Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor: Immunotherapy Combinations (ST-Analytics)

整体项目总结 建议的U54程序时空肿瘤分析用于指导序贯靶向抑制物-- 免疫疗法组合(ST-Analytics)旨在发展最近的概念进展，即 靶向抑制物+癌症免疫疗法(IT)的联合治疗可能会产生更多的患者 如果这些治疗是按顺序进行的，而不是同时进行的，那么就会受益。回溯性分析 临床数据与同种小鼠黑色素瘤模型体内治疗模型的结合 支持这一概念。事实上，在黑色素瘤中出现的情况是免疫因素可以起到很强的作用 在MAPK抑制剂(MAPKi)治疗中的作用，以及引入免疫检查点阻断(ICB) 可以为根除原发肿瘤和远端转移瘤(包括脑转移瘤)做好准备 随后将其与MAPKi相结合。这一观察结果为基于免疫的策略打开了大门，例如 如ICB或过继细胞疗法(ACT)，作为预防MAPKi耐药的序贯组合药物。 然而，这个概念引入了许多新的变量，包括剂量、顺序和时间。这可以 使设计和执行可以产生统计意义重大结果的临床试验变得不切实际。这 是我们在建议的ST-Analytics U54中解决的科学和翻译问题。 ST-Analytics U54中心由加州大学洛杉矶分校格芬医学院的顶尖科学家组成， 和耶鲁大学，由两个研究项目和两个研究核心组成，每个项目将两者整合在一起 最先进的实验和计算工作。此结构进一步设计为将 科学、实验、计算和管理资源，以开发捕获 引入单一疗法肿瘤启动动力学，并将该数据库应用于预测的发展。 可以为设计这样的靶向抑制剂-免疫治疗序列组合提供信息的计算机模型 临床试验。这需要在最先进的实验、 前沿计算和现实的疾病模型，通过高度的分析不断校准 相关的，活组织检查的患者肿瘤。由此产生的科学也为高影响力提供了令人兴奋的机会 向外伸展。我们建议通过利用长期的系统教育来抓住这些机会 ISB的外展计划已经影响了所有50个州的K-12 STEM教育，并将 将重点放在那些历史上在STEM中代表性不足的社区。

Overall Project Summary The proposed U54 program Spatiotemporal Tumor Analytics for Guiding Sequential Targeted-Inhibitor -- Immunotherapy Combinations (ST-Analytics) is designed to develop the recent conceptual advance that targeted inhibitor + cancer immunotherapy (IT) combination treatments may yield significantly greater patient benefit if those treatments are administered in sequence rather than simultaneously. Analysis of retrospective clinical data coupled with in vivo therapeutic modeling using syngeneic models of murine melanoma strongly support this concept. In fact, the picture that has emerged in melanoma is that immune factors can play a strong role in driving resistance to MAPK inhibitor (MAPKi) therapy, and that lead-in immune checkpoint blockade (ICB) can ‘prime’ both the primary tumor and distal metastases (including brain metastases) for eradication when the IT is subsequently combined with MAPKi. This observation opens the doors for immune based strategies, such as ICB or adoptive cell therapy (ACT), as sequential combinatorial agents to prevent MAPKi resistance. However, this concept introduces a number of new variables, including dosing, sequence, and timing. This can make the design and execution of clinical trials that can yield statistically significant outcomes impractical. This is the scientific and translational problem we address in the proposed ST-Analytics U54. The ST-Analytics U54 center is populated by leading scientists at the ISB, the UCLA Geffen School of Medicine, and Yale, and is comprised of two research projects and two research cores, with each project integrating both state-of-the-art experimentation and computational work. This structure is further designed to bring together the scientific, experimental, and computational and administrative resources to develop a data base that captures the kinetics of lead-in monotherapy tumor priming, and apply that data base to the development of predictive in silico models that can inform the design of such targeted inhibitor – immunotherapy sequence combinations for clinical trials. This requires close integration and cycles of iteration between of state-of-the-art experimentation, leading edge computation, and realistic disease models, continuously calibrated through the analysis of highly relevant, biopsied patient tumors. The resulting science also provides exciting opportunities for high impact STEM outreach. We propose to act on those opportunities by leveraging a long-standing systems education outreach program at ISB that already has impacted K-12 STEM education in all 50 states, and places an emphasis on those communities that have been historically under-represented in STEM.