The Cellular Geography of Therapeutic Resistance in Cancer

大多数死于癌症的患者这样做是因为他们的癌症对现有的治疗方法具有抗药性， 本质上，或者随着治疗的进展。然而，驱动阻力的基本机制 仍然在很大程度上未知。肿瘤由恶性和非恶性肿瘤的复杂多细胞生态系统组成。 恶性细胞，以及它们的组成、状态、空间组织和相互作用的变化， 治疗抵抗因此，非常需要绘制肿瘤细胞的图谱，它们的空间分布， 组织和相互作用，因为它们在对治疗的抵抗中动态变化。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， 但利用这一非凡的机会，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; 并展示了如何使用图谱来制定和测试新的抗性预测模型。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地图集，以了解癌症细胞的变化， 肿瘤生态系统导致治疗耐药表现在：（1）对CDK 4/6抑制的原发性和获得性耐药 （2）转移性黑色素瘤中对免疫检查点阻断的原发性和获得性抗性; （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; 近似相等的电阻率和响应率，以允许在状态之间进行比较;以及 重要的临床经验，并建立在我们中心的大量初步结果。为了建立地图集， 我们每年将从三种肿瘤类型的切除和活检中收集至少100份生物标本， 分析他们与组织病理学数据，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个数据模态的关键特征， 并将它们整合到一个图谱中，以确定是什么预测和支撑了耐药性。我们建立在 在两个主要癌症中心（DFCI，MGH）和四个研究中心建立了完善的跨学科团队 机构（布罗德，哈佛，斯坦福大学，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（生物标本;约翰逊，Wagle），空间和单细胞 基因组学（Shalek，Rozenblatt-Rosen，Nolan，Zhuang），以及计算生物学和数据科学（Regev， 货车艾伦，恩格尔哈特）。我们的图谱将允许识别肿瘤耐药的预测性生物标志物 生态系统和治疗靶点发现，靶向复杂肿瘤生态系统的各个方面。

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.