Development of a multi-omic clinical decision platform to guide personalized therapy

项目总结 大数据时代为基因组和系统生物学方法的应用打开了大门 生命科学和精确医学的当前挑战。这些领域的一个关键挑战是如何 确定研究结果的优先顺序，以验证和确定可转化为更好结果的可操作的见解 对病人来说。在这方面，我们召集了一个多学科的科学家和医生小组，来自 学术界和工业界，专注于创建结合高通量分析的发现管道 具有先进的统计和机器学习方法的技术，以生成预测工具 使我们能够快速从大数据转向更好的诊断和治疗。在这方面，我们建议适用 这些方法用于开发计算临床决策工具，该工具将改进疾病预测和 多发性骨髓瘤(MM)的治疗计划，这是一种起源于骨髓浆细胞的无法治愈的癌症 每年影响30,000多名患者。尽管在数量上取得了一些进展， 对于这些患者，可用治疗方案的多样性，复发仍然不可避免，多发性骨髓瘤最终 仍然是一个绝症诊断。在这个项目中开发的临床分析和计算流水线将 将针对骨髓瘤患者的靶向测序小组和克隆性评估与RNA- 测序和药物再利用，以扩大多发性骨髓瘤患者的治疗选择。我们将开发这一独特的 工具的具体目标如下：(1)开发集成的基因组临床决策工具，以指导精确度 多发性骨髓瘤的治疗，并使用PDX分析验证治疗建议，以及(2)验证多发性骨髓瘤的精确度 药物平台在预期的临床试验中，并产生针对克隆的治疗建议。至 为了实现这些目标，我们将整合一个癌症遗传股份有限公司的S焦点：：骨髓瘤小组，一个有针对性的小组 专门设计用来询问骨髓瘤中通常改变的所有基因和拷贝数变化， 并进入利用RNA测序数据的计算药物选择流水线 和药物再利用算法来生成与患者独特的治疗建议相匹配的治疗建议 疾病概况。这些建议将在骨髓瘤的小鼠化身中得到验证，以确认和改进 毒品预测。我们将在对100名患者进行的前瞻性临床试验中实施我们的分析，以确定 由我们的管道产生的治疗决定实现了护理标准的改善。最后，我们会 对复发患者进行克隆建模，以回顾评估克隆特异性治疗反应。 这些研究的完成将产生一种可用于临床的分析和计算工具，它将指导MM 精确的治疗决策，并根据患者独特的癌症特征为新的治疗策略提供信息。 基因组学 克隆性 建模

PROJECT SUMMARY The era of “big data” has opened the door for genomic and systems biology approaches to be applied to current challenges in life sciences and precision medicine. One critical challenge in these areas is how to prioritize research findings to validate and identify actionable insights that can translate into better outcomes for patients. In this regard, we have assembled a multidisciplinary group of scientists and physicians from academia and industry with a focus on creating discovery pipelines that combine high-throughput profiling technologies with advanced statistical and machine learning approaches to generate predictive tools that enable us to move rapidly from big data to better diagnoses and treatment. In this regard, we propose to apply these approaches to develop a computational clinical decision tool that will improve disease forecasting and treatment plans for Multiple Myeloma (MM), an incurable cancer that originates in bone marrow plasma cells and affects more than 30,000 patients a year. Though there have been some advances in the number and diversity of available therapeutic options for these patients, relapse remains inevitable, and MM ultimately remains a terminal diagnosis. The clinical assay and computational pipeline developed in this project will combine a targeted sequencing panel specific to myeloma patients and clonality estimates with RNA- sequencing and drug repurposing to expand therapeutic options for MM patients. We will develop this unique tool with the following specific aims: (1) Develop an integrated genomic clinical decision tool to guide precision treatment of MM and validate therapy recommendations using PDX profiling, and (2) Validate MM precision medicine platform in a prospective clinical trial and generate clone-specific treatment recommendations. To achieve these objectives, we will integrate a Cancer Genetic, Inc.'s FOCUS::Myeloma panel, a targeted panel designed to specifically interrogateall the genes and copy number alterations commonly altered in myeloma, and into a computational drug selection pipeline that utilizes RNA-sequencing data and drug repurposing algorithms to generate therapeutic recommendations matched to a patient's unique disease profile. These recommendations will be validated in mouse avatars of myeloma to confirm and refine drug predictions. We will implement our assay in a prospective clinical trial of 100 patients to determine if the treatment decisions generated by our pipeline achieves an improvement in standard-of-care. Finally, we will perform clonal modeling on relapsed patients to retrospectively evaluate clone-specific treatment responses. Completion of these studies will result in a clinic-ready assay and computational tool that will guide MM precision treatment decisions and inform new therapeutic strategies based on a patient's unique cancer profile. genomic clonal modeling