Thanintorn Nattapon, Wang Juexin, Ersoy Ilker, Al-Taie Zainab, Jiang Yuexu, Wang Duolin, Verma Megha, Joshi Trupti, Hammer Richard, Xu Dong, Shin Dmitriy
Department of Pathology and Anatomical Sciences, USA.
Pac Symp Biocomput. 2016;21:417-28.
Realization of precision medicine ideas requires significant research effort to be able to spot subtle differences in complex diseases at the molecular level to develop personalized therapies. It is especially important in many cases of highly heterogeneous cancers. Precision diagnostics and therapeutics of such diseases demands interrogation of vast amounts of biological knowledge coupled with novel analytic methodologies. For instance, pathway-based approaches can shed light on the way tumorigenesis takes place in individual patient cases and pinpoint to novel drug targets. However, comprehensive analysis of hundreds of pathways and thousands of genes creates a combinatorial explosion, that is challenging for medical practitioners to handle at the point of care. Here we extend our previous work on mapping clinical omics data to curated Resource Description Framework (RDF) knowledge bases to derive influence diagrams of interrelationships of biomarker proteins, diseases and signal transduction pathways for personalized theranostics. We present RDF Sketch Maps - a computational method to reduce knowledge complexity for precision medicine analytics. The method of RDF Sketch Maps is inspired by the way a sketch artist conveys only important visual information and discards other unnecessary details. In our case, we compute and retain only so-called RDF Edges - places with highly important diagnostic and therapeutic information. To do this we utilize 35 maps of human signal transduction pathways by transforming 300 KEGG maps into highly processable RDF knowledge base. We have demonstrated potential clinical utility of RDF Sketch Maps in hematopoietic cancers, including analysis of pathways associated with Hairy Cell Leukemia (HCL) and Chronic Myeloid Leukemia (CML) where we achieved up to 20-fold reduction in the number of biological entities to be analyzed, while retaining most likely important entities. In experiments with pathways associated with HCL a generated RDF Sketch Map of the top 30% paths retained important information about signaling cascades leading to activation of proto-oncogene BRAF, which is usually associated with a different cancer, melanoma. Recent reports of successful treatments of HCL patients by the BRAF-targeted drug vemurafenib support the validity of the RDF Sketch Maps findings. We therefore believe that RDF Sketch Maps will be invaluable for hypothesis generation for precision diagnostics and therapeutics as well as drug repurposing studies.
实现精准医学理念需要大量的研究工作，以便能够在分子水平上发现复杂疾病中的细微差异，从而开发个性化疗法。这在许多高度异质性癌症病例中尤为重要。此类疾病的精准诊断和治疗需要审视大量的生物学知识，并结合新颖的分析方法。例如，基于通路的方法可以揭示个体患者病例中肿瘤发生的方式，并确定新的药物靶点。然而，对数百条通路和数千个基因进行全面分析会产生组合爆炸，这对医疗从业者在医疗现场处理来说具有挑战性。在此，我们扩展了之前将临床组学数据映射到经过整理的资源描述框架（RDF）知识库的工作，以推导生物标志物蛋白、疾病和信号转导通路之间相互关系的影响图，用于个性化诊疗。我们提出了RDF草图地图——一种降低精准医学分析知识复杂性的计算方法。RDF草图地图的方法灵感来源于草图艺术家仅传达重要视觉信息并摒弃其他不必要细节的方式。在我们的案例中，我们仅计算并保留所谓的RDF边——具有高度重要诊断和治疗信息的位置。为此，我们通过将300个京都基因与基因组百科全书（KEGG）地图转化为高度可处理的RDF知识库，利用了35个人类信号转导通路地图。我们已经证明了RDF草图地图在造血系统癌症中的潜在临床应用价值，包括分析与毛细胞白血病（HCL）和慢性粒细胞白血病（CML）相关的通路，在这些分析中，我们将待分析的生物实体数量减少了多达20倍，同时保留了最可能重要的实体。在与HCL相关通路的实验中，生成的前30%通路的RDF草图地图保留了有关导致原癌基因BRAF激活的信号级联的重要信息，而BRAF通常与另一种癌症黑色素瘤相关。最近关于BRAF靶向药物维莫非尼成功治疗HCL患者的报道支持了RDF草图地图研究结果的有效性。因此，我们相信RDF草图地图对于精准诊断和治疗的假设生成以及药物再利用研究将具有极高的价值。
