Laboratory investigation; a journal of technical methods and pathology · 2026
Cross-Platform Comparative Spatial Transcriptomics in High-Grade Serous Carcinoma.
СтатьяСравнительное исследованиеНазвание и аннотация: Перевод в очереди
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Youssef Omar, Selander Lars, Zheng Shuyu, Csellar Lilla, Hänninen Satu, Tang Jing, Carpén Olli, Laury Anna Ray
Аннотация
PURPOSE: High-grade serous carcinoma of the ovary/fallopian tube (HGSC) is a remarkably heterogeneous tumor. The purpose of this study was to directly compare the reproducibility of transcriptomic profiles and biologically relevant molecular features across 2 spatial transcriptomics platforms-GeoMx Digital Spatial Profiler (GeoMx) and Visium Spatial Gene Expression (Visium)-using artificial intelligence (AI)-defined tumor regions associated with clinical outcome.
MATERIALS AND METHODS: A combination of AI-identified tumor regions and Visium Spatial Gene Expression (Visium) was previously applied to formalin-fixed, paraffin-embedded HGSC samples to identify molecular features associated with disease outcome. Here, we apply GeoMx Digital Spatial Profiler (GeoMx) to the same formalin-fixed, paraffin-embedded cohort (n = 16) to directly compare transcriptional profiles between platforms. We analyzed AI-identified regions, including high-confidence (HC) and lower-confidence "background tumor" areas from the same tumor.
RESULTS: A total of 8011 differentially expressed genes were identified by both platforms, showing strong concordance (r = 0.72, P = 2.2 × 10-16), and pathway enrichment patterns, including tumor necrosis factor-α signaling and p53 pathways, were similar. Outcome-related genes of interest exhibited distinct expression patterns between HC and background tumor regions: JUN, ITGB8, KRT7, and TACSTD2 were enriched in the short platinum-free interval group, whereas PPIB, GSTP1, and LPAR3 were associated with long platinum-free interval. Additionally, RNA in situ hybridization validated GeoMx-identified HC region-specific markers. Higher NR1D2 and TREX1 expression in short platinum-free interval tumors and elevated H1-5 and HMGB3 expression in long platinum-free interval tumors were detected, consistent with the differential expression trends observed in GeoMx.
CONCLUSIONS: Together, our results demonstrate that key transcriptomic features of HGSC are consistently captured across spatial transcriptomic technologies, supporting their translational utility. The ability to accurately replicate transcriptomic profiles across 2 different platforms expands translational applications and enhances the identification of biologically significant molecular features relevant to HGSC outcomes.
MATERIALS AND METHODS: A combination of AI-identified tumor regions and Visium Spatial Gene Expression (Visium) was previously applied to formalin-fixed, paraffin-embedded HGSC samples to identify molecular features associated with disease outcome. Here, we apply GeoMx Digital Spatial Profiler (GeoMx) to the same formalin-fixed, paraffin-embedded cohort (n = 16) to directly compare transcriptional profiles between platforms. We analyzed AI-identified regions, including high-confidence (HC) and lower-confidence "background tumor" areas from the same tumor.
RESULTS: A total of 8011 differentially expressed genes were identified by both platforms, showing strong concordance (r = 0.72, P = 2.2 × 10-16), and pathway enrichment patterns, including tumor necrosis factor-α signaling and p53 pathways, were similar. Outcome-related genes of interest exhibited distinct expression patterns between HC and background tumor regions: JUN, ITGB8, KRT7, and TACSTD2 were enriched in the short platinum-free interval group, whereas PPIB, GSTP1, and LPAR3 were associated with long platinum-free interval. Additionally, RNA in situ hybridization validated GeoMx-identified HC region-specific markers. Higher NR1D2 and TREX1 expression in short platinum-free interval tumors and elevated H1-5 and HMGB3 expression in long platinum-free interval tumors were detected, consistent with the differential expression trends observed in GeoMx.
CONCLUSIONS: Together, our results demonstrate that key transcriptomic features of HGSC are consistently captured across spatial transcriptomic technologies, supporting their translational utility. The ability to accurately replicate transcriptomic profiles across 2 different platforms expands translational applications and enhances the identification of biologically significant molecular features relevant to HGSC outcomes.