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

Authors

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

Publication Date

4-10-2023

Journal

Tomography

DOI

10.3390/tomography9020066

PMID

37104137

PMCID

PMC10144684

PubMedCentral® Posted Date

4-10-2023

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Triple Negative Breast Neoplasms, Magnetic Resonance Imaging, Image Processing, Computer-Assisted, magnetic resonance imaging (MRI), multi-omics, breast cancer, cancer informatics, cancer modeling, radiomics

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.

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