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

开发多组学临床决策平台来指导个性化治疗

• 批准号: 10624792
• 负责人: Samir Parekh
• 金额: $ 52.75万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-02-06 至 2025-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10624792
• 关键词: Academia; Address; Affect; Algorithms; Alzheimer' s Disease; Animal Model; Area; Big Data; Biological Assay; Biological Sciences; Bone Marrow; Classification; Clinical; Clinical Data; Clinical Trials; Clonality; Collaborations; Computer Models; DNA Sequence Alteration; Data; Development; Diagnosis; Disease; Drug resistance; Employment; Event; Genes; Genetic; Genetic Diseases; Genetic Heterogeneity; Genomics; Health; Hematopoietic Neoplasms; Heterogeneity; Industry; Inflammatory Bowel Diseases; Intake; Learning; Machine Learning; Malignant Neoplasms; Measurement; Modeling; Monitor; Multiple Myeloma; Mus; Patient-Focused Outcomes; Patients; Peripheral arterial disease; Persons; Pharmaceutical Preparations; Pharmacotherapy; Physicians; Plasma Cells; Precision therapeutics; Prediction of Response to Therapy; Primary Neoplasm; RNA; Recommendation; Refractory; Relapse; Research; Research Priority; Sampling; Schizophrenia; Scientist; Selection for Treatments; Solid Neoplasm; Stream; Suggestion; Systems Biology; Systems Integration; Technology; Testing; Therapeutic; Time; Translating; cancer genetics; clinic ready; clinical decision support; clinically actionable; computational pipelines; computerized tools; data integration; design; disorder subtype; drug repurposing; efficacy testing; genomic data; genomic profiles; improved; improved outcome; in vivo; individual patient; insight; longitudinal design; medical schools; mouse model; multidisciplinary; multiple omics; next generation; novel; novel therapeutic intervention; novel therapeutics; patient derived xenograft model; personalized medicine; personalized therapeutic; pilot trial; point of care; precision medicine; predictive tools; profiles in patients; prospective; relapse patients; risk stratification; standard of care; statistical and machine learning; support tools; targeted sequencing; tool; transcriptome sequencing; transcriptomics; treatment planning; treatment response; tumor; tumor xenograft

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

项目摘要 “大数据”时代为基因组学和系统生物学方法打开了大门， 生命科学和精准医学的当前挑战。这些领域的一个关键挑战是如何 优先考虑研究结果，以验证和确定可转化为更好结果的可操作见解 对患者在这方面，我们召集了一个多学科的科学家和医生小组， 学术界和工业界，重点是创建结合了联合收割机高吞吐量分析的发现管道 技术与先进的统计和机器学习方法，以生成预测工具， 使我们能够迅速从大数据转向更好的诊断和治疗。在这方面，我们建议 这些方法开发了一种计算临床决策工具，将改善疾病预测， 多发性骨髓瘤（MM）的治疗计划，MM是一种起源于骨髓浆细胞的无法治愈的癌症 每年有超过3万名患者受到影响。尽管在数量上有了一些进步， 尽管这些患者的治疗选择多种多样，但复发仍是不可避免的，MM最终 仍然是晚期诊断。本项目中开发的临床分析和计算管道将 联合收割机将骨髓瘤患者特异性靶向测序组和克隆性估计与RNA- 测序和药物再利用，以扩大MM患者的治疗选择。我们将开发这种独特的 该工具具有以下具体目标：（1）开发一个集成的基因组临床决策工具，以指导精确度 治疗MM并使用PDX分析验证治疗建议，以及（2）治疗MM的精确度 在前瞻性临床试验中使用药物平台，并生成克隆特异性治疗建议。到 为了实现这些目标，我们将整合一个癌症遗传公司。的焦点：：骨髓瘤小组，一个有针对性的小组 旨在特异性地询问骨髓瘤中常见的所有基因和拷贝数改变， 并进入一个利用RNA测序数据的计算药物选择管道， 和药物再利用算法，以生成与患者的独特 疾病概况这些建议将在骨髓瘤的小鼠化身中进行验证，以确认和完善 药物预测我们将在100名患者的前瞻性临床试验中实施我们的检测方法，以确定是否 我们的管道产生的治疗决定实现了护理标准的提高。最后我们将 对复发患者进行克隆建模，以回顾性评估克隆特异性治疗反应。 这些研究的完成将产生一个临床就绪的测定和计算工具，将指导MM 精确的治疗决策，并根据患者独特的癌症特征制定新的治疗策略。 基因组 克隆 建模