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

项目2项目总结 皮肤黑色素瘤成为临床靶向治疗的早期例子 开发第一个BRAFV600MUT特异性抑制剂(BRAFi)，vemurafenib1。对BRAFi的抵抗是很常见的， 最初被归因于癌细胞内在因素，重新激活MAPK通路信号3-7。 与MEKi8联合治疗是为了对抗这种耐药性，但只有四分之一的患者接受了 这一组合可持续五年。事实上，最近的数据表明，癌细胞-外在因素，包括 免疫因子3、10-13在MAPK途径抑制剂耐药中发挥重要作用 突出肿瘤免疫微环境(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)都产生抵抗力，导入ICB可以 原发灶和远端转移瘤均需在随后联合ICB时根治 MAPKi14.虽然这表明基于免疫的策略，如ICB或过继细胞疗法(ACT)，可以 作为预防MAPKi耐药的序贯组合制剂。然而，它也使情况变得非常复杂 候选治疗方案的设计，因为需要测试多个序列和序列计时。 这可能会使临床试验设计不切实际。我们建议开发应用迭代和主动的方法 学习通过空间和时间多组学分析进行深度表型分析，以在计算机模型中产生预测性 这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的一个关键要素是将基于多尺度代理的模型(ABM)迭代开发为 代表当时的时间。ABM最初是根据现有数据构建的，包括以下内容的初步结果 生物库肿瘤标本和公共组学数据库，以及我们在癌症领域的广泛经验 基因组图谱(TCGA)。然后，它们通过系统生物学启发的量化迭代循环进行进化 实验、分析、建模和验证，来自项目1和项目2的实验数据。

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.