Interrogating the response of the tumor microenvironment to combination immunotherapy using a microfluidic platform

使用微流控平台探究肿瘤微环境对联合免疫疗法的反应

• 批准号: 10397985
• 负责人: ALBERT FOLCH
• 金额: $ 52.69万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-05 至 2026-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10397985
• 关键词: Activation Analysis; Address; Antibodies; Antineoplastic Agents; Attention; Biological Assay; Biopsy; CD8-Positive T-Lymphocytes; Cancer Biology; Cell Communication; Cell Death; Cells; Clinical Trials; Colorectal; Combination immunotherapy; Combined Modality Therapy; Complex; Computer Analysis; Cytotoxic agent; Data; Development; Drug resistance; Endothelial Cells; Extracellular Matrix; Fibroblasts; Gene Expression Profiling; Glioblastoma; Goals; Healthcare; Human; Image; Immobilization; Immune; Immune checkpoint inhibitor; Immunomodulators; Immunooncology; Investigation; Knowledge; Label; Lead; Malignant Neoplasms; Malignant neoplasm of pancreas; Metastatic Neoplasm to the Liver; Methods; Microfluidics; Molecular; Molecular Analysis; Molecular Target; Monoclonal Antibodies; Mus; Neoplasm Metastasis; PD-1/PD-L1; Pathway interactions; Patients; Pharmaceutical Preparations; Pharmacology; Pharmacotherapy; Phase; Phenotype; Production; Protein Array; Proteins; Pyroxylin; Resources; Role; Sampling; Signal Transduction; Signaling Molecule; Slice; Solid Neoplasm; Statistical Models; System; Systems Biology; T-Lymphocyte; Technology; Testing; Time; Tissues; Transgenic Mice; Tumor Tissue; Xenograft procedure; base; biological systems; cancer cell; cancer imaging; cancer therapy; checkpoint therapy; chemotherapy; cytokine; design; drug action; drug discovery; drug mechanism; drug testing; experimental study; fighting; in vivo; innovation; insight; macrophage; migration; miniaturize; molecular phenotype; nano-string; neoplastic cell; novel therapeutics; pancreatic neoplasm; preservation; protein profiling; recruit; response; small molecule; spatiotemporal; targeted treatment; therapeutically effective; tool; transcriptomics; triple-negative invasive breast carcinoma; tumor; tumor growth; tumor heterogeneity; tumor microenvironment; tumor-immune system interactions; tumorigenesis; two photon microscopy; two-photon

ABSTRACT The complexity of cell-cell interactions in the tumor represents one of the biggest barriers to understanding cancer and to developing effective therapeutics. Cancer cells constantly interact with fibroblast cells, endothelial cells, immune cells, signaling molecules and the extracellular matrix in the tumor microenvironment (TME). The interactions between host and cancer cells are complex, with effects that may be tumor-suppressive or tumor-promoting. For example, macrophages are first recruited to fight cancer; however, interactions with cancer cells can render them tumor-supportive. Thus, enabling a greater understanding of the complexities of the interaction between cancer cells and their microenvironment can lead to a better understanding of mechanisms of drug resistance, identification of new molecular targets and help address the large unmet needs in treating cancer. Quantitative technologies, such as ours, that integrate ex vivo drug treatments and assess pharmacological responses with cellular and molecular phenotypes in native tissues, should accelerate the discovery and development of novel therapeutics. Yet present tools to study drug responses and the TME have not kept up with drug testing needs. Given the nearly infinite number of potential combinations and limited resources to actually test them, there is a great need for testing platforms that use human, intact tumor tissue ex vivo to predict in vivo responses to combination immunotherapies in miniaturized, multiplexed formats that retain as much of the TME as possible. In our first R01 cycle, we developed a microfluidic platform (called Oncoslice) that allows for selective spatiotemporal exposure of organotypic cultures to dozens of drug conditions, and we demonstrated its utility with cell death assays on xenograft and patient GBM slices. In this second R01 cycle, our goal is to apply our Oncoslice platform to evaluate combination immunotherapies and their interaction with the TME. The platform will be applied to two difficult-to-treat solid tumor types – pancreatic cancer (PCa, mouse, and human) and triple-negative breast cancer (TNBC, mouse). Our recent finding that intratumoral migration of CD8+ T cells is a necessary precursor to anti-tumor activity in response to immune checkpoint inhibitor therapy in pancreatic cancer lends credence to our assertion that developing a micro-scale understanding of cell-cell interactions in the TME is critical. Both PCa and TNBC are extremely heterogeneous and respond poorly to current immune checkpoint inhibitors. New developments to our platform will include live imaging of dynamic changes in the immune TME, tissue and cytokine sampling, selective transcriptomics, and protein pathway profiling. We will integrate phenotypic responses in slices with molecular data to build predictive statistical models. Together, these approaches will establish an innovative platform for immuno-modulatory drug discovery designed to provide insights into the drug's mechanism of action and the TME's role in cancer treatment.

摘要 肿瘤细胞间相互作用的复杂性是理解肿瘤的最大障碍之一。 癌症和开发有效的治疗方法。癌细胞不断与成纤维细胞、内皮细胞相互作用 肿瘤微环境(TME)中的细胞、免疫细胞、信号分子和细胞外基质。 宿主和癌细胞之间的相互作用是复杂的，其影响可能是抑制肿瘤或 促进肿瘤生长。例如，巨噬细胞首先被招募来对抗癌症；然而，与癌症的相互作用 细胞可以使它们支持肿瘤。因此，能够更好地理解 癌细胞与其微环境的相互作用有助于更好地理解其作用机制 耐药，识别新的分子靶点，并帮助解决治疗中未得到满足的大量需求 癌症。量化技术，如我们的，整合了体外药物治疗和评估 天然组织中细胞和分子表型的药理反应，应该会加速 新疗法的发现和发展。 然而，目前研究药物反应的工具和TME还没有跟上药物测试的需求。给定 几乎无限数量的潜在组合和有限的资源来实际测试它们，有很大的需求 用于测试平台，该平台使用体外完整的人类肿瘤组织来预测体内对联合治疗的反应 免疫疗法采用小型化、多元化的形式，尽可能多地保留TME。 在我们的第一个R01周期中，我们开发了一个微流控平台(称为OnCoslice)，它允许选择性地 器官类型培养在数十种药物条件下的时空暴露，我们展示了它的实用性。 对异种移植和患者的基底膜切片进行细胞死亡分析。在第二个R01周期中，我们的目标是应用我们的 Oncolice平台评估联合免疫疗法及其与TME的相互作用。这个 Platform将应用于两种难以治疗的实体肿瘤类型-胰腺癌(PCa、小鼠和人类) 和三阴性乳腺癌(TNBC，小鼠)。我们最近发现CD8+T细胞在肿瘤内的迁移 是胰腺免疫检查点抑制剂治疗中抗肿瘤活性的必要前驱 癌症支持了我们的断言，即发展对细胞-细胞相互作用的微观理解 TME是至关重要的。PCa和TNBC都是高度异质性的，对当前的免疫反应很差 检查点抑制药。我们平台的新开发将包括对 免疫TME、组织和细胞因子采样、选择性转录组分和蛋白质路径分析。我们会 将切片中的表型反应与分子数据相结合，以建立预测性统计模型。一起， 这些方法将建立一个免疫调节性药物发现的创新平台，旨在 提供对该药物的作用机制和TME在癌症治疗中的作用的见解。