Biology subjects

Ohn, J.

Publications and source records attributed to Ohn, J..

4 recordsLinked to original sources

bioRxiv2026

Spatial tumor microenvironmental architecture of chemoradiotherapy-resistant residual esophageal squamous cell carcinoma

PurposeResidual ESCC after neoadjuvant CCRT reflects incomplete treatment response and carries a high risk of recurrence. We sought to define the spatial tumor microenvironmental features of CCRT-resistant residual ESCC -- using tumor regression grade (TRG) 2-3 tumors as a model of poor pathologic response -- with mechanistic refinement at single-cell resolution and outcome assessment in internal and external cohorts. Experimental DesignWe profiled post-CCRT ESCC tissue microarray (TMA) cores by Visium FFPE whole-transcriptome spatial transcriptomics and orthogonal Xenium single-cell-resolution in situ profiling on serial sections from the same TMA blocks. Spot-level cell-type composition was inferred by CellDART deconvolution against a published ESCC single-cell RNA sequencing (scRNAseq). Local malignant-cell-enriched regions were defined by CancerFinder. We additionally re-analyzed the scRNAseq dataset to characterize SPP1 and CXCL5 macrophage states and their ligand-receptor signaling using CellChat. Outcome associations were evaluated in the SNUH cohort and externally tested in the TCGA ESCC cohort. ResultsTRG 2-3 residual tumors exhibited effector immune-cell exclusion from malignant-cell-enriched regions, SPP1/CXCL5 macrophage accumulation, microvascular rarefaction, and proliferative-metabolic reprogramming. Single-cell re-analysis confirmed that CXCL5 macrophages constitute a transcriptional subset of the broader SPP1 macrophage population. CellChat showed that SPP1 macrophages dominantly signal through SPP1-CD44 and SPP1-integrin axes to stromal and epithelial targets, and additionally engage immunosuppressive NECTIN2-TIGIT, CD86-CTLA4, and LGALS9-HAVCR2 programs with CD8 T cells, providing a mechanistic context for local immune exclusion. Visium LIANA and Xenium distance-gradient profiling localized SPP1-associated signaling preferentially to tumor cells and CAFs. Higher SPP1 expression and lower endothelial abundance were associated with shorter disease-free survival in the SNUH cohort. Higher SPP1 expression was also associated with shorter disease-free survival in the independent TCGA ESCC cohort. ConclusionsCCRT-resistant residual ESCC is characterized by a spatially organized tumor microenvironmental niche centered on SPP1-associated macrophage programs and microvascular rarefaction. These spatially resolved findings identify candidate macrophage- and vasculature-targeted axes for overcoming treatment resistance. Translational RelevancePatients with esophageal squamous cell carcinoma (ESCC) who harbor residual disease after neoadjuvant chemoradiotherapy (CCRT) remain at high risk of recurrence, yet the spatial biology of this resistant residual state has been poorly defined. Here, we combine Visium whole-transcriptome and Xenium single-cell-resolution spatial transcriptomics with single-cell RNA-seq re-analysis and external TCGA context to define the tumor microenvironmental architecture of CCRT-resistant residual ESCC. The resistant niche is characterized by effector immune-cell exclusion from malignant-cell-enriched regions, accumulation of SPP1/CXCL5 macrophage programs that signal to tumor cells and cancer-associated fibroblasts through SPP1-CD44 and SPP1-integrin axes, microvascular rarefaction, and proliferative-metabolic reprogramming. These findings define a coordinated spatial tumor microenvironmental signature of CCRT-resistant residual ESCC and nominate macrophage-tumor/stromal interactions and vascular injury as biologic axes that may help guide future strategies to overcome treatment resistance.

cancer biology↗

bioRxiv2024

Spatial transcriptomics unveils landscape of resistance to concurrent chemo-radiotherapy in hypopharyngeal squamous cell carcinoma: the role of SPP1+ macrophages

Hypopharyngeal squamous cell carcinoma (SCC) is a highly aggressive cancer with a poor prognosis, particularly in advanced stages where concurrent chemoradiotherapy (CCRT) is used for treatment. However, resistance to CCRT poses a significant challenge, often leading to treatment failure and disease progression. This study explores the tumor microenvironment (TME) of hypopharyngeal SCC to understand the molecular mechanisms underlying CCRT resistance. Using spatial transcriptomics (ST), we analyzed tissue samples from patients with locally advanced hypopharyngeal SCC, distinguishing between those who were CCRT-resistant and those who were CCRT-naive. The analysis revealed six distinct cellular clusters within the TME, including a prominent epithelio-immune cellular area in CCRT-resistant tissues. SPP1 was identified as a key gene with significantly higher expression in CCRT-resistant samples, specifically within macrophages. Further investigation showed that SPP1+ macrophages interacted with malignant epithelial cells through SPP1-CD44 and SPP1-ITGB1 ligand-receptor pairs. These interactions were primarily localized in the peri-tumoral and intra-tumoral regions, highlighting their potential role in driving CCRT resistance. Our findings suggest that SPP1+ macrophages contribute to the resistant phenotype in hypopharyngeal SCC by modulating the TME and interacting with cancer cells. Understanding these interactions offers valuable insights into the mechanisms of CCRT resistance and may inform the development of targeted therapies to improve patient outcomes.

cancer biology↗

bioRxiv2023

SpatialSPM: Statistical parametric mapping for the comparison of gene expression pattern images in multiple spatial transcriptomic datasets

Spatial transcriptomic (ST) techniques help us understand the gene expression levels in specific parts of tissues and organs, providing insights into their biological functions. Even though ST dataset provides information on the gene expression and its location for each sample, it is challenging to compare spatial gene expression patterns across tissue samples with different shapes and coordinates. Here, we propose a method that reconstructs ST data into multi-dimensional image matrices to ensure comparability across different samples through spatial registration process. We demonstrated the applicability of this method by using two mouse brain ST datasets to investigate and directly compare gene expression in a specific anatomical region of interest, pixel by pixel, across various biological statuses. It can produce statistical parametric maps to find specific regions with differentially expressed genes across tissue samples. Our approach provides an efficient way to analyze ST datasets and may offer detailed insights into various biological conditions.

bioinformatics↗

bioRxiv2023

GeneDART: Extending gene coverage in image-based spatial transcriptomics by deep learning-based domain adaptation with barcode-based RNA-sequencing data

Spatial transcriptomics (ST) technologies provide comprehensive biological insights regarding cell-cell interactions and peri-cellular microenvironments. ST technologies are divided into two categories: imaging-based (I-B) and barcode-based (B-B). I-B ST technologies provide high resolution and sensitivity but have limited gene coverage. B-B ST technologies can analyze the whole transcriptome but have lower spatial resolution. To address these limitations, we propose a deep learning-based model that integrates I-B and B-B ST technologies to increase gene coverage while preserving high resolution. A model, trained by a neural network with an adversarial loss based on I-B and B-B datasets from human breast cancer tissue, was able to extend gene coverage to whole transcripts-level and accurately predict gene expression patterns in the I-B dataset with a high resolution. This novel methodology, named GeneDART, could enable researchers to utilize B-B and I-B ST datasets in a complementary way.

bioinformatics↗