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。

• 批准号: 10212771
• 负责人: Anil Korkut
• 金额: $ 54.05万
• 依托单位: OREGON HEALTH & SCIENCE UNIVERSITY
• 依托单位国家: 美国
• 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/10212771
• 关键词: Algorithms; Anatomy; Automobile Driving; Bioinformatics; Biological; 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; 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; triple-negative invasive breast carcinoma; tumor; tumor heterogeneity; tumor-immune system interactions; virtual

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.

摘要在三阴性乳腺癌和高级别浆液性卵巢癌中，耐药的出现 实际上不可避免，并导致长期患者结果令人沮丧。该小组将测试 假设肿瘤生态系统迅速适应治疗产生的压力，导致肿瘤的快速生长。 抵抗的出现。作为一个必然的结果，阻断肿瘤细胞和免疫系统的适应性反应， 微环境将阻断耐药性的出现。目标是监测 跨时间和空间尺度的适应性反应，单细胞精度，预测对未经测试的 组合扰动，并验证预测的药物组合，为未来的临床试验提供动力。交互式 我们已经组建了一个拥有不同和互补专业知识和长期合作历史的团队：癌症和 系统生物学和治疗学（米尔斯，联系PI，OHSU），计算生物学/图像分析（Korkut，PI， MDACC; Goelop，OHSU），生物信息学和系统生物学（Liang，PI，MDACC），单细胞转录组学 和蛋白质组学（Mohammed，OHSU），分子和解剖病理学（Corless，OHSU; Sahin，MDACC），以及 卵巢癌和乳腺癌转化研究（Westin，MDACC; Mitri，OHSU）。我们将追踪两个具体的 目标。目标1。开发新的算法来创建对治疗压力的适应性反应的机制图。 该团队将创新算法，以建立涵盖肿瘤细胞信号传导的数据驱动和预测模型， 微环境和免疫调节。一个广泛的预先存在的细胞纵向蛋白质组学数据集 细胞系、异种移植物、新型鼠可移植同基因模型、PDX和患者样品将作为 实验数据和约束驱动模型构建。建模方法将识别细胞 脆弱性产生的适应性反应，治疗压力和预测的反应， 组合扰动该团队还将确定治疗靶向是否能从蛋白质组学上“引导” 将异质性肿瘤转化为更易于治疗的同质状态。为此，我们将使用国家- 最先进的多路复用成像为基础的蛋白质组学技术，以制定和实施数据驱动的模型 在空间和单细胞的精度。单细胞，数据驱动的建模将展示靶向治疗如何 改变肿瘤和免疫微环境，导致新的靶向治疗 或免疫疗法组合可以利用。目标2.以适应性为靶点的合理药物组合 在相关环境中对治疗的反应。研究小组将使用细胞系、异种移植物、PDX和新型鼠 可移植的同基因模型，以验证合理药物组合的治疗可处理性 目标1下的数据驱动模型预测。重要的是，实验评估将告知和 通过迭代数据采集和随后的重建来改进计算模型。新疗法 将通过其他基金支持的临床试验对联合用药进行评估。新出现的原则和工具 非常适用于其他癌症谱系，并可提供广泛的益处。