Bioinformatic Tools in Cancer Research

癌症研究中的生物信息工具

• 批准号: 8350232
• 负责人: Kenneth Buetow
• 金额: $ 75.46万
• 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/8350232
• 关键词: 1-Phosphatidylinositol 3-Kinase; ABCB1 gene; ABL1 gene; Acute Lymphocytic Leukemia; Adjuvant Chemotherapy; Aftercare; Alleles; Architecture; Atlases; Attention; B-Cell Development; BRCA1 gene; BRCA2 gene; Behavior; Bioinformatics; Biological Process; Bortezomib; Breast Cancer Cell; CDKN2A gene; California; Cancer cell line; Candidate Disease Gene; Carcinoma; Cell Cycle; Cell Line; Cells; Cessation of life; Childhood Acute Lymphocytic Leukemia; Chimeric Proteins; Chromosome Mapping; Clinical; Collection; Communities; Complex; Constitutional; Coupled; Cyst; Cytogenetics; DNA Sequence Rearrangement; Dasatinib; Data; Data Set; Developmental Therapeutics Program; Dideoxy Chain Termination DNA Sequencing; Dose; Doxorubicin; Drug Exposure; Drug usage; ERBB2 gene; Epidermal Growth Factor Receptor; Epigenetic Process; Exposure to; Frequencies; Gene Combinations; Gene Expression; Gene Expression Alteration; Gene Expression Profile; Gene Mutation; Generations; Genes; Genetic; Genome; Genome Mappings; Genomics; Glioblastoma; Growth Factor; Heterogeneity; Human; Imagery; Individual; JAK1 gene; JAK2 gene; JAK3 gene; Janus kinase; Laboratories; Lesion; Linkage Disequilibrium; Malignant Neoplasms; Malignant neoplasm of ovary; Maps; Methodology; Methods; Methylation; MicroRNAs; Modeling; Molecular; Molecular Profiling; Mutate; Mutation; Mutation Analysis; Mutation Detection; National Human Genome Research Institute; Nucleotides; Outcome; PIK3CA gene; PTEN gene; Paclitaxel; Pathologist; Pathology; Pathway Analysis; Pathway interactions; Patients; Pattern; Pharmaceutical Preparations; Phase; Phenotype; Phosphotransferases; Population; Primary Neoplasm; Process; Proteins; Publications; RB1 gene; Radiation; Recurrence; Regimen; Reporting; Research; Resistance; Sampling; Sentinel; Signal Pathway; Signal Transduction; Somatic Mutation; Structure; Support System; System; Systems Analysis; TP53 gene; Technology; Testing; The Cancer Genome Atlas; Therapeutic Intervention; Time; Tissues; Tumor Tissue; Universities; Validation; Variant; Work; analytical tool; anticancer research; base; cancer genome; cancer type; cell growth; drug development; drug sensitivity; expectation; fusion gene; genome sequencing; genome-wide; high risk; inhibitor/antagonist; insertion/deletion mutation; insight; kinase inhibitor; leukemia/lymphoma; member; mutant; next generation; novel; research study; response; time interval; tool; tumor

The Buetow laboratory has developed analytical tools for large-scale, multi-dimensional cancer genome data. We have developed sensitive mutation detection methods (SNPdetector and IndelDetector) for identifying somatic mutations in tumor tissues; we developed the Cancer Genome Workbench (CGWB), a visualization tool that integrates somatic mutation data with copy number alteration, gene expression, methylation and microRNA expression. CGWB has integrated data from TCGA, TARGET, the Sanger Center COSMIC initiative, NHGRI Tumor Sequencing Project (TSP), whole genome somatic mutation data from the Vogelstein laboratory at Johns Hopkins University and GlaxoSmithKline Cancer Cell Line Genomic Profiling Data, thereby facilitating the integration and interpretation of diverse, high-quality raw data for the entire cancer research community. The Buetow laboratory is an active member of the TCGA consortium and has contributed analysis to the GBM flagship publication. For example, the laboratory developed the first computational pipeline for mutation detection and brought the novel NF1 mutation finding to the consortium for validation. Since this publication, an additional collection of GBM samples and genes have been sequenced. These extended data find 130 validated somatic mutations and 28 putative somatic mutations. With the exception of ERBB2 there is no significant difference between the somatic mutations reported in the original samples and those in the additional samples among the original 8 genes (i.e. TP53, EGFR, PTEN, NF1, ERBB2, RB1, PIK3R1 and PIK3CA). No somatic mutations were found in the additional 89 samples for ERBB2. Interestingly, 7 out of 10 samples from the original 91 with ERBB2 mutations were in secondary or recurrent GBM samples that were treated with neo-adjuvant chemotherapy and radiation. The results suggest that the ERBB2 mutations may arise only in secondary GBM. The TCGA consortia are still preparing its flagship publication. To date, the Buetow laboratory contribution includes the initial computational identification of somatic mutations, somatic allele loss, and discovery of fusion gene products. Our analysis of the Sanger sequencing data first identified that all TCGA ovarian cancer samples have TP53 somatic mutations. The 100% TP53 mutation rate coupled with the gross somatic copy number alteration suggested that these tumor samples are high-grade cyst carcinomas, a finding which was later confirmed when pathology data became available. We brought to the attention of the consortia truncation mutations in BRCA1 and BRCA2. Somatic and germline truncation mutations were found in approximately 25% of the patients. By examining the integrated view of sequencing, copy number alterations in these samples, we also discovered that LOH at the BRCA1 locus was found in all samples (100%); most of which appeared to be caused by copy-neutral LOH. 50% of the samples have LOH at the BRCA2 locus including one sample that has a reversion from mutation to wild-type: its germline frame shift mutant allele was lost by LOH. These findings were later verified by examination of next-gen sequencing data. In addition to TP53 and BRCA1/2, we have also detected high-frequency mutations in TTN and MUC16. The sample that has the BRCA2 reversion mutation appears to have fusion proteins that arise from amplification and deletion break-points occurring in two fusion partner. The Buetow laboratory is responsible for analyzing mutations for childhood acute lymphoblastic leukemia TARGET project. The laboratory has identified novel recurrent activating mutations in the Janus kinases JAK1 (n = 3), JAK2 (n = 16), and JAK3 (n = 1) in 20 (10.7%) of 187 BCR-ABL1-negative, high-risk pediatric ALL cases. The JAK1 and JAK2 mutations involved highly conserved residues in the kinase and pseudokinase domains and resulted in constitutive JAK-STAT activation and growth factor independence of Ba/F3-EpoR cells. The presence of JAK mutations was significantly associated with alteration of IKZF1 (70% of all JAK-mutated cases and 87.5% of cases with JAK2 mutations; p-value = 0.001) and deletion of CDKN2A/B (70% of all JAK-mutated cases and 68.9% of JAK2-mutated cases). The JAK-mutated cases had a gene expression signature similar to that of BCR-ABL1 in pediatric ALL, and they had a poor outcome. These results suggest that inhibition of JAK signaling is a logical target for therapeutic intervention. The COG Phase 1 Consortium is now developing a trial, expected to start in 2010, of a JAK inhibitor. Systematic analysis of all 185 validated somatic mutations identified four key pathways that are highly mutated in ALL: RAS signaling (39%), JAK signaling (10%), p53/RB signaling (6%), and B-cell development (14%). The RAS signaling pathway has the highest mutation rate and the mutations are over-represented in two gene expression clusters that correspond to patients with good clinical outcome and those with no sentinel cytogenetic lesions (p value < 0.0001). In contrast, no mutation has been found in the PI-3K pathway. In addition to studying primary tumors, the Buetow laboratory has analyzed the gene expression profile for the NCI60 cell lines in a time-course drug induction experiment carried out by DCTD DTP Drs. Anne Monk and Jim Doroshow. The NCI60 represent the front end of the NCI's drug development platform in which candidate agents are tested for effects on cell growth inhibition and death. Many thousands of compounds have now been screened utilizing this panel. Multidimensional molecular characterizations of the constitutional state of the multi-cancer type panel have been performed, including candidate gene mutation analysis, copy number assessment, and gene expression profiles. We have analyzed gene expression profiles after 2hr, 6hr and 24hr exposure to the following five drugs using both a low and a high dose regimen: doxorubicin, bortezomib, dastinib, taxol and sunitinib. The global gene expression changes induced by drug exposure are quite different. Soxorubicin: low dose patterns appear one time interval behind the high-dose treatment patterns; Bortezomib: massive expression changes occur at early time points and there is no major difference between low and high dose. Taxol low-dose and high-dose have similar effect. Dasatinib: triangle pattern indicating sensitive cell lines have expression changes early on while the resistant cell lines have no change all the way through. Sunitinib: only high-dose treatment has resulted in dramatic expression changes. To identify gene expression signatures related to drug sensitivity, we are: a) using GLM models to calculate expression correlation to GI50; b) identifying pathways with over-representation of induced or reduced genes using Fisher Ominibus test; c) applying PathOlogist analysis before and after the treatment to identify significantly altered pathways; and d) performing gene-rank analysis to evaluate similarity in drug response profile by drug, dose, time and tissue. BTG Cell cycle p53 Induced at 2hr Induced at 6hr Induced at 24hr Reduced at 24hr For comparison of cross-drug response (d) we used the Komogorov-Smirnov test to compare the top 200 most up-regulated and most down-regulated genes after drug treatment. Of the five drugs we analyzed, we see strong correlation of the profiles of the same drug treatment. However, cross-drug analysis reveals that dasatinib and sunitinib show similar profiles which is consistent with expectations as both drugs are kinase inhibitors. Response to a drug can vary from one cell line to the other and appears to be influenced by the genomic composition. For example, all 11 cell lines that have p53 wild-type, p16 deletion and MDR1-negative are sensitive to doxorubicin.

Buetow实验室开发了用于大规模、多维癌症基因组数据的分析工具。我们开发了敏感的突变检测方法（snp检测器和indel检测器），用于识别肿瘤组织中的体细胞突变；我们开发了癌症基因组工作台（Cancer Genome Workbench， CGWB），这是一个整合体细胞突变数据、拷贝数改变、基因表达、甲基化和microRNA表达的可视化工具。CGWB整合了来自TCGA、TARGET、Sanger中心COSMIC计划、NHGRI肿瘤测序项目（TSP）的数据、来自约翰霍普金斯大学Vogelstein实验室的全基因组体细胞突变数据和葛兰素史克癌症细胞系基因组分析数据，从而促进了整个癌症研究界对各种高质量原始数据的整合和解释。Buetow实验室是TCGA联盟的活跃成员，并为GBM旗舰出版物提供了分析。例如，该实验室开发了第一个用于突变检测的计算管道，并将新的NF1突变发现提交给联盟进行验证。自本论文发表以来，又对GBM样本和基因进行了测序。这些扩展的数据发现了130个有效的体细胞突变和28个假定的体细胞突变。除ERBB2外，原8个基因（即TP53、EGFR、PTEN、NF1、ERBB2、RB1、PIK3R1和PIK3CA）中所报告的体细胞突变在原样品和附加样品中无显著差异。在另外89份样本中未发现ERBB2的体细胞突变。有趣的是，在最初的91例ERBB2突变样本中，10例样本中有7例是继发性或复发性GBM样本，这些样本接受了新辅助化疗和放疗。结果表明，ERBB2突变可能仅发生在继发性GBM中。TCGA联盟仍在准备其旗舰出版物。迄今为止，Buetow实验室的贡献包括体细胞突变的初步计算鉴定、体细胞等位基因丢失和融合基因产物的发现。我们对Sanger测序数据的分析首先确定了所有TCGA卵巢癌样本都有TP53体细胞突变。100%的TP53突变率加上总体体细胞拷贝数的改变表明这些肿瘤样本是高级别囊肿癌，这一发现后来在获得病理数据时得到证实。我们引起了BRCA1和BRCA2的联合体截断突变的注意。在大约25%的患者中发现体细胞和种系截断突变。通过对这些样本的测序、拷贝数改变进行综合分析，我们还发现BRCA1位点的LOH在所有样本中都存在（100%）；其中大部分似乎是由复制中性LOH引起的。50%的样本在BRCA2位点存在LOH，其中一个样本从突变逆转为野生型：其种系框移突变等位基因因LOH而丢失。这些发现后来通过检查下一代测序数据得到了证实。除了TP53和BRCA1/2外，我们还检测到了TTN和MUC16的高频突变。具有BRCA2逆转突变的样本似乎具有由两个融合伙伴中发生的扩增和删除断点产生的融合蛋白。Buetow实验室负责分析儿童急性淋巴细胞白血病TARGET项目的突变。该实验室在187例bcr - abl1阴性的高危儿童ALL病例中发现了JAK1 （n = 3）、JAK2 （n = 16）和JAK3 (n = 1) Janus激酶的新型复发性激活突变。JAK1和JAK2突变涉及激酶和假激酶结构域的高度保守残基，导致Ba/F3-EpoR细胞的组成性JAK-STAT激活和生长因子独立性。JAK突变的存在与IKZF1的改变（占所有JAK突变病例的70%，占JAK2突变病例的87.5%，p值= 0.001）和CDKN2A/B的缺失（占所有JAK突变病例的70%，占JAK2突变病例的68.9%）显著相关。jak突变病例的基因表达特征与儿科ALL的BCR-ABL1相似，预后较差。这些结果表明，抑制JAK信号是治疗干预的逻辑目标。COG第一阶段联盟目前正在开发一种JAK抑制剂的试验，预计将于2010年开始。对所有185个体细胞突变的系统分析确定了all中高度突变的四个关键途径：RAS信号（39%）、JAK信号（10%）、p53/RB信号（6%）和b细胞发育（14%）。RAS信号通路的突变率最高，突变在两个基因表达簇中被过度代表，这两个基因表达簇对应于临床结果良好的患者和没有前哨细胞遗传学病变的患者（p值&lt； 0.0001）。相比之下，PI-3K通路未发现突变。除了研究原发肿瘤外，Buetow实验室还在DCTD DTP博士进行的时间过程药物诱导实验中分析了NCI60细胞系的基因表达谱。安妮·蒙克和吉姆·多罗休。NCI60代表了NCI药物开发平台的前端，在该平台上测试候选药物对细胞生长抑制和死亡的影响。成千上万的化合物现在已经利用这个面板进行了筛选。多种癌症类型面板的结构状态的多维分子表征已经执行，包括候选基因突变分析，拷贝数评估和基因表达谱。我们分析了使用低剂量和高剂量方案暴露于以下五种药物2小时、6小时和24小时后的基因表达谱：阿霉素、硼替佐米、达斯替尼、紫杉醇和舒尼替尼。药物暴露引起的整体基因表达变化有很大差异。柔柔比星：低剂量模式比高剂量模式晚一个时间间隔出现；硼替佐米：大量表达改变发生在早期时间点，低剂量和高剂量之间无明显差异。紫杉醇低剂量和高剂量效果相似。达沙替尼：三角形模式表明敏感细胞系在早期有表达变化，而耐药细胞系在整个过程中没有变化。舒尼替尼：仅大剂量治疗已导致显著的表达变化。为了确定与药物敏感性相关的基因表达特征，我们：a)使用GLM模型计算与GI50的表达相关性；b)使用Fisher Ominibus试验确定过度表达诱导或减少基因的途径；c)在治疗前后应用病理学家分析，以确定显著改变的通路；d)进行基因等级分析，以评估药物、剂量、时间和组织在药物反应谱上的相似性。BTG细胞周期p53 2小时诱导6小时诱导24小时诱导24小时降低交叉药物反应比较(d)我们使用Komogorov-Smirnov检验比较药物治疗后前200个上调和下调最多的基因。在我们分析的五种药物中，我们看到了同一种药物治疗的很强的相关性。然而，交叉药物分析显示，达沙替尼和舒尼替尼表现出相似的特征，这与预期一致，因为这两种药物都是激酶抑制剂。对药物的反应可能因细胞系而异，并且似乎受基因组组成的影响。例如，所有11种p53野生型、p16缺失和mdr1阴性的细胞系都对阿霉素敏感。