# WASHINGTON UNIVERSITY CO-CLINICAL IMAGING RESEARCH RESOURCE

> **NIH NIH U24** · WASHINGTON UNIVERSITY · 2021 · $624,395

## Abstract

ABSTRACT
Quantitative imaging (QI) is positioned to play a central role in providing clinically relevant information about a
tumor’s biology and its microenvironment to aid in the design of adaptive therapy trials. While standardization
of QI methods have had a great impact in advancing clinical applications of QI to assess and predict response
to therapy, preclinical imaging remains a critical component in the translational pipeline of validating advanced
QI methods for applications in drug discovery and assessment of response to therapy. Thus, developing QI
standards that transcend species and modalities is critical in advancing the application of QI. Standardization of
preclinical QI has been limited primarily for two reasons: first, use of unrealistic animal models of cancer, such
as established cell lines, to validate QI methods, and second, logistical and technical challenges inherent in
preclinical imaging. More realistic preclinical cancer models are thought to be provided by transplantable,
patient-derived cancer tissue xenograft (PDX). Leveraging the infrastructure available at Mallinckrodt Institute
of Radiology (MIR), Human & Mouse Linked Evaluation of Tumors (HAMLET) Core of the Institute of Clinical
and Translational Sciences (ICTS), and the Siteman Cancer Center, the WU-C2IR2 will harmonize, optimize,
validate, and implement preclinical-clinical QI algorithms, bi-directionally, in a context of an active co-clinical
TNBC trial. Interfacing with the HAMLET Core, the Resource will generate credentialed PDX mice matched to
the patient’s tumor sub-type in addition to PDX mice generated from tumor biopsies/engraftments. QI algorithms
will be optimized and validated in both settings and implemented in the co-clinical trial taking advantage of
simultaneous PET/MR Biograph mMR to evaluate the efficacy of advanced multi-parametric PET and MR
imaging methods to assess response to therapy in TNBC. To compliment QI efforts, multi-scale analytics of
tissue samples will be collected including RNA Seq, pathology, Exome sequencing, among others to integrate
with QI to facilitate prediction of response to therapy. All data will be uploaded to a dynamic and modular
informatics resource available to the co-clinical community to test new algorithms, and mine for novel leads
integrating imaging and multi-scale analytic data to predict therapeutic response in TNBC. Thus, the Resource
will rigorously optimize the utility multi-parametric quantitative PET/MR imaging in assessing response to therapy
in realistic animal models of cancer and patients and develop QI standards that transcend species and modalities
which is critical in advancing the application of QI.

## Key facts

- **NIH application ID:** 10107776
- **Project number:** 5U24CA209837-05
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** JOSEPH J. H. ACKERMAN
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $624,395
- **Award type:** 5
- **Project period:** 2017-03-17 → 2022-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10107776

## Citation

> US National Institutes of Health, RePORTER application 10107776, WASHINGTON UNIVERSITY CO-CLINICAL IMAGING RESEARCH RESOURCE (5U24CA209837-05). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10107776. Licensed CC0.

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