Bioinformatics: Characterizing Brain Tumor Data

生物信息学：表征脑肿瘤数据

• 批准号: 10926324
• 负责人: Mark Gilbert
• 金额: $ 89.5万
• 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/10926324
• 关键词: Acceleration; Animals; Area; Belief; Big Data; Biochemical Pathway; Bioinformatics; Biological; 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; Explosion; Formalin; Freezing; Functional disorder; Funding; Gene Expression Profile; Gene Expression Profiling; Generations; Genes; Genetic; 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; Monitor; Morphology; Natural History; Nature; Nucleic Acids; Paraffin Embedding; Patients; Pattern; Pharmacotherapy; Phenotype; Process; Proteins; Proteomics; RNA; RNA chemical synthesis; Research; Research Personnel; Resolution; Resources; Sampling; Science; Signal Transduction Pathway; Source; System; Techniques; Technology; The Cancer Genome Atlas; Therapeutic; Time; Tissue Sample; Tissues; Translating; Treatment Efficacy; Tumor Biology; Tumor Tissue; Variant; Work; analytical tool; 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; mRNA Translation; metabolomics; methylation pattern; molecular diagnostics; molecular targeted therapies; neuro-oncology; novel; novel therapeutics; older patient; participant enrollment; patient prognosis; patient stratification; peripheral blood; precision medicine; programs; prospective; protein aminoacid sequence; protein distribution; sample collection; small molecule; stem cells; therapeutic biomarker; therapeutic target; three dimensional structure; transcriptomics; treatment response; treatment stratification; 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 1000 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、分发和分析技术相结合以支持包括生物医学在内的多个科学研究领域的混合科学，生物信息学受到生物学中数据生成过程的极大加速的推动。在过去的几年里，NOB已经招募了1000多名患者参加自然历史研究，该研究要求收集肿瘤组织以及外周血样本以检测生殖系DNA。这包括数量空前的罕见中枢神经系统癌症患者。此外，样品采集伴随着仔细和前瞻性的临床数据采集，允许前所未有的大量匹配的分子和临床数据，从而实现各种分析。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了解，并增强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。伴随诊断的精准医学是加速药物开发进程和改善治疗管理的驱动力之一。NCI基因组数据共享中心(GDC)于2016年启动，为NCI倡议和癌症研究项目提供单一来源的数据，包括TCGA和TARGET，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的数量翻了一番，并为我们提供了基于一个数据库制定计算派生的假设的优势，从而能够在完全不同的数据库上验证这些假设。例如，NOB生物信息学在数据库上花费了大量的时间来尝试和了解更具侵袭性的表型的生物学基础，因此与年轻的基底膜相比，来自老年患者的基底膜存活时间更短。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miRNAs，以及特定的全基因组甲基化模式和特定的染色体数量变异，以区分年龄较大的和较年轻的基底膜。我们正在利用其中的一些发现进行湿实验，以更好地诠释这些发现的意义。此外，在与NCI ClinOmics(现为COMPASS)计划的合作中，我们分析和创建了罕见的中枢神经系统肿瘤(如脊索瘤)的基因组图谱数据，并剖析了信号转导途径，并帮助设计了新的治疗方法。除了对登记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类胶质瘤启动细胞/胶质瘤干细胞(GIC/GSC)系进行了基因组规模的分析。这种表征既是在初级细胞水平上(包括通过传代进化)，也是评估不同处理(分化、动物传代、药物治疗等)对细胞生物学行为的影响。

项目成果

Clinical outcome assessment trends in clinical trials-Contrasting oncology and non-oncology trials.

• DOI: 10.1002/cam4.6325
• 发表时间: 2023-08
• 期刊: Cancer medicine
• 影响因子: 4
• 作者: Kim Y;Gilbert MR;Armstrong TS;Celiku O
• 通讯作者: Celiku O

Synthetic lethality-mediated precision oncology via the tumor transcriptome.

• DOI: 10.1016/j.cell.2021.03.030
• 发表时间: 2021-04-29
• 期刊: CELL
• 影响因子: 64.5
• 作者: Lee, Joo Sang;Nair, Nishanth Ulhas;Dinstag, Gal;Chapman, Lesley;Chung, Youngmin;Wang, Kun;Sinha, Sanju;Cha, Hongui;Kim, Dasol;Schperberg, Alexander, V;Srinivasan, Ajay;Lazar, Vladimir;Rubin, Eitan;Hwang, Sohyun;Berger, Raanan;Beker, Tuvik;Ronai, Ze'ev;Hannenhalli, Sridhar;Gilbert, Mark R.;Kurzrock, Razelle;Lee, Se-Hoon;Aldape, Kenneth;Ruppin, Eytan
• 通讯作者: Ruppin, Eytan

BOIN: a novel Bayesian design platform to accelerate early phase brain tumor clinical trials.

BOIN：一种新颖的贝叶斯设计平台，可加速早期脑肿瘤临床试验。

• DOI: 10.1093/nop/npab035
• 发表时间: 2021
• 期刊: Neuro-oncology practice
• 影响因子: 2.7
• 作者: Yuan,Ying;Wu,Jing;Gilbert,MarkR
• 通讯作者: Gilbert,MarkR