Predictive experiment-based multiscale models of the tumor immune microenvironment and immunotherapy in breast cancer

基于预测实验的肿瘤免疫微环境和乳腺癌免疫治疗的多尺度模型

• 批准号: 10476999
• 负责人: ALEKSANDER S. POPEL
• 金额: $ 51.72万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-02-13 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10476999
• 关键词: 3-Dimensional; Accounting; Animal Model; Animals; Biological Products; Biopsy; Biopsy Specimen; Blood; Breast Cancer Model; Breast Cancer Patient; CD8-Positive T-Lymphocytes; CTLA4 gene; Cancer Biology; Cancer Model; Cells; Clinical; Clinical Data; Clinical Pathology; Clinical Research; Clinical Trials; Clinical Trials Design; Collaborations; Combination immunotherapy; Communities; Comprehensive Cancer Center; Computer Analysis; Computer Models; Computing Methodologies; Conduct Clinical Trials; Data; Data Analyses; Development; Diagnosis; Differential Equation; Dose; Drug Industry; Ensure; Epigenetic Process; FOXP3 gene; Female; Flow Cytometry; Goals; Hybrids; Image Analysis; Immune; Immune checkpoint inhibitor; Immune response; Immunocompetent; Immunofluorescence Microscopy; Immunogenomics; Immunologic Markers; Immunomodulators; Immunotherapy; Laboratories; Ligands; Malignant Neoplasms; Mammary Neoplasms; Medicine; Methodology; Methods; Modality; Modeling; Molecular; Molecular Biology; Mus; Myeloid-derived suppressor cells; Neoplasm Metastasis; OX40; Pathology; Patients; Peripheral; Pharmaceutical Preparations; Pharmacology; Prognosis; Protein Array; Protocols documentation; Radiology Specialty; Regimen; Regulatory T-Lymphocyte; Reporting; Reproducibility; Research; Research Personnel; Research Project Summaries; Resected; Role; Sampling; Source; Spatial Distribution; Specimen; System; Systems Biology; T-Lymphocyte; Tissues; Treatment Protocols; Uncertainty; United States; Validation; animal data; anti-CTLA4; anti-PD-1; anti-PD-L1; base; cancer cell; cancer drug resistance; cancer immunotherapy; cancer subtypes; cancer therapy; chemotherapy; clinical application; cohort; combinatorial; computer code; computer studies; design; drug development; effective therapy; effector T cell; epigenetic drug; experimental study; immune checkpoint; immune checkpoint blockade; immunogenic; immunotherapy clinical trials; in silico; insight; lymph nodes; malignant breast neoplasm; molecular marker; mouse model; multi-scale modeling; novel; objective response rate; outcome prediction; pathology imaging; potential biomarker; predictive marker; programmed cell death ligand 1; programmed cell death protein 1; response; spatiotemporal; therapy development; triple-negative invasive breast carcinoma; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor-immune system interactions; virtual clinical trial

Project Summary This research will focus on immunotherapy and the tumor microenvironment in breast cancer, and particularly triple-negative breast cancer (TNBC), which is highly metastatic, has the worst prognosis among breast cancer subtypes, and is lacking effective therapies. Immunotherapy is changing the paradigm of cancer treatment, but in breast cancer the response rate to single agent immune checkpoint blockade is low, compared to more immunogenic cancers. A quantitative understanding of the complexity of the immune-cancer interactions is presently insufficient. The long-term goal of this project is to develop predictive, mechanistic clinically- and experimentally-based computational models of breast cancer, taking into account the immune-cancer interactions, and apply them to modeling cancer immunotherapy. The project will be a close collaboration between computational, clinical, and experimental researchers. We will formulate quantitative systems pharmacology (QSP) ordinary differential equation-based models comprising tumor (primary and metastasis), lymph nodes, and blood and peripheral compartments; we will also formulate spatio-temporal three- dimensional agent-based and hybrid tumor models that will describe tumor heterogeneity that is a hallmark of cancer. Transport of ligands and drugs will be modeled by partial differential equations. The data for these spatial models will be derived from our computational analysis of clinical pathology images where we will determine the spatial distributions of immune cells, such as CD8+ T cells, regulatory T cells, and myeloid- derived suppressor cells, and molecular markers such as PD-1, PD-L1, PD-L2, FoxP3, and LAG-3. The distributions will be used to parameterize and validate the models; part of these data will serve as the input to computational models and part for model validation. We will conduct state-of-the-art sensitivity analysis and uncertainty quantification. The computer codes will be reported in the form to share with the research community, to ensure reproducibility. The clinical data will be derived from several breast cancer immunotherapy clinical trials in which immune checkpoints CTLA-4, PD-1, and PD-L1 are targeted, in combination with immunomodulating agents, e.g. epigenetic. Clinical data will be supplemented with experimental data obtained from syngeneic mouse models with orthotopic triple-negative breast cancer tumors, with the experimental protocols mimicking the clinical trials. A variety of experimental methods will be used to provide a plethora of data for model parameterization and validation, including flow cytometry, immunofluorescence microscopy, protein arrays, and molecular biology. Additional immune checkpoints will be explored experimentally and computationally, such as OX40 and LAG-3. The research will contribute to a fundamental understanding of breast cancer biology, to the identification of potential biomarkers, and will aid in design and interpretation of clinical trials. The synergistic combination of computational, clinical, and experimental studies will provide significant insights into breast cancer immunotherapy.

项目摘要 这项研究将集中在乳腺癌的免疫治疗和肿瘤微环境，特别是 三阴性乳腺癌（TNBC）是一种高度转移性的乳腺癌，在乳腺癌中预后最差 亚型，缺乏有效的治疗方法。免疫疗法正在改变癌症治疗的模式， 在乳腺癌中，对单一药剂免疫检查点阻断的应答率较低，相比之下， 免疫原性癌症对免疫-癌症相互作用复杂性的定量理解是 目前还不够。该项目的长期目标是开发预测性的，机械的临床-和 基于实验的乳腺癌计算模型，考虑到免疫癌症 相互作用，并将其应用于癌症免疫疗法建模。该项目将是一个密切合作 在计算、临床和实验研究者之间。我们将制定量化系统 药理学（QSP）基于常微分方程的模型，包括肿瘤（原发性和转移）， 淋巴结，血液和外周隔室;我们还将制定时空三- 三维基于代理和混合肿瘤模型，将描述肿瘤异质性，这是一个标志， 癌配体和药物的运输将由偏微分方程建模。这些数据 空间模型将从我们对临床病理学图像的计算分析中得出， 确定免疫细胞的空间分布，例如CD 8 + T细胞、调节性T细胞和髓样- 衍生的抑制细胞和分子标志物，如PD-1、PD-L1、PD-L2、FoxP 3和LAG-3。的 分布将用于参数化和验证模型;这些数据的一部分将作为输入， 计算模型和模型验证部分。我们将进行最先进的敏感性分析， 不确定性量化计算机代码将以表格形式报告，以便与研究人员共享 社区，以确保可重复性。临床数据将来自几种乳腺癌 靶向免疫检查点CTLA-4、PD-1和PD-L1的免疫治疗临床试验， 与免疫调节剂（例如表观遗传）组合。临床数据将补充 从具有原位三阴性乳腺癌的同基因小鼠模型获得的实验数据 肿瘤，实验方案模仿临床试验。将采用多种实验方法 用于为模型参数化和验证提供大量数据，包括流式细胞术， 免疫荧光显微术、蛋白质阵列和分子生物学。额外的免疫检查点将 实验和计算探索，如OX 40和LAG-3。这项研究将有助于 基本了解乳腺癌生物学，以确定潜在的生物标志物，并将有助于 临床试验的设计和解释。计算、临床和 实验研究将为乳腺癌免疫治疗提供重要的见解。