# Evolutionary dynamics and microenvironmental determinants of metastatic breast cancer

> **NIH NIH U54** · STANFORD UNIVERSITY · 2022 · $1,842,753

## Abstract

Abstract/Project Summary
Metastatic breast cancer and relapse following therapy are dependent on (1) development of intrinsic resistance
to targeted and endocrine therapies, and (2) resistance to recognition and destruction of cancer cells by the
immune system. The Stanford Breast Metastasis Center (SBMC) is focused on (1) quantifying the timing of
metastatic dissemination in breast cancer (2) functionally delineating the contribution of cellular and
microenvironmental crosstalk on metastatic proclivity, and (3) characterizing the mechanisms of responses by
metastatic cells to therapies.
 In order to achieve these goals, mechanistic computational models that capture dynamic and
emergent tumor cell intrinsic and extrinsic properties are needed as are clinically annotated longitudinal
tissue cohorts and experimental models that capture disease heterogeneity. The SBMC addresses each of these
outstanding challenges. First, we have established an unparalleled collection of clinically annotated breast
cancer cohorts sampled through treatment and metastasis, including both prospective and retrospective
longitudinal cohorts, with multiple metastatic sites. We leverage a living biobank of breast cancer patient-
derived organoids (PDOs) from primary tumors and metastases that recapitulate the heterogeneity of
disease, high-risk of relapse subgroups and tumor-immune interactions and greatly facilitating the proposed
functional studies. We characterize these vast tissue resources and model systems using state-of-the-art
molecular profiling technologies to probe tumor tissue in situ at single cell and subcellular resolution. Specifically,
with Multiplexed Ion Beam Imaging by Time of Flight (MIBI-TOF) and matrix-assisted laser desorption ionization
imaging (MALDI) we simultaneously visualize the composition, lineage, function and spatial distribution of tumor
and stromal cell populations and perform co-registered analysis of the glycome. We integrate these data within
the genomic landscape of metastatic disease and analyze these data within robust machine learning and
computational frameworks to uncover disease dynamics and features associated with clinical outcomes.
Lastly, we conduct genome-scale CRISPR screens in 3D breast cancer models to systematically define
oncogenic dependencies, therapeutic vulnerabilities and macrophage-tumor cell interactions.
 This integrated systems biology and functional genomics approach will contribute to a quantitative and
mechanistic understanding of metastatic breast cancer and the dynamic relationship between tumor cells and
the host, with implications for therapeutic targeting.

## Key facts

- **NIH application ID:** 10488697
- **Project number:** 5U54CA261719-02
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Christina N Curtis
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $1,842,753
- **Award type:** 5
- **Project period:** 2021-09-14 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10488697

## Citation

> US National Institutes of Health, RePORTER application 10488697, Evolutionary dynamics and microenvironmental determinants of metastatic breast cancer (5U54CA261719-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10488697. Licensed CC0.

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