# Understanding the Impact of Microscale and Nanoscale Heterogeneity and Resistance

> **NIH NIH U54** · OREGON HEALTH & SCIENCE UNIVERSITY · 2020 · $461,333

## Abstract

ABSTRACT- Project 3 
The goals of this Project are to use a spatial systems approach to identify molecular networks that control 
development of resistance-associated heterogeneity in triple negative breast cancers (TNBCs) and to use this 
information to devise multidrug treatments that will be effective in heterogeneous TNBCs. Our focus is on 
heterogeneity that arises from epigenomic plasticity intrinsic to cancer cells and from extrinsic signals from the 
diverse microenvironments into which TNBC cells disperse. Individual cells within a TNBC exhibit variable 
phenotypes and respond variably to treatment so that establishing durable control of TNBCs is notoriously 
difficult. We will explore the mechanisms by which individual cells in TNBC tissues respond to perturbations 
induced by microenvironment interactions and/or drugs. Our approach is based on the concept that the 
phenotype and response to therapy of every cell in a heterogeneous TNBC tissue is influenced by its intrinsic 
epigenomic status and by the microenvironmental signals it receives. In short, every cancer cell- 
microenvironment-drug interaction in a heterogeneous experimental tissue or clinical specimen is an 
independent experiment of nature. We propose to analyze ensembles of such interactions in TNBC tissues 
before and after treatment to determine the impact of local environmental signals on cancer cell phenotype and 
therapeutic response. We will accomplish this using cmIF to stain cancer cells for quantitative analysis of 
proliferative status, differentiation state, and expression levels of proteins that report on control network 
activity. We will quantify cancer cell-microenvironment interactions at the microscale using multicolor 
fluorescence microscopy and at the nanoscale using multispectral super resolution fluorescence microscopy 
(MSSRM) and 3D scanning electron microscopy. We will use custom image analysis techniques developed in 
the Imaging Core to quantify cell and microenvironment components and machine/deep learning strategies to 
identify microenvironment-cancer cell interactions that influence phenotype. This work will guide development 
of dynamic models of spatially dependent control network-microenvironment interactions that can be used to 
devise therapeutic strategies to control TNBCs. The approach is statistically powerful since every tissue 
section contains details about tens of thousands of cell-microenvironment interactions. This work is 
encompassed in three Aims. Aim 1 will develop cyclic multiplex immunofluorescence (cmIF), multiscale image 
analysis, and machine learning procedures needed to identify molecular control networks in individual cells in 
TNBC tissues that respond to signals from microenvironmental cells and proteins (MEPs) and that influence 
phenotype and/or therapeutic response. Aim 2 will elucidate the effects of microenvironmental cells and high 
impact proteins on TNBC control network activity, phenotype, and therapeutic...

## Key facts

- **NIH application ID:** 9964684
- **Project number:** 5U54CA209988-04
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** JOE W. GRAY
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $461,333
- **Award type:** 5
- **Project period:** 2020-05-01 → 2022-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9964684

## Citation

> US National Institutes of Health, RePORTER application 9964684, Understanding the Impact of Microscale and Nanoscale Heterogeneity and Resistance (5U54CA209988-04). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9964684. Licensed CC0.

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