# Quantitative characterization of tumor heterogeneity using habitat imaging for the prediction of patient outcome in triple negative breast cancer

> **NIH NIH K99** · UNIVERSITY OF WASHINGTON · 2024 · $101,458

## Abstract

PROJECT SUMMARY
The overall goal of this training proposal is to employ quantitative imaging to noninvasively characterize tumor
microenvironmental heterogeneity in triple negative breast cancer (TNBC) for the prediction of treatment
response and outcome. TNBC is an aggressive breast cancer subtype with notable diversity in disease biology
and clinical presentation. Recently-approved immunotherapies introduce exciting new avenues for neoadjuvant
treatment of TNBC; however, response to immunotherapy is variable and treatment can entail significant adverse
effects and financial cost. Variable response to therapy can be attributed in-part to heterogeneity of the tumor
microenvironment, affecting therapeutic delivery and efficacy. Through an innovative approach known as “habitat
imaging”, multiparametric magnetic resonance imaging (mpMRI) can be used to spatially resolve local
microenvironments within a lesion into distinct tumor subregions, or habitats. For the research component of this
proposal, we propose to use quantitative breast imaging and informatics techniques to identify tumor habitats
and whole-lesion habitat signatures for TNBC patient stratification. We hypothesize that imaging-derived habitat
signatures identified prior to treatment can aid in stratifying TNBC patients with increased probability of achieving
pCR and/or decreased risk of recurrence. We will test this hypothesis through the following specific aims: Aim 1
(K99) To retrospectively identify tumor habitat signatures from pretreatment mpMRI to stratify TNBC patients
and predict treatment outcome; Aim 2 (R00) To prospectively employ habitat imaging using hybrid positron
emission tomography (PET)/MRI for improved TNBC patient stratification. Successful completion of my research
aims will provide a clinically-translatable methodology for improved understanding of an individual’s lesion
physiology that could guide personalized treatment strategies for optimal patient outcome. To provide me with
the necessary training to successfully carry out these research aims, this proposal outlines a mentored-training
plan with three areas of focus: 1) strengthen my expertise in clinical breast cancer research, 2) receive additional
training in computational pathology, and 3) obtain educational training and hands-on experience with PET/MRI
to prepare for research in the independent R00 phase and beyond. This training program will be executed under
the direct mentorship of NIH-funded researchers and clinicians specializing in medical oncology, nuclear
medicine, pathology and radiology, and take place within the well-equipped and established cancer research
environment at the University of Washington and Fred Hutch Cancer Center. As outlined in my career
development plan, funding from this proposal will be used to dedicate time for educational workshops and training
seminars, along with regular meetings with my mentorship team. Together, this training and research proposal
will ensure that I am...

## Key facts

- **NIH application ID:** 10947543
- **Project number:** 1K99CA293004-01
- **Recipient organization:** UNIVERSITY OF WASHINGTON
- **Principal Investigator:** Anum Syed Kazerouni
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $101,458
- **Award type:** 1
- **Project period:** 2024-07-16 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10947543

## Citation

> US National Institutes of Health, RePORTER application 10947543, Quantitative characterization of tumor heterogeneity using habitat imaging for the prediction of patient outcome in triple negative breast cancer (1K99CA293004-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10947543. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*