Biology subjects

Kang, C. H.

Publications and source records attributed to Kang, C. H..

3 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↗

bioRxiv2026

Distinct Spatial Programs of Response versus Resistance in Non-Small Cell Lung Cancer after Neoadjuvant Chemoimmunotherapy

BackgroundNeoadjuvant chemoimmunotherapy (nCIT) has become a standard treatment for locally advanced resectable non-small cell lung cancer (NSCLC), yet the spatial biology underlying treatment resistance remains poorly understood. We used spatial transcriptomics to define the microenvironmental architecture of residual cancers in patients who did not achieve major pathologic response (non-MPR) compared with those who did (MPR). MethodsSpatial transcriptomics was performed on 10 formalin-fixed paraffin-embedded (FFPE) tumor blocks (5 MPR, 5 non-MPR) obtained from 8 patients treated with nCIT. A deep learning algorithm was applied to detect viable residual cancer spots from treatment-induced fibrosis and necrosis. Spatial deconvolution, distance modeling, ligand-receptor analysis, and functional pathway scoring were integrated to characterize niche-specific programs. ResultsMPR cancer core displayed an immune-permissive remodeling environment with deep infiltration of cytotoxic CD8+ T cells, mature dendritic cells (LAMP3+, CCR7+), and active efferocytosis signaling (APOE-TREM2), alongside robust MHC class II expression. Non-MPR cancer core, by contrast, exhibited spatial immune exclusion: a dense fibroblast barrier reinforced by TIMP1-CD63 signaling and Treg-enriched boundaries physically restricted effector T cell access to the cancer core. Residual cancer cells in non-MPR samples maintained active cell cycling and independently upregulated cytochrome P450-mediated drug detoxification and DNA damage response pathways without inducing MHC class II expression -- effectively decoupling intrinsic survival from immune recognition. The non-MPR core also showed a hyper-metabolic profile, including elevated glutathione metabolism consistent with antioxidant buffering against chemotherapy-induced oxidative stress. TROP2 was broadly expressed across the non-MPR cancer core and co-localized with DNA damage response and nuclear factor erythroid 2-related factor 2 resistance signatures. ConclusionsResidual cancer cores in non-MPR tumors appear to represent evolved resistant niches sustained by structural immune exclusion, metabolic rewiring, and DNA repair proficiency. These findings highlight the spatial co-localization of epithelial anchors, such as TROP2, with intrinsic resistance pathways, providing a structural rationale for developing novel precision therapeutic strategies to bypass stromal barriers and overcome the cancer cores intrinsic repair capacity.

cancer biology↗

bioRxiv2023

A leaky integrate-and-fire computational model based on the connectome of the entire adult Drosophila brain reveals insights into sensorimotor processing

The forthcoming assembly of the adult Drosophila melanogaster central brain connectome, containing over 125,000 neurons and 50 million synaptic connections, provides a template for examining sensory processing throughout the brain. Here, we create a leaky integrate-and-fire computational model of the entire Drosophila brain, based on neural connectivity and neurotransmitter identity, to study circuit properties of feeding and grooming behaviors. We show that activation of sugar-sensing or water-sensing gustatory neurons in the computational model accurately predicts neurons that respond to tastes and are required for feeding initiation. Computational activation of neurons in the feeding region of the Drosophila brain predicts those that elicit motor neuron firing, a testable hypothesis that we validate by optogenetic activation and behavioral studies. Moreover, computational activation of different classes of gustatory neurons makes accurate predictions of how multiple taste modalities interact, providing circuit-level insight into aversive and appetitive taste processing. Our computational model predicts that the sugar and water pathways form a partially shared appetitive feeding initiation pathway, which our calcium imaging and behavioral experiments confirm. Additionally, we applied this model to mechanosensory circuits and found that computational activation of mechanosensory neurons predicts activation of a small set of neurons comprising the antennal grooming circuit that do not overlap with gustatory circuits, and accurately describes the circuit response upon activation of different mechanosensory subtypes. Our results demonstrate that modeling brain circuits purely from connectivity and predicted neurotransmitter identity generates experimentally testable hypotheses and can accurately describe complete sensorimotor transformations.

neuroscience↗