Bioinformatics: Characterizing Brain Tumor Data

生物信息学：表征脑肿瘤数据

• 批准号: 10486975
• 负责人: Mark Gilbert
• 金额: $ 72.91万
• 依托单位: DIVISION OF BASIC SCIENCES - NCI
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10486975
• 关键词: Acceleration; Amino Acid Sequence; Animals; Area; Belief; Big Data; Biochemical Pathway; Bioinformatics; Biological; Biological Markers; Biological Process; Biology; Biomedical Research; Blood specimen; Brain Neoplasms; Cancer Genome Anatomy Project; Cancer Research Project; Cell Line; Cells; Central Nervous System Neoplasms; Chordoma; Classification; Clinical Data; Clinical Trials; Collaborations; Collection; Complex; Computer Analysis; DNA; DNA Sequence; DNA biosynthesis; Data; Data Analyses; Databases; Decision Making; Development; Disease; Enrollment; Evolution; Formalin; Freezing; Functional disorder; Funding; Gene Expression Profile; Gene Expression Profiling; Gene Proteins; Generations; Genes; Genetic Translation; Genomic Data Commons; Genomics; Glioma; Human; Human Genome Project; Hybrids; Impact evaluation; In Vitro; Information Storage; Laboratories; Link; Mainstreaming; Malignant Neoplasms; Malignant neoplasm of central nervous system; Maps; MicroRNAs; Microarray Analysis; Mining; Modality; Molecular; Molecular Analysis; Molecular Genetics; Monitor; Morphology; Natural History; Nature; Nucleic Acids; Paraffin Embedding; Patients; Pattern; Pharmacotherapy; Phenotype; Process; Prognosis; Proteins; Proteomics; RNA; RNA chemical synthesis; Research; Research Personnel; Resolution; Resources; Sampling; Science; Signal Transduction Pathway; Source; Structural Protein; System; Techniques; Technology; The Cancer Genome Atlas; Therapeutic; Time; Tissue Sample; Tissues; Translating; Treatment Efficacy; Tumor Biology; Tumor Tissue; Variant; Work; analytical tool; base; bioinformatics tool; cDNA Arrays; cell behavior; clinical care; clinical practice; companion diagnostics; data acquisition; data streams; data warehouse; design; differential expression; driving force; drug development; genome wide methylation; genome-wide; histone methylation; improved; in vivo; laboratory experiment; metabolomics; methylation pattern; molecular diagnostics; molecular targeted therapies; neuro-oncology; novel; novel therapeutics; older patient; patient stratification; peripheral blood; precision medicine; programs; prospective; protein distribution; protein metabolite; sample collection; small molecule; stem cells; therapeutic target; three dimensional structure; transcriptomics; treatment response; user-friendly; whole genome

As a hybrid science that links biological data with techniques for information storage, distribution, and analysis to support multiple areas of scientific research, including biomedicine, Bioinformatics has been driven by the great acceleration in data-generation processes in biology. The NOB for the past years has enrolled over 800 patients on the Natural History study which mandates the collection of tumor tissue as well as peripheral blood samples for germline DNA. This includes an unprecedented number of patients with rare CNS cancers. Moreover, the sample collection is accompanied by careful and prospective clinical data acquisition, allowing an unprecedented wealth of matched molecular and clinical data permitting a wide variety of analyses. Advances in the molecular analysis of genes, proteins and metabolites have greatly improved our understanding of biological processes and disease and have increased our ability to monitor treatment response and stratify patients to improve treatment efficacy. Precision medicine facilitated by companion diagnostics is one of the driving forces accelerating the drug development process and improving therapeutic management. Launched in 2016, the NCI Genomic Data Commons (GDC) provides a single source for data from NCI initiatives and cancer research projects, including TCGA and TARGET, and the analytical tools needed to mine them. The new initiated NOB Bioinformatics has extended our bioinformatics and computational analyses efforts to utilize the GDC databases. For example, using the TCGA data effectively doubles the number of GBMs we have to work with and affords us the advantage of formulating computationally derived hypothesis based on one database with the ability to validate those hypotheses on a totally different database. For example, a significant amount of time has been spent by NOB Bioinformatics on the databases to try and understand the biologically basis for the more aggressive phenotype and thus shorter survival of GBMs from older patients compared to those of younger GBMs. To date we have found a very interesting set of differentially expressed genes and miRNAs as well as specific genome wide methylation patterns and specific chromosomal number variants that differentiate older versus younger GBMs. We are in the process of using some of these findings to perform wet lab experiments to better annotate the significance of these findings. Furthermore, in collaborated with NCI ClinOmics (now COMPASS) program we analyzed and created genomic profiling data from the rare CNS tumors, such as chordomas, and dissected signal transduction pathways, and aided in the design of novel therapeutics. In addition to characterizing the samples from patients enrolled, the NOB Bioinformatics has generated genomic-scale analyses of the many human glioma initiating cells/glioma stem cells (GIC/GSC) lines produced in laboratory. This characterization is both at the primary cell level (including evolution through passages) as well as evaluation of the impact of different treatments (differentiation, animal passages, drug treatment, etc) on the biological behavior of the cells.

作为一门将生物数据与信息存储，分发和分析技术联系起来的混合科学，以支持包括生物医学在内的多个科学研究领域，生物信息学一直受到生物学数据生成过程加速的推动。在过去的几年里，NOB已经招募了800多名患者参加自然史研究，该研究要求收集肿瘤组织和外周血样本进行生殖系DNA检测。这包括前所未有数量的罕见CNS癌症患者。此外，样本采集伴随着仔细和前瞻性的临床数据采集，允许前所未有的匹配分子和临床数据的财富，允许各种各样的分析。基因、蛋白质和代谢物分子分析的进展极大地提高了我们对生物过程和疾病的理解，并提高了我们监测治疗反应和对患者进行分层以提高治疗效果的能力。伴随诊断促进的精准医学是加速药物开发过程和改善治疗管理的驱动力之一。NCI Genomic Data Commons（GDC）于2016年推出，为NCI计划和癌症研究项目（包括TCGA和TARGET）的数据提供了单一来源，以及挖掘这些数据所需的分析工具。新发起的NOB生物信息学扩展了我们的生物信息学和计算分析工作，以利用GDC数据库。例如，使用TCGA数据有效地使我们必须处理的GBM的数量增加了一倍，并且为我们提供了基于一个数据库制定计算导出的假设的优势，并且能够在完全不同的数据库上验证这些假设。例如，NOB Bioinformatics在数据库上花费了大量时间，试图了解老年患者GBM更具侵袭性表型的生物学基础，因此与年轻GBM相比，老年患者的GBM生存期较短。到目前为止，我们已经发现了一组非常有趣的差异表达基因和miRNA，以及特定的全基因组甲基化模式和特定的染色体数目变异，这些变异可以区分老年与年轻的GBM。我们正在使用其中的一些发现来进行湿实验室实验，以更好地诠释这些发现的意义。此外，在与NCI ClinOmics（现为COMPASS）项目的合作中，我们分析并创建了罕见CNS肿瘤（如脊索瘤）的基因组谱数据，并解剖了信号转导通路，并帮助设计了新的治疗方法。除了对入选患者的样本进行表征外，NOB生物信息学还对实验室生产的许多人脑胶质瘤起始细胞/胶质瘤干细胞（GIC/GSC）系进行了基因组规模的分析。这种表征既在原代细胞水平（包括传代进化），也在评价不同处理（分化、动物传代、药物处理等）对细胞生物学行为的影响。