权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Mechanistic maps of adaptive responses to therapeutic stress to optimize combination therapies.

对治疗应激的适应性反应的机制图，以优化联合疗法。

基本信息

批准号：
10376362
负责人：
Anil Korkut
金额：
$ 51.64万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-01 至 2026-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10376362
关键词：
Algorithms Anatomy Automobile Driving Bioinformatics Breast Cancer Biology Cell Line Cells Clinical Research Clinical Trials Collaborations Combination immunotherapy Combined Modality Therapy Computational Biology Computer Models DNA Damage Data Data Set Data Sources Disease Drug Combinations Drug resistance Ecosystem Funding Funding Mechanisms Future Generations Heterogeneity Image Analysis Immune checkpoint inhibitor Immunofluorescence Immunologic Immunology Immunomodulators In Vitro Isogenic transplantation Lead Libraries MEKs Malignant Neoplasms Malignant neoplasm of ovary Maps Modeling Molecular Monitor Mus Network-based Oncogenic Outcome Ovarian Pathology Pathway interactions Patient-Focused Outcomes Patients Periodicity Pharmaceutical Preparations Pre-Clinical Model Prediction of Response to Therapy Process Proteins Proteomics Recording of previous events Resistance Sampling Serous Signal Transduction Stress Systems Biology Technology Testing Therapeutic Tissues Transplantation University of Texas M D Anderson Cancer Center Validation Xenograft procedure base biological adaptation to stress cancer clinical trial clinical trial implementation clinically relevant combinatorial computerized tools data acquisition data-driven model design drug relapse immune checkpoint immunoregulation improved in vivo in vivo Model innovation insight malignant breast neoplasm mathematical model multiplexed imaging neoplastic cell new therapeutic target novel novel drug combination novel therapeutics pre-clinical assessment predicting response predictive modeling programmed cell death ligand 1 programs resistance mechanism response spatiotemporal targeted treatment therapeutic target therapy resistant tool transcriptomics translational cancer research treatment response triple-negative invasive breast carcinoma tumor tumor heterogeneity tumor-immune system interactions virtual

项目摘要

Summary. In triple-negative breast cancer and high-grade serous ovarian cancer, the emergence of resistance to therapy is virtually inevitable and contributes to dismal long-term patient outcomes. The team will test the hypothesis that tumor ecosystems rapidly adapt to stress engendered by therapies, leading to the rapid emergence of resistance. As a corollary, blocking adaptive responses in tumor cells and the immune microenvironment will interdict the emergence of resistance. The objective is to monitor mechanisms underlying adaptive responses across temporal and spatial scales with single-cell precision, predict responses to untested combinatorial perturbations, and validate predicted drug combinations, fueling future clinical trials. An interactive team with diverse and complementary expertise and long collaboration history has been assembled: cancer and systems biology and therapeutics (Mills, contact PI, OHSU), computational biology/image analysis (Korkut, PI, MDACC; Goecks, OHSU), bioinformatics and systems biology (Liang, PI, MDACC), single-cell transcriptomics and proteomics (Mohammed, OHSU), molecular and anatomic pathology (Corless, OHSU; Sahin, MDACC), and ovarian and breast cancer translational research (Westin, MDACC; Mitri, OHSU). We will pursue two specific aims. Aim 1. Develop novel algorithms to create mechanistic maps of adaptive responses to therapeutic stress. The team will innovate algorithms to build data-driven and predictive models encompassing tumor cell signaling, microenvironment, and immune modulation. An extensive pre-existing longitudinal proteomics dataset of cell lines, xenografts, novel murine transplantable syngeneic models, PDXs, and patient samples will serve as the experimental data and constraints driving model construction. The modeling approaches will identify cellular vulnerabilities arising from adaptive responses to therapeutic stress and predict responses to untested combinatorial perturbations. The team will also determine whether therapeutic targeting “steers” proteomically heterogeneous tumors to a more therapeutically tractable homogenous state. For this purpose, we will use state- of-the-art multiplexed imaging-based proteomics technologies to formulate and implement data-driven models at spatial and single-cell precision. The single-cell, data-driven modeling will demonstrate how targeted therapies alter the tumor and immune microenvironment, leading to therapeutic vulnerabilities that new targeted therapy or immunotherapy combinations could exploit. Aim 2. Validate rational drug combinations targeting adaptive responses to therapy in relevant settings. The team will use cell lines, xenografts, PDXs, and novel murine transplantable syngeneic models to validate the therapeutic tractability of the rational drug combinations predicted by the data-driven models under Aim 1. Importantly, the experimental assessment will inform and improve the computational models through iterative data acquisition and subsequent remodeling. Novel therapy combinations will be assessed through clinical trials supported by other funds. The emerging principles and tools are highly applicable to other cancer lineages and could provide broad benefits.

总结。在三阴性乳腺癌和高级浆液性卵巢癌中，出现耐药性