Comparative analysis between patient-derived models of pancreatic ductal adenocarcinomas and matched tumor specimens

患者来源的胰腺导管腺癌模型与匹配肿瘤标本之间的比较分析

• 批准号: 10238080
• 负责人: Jonathan Brody
• 金额: $ 71.43万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-12 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10238080
• 关键词: 3-Dimensional; Affect; Algorithms; Automobile Driving; Bioinformatics; Biologic Characteristic; Biological; Biological Models; Biology; CDKN2A gene; Cancer Biology; Cancer Etiology; Cancer Model; Cell Death; Cell Proliferation; Cells; Cessation of life; Clinical; Clinical Oncology; Clinical Trials; Communities; Complement; Complex; Computational Biology; Computational algorithm; Critical Pathways; DNA Damage; DNA Repair; Data; Database Management Systems; Development; Disease; Disease model; Drug resistance; Elements; Endothelium; Enrollment; Ensure; Epigenetic Process; Generations; Genes; Genetic; Genetic Heterogeneity; Human; Human Characteristics; Immune; Inflammation; Inter-tumoral heterogeneity; KRAS2 gene; Knowledge; Laboratories; Libraries; MADH4 gene; Malignant Neoplasms; Malignant neoplasm of pancreas; Measures; Mesenchymal; Metadata; Metastatic Neoplasm to the Liver; Modeling; Molecular; Molecular Profiling; Mutation; Oncogenic; Oregon; Organoids; Outcome; Pancreas; Pancreatic Adenocarcinoma; Pancreatic Ductal Adenocarcinoma; Parents; Pathology; Pathway interactions; Patient-Focused Outcomes; Patients; Periodicity; Pharmaceutical Preparations; Phenotype; RNA; Regulator Genes; Regulatory Pathway; Research; Resistance; Sampling; Signal Pathway; Specimen; Standardization; Stromal Cells; Survival Rate; Synapses; TP53 gene; Testing; Therapeutic; Translating; Tumor Immunity; United States; Work; arm; base; bioprinting; cancer cell; cell type; chemotherapy; cohort; comparative; data harmonization; design; drug response prediction; drug sensitivity; exome sequencing; improved; in vivo; inhibitor/antagonist; insight; multidisciplinary; multiple omics; neoplastic; neoplastic cell; novel therapeutic intervention; pancreatic cancer model; pancreatic ductal adenocarcinoma model; patient derived xenograft model; patient response; phosphoproteomics; resistance mechanism; response; sample collection; standard of care; targeted treatment; therapeutically effective; three dimensional structure; tissue registry; tool; transcriptome sequencing; tumor; tumor microenvironment; wiki

PROJECT SUMMARY 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.

项目摘要 胰腺导管腺癌（PDA）是致命的癌症，5年生存率<10％；预测 到2020年成为美国与癌症相关死亡的第二大原因。 在许多PDA病例中，基因（KRAS，TP53，CDKN2A和SMAD4）很常见。但是，PDA可以是 表型分类为多种肿瘤亚型，每种都有多种类型的基质和抗肿瘤 免疫。仅在PDA治疗中仅取得了增量的临床进展，这可能是由于 该研究可用的胰腺癌的经过通知和验证的患者衍生模型的匮乏 社区。作为翻译使用患者来源癌症模型（PDMC）的第一步，我们必须确定 每种类型的PDMC的优势和局限性，包括PDMC是否反映了遗传和生物学 人，父肿瘤的特征。在此，我们提出了一个旨在扩展的多机构项目 我们现有的PDA PDMC库，并描绘了哪种模型最能代表其父母的特定方面 肿瘤。捕获肿瘤间异质性并可以维持亲构的调节途径的PDMC 需要至关重要的是，以更好地增强当前疗法并确定新颖的治疗策略。我们是 当前收集PDA标本并生成有条件重新编程的细胞（CRC），类器官（ORG）， 通过俄勒冈州胰腺组织注册表和靶向治疗 （即基于PARP抑制剂）临床试验。生成的PDMC具有良好的临床结果和药物 响应数据。在这里，我们将系统地和彻底配置与PDMC匹配以确定重要性 关键分子网络（包括KRAS，MYC，DDR，HUR和炎症）和表型亚群 最好与患者各自的肿瘤相匹配。我们还将通过添加来构建更复杂的PDMC 在简单的PDMC中，可能会恢复表型的父肿瘤微环境的元素。 补充药物敏感性研究将在简单和复杂的PDMC中进行测试，作为另一个指标 它们与父肿瘤和患者反应的相关性。为了执行这项工作，我们组装了一个多 具有临床肿瘤学，标本收集/加工，病理，癌症模型专业知识的纪律团队 一代，肿瘤微环境，计算生物学，RNA生物学，DNA修复和数据库 管理。工作将以三个特定的目的进行：AIM 1，生成和验证PDMC；确定是否 匹配的父肿瘤可以保留关键的PDA信号通路； AIM 2，从临床上识别PDMC 跟踪的标本可以最好地预测患者的药物反应；识别和靶向电阻的关键途径； AIM 3：确定在简单PDMC中丢失但可以恢复的信号通路和药物反应 通过添加父肿瘤的已知元素（例如，间质，内皮和免疫细胞）。一个 这项研究的总体交付将是共享特征良好的，经过验证的PDMC和分子见解 与胰腺癌社区的PDA生物学和药物反应。