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

将基因型与个性化治疗相匹配：开发决策支持基础设施以增强精准医疗的价值

• 批准号: 10645785
• 负责人: Valsamo Anagnostou
• 金额: $ 40.77万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10645785
• 关键词: Acceleration; Address; Architecture; Cancer Biology; Cancer Patient; Caring; Characteristics; Clinical; Clinical Data; Clinical Research; Clinical Trials; Communities; Complex; Computational algorithm; Consumption; Coupled; Data; Data Analyses; Data Element; Databases; Decision Support Systems; Development; Elements; Environment; Face; Foundations; Genes; Genomics; Genotype; Goals; Health; Health system; Healthcare Systems; Immunotherapy; Individual; Informatics; Infrastructure; Ingestion; Institution; Intuition; Investigational Drugs; Knowledge; Label; Level of Evidence; Link; Literature; Mainstreaming; Malignant Neoplasms; Manuals; Maps; Medical; Medical center; Modality; Molecular; Molecular Target; Mutation; Natural Language Processing; Oncologist; Outcome; Patient-Focused Outcomes; Patients; Performance; Population; Precision therapeutics; Process; Records; Registries; Reporting; Research; Resources; Sampling; Solid; Source; Standardization; Terminology; Testing; Therapeutic; Therapy trial; Time; United States Food and Drug Administration; Variant; Vertebral column; Woman; actionable mutation; advanced analytics; analytical tool; clinical decision-making; clinical trial enrollment; community setting; data modeling; data standards; design; drug development; genomic data; health care disparity; health care settings; health disparity; improved; improved outcome; individual patient; knowledgebase; malignant breast neoplasm; multiple data sources; next generation sequence data; next generation sequencing; operation; personalized medicine; pragmatic implementation; precision medicine; precision oncology; standard of care; targeted treatment; treatment trial; tumor; tumor diagnostic

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.

项目摘要 尽管在精确肿瘤学方面取得了进展，但临床医生通常会面临大量和接下来的各种 发电测序和分子数据经常被直观地处理以支持高风险 决定。总体而言，目前可用的资源可以帮助下一代序列数据解释 受到手动执行，复杂，耗时和容易出错的基因查询的限制，最终 缺乏以可扩展方式优先考虑新兴疗法的必要信息。重要的是， 缺乏先进的分析工具，基因组与临床数据的整合受到了严重阻碍 该基因组靶标与分子驱动的疗法相匹配。这些障碍以及健康差异 扩大了指数增加药物发育领域的差距和实际收益 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 在基因组定义的组中启用临床决策。我们建议开发精确的肿瘤学 可行的下一代的自动化，可扩展和精确匹配的决策支持框架 采用靶向疗法的测序结果。然后，我们将在几个临床上测试其临床实用性和价值 约翰·霍普金斯分子肿瘤委员会内的设置，约翰·霍普金斯（Johns Hopkins）与社区合作医疗 在两项正在进行的针对乳腺癌女性的临床试验中中心。提高推广性 我们的分析工具包经过了当地的学术环境，我们设计了平台的架构 这样它允许从多个来源摄入和协调下一代序列数据， 实施通用数据模型以将临床元素映射到标准化术语和杠杆作用 集合自然语言处理，以产生可行的突变靶向疗法对。这些 属性为工具包在医疗保健中的潜在广泛使用和实施奠定了基础 在我们当地的学术环境之外的设置。 虽然在肿瘤分析的高级诊断中已经取得了重大进展，但固体骨架 缺乏支持医疗保健系统内外的实际实施。基础 拟议的研究的前提是它将点燃跨机构现实世界基因组数据分析 针对卫生系统和患者有益的计划和基因型驱动的临床试验。尤其， 我们的精确肿瘤学决策支持平台将增强精确肿瘤学的实施 不容易获得临床基因组学专业知识的机构。我们设想这个 简化的自动且可扩展的过程将改善护理，增强患者的结果并定义国家 如何选择和针对个别患者量身定制治疗的标准。