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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：
10238909
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/10238909
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

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.

项目总结