Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 13319-13323· 0 citations· 27 references
TL;DR
This lecture-style tutorial surveys the algorithmic foundations and recent advances in method development that make them practical and impactful for precision medicine, and outlines opportunities for the KDD community to contribute principled data mining and learning approaches to multimodal spatial biology.
Abstract
Spatial omics (SO) technologies enable spatially resolved molecular profiling, while hematoxylin and eosin (H&E) imaging remains the gold standard for morphological assessment in clinical pathology. Recent computational advances increasingly place H&E images at the center of SO analysis, bridging morphology with transcriptomic, proteomic, and other spatial molecular modalities. This lecture-style tutorial surveys the algorithmic foundations and recent advances in method development that make them practical and impactful for precision medicine. Following the tutorial flow, we first introduce key SO modalities and data abstractions (tiles/patches, spots, cells, and spatial graphs) and articulate problems to address and motivations, emphasizing multi-scale mismatch, structured spatial dependence, weak supervision, and domain shift across cohorts and sites. We then trace the evolution of modern multimodal representation learning, highlighting graph neural networks, transformer-based architectures, and encoder–decoder designs. The core of the tutorial systematically organizes contemporary methods into three categories: (i) integration methods, which jointly model paired multimodal measurements; (ii) mapping methods, which predict spatial molecular profiles from H&E images; and (iii) foundation models (FMs), which learn transferable representations from large-scale spatial datasets via self-supervised pretraining, contrastive objectives, etc. This tutorial also discusses applications of generative modeling to support imputation and data augmentation. Throughout, we connect methods to real biomedical endpoints (e.g., tumor microenvironment characterization, biomarker discovery, and cohort-level stratification). We further summarize actionable modeling directions enabled by current architectures and delineate persistent gaps driven by data, biology, and technology that are unlikely to be resolved by model design alone. The tutorial concludes with open challenges in interpretability, reliability, privacy, and clinical translation, outlining opportunities for the KDD community to contribute principled data mining and learning approaches to multimodal spatial biology.
Histopathologic evaluation remains central to cancer diagnosis and treatment planning, yet the molecular programs underlying distinct tissue morphologies aren't routinely accessible in clinical workflows. Spatial transcriptomic/proteomic platforms provide region-specific molecular measurements but are limited by cost, throughput, and scalability. Most computational pathology models rely on either bulk tissue-based gene expression or a focused gene/protein expression-panel prediction, thereby obscuring subregion-specific morpho-molecular relationships and limiting spatial interpretation of a wider gene/protein expression network. This limitation is particularly significant in triple-negative breast cancer (TNBC), which exhibits pronounced spatial heterogeneity across tumor, stroma, and immune compartments. We developed X-SPATIO, a spatially compatible computational pipeline designed to directly link hematoxylin and eosin (H&E) morphology with region-matched mRNA and protein expression. The model was trained on H&E-defined regions of interest paired with spatially-resolved omics data obtained from GeoMx Digital Spatial Profiling. Using a multiple-instance learning approach, X-SPATIO captures morpho-molecular associations, generating spatio-morphologic attention maps that indicate predictive tissue regions. X-SPATIO demonstrated strong performance across biologically relevant spatial biomarkers, achieving area under the curve values ranging from 0.79-0.97. Attention maps revealed spatial patterns consistent with known biology, indicating alignment between learned features and tissue organization. By integrating spatial molecular ground truth with routine histopathology, X-SPATIO enables cost-effective inference of spatial biomarker expression and establishes a foundation for biologically grounded discovery and precision oncology in TNBC.
Vibha R. Rao, Madhumala K. Sadanandappa, C. Black et al.· American Journal of Patholog...· 0 citations
ABSTRACT
The adoption of whole-slide imaging is establishing a new paradigm in digital pathology. However, the translation of artificial intelligence (AI) from research to clinical practice faces significant hurdles, largely due to a misalignment between algorithmic advances and the practical demands of pathological diagnosis and prognosis. In this review, we propose a dual-perspective framework to systematically bridge this gap by linking core clinical tasks with cutting-edge deep learning methodologies. We present a comprehensive overview of the field from 2020 to 2025, analyzing how architectures such as convolutional neural networks, vision transformers, and graph neural networks are being adapted for diagnostic classification, tissue segmentation, and prognostic prediction. A key contribution is our novel algorithm-clinical task mapping framework, which offers practical guidance for selecting and designing AI solutions tailored to specific clinical goals. We also highlight emerging trends that minimize reliance on costly annotations-including weakly supervised and self-supervised learning-as well as advances in predicting immunohistochemistry results directly from hematoxylin and eosin-stained slides. Finally, we address critical challenges related to model interpretability, regulatory approval, and multicenter generalization, and outline a future pathway focused on developing integrated, trustworthy, and equitable AI systems that enhance, rather than replace, the expertise of pathologists.
Yun-qiu Gao, Teng Ma, Lisha Li et al.· Chinese Medical Journal· 0 citations
Spatial proteomics technologies have transformed our understanding of complex tissue architecture in cancer but present unique challenges for computational analysis1. Each study uses a different marker panel and protocol, and most methods are tailored to single cohorts, which limits knowledge transfer and robust biomarker discovery. Here we present Virtual Tissues (VirTues), a general-purpose foundation model for spatial proteomics that learns marker-aware, multi-scale representations of proteins, cells, niches and tissues directly from multiplex imaging data. From a single pretrained backbone, VirTues supports marker reconstruction, cell segmentation and typing, niche annotation, spatial biomarker discovery and patient stratification, including zero-shot annotation across heterogeneous panels and datasets. In triple-negative breast cancer, VirTues-derived biomarkers predict anti-PD-L1 chemo-immunotherapy response2 and stratify disease-free survival in an independent cohort3, outperforming state-of-the-art biomarkers derived from the same datasets and current clinical stratification schemes.
Johann Wenckstern, Eeshaan Jain, Benedikt von Querfurth et al.· Nature· 0 citations
Rare cancers present persistent challenges in biomarker discovery and clinical translation due to limited sample availability, histopathologic heterogeneity, and fragmented molecular data. To overcome these barriers, we developed an artificial intelligence (AI)-driven integrative framework that combines digital pathology with multi-omics profiling to enable cell-type–resolved characterization of tumor biology in rare and environmentally associated cancers. Our approach leverages whole-slide imaging and computational pathology algorithms to perform high-resolution cell typing and spatial characterization of the tumor microenvironment. These spatially informed features are integrated with matched transcriptomic and proteomic data using machine learning models to identify robust, biologically interpretable biomarkers. By linking cellular architecture with molecular signatures, our framework captures tumor heterogeneity on both structural and functional levels. As a proof-of-concept, we applied this platform to arsenic-associated bladder cancer, an exposure-driven malignancy with regionally rare incidence but significant global health impact. We identified distinct cell-type–specific gene and protein expression patterns associated with disease risk and progression. Integrative modeling revealed key pathways linking environmental exposure, tumor organization, and immune microenvironment dynamics. Biomarker candidates demonstrated reproducibility across independent cohorts and tissue-based validation datasets. Importantly, this framework is designed for translational scalability, incorporating predictive modeling for patient stratification and deployment through cloud-based analytical pipelines. By enabling the integration of histopathologic features with multi-omics data in low-sample settings, our approach addresses a critical gap in rare cancer research and supports the development of clinically actionable biomarkers. This study highlights the power of AI-driven digital pathology combined with transcriptomic and proteomic integration to uncover cell-type–specific mechanisms underlying rare cancers. Our platform provides a generalizable strategy to advance precision oncology, improve diagnostic accuracy, and facilitate equitable access to data-driven care for patients with rare and understudied malignancies.
Sandeep K. Singhal. AI-Enabled Digital Pathology and Multi-Omics Integration for Cell-Type–Resolved Biomarker Discovery in Environment-Associated Cancers [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr A026.
Tumor heterogeneity remains one of the greatest challenges in precision oncology because malignant tissues exhibit extensive spatial, molecular, cellular, and microenvironmental diversity that continuously evolves during disease progression and therapeutic intervention. Recent advances in spatial omics technologies and foundation artificial intelligence (AI) models have created unprecedented opportunities to decode this complexity by integrating high-dimensional molecular, histopathological, imaging, and clinical information into unified computational frameworks. Foundation AI models, multimodal transformer architectures, graph neural networks, self-supervised learning, and generative artificial intelligence enable generalized representation learning across spatial transcriptomics, spatial proteomics, spatial metabolomics, digital pathology, radiological imaging, genomics, transcriptomics, epigenomics, laboratory biomarkers, circulating tumor DNA, wearable physiological monitoring, electronic health records, and longitudinal clinical outcomes. These intelligent computational systems support comprehensive characterization of tumor heterogeneity, biomarker discovery, prognostic prediction, therapeutic optimization, immunotherapy selection, digital twin simulation, adaptive disease monitoring, and evidence-based clinical decision support. Emerging technologies including multimodal large language models, federated learning, reinforcement learning, retrieval-augmented generation, explainable artificial intelligence, and agentic AI further strengthen spatial oncology by enabling collaborative, privacy-preserving, transparent, and continuously adaptive biomedical intelligence. Despite remarkable technological progress, important scientific, technical, ethical, and regulatory challenges remain regarding multimodal data harmonization, computational scalability, spatial resolution, interoperability, clinical validation, and equitable implementation. This review provides a comprehensive overview of spatial omics and foundation AI models, emphasizing their role in decoding tumor heterogeneity for precision oncology and personalized cancer medicine.
Dr. Pritam Sen· International journal of adv...· 0 citations
Spatial transcriptomics facilitates tissue microenvironment analysis by retaining gene expression alongside spatial context, with spatial domain detection being crucial. Conventional clustering or graph-based approaches often fail to capture global spatial dependencies and low-dimensional features due to complex nonlinear patterns and intricate neighborhood structures, limiting both accuracy and generalizability. We introduce stKAN, a novel framework integrating Kolmogorov-Arnold Network with variational autoencoder to effectively model spatially resolved gene expression with graph attention network. StKAN fuses spatial information, gene expression, and optional morphological features, and applies contrastive learning to identify biologically coherent domains. Leveraging explicit function decomposition, it ensures flexible adaptation to diverse data scales. Evaluated on seven spatial transcriptomics datasets, stKAN outperforms existing methods in domain detection accuracy and robustness. It shows strong potential for downstream analyses, offering deeper insights into disease pathology and tumor invasion. By bridging deep learning and spatial context, stKAN advances spatial biology with enhanced generalizability.
Jing Lin, Aijing Feng, Yankun Cao et al.· Computational biology and ch...· 0 citations