Advancing immunotherapy through cross species studies of immune cell responses and immune checkpoint inhibitor effects in dogs and humans with invasive urinary bladder cancer

通过对患有侵袭性膀胱癌的狗和人类的免疫细胞反应和免疫检查点抑制剂作用的跨物种研究来推进免疫治疗

• 批准号: 10651879
• 负责人: DEBORAH W KNAPP
• 金额: $ 55.25万
• 依托单位: PURDUE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10651879
• 关键词: Address; Advanced Malignant Neoplasm; Adverse event; Affect; Animal Cancer Model; Animal Model; Behavior; Bioinformatics; Cancer Model; Canis familiaris; Cells; Cellular Indexing of Transcriptomes and Epitopes by Sequencing; Clinic; Clinical; Clinical Data; Clinical Trials; Complement; Data; Data Commons; Data Set; Deposition; Disease; Disease remission; Drug Kinetics; Experimental Models; FDA approved; Failure; Genomics; Goals; Health; Health Care Costs; Human; Immune; Immune checkpoint inhibitor; Immune response; Immunobiology; Immunology; Immunotherapy; Invaded; Literature; Malignant Neoplasms; Malignant neoplasm of urinary bladder; Medical Oncology; MicroRNAs; Minority; Mission; Modeling; Molecular; NCI Center for Cancer Research; Nature; Neoplasm Metastasis; Outcome; Pathologic; Pathology; Patients; Persons; Prediction of Response to Therapy; Public Health; Publishing; RNA analysis; Reporting; Research; Safety; Sampling; TYRO3 gene; Transitional Cell Carcinoma; United States; United States National Institutes of Health; Universities; Work; anti-PD-L1 antibodies; cancer heterogeneity; cancer immunotherapy; canine model; checkpoint therapy; comparative; improved; microbiome research; molecular subtypes; neoplastic cell; outcome prediction; pre-clinical; prevent; programmed cell death protein 1; response; single-cell RNA sequencing; success; transcriptome sequencing; treatment response; tumor; tumor microenvironment

Project Summary / Abstract Invasive urinary bladder cancer (invasive urothelial carcinoma, InvUC) is lethal in 50% of patients. Immune checkpoint inhibitors (ICIs) can cause dramatic remission of advanced InvUC, but only ~20% of patients have this level of benefit. Pre-clinical animal models are critical for research to improve ICI outcomes, but experimental models lack many of the hallmark features of human cancer and are poor predictors of outcomes in humans. To address the gap in relevant animal cancer models for immunotherapy research, we will study dogs with naturally-occurring InvUC as canine InvUC closely mimics the human condition in pathology, molecular features including luminal and basal subtypes, clinical presentation, local invasion, and frequent metastasis. The proposed work will strengthen the canine InvUC model by defining immune cell responsiveness and ICI outcomes, with comparison to human studies. Our long range goal is to improve the outlook for people with InvUC. The objective of this proposal is to address the gap by determining the suitability of canine InvUC to serve as a model to improve ICI therapy in humans. The central hypothesis is marked similarities will exist between dogs and humans in the immune cell responses in InvUC, and ICI therapy effects including immune adverse events, antitumor activity, immunological responses, and predictors of treatment success and failure. Some differences between dogs and humans are expected, with these also being informative. The hypothesis is formulated and based on strong evidence in the literature and preliminary data. The rationale is that demonstrating the shared immune cell responses and ICI effects between dogs and humans with InvUC will allow the canine model to be fully employed to improve ICI therapy for humans. The objective will be accomplished through two specific aims: (1) determine similarities and differences between dogs and humans in the immune cell responsiveness to InvUC, and (2) determine the safety and antitumor activity of a canine PD-L1 antibody and predictors of success and failure in dogs with InvUC with comparison to findings in humans. The approach will be to: (1) perform dog-human comparison of InvUC through analyses of RNA-seq, scRNA-seq, WGS, and CITE-seq data, and (2) conduct a clinical trial of an ICI, our canine PD-L1 antibody, in dogs with InvUC to assess antitumor activity, pharmacokinetics, adverse event profile, and correlative sequencing and clinical data to predict outcomes, and to compare results to those from human PD- L1 antibody trials. The expected results will define shared immune cell responses and ICI effects in dogs and humans, expand the understanding of predictors of ICI therapy, and justify use of the canine model to improve ICI and other immunotherapies in humans. Samples from dogs in the ICI trial will also be made available for other immune, microRNA, and microbiome research by our collaborators and beyond. We look forward to contributing to the Canine Cancer Immunotherapy Network and depositing our study data in the NCI's Integrated Canine Data Commons, to complement two InvUC data sets that we have previously deposited.

项目概要/摘要 浸润性膀胱癌（浸润性尿路上皮癌，InvUC）对 50% 的患者来说是致命的。免疫 检查点抑制剂 (ICIs) 可以使晚期 InvUC 显着缓解，但只有约 20% 的患者得到缓解 这个级别的福利。临床前动物模型对于改善 ICI 结果的研究至关重要，但是 实验模型缺乏人类癌症的许多标志性特征，并且不能很好地预测结果 在人类中。为了弥补免疫治疗研究相关动物癌症模型的空白，我们将研究 患有天然 InvUC 的狗，因为犬 InvUC 的病理学状况与人类状况非常相似， 分子特征，包括管腔和基底亚型、临床表现、局部侵袭和常见的 转移。拟议的工作将通过定义免疫细胞来加强犬 InvUC 模型 反应性和 ICI 结果，与人类研究进行比较。我们的长期目标是改善 InvUC 患者的前景。该提案的目的是通过确定适用性来弥补差距 犬 InvUC 作为改善人类 ICI 治疗的模型。中心假设已标记 狗和人类在 InvUC 的免疫细胞反应和 ICI 治疗效果方面存在相似之处 包括免疫不良事件、抗肿瘤活性、免疫反应和治疗预测因素 成功与失败。狗和人类之间存在一些差异是预料之中的，这些差异也被 内容丰富。该假设是基于文献和初步数据中的有力证据而制定的。 理由是证明狗和狗之间共有的免疫细胞反应和 ICI 效应 患有 InvUC 的人类将允许充分利用犬类模型来改善人类的 ICI 治疗。这 目标将通过两个具体目标来实现：（1）确定之间的异同 狗和人类的免疫细胞对 InvUC 的反应性，以及（2）确定其安全性和抗肿瘤性 犬 PD-L1 抗体的活性以及 InvUC 犬成功和失败的预测因子的比较 人类的发现。该方法将是：（1）通过分析对 InvUC 进行狗与人的比较 RNA-seq、scRNA-seq、WGS 和 CITE-seq 数据，以及 (2) 对我们的犬 PD-L1 ICI 进行临床试验 抗体，在患有 InvUC 的狗中评估抗肿瘤活性、药代动力学、不良事件概况和 相关测序和临床数据来预测结果，并将结果与​​人类 PD 的结果进行比较 L1 抗体试验。预期结果将定义狗和动物中共同的免疫细胞反应和 ICI 效应 人类，扩大对 ICI 治疗预测因子的理解，并证明使用犬模型来改善 ICI 和其他人类免疫疗法。 ICI 试验中狗的样本也将提供给 我们的合作者及其他人进行的其他免疫、microRNA 和微生物组研究。我们期待 为犬癌症免疫治疗网络做出贡献并将我们的研究数据存入 NCI 集成犬类数据共享，以补充我们之前存储的两个 InvUC 数据集。