# Integrative single cell analyses of inflammation-derived circulating hybrid cells to identify aggressive disease

> **NIH NIH R01** · OREGON HEALTH & SCIENCE UNIVERSITY · 2024 · $585,409

## Abstract

PROJECT SUMMARY
The lifetime risk for acquiring colorectal cancer (CRC) is 7%, with an astounding rate of disease recurrence in
32% of newly diagnosed patients after their “successful” treatment. Patient with recurrent disease have a dismal
14.3% five-year survival. Lack of effective biomarkers hampers early detection of pre-metastatic disease,
impacting overall survival from CRC. We identified a promising disseminated tumor cell—a product of
macrophage (MФ) and cancer cell fusion—that harbors genotypic and phenotypic features of both cells of origin.
Detectable along the metastatic cascade, hybrid cells can initiate tumor growth, migrate in response to MФ
receptor-ligand chemotaxis, and seed metastatic sites. In peripheral blood, hybrids, named circulating hybrid
cells (CHCs) outnumber conventionally defined circulating tumor cells (CTCs) in CRC patients, overcoming the
sensitivity of CTC—a primary barrier—to usage as a biomarker for disease. CHCs are phenotypically diverse
and reflect protein expression of the primary tumor. Based on these exciting findings, we propose that hybrid
cells subpopulations harbor discrete phenotypes of pre-metastatic cells that can be identified and defined using
single cell image-based phenotyping through multiplexed imaging and multimodal integration with –omics. To
this end, we will analyze CHCs derived from early stage and metastatic tumors for image-based phenotyping
with single cell gene expression. Utilizing quantitative and advanced image analytics including deep learning
approach for image-based cell profiling, we will define inter/subcellular spatial features in single cells to identify
new subpopulations and differentiate discrete phenotypic populations associated with metastatic signatures. In
addition, the application of both imaging and genomic technologies to the same specimen independently
measures highly dimensional, yet non-orthogonal, sets of cellular features. Multimodal integration of imaging
and single cell data will quantify systems-level biological functions of cellular subpopulation and enhance imaging
biomarker panel to gain biomarker specificity and sensitivity for validation in a discrete CRC patient cohort. Our
overall goal is to develop a novel tumor biomarker, based upon CHC phenotyping and –omics analyses that can
be used to provide new quantitative insights and develop machine-driven prediction with superior accuracy for
identifying risk of metastases in CRC patients to ultimately impact survival.

## Key facts

- **NIH application ID:** 10840799
- **Project number:** 5R01CA253860-04
- **Recipient organization:** OREGON HEALTH & SCIENCE UNIVERSITY
- **Principal Investigator:** Young Hwan Chang
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $585,409
- **Award type:** 5
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10840799

## Citation

> US National Institutes of Health, RePORTER application 10840799, Integrative single cell analyses of inflammation-derived circulating hybrid cells to identify aggressive disease (5R01CA253860-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10840799. Licensed CC0.

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