High-Throughput Functional Annotation of Fusion Gene Cancer Drivers

融合基因癌症驱动因素的高通量功能注释

• 批准号: 9250699
• 负责人: Kenneth L. Scott
• 金额: $ 20.72万
• 依托单位: BAYLOR COLLEGE OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-04-01 至 2018-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9250699
• 关键词: ABL1 gene; Address; Behavior; Biological Assay; Biological Process; Biology; Breast; Cancer Patient; Candidate Disease Gene; Cell Proliferation; Cell Survival; Cell model; Cells; Chromosomal Rearrangement; Clinic; Computer Simulation; DNA; Data; Data Set; Diagnostic; ETV6 gene; Engineering; Enrollment; Epithelial; Event; FDA approved; Face; Funding Mechanisms; Fusion Oncogene Proteins; Gene Fusion; Gene Targeting; Generations; Genes; Genetic; Goals; Human; IL3 gene; Implant; Individual; Malignant Neoplasms; Methodology; Modeling; Mus; NTRK3 gene; Nature; Neoplasm Metastasis; Oncogenes; Oncogenic; Open Reading Frames; Pathway interactions; Patient Care; Patient-Focused Outcomes; Performance; Phenotype; Phosphotransferases; Program Development; Proliferating; Protein Kinase; Proteomics; Research; Sampling; System; Technology; Testing; The Cancer Genome Atlas; Therapeutic; Transcript; Validation; addiction; base; bcr-abl Fusion Proteins; cancer cell; cancer genome; cancer therapy; cancer type; clinically relevant; drug development; drug discovery; early detection biomarkers; experience; functional genomics; fusion gene; genomic aberrations; improved; in vivo; malignant breast neoplasm; melanocyte; melanoma; new therapeutic target; next generation sequencing; novel; personalized medicine; prevent; public health relevance; screening; technology development; therapeutic target; treatment strategy; tumor; tumorigenesis; validation studies

 DESCRIPTION (provided by applicant): As we enter the era of personalized medicine, characterization of the cancer genome will continue to influence diagnostic and therapeutic decisions in the clinic. Recognizing this, large-scale efforts by The Cancer Genome Atlas (TCGA) and others are generating a compendium of genomic aberrations found across major cancer types with the goal of identifying new therapeutic targets and early detection biomarkers. The challenge now is to find ways to identify functional "driver" aberrations, as targeting driver events or their activated pathways offers the greatest hope of improving patient outcomes. Oncogenic transcript fusions resulting from chromosomal rearrangements represent an important class of such events, and the successful targeting of fusion oncoproteins such as BCR-ABL1 and EML4-ALK provide strong rationale for comprehensive testing of fusion genes identified in cancer. Unfortunately, the functional interrogation of fusion genes is complicated by the large quantity identified, inability to accurately predict those with driver activity and significant technical roadblocks preventing fusion gene construction for biological assays. To address these challenges, we developed novel methodologies for (1) in silico annotation of NGS data to select novel in-frame gene fusions across diverse tumor types, (2) high-throughput fusion gene construction using a novel recombineering strategy and our platform of >35,000 human open reading frame (ORF) clones, and (3) lentiviral delivery of fusion genes to cell models to identify those with driver activity and responsiveness to available therapeutics. The goal of our project is to scale these technologies for the comprehensive analysis of gene fusions in cancer, ultimately allowing functionalization of thousands of fusion events across diverse cancer types. In Aim 1 we will scale fusion gene construction to model up to 296 fusion genes chosen for their inclusion of druggable protein kinase domains. In Aim 2, fusion genes will be entered into our existing Ba/F3 driver screening platform, which rapidly quantitates the ability of each fusion gene to induce cell survival and proliferation. In Aim 3 we will functionally validate the top fusion drivers identified in Aim 2, followed by analysis of their activity and therapeutic sensitivities by proteomic profiling and compound screening, respectively. Finally, we will subject the top fusions to in vivo validation using engineered context- specific cell models in mice that provide the appropriate genetic and microenvironmental contexts for driver gene validation. This level of technology development, which is widely applicable to all cancer types, will have a sustained impact by creating unique opportunities for transformative research. These systems will reveal the highest priority fusion gene targets to enroll in deep mechanistic biology studies, drug discovery and development programs ultimately leading to personalized treatment strategies.

 描述（由申请人提供）：随着我们进入个性化医疗时代，癌症基因组的表征将继续影响临床诊断和治疗决策。认识到这一点，癌症基因组图谱（TCGA）和其他人的大规模努力正在产生一个在主要癌症类型中发现的基因组畸变的纲要，目的是确定新的治疗靶点和早期检测生物标志物。现在的挑战是找到识别功能性“驱动”畸变的方法，因为针对驱动事件或其激活的通路提供了改善患者结局的最大希望。由染色体重排引起的致癌转录物融合代表了一类重要的此类事件，融合癌蛋白（如BCR-ABL 1和EML 4-ALK）的成功靶向为全面检测癌症中鉴定的融合基因提供了强有力的依据。不幸的是，融合基因的功能询问是复杂的， 鉴定的数量大，不能准确预测具有驱动活性的那些，以及阻止用于生物测定的融合基因构建的重大技术障碍。 为了解决这些挑战，我们开发了新的方法，用于（1）NGS数据的计算机注释，以选择跨不同肿瘤类型的新的框内基因融合，（2）使用新的重组工程策略和我们的> 35，000个人开放阅读框（ORF）克隆的平台的高通量融合基因构建，和（3）将融合基因慢病毒递送至细胞模型以鉴定具有驱动活性和对可用治疗剂的应答性的那些。我们项目的目标是扩展这些技术，用于癌症基因融合的综合分析，最终允许不同癌症类型的数千个融合事件的功能化。在目标1中，我们将扩大融合基因的构建，以模拟多达296个融合基因，这些融合基因被选择用于包含可药物化的蛋白激酶结构域。在目标2中，融合基因将被输入我们现有的Ba/F3驱动筛选平台，该平台快速定量融合基因的能力。 每个融合基因诱导细胞存活和增殖。在目标3中，我们将在功能上验证目标2中确定的顶级融合驱动程序，然后分别通过蛋白质组学分析和化合物筛选分析其活性和治疗敏感性。最后，我们将使用小鼠中的工程化背景特异性细胞模型对最高融合体进行体内验证，所述小鼠为驱动基因验证提供适当的遗传和微环境背景。 这种广泛适用于所有癌症类型的技术发展水平将通过为变革性研究创造独特的机会而产生持续的影响。这些系统将揭示最高优先级的融合基因靶点，以参与深入的机制生物学研究，药物发现和开发计划，最终导致个性化的治疗策略。