Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials

Emel Alkim; Heidi Dowst; Julie DiCarlo; Lacey E Dobrolecki; Anadulce Hernández-Herrera; David A Hormuth 2nd; 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

doi:10.3390/tomography9020066

Toward Practical Integration of Omic and Imaging Data in Co-Clinical Trials

Tomography. 2023 Apr 10;9(2):810-828. doi: 10.3390/tomography9020066.

Authors

Emel Alkim¹, Heidi Dowst², Julie DiCarlo^{3

4}, Lacey E Dobrolecki^{5

6}, Anadulce Hernández-Herrera², David A Hormuth 2nd^{3

4}, Yuxing Liao⁵, Apollo McOwiti², Robia Pautler⁷, Mothaffar Rimawi^{5

8}, Ashley Roark^{5

8}, Ramakrishnan Rajaram Srinivasan⁵, Jack Virostko^{3

4

9

10}, Bing Zhang⁵, Fei Zheng², Daniel L Rubin^{1

11

12}, Thomas E Yankeelov^{3

4

9

10

13

14}, Michael T Lewis^{2

5

6}

Affiliations

¹ Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA 94305, USA.
² Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA.
³ Oden Institute for Computational Engineering and Sciences, Austin, TX 78712, USA.
⁴ Livestrong Cancer Institutes, Austin, TX 78712, USA.
⁵ Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA.
⁶ Department of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, TX 77030, USA.
⁷ Department of Physiology, Baylor College of Medicine, Houston, TX 77030, USA.
⁸ Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA.
⁹ Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA.
¹⁰ Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA.
¹¹ Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹² Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
¹³ Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA.
¹⁴ Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Abstract

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.

Keywords: breast cancer; cancer informatics; cancer modeling; magnetic resonance imaging (MRI); multi-omics; radiomics.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Triple Negative Breast Neoplasms* / pathology

Grants and funding

This research was funded by National Institutes of Health (grant number CA226110, CA125123), Cancer Prevention and Research Institute of Texas (grant number RP170691, RP220646, RR160005, RP220225).