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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

基本信息

批准号：
10684121
负责人：
ALEKSANDER S. POPEL
金额：
$ 52.78万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
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/10684121
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 Drug resistance Ensure Epigenetic Process FOXP3 gene Female Flow Cytometry Goals Hybrids Image Analysis Immune Immune checkpoint inhibitor Immune response Immunocompetent Immunofluorescence Microscopy Immunogenomics Immunologic Markers 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 cancer cell 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 immune modulating agents immunogenic immunotherapy clinical trials in silico inhibitor therapy 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 progression tumor-immune system interactions virtual clinical trial

项目摘要

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免疫治疗和乳腺癌的肿瘤微环境，特别是三阴性乳腺癌是乳腺癌中预后最差的一种，具有高度转移性。亚型，缺乏有效的治疗方法。免疫疗法正在改变癌症治疗的范式，但在乳腺癌患者中，对单剂免疫检查点阻断的应答率较低，而对免疫原性癌症。对免疫-癌症相互作用的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、机械性的临床应用--以及基于实验的乳腺癌计算模型，考虑免疫癌症相互作用，并将其应用于癌症免疫治疗的建模。该项目将是一次密切的合作在计算、临床和实验研究人员之间。制定量化制度。药理学(QSP)基于常微分方程式的模型，包括肿瘤(原发和转移)，淋巴、血液和外周隔间；我们还将制定时空三个- 将描述肿瘤异质性的基于维度代理的和混合的肿瘤模型癌症。配体和药物的传输将由偏微分方程式来模拟。这些产品的数据空间模型将从我们对临床病理图像的计算分析中获得，我们将在确定免疫细胞的空间分布，如CD8+T细胞、调节性T细胞和髓系细胞- 衍生的抑制细胞，以及分子标记，如PD-1、PD-L1、PD-L2、FoxP3和LAG-3。这个分布将用于对模型进行参数化和验证；这些数据的一部分将用作计算模型和用于模型验证的部分。我们将进行最先进的敏感性分析和不确定性量化。计算机代码将以表格形式报告，以便与研究人员分享社区，以确保可重现性。临床数据将来自几种乳腺癌针对免疫检查点CTLA-4、PD-1和PD-L1的免疫治疗临床试验，与免疫调节剂结合，例如表观遗传学。临床数据将补充同基因小鼠原位三阴性乳腺癌模型的实验数据肿瘤，实验方案模仿临床试验。将有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、免疫荧光显微镜、蛋白质阵列和分子生物学。额外的免疫检查站将是通过实验和计算探索，如OX40和LAG-3。这项研究将有助于对乳腺癌生物学的基本了解，有助于识别潜在的生物标志物，并将有助于临床试验的设计和解释。计算、临床和技术的协同组合实验研究将为乳腺癌的免疫治疗提供重要的见解。

项目成果

期刊论文数量（75）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

A computational multiscale agent-based model for simulating spatio-temporal tumour immune response to PD1 and PDL1 inhibition.

DOI：
10.1098/rsif.2017.0320
发表时间：
2017-09
期刊：
Journal of the Royal Society, Interface
影响因子：
0
作者：
Gong C;Milberg O;Wang B;Vicini P;Narwal R;Roskos L;Popel AS
通讯作者：
Popel AS

Simultaneous blockade of IL-6 and CCL5 signaling for synergistic inhibition of triple-negative breast cancer growth and metastasis.

DOI：
10.1186/s13058-018-0981-3
发表时间：
2018-06-14
期刊：
Breast cancer research : BCR
影响因子：
0
作者：
Jin K;Pandey NB;Popel AS
通讯作者：
Popel AS

Breast cancer cells condition lymphatic endothelial cells within pre-metastatic niches to promote metastasis.

DOI：
10.1038/ncomms5715
发表时间：
2014-09-02
期刊：
NATURE COMMUNICATIONS
影响因子：
16.6
作者：
Lee, Esak;Fertig, Elana J.;Jin, Kideok;Sukumar, Saraswati;Pandey, Niranjan B.;Popel, Aleksander S.
通讯作者：
Popel, Aleksander S.

Crosstalk between cancer cells and blood endothelial and lymphatic endothelial cells in tumour and organ microenvironment.