What question did this study set out to answer?

The research aims to develop an AI-driven framework to analyze spatial tumor-immune interactions in bladder cancer.

April 5, 2026Cancer Research0 citations

AI-Driven Spatial Classification of Tumor-Infiltrating Lymphocytes in Bladder Cancer

SSSonalika Singhal ADAnushka Dikshit SSS.P. Singhal

Key Points

The research aims to develop an AI-driven framework to analyze spatial tumor-immune interactions in bladder cancer.
Developed an AI framework integrating multiplex fluorescence in situ hybridization (mFISH) and digital pathology.
Analyzed formalin-fixed paraffin-embedded samples with RNAscope mFISH targeting arsenic-responsive genes.
Used deep learning for automated cell classification and detection of TIL patches.
Quantified immune-tumor proximity and spatial context using AI clustering algorithms.
Achieved over 92% accuracy in classifying tumor, stromal, and lymphocytic populations.
Low-grade tumors showed densely packed TILs with inter-TIL distances of less than 30 μm, while high-grade tumors had TIL density reduced by over 3-fold.
Identified distinct immune phenotypes corresponding with histologic grades and environmental exposures.

Abstract

Abstract The integration of artificial intelligence (AI) with digital pathology provides unprecedented opportunities to decode the spatial complexity of tumor-immune interactions. We developed an AI-driven spatial pathology framework that combines multiplex fluorescence in situ hybridization (mFISH) and deep-learning-based digital histopathology to classify cells and identify tumor-infiltrating lymphocyte (TIL) patches in bladder tumors. Formalin-fixed paraffin-embedded samples were analyzed using RNAscope mFISH targeting three arsenic-responsive genes - NKIRAS2, AKTIP, and HLA-DQA1. Whole-slide images were co-registered with H92% accuracy in distinguishing tumor, stromal, and lymphocytic populations. Quantitative mapping revealed that low-grade tumors contained densely packed TIL clusters with a mean inter-TIL distance of 30 μm, reflecting robust immune infiltration. In contrast, high-grade tumors exhibited a 3-fold reduction in TIL patch density and increased spatial separation between lymphocytes and tumor boundaries, consistent with immune exclusion. Spatial co-expression of NKIRAS2-AKTIP was enriched in immune-cold regions, while HLA-DQA1 expression correlated with higher TIL density and active immune interfaces. Pathway enrichment analysis of TIL-proximal regions highlighted upregulation of inflammatory and oxidative stress pathways, whereas immune-depleted zones showed activation of DNA repair and cell proliferation signatures. Gradient-based quantification of TIL patch density and co-expression topology stratified tumors into immune-inflamed, immune-intermediate, and immune-cold phenotypes that correlated with histologic grade and exposure signatures. This integrative approach bridges molecular multiplex imaging with spatial computational pathology, establishing a scalable framework for digital immune landscape mapping. Our findings highlight how environmental exposures reshape tumor-immune architecture and demonstrate the potential of AI-guided pathology for precision immunoprofiling and exposure-related cancer risk assessment. Citation Format: Sonalika Singhal, Anushka Dikshit, Samarth Singhal, Kevin L. Gardner. AI-driven spatial cell classification and tumor-infiltrating lymphocyte (TIL) patch analysis reveal spatial immunoarchitecture in arsenic-associated bladder cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 4001.

AI से पूछें

Bookmark

View Full Paper