Pancreatic ductal adenocarcinoma (PDA) is a lethal cancer, with a 5-year survival rate of < 10%; it is predicted to become the 2nd leading cause of cancer-related deaths in the US by 2020. Somatic alterations of four driver genes (KRAS, TP53, CDKN2A, and SMAD4) are common among many cases of PDA; however, PDA can be phenotypically categorized into multiple neoplastic subtypes, each with myriad types of stroma and anti-tumor immunity. Only incremental clinical advances have been made in the treatment of PDA, potentially due to the paucity of well-annotated and validated patient-derived models of pancreatic cancer available to the research community. As a first step to translating the use of patient-derived models of cancer (PDMCs), we must identify the strengths and limitations of each type of PDMC, including whether PDMCs mirror genetic and biologic characteristics of the human, parent tumor. Herein, we propose a multi-institutional project designed to extend our existing library of PDA PDMCs and depict which model(s) best represent specific aspects of their parent tumors. PDMCs that capture an inter-tumor heterogeneity and can maintain pro-oncogenic regulatory pathways are critically needed to better enhance current therapies and identify novel therapeutic strategies. We are currently collecting PDA specimens and generating conditionally re-programmed cells (CRC), organoids (ORG), and patient-derived xenografts (PDX) through the Oregon Pancreas Tissue Registry and from a targeted therapy (i.e., PARP inhibitor-based) clinical trial. The PDMCs generated have well-annotated clinical outcomes and drug response data. Here, we will systematically and thoroughly profile matched PDMCs to determine the significance of key molecular networks (including KRAS, MYC, DDR, HuR, and inflammation) and phenotypic subpopulations that best match their respective tumors from patients. We will also build more complex PDMCs by adding elements of the parent tumor microenvironment that can restore phenotypes absent in simple PDMCs. Complementary drug sensitivity studies will be tested in both simple and complex PDMCs as another metric of their relatedness to the parent tumor and patient responses. To perform this work, we have assembled a multi- disciplinary team with expertise in clinical oncology, specimen collection/processing, pathology, cancer model generation, tumor microenvironment, computational biology, RNA biology, DNA repair, and database management. Work will be performed in three specific aims:
Aim 1, generate and validate PDMCs; determine if key PDA signaling pathways are conserved with the matched parent tumor;
Aim 2, identify PDMCs from clinically tracked specimens that best predict drug responses in patients; identify and target key pathways of resistance;
Aim 3 : identify signaling pathways and drug responses that are lost in simple PDMCs but that can be restored by adding known elements of the parent tumor (e.g., stromal mesenchymal, endothelial and immune cells). An overarching deliverable of this study will be to share well-characterized, validated PDMCs and molecular insights into PDA biology and drug responses with the pancreatic cancer community.
Pancreatic adenocarcinoma is one of the most lethal of human cancers and is predicted to become the second leading cause of cancer related deaths in the United States by 2020. Traditional chemotherapy is currently the only recommended treatment, and the 5-year survival rate for PDA is less than 10%. There is therefore a critical need to generate robust patient-derived laboratory models of PDA that can be used to increase our understanding of the biology of this deadly disease and to identify better therapeutic strategies for improving patient outcomes.
