# INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER

> **NIH NIH U24** · BAYLOR COLLEGE OF MEDICINE · 2020 · $632,900

## Abstract

Project Summary
Triple negative breast cancer (TNBC) is a very challenging disease because it is biologically aggressive, there
are no targeted therapies, and, consequently, patients have poor prognosis. Although immunotherapy is
promising for treating many cancers, TNBC lacks specific molecular targets, no predictive biomarkers to
chemotherapy response have yet been identified, and treatment response is difficult to evaluate using current
biomarker assessments. Patient-derived xenograft (PDX) models of TNBC offer the exciting opportunity of
evaluating this disease in terms of molecular features (e.g., genomic copy number, whole exome sequence, and
mRNA expression) to identify candidate “omic” biomarkers that best predict the ultimate response to treatment
and could provide surrogate endpoints to validate novel imaging biomarkers in co-clinical trial human trails.
Moreover, emerging quantitative MRI methods, such as dynamic contrast enhanced magnetic resonance
imaging (DCE-MRI) and diffusion weighted MRI (DW-MRI), contain rich physiological signals in the images for
predicting treatment response, but it is challenging to integrate both animal and human data to reliably predict the
treatment response. A paradigm of “co-clinical trials” is emerging in which new treatments are evaluated in
animals, and the results guide treatments in clinical trials, but there is a paucity of informatics tools and resources
to enable analyses in such animal-to-human work. We believe that an informatics-based methodology that
integrates molecular `omics' and imaging data will propel advances in TNBC by enabling development of
machine learning models to predict the response to therapies. In order to develop research resources that will
encourage consensus on how quantitative imaging methods are optimized to improve the quality of imaging
results for co-clinical trials, we will leverage an ongoing co-clinical trial we are undertaking to pursue the following
specific aims: (1) Identify molecular biomarkers that predict response in TNBC patient-derived xenografts (PDX);
(2) Identify quantitative MRI biomarkers that predict response in TNBC patient-derived xenografts; and (3)
Evaluate our informatics tools in a prospective co-clinical trial. Our proposed research is significant and
innovative because it leverages advances in basic cancer biology, state-of-the-art imaging technologies, and
informatics methods to develop a resource to catalyze discovery in this important disease. Our PDX-based
approach will provide the cancer community with a rational, iterative, combined pre-clinical and clinical
methodology and supporting data resource for making progressively more refined and personalized therapeutic
regimens for TNBC patients. Our methods and tools will likely also generalize to other cancers and could,
therefore, substantially benefit the care of all cancer patients.

## Key facts

- **NIH application ID:** 10020941
- **Project number:** 5U24CA226110-02
- **Recipient organization:** BAYLOR COLLEGE OF MEDICINE
- **Principal Investigator:** Michael T. Lewis
- **Activity code:** U24 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $632,900
- **Award type:** 5
- **Project period:** 2019-09-19 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10020941

## Citation

> US National Institutes of Health, RePORTER application 10020941, INTEGRATING OMICS AND QUANTITATIVE IMAGING DATA IN CO-CLINICAL TRIALS TO PREDICT TREATMENT RESPONSE IN TRIPLE NEGATIVE BREAST CANCER (5U24CA226110-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10020941. Licensed CC0.

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