188 Single-Cell Genomic Profiling of the Human Metastatic Prostate Spine Tumor Microenvironment
INTRODUCTION: Although 90% of spine tumors develop metastases with debilitating sensorimotor complications and spinal instability, spine tumor patients have limited eligibility to participate in clinical trials and continuously lack therapeutic targets and prognostication markers of disease. Thus, there is a distinct need for molecular profiling of spine tumors. METHODS: Patients undergoing spine surgery with the Department of Neurosurgery at Duke University were consented (IRB Protocol #Pro00101198) for tissue collection. Vertebral osseous metastases and adjacent normal tissue were confirmed histologically (H&E, immunohistochemistry) and radiologically (MRI, PET, CT, and x-ray). Tissues were manually and enzymatically homogenized in serum-free media with collagenase A and DNAse I to obtain a single cell suspension. Cells were then filter sterilized, erythrolyzed, counted to determine live-death ratio, resuspended to 10-20 million cells per mL, then cryopreserved for downstream standard library preparation for single-cell sequencing using the 10X genomics platform. Downstream analysis was performed using Seurat pipeline, InferCNV analysis, and SingleR. RESULTS: P404S and PSA expression confirmed tumor was prostate in origin. Radiological imaging and H&E staining confirmed the presence of T7 osseous metastasis. Uniform manifold approximation and project (UMAP) plots identified 12 unique clusters of immune cell subpopulations between normal and tumorous vertebrae, with their own subsets of differentially expressed genes, emphasizing metabolic enzyme UDP-6-glucose-dehydrogenase (UGDH). CONCLUSIONS: We validate a protocol to generate granular single cell molecular profiling in other metastatic spine tumor cancer types. Applications of this protocol can aid in identifying cancer-specific therapeutic targets, molecular predictors of disease, and potentiate increased eligibility of patients for clinical trials.
Authors
- Eli Johnson (ORCID: https://orcid.org/0000-0003-1960-0789)
- Meghan Price (ORCID: https://orcid.org/0000-0002-2683-9758)
- Courtney Rory Goodwin (ORCID: https://orcid.org/0000-0002-6540-2751)
- César Baëta (ORCID: https://orcid.org/0000-0001-6054-5369)
- Annee Nguyen (ORCID: https://orcid.org/0000-0002-5550-9053)
- Corinne Haines (ORCID: https://orcid.org/0000-0003-0061-6983)
- Harrison Hockenberry
Publication Details
- Journal
- Neurosurgery
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1227/neu.0000000000003360_188
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00