# Engineered biomimetic collective cancer invasion models for screening chemotherapeutic agents

> **NIH NIH R01** · JOHNS HOPKINS UNIVERSITY · 2024 · $443,191

## Abstract

PROJECT SUMMARY
Metastasis is the primary cause of cancer mortality, yet few breast cancer drugs effectively inhibit metastasis.
Breast cancer cells use collective migration to remodel and align surrounding extracellular matrix (ECM) fibrils,
which facilitates invasion. Aligned tumor stroma topography can induce cluster budding and dissemination of
breast cancer cells. The goal of this project is to identify chemotherapeutic drugs using engineered biomimetic
tumor invasion models and to evaluate therapeutic feasibility of inhibiting the target genes involved in breast
cancer dissemination. To achieve this goal, we developed a quasi-3D nanotopographically patterned substrate
and are incorporating it into a nanopatterned impedance electrode array (nanoIEA) to quantify collective cell
migration and proliferation in real-time at high-throughput. We are validating a 3D aligned collagen fiber hydrogel
model with control over fiber alignment that recapitulates the fiber dimension and orientation of in vivo breast
tumor stroma. These models markedly promote breast cancer cluster dissemination and increase its resistance
to chemotherapy. In our preliminary study, we have identified differentially expressed genes via RNA-seq
between ‘disseminated tumor cell clusters’ and ‘non-disseminated tumor cells’ using the quasi-3D model. We
will pursue three aims that leverage our expertise in cancer molecular biology/genomics (Ahn), tissue
engineering (Kim), machine learning (ML)-based image analysis (Lee), cancer organoids/metastasis (Ewald),
and pharmacology/drug development (Liu). Human breast cancer patient-derived xenograft (PDX) cell
clusters/organoids will be investigated in this project. In Aim 1, we will evaluate effects of the following drugs on
collective cell migration and on growth using the nanoIEA: [a] the 23 oncology drugs (out of 147 drugs we tested)
which most significantly inhibited the viability of breast cancer cells in the quasi-3D model, [b] the 73 non-
oncology drugs which inhibited the viability of 22 breast cancer cell lines by at least 4-fold in conventional 2D
culture, and [c] the 95 inhibitors of target genes (CYP1A1, CYP1A2, CYP1B1). In Aim 2, we will characterize
phenotypic responses of breast cancer cells/organoids to the identified drug candidates from Aim 1 using live
cell microscopy and ML analyses. Phenotypic changes (e.g., motility, morphology) will be quantified to contrast
subpopulations (non-invasive vs. invasive) and drug-treated cells vs. untreated. In Aim 3, we will evaluate
therapeutic feasibility of regulating the target genes to inhibit cancer invasion. We will determine expressions of
target genes at protein levels in PDX organoids, then correlate these with organoid invasiveness in the 3D model.
We will then determine how inhibition of the target genes influences chemosensitivity of PDX organoids and
suppresses their invasiveness. This project will increase our understanding of the mechanisms of topography-
induced bre...

## Key facts

- **NIH application ID:** 10804155
- **Project number:** 1R01CA279948-01A1
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Eun Hyun Ahn
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $443,191
- **Award type:** 1
- **Project period:** 2024-06-13 → 2029-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10804155

## Citation

> US National Institutes of Health, RePORTER application 10804155, Engineered biomimetic collective cancer invasion models for screening chemotherapeutic agents (1R01CA279948-01A1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10804155. Licensed CC0.

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