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着大量和多样化的下一步- 经常进行直观处理以支持高风险的世代测序和分子数据 决定。总体而言，当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、耗时且容易出错的基因查询，并最终 缺乏必要的信息，无法以可扩展的方式确定新疗法的优先顺序。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 将基因组靶标与分子驱动的疗法相匹配。这些障碍，加上健康差距， 扩大成倍增长的药物开发领域与以下方面的实际好处之间的差距 癌症患者。 拟议研究的总体目标是将临床与计算精度肿瘤学和 在基因定义的群体中实现临床决策。我们建议发展一种精确的肿瘤学 可自动、可扩展且精确匹配下一代可操作产品的决策支持框架 通过靶向治疗对研究结果进行排序。然后我们将在几个临床应用中测试它的临床实用性和价值 约翰霍普金斯大学分子肿瘤委员会内的环境，约翰霍普金斯大学合作社区医疗 在两个正在进行的乳腺癌女性临床试验中也是如此。增强可推广性 我们的分析工具包经过了我们当地的学术环境，我们设计了平台的架构 使得它允许摄取和协调来自多个源的下一代序列数据， 实施通用数据模型以将临床元素映射到标准化术语和利用 整合自然语言处理，以生成可操作的突变靶向治疗对。这些 属性为该工具包在医疗保健中的潜在广泛使用和实现提供了基础 在我们当地学术环境之外的环境。 虽然在肿瘤特征分析的高级诊断方面取得了重大进展，但坚实的骨干 缺乏支持卫生保健系统内部和跨保健系统的实际执行的能力。潜在的 拟议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 将有益于卫生系统和患者的倡议和以基因为导向的临床试验。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在 不容易获得临床基因组学内部专业知识的机构。我们设想这一点 简化的自动化和可扩展流程将改善护理、改善患者结局并定义国家 如何为个别患者选择和量身定做治疗的标准。

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.