Esophageal adenocarcinoma (EA) usually develops following Barrett esophagus as a consequence of long-term GERD. Its incidence has been rising significantly in the Western world, faster than most other cancer subtypes. As an emerging global health problem with few specific treatment options, it requires strong research focus and effort.

We have utilized modern single cell and spatial transcriptomics technologies from 10x Genomics to characterize transcriptomic profiles of tumor and stromal cell populations in EA and their spatial relationships. We applied Xenium 5,000 human gene spatial transcriptomics on tumor and stroma samples derived from 30 patients (matched single cell and spatial transcriptomics). We identified multiple tumor cell subpopulations which displayed spatially distinct distribution. Notably, a minor tumor cell cluster was present in all samples with high expression of proliferation markers. This tumor subpopulation was tightly interacting with other tumor clusters and possibly contributing to tumor propagation. In-depth characterization of all tumor subpopulations will help pinpoint their respective contributions to tumor development and tumor-stroma interactions.

• From images to biological insights: How high-dimensional spatial proteomics and multiplexed tissue imaging can reveal tissue architecture, cellular interactions, and disease-associated microenvironments that are difficult to capture with conventional approaches.
• Integrating spatial data for biological and clinical discovery: Applications of spatially resolved tissue analysis to understanding disease mechanisms, identifying predictive biomarkers, and improving diagnosis and treatment response assessment.
• Moving toward scalable and reproducible spatial biology: Opportunities and challenges in data generation, image analysis, harmonization, and integration of spatial molecular measurements with computational pathology and AI.

• Advances in spatial multi-omics including proteomics and transcriptomics allow for the analysis of an unprecedented number of single-cell spatial features.

• The spatial integration of proteomics and transcriptomics allows for the identification of novel biomarkers with direct therapeutic potential.
• This presentation will demonstrate the utilization of highly multiplex spatial transcriptomics and proteomics for the identification of novel therapeutic biomarkers in EGFR-mutant NSCLC.

• What technical barriers remain? (sensitivity, resolution, multiplexing)
• How to overcome data challenges
• Scaling impact across research and medicine. What will this look like?
• Standardization, reproducibility, and data interoperability across spatial multi-omics platforms