Residual disease: unraveling immunosurveillance and immune evasion of disseminated tumor cells

残留疾病：解开播散性肿瘤细胞的免疫监视和免疫逃避

• 批准号: 9980810
• 负责人: JOAN MASSAGUE
• 金额: $ 43.33万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-26 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9980810
• 关键词: Address; Antineoplastic Agents; Bar Codes; Behavior; Biological Models; Biological Response Modifiers; Biology; Breast cancer metastasis; CRISPR library; Cancer Biology; Cell-Mediated Cytolysis; Cells; Cessation of life; Clinical; Cytolysis; Dimensions; Distant; Distant Metastasis; Down-Regulation; Equilibrium; Evolution; Expression Profiling; Gene Expression Profile; Genes; Genetic Transcription; Goals; Growth; Heterogeneity; Immune Evasion; Immune system; Immunologic Surveillance; Immunology; In Vitro; Individual; Innate Immune System; Knowledge; Ligands; Lung Adenocarcinoma; Malignant Neoplasms; Mathematics; Mediating; Memorial Sloan-Kettering Cancer Center; Methods; Modeling; Molecular; NCI Center for Cancer Research; Natural Immunity; Natural Killer Cells; Neoplasm Metastasis; Organ; Pathway Analysis; Phenotype; Population; Population Dynamics; Predisposition; Primary Neoplasm; Proliferating; Recording of previous events; Regulatory Pathway; Relapse; Reporter; Residual Tumors; Residual state; Resistance; Stress; System; Systems Biology; Technology; Therapeutic; Therapeutic Effect; Therapeutic Intervention; Time; Tissues; Variant; Work; base; cancer cell; cancer stem cell; cell type; comparative; immune clearance; immunoregulation; in vivo; insight; learning algorithm; learning strategy; live cell imaging; mathematical model; mouse model; neoplastic cell; novel; novel strategies; predictive modeling; pressure; prevent; response; sensor; single cell analysis; supervised learning; trait; tumor; unsupervised learning

Project III: Latent metastasis: Immune Regulation Of Disseminated Cancer Stem Cells PROJECT SUMMARY: The goal of Project III is to discover mechanisms that critically regulate immune evasion by disseminated tumor cells (DTCs) and their evolution as latent metastatic entities. We will use an integrated approach that combines single-cell interrogation methods with unique biological models of latent metastasis from breast cancer and lung adenocarcinoma, and novel computational strategies. Distant metastasis underlies the overwhelming majority of cancer-related deaths and its inception is exceedingly variable. Residual DTCs may outgrow immediately or, more frequently, linger in a viable state of replicative quiescence or mass dormancy for months to years after infiltrating distant organs. This latency state of DTCs is accompanied with significant resistance to anti-neoplastic therapy, which typically targets actively dividing tumor cells. Moreover, latent DTCs somehow evade immune surveillance. The biology underlying these adaptive abilities remains poorly understood and factors governing DTC population dynamics, whether stochastic or deterministic, remain unknown. We aim to address this significant knowledge gap by combining massively parallel, single-cell RNA expression profiling using a bead-based molecular barcoding technology with unsupervised learning methods to identify stable/transitory cell states within latent, residual disease and their molecular control mechanisms. As a complementary approach, individual cell responses to molecular perturbations will be dynamically tracked by live cell imaging. In Aim 1 we propose to determine whether the latent state pre-exists in the primary tumor or is induced by the stress of immunosurveillance in a host tissue. In Aim 2 we will model the evolutionary dynamics of metastatic cells as they exit latency under growth permissive and immunoediting conditions. Moreover, in Aim 3 we will identify key regulators of metastatic immune evasion by probing transcriptional heterogeneity in quiescent subpopulations differentially sensitive to NK-cell mediated elimination. The amalgamation of these approaches, combined with our deep understanding of the biology of cancer metastasis, will promote the discovery of therapeutic strategies to eradicate or control metastasis from its earliest stages of inception.

项目III：潜在转移：播散性癌症干细胞的免疫调节 项目概要： 项目III的目标是发现播散性肿瘤严重调节免疫逃避的机制， 细胞（DTC）及其作为潜在转移实体的演变。我们将采用综合方法， 具有乳腺癌潜在转移的独特生物模型的单细胞询问方法， 肺腺癌和新的计算策略。远处转移导致了 大多数与癌症有关的死亡及其开始是非常可变的。残留故障诊断码可能会消失 立即或更经常地，在几个月的复制静止或集体休眠的可行状态中徘徊 到数年后才扩散到远处器官DTC的这种潜伏状态伴随着显著的阻力 涉及抗肿瘤治疗，其通常靶向活跃分裂的肿瘤细胞。此外，潜在的DTC以某种方式 逃避免疫监视这些适应能力背后的生物学机制仍然知之甚少， 控制DTC种群动态的因素，无论是随机的还是确定的，仍然是未知的。我们的目标是 通过结合大规模平行的单细胞RNA表达谱分析， 使用基于珠的分子条形码技术和无监督学习方法来识别 潜伏性、残留性疾病中的稳定/暂时性细胞状态及其分子控制机制。作为 作为一种互补的方法，单个细胞对分子扰动的反应将被动态跟踪， 活细胞成像在目标1中，我们提出确定潜伏状态是否预先存在于初级状态中。 肿瘤或由宿主组织中的免疫监视应激诱导。在目标2中，我们将对 转移性细胞在生长许可下退出潜伏期时的进化动力学， 免疫编辑条件。此外，在目标3中，我们将确定转移性免疫逃避的关键调节因子 通过探测对NK细胞介导的细胞因子敏感的静止亚群中的转录异质性， 淘汰这些方法的融合，结合我们对生物学的深刻理解， 癌症转移，将促进发现治疗策略，以根除或控制转移， 最早的阶段。