High-Throughput Functional Annotation of Fusion Gene Cancer Drivers

 描述(申请人提供)：随着我们进入个性化医学时代，癌症基因组的特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)和其他机构的大规模努力正在产生一份主要癌症类型的基因组异常概要，目的是确定新的治疗靶点和早期检测生物标记物。现在的挑战是找到识别功能性“司机”异常的方法，因为针对司机事件或其激活的通路提供了改善患者预后的最大希望。由染色体重排引起的癌基因转录融合是这类事件的重要类别，而BCR-ABL1和EML4-ALK等融合癌蛋白的成功靶向为全面检测在癌症中发现的融合基因提供了强有力的理论基础。不幸的是，融合基因的功能询问因 大量识别，无法准确预测那些具有司机活动的人，以及阻碍融合基因构建用于生物检测的重大技术障碍。为了应对这些挑战，我们开发了用于以下方面的新方法：(1)通过电子计算机注释NGS数据以选择跨不同肿瘤类型的新型框内基因融合；(2)利用新型重组工程策略和我们的35,000个人类开放阅读框架(ORF)克隆平台构建高通量融合基因；以及(3)以慢病毒形式将融合基因导入细胞模型，以确定那些具有驱动活性和对现有治疗药物有反应性的融合基因。我们项目的目标是扩大这些技术的规模，以全面分析癌症中的基因融合，最终允许各种癌症类型的数千种融合事件的功能化。在目标1中，我们将扩大融合基因构建的规模，以模拟多达296个融合基因，这些融合基因包括可药物蛋白激酶结构域。在目标2中，融合基因将被输入到我们现有的BA/F3司机筛选平台中，这将快速量化 每个融合基因都能诱导细胞存活和增殖。在目标3中，我们将从功能上验证目标2中确定的顶级融合驱动因素，然后分别通过蛋白质组学分析和化合物筛选分析它们的活性和治疗敏感性。最后，我们将在小鼠体内使用特定于环境的细胞模型对顶级融合进行体内验证，该模型为驱动基因验证提供了适当的遗传和微环境环境。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。这些系统将揭示最优先的融合基因靶点，以登记参加深度机械生物学研究、药物发现和开发计划，最终导致个性化治疗策略。

 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.