# Washington University Co-Clinical Imaging Research Resource

> **NIH NIH U24** · WASHINGTON UNIVERSITY · 2022 · $665,295

## Abstract

ABSTRACT
Breast cancer (BC) is the most common cancer diagnosed in women. Approximately 70% of BCs are estrogen
receptor (ER) positive (ER+) and human epidermal growth factor receptor 2 negative (HER2-). Endocrine
therapy (ET) reduces recurrence risk and improves survival for many in this group. However, despite standard
of care and adjuvant ET, over 20% patients with ER+/HER2- BC experience metastatic recurrence in the years
to come, and virtually all patients with metastatic disease eventually experience disease progression on ET due
to intrinsic or acquired resistance mechanisms. There are currently no biomarkers that reliably identify which of
these advanced breast cancer patients will benefit from ET-based approaches so that chemotherapy could be
avoided or delayed. To address this unmet need, the objective of this proposal is to develop co-clinical
quantitative PET/CT imaging strategies integrated genoproteomic discovery to predict response to ET in patients
with ER+/HER2- metastatic breast cancer (MBC). To that end, we will interface with a recently awarded phase
II multicenter Translational Breast Cancer Research Consortium (TBCRC) trial to assess the functional status of
estrogen receptor in patients with ER+/HER2- MBC. The U24 will have three specific aims: in Aim 1 we will
optimize animal modeling and the quantitative accuracy of PET imaging agents of response to ET in ER+/HER2-
BC patient-derived tumor xenografts (PDX). In Aim 2 we will implement optimal quantitative methods to predict
response to ET in ER+/HER2- and integrate with multi-scale genoproteomic data across the co-clinical trial. And
in Aim 3 we will populate content from the co-clinical investigation on a web-accessible research resource and
expand capabilities of co-clinical database (CCDB). In addition, high value multi-scale analytic data will be
generated, including whole exome sequencing (WES), RNASeq, pathology, and CODetection by indEXing
(CODEX) to characterize tumor heterogeneity. All data will be uploaded to an 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. Overall, this proposal aims to have a far-reaching and high impact
on the implementation of precision medicine in identifying, stratifying, and predicting response to ET+CDK4/6i in
patients with ER+/HER2- MBC, integrating quantitative imaging with genoproteomic discovery.

## Key facts

- **NIH application ID:** 10429189
- **Project number:** 2U24CA209837-06
- **Recipient organization:** WASHINGTON UNIVERSITY
- **Principal Investigator:** Li Ding
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $665,295
- **Award type:** 2
- **Project period:** 2017-03-17 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10429189

## Citation

> US National Institutes of Health, RePORTER application 10429189, Washington University Co-Clinical Imaging Research Resource (2U24CA209837-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10429189. Licensed CC0.

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