PROJECT 2: Development and Refinement of Predictive Models for Designing Immunotherapy Combination Treatments

项目2项目概要 皮肤黑色素瘤成为靶向治疗的早期例子， 第一个BRAFV 600 MUT特异性抑制剂（BRAFi）vemurafenib的开发。对BRAFi的抗性是常见的2， 并且最初归因于重新激活MAPK途径信号传导的癌细胞内在因子3 - 7。BRAFi在 与MEKi8的组合被开发用于对抗这种耐药性，但只有四分之一的患者接受MEKi8治疗， 这种组合存活了五年。事实上，最近的数据表明，癌细胞的外在因素，包括 免疫因子3，10 - 13，在对MAPK途径抑制剂的抗性发展中起重要作用，因此 强调肿瘤免疫微环境（TIME）的作用。引人注目的是，在同基因黑色素瘤模型中， 对MAPKi和免疫检查点阻断（ICB）产生抗性，导入ICB可以"引发" 当ICB随后与以下药物联合时，原发肿瘤和远端转移灶均需根除 MAPKi14.虽然这表明基于免疫的策略，如ICB或过继细胞疗法（ACT）， 用作顺序组合剂以防止MAPKi抗性。然而，它也大大复杂化了 候选治疗方案的设计，因为需要测试多个序列和序列定时。 这可能使临床试验设计不切实际。我们建议开发的方法，应用迭代和积极的 学习使用空间和时间多组学分析进行深度表型分析，以产生预测性计算机模型 可以为设计序贯免疫疗法-靶向抑制剂联合疗法提供指导。. 项目2的一个关键要素是多尺度基于代理的模型（ABM）的迭代开发， 时间的代表。ABM最初是根据现有数据构建的，包括 生物库肿瘤标本和公共组学数据库，以及我们在癌症研究领域的丰富经验， 基因组图谱（TCGA）。然后，它们通过系统生物学启发的定量迭代循环进化， 实验，分析，建模和验证，从项目1和2的实验数据中提取。

Project 2 Project Summary Cutaneous melanoma became an early example for treatment with targeted therapy with the clinical development of the first BRAFV600MUT-specific inhibitor (BRAFi), vemurafenib1. Resistance to BRAFi is common2, and was initially ascribed to cancer cell intrinsic factors that reactivate MAPK pathway signaling3–7. BRAFi in combination with MEKi8 was developed to combat such resistance, but only a quarter of patients treated with this combination survive for five years9. In fact, recent data suggest that cancer cell-extrinsic factors, including immune factors3,10–13, can play important roles in resistance development to MAPK pathway inhibitors, thus highlighting the role of the tumor-immune microenvironment (TIME). Strikingly, in syngeneic melanoma models that develop resistance against both MAPKi and immune checkpoint blockade (ICB), lead-in ICB can ‘prime’ both the primary tumor and distal metastases for eradication when the ICB is subsequently combined with MAPKi14. While this suggests that immune based strategies, such as ICB or adoptive cell therapy (ACT), can serve as sequential combinatorial agents to prevent MAPKi resistance. However, it also significantly complicates the design of candidate treatment regimens, since multiple sequences and sequence timings need to be tested. This can make clinical trials design impractical. We propose to develop methods that apply iterative and active learning to deep phenotyping with spatial and temporal multi-omics assays to yield predictive in silico models that can provide guidance for designing sequential immunotherapy - targeted inhibitor combination therapies. . A key element of Project 2 is the iterative development of multiscale Agent Based Models (ABMs) as a virtual representation of the TIME. ABMs are initially constructed from existing data, including preliminary results from biobanked tumor specimens and public omics data bases, and from our extensive experience within the Cancer Genome Atlas (TCGA). They are then evolved through a systems biology-inspired iterative cycle of quantitative experimentation, analysis, modeling, and validation, drawing from experimental data from both Projects 1 and 2.