High-Throughput Tumor Genomic Profiling by Massively Parallel Sequencing

描述(申请人提供)：癌症诊断和治疗决策历来是基于起源和扩散的解剖位置；然而，新兴的范例结合了给定肿瘤的关键遗传属性来预测临床行为并指定靶向治疗的最佳使用。最终，个性化癌症药物的交付将需要对临床和翻译领域中所有具有治疗信息性的肿瘤基因组变化进行系统的表征。此前，我们开发和部署了OncoMap，这是一个基于质谱学基因分型的平台，可以高通量分析数十个癌症基因的数百个已知突变。该平台表现良好，并发起了一项强有力的翻译肿瘤学努力。然而，质谱学基因分型技术在范围、检测灵敏度和可识别的基因组改变类型方面仍然有限。最近，通过大规模并行测序(MPS)技术使多方面的肿瘤表征成为可能，既技术上可行，经济上也可获得。因此，该应用程序的目标是将OncoMap方法迁移到MPS平台(Illumina)，支持解决方案阶段外显子捕获和样本条形码等创新。与我们在Dana-Farber癌症研究所和布罗德研究所的同事一起，我们已经生成了初步数据，显示了每个组件的可行性和前景，从而提高了以低样本成本进行全面肿瘤测序的可能性。因此，在这个R33应用程序中，我们将实现一个用于系统化肿瘤基因组图谱的变革性平台，我们称之为“MPS-OncoMap”。为了实现这一目标，我们将优化样本条码技术、解决方案阶段外显子捕获和单分子测序的方法，以实现同时对至少12个肿瘤样本的约150个癌症基因进行稳健的突变分析(碱基突变、扩增和缺失)。我们将使用来自癌细胞系、冰冻肿瘤和福尔马林固定的、石蜡包埋的肿瘤组织的DNA来测试MPS-OncoMap的性能，对于这些组织，人们已经知道了这些组织的多基因改变。最后，我们将在生产规模上实施MPS-OncoMap，以实现对许多翻译肿瘤学应用程序的系统分析。实现这些目标可能会为全面的肿瘤基因组图谱提供一条明确的道路，在翻译和临床肿瘤学领域具有深远的影响。 与公共卫生相关：高通量癌症基因突变图谱和大规模平行测序PI：Levi A.Garraway，M.D.，Ph.D.和Laura E.MacConaill，Ph.D.许多癌症是由DNA突变引起蛋白质或细胞过程改变引起的。使用针对这些蛋白的抗癌药物可以带来显著的临床益处，但为了这种变化的存在而描述每个癌症患者的肿瘤仍然是不切实际的。这项应用寻求采用一种强大的新测序技术与其他创新一起，使其能够在临床肿瘤标本中检测许多信息丰富的癌症基因突变。一旦实施，这种方法可能会加速个性化癌症药物的到来。

DESCRIPTION (provided by applicant): Cancer diagnosis and treatment decisions have historically been based on anatomic sites of origin and spread; however, the emerging paradigm incorporates key genetic attributes of a given tumor to predict clinical behavior and specify the optimal use of targeted therapeutics. Ultimately, the delivery of personalized cancer medicine will require systematic characterization of all therapeutically informative tumor genomic alterations in the clinical and translational arena. Previously, we developed and deployed OncoMap, a mass spectrometric genotyping-based platform that enables high-throughput profiling of hundreds of known mutations across dozens of cancer genes. This platform performs well and has launched a robust translational oncology effort. However, the mass spectrometric genotyping technology remains limited in scope, assay sensitivity, and the type of genomic alteration that can be identified. Recently, it has become possible to render multi-faceted tumor characterization both technologically feasible and economically accessible through massively parallel sequencing (MPS) technology. Thus, the goal of this application is to migrate the OncoMap approach to an MPS platform (Illumina), empowered by innovations such as solution-phase exon capture and sample barcoding. Together with our colleagues at the Dana-Farber Cancer Institute and Broad Institute, we have generated preliminary data showing the feasibility and promise of each of these components, thereby raising the possibility of comprehensive tumor sequencing at a low per-sample cost. Accordingly, in this R33 application we will implement a transformative platform for systematic tumor genomic profiling, which we call "MPS-OncoMap." To accomplish this we will optimize the methodology for sample barcoding technology, solution-phase exon capture, and single-molecule sequencing to enable robust mutation profiling (base mutations, amplifications, and deletions) across ~150 cancer genes in at least 12 tumor samples simultaneously. We will test the performance of MPS-OncoMap using DNA from cancer cell lines, frozen tumors, and formalin-fixed, paraffin-embedded tumor tissue for which the "ground truth" is known for multiple genetic alterations. Finally, we will implement MPS-OncoMap at production scale to enable systematic analyses for many translational oncology applications. Achieving these Aims may inform a definitive path to comprehensive tumor genomic profiling with far-reaching impact in the translational and clinical oncology arena. PUBLIC HEALTH RELEVANCE: High-Throughput Cancer Gene Mutation Profiling y Massively Parallel Sequencing PIs: Levi A. Garraway, M.D., Ph.D. and Laura E. MacConaill, Ph.D. Many cancers are caused by mutations in the DNA that give rise to altered proteins or cellular processes. The use of anticancer drugs targeting such proteins can lead to significant clinical benefit, but it remains impractical to profile the tumor of each cancer patient for the presence of such alterations. This application seeks to adapt a powerful new sequencing technology together with other innovations to make it possible to detect many informative cancer gene mutations in clinical tumor specimens. Once implemented, this approach could speed the advent of personalized cancer medicine.