases but also included primary pancreatic tumors and other metastatic sites such as peritoneal, omental, lymph node, lung, and brain lesions. KRAS mutation analysis using droplet digital PCR was used to confirm neoplastic organoid cultures and identify pseudonormal epithelial outgrowth, which occurred in approximately 25–40% of samples. Overall, malignant PDOs were successfully established from approximately 48% of patient biopsies. Interestingly, successful organoid establishment was associated with patients who experienced more rapid clinical progression, and organoid morphological features also correlated with patient outcomes. Molecular characterization revealed that PDOs were more frequently generated from tumors belonging to the classical transcriptional subtype and from tumors harboring KRAS and TP53 mutations. Established PDO lines were expanded, biobanked, and subjected to high-throughput pharmacotyping against a library of more than 120 standard-of-care and investigational compounds. The median time from tissue receipt to drug screening data was 70 days, with several lines yielding results within two months, enabling pharmacotyping data to be presented during monthly molecular tumor boards. In several cases, PDO drug response profiles were considered alongside genomic and clinical data to help inform second-line therapy decisions following disease progression. Ongoing work integrates PDO pharmacologic response data with genomic and transcriptomic profiling to identify predictive biomarkers of therapy response and resistance. Our results thus far demonstrated some concordance with patient responses to first-line chemotherapy, particularly gemcitabine/nab-paclitaxel. In parallel, PDO models are being used to evaluate emerging targeted therapies, including RAS inhibitors and combination strategies aimed at PDAC vulnerabilities. Together, these studies highlight the potential for patient-derived cancer models to accelerate translational discovery and support clinically actionable precision medicine approaches in pancreatic cancer. Citation Format: Amber N Habowski, Fatim Kouassi, Hardik Patel, James Rouse, Caitlin Tsang, Luce Kelly, Dennis Plenker, Gun Ho Jang, Deepthi Budagavi, Grainne O'Kane, Raditya Utama, Julie Wilson, Anna Dodd, Stephanie Ramotar, Julien Hohenleitner, Kimberly Perez, Robert C Grant, Eileen M O’Reilly, Steven Gallinger, Dan A Laheru, Brian M Wolpin, Andrew J Aguirre, Kenneth H Yu, Elizabeth M Jaffee, Jennifer Knox, Daniel A King, Faiyaz Notta, David A Tuveson. Human cancer models: From patient-derived systems to clinical translation abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts) ; 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86 (8Suppl): Abstract nr SY36-02.