Bioinformatic Tools in Cancer Research

癌症研究中的生物信息工具

• 批准号: 7970387
• 负责人: Kenneth Buetow
• 金额: $ 96.9万
• 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/7970387
• 关键词: ABCB1 gene; Acute Lymphocytic Leukemia; Affect; Alternative Splicing; Atlases; Bioinformatics; Biological; Bortezomib; Cancer Etiology; Cancer Therapy Evaluation Program; Candidate Disease Gene; Cell Line; Chemicals; Child; Chimeric Proteins; Clinical; Clinical Trials; Collaborations; Dasatinib; Data; Data Analyses; Development; Diagnosis; Doxorubicin; Drug Delivery Systems; Epidermal Growth Factor Receptor; Event; Failure; Gene Expression; Gene Expression Profile; Genes; Genetic Transcription; Genomics; Genotype; Germ-Line Mutation; Glioblastoma; Growth; Hospitals; Hour; Imagery; Investigation; KRAS2 gene; Leadership; Loss of Heterozygosity; Malignant Neoplasms; Manuscripts; Maps; Methods; Mutate; Mutation; Nature; Neuroblastoma; Oncology Group; Outcome; Ovarian; Paclitaxel; Paper; Pathway interactions; Patients; Pharmaceutical Preparations; Phase I Clinical Trials; Pilot Projects; Protein Tyrosine Kinase; Publishing; Receptor Tyrosine Kinase Gene; Recurrence; Refractory; Reporting; Research; Saint Jude Children' s Research Hospital; Sampling; Somatic Mutation; TP53 gene; Time; Translating; Update; Validation; analytical method; analytical tool; anticancer research; base; cancer genome; cancer genomics; cancer therapy; cell growth; drug mechanism; drug sensitivity; effective therapy; inhibitor/antagonist; insertion/deletion mutation; insight; member; mutant; next generation; novel; ovarian neoplasm; protein function; response; tool; tumor

Multi-dimensional cancer genomic data generated by large-scale investigations of tumor genomic alterations such as the The Cancer Genome Atlas Project (TCGA) is expected to greatly facilitate understanding of cancer etiology, identification of novel drug targets and development of personalized treatment based on tumor genomic profiling. Our group has developed analytical tools and has actively participated in the analysis of the integrated genomic data for TCGA. We were the first to identify NF1 as one of the most frequently mutated genes in glioblastoma multiforme (GBM), a finding that was later validated and reported in the TCGA network paper published in Nature (1). Our visualization tools were used by the TCGA network members in identifying core pathways involved in GBM. Recently we discovered and reported to TCGA project leadership extensive loss of heterozygosity and reversion of germline mutations in BRCA1/2 in TCGA ovarian tumors. These findings have been validated by Next-generation sequencing data. We are also collaborating with TCGA project team on preparing a manuscript that gives an update of mutation profile of the significant genes identified in the pilot study. Besides contributing to TCGA, our group has been responsible for analyzing mutations for NCI's Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. Recently we identified novel recurrent somatic mutations that were only found in acute lymphoblastic leukemia (ALL) patients with poor clinical outcome (2, 3). These mutations cause constitutive activation of the JAK receptor tyrosine kinase gene, and the availability of chemical inhibitors of this gene suggest that this finding can be translated into a novel therapy for poor outcome ALL patients (2). Phase I clinical trial based on this finding is currently being organized by NCI CTEP and Childrens Oncology Group. We are currently preparing a manuscript that summarizes the major biological pathways affected by somatic mutations and copy number alterations in ALL. We have also identified activating somatic mutations in ALK in TARGET neuroblastoma (NBL) tumors. A clinical trial of ALK inhibition therapy for children with refractory NBL is ongoing under the leadership of TARGET NBL. We have ongoing collaboration with Drs. James Downing and Charles Mulligan at St. Jude Childrens Hospital to compare the somatic mutation changes occurred at diagnosis and those at the relaps to gain insight into biological pathways that involved in failure to treatment. We have developed novel analytical methods for analyzing Next-generation sequencing data focusing on the analysis of fusion protein and insertion/deletions. These events are difficult to be detected by current Next-gen mapping methods. In an analysis of Next-gen data of three ALL samples, we have identified novel insertion/deletions that can disrupt protein function and alternative splicing events that affect 25% of the transcriptome. We will participate in the Next-gen sequencing data analysis of TCGA Ovarian and GBM for the TCGA project when the data is available. Understanding the mechanisms of drug response is critical for developing effective cancer therapy. In collaboration with Drs. Jim Doroshow and Anne Monks, we analyzed the correlation between drug-induced gene expression changes and drug sensitivity in the NCI-60 cell line panel. Expression data were collected from untreated cell lines as well as those treated with low- or high-concentration drug for 2hr, 6hr and 24hr. Drug sensitivity was calculated as GI50 (i.e. 50% growth inhibitory concentration) for each cell line. To date, we have analyzed data collected from doxorubicin, bortezomib, taxol, dasatinib, and sunitinib. Each drug is unique in the onset of massive cellular transcription change, response to low versus high treatment and affected biological pathways. For doxorubicin, we identified that the dual contribution of MDR-1 and activation of the p53 pathway to DOXO-sensitivity, establishing a strong association between the p53 pathway activation with the composite genotype of wild-type p53 mutant p16 across the NCI60 cell line panel. For dasatinib, induction of MYC at 2 hours appear to be a hallmark associated with drug sensitivity. For sunitinib, activation of tyrosine kinase genes involved in cellular growth such as EGFR, STAT and KRAS appear to be associated with drug sensitivity. Experimental validation is ongoing to evaluate the candidate genes identified by our analysis.

通过大规模肿瘤调查产生的多维癌症基因组数据 基因组改变，如癌症基因组图谱计划（TCGA），预计将大大 促进对癌症病因学的理解，识别新的药物靶点， 开发基于肿瘤基因组图谱的个性化治疗。我们集团 开发了分析工具，并积极参与了对 TCGA的基因组数据。我们是第一个确定NF 1是最常见的突变之一， 多形性胶质母细胞瘤（GBM）中的基因，这一发现后来得到证实，并在 TCGA网络论文发表在Nature（1）上。TCGA使用了我们的可视化工具 网络成员在确定参与GBM的核心途径。最近我们发现， 向TCGA项目领导报告了广泛的杂合性丢失和生殖系回复突变 TCGA卵巢肿瘤中BRCA 1/2突变的研究这些发现得到了 下一代测序数据。我们还与TCGA项目团队合作， 一份手稿，提供了一个更新的突变谱的重要基因中确定的， 试点研究。除了为TCGA做出贡献，我们小组还负责分析 NCI的治疗应用研究，以产生有效的治疗 （目标）项目。最近，我们发现了新的复发性体细胞突变， 在临床结果较差的急性淋巴细胞白血病（ALL）患者中发现（2，3）。这些 突变引起JAK受体酪氨酸激酶基因的组成性激活， 该基因的化学抑制剂的可用性表明，这一发现可以被翻译为 转化为治疗预后不良ALL患者的新疗法（2）。基于此的I期临床试验 这项研究目前由NCI CTEP和儿童肿瘤学小组组织。我们 目前正在准备一份手稿，总结了受影响的主要生物途径， ALL中的体细胞突变和拷贝数改变。我们还发现， 靶向神经母细胞瘤（NBL）肿瘤中ALK的体细胞突变。ALK的临床试验 在TARGET的领导下，对难治性NBL儿童的抑制治疗正在进行中 NBL。我们正在与圣裘德的James Downing和Charles Mulligan博士合作 儿童医院比较诊断时发生的体细胞突变变化和 通过这些重复的实验来深入了解导致治疗失败的生物学途径。我们 开发了新的分析方法，用于分析下一代测序数据聚焦 对融合蛋白和插入/缺失的分析。这些事件很难被 通过当前的下一代映射方法检测到。在对三种ALL的下一代数据的分析中， 样品，我们已经确定了新的插入/缺失，可以破坏蛋白质的功能， 影响25%转录组的可变剪接事件。我们将参加 TCGA项目的TCGA Ovarian和GBM的下一代测序数据分析 服务器租用-美国服务租用了解药物反应的机制对于开发 有效的癌症治疗。在与Jim Doroshow和Anne Monks博士的合作中，我们分析了 药物诱导的基因表达变化与药物敏感性之间的相关性 NCI-60细胞系组。从未处理的细胞系以及未处理的细胞系收集表达数据。 低浓度或高浓度药物处理2小时、6小时和24小时。药敏 计算为每种细胞系的GI 50（即50%生长抑制浓度）。迄今为止，我们 分析了从阿霉素、硼替佐米、紫杉醇、达沙替尼和舒尼替尼收集的数据。 每种药物在大规模细胞转录变化的发生、对低水平的反应方面都是独特的 与高治疗和受影响的生物途径相比。对于阿霉素，我们发现， MDR-1和p53通路活化对DOXO敏感性的双重作用， 在p53途径激活与复合物之间建立强关联 NCI 60细胞系组中野生型p53突变型p16的基因型。对于达沙替尼， 2小时MYC诱导似乎是与药物敏感性相关的标志。为 舒尼替尼，激活参与细胞生长的酪氨酸激酶基因，如EGFR、STAT 和KRAS似乎与药物敏感性有关。实验验证正在进行中， 评估通过我们的分析鉴定的候选基因。