The Cellular Geography of Therapeutic Resistance in Cancer

癌症治疗耐药的细胞地理学

• 批准号: 10259732
• 负责人: BRUCE E. JOHNSON
• 金额: $ 239.78万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-24 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10259732
• 关键词: Address; Affect; Algorithms; Atlases; Automobile Driving; Biological Assay; Biological Markers; Biopsy; Boston; Breast Melanoma; CDK4 gene; Cancer Center; Cell Line; Cells; Clinic; Clinical; Clinical Data; Coculture Techniques; Cohort Analysis; Collection; Colon Carcinoma; Complex; Computational Biology; Data; Data Science; Ecosystem; Excision; Experimental Designs; Genomics; Geography; Histologic; Human; Immune; Immunology; Immunotherapy; Institution; Large Intestine Carcinoma; Lead; Leadership; Malignant - descriptor; Malignant Neoplasms; Maps; Measures; Metastatic Melanoma; Microsatellite Repeats; Modality; Non-Malignant; Organoids; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Proteins; RNA; Research; Research Design; Resistance; Resolution; Risk; Sampling; Testing; Tissues; Treatment outcome; Validation; anticancer research; base; cancer therapy; cell community; cell type; experience; experimental study; genomic data; immune checkpoint blockade; improved outcome; innovation; malignant breast neoplasm; member; neoplastic cell; next generation; novel; patient stratification; precision oncology; predictive marker; predictive modeling; prospective; response; single-cell RNA sequencing; spatial integration; therapeutic target; therapeutically effective; therapy resistant; treatment response; treatment strategy; tumor; tumor progression

Most patients who die from cancer do so because their cancer is resistant to available therapies, either intrinsically, or as it evolves in response to treatment. However, the fundamental mechanisms driving resistance remain largely unknown. Tumors are comprised of a complex multicellular ecosystem of malignant and non- malignant cells, and changes in their composition, states, spatial organization and interactions are central to therapeutic resistance. Thus, there is an enormous need to chart an atlas of a tumor's cells, their spatial organization and interactions as those change dynamically in resistance to therapy. Technological breakthroughs in spatial and single-cell genomics, including many innovations by our team, now put an atlas within reach, but harnessing this remarkable opportunity, requires collection of multiple spatial and single cell genomics data in clinical samples; novel study design strategies; new experimental and computational strategies to integrate across cellular and spatial data; algorithms to construct tumor atlases that capture the resistant state; and showing how to use an atlas to formulate and test new predictive models of resistance. The Boston Human Tumor Atlas Network Research Center (HTA-RC) will address each of these challenges by creating three comprehensive atlases of the cellular geography of human cancer to understand how changes in the tumor ecosystem lead to therapeutic resistance in: (1) Primary and acquired resistance to CDK4/6 inhibition in breast cancer; (2) Primary and acquired resistance to immune checkpoint blockade in metastatic melanoma; and (3) Primary resistance to immunotherapy in microsatellite stable (MSS) colorectal carcinoma (CRC) compared with microsatellite instable (MSI) CRC. All three tumors types tackle an unmet clinical need; have an approximately equal rate of resistance and response to allow comparisons between states; and harness significant clinical experience and build on substantial preliminary results at our center. To construct the atlases, we will collect at least 100 biospecimens per year from resections and biopsies of the three tumor types and analyze them with histopathological data, high-resolution spatial multiplex RNA and protein data, single- cell genomics data, and temporal clinical data. Our algorithms will recover key features of each data modality, and integrate them into a single atlas to determine what predicts and underlies resistance. We build on a well-established interdisciplinary team in two major cancer centers (DFCI, MGH) and four research institutions (Broad, Harvard, Stanford, Princeton). Our leadership (Haining, Regev) and Units comprise of foremost experts and pioneers in clinical genomics (Biospecimens; Johnson, Wagle), spatial and single cell genomics (Shalek, Rozenblatt-Rosen, Nolan, Zhuang), and computational biology and data science (Regev, Van Allen, Engelhardt). Our atlases will allow identification of predictive biomarkers of resistance in the tumor ecosystem, and therapeutic target discovery, targeting diverse facets of the complex tumor ecosystem.

大多数死于癌症的患者之所以这样做，是因为他们的癌症对可用疗法有抵抗力 本质上，或者随着治疗的响应而演变。但是，驱动阻力的基本机制 在很大程度上未知。肿瘤由恶性和非 - 恶性细胞及其组成，状态，空间组织和相互作用的变化对于 治疗性抗性。因此，巨大的需要图表肿瘤细胞的地图集，它们的空间 组织和互动随着对治疗的抵抗动态变化。技术 空间和单细胞基因组学的突破，包括我们团队的许多创新，现在放了一个Atlas 触手可及，但要利用这一非凡的机会，需要收集多个空间和单个单元格 临床样品中的基因组数据；新颖的研究设计策略；新的实验和计算策略 跨细胞和空间数据集成；构建捕获抗性状态的肿瘤图谱的算法； 并展示如何使用地图集制定和测试新的电阻预测模型。波士顿人 肿瘤地图集网络研究中心（HTA-RC）将通过创建三个来应对这些挑战 人类癌细胞地理的综合图像，以了解如何变化 肿瘤生态系统导致治疗性抗性：（1）对CDK4/6抑制的主要和获得性抗性 在乳腺癌中； （2）转移性黑色素瘤中对免疫检查点阻滞的一级和获得性抗性； （3）微卫星稳定（MSS）结直肠癌（CRC）中对免疫疗法的一级耐药性 与微卫星（MSI）CRC相比。所有三种肿瘤类型都解决了未满足的临床需求。有一个 电阻率和响应速度大致相等，以允许状态之间进行比较；和安全带 在我们中心，临床经验丰富，并以实质性的初步结果为基础。为了构建地图集， 我们将从三种肿瘤类型的切除和活检中每年至少收集100个生物测量和 通过组织病理学数据，高分辨率空间多重RNA和蛋白质数据分析它们 细胞基因组学数据和时间临床数据。我们的算法将恢复每种数据模式的关键功能， 并将它们整合到一个地图集中，以确定什么预测和基础阻力。我们以一个 两个主要癌症中心（DFCI，MGH）和四项研究的跨学科团队和四个研究 机构（Broad，Harvard，Stanford，Princeton）。我们的领导（Haining，Regev）和单位包括 临床基因组学（Biospecimens; Johnson，Wagle），空间和单细胞的最重要专家和开拓者 基因组学（Shalek，Rozenblatt-Rosen，Nolan，Zhuang）和计算生物学与数据科学（Regev，Regev， 范·艾伦（Van Allen），恩格哈特（Engelhardt））。我们的地图酶将允许识别肿瘤中抗性的预测生物标志物 生态系统和治疗靶标发现，靶向复杂肿瘤生态系统的各个方面。