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: 10703682
• 负责人: Samir Parekh
• 金额: $ 8.2万
• 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/10703682
• 关键词: Academia; Address; Affect; Algorithms; Alzheimer' s Disease; Animal Model; Area; Big Data; Biological Assay; Biological Sciences; Bone Marrow; Classification; Clinic; Clinical; Clinical Data; Clinical Trials; Clonality; Computer Models; DNA Sequence Alteration; Data; Development; Diagnosis; Disease; Drug resistance; Employment; Event; Gene Dosage; 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; Sampling; Schizophrenia; Scientist; Selection for Treatments; Solid Neoplasm; Stream; Suggestion; System; Systems Biology; Technology; Testing; Therapeutic; Time; Translating; base; cancer genetics; clinical decision support; clinically actionable; computational pipelines; computerized tools; 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

COVID-19 vaccination substantially reduced morbidity and mortality associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and severe illness. However, many questions remain about the efficacy of vaccines and the durability and robustness of immune responses, especially in immunocompromised persons. SeroNet has several institutions focused on conducting epidemiologic studies in immunocompromised populations using either EMR-based real-world approaches and/or building novel prospective cohorts with longitudinal assessments. When combined, there are substantive populations of patients with cancer, including hematological malignancies, solid cancers, and hematopoietic cell transplants. The cohort groups are accrued and followed prospectively for endpoints of interest and impacts of various immunotherapies/cancer treatments. In addition, there are large number of patients with autoimmune diseases, patients living with HIV and SOTR. We propose to leverage the existing infrastructure to establish the SeroNet ‘Pooling Project.’ Our general approach will be to establish a forum to coordinate a synchronized effort to pool individual-level data on immuno-compromised populations across multiple SeroNet sites. Our proposal will leverage the existing SeroNet Epi Ops and Data Ops Working Groups infrastructure to formulate a plan of action to harmonize, synchronize and transfer data across participating sites and facilitate meta-analyses of individual-level data to answer critical research questions in immunocompromised populations. We plan to facilitate parallel data harmonization and analyses on specific immunocompromised populations using a model where different sites are assigned to lead or co-leading different research questions. Lastly, we plan to disseminate results through scientific publications, adherence to data sharing policies and wider community-level communications of SeroNet findings.

COVID-19疫苗接种大幅降低了与严重急性呼吸系统综合征冠状病毒2（SARS-CoV-2）感染和重症相关的发病率和死亡率。然而，关于疫苗的效力以及免疫应答的持久性和稳健性，特别是在免疫功能低下的人中，仍然存在许多问题。SeroNet有几个机构专注于使用基于EMR的真实世界方法和/或通过纵向评估建立新的前瞻性队列在免疫功能低下人群中进行流行病学研究。当合并时，存在大量癌症患者群体，包括血液恶性肿瘤、实体癌和造血细胞移植。对队列组进行累积，并前瞻性随访各种免疫疗法/癌症治疗的关注终点和影响。此外，还有大量自身免疫性疾病患者、HIV和SOTR患者。我们建议利用现有的基础设施来建立SeroNet“池项目”。我们的总体方法是建立一个论坛，协调多个SeroNet站点的免疫功能低下人群的个人水平数据的同步工作。我们的提案将利用现有的SeroNet Epi Ops和Data Ops Working Groups基础设施，制定一项行动计划，以协调、同步和传输参与研究中心的数据，并促进对个人水平数据的荟萃分析，以回答免疫功能低下人群的关键研究问题。我们计划使用一种模型，在该模型中，不同的研究中心被分配领导或共同领导不同的研究问题，以促进对特定免疫功能低下人群的并行数据协调和分析。最后，我们计划通过科学出版物、遵守数据共享政策和在社区一级更广泛地传播SeroNet调查结果来传播结果。