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Determining tumor microsatellite status has significant clinical value because tumors that are microsatellite instability-high (MSI-H) or mismatch repair deficient (dMMR) respond well to immune checkpoint inhibitors (ICIs) and oftentimes not to chemotherapeutics. We propose MSI-SEER, a deep Gaussian process-based Bayesian model that analyzes H&E whole-slide images in weakly-supervised-learning to predict microsatellite status in gastric and colorectal cancers. We performed extensive validation using multiple large datasets comprised of patients from diverse racial backgrounds. MSI-SEER achieved state-of-the-art performance with MSI prediction by integrating uncertainty prediction. We achieved high accuracy for predicting ICI responsiveness by combining tumor MSI status with stroma-to-tumor ratio. Finally, MSI-SEER’s tile-level predictions revealed novel insights into the role of spatial distribution of MSI-H regions in the tumor microenvironment and ICI response.

In histopathology, acquiring subcellular-level three-dimensional (3D) tissue structures efficiently and without damaging the tissues during serial sectioning and staining remains a formidable challenge. We address this by integrating holotomography with deep learning and creating 3D virtual hematoxylin and eosin (H&E) images from label-free thick cancer tissues. This method involves measuring the tissues’ 3D refractive index (RI) distribution using holotomography, followed by processing with a deep learning-based image translation framework to produce virtual H&E staining in 3D. Applied to colon cancer tissues up to 50 µm thick—far surpassing conventional slide thickness—this technique provides direct methodological validation through chemical H&E staining. It reveals quantitative 3D microanatomical structures of colon cancer with subcellular resolution. Further validation of our method’s repeatability and scalability is demonstrated on gastric cancer samples across different institutional settings. This innovative 3D virtual H&E staining method enhances histopathological efficiency and reliability, marking a significant advancement in extending histopathology to the 3D realm and offering substantial potential for cancer research and diagnostics. Acquiring subcellular-level three-dimensional (3D) tissue structures efficiently without damaging the tissue remains challenging in histopathology. Here, the authors integrate holotomography with deep learning to generate 3D virtual H&E staining images from label-free thick cancer tissues, and apply this approach to colon and gastric cancer samples.

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multimodal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with subcellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a substantial advancement in multimodal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by subcellular resolution spatial omics technologies. MISO (MultI-modal Spatial Omics) integrates two or more spatial omics modalities, despite differences in data quality and spatial resolution for improved feature extraction and clustering to reveal biologically meaningful tissue organization.

Spatial molecular profiling has provided biomedical researchers valuable opportunities to better understand the relationship between cellular localization and tissue function. Effectively modeling multi-modal spatial omics data is crucial for understanding tissue complexity and underlying biology. Furthermore, improvements in spatial resolution have led to the advent of technologies that can generate spatial molecular data with sub-cellular resolution, requiring the development of computationally efficient methods that can handle the resulting large-scale datasets. MISO (MultI-modal Spatial Omics) is a versatile algorithm for feature extraction and clustering, capable of integrating multiple modalities from diverse spatial omics experiments with high spatial resolution. Its effectiveness is demonstrated across various datasets, encompassing gene expression, protein expression, epigenetics, metabolomics, and tissue histology modalities. MISO outperforms existing methods in identifying biologically relevant spatial domains, representing a significant advancement in multi-modal spatial omics analysis. Moreover, MISO’s computational efficiency ensures its scalability to handle large-scale datasets generated by sub-cellular resolution spatial omics technologies. MISO (MultI-modal Spatial Omics) integrates two or more spatial omic modalities, despite differences in data quality and spatial resolution for improved feature extraction and clustering to reveal biologically meaningful tissue organization.

Background Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. Methods A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. Results Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression ( p  = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes. Conclusions Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC. Plain language summary In this study, we investigated whether differences in the immune cell population surrounding head and neck cancers impact disease progression. We used advanced computer programs to analyze tissue samples from tumors and nearby lymph nodes, a part of the immune system. These tumor and lymph node samples were stained to show the structure of the tissue and to identify the different types of immune cells present. We grouped patients into different categories based on differences in their immune cells. We found that patients with certain patterns of immune cells tended to have better outcomes. This method could help doctors predict how well patients will respond to treatments. Nakkireddy, Jang, Kim, et al. explore tumor immune microenvironment (TME) types in HPV-positive oropharyngeal squamous cell carcinoma. Deep learning analysis of tumor and lymph node tissues identifies immune cell patterns that correlate with improved prognosis.

Abstract Background Deep learning techniques excel at identifying tumor-infiltrating lymphocytes (TILs) and immune phenotypes in hematoxylin and eosin (H&E)-stained slides. However, their ability to elucidate detailed functional characteristics of diverse cellular phenotypes within tumor immune microenvironment (TME) is limited. We aimed to enhance our understanding of cellular composition and functional characteristics across TME regions and improve patient stratification by integrating H&E with adjacent immunohistochemistry (IHC) images. Methods A retrospective study was conducted on patients with Human Papillomavirus-positive oropharyngeal squamous cell carcinoma (OPSCC). Using paired H&E and IHC slides for 11 proteins, a deep learning pipeline was used to quantify tumor, stroma, and TILs in the TME. Patients were classified into immune inflamed (IN), immune excluded (IE), or immune desert (ID) phenotypes. By registering the IHC and H&E slides, we integrated IHC data to capture protein expression in the corresponding tumor regions. We further stratified patients into specific immune subtypes, such as IN, with increased or reduced CD8+ cells, based on the abundance of these proteins. This characterization provided functional insight into the H&E-based subtypes. Results Analysis of 88 primary tumors and 70 involved lymph node tissue images reveals an improved prognosis in patients classified as IN in primary tumors with high CD8 and low CD163 expression (p = 0.007). Multivariate Cox regression analysis confirms a significantly better prognosis for these subtypes. Conclusions Integrating H&E and IHC data enhances the functional characterization of immune phenotypes of the TME with biological interpretability, and improves patient stratification in HPV( + ) OPSCC.