Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials
Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Tomography |
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| Online Access: | https://www.mdpi.com/2379-139X/9/2/66 |
| _version_ | 1797603390221451264 |
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| author | Emel Alkim Heidi Dowst Julie DiCarlo Lacey E. Dobrolecki Anadulce Hernández-Herrera David A. Hormuth Yuxing Liao Apollo McOwiti Robia Pautler Mothaffar Rimawi Ashley Roark Ramakrishnan Rajaram Srinivasan Jack Virostko Bing Zhang Fei Zheng Daniel L. Rubin Thomas E. Yankeelov Michael T. Lewis |
| author_facet | Emel Alkim Heidi Dowst Julie DiCarlo Lacey E. Dobrolecki Anadulce Hernández-Herrera David A. Hormuth Yuxing Liao Apollo McOwiti Robia Pautler Mothaffar Rimawi Ashley Roark Ramakrishnan Rajaram Srinivasan Jack Virostko Bing Zhang Fei Zheng Daniel L. Rubin Thomas E. Yankeelov Michael T. Lewis |
| author_sort | Emel Alkim |
| collection | DOAJ |
| description | Co-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in “triple-negative” breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment. |
| first_indexed | 2024-03-11T04:29:29Z |
| format | Article |
| id | doaj.art-e0d87f8b7a8e4f92951123a867d724ef |
| institution | Directory Open Access Journal |
| issn | 2379-1381 2379-139X |
| language | English |
| last_indexed | 2024-03-11T04:29:29Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Tomography |
| spelling | doaj.art-e0d87f8b7a8e4f92951123a867d724ef2023-11-17T21:36:42ZengMDPI AGTomography2379-13812379-139X2023-04-019281082810.3390/tomography9020066Toward Practical Integration of Omic and Imaging Data in Co-Clinical TrialsEmel Alkim0Heidi Dowst1Julie DiCarlo2Lacey E. Dobrolecki3Anadulce Hernández-Herrera4David A. Hormuth5Yuxing Liao6Apollo McOwiti7Robia Pautler8Mothaffar Rimawi9Ashley Roark10Ramakrishnan Rajaram Srinivasan11Jack Virostko12Bing Zhang13Fei Zheng14Daniel L. Rubin15Thomas E. Yankeelov16Michael T. Lewis17Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USADan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USAOden Institute for Computational Engineering and Sciences, Austin, TX 78712, USALester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USADan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USAOden Institute for Computational Engineering and Sciences, Austin, TX 78712, USALester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USADan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USADepartment of Physiology, Baylor College of Medicine, Houston, TX 77030, USALester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USALester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USALester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USAOden Institute for Computational Engineering and Sciences, Austin, TX 78712, USALester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USADan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USADepartment of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USAOden Institute for Computational Engineering and Sciences, Austin, TX 78712, USADan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USACo-clinical trials are the concurrent or sequential evaluation of therapeutics in both patients clinically and patient-derived xenografts (PDX) pre-clinically, in a manner designed to match the pharmacokinetics and pharmacodynamics of the agent(s) used. The primary goal is to determine the degree to which PDX cohort responses recapitulate patient cohort responses at the phenotypic and molecular levels, such that pre-clinical and clinical trials can inform one another. A major issue is how to manage, integrate, and analyze the abundance of data generated across both spatial and temporal scales, as well as across species. To address this issue, we are developing MIRACCL (molecular and imaging response analysis of co-clinical trials), a web-based analytical tool. For prototyping, we simulated data for a co-clinical trial in “triple-negative” breast cancer (TNBC) by pairing pre- (T0) and on-treatment (T1) magnetic resonance imaging (MRI) from the I-SPY2 trial, as well as PDX-based T0 and T1 MRI. Baseline (T0) and on-treatment (T1) RNA expression data were also simulated for TNBC and PDX. Image features derived from both datasets were cross-referenced to omic data to evaluate MIRACCL functionality for correlating and displaying MRI-based changes in tumor size, vascularity, and cellularity with changes in mRNA expression as a function of treatment.https://www.mdpi.com/2379-139X/9/2/66magnetic resonance imaging (MRI)multi-omicsbreast cancercancer informaticscancer modelingradiomics |
| spellingShingle | Emel Alkim Heidi Dowst Julie DiCarlo Lacey E. Dobrolecki Anadulce Hernández-Herrera David A. Hormuth Yuxing Liao Apollo McOwiti Robia Pautler Mothaffar Rimawi Ashley Roark Ramakrishnan Rajaram Srinivasan Jack Virostko Bing Zhang Fei Zheng Daniel L. Rubin Thomas E. Yankeelov Michael T. Lewis Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials Tomography magnetic resonance imaging (MRI) multi-omics breast cancer cancer informatics cancer modeling radiomics |
| title | Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials |
| title_full | Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials |
| title_fullStr | Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials |
| title_full_unstemmed | Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials |
| title_short | Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials |
| title_sort | toward practical integration of omic and imaging data in co clinical trials |
| topic | magnetic resonance imaging (MRI) multi-omics breast cancer cancer informatics cancer modeling radiomics |
| url | https://www.mdpi.com/2379-139X/9/2/66 |
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