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Assessing the power of primary drug-screens to predict clinical response.

评估初级药物筛选预测临床反应的能力。

基本信息

批准号：
10092124
负责人：
Kevin Matthew Watanabe-Smith
金额：
$ 13.8万
依托单位：
OREGON HEALTH & SCIENCE UNIVERSITY
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：
2020-02-01 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10092124
关键词：
ABL1 gene Accounting Address Advisory Committees Aftercare Biological Biological Assay Cancer Patient Cancer cell line Cell Line Chronic Myeloid Leukemia Clinical Clinical Data Clinical Trials Combined Modality Therapy Committee Members Complex Computer Models Computing Methodologies Data Data Analyses Data Set Data Sources Decision Making Development Diagnosis Drug Combinations Drug Screening ERBB2 gene Evaluation Faculty Freezing Future Genomics German population Goals Guidelines In Vitro Institutes Lead Learning Leukemic Cell Machine Learning Malignant Neoplasms Mediating Mentors Methods Modeling Morphologic artifacts Oncology Outcome Patient Care Patient-Focused Outcomes Patients Pharmaceutical Preparations Pharmacotherapy Phase Positioning Attribute Prediction of Response to Therapy Predictive Value Process Randomized Clinical Trials Reading Research Personnel Resistance Risk Sample Size Sampling Screening for cancer Selection for Treatments Source Subgroup System Systems Biology Technology Testing Treatment Protocols Validation Weight Work arm base cancer biomarkers cancer cell cancer subtypes cancer therapy care outcomes clinical decision-making clinical predictors cohort combinatorial conventional therapy design drug response prediction drug sensitivity drug testing exhaustion experimental study high-throughput drug screening improved individual patient inhibitor/antagonist large datasets leukemia malignant breast neoplasm model design multiple omics multitask new combination therapies novel therapeutics patient screening patient stratification phosphoproteomics precision medicine precision oncology predictive test primary outcome prognostic response screening standard of care survival prediction targeted agent targeted cancer therapy targeted treatment transcriptome sequencing transcriptomics treatment arm treatment response tumor tumor heterogeneity validation studies

项目摘要

PROJECT SUMMARY Precision oncology relies on the hypothesis that further characterizing a patient's tumor will lead to better predictions of treatment response. While this approach effectively breaks diagnoses into smaller and smaller subtypes likely to respond to a given targeted inhibitor, the downside is it results in highly fragmented clinical data spread across multiple treatment arms. The more progress the field makes in assigning new therapies, the harder it will be to accrue adequate sample size to test another therapy. Direct screening of cancer cell lines and primary samples on panels of targeted inhibitors is a uniquely promising approach to this problem, turning every patient sample into a hundred mini experiments, but clinical validation of in vitro drug-response predictions have been hampered by limited numbers of patients who are screened and actually treated with any given drug. Such an evaluation is critical to determine the predictive value gained from drug screening of patient samples. Over the past seven years the Knight Cancer Institute has performed drug screening paired alongside genomic and/or RNA sequencing for over 600 primary leukemic samples. Within two years, we will have accumulated over 200 patients not only screened for in vitro drug response but then treated with matched targeted inhibitors. I will leverage this existing and growing dataset to interrogate the power of in vitro drug screening data to predict clinical response using retrospective data. I will establish a robust framework for primary drug screening and analysis, build interpretable models for clinical decision making, and explore mechanisms controlling drug response. This project will result in improvements to high-throughput drug screening, a thorough accounting of the predictive power of in vitro drug screening, and candidates for treatment combinations in resistant tumors. My goal is to become an independent investigator and cross-disciplinary leader in patient sample multi-omic profiling, targeted therapy selection, and translational oncology. During my mentored phase I will be receiving guidance from Dr. Emek Demir, an expert in computational modeling of systems biology, Dr. Jeffery Tyner, a leader in patient sample drug screening and validation, and Dr. Brian Druker, a pioneer of targeted cancer therapy and director of the Knight Cancer Institute. I will also improve my statistical understanding of complex systems by working with my advisory committee member Dr Tomi Mori, learn to integrate large datasets and predicting patient outcomes from Dr. Shannon McWeeney, and improve upon existing drug screening platforms and analysis methods with Dr. Laura Heiser. I am determined to attain an independent faculty position and my mentors have committed to assisting me in the application and transition process.

项目总结