Predicting Transcriptional and Epigenetic Networks in Cancer from Sequencing Data

从测序数据预测癌症中的转录和表观遗传网络

• 批准号: 10310467
• 负责人: Jun S Song
• 金额: $ 31.96万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-12-16 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10310467
• 关键词: Address; Binding; Binding Sites; Cancer Patient; Cells; Chromatin; Complex; Computing Methodologies; DNA; DNA Sequence Alteration; Data; Development; Drug resistance; Epigenetic Process; Family; Family member; Frequencies; Funding; Gene Expression Regulation; Genetic; Genetic Transcription; Genome; Genomics; Glioblastoma; Goals; High Prevalence; Individual; Knock-out; Knowledge; Link; Machine Learning; Malignant Neoplasms; Mediating; Methods; Molecular; Molecular Target; Mutation; Nature; Oncogenic; Patients; Pattern; Phase; Point Mutation; Protein Isoforms; RNA-Directed DNA Polymerase; Recurrence; Research; Role; Somatic Mutation; Specificity; TERT gene; Techniques; Telomerase; Telomere Maintenance; Testing; Therapeutic; Time; Toxic effect; Transcriptional Regulation; Untranslated RNA; Validation; Variant; Work; anticancer research; base; bioinformatics resource; c-myc Genes; cancer cell; cancer genomics; cancer type; computer framework; computing resources; deep learning; dimer; genetic variant; genome-wide; individual patient; insight; knowledge of results; melanoma; member; mutant; novel; novel therapeutic intervention; promoter; recruit; resistance mechanism; telomere; therapeutic target; tool; transcription factor; treatment response

Limitless replicative potential is a key hallmark of cancer and critically depends on telomere maintenance. Many cancers thus aberrantly reactivate the telomerase reverse transcriptase (TERT), a catalytic subunit of the telomerase complex that elongates telomere. It has been recently discovered that this common path to immortality in multiple cancers is through two activating point mutations in the TERT promoter (TERTp), found in more than 50 different cancer types, often at strikingly high frequencies, e.g. roughly 83% in glioblastomas (GBM) and 71% in melanomas. In the previous funding period, the PI has identified the molecular function of these highly recurrent mutations, demonstrating that the transcription factor (TF) GABP binds the mutant TERTp with exquisite specificity, but not the wild-type TERTp. The high prevalence of TERTp mutations across multiple cancer types and the selectivity of GABP recruitment to mutant TERTp thus provide an unprecedented opportunity for treating a large number of cancer patients with minimal toxicity to healthy cells. Despite the clear significance of this opportunity, however, several important questions surrounding the molecular functions and modulators of TERTp mutations remain poorly understood, hindering the development of effective and safe therapeutic strategies. Our long-term goal is to establish a rigorous computational framework for understanding the aberrant transcriptional and epigenetic networks in cancers and to apply the resulting knowledge to devise novel therapeutic strategies that account for the genetic background of individual patients and that can a priori predict and avoid potential resistance mechanisms. The objective of our current renewal proposal is to develop powerful computational methods for transforming our knowledge about the non-coding TERTp mutations into an effective and safe molecular target. At the same time, the resulting methods will help resolve several outstanding challenges in the field of transcriptional gene regulation and have broad applications in cancer genomics. We will accomplish our objective my pursuing the following Aims: (1) Develop and test a computational framework for inferring sequence features that determine the distinct and shared binding patterns of paralogous TFs; (2) Develop and validate integrative tools for discovering the molecular basis of genetic interactions between germline variations and oncogenic mutations; (3) Develop and apply computational methods for studying the role of DNA helical phase between adjacent binding motifs in recruiting ETS factors to chromatin; (4) Perform a systematic genomic characterization of the effects of knocking out GABPB1L in TERTp-mutant cancer cells and healthy cells. The results of this proposal will have a broad impact on cancer research by providing powerful tools for studying paralogous oncogenic TFs and revealing novel insights into a highly promising therapeutic strategy.

无限的复制潜力是癌症的一个关键标志，并且严重依赖于端粒的维持。许多 因此，癌症异常地重新激活端粒酶逆转录酶（TERT），端粒酶逆转录酶的催化亚基。 延长端粒的端粒酶复合物。最近发现，这条通往 在多种癌症中的永生是通过在TERT启动子（TERTp）中的两个激活点突变， 在50多种不同的癌症类型中，通常以惊人的高频率，例如在胶质母细胞瘤中约为83% (GBM)在黑色素瘤中为71%。在上一个资助期内，PI已经确定了 这些高度重复的突变，表明转录因子（TF）GABP结合突变的TERTp 具有很强的特异性，但野生型TERTp没有TERTp突变的高患病率在多个 癌症类型和GABP募集突变TERTp的选择性因此提供了前所未有的 这为以最小的健康细胞毒性治疗大量癌症患者提供了机会。尽管显然 然而，这个机会的重要性，围绕分子功能的几个重要问题， TERTp突变的调节剂仍然知之甚少，阻碍了有效和安全的药物的开发。 治疗策略 我们的长期目标是建立一个严格的计算框架， 癌症中的转录和表观遗传网络，并应用由此产生的知识来设计新的 治疗策略，说明个别患者的遗传背景，并可以先验预测 并避免潜在的阻力机制。我们目前更新建议的目标是发展强大的 将我们关于非编码TERTp突变的知识转化为有效的计算方法， 和安全的分子靶点。同时，由此产生的方法将有助于解决几个悬而未决的问题， 在转录基因调控领域的挑战，并在癌症基因组学中有广泛的应用。我们 我将实现我们的目标，我追求以下目标：（1）开发和测试计算框架 用于推断决定旁系同源TF的独特和共享结合模式的序列特征;（2） 开发和验证综合工具，以发现遗传相互作用的分子基础， 生殖系变异和致癌突变;（3）开发和应用计算方法研究 相邻结合基序之间的DNA螺旋相在将ETS因子募集到染色质中的作用;（4）进行 在TERTp突变癌细胞中敲除GABPB 1 L的作用的系统基因组表征， 健康的细胞 这项提案的结果将通过提供强大的研究工具对癌症研究产生广泛的影响。 旁系同源致癌TF，并揭示了一个非常有前途的治疗策略的新见解。