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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

基本信息

批准号：
10337223
负责人：
Samir Parekh
金额：
$ 52.51万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
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/10337223
关键词：
Academia Address Affect Algorithms Alzheimer&apos 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

项目摘要

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

项目总结