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.

项目摘要 Diversity PDX开发和测试中心（D-PDTC）的总体目标是 改善胃癌和肺癌的癌症精准医疗方法。这将通过以下方式实现： 120多例新少数民族胃和肺PDX肿瘤的大规模开发和分子特征 模型，并利用这些体外和临床前PDX试验，以测试分子匹配的 作为单一药剂或组合施用的治疗剂。药物反应性的统计学检验将 以及将治疗反应与分子生物学相关的数据整合策略的应用 个体PDX和患者肿瘤的特征。生物信息学核心（UBC）的目标是提供 为多个阶段的拟议研究提供广泛的数据分析支持，包括研究设计、分子生物学和生物学方法。 PDX的表征、靶标选择以及药物反应的统计相关性和建模。的 UBC将通过汇集具有优势的关键人员来建立，并具有相当的能力 在基因组学，生物信息学和生物统计学，并利用现有的基础设施在UCD 综合癌症中心，特别是生物储存库，基因组学和生物统计学共享资源。 UBC将通过与其调查人员的动态互动，促进我们的U 54的无缝集成， 并通过一个专用的UCaTS生物信息学核心关系数据库的实施，将作为一个 数据存储库和接口，用于连接项目设计、样品跟踪、数据分析和最终数据共享。 对于PDX模型和相关患者肿瘤的遗传表征，我们将应用一套 建立了生物信息学分析管道，在高性能超快速下一代 测序数据分析平台，用于处理全外显子组测序和RNA测序数据， 生成由完全注释的1）基因组变体和2）转录物组成的基本结果数据集 对应的表达式值。其他肿瘤特性可以在指导中发挥重要作用 还将定义治疗决定，例如突变负荷。随后，综合生物信息学 方法将应用于目标优先级和治疗匹配，随后将进行生物统计 分析以评估药物反应和新型分子生物标志物的准确性。UBC还将 与PDXNet、PDMR-FNLCR和PDTC合作，共享数据并参与PDX试验。