Bioinformatics Core

生物信息学核心

• 批准号: 10733394
• 负责人: CLIFFORD G TEPPER
• 金额: $ 4.02万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2028-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10733394
• 关键词: Antineoplastic Agents; Asian Americans; Biochemical; Bioinformatics; Biological; Biometry; Biostatistics Shared Resource; California; Cancer Model; Cancer Patient; Characteristics; Clinical; Collaborations; Comprehensive Cancer Center; Conduct Clinical Trials; Correlation Studies; Data; Data Analyses; Data Analytics; Data Set; Dedications; Development; Drug Combinations; Drug Modelings; Epidermal Growth Factor Receptor; Frequencies; Gene Expression; Gene Mutation; Genes; Genetic; Genomics; Goals; Human Resources; In Vitro; Individual; Infrastructure; Latino Population; Light; Link; Lung; Lung Adenocarcinoma; Lung Neoplasms; Malignant Neoplasms; Malignant neoplasm of lung; Methods; Minority; Modeling; Molecular; Mutation; Native Hawaiian or Other Pacific Islander; Non-Small-Cell Lung Carcinoma; Patients; Performance; Pharmaceutical Preparations; Pilot Projects; Population; Property; Regimen; Research Design; Research Personnel; Research Project Grants; Resistance; Sampling; Services; Smoker; Statistical Models; Stomach; Stomach Neoplasms; Testing; Texas; Therapeutic; Therapeutic Agents; Transcript; Treatment Efficacy; Tumor Subtype; Universities; analysis pipeline; biobank; data integration; data repository; data sharing; design; drug testing; efficacy testing; ethnic minority; exome sequencing; genetic variant; genomic data; high throughput analysis; improved; individual patient; malignant stomach neoplasm; minority patient; molecular marker; mutant; never smoker; next generation sequencing; novel; novel therapeutics; patient derived xenograft model; pre-clinical; precision oncology; racial minority; relational database; repository; response; targeted treatment; therapeutic target; transcriptome sequencing; treatment response; treatment strategy; tumor; tumor heterogeneity; tumor xenograft

PROJECT SUMMARY The overall objective of the proposed Diversity PDX Development and Testing Center (D-PDTC) is directed at improving cancer precision medicine approaches for gastric and lung cancers. This will be achieved through the large-scale development and molecular characterization of over 120 new minority gastric and lung PDX tumor models and to utilize these for in vitro and pre-clinical PDX trials to test the efficacy of molecularly-matched therapeutics administered as single agents or combination. Statistical testing for drug-responsiveness will be performed, as well as the application of data integration strategies that relate treatment responses with molecular characteristics of individual PDXs and patients’ tumors. The goal of Bioinformatics Core (UBC) is to provide extensive data analytic support for the proposed studies at multiple stages including study design, molecular characterization of the PDXs, target selection, and statistical correlation and modeling of drug responses. The UBC will be established, and have considerable capabilities, by bringing together key personnel with strengths in genomics, bioinformatics, and biostatistics, and by leveraging the existing infrastructure at the UCD Comprehensive Cancer Center, specifically the Biorepository, Genomics, and Biostatistics Shared Resources. The UBC will facilitate the seamless integration of our U54 through dynamic interactions with its investigators, and via the implementation of a dedicated UCaTS Bioinformatics Core relational database that will serve as a data repository and interface for linking project design, sample tracking, data analysis, and eventual data sharing. For genetic characterization of the PDX models, and associated patient tumors, we will apply a suite of established bioinformatics analysis pipelines implemented on a high-performance ultra-rapid next-generation sequencing data analysis platform for processing whole-exome sequencing and RNA-sequencing data to generate the fundamental results datasets consisting of fully annotated 1) genomic variants and 2) transcripts with corresponding expression values. Additional tumor properties that can figure prominently in guiding therapeutic decisions, such as mutational burden will also be defined. Subsequently, integrative bioinformatics approaches will be applied for target prioritization and treatment matching, which will be followed by biostatistical analyses to evaluate both drug responses and the accuracy of novel molecular biomarkers. The UBC will also collaborate with the PDXNet, PDMR-FNLCR, and PDTCs for data sharing and participation in PDX trials.

项目总结 拟议的多样性PDX开发和测试中心(D-PDTC)的总体目标是 改进胃癌和肺癌的癌症精准医学方法。这将通过以下方式实现 120多例新发现的少数族裔胃和肺PDX肿瘤的大规模发展及其分子特征 模型，并利用这些模型进行体外和临床前的PDX试验，以测试分子匹配的疗效 作为单一药物或组合使用的治疗药物。药物敏感性的统计测试将是 以及将治疗反应与分子联系起来的数据集成策略的应用 个体PDX和患者肿瘤的特点。生物信息学核心(UBC)的目标是提供 在多个阶段为拟议研究提供广泛的数据分析支持，包括研究设计、分子 PDX的表征，目标选择，以及药物反应的统计相关性和建模。这个 UBC将成立，并将拥有相当大的能力，通过汇集有实力的关键人员 在基因组学、生物信息学和生物统计学方面，并利用UCD的现有基础设施 综合癌症中心，特别是生物库、基因组学和生物统计共享资源。 UBC将通过与其调查人员的动态互动促进我们的U54的无缝整合， 并通过实施专门的UCATS生物信息学核心关系数据库，该数据库将作为 用于连接项目设计、样品跟踪、数据分析和最终数据共享的数据存储库和接口。 对于PDX模型和相关患者肿瘤的基因特征，我们将应用一套 已建立的生物信息学分析流水线在高性能超快下一代上实施 一种处理全外显子组测序和RNA测序数据的测序数据分析平台 生成包含完整注释的1)基因组变体和2)转录本的基本结果数据集 并具有相应的表达式值。可在指导中突出显示的其他肿瘤特性 治疗决定，如突变负担也将被定义。随后，综合生物信息学 将采用确定目标优先顺序和治疗匹配的方法，随后将采用生物统计学方法 分析以评估药物反应和新型分子生物标记物的准确性。UBC还将 与PDXNet、PDMR-FNLCR和PDTC合作，共享数据并参与PDX试验。