Single-Cell, Spatial and Functional Dissection of Cancer Cell States, Co-Evolving Ecosystems, and Vulnerabilities During Tumor Progression and Metastasis

侵袭性癌症通常缺乏可消除的突变，并且对免疫检查点没有反应。 阻断，因此仅从靶向和免疫治疗中获得适度的临床益处。的异质性 肿瘤微环境（TME）中的转化细胞和健康细胞都是肿瘤微环境的关键障碍。 在癌症患者中实现更持久的反应。使用多组学方法的最新见解表明， 癌细胞可以以多种转录上不同但共存的状态存在，其中一些是 已经准备好转移进展或耐药性。这些状态的可塑性，的能力 癌细胞在多种状态下重新编程，无论是自发的还是由于药物干扰， 它们通过旁分泌分子相互作用与其他TME亚群稳态共存， 通过促进耐药性的出现，肿瘤进展， 以及创造一个促恶性肿瘤的免疫抑制环境恶性状态和转变只是部分 解释了体细胞突变的顺序收购，这表明它们是从一个品种的整合 细胞内在和外在分子线索，决定他们的谱系归属，建立， 相互转换到目前为止，一些技术，临床和分析挑战阻碍了全面的 了解患者这些过程的自然生物学。项目2致力于解决 恶性细胞和健康细胞的可变性和可塑性，通过开发和应用 一系列技术和分析工具，用于在单细胞水平上剖析癌症异质性，以及 提名、验证和测试肿瘤进展和治疗反应的新驱动因素， 阻力我们将在一个明确的生物学背景下描述这些概念，即从一个初级阶段到另一个初级阶段的发展过程。 转移到脑转移性疾病为此，我们将利用CAST的一系列创新 研究者，包括（a）来自存档组织的多模式单细胞分析，（B）同时低通 相同细胞池的全基因组测序（lpWGS），（c）整合的单细胞和空间单细胞测序， 转录组学，（d）分析方法，以整合和模拟多模态单细胞数据的空间，时间和 细胞之间相互作用的背景，（e）阐明细胞状态稳定性和转换的工具，（f） 基因组编辑扰动与单细胞读出，可以连接到药物筛选通过基因表达 分析，以及（g）基于网络的主调节器分析，以阐明 转录细胞状态。这将通过实验创新来扩展，即（h）准确地模拟肿瘤 体内进展并重现整个人类生态系统，（i）能够标记转移性小生境， 和（j）提供一个平台来测试癌细胞内在的药理学调节， 以及从人类研究和建模预测的肿瘤微环境特征。提出的创新 该框架将广泛应用于其他癌症背景。

Aggressive cancers often lack pharmacologically actionable mutations and do not respond to immune checkpoint blockade, thus deriving only modest clinical benefit from targeted and immune therapy. The heterogeneity of both transformed and healthy cells in the Tumor Microenvironment (TME) represents a critical obstacle to achieving more durable response in cancer patients. Recent insights, using multi-omics approaches, have shown that cancer cells can exist in a variety of transcriptionally distinct, yet co-existing states, some of which are already primed for metastatic progression or drug resistance. The plasticity of these states—i.e., the ability of cancer cells to reprogram across multiple states, either spontaneously or because of drug perturbations—and their homeostatic coexistence with other TME subpopulation, via paracrine molecular interactions, creates a constant challenge to therapeutic approaches by fostering the emergence of drug-resistance, tumor progression, and the creation of a pro-malignant, immunosuppressive milieu. Malignant states and transitions are only partially explained by sequential acquisition of somatic mutations, suggesting that they result from integration of a variety of cell-intrinsic and -extrinsic molecular cues that determine their lineage attribution, establishment, and interconversion. To date, several technical, clinical, and analytical challenges have hampered a comprehensive understanding of the natural biology of these processes in patients. Project 2 is dedicated to resolving the variability and plasticity of malignant cells and of the healthy cells that define the TME by developing and applying a battery of technical and analytical tools for the dissection of cancer heterogeneity at the single-cell level, and for the nomination, validation and testing of novel drivers of tumor-progression and therapy response and resistance. We will delineate these concepts in a defined biological context, that is the progression from a primary tumor towards brain-metastatic disease. To this end, we will leverage a series of innovations from CaST investigators, including (a) multi-modal single-cell profiling from archival tissues, (b) simultaneous low-pass whole-genome sequencing (lpWGS) of the same cell pool, (c) integrated single-cell and spatial single-cell transcriptomics, (d) analytical approaches to integrate and model multi-modal single-cell data in space, time and context of interactions among cells, (e) tools to elucidate cell state stability and transitions, (f) combinations of genome-editing perturbations with single-cell read outs that can be linked to drug screens via gene expression profiling, and (g) network-based Master Regulator analyses to elucidate mechanistic determinants of transcriptional cell state. This will be extended by experimental innovations, that (h) accurately model tumor progression in vivo and recapitulate entire human ecosystems, (i) enable labeling of metastatic niches coupled with single-cell genomics, and (j) provide a platform to test pharmacological modulation of cancer cell intrinsic and tumor-microenvironmental features predicted from human studies and modeling. The presented innovative framework will be broadly application to other cancer contexts.