Project 3: Neoplastic Cell Evolution

项目3：肿瘤细胞进化

• 批准号: 10392869
• 负责人: Darryl K Shibata
• 金额: $ 26.35万
• 依托单位: ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-04-12 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10392869
• 关键词: Algorithms; Arizona; Awareness; Biological Markers; Cancer Prognosis; Cell Density; Cells; Characteristics; Clinical; Colorectal Cancer; Correlation Studies; Data; Ecology; Elephants; Environment; Evolution; Frequencies; Genomics; Genotype; Heterogeneity; Histology; Human; Image Analysis; Immune response; In Situ; Indolent; Infiltration; Knowledge; Lead; Link; Longterm Follow-up; Lymphocyte; Lymphoid; Malignant Neoplasms; Maps; Measurement; Measures; Methods; Microscope; Modality; Modeling; Mus; Mutation; Neoplasms; Outcome; Patient risk; Patients; Population; Privatization; Process; Prognosis; Prognostic Marker; Progression-Free Survivals; Reaction; Resolution; Role; Sampling; Scanning; Side; Slide; Somatic Cell; Stromal Cells; System; T-Lymphocyte; Testing; Time; Treatment outcome; Validation; base; cancer genome; cancer recurrence; carcinogenicity; cohort; computerized; density; genome sequencing; genomic data; improved; indexing; individual patient; innovation; life history; neoantigens; neoplastic; neoplastic cell; outcome prediction; predict clinical outcome; predictive marker; prognostic value; response; risk stratification; supportive environment; tool; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; whole genome

Project 3 Abstract Cancer is an evolutionary process where a single cell grows into a visible tumor after it has acquired multiple driver alterations and has created, or finds itself within, a permissive microenvironment. We will study fundamental parameters of evolution (mutation, drift, selection, "MDS"), and microenvironmental features to better understand their roles in colorectal cancer (CRC) outcomes. The innovation is that MDS-based microenvironmental-aware biomarkers are direct measures of evolution. Such evolution-based biomarkers reflect fundamental mechanisms for understanding what aspects of evolution most impact survival. To fully characterize tumor evolution it is necessary to measure both how tumor cells evolve and the host ecology. In Aim 1, mutations detected by whole genome sequencing of 4 regions of 200 stage II and III CRCs with long-term follow-up will be classified as public (clonal) and private (subclonal). CRCs will then be subclassified with newly developed algorithms that can quantify selection (positive, neutral, negative) based on subclonal mutation frequencies, where selection preferentially increases (positive) or decreases (negative) subclonal mutation frequency. The same studies will be performed on small numbers of mouse and elephant tumors to test whether neoplastic MDS parameters differ between species. In Aim 2, we will scan microscope sections from the exact same regions sequenced in Aim 1, using a unique automated computational image analysis platformthat can identify cells and quantify tumor microenvironments with respect to lymphocytes and stromal cells. In particular, we will quantify potential immunoediting by the host, using lymphocyte colocalization (Morisita index), lymphocyte density and Immunoscore, because prior studies indicate such host responses lead to significantly better outcomes. To determine if host ecological heterogeneity reflects responses to specific tumor subclones, we will overlay private mutation distributions on the same microscope slides. A correlation between a specific subclone with a specific microenvironment is consistent with selection. No correlation between specific ecological niches and tumor subclones is more consistent with neutral evolution, where all subclones are equally well-adapted and subject to the same selection. We will combine tumor evolution and the host reaction into a single evolution-ecology (Evo-Eco) index that summarizes the underlying evolutionary struggle. For example, patients with aggressive tumors (positive selection) and supportive environments are likely to have poorer outcomes relative to patients with tumorsunder negative selection and repressive environments. We will validate any promising Evo-Eco index on a validation cohort of ~100 more CRCs in Aim 3. Data from this Project will be also analyzed in Project 1, and compared with the normal crypts in Project 2 to determine if and how MDS parameters change in neoplasia. If successful, these studies will advance the measurement and understanding of MDS parameters in human cancer, and could yield improved CRC predictive biomarkers.

项目3摘要 癌症是一个进化过程，单个细胞获得后生长为可见的肿瘤。 多个驱动程序的改变，并已经创建或发现自己处于一个宽松的微环境中。我们将学习 进化的基本参数（突变、漂移、选择、“MDS”）和微环境特征 更好地了解它们在结直肠癌 (CRC) 结果中的作用。创新在于基于MDS 微环境感知生物标志物是进化的直接衡量标准。这种基于进化的生物标志物 反映了理解进化的哪些方面对生存影响最大的基本机制。为了充分 为了表征肿瘤的进化，有必要测量肿瘤细胞的进化方式和宿主生态。 在目标 1 中，通过对 200 个 II 期和 III 期 CRC 的 4 个区域进行全基因组测序检测到突变 长期随访将分为公共（克隆）和私人（亚克隆）。然后 CRC 将是 使用新开发的算法进行子分类，可以根据以下条件量化选择（积极、中立、消极） 亚克隆突变频率，其中选择优先增加（正）或减少（负） 亚克隆突变频率。同样的研究也将在少量老鼠和大象身上进行 肿瘤来测试肿瘤性 MDS 参数在物种之间是否存在差异。在目标 2 中，我们将扫描显微镜 使用独特的自动计算图像，来自目标 1 中测序的完全相同区域的切片 分析平台，可以识别细胞并量化有关淋巴细胞和肿瘤微环境 基质细胞。特别是，我们将使用淋巴细胞共定位来量化宿主潜在的免疫编辑 （Morisita 指数）、淋巴细胞密度和免疫评分，因为先前的研究表明此类宿主反应会导致 以获得显着更好的结果。确定宿主生态异质性是否反映了对特定环境的反应 肿瘤亚克隆，我们将在相同的显微镜载玻片上叠加私人突变分布。相关性 特定亚克隆与特定微环境之间的选择是一致的。没有相关性 特定生态位和肿瘤亚克隆之间更符合中性进化，其中所有 亚克隆同样具有良好的适应性并受到相同的选择。我们将结合肿瘤的进化和 宿主反应转化为单一进化生态学 (Evo-Eco) 指数，该指数总结了潜在的进化 斗争。例如，患有侵袭性肿瘤（正选择）和支持性环境的患者 相对于处于负选择和抑制状态下的肿瘤患者，其结果可能较差 环境。我们将在 Aim 中的约 100 个 CRC 验证队列中验证任何有前景的 Evo-Eco 指数 3. 本项目的数据也将在项目1中进行分析，并与项目2中的正常地窖进行比较 确定肿瘤中 MDS 参数是否发生变化以及如何变化。如果成功的话，这些研究将推动 测量和了解人类癌症中的 MDS 参数，并可以改善 CRC 预测性生物标志物。