Matching genotypes with personalized therapies: Development of a decision support infrastructure to augment the value of precision medicine

项目摘要 尽管在精确肿瘤学方面取得了进展，但临床医生通常面临大量和各种各样的下一个- 代测序和分子数据，这些数据经常被直观地处理，以支持高风险 决策总的来说，目前可用的资源，有助于下一代序列数据解释 受限于手动执行的、复杂的、耗时的和容易出错的基因查询，并且最终 缺乏以可扩展的方式对新兴疗法进行优先排序的必要信息。重要的是 由于缺乏先进的分析工具，基因组与临床数据的整合受到严重阻碍 将基因组靶点与分子驱动疗法相匹配。这些障碍加上健康方面的差距， 扩大呈指数增长的药物开发领域与实际利益之间的差距， 癌症患者。 拟议研究的总体目标是将临床与计算精确肿瘤学联系起来， 使临床决策在基因组定义的群体。我们建议开发一种精确的肿瘤学 决策支持框架，用于自动化、可扩展和精确匹配可操作的下一代 用靶向疗法对结果进行测序。然后，我们将测试其临床效用和价值，在几个临床 约翰霍普金斯分子肿瘤委员会的设置，在约翰霍普金斯合作社区医疗 中心以及两个正在进行的乳腺癌妇女临床试验。为了提高普遍性， 我们的分析工具包过去我们当地的学术环境，我们设计了平台的架构 使得它允许从多个来源摄取和协调下一代序列数据， 实施通用数据模型，将临床要素映射到标准化术语，并利用 集成自然语言处理以生成可操作的突变靶向治疗对。这些 属性为该工具包在卫生保健中的潜在广泛使用和实施提供了基础 在我们当地的学术环境之外。 虽然在用于肿瘤分析的先进诊断方面已经取得了重大进展，但一个坚实的支柱仍然存在。 在卫生保健系统内部和整个卫生保健系统中缺乏支持实际执行的机制。底层 这项研究的前提是，它将点燃跨机构的真实世界基因组数据分析 这些倡议和基因型驱动的临床试验将有益于卫生系统和患者。值得注意的是， 我们的精准肿瘤学决策支持平台将加强精准肿瘤学的实施， 这些机构不容易获得临床基因组学方面的内部专业知识。我们设想， 简化的自动化和可扩展的流程将改善护理，提高患者的治疗效果， 如何选择治疗方法并为患者量身定制。

Project Summary Despite the progress made in precision oncology, clinicians typically face a vast volume and variety of next- generation sequencing and molecular data that is frequently intuitively processed to support high-stakes decisions. Overall, currently available resources that assist with next-generation sequence data interpretation are limited by manually performed, complex, time-consuming, and error-prone gene queries and ultimately lack the necessary information for prioritizing emerging therapies in a scalable manner. Importantly, the integration of genomic with clinical data has been severely hampered by the lack of advanced analytical tools that match genomic targets with molecularly-driven therapies. These barriers, together with health disparities, widen the gap between an exponentially increasing drug development field and the actual benefits for patients with cancer. The overarching goal of the proposed research is to link clinical with computational precision oncology and enable clinical decision-making in genomically defined groups. We propose to develop a precision oncology decision support framework for automated, scalable, and precise matching of actionable next-generation sequencing findings with targeted therapies. We will then test its clinical utility and value in the several clinical settings within the Johns Hopkins Molecular Tumor Board, in Johns Hopkins partnering community medical centers as well within two ongoing clinical trials for women with breast cancer. To enhance the generalizability of our analytical toolkit past our local academic environment, we have designed the platform's architecture such that it allows for ingestion and harmonization of next-generation sequence data from multiple sources, implements a common data model to map clinical elements to standardized terminologies and leverages ensemble natural language processing to generate actionable mutation-targeted therapy pairs. These attributes provide the foundation for the toolkit's potential widespread use and implementation in health care settings outside our local academic environment. While significant advances have been made in advanced diagnostics for tumor profiling, a solid backbone that supports the practical implementation within and across health care systems is lacking. The underlying premise of the proposed research is that it will ignite cross-institutional real-world genomic data analysis initiatives and genotype-driven clinical trials that will be beneficial for health systems and patients. Notably, our precision oncology decision support platform will enhance the implementation of precision oncology at institutions that do not readily have access to in-house expertise in clinical genomics. We envision that this streamlined automatic and scalable process will improve care, enhance patient outcomes and define national standards in how treatments are selected and tailored to individual patients.