Tojo Arinobu
Division of Molecular Therapy, Advanced Clinical Research Center, The Institute of Medical Science, The University of Tokyo.
Rinsho Ketsueki. 2017;58(10):1913-1917. doi: 10.11406/rinketsu.58.1913.
Next generation sequencing (NGS) of cancer genomes is now becoming a prerequisite for accurate diagnosis and proper treatment in clinical oncology. Because the genomic regions for NGS expand from a certain set of genes to the whole exome or whole genome, the resulting sequence data becomes incredibly enormous and makes it quite laborious to translate the genomic data into medicine, so-called annotation and curation. We organized a clinical sequencing team and established a bidirectional (bed-to-bench and bench-to-bed) system to integrate clinical and genomic data for hematological malignancies. We also started a collaborative research project with IBM Japan to adopt the artificial intelligence Watson for Genomics (WfG) to the pipeline of medical informatics. Genomic DNA was prepared from malignant as well as normal tissues in each patient and subjected to NGS. Sequence data was analyzed using an in-house semi-automated pipeline in combination with WfG, which was used to identify candidate driver mutations and relevant pathways from which applicable drug information was deduced. Currently, we have analyzed more than 150 patients with hematological disorders, including AML and ALL, and obtained many informative findings. In this presentation, I will introduce some of the achievements we have made so far.
癌症基因组的下一代测序（NGS）如今已成为临床肿瘤学中准确诊断和合理治疗的先决条件。由于NGS的基因组区域从特定的一组基因扩展到整个外显子组或整个基因组，所产生的序列数据变得极其庞大，使得将基因组数据转化为医学应用（即所谓的注释和整理）变得相当费力。我们组建了一个临床测序团队，并建立了一个双向（临床到实验室和实验室到临床）系统，以整合血液系统恶性肿瘤的临床和基因组数据。我们还启动了一个与日本IBM的合作研究项目，将人工智能基因组学沃森（WfG）应用于医学信息学流程。从每位患者的恶性组织以及正常组织中提取基因组DNA，并进行NGS检测。使用内部半自动流程结合WfG对序列数据进行分析，该流程用于识别候选驱动突变和相关通路，并从中推导适用的药物信息。目前，我们已经分析了150多名血液系统疾病患者，包括急性髓系白血病（AML）和急性淋巴细胞白血病（ALL），并获得了许多有价值的发现。在本次报告中，我将介绍我们到目前为止所取得的一些成果。
