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

癌细胞状态、共同进化生态系统以及肿瘤进展和转移过程中的脆弱性的单细胞、空间和功能剖析

• 批准号: 10729386
• 负责人: Benjamin Izar
• 金额: $ 38.49万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-19 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729386
• 关键词: Address; Behavior; Binding; Biological; Biology; Brain; Brain Neoplasms; Cancer Center; Cancer Patient; Cell Communication; Cells; Cephalic; Chronology; Clinical; Complex; Coupled; Cues; Cutaneous Melanoma; Data; Dedications; Dependence; Disease; Disease Progression; Dissection; Drug Screening; Drug resistance; Ecosystem; Event; Fostering; Future; Gene Expression Profiling; Genetic Transcription; Genome; Genomics; Goals; Heterogeneity; Human; Immune checkpoint inhibitor; Immunosuppression; Immunotherapy; In Situ; Label; Ligands; Link; Logic; Malignant - descriptor; Malignant Neoplasms; Mediating; Metastatic malignant neoplasm to brain; Methods; Minority; Modeling; Molecular; Neoplasm Metastasis; Network-based; Non-Small-Cell Lung Carcinoma; Patients; Pharmaceutical Preparations; Pharmacotherapy; Phenotype; Primary Neoplasm; Printing; Process; Proteins; Publications; Research Personnel; Resistance; Resolution; Series; Signal Transduction; Site; Slice; Somatic Mutation; Stratification; System; Testing; Therapeutic; Time; Tissues; Validation; actionable mutation; analytical tool; cancer cell; cancer heterogeneity; cell behavior; checkpoint therapy; clinically relevant; computerized tools; design; genome editing; genome sequencing; human model; immune checkpoint blockade; improved; in vivo; innovation; insight; machine learning framework; malignant state; multimodality; multiple omics; novel; paracrine; pharmacologic; programs; receptor; recruit; response; spatiotemporal; targeted treatment; therapeutic development; tool; transcriptomics; treatment response; tumor; tumor microenvironment; tumor progression; tumor-immune system interactions; tumorigenic; whole genome

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.

侵袭性癌症通常缺乏药理学上可行的突变，并且对免疫检查点没有反应 阻断，因此从靶向和免疫治疗中只能获得有限的临床益处。 肿瘤微环境（TME）中的转化细胞和健康细胞都是肿瘤微环境（TME）中的一个关键障碍。 最近的见解表明，使用多组学方法可以使癌症患者获得更持久的反应。 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 已经准备好进行转移进展或耐药性。这些状态的可塑性，即能力。 癌细胞可以自发地或由于药物扰动而在多种状态下重新编程，并且 它们通过旁分泌分子相互作用与其他 TME 亚群稳态共存，创造了一种 通过促进耐药性的出现、肿瘤进展、 恶性状态和转变只是部分的。 通过体细胞突变的连续获得来解释，表明它们是多种整合的结果 细胞内在和外在的分子线索决定了它们的谱系归属、建立和 迄今为止，一些技术、临床和分析挑战阻碍了全面的转换。 项目 2 致力于了解患者这些过程的自然生物学。 恶性细胞和健康细胞的变异性和可塑性通过开发和应用来定义 TME 一系列用于在单细胞水平上剖析癌症异质性的技术和分析工具，以及 用于肿瘤进展和治疗反应的新驱动因素的提名、验证和测试 我们将在定义的生物学背景下描述这些概念，即从初级的进展。 为此，我们将利用 CaST 的一系列创新。 研究人员，包括（a）来自档案组织的多模式单细胞分析，（b）同时低通 同一细胞库的全基因组测序（lpWGS），（c）集成单细胞和空间单细胞 转录组学，(d) 整合和建模空间、时间和多模态单细胞数据的分析方法 细胞之间相互作用的背景，（e）阐明细胞状态稳定性和转变的工具，（f）组合 通过单细胞读出进行基因组编辑扰动，可以通过基因表达与药物筛选相关联 分析，以及（g）基于网络的主调节器分析，以阐明 这将通过实验创新得到扩展，即 (h) 准确地模拟肿瘤。 体内进展并概括整个人类生态系统，（i）能够标记耦合的转移生态位 与单细胞基因组学，以及（j）提供一个平台来测试癌细胞内在的药理学调节 以及通过人体研究和建模预测的肿瘤微环境特征。 该框架将广泛应用于其他癌症环境。