# Sorting and characterization of cancer cells based on metabolic phenotype > **NIH NIH R21** · VANDERBILT UNIVERSITY · 2022 · $222,296 ## Abstract PROJECT SUMMARY/ABSTRACT Altered metabolism is a hallmark of cancer, and therapeutic intervention of this altered feature is emerging and holds significant potential. Recent work has found that breast cancer cells exhibit dramatic differences in their glycolysis versus oxidative phosphorylation (OXPHOS) metabolic phenotype within the primary tumor and metastases, and between metastases at different organs. This heterogeneity in metabolic phenotype may be a result of genetic heterogeneity or cellular plasticity and metabolic adaptation to the local microenvironment. Metabolic heterogeneity and plasticity may contribute to therapeutic resistance to treatments that target a specific metabolic pathway. The field generally believes that cellular metabolic adaptation and plasticity facilitate their survival and colonization during metastasis. However, it not clear whether a change in metabolic phenotype in the primary tumor can predict metastatic outcome. In this project, we propose to phenotypically sort breast cancer cells into subpopulations with distinct glycolysis or OXPHOS phenotypes, and use these sorted subpopulations to test the hypothesis that the initial metabolic phenotype and heterogeneity determine the metastatic outcome against the alternative hypothesis that metabolic adaptation to the local microenvironment and phenotypical switching contribute to metastatic outcome regardless of the initial metabolic heterogeneity. By expressing a fluorescent biosensor in the cells for cellular glycolysis versus OXPHOS reliance, we have obtained preliminary data supporting the feasibility of cell sorting based on this metabolic feature. In Aim 1, we will optimize the engineering approach for cell sorting based on cellular metabolic phenotype. Fluorescence-activated cell sorting (FACS) will be coupled with metabolic biosensors, and automated microscopy, photoactivation and fluorescent labeling of cells for cell separation. In Aim 2, we will use the sorted metabolic subpopulations to test our overall hypotheses in vitro and in vivo that initial metabolic phenotype predicts metastatic outcome. Engineered systems mimicking the environmental conditions at the primary and secondary sites, and in circulation will be designed to characterize cell migration, proliferation, and survival of the subpopulations, as well as their metabolic adaptation. We will examine the metastatic potential of these subpopulations in a mouse model and determine their metabolic adaptations at different stages along the metastatic cascade. The innovative aspects of this proposal are the concept to sort by metabolic phenotype and the goal of uncovering the role of initial metabolic phenotype in the broader metastatic cascade. This project will use the novel engineered cell sorting approach to dissect the respective roles of metabolic heterogeneity and adaptability in breast cancer metastasis, thus laying the foundation for future work to identify the key molecular pathways to precise... ## Key facts - **NIH application ID:** 10467279 - **Project number:** 1R21CA271715-01 - **Recipient organization:** VANDERBILT UNIVERSITY - **Principal Investigator:** Cynthia A. Reinhart-King - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2022 - **Award amount:** $222,296 - **Award type:** 1 - **Project period:** 2022-04-01 → 2024-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10467279 ## Citation > US National Institutes of Health, RePORTER application 10467279, Sorting and characterization of cancer cells based on metabolic phenotype (1R21CA271715-01). Retrieved via AI Analytics 2026-06-24 from https://api.ai-analytics.org/grant/nih/10467279. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*