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<p>Bladder cancer exhibits marked spatial heterogeneity in gene expression and immune infiltration. In this exploratory pilot study, we integrate multiplex fluorescence in situ hybridization (mFISH) with AI-assisted digital pathology to characterize the spatial distribution of a previously validated three-gene arsenic-responsive risk model (<i>NKIRAS2</i>,<i> AKTIP</i>,<i> HLA-DQA1</i>). Initially identified in arsenic-exposed individuals and associated with bladder cancer risk, this gene panel achieved 94% training and 75% validation AUC in prior genomic models (PMC8760535). We analyzed five bladder tumor specimens using whole-slide mFISH imaging and HoverNet-based nuclear segmentation to quantify gene expression at single-cell resolution. Spatial profiling revealed elevated expression scores in tumor-adjacent regions, with a strong positive correlation to tumor grade (Pearson’s <i>r</i> = 0.83). These gene-enriched regions exhibited spatial clustering of tumor cells. Additionally, tumor-infiltrating lymphocyte (TIL) density was inversely correlated with tumor grade, suggesting immune exclusion in high-grade tumors. Our findings demonstrate the feasibility of combining spatial transcriptomics with AI-driven histopathological analysis for biomarker validation. This integrative framework provides a foundation for future population-scale studies leveraging spatial omics to evaluate arsenic-associated gene signatures and assess their relevance in bladder cancer risk stratification and disease progression.</p>

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A novel spatial framework to validate arsenic exposure gene expression profiling in bladder cancer using multiplex FISH and AI-powered digital pathology

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月30日

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  • Sonalika Singhal,
  • Samarth Singhal,
  • Kevin L. Gardner,
  • Anushka Dikshit,
  • Emerald Doolittle,
  • Donald Sens,
  • Mary A. Sens,
  • Michael L. Miller,
  • Sai Tun Hein Aung,
  • Sandeep K. Singhal

摘要

Bladder cancer exhibits marked spatial heterogeneity in gene expression and immune infiltration. In this exploratory pilot study, we integrate multiplex fluorescence in situ hybridization (mFISH) with AI-assisted digital pathology to characterize the spatial distribution of a previously validated three-gene arsenic-responsive risk model (NKIRAS2, AKTIP, HLA-DQA1). Initially identified in arsenic-exposed individuals and associated with bladder cancer risk, this gene panel achieved 94% training and 75% validation AUC in prior genomic models (PMC8760535). We analyzed five bladder tumor specimens using whole-slide mFISH imaging and HoverNet-based nuclear segmentation to quantify gene expression at single-cell resolution. Spatial profiling revealed elevated expression scores in tumor-adjacent regions, with a strong positive correlation to tumor grade (Pearson’s r = 0.83). These gene-enriched regions exhibited spatial clustering of tumor cells. Additionally, tumor-infiltrating lymphocyte (TIL) density was inversely correlated with tumor grade, suggesting immune exclusion in high-grade tumors. Our findings demonstrate the feasibility of combining spatial transcriptomics with AI-driven histopathological analysis for biomarker validation. This integrative framework provides a foundation for future population-scale studies leveraging spatial omics to evaluate arsenic-associated gene signatures and assess their relevance in bladder cancer risk stratification and disease progression.