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，单个细胞在获得肿瘤后生长为可见的肿瘤。 多个驱动程序更改，并且已经创建或发现自己处于允许的微环境中。我们会研究 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)预后中的作用。创新之处在于，基于MDS的 微环境感知生物标记物是进化的直接衡量标准。这种基于进化的生物标记物 反映了解进化的哪些方面对生存影响最大的基本机制。充分地 表征肿瘤的进化，有必要同时测量肿瘤细胞如何进化和宿主生态。 在目标1中，通过200个II和III期CRC的4个区域的全基因组测序检测到突变 有长期随访的将分为公有(克隆)和私有(亚克隆)。然后，CRC将被 使用新开发的算法进行细分，这些算法可以根据以下条件量化选择(正面、中性、负面) 亚克隆突变频率，其中选择优先增加(正)或降低(负) 亚克隆突变频率。同样的研究将在少量的老鼠和大象身上进行 测试肿瘤MDS参数是否因物种不同而不同。在目标2中，我们将扫描显微镜 在AIM 1中，使用独特的自动计算图像对完全相同区域的部分进行了测序 分析平台，可以识别细胞和量化肿瘤微环境与淋巴细胞和 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，这种宿主反应导致 以获得显著更好的结果。确定寄主生态异质性是否反映了对特定 肿瘤亚克隆，我们将在相同的显微镜载玻片上覆盖私人突变分布。一种相关性 特定亚克隆与特定微环境之间的选择是一致的。无相关性 在特定的生态位和肿瘤亚克隆之间更符合中性进化，其中所有 亚克隆同样适应得很好，并受到相同的选择。我们将把肿瘤的进化和 宿主反应成一个单一的进化-生态(Evo-Eco)指数，该指数总结了潜在的进化 挣扎。例如，患有侵袭性肿瘤的患者(积极选择)和支持性环境是 与处于负面选择和压抑状态下的肿瘤患者相比，预后可能更差 环境。我们将在AIM的大约100多个CRC的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中的正常墓穴进行比较，以 确定肿瘤中MDS参数是否改变以及如何改变。如果成功，这些研究将推进 测量和了解人类癌症中的MDS参数，并可产生改善的CRC 预测性生物标志物。