The Cellular Geography of Therapeutic Resistance in Cancer

大多数死于癌症的患者是因为他们的癌症对现有的治疗方法有抵抗力。 从本质上讲，或者随着它对治疗的反应而演变。然而，驱动耐药性的基本机制 在很大程度上仍然不为人知。肿瘤由一个复杂的多细胞生态系统组成，包括恶性肿瘤和非肿瘤细胞。 恶性细胞及其组成、状态、空间组织和相互作用的变化是 治疗抵抗力。因此，非常需要绘制肿瘤细胞的图谱，它们的空间 随着治疗阻力的动态变化，组织和相互作用也会发生变化。工艺性 空间和单细胞基因组学方面的突破，包括我们团队的许多创新，现在把地图集 触手可及，但利用这一非凡的机会，需要收集多个空间和单个单元 临床样本中的基因组数据；新的研究设计策略；新的实验和计算策略 整合细胞和空间数据；构建捕捉耐药状态的肿瘤图谱的算法； 并展示如何使用地图集来制定和测试新的耐药性预测模型。《波士顿人报》 肿瘤图谱网络研究中心(HTA-RC)将通过创建三个 人类癌症细胞地理的综合图谱，以了解人类癌症细胞地理的变化 肿瘤生态系统在以下方面导致治疗耐药性：(1)对CDK4/6抑制的原发和获得性耐药性 乳腺癌；(2)转移性黑色素瘤对免疫检查点阻断的原发和获得性抵抗； (3)微卫星稳定(MSS)结直肠癌(CRC)对免疫治疗的原发耐药 与微卫星不稳定(MSI)CRC相比。所有三种肿瘤类型都解决了未得到满足的临床需求；具有 阻力和响应率大致相等，以允许在状态之间进行比较；以及驾驭 重要的临床经验，并在我们中心取得了实质性的初步成果。为了建造地图集， 我们每年将从这三种肿瘤的切除和活检中收集至少100份生物标本，并 用组织病理学数据、高分辨率空间多重RNA和蛋白质数据、单个- 细胞基因组学数据和临时临床数据。我们的算法将恢复每种数据模式的关键特征， 并将它们整合到一个单独的图谱中，以确定什么是抵抗的预测和基础。我们建立在 在两个主要癌症中心(DFCI、MGH)和四项研究中建立了完善的跨学科团队 院校(布罗德、哈佛、斯坦福、普林斯顿)。我们的领导层(海宁、雷格夫)和单位包括 临床基因组学(生物基因组学；约翰逊，瓦格尔)、空间和单细胞领域的顶尖专家和先驱 基因组学(Shalek，Rozenblatt-Rosen，Nolan，Zang)和计算生物学和数据科学(Regev， 范·艾伦、恩格尔哈特)。我们的图谱将有助于识别肿瘤耐药的预测生物标记物。 生态系统和治疗靶点发现，针对复杂的肿瘤生态系统的不同方面。

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.