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

项目III：潜伏转移：播散性肿瘤干细胞的免疫调节 项目总结： 项目III的目标是发现对播散性肿瘤的免疫逃逸进行关键调控的机制 细胞(DCs)及其作为潜在转移实体的进化。我们将使用一种综合方法，将 乳腺癌和乳腺癌潜在转移独特生物学模型的单细胞问诊方法 肺腺癌和新的计算策略。远处转移是压倒性的基础 大多数与癌症相关的死亡和它的开始是非常不稳定的。剩余的DTC可能会超过 立即或更频繁地，徘徊在复制静止或集体休眠的可行状态数月 在渗透到远处器官之后的几年里。DTC的这种潜伏期状态伴随着显著的阻力 到抗肿瘤治疗，这种治疗通常以积极分裂的肿瘤细胞为目标。此外，潜在的DTC不知何故 逃避免疫监视。这些适应能力背后的生物学基础仍然知之甚少， 控制DTC种群动态的因素，无论是随机的还是确定性的，仍然未知。我们的目标是 通过结合大规模并行的单细胞RNA表达谱来解决这一重大知识差距 使用基于珠子的分子条形码技术和非监督学习方法来识别 潜伏期、残留期的稳定/暂时性细胞状态及其分子调控机制。作为一名 补充方法，单个细胞对分子扰动的反应将通过以下方式动态跟踪 活细胞成像。在目标1中，我们建议确定潜伏态是否预先存在于初级 肿瘤或由宿主组织中的免疫监视应激诱导的。在目标2中，我们将对 转移细胞在生长许可条件下潜伏期结束时的进化动力学 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控因素。 通过探索对NK细胞介导的不同敏感的静止亚群的转录异质性 淘汰赛。这些方法的融合，结合我们对生物的深刻理解 癌症转移，将促进发现根除或控制癌症转移的治疗策略 它成立的最早阶段。

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.