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

总结。在三阴性乳腺癌和高级别浆液性卵巢癌中，出现耐药 接受治疗几乎是不可避免的，也是导致患者长期结局糟糕的原因之一。该团队将测试 假设肿瘤生态系统迅速适应治疗产生的压力，导致快速 抵抗力的出现。作为推论，阻断肿瘤细胞和免疫系统的适应性反应 微环境将阻断抗性的出现。目标是监控潜在的机制 具有单细胞精度的跨时间和空间尺度的自适应响应，预测未经测试的响应 组合扰动，并验证预测的药物组合，推动未来的临床试验。一个互动的 具有多样化和互补性的专业知识和长期合作历史的团队已经组成：癌症和 系统生物学和治疗学(Mills，Contact Pi，OHSU)，计算生物学/图像分析(Korkut，Pi， 生物信息学和系统生物学(梁、派、MDACC)、单细胞转录学 和蛋白质组学(Mohammed，OHSU)、分子和解剖病理学(Corless，OHSU；Sahin，MDACC)，以及 卵巢癌和乳腺癌的转化研究(威斯汀，MDACC；Mitri，OHSU)。我们将追求两个具体的目标 目标。目的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，以建立包含肿瘤细胞信号的数据驱动和预测模型， 微环境，和免疫调节。广泛存在的细胞纵向蛋白质组学数据集 细胞系、异种移植、新的小鼠可移植同基因模型、PDX和患者样本将作为 实验数据和约束驱动模型构建。建模方法将识别细胞 对治疗压力的适应性反应引起的脆弱性和对未经测试的反应的预测 组合摄动。该团队还将确定治疗靶向是否在蛋白质组学上起到了“引导”作用 异质肿瘤转变为更易于治疗的同质状态。为此，我们将使用州- 最先进的基于多路成像的蛋白质组学技术来制定和实现数据驱动的模型 在空间和单元格精度上。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，导致治疗脆弱性即新的靶向治疗 或者免疫疗法组合可以利用。目的2.验证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。该团队将使用细胞系、异种移植、PDX和新的小鼠 用于验证合理药物组合的治疗可操作性的可移植同基因模型 由目标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后续重塑来改进计算模型。新疗法 组合将通过由其他基金支持的临床试验进行评估。新兴的原则和工具 高度适用于其他癌症谱系，并可能提供广泛的好处。

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.