Bioinformatics Core

生物信息学核心

基本信息

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
项目类别：
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

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

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项目摘要

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.

项目摘要拟议多样性PDX开发和测试中心（D-PDTC）的总体目标针对改善胃癌和肺癌的癌症精度药物方法。这将通过超过120种新的少数胃和肺PDX肿瘤的大规模发育和分子表征模型并利用这些模型进行体外和临床前PDX试验来测试分子匹配的有效性治疗药作为单个药物或组合。药物反应性的统计测试将是进行的，以及将治疗反应与分子相关的数据集成策略的应用单个PDX和患者肿瘤的特征。生物信息学核心（UBC）的目标是提供在多个阶段为拟议研究的大量数据分析支持，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 UBC将建立并具有相当大的能力，通过将重要人员汇集在一起在基因组学，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学共享资源。 UBC将通过与研究人员的动态相互作用，促进U54无缝集成，通过实施专用的UCATS生物信息学核心关系数据库，该数据库将作为一个用于链接项目设计，示例跟踪，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遗传表征，我们将应用一套建立的生物信息学分析管道在高性能超增强下一代上实施测序数据分析平台，用于处理全外观测序和RNA测序数据生成由完全注释的基本结果数据集1）基因组变体和2）成绩单具有相应的表达值。在引导方面可以显着构想的其他肿瘤特性还将定义治疗决定，例如突变烧伤。随后，综合生物信息学方法将用于目标优先级和治疗匹配，然后将其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也将与PDXNET，PDMR-FNLCR和PDTCS合作，以进行数据共享和参与PDX试验。