Bioinformatic Tools in Cancer Research

癌症研究中的生物信息工具

• 批准号: 8158466
• 负责人: Kenneth Buetow
• 金额: $ 68.31万
• 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/8158466
• 关键词: Bioinformatics; anticancer research; tool

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's COSMIC initiative, NHGRI's 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 is still preparing its flagship publication. To date, the Buetow laboratory's 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 frameshift mutant allele was lost by LOH. These findings were later verified by examination of nex-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 less than 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's 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's 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。我们开发了灵敏的突变检测方法（SNPDetector和IndelDetector）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 (CGWB)，这是一种可视化工具，将体细胞突变数据与拷贝数改变、基因表达、甲基化和 microRNA 表达相结合。 CGWB整合了来自TCGA、TARGET、桑格中心的COSMIC计划、NHGRI的肿瘤测序项目（TSP）、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细胞系基因组分析数据，从而促进整个癌症研究界多样化、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 旗舰出版物做出了分析。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。自本文发表以来，额外收集的 GBM 样本和基因已被测序。这些扩展数据发现了 130 个已验证的体细胞突变和 28 个假定的体细胞突变。除ERBB2外，原始8个基因（即TP53、EGFR、PTEN、NF1、ERBB2、RB1、PIK3R1和PIK3CA）中原始样本中报告的体细胞突变与附加样本中报告的体细胞突变没有显着差异。在另外 89 个 ERBB2 样本中未发现体细胞突变。有趣的是，原始 91 个带有 ERBB2 突变的样本中，10 个样本中有 7 个是接受新辅助化疗和放疗的继发性或复发性 GBM 样本。结果表明 ERBB2 突变可能仅出现在继发性 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、体细胞等位基因丢失的初步计算鉴定以及融合基因产物的发现。我们对桑格测序数据的分析首先发现所有 TCGA 卵巢癌样本都存在 TP53 体细胞突变。 100% 的 TP53 突变率加上总体细胞拷贝数改变表明这些肿瘤样本是高级别囊性癌，这一发现后来在病理学数据可用时得到证实。我们提请注意 BRCA1 和 BRCA2 中的联合体截短突变。大约 25% 的患者发现体细胞和种系截短突变。通过检查这些样本中测序、拷贝数变化的综合视图，我们还发现所有样本中都发现了 BRCA1 位点的 LOH（100%）；其中大部分似乎是由复制中性 LOH 引起的。 50% 的样品在 BRCA2 基因座处具有 LOH，其中包括一个从突变回复为野生型的样品：其种系移码突变等位基因因 LOH 而丢失。这些发现后来通过下一代测序数据的检查得到了验证。除了TP53和BRCA1/2之外，我们还检测到了TTN和MUC16的高频突变。具有 BRCA2 回复突变的样品似乎具有由两个融合伙伴中发生的扩增和缺失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 阴性高危儿科 ALL 病例中的 20 例 (10.7%) 中发现了 Janus 激酶 JAK1 (n = 3)、JAK2 (n = 16) 和 JAK3 (n = 1) 中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中高度保守的残基，并导致Ba/F3-EpoR细胞的组成型JAK-STAT激活和生长因子独立性。 JAK 突变的存在与 IKZF1 的改变（所有 JAK 突变病例的 70% 和 JAK2 突变病例的 87.5%；p 值 = 0.001）和 CDKN2A/B 的缺失（所有 JAK 突变病例的 70% 和 JAK2 突变病例的 68.9%）显着相关。 JAK 突变病例的基因表达特征与儿童 ALL 中的 BCR-ABL1 相似，但预后较差。这些结果表明，抑制 JAK 信号传导是治疗干预的合理目标。 COG 1 期联盟目前正在开发一项 JAK 抑制剂试验，预计将于 2010 年开始。对所有 185 个经过验证的体细胞突变的系统分析确定了 ALL 中高度突变的 4 个关键途径：RAS 信号传导 (39%)、JAK 信号传导 (10%)、p53/RB 信号传导 (6%) 和 B 细胞发育 (14%)。 RAS信号通路具有最高的突变率，并且突变在两个基因表达簇中过度表达，这两个基因表达簇对应于具有良好临床结果的患者和没有前哨细胞遗传学病变的患者（p值小于0.0001）。相比之下，PI-3K 通路中未发现突变。除了研究原发性肿瘤外，Buetow 实验室还在 DCTD DTP 的 Drs. 进行的时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·多罗肖。 NCI60 代表 NCI 药物开发平台的前端，其中测试候选药物对细胞生长抑制和死亡的影响。现在已经利用该面板筛选了数千种化合物。已经对多癌症类型组的构成状态进行了多维分子表征，包括候选基因突变分析、拷贝数评估和基因表达谱。我们使用低剂量和高剂量方案分析了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不同。索柔比星：低剂量模式出现在高剂量治疗模式后一时间间隔；硼替佐米：在早期时间点发生大量表达变化，低剂量和高剂量之间没有重大差异。紫杉醇：低剂量和高剂量有相似的效果。达沙替尼：三角形图案表明敏感细胞系早期有表达变化，而耐药细胞系自始至终没有变化。舒尼替尼：只有高剂量治疗才会导致显着的表达变化。为了识别与药物敏感性相关的基因表达特征，我们： a) 使用 GLM 模型来计算与 GI50 的表达相关性； b) 使用Fisher's Ominibus检验识别诱导或减少基因过度表达的途径； c) 在治疗前后应用病理学家分析来识别显着改变的途径； d) 进行基因排序分析，以评估药物、剂量、时间和组织的药物反应谱的相似性。 BTG 细胞周期 p53 2 小时诱导 6 小时诱导 24 小时诱导 24 小时减少 为了比较交叉药物反应 (d)，我们使用 Komogorov-Smirnov 检验来比较药物治疗后前 200 个最上调和最下调的基因。在我们分析的五种药物中，我们发现相同药物治疗的情况具有很强的相关性。然而，交叉药物分析表明，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。一种细胞系与另一种细胞系对药物的反应可能有所不同，并且似乎受到基因组组成的影响。例如，所有 11 个具有 p53 野生型、p16 缺失和 MDR1 阴性的细胞系都对阿霉素敏感。