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都是因为他们的癌症对现有疗法有抵抗力 本质上，或者随着治疗的反应而演变。然而，驱动耐药性的基本机制 仍然很大程度上不为人所知。肿瘤由恶性和非复杂的多细胞生态系统组成 恶性细胞及其组成、状态、空间组织和相互作用的变化是恶性细胞的核心 治疗抵抗。因此，非常需要绘制肿瘤细胞及其空间分布图谱。 组织和相互作用随着对治疗的抵抗而动态变化。技术性 空间和单细胞基因组学的突破，包括我们团队的许多创新，现在将一个图谱 触手可及，但要利用这个非凡的机会，需要收集多个空间和单个细胞 临床样本中的基因组学数据；新颖的研究设计策略；新的实验和计算策略 整合细胞和空间数据；构建捕获耐药状态的肿瘤图谱的算法； 并展示如何使用图谱来制定和测试新的耐药预测模型。波士顿人 肿瘤图谱网络研究中心 (HTA-RC) 将通过创建三个 人类癌症细胞地理学的综合图谱，以了解 肿瘤生态系统导致以下方面的治疗耐药：（1）对 CDK4/6 抑制的原发性和获得性耐药 乳腺癌； (2) 转移性黑色素瘤对免疫检查点阻断的原发性和获得性耐药； (3) 微卫星稳定 (MSS) 结直肠癌 (CRC) 对免疫治疗的原发性耐药 与微卫星不稳定 (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 电阻率和响应率大致相等，以便在状态之间进行比较；和马具 丰富的临床经验，并以我们中心的大量初步结果为基础。为了构建地图集， 我们每年将从三种肿瘤类型的切除和活检中收集至少 100 个生物样本 使用组织病理学数据、高分辨率空间多重 RNA 和蛋白质数据、单 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态的关键特征， 并将它们整合到一个地图集中，以确定阻力的预测和基础。我们建立在 两个主要癌症中心（DFCI、MGH）和四个研究机构拥有完善的跨学科团队 机构（博德大学、哈佛大学、斯坦福大学、普林斯顿大学）。我们的领导层（海宁、雷格夫）和单位包括 临床基因组学（生物样本；Johnson、Wagle）、空间和单细胞领域最重要的专家和先驱 基因组学（Shalek、Rozenblatt-Rosen、Nolan、Zhang）以及计算生物学和数据科学（Regev、 范艾伦，恩格尔哈特）。我们的图谱将能够识别肿瘤耐药性的预测生物标志物 生态系统和治疗靶点发现，针对复杂肿瘤生态系统的各个方面。