UCSC-Buck Genome Data Analysis Center for the Genomic Data Analysis Network v2.0

UCSC-Buck 基因组数据分析中心基因组数据分析网络 v2.0

• 批准号: 10300936
• 负责人: Christopher Benz
• 金额: $ 41.29万
• 依托单位: UNIVERSITY OF CALIFORNIA SANTA CRUZ
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-07 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10300936
• 关键词: Address; Atlases; Automobile Driving; Behavior; Cancer Patient; Cell Extracts; Cells; Characteristics; Clinical; Clinical Protocols; Clinical Trials; Clinical Trials Network; Collaborations; Collection; Competence; Complex; Computing Methodologies; Data; Data Set; Evolution; Future; Gene Expression Profile; Genome; Genome Data Analysis Center; Genome Data Analysis Network; Genomics; Glean; Goals; Health; Human; Knowledge; Lead; Machine Learning; Maps; Messenger RNA; Modality; Molecular; Outcome; Pathway Analysis; Pathway interactions; Patient-Focused Outcomes; Patients; Pattern; Play; Prediction of Response to Therapy; Predictive Value; Prognosis; Publications; Role; Sampling; Somatic Mutation; Supervision; The Cancer Genome Atlas; Training; Tumor Subtype; Visualization; Work; base; cancer therapy; cell type; computerized tools; data exploration; gene expression database; genomic data; individual patient; innovation; learning classifier; mRNA sequencing; machine learning method; multiple omics; neoplastic cell; novel; patient response; predicting response; pressure; prognostic value; single cell mRNA sequencing; survival outcome; treatment arm; treatment response; tumor; tumor heterogeneity; tumor microenvironment; tumor progression; working group

ABSTRACT Tumor heterogeneity -- the complex mix of tumor subclones, the cell-of-origin that first became transformed, the evolution of tumor subclones under selective pressures of the body and due to treatment, and the interplay of these cells with the tumor microenvironment (TME) -- contributes to the character, behavior, and mystery of tumors and is a key determinant of cancer progression and a patient’s response to therapy. Large-scale genomics projects like the Cancer Genome Atlas (TCGA) and the Genome Data Analysis Network (GDAN) have revealed important characteristics and patterns from a multi-omics overview of various tumor types. However, it remains a mystery on how to maximize the use of these data to choose the best course of treatment for an individual patient. The proposed GDAN will close this gap in knowledge by collecting clinical information and outcomes endpoints alongside the multiple omics platforms that will provide key linkages upon which to train supervised computational approaches. We propose to contribute our key competencies of pathway analysis, integrative machine-learning, mRNA-seq analysis, assessment of driving somatic mutations, and visualization of high-throughput datasets to serve the future GDAN analysis working groups (AWGs) to achieve these goals. We will collect and share widely a database of gene expression signatures that capture cell state information gleaned from the large collection of single-cell mRNA sequencing data such as from the Human Cell Atlas (Aim 1). In addition, we will contribute our existing, and novel extensions to, machine-learning approaches like AKIMATE to maximally use these signatures and others in combination with AWG-approved omics datasets as features to train accurate predictors of response for the GDAN’s studies like ALCHEMIST (Aim 2). Our proposal will adapt the TumorMap to benefit weekly analysis and bolster the exploration and publication of results. Specifically, we will work with the group to create new maps that show the TME and TIC comparisons of the patient samples separately to help elucidate new important subtypes implied by the collected data (Aim 3). As we have done for the past twelve years for TCGA and the GDAN, we propose to continue working closely with the consortium in these endeavors to significantly enrich our understanding of the molecular and cellular basis of tumor heterogeneity and its influence on cancer progression and treatment response.

摘要 肿瘤异质性--肿瘤亚克隆的复杂混合，最初转化的原始细胞， 肿瘤亚克隆在身体的选择性压力下和由于治疗的演变，以及它们之间的相互作用 这些细胞与肿瘤微环境（TME）的关系-有助于肿瘤的特征，行为和神秘性。 是癌症进展和患者对治疗反应的关键决定因素。大规模 癌症基因组图谱（TCGA）和基因组数据分析网络（GDAN）等基因组学项目 已经从各种肿瘤类型的多组学概述中揭示了重要的特征和模式。 然而，如何最大限度地利用这些数据来选择最佳的课程仍然是一个谜。 为个别患者提供治疗。拟议的GDAN将通过收集临床数据， 信息和成果终点以及多个组学平台，这些平台将在以下方面提供关键联系 用来训练有监督的计算方法我们建议贡献我们的关键能力， 途径分析，综合机器学习，mRNA-seq分析，驱动体细胞突变的评估， 高通量数据集的可视化，以服务于未来的GDAN分析工作组（AWG）， 以实现这些目标。我们将收集并广泛分享基因表达特征数据库， 从大量的单细胞mRNA测序数据中收集的细胞状态信息，例如来自 人类细胞图谱（Aim 1）。此外，我们将贡献我们现有的，和新的扩展， 机器学习方法，如AKIMATE，最大限度地使用这些签名和其他结合 AWG批准的组学数据集作为特征，用于为GDAN的研究训练准确的响应预测因子， 炼金术士（目标2）。我们的提案将调整TumorMap，以利于每周分析，并支持 探索和公布成果。具体来说，我们将与该小组合作，创建新的地图， 分别对患者样本进行TME和TIC比较，以帮助阐明新的重要亚型 所收集的数据（目标3）暗示。正如我们过去12年为TCGA和GDAN所做的那样，我们 我建议继续与该财团密切合作，以大大丰富我们的 了解肿瘤异质性的分子和细胞基础及其对癌症的影响 进展和治疗反应。