# Mapping the single cell state basis of metastasis in space and time

> **NIH NIH U01** · JOHNS HOPKINS UNIVERSITY · 2024 · $645,588

## Abstract

We propose to leverage recent advances in machine learning and systems biology to enable high
dimensional molecular assessment of the dynamic cell state transitions driving metastasis. We hypothesize that
the interaction between a cancer cell's intrinsic reactivation of developmental programs with its spatiotemporal
context determines its metastatic potential. We will exploit developmental changes in the mammary epithelium
to define their cell state basis and map the aberrant reuse of these transcriptional programs in metastatic disease.
 Both normal mammary epithelium and breast tumors undergo dramatic changes in differentiation and
tissue architecture, and loss of differentiation correlates with poor patient outcomes. We developed 3D culture
assays that recapitulate epithelial morphogenesis and cancer growth, invasion, and metastatic colony formation.
The key concepts arising are that: (1) a conserved process of dedifferentiation and loss of polarity accompanies
both normal and neoplastic morphogenesis and (2) the cancer cells in luminal and basal breast cancer
recapitulate basal epithelial and hybrid EMT programs. Recent advances in single cell sequencing, spatial
transcriptomics, and machine learning enable transcriptome-wide resolution of these states in tissue, quantitative
comparison of normal and cancerous cell states, and the identification of targetable cell state regulators.
Aim 1: Map cell states in space and time during development, tumor formation, and metastasis. We will
generate scRNA-seq data from normal glands, ductal carcinoma in situ, and invasive tumors collected at different
ages and also longitudinally in 3D culture. We will use our CoGAPS algorithm to infer cell states and their
temporal progression. We will then use our patternMarker2 statistic to identify cell state makers for MERSCOPE
analysis in tissue. We will map these states in normal glands, primary tumors, and metastases isolated from
genetically engineered mouse models (GEMM) and patient derived xenografts (PDX).
Aim 2: Model the dynamics of differentiation state during development and cancer progression. To define
the effect of cell state on metastatic progression, we will construct an ecosystem-style multinomial diversity
model. We will initialize the model with literature-based parameter values to predict the interactions between cell
type and cell state. We will then extend the model to use the weights assigned by CoGAPS to each cell, thereby
linking gene regulatory programs to the cell state changes driving metastasis.
Aim 3: Validate candidate regulators of metastatic cell state transitions in 3D culture and in vivo.
To isolate the genes regulating metastasis, we will use our transfer learning algorithm, projectR, to score each
cancer cell for its relative utilization of scRNA-seq-defined molecular programs. We will then use our
projectionDriver statistic to identify differentially expressed (DE) genes at sites of cancer invasion, relative to the
tumor inte...

## Key facts

- **NIH application ID:** 10928210
- **Project number:** 5U01CA284090-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Andrew Josef Ewald
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $645,588
- **Award type:** 5
- **Project period:** 2023-09-12 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10928210

## Citation

> US National Institutes of Health, RePORTER application 10928210, Mapping the single cell state basis of metastasis in space and time (5U01CA284090-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10928210. Licensed CC0.

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