Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective, and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we found that PDO therapeutic profiles paralleled patient outcomes and that PDOs enabled longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection. New approaches to prioritize treatment strategies are urgently needed to improve survival and quality of life for patients with pancreatic cancer. Combined genomic, transcriptomic, and therapeutic profiling of PDOs can identify molecular and functional subtypes of pancreatic cancer, predict therapeutic responses, and facilitate precision medicine for patients with pancreatic cancer. .

High-density SNP genotyping arrays can be designed for any species given sufficient sequence information of high quality. Two high-density SNP arrays relying on the Infinium iSelect technology (Illumina) were designed for use in the conifer white spruce (Picea glauca). One array contained 7338 segregating SNPs representative of 2814 genes of various molecular functional classes for main uses in genetic association and population genetics studies. The other one contained 9559 segregating SNPs representative of 9543 genes for main uses in population genetics, linkage mapping of the genome and genomic prediction. The SNPs assayed were discovered from various sources of gene resequencing data. SNPs predicted from high-quality sequences derived from genomic DNA reached a genotyping success rate of 64.7%. Nonsingleton in silico SNPs (i.e. a sequence polymorphism present in at least two reads) predicted from expressed sequenced tags obtained with the Roche 454 technology and Illumina GAII analyser resulted in a similar genotyping success rate of 71.6% when the deepest alignment was used and the most favourable SNP probe per gene was selected. A variable proportion of these SNPs was shared by other nordic and subtropical spruce species from North America and Europe. The number of shared SNPs was inversely proportional to phylogenetic divergence and standing genetic variation in the recipient species, but positively related to allele frequency in P. glauca natural populations. These validated SNP resources should open up new avenues for population genetics and comparative genetic mapping at a genomic scale in spruce species.

Pancreatic ductal adenocarcinoma (PDAC) is the most lethal common malignancy, with little improvement in patient outcomes over the past decades. Recently, subtypes of pancreatic cancer with different prognoses have been elaborated; however, the inability to model these subtypes has precluded mechanistic investigation of their origins. Here, we present a xenotransplantation model of PDAC in which neoplasms originate from patient-derived organoids injected directly into murine pancreatic ducts. Our model enables distinction of the two main PDAC subtypes: intraepithelial neoplasms from this model progress in an indolent or invasive manner representing the classical or basal-like subtypes of PDAC, respectively. Parameters that influence PDAC subtype specification in this intraductal model include cell plasticity and hyperactivation of the RAS pathway. Finally, through intratumoral dissection and the direct manipulation of RAS gene dosage, we identify a suite of RAS-regulated secreted and membrane-bound proteins that may represent potential candidates for therapeutic intervention in patients with PDAC. SIGNIFICANCE: Accurate modeling of the molecular subtypes of pancreatic cancer is crucial to facilitate the generation of effective therapies. We report the development of an intraductal organoid transplantation model of pancreatic cancer that models the progressive switching of subtypes, and identify stochastic and RAS-driven mechanisms that determine subtype specification.

Pancreatic cancer is the most lethal common solid malignancy. Systemic therapies are often ineffective and predictive biomarkers to guide treatment are urgently needed. We generated a pancreatic cancer patient-derived organoid (PDO) library that recapitulates the mutational spectrum and transcriptional subtypes of primary pancreatic cancer. New driver oncogenes were nominated and transcriptomic analyses revealed unique clusters. PDOs exhibited heterogeneous responses to standard-of-care chemotherapeutics and investigational agents. In a case study manner, we find that PDO therapeutic profiles paralleled patient outcomes and that PDOs enable longitudinal assessment of chemosensitivity and evaluation of synchronous metastases. We derived organoid-based gene expression signatures of chemosensitivity that predicted improved responses for many patients to chemotherapy in both the adjuvant and advanced disease settings. Finally, we nominated alternative treatment strategies for chemorefractory PDOs using targeted agent therapeutic profiling. We propose that combined molecular and therapeutic profiling of PDOs may predict clinical response and enable prospective therapeutic selection. Citation Format: Herve Tiriac, Pascal Belleau, Dannielle Engle, Dennis Plenker, Astrid Deschenes, Tim Somerville, Fieke Froeling, Richard Moffitt, Jennifer Knox, Alexander Krasnitz, Steven Gallinger, David Tuveson. Organoid profiling identifies common responders to chemotherapy in pancreatic cancer [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr C57.