Integrative spatial omics and artificial intelligence: transforming cancer research with omics data and AI.

Recent advances in spatial transcriptomics and spatial proteomics have enabled increasingly complex questions on the nature of gene regulation and expression in cellular subtypes in tumor tissue and the tumor microenvironment. However, most spatial omics techniques do not profile the epigenomic landscape responsible for downstream gene expression. Furthermore, current spatial technologies have yet to profile the epigenome and transcriptome simultaneously, and thus it remains a challenge to correlate multi-omics data across sections of extremely heterogenous tumor tissue. Recently, co-profiling of spatial epigenomics and transcriptomics using principles of Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq) has been demonstrated on normal brain tissue. Joint spatial profiling of chromatin states and whole transcriptome in tissue allows for parallel characterization of gene regulation programs across all cell types, while preserving the tissue architecture for greater understanding of the cellular environment. Here we present the first application of spatial ATAC-seq and spatial transcriptomics on the same tissue section to characterize the tumor microenvironment of an invasive gastric adenocarcinoma (GAC) and adjacent normal tissue. GAC is the fifth most common cancer and commonly exhibits mutations in epigenetic modifiers, including ARID1A and MLL1-4. Distinct spatial clusters representing different cell subtypes were identified via both spatial chromatin accessibility and spatial transcriptomics. Spatial ATAC-seq profiling of accessible regulatory elements correlated well with RNA expression of target genes. Spatial patterns of transcription factor motif accessibility also correlated well with the observed transcriptional program of tumor tissue. When compared to adjacent normal tissue, spatial co-profiling of chromatin accessibility and the transcriptome revealed that the epigenetic landscape is significantly altered in tumorigenesis of GAC. Future work will focus on development of co-profiling of histone modifications and the transcriptome to enable the study of another layer of the epigenomic landscape, especially as targeting epigenetic modifiers such as EZH2 has been identified as a potential therapeutic strategy in GACs. Overall, we present a solution to profile multiple layers of gene regulation and expression with spatial context, which can be applied to most tumor types for better understanding of tumorigenesis and the consequences of new targeted therapies. Citation Format: Katelyn J. Noronha, Jennifer M. Garbarino, Daniel Massucci, Abigail R. Tyree, Colin Ng. Simultaneous spatial epigenomic and transcriptomic analysis of gastric adenocarcinoma reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4407.

Recent advances in spatial transcriptomics and spatial proteomics have enabled increasingly complex questions on the nature of gene regulation and expression in cellular subtypes in tumor tissue and the tumor microenvironment. However, most spatial omics techniques do not profile the epigenomic landscape responsible for downstream gene expression. Furthermore, current spatial technologies have yet to profile the epigenome and transcriptome simultaneously, and thus it remains a challenge to correlate multi-omics data across sections of extremely heterogenous tumor tissue. Co-profiling of spatial epigenomics and transcriptomics using principles of Deterministic Barcoding in Tissue for spatial omics sequencing (DBiT-seq) has been demonstrated on normal brain tissue. Joint spatial profiling of chromatin states and whole transcriptome in tissue allows for parallel characterization of gene regulation programs across all cell types, while preserving the tissue architecture for greater understanding of the cellular environment. Here we present the first application of spatial ATAC-seq and spatial transcriptomics on the same tissue section to characterize the tumor microenvironment of an infiltrating ductal carcinoma, the most common type of breast cancer, and adjacent normal tissue. Distinct spatial clusters representing different cell subtypes were identified via both spatial chromatin accessibility and spatial transcriptomics. Spatial ATAC-seq profiling of accessible regulatory elements correlated well with RNA expression. Spatial patterns of transcription factor motif accessibility also correlated well with the observed transcriptional program of tumor tissue. When compared to adjacent normal tissue, spatial co-profiling of chromatin accessibility and the transcriptome revealed that the epigenetic landscape is significantly altered in the tumor. Future work will focus on development of co-profiling of histone modifications and the transcriptome to enable the study of another layer of the epigenomic landscape, especially as targeting epigenetic modifiers such as HDACis in breast cancer is being tested in clinical trials with other standard of care therapeutics. Overall, we present a solution to profile multiple layers of gene regulation and expression with spatial context, which can be applied to most tumor types for better understanding of tumorigenesis and the consequences of new targeted therapies. Citation Format: Katelyn Noronha, Molly Wetzel, Gumaro Rojas, Jennifer Garbarino, James McGann, Jose Perez, Machele Riccio, Danijela Pavic, Jaden Joaquin, Joshua Barnett, Jeffrey Sabina, Colin Ng. Simultaneous spatial epigenomic and transcriptomic analysis of human breast cancer reveals regulatory patterns governing tumor and microenvironment architecture at the cellular level [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5319.

Introduction: Patients with high-risk non-muscle invasive bladder cancer (NMIBC) are recommended treatment with Bacillus Calmette-Guérin (BCG). The therapeutic effect of BCG is highly dependent on the host immune system and the tumor microenvironment (TME). The cellular composition and the functional status of the TME have been shown to play crucial roles for treatment efficacy. However, much is still unknown regarding the cellular interactions and mechanisms of BCG. Using spatial proteomics and transcriptomics we investigated the immune cell landscape in a cohort of BCG treated patients. Materials and methods: A total of 150 tumors from 105 patients with NMIBC treated with BCG were included on a tissue microarray (TMA). The TMA included paired pre- and post-BCG tumors. We analyzed the tumor tissue using Imaging Mass Cytometry (IMC; StandardBioTools) for simultaneous detection of 35 immune and tumor-related proteins at single cell resolution. Ilastik was used to generate probability maps and DeepCell Mesmer was used for cell segmentation. Markers were quantified for the mean intensity per cell. The GeoMX Digital Spatial Profiling (DSP) was used for spatial proteomics and transcriptomics profiling of tumor and stromal areas, targeting 63 protein targets and whole transcriptome amplification, respectively. Results: After computing a neighborhood graph using the log normalised and z-scaled signal and embedding it using UMAP, leiden clustering was performed on 360, 673 cells to detect communities of unique cell types by interrogating the presence of phenotypic markers. We identified major lineages such as macrophages, CD8 T cells, dendritic cells, natural killer (NK) cells and neutrophils in the TME of analyzed tumors. We observed more macrophages, CD4 T cells and CD8 T cells after BCG (p=0.02, p<0.001 and p=0.004, respectively) and similarly, we found enrichment of cellular neighborhoods composed of immune cells after treatment. Patients who progressed to muscle-invasive bladder cancer after BCG had higher numbers of CD4 T cells, CD8 T cells and NK cells prior to BCG (p=0.034, p=0.023 and p<0.001, respectively). Prolonged exposure to interferons has been associated with immune evasion and tumor cell proliferation. In concordance, we found high pretreatment expression of interferon signaling pathways in tumor regions from patients with a signature of CD8 T cell exhaustion after BCG using GeoMx DSP transcriptomic analysis. Conclusion: The composition and functional status of the TME is associated with clinical and biological features such as immune cell abundance, CD8 T cell exhaustion, and progression. A greater understanding of the TME may help identify patients unresponsive to BCG and improve the understanding of biological differences in tumor development and aggressiveness ultimately improving patient outcomes. Citation Format: Trine Strandgaard, Tine Ginnerup Andreasen, Tessa Jane Divita, Liina Salminen, Sean Houghton, Kasper Thorsen, Iver Nordentoft, Iyinyeoluwa Okulate, Alexander Schmitz, Søren Riis Paludan, John Sfakianos, Amir Horowitz, Jørgen Bjerggaard Jensen, Lars Dyrskjøt. Spatial proteomics and transcriptomics reveal an altered immune cell landscape in bladder cancer patients unresponsive to BCG treatment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 769.

Introduction: Patients with high-risk non-muscle invasive bladder cancer (NMIBC) are recommended treatment with Bacillus Calmette-Guérin (BCG). The therapeutic effect of BCG is highly dependent on the host immune system and the tumor microenvironment (TME). The cellular composition and the functional status of the TME have been shown to play crucial roles for treatment efficacy. However, much is still unknown regarding the cellular interactions and mechanisms of BCG. Using spatial proteomics and transcriptomics, we investigated the immune cell landscape in a cohort of BCG treated patients. Materials and methods: A total of 183 tumors from 111 patients with NMIBC treated with BCG were included on a tissue microarray (TMA). The TMA included paired pre- and post-BCG tumors. We analyzed the tumor tissue using Imaging Mass Cytometry (IMC; StandardBioTools) for simultaneous detection of 35 immune and tumor-related proteins at single cell resolution. Ilastik was used to generate probability maps and DeepCell Mesmer was used for cell segmentation. Markers were quantified for the mean intensity per cell. GeoMX Digital Spatial Profiling (DSP) was used for spatial proteomics and transcriptomics profiling of tumor and stromal areas, targeting 63 protein targets and whole transcriptome amplification, respectively. Results: We identified a total of 364,504 cells in the tumor tissue using IMC. After computing a neighborhood graph using the log normalised and z-scaled signal and embedding it using UMAP, leiden clustering was performed to detect communities of unique cell types by interrogating the presence of phenotypic markers. We identified major lineages such as macrophages, CD8 T cells, dendritic cells (DCs), NK cells and neutrophils in the TME of analyzed tumors. Specifically, we observed more CD8 T cells after BCG (p=0.015), especially in females (p=0.048). Females also had higher levels of DCs and macrophages after treatment compared to males (p=0.024 and p=0.048). Patients who progressed to MIBC after BCG had higher numbers of CD8 T cells and NK cells prior to BCG (p=0.039 and p=0.021). Prolonged exposure to interferons has been associated with immune evasion and tumor cell proliferation. In concordance, we found high pretreatment protein expression of IFNα and IFNγ in tumor regions from patients with a pronounced signature of CD8 T cell exhaustion after BCG treatment using GeoMX DSP proteomic analysis. Data analysis is currently ongoing, and additional results will be presented at the conference. Conclusion: The composition and functional status of the TME is associated with clinical and biological features such as immune cell abundance, CD8 T cell exhaustion, sex and progression. A greater understanding of the TME may help identify patients unresponsive to BCG earlier and improve the understanding of biological differences in tumor development and aggressiveness. This may ultimately improve patient outcomes. Citation Format: Trine Strandgaard, Tine Ginnerup Andreasen, Tessa Jane Divita, Liina Salminen, Sean Houghton, Kasper Thorsen, Iver Nordentoft, Iyinyeoluwa Okulate, Alexander Schmitz, Søren Riis Paludan, John Sfakianos, Amir Horowitz, Jørgen Bjerggaard Jensen, Lars Dyrskjøt. Spatial proteomics and transcriptomics reveal an altered immune cell landscape in bladder cancer patients unresponsive to BCG treatment [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR004.

BackgroundRecent technologies enabling the study of spatial biology include multiple high-dimensional spatial imaging methods that have rapidly emerged with different capabilities evaluating tissues at different resolutions for different sample formats. Platforms like Xenium (10x Genomics) and PhenoCycler-Fusion (Akoya Biosciences) enable single-cell resolution analysis of gene and protein expression in archival FFPE tissue slides. However, a key limitation is the absence of systematic methods to ensure tissue quality, marker integrity, and data reproducibility.ObjectiveWe seek to optimize the technical methods for spatial work by addressing preanalytical challenges with various tissue and tumor types, including a decalcification protocol for processing FFPE bone marrow core specimens to preserve nucleic acids for effective spatial proteomics and transcriptomics. This study characterizes a multicancer tissue microarray (TMA) and a molecular- and protein-friendly decalcification protocol that supports downstream spatial biology investigations.MethodsWe developed a multi-cancer tissue microarray (TMA) and processed bone marrow core samples using a molecular- and protein-friendly decalcification protocol. PhenoCycler high-plex immunohistochemistry (IHC) generated spatial proteomics data, analyzed with QuPath and single-cell analysis. Xenium provided spatial transcriptomics data, analyzed via Xenium Explorer and custom pipelines.ResultsResults showed that PhenoCycler and Xenium platforms applied to TMA sections of tonsil and various tumor types achieved good marker concordance. Bone marrow decalcification with our optimized protocol preserved mRNA and protein markers, allowing Xenium analysis to resolve all major cell types while maintaining tissue morphology.ConclusionsWe have shared our preanalytical verification of tissues and demonstrate that both the PhenoCycler-Fusion high-plex spatial proteomics and Xenium spatial transcriptomics platforms work well on various tumor types, including marrow core biopsies decalcified using a molecular- and protein-friendly decalcificationprotocol. We also demonstrate our laboratory's methods for systematic quality assessment of the spatial proteomic and transcriptomic data from these platforms, such that either platform can provide orthogonal confirmation for the other.

Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of DL-based pipelines used in preprocessing highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients. Since its introduction into the field of oncology, deep learning (DL) has impacted clinical discoveries and biomarker predictions. DL-driven discoveries and predictions in oncology are based on a variety of biological data such as genomics, proteomics, and imaging data. DL-based computational frameworks can predict genetic variant effects on gene expression, as well as protein structures based on amino acid sequences. Furthermore, DL algorithms can capture valuable mechanistic biological information from several spatial "omics" technologies, such as spatial transcriptomics and spatial proteomics. Here, we review the impact that the combination of artificial intelligence (AI) with spatial omics technologies has had on oncology, focusing on DL and its applications in biomedical image analysis, encompassing cell segmentation, cell phenotype identification, cancer prognostication, and therapy prediction. We highlight the advantages of using highly multiplexed images (spatial proteomics data) compared to single-stained, conventional histopathological ("simple") images, as the former can provide deep mechanistic insights that cannot be obtained by the latter, even with the aid of explainable AI. Furthermore, we provide the reader with the advantages/disadvantages of the DL-based pipelines used in preprocessing the highly multiplexed images (cell segmentation, cell type annotation). Therefore, this review also guides the reader to choose the DL-based pipeline that best fits their data. In conclusion, DL continues to be established as an essential tool in discovering novel biological mechanisms when combined with technologies such as highly multiplexed tissue imaging data. In balance with conventional medical data, its role in clinical routine will become more important, supporting diagnosis and prognosis in oncology, enhancing clinical decision-making, and improving the quality of care for patients.

Spatial omics technology integrates the concept of space into omics research and retains the spatial information of tissues or organs while obtaining molecular information. It is characterized by the ability to visualize changes in molecular information and yields intuitive and vivid visual results. Spatial omics technologies include spatial transcriptomics, spatial proteomics, spatial metabolomics, and other technologies, the most widely used of which are spatial transcriptomics and spatial proteomics. The tumor microenvironment refers to the surrounding microenvironment in which tumor cells exist, including the surrounding blood vessels, immune cells, fibroblasts, bone marrow-derived inflammatory cells, various signaling molecules, and extracellular matrix. A key issue in modern tumor biology is the application of spatial omics to the study of the tumor microenvironment, which can reveal problems that conventional research techniques cannot, potentially leading to the development of novel therapeutic agents for cancer. This paper summarizes the progress of research on spatial transcriptomics and spatial proteomics technologies for characterizing the tumor immune microenvironment.

Background: Pulmonary pleomorphic carcinoma (PPC) is a rare and aggressive subtype of non-small cell lung cancer characterized by the presence of both epithelial and sarcomatoid components. We conducted multi-omics analyses on surgically resected specimens from nine primary PPC cases to explore characteristics of this tumor at molecular level. Materials and Methods: Whole-exome sequencing and RNA sequencing on bulk tumor specimens to identify mutational profiles. Spatial transcriptomics using the Digital Spatial Profiler was employed to analyze gene expression separately for epithelial and sarcomatoid tumor components. Single-cell RNA sequencing (scRNA-seq) was conducted on three cases to explore intratumoral heterogeneity and clonal evolution. Gene signature based on extracellular matrix (ECM) remodeling was developed and validated in public lung cancer datasets to assess its association with tumor aggressiveness and prognosis. Results: Mutational analyses revealed MET exon 14 skipping in two cases and an ALK-EML4 fusion in one case, while no EGFR mutations were observed. We found various differential expressed genes (DEGs) between components. Further analysis (GSEA and Transcriptional factor scoring) revealed there was a common enrichment trend observed among pathways related to MYC, such as those involved in cell proliferation, division, and the cell cycle. On the other hand, significant differences were observed in pathways related to cancer invasion and cell-cell interactions, such as extracellular matrix (ECM) remodeling and hypoxia. identified an atypical cell population bridging epithelial cells and fibroblasts, which we defined as sarcomatoid cells. The clusters defined as epithelial tumor cells and sarcomatoid cells showed upregulation of DEGs corresponding to the respective component identified through spatial transcriptomics. Trajectory analysis visualized tumor evolution, suggesting a transition from epithelial tumor cells to sarcomatoid cells. A consistent upregulation of seven ECM remodeling-related genes was observed in both tumor components compared to normal lung tissue. When applied to a GEO lung adenocarcinoma cohort, this gene signature was associated with more aggressive histological subtypes (P < 0.01). Survival analysis using TCGA demonstrated that high ECM remodeling signature scores correlated with significantly poorer prognosis (P < 0.01). Conclusion: This study provides a comprehensive molecular analysis of PPC, revealing both shared and distinct features between its components. The trajectory analysis supports the hypothesis of a common cellular origin for these components and highlights the transition to a more aggressive sarcomatoid phenotype. These findings provide new insights into the biology of PPC and identify potential therapeutic targets to address its intratumoral heterogeneity and clonal evolution. Citation Format: Atsushi Matsuoka, Kazuhiko Shien, Shuta Tomida, Hirofumi Inoue, Kazuya Hisamatsu, Ryota Fujiwara, Kousei Ishimura, Ryunosuke Fujii, Ryo Yoshichika, Tomoaki Higashihara, Naohiro Hayashi, Kazuhiro Okada, Fumiaki Mukohara, Mao Yoshikawa, Ken Suzawa, Hidetaka Yamamoto, Shinichi Toyooka. Spatial and single-cell transcriptomics reveal molecular heterogeneity in pulmonary pleomorphic carcinoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2664.

Background: Spatially resolved transcriptomics is a novel and already highly recognized method that allows RNA sequencing results to be annotated with local tissue phenotypes. The NanoString GeoMx Digital Spatial Profiling (DSP) Platform allows users to collect RNA expression data from manually selected Regions of Interest (ROIs) on FFPE tissue sections. Here, we extensively evaluated data from the DSP platform with its associated pipeline and identify significant background noise interference issues which compromise data interpretation. Alternative and more suitable workflows are presented for correct data analysis. Methods: In this study, 12 paired tumor samples were collected from six glioma patients who underwent two separate resections. For all patients, the first resection was a low grade astrocytoma (WHO grade II or III) and the second resection was a high grade astrocytoma (WHO grade IV). The DSP platform was used to collect expression data of 1,800 genes from 72 ROIs (i.e. 6 per sample). Biological replicates were made of eight tumors from four patients. Gene expression data was normalized with both standard NanoString methods and several alternative methods (e.g. DeSeq2, gamma fit correction and quantile normalization). Weighted Gene Co-expression Network analysis (WGCNA) was used for biological validation. In addition to our own study, six publicly available NanoString DSP datasets were evaluated. Results: Data distributions of all glioma samples, when exposed to standard data processing, were burdened with significant background noise interference. Notably, differences in noise interference were largest between biologically distinct tumor subgroups (i.e. between first and second glioma resections), which was confirmed in replicate experiments. The noise interference patterns were also present in all six publicly available NanoString DSP datasets which will invariably lead to incorrect interpretation of the underlying biology. To correct for noise interference, we tested several normalization methods. The relatively crude quantile normalization method provided the least biased result and showed the highest concordance with bulk RNA sequencing data. To evaluate the biological validity of our alternative approach, we used T cell counts from our tissue regions as an independent parameter, that were quantified using immune fluorescence. Unsupervised WGCNA identified gene clusters enriched for lymphocyte genes that highly correlated with T cell quantities in ROIs, confirming that alternative normalization can extract a biological signal from the DSP platform. Conclusion: The DSP Platform platform suffers from significant noise interference when using standard analysis tools that obscure its results. Here, we revised the workflow and provide an alternative normalization that adequately addresses noise interference and enables correct interpretation of gene expression data. Citation Format: Levi van Hijfte, Marjolein Geurts, Wies R. Vallentgoed, Paul H. Eilers, Peter A. Sillevis Smitt, Reno Debets, Pim J. French. Spatial transcriptomics: Data processing revisited to address noise interference [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1228.

BackgroundThe tumor microenvironment (TME) of non-small cell lung cancer (NSCLC) undergoing immune checkpoint inhibitor (ICI) treatment is poorly understood. Spatially-resolved single-cell analyses are necessary to identify co-enriched cellular interactions and...

Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.

Recent advances in multimodal artificial intelligence (AI) have demonstrated promising potential for generating the currently expensive spatial transcriptomics (ST) data directly from routine histology images, offering a means to reduce the high cost and time-intensive nature of ST data acquisition. However, the increasing resolution of ST-particularly with platforms such as Visium HD achieving or finer-introduces significant computational and modeling challenges. Conventional spot-by-spot sequential regression frameworks become inefficient and unstable at this scale, whereas the inherent extreme sparsity and low expression levels of high-resolution ST further complicate both prediction and evaluation. To address these limitations, we propose Img2ST-Net, a high-definition (HD) histology-to-ST generation framework for efficient and parallel high-resolution ST prediction. Unlike conventional spot-by-spot inference methods, Img2ST-Net employs a fully convolutional architecture to generate dense, HD gene expression maps in a parallelized manner. By modeling HD ST data as super-pixel representations, the task is reformulated from image-to-omics inference into a super-content image generation problem with hundreds or thousands of output channels. This design not only improves computational efficiency but also better preserves the spatial organization intrinsic to spatial omics data. To enhance robustness under sparse expression patterns, we further introduce SSIM-ST, a structural-similarity-based evaluation metric tailored for high-resolution ST analysis. Evaluations on two public Visium HD datasets at 8 and resolutions demonstrate that Img2ST-Net outperforms state-of-the-art methods in both accuracy and spatial coherence. On the Breast Cancer dataset at , Img2ST-Net achieves a mean squared error (MSE) of 0.1657 and a structural similarity index of 0.0937, whereas on the Colorectal Cancer dataset, it reaches an MSE of 0.7981 and a mean absolute error of 0.5208. These results highlight its ability to capture fine-grained gene expression patterns. In addition, our region-wise modeling significantly reduces training time without sacrificing performance, achieving up to 28-fold acceleration over conventional spot-wise methods. Ablation studies further validate the contribution of contrastive learning in enhancing spatial fidelity. The source code has been made publicly available at https://github.com/hrlblab/Img2ST-Net. We present a scalable, biologically coherent framework for high-resolution ST prediction. Img2ST-Net offers a principled solution for efficient and accurate ST inference at scale. Our contributions lay the groundwork for next-generation ST modeling that is robust and resolution-aware.

Introduction: Triple-negative breast cancer (TNBC) is an aggressive subtype with poor prognoses and limited biomarkers for predicting treatment outcomes [1]. Tumor-infiltrating lymphocytes and PD-L1 expression, while associated with immune checkpoint inhibitor (ICI) efficacy, fail to predict responses reliably [2, 3]. Spatial transcriptomics is a cutting-edge technology that enables the precise mapping of gene expression within tissue samples. However, the high cost of sequencing limits its clinical utility. This study aims to develop cost-effective predictive biomarkers by integrating spatial transcriptomic data and machine learning to impute gene expression from widely available Hematoxylin and Eosin (H&E) images. Methods: This study involves two cohorts of TNBC patients treated with pembrolizumab (N=75) and durvalumab (N=57) in the neoadjuvant setting. Digital Spatial Profiling (DSP) was used to generate Spatial transcriptomics data. Gene signatures predicting ICI outcomes were developed and validated using Least Absolute Shrinkage and Selection Operator (LASSO) regression models. Imputed gene expression was derived from H&E images, employing adaptive spatial graph neural networks (asGNN [4]), and evaluated against DSP data on a subset of patients. Signatures were trained using imputed transcriptomics directly and in combination with a hold-out set of H&E images. Prediction accuracies were evaluated against spatial transcriptomics and clinical outcomes using mean squared error and AUC. Results: Imputed gene expression from H&E images closely mirrors spatial transcriptomic profiles. Imputed spatial gene expression using asGNN achieved better prediction of DSP expression than competing methods (HE2RNA [5] and Sequoia [6]) and non-adaptive GNN methods (p=0.031). Signatures trained directly using H&E imputed spatial expression achieved accurate prediction of treatment outcomes (AUC=0.75±0.24). Integrating the DSP transcriptomics further increased the accuracy of the predictions; the signatures trained using imputed transcriptomics combined with DSP data achieved higher prediction accuracy (AUC=0.85±0.08), which significantly outperformed the non-DSP trained models (p=0.021). Conclusion: This study demonstrates the feasibility of leveraging H&E images for gene expression imputation in TNBC. Future work will optimize models across diverse cancer types and expand validation with independent datasets. This innovation advances personalized medicine by bridging the gap between cutting-edge spatial transcriptomics and routine clinical diagnostics. Citation Format: Thazin Nwe Aung, Tianci Song, Lajos Pusztai, Mark Gerstein, David L. Rimm, Jonathan H. Warrell. Integrating digital spatial profiling and H&E images to develop predictive biomarkers for immunotherapy outcomes in triple-negative breast cancer from imputed spatial gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 2424.

061 Spatial proteomics and transcriptomics with digital spatial profiling reveals overlapping, but distinct inflammatory pathways in discoid lupus and lichen planus

Mini-bulk or single-spot spatial transcriptomic technologies, such as GeoMX Digital Spatial Profiler (DSP), have revolutionized our ability to probe spatial heterogeneity and examine transcripts at a subcellular level. In cancer research, this level of granularity is crucial for uncovering the distinct gene expression signatures within specific compartments or structures of interest such as tumor, immune, stroma, and tertiary lymphoid structures. However, in most GeoMX DSP studies, the spatial information obtained from multiplex immunofluorescence imaging has been primarily used for identifying the regions of interest (ROI), rather than as an integral part of the downstream transcriptomic data interpretation. To fix this missed opportunity, we developed a machine learning based analytical framework to fully leverage the rich spatial context provided by the in situ imaging modality. The framework has two main functionalities. (1) ROI cropping and cell segmentation: our framework enables the automated cropping of ROI images, followed by a cell segmentation process utilizing a multi-color-optimized deep learning model pre-trained based on the TissueNet. (2) Cell typing and integrative spatial analysis: the pipeline extracts cell-level morphological features (e.g. cell size and circularity), and AI-driven characteristics. These features are subsequently employed for cell typing and the quantification of cell mixture. The framework is equipped to perform a series of advanced spatial analyses, such as spatial metrics calculation and consensus clustering analysis with the matched gene expression data. To validate the framework, we conducted GeoMX DSP analyses on samples obtained from bladder cancer and upper tract urothelial carcinoma, encompassing 56 ROIs annotated by pathologists. Our results demonstrated high accuracy, with the calculated tumor-immune proportions aligning precisely with the original annotations. We further compared the cell deconvolution results of our framework with those obtained using SpatialDecon. Consistency was noted across both methods, with a high correlation reaching 0.90. However, we observed that SpatialDecon tends to underestimate tumor purity in tumor core regions, with discrepancies as large as 0.30, and overestimating tumor purity in stromal regions. Additionally, analysis of ROIs in tumor margins revealed that SpatialDecon consistently overestimates the overall proportions of immune cells. In conclusion, our results underscore the importance of integrating in situ imaging with subcellular-level spatial transcriptomics for a more accurate and reliable analysis of tumor tissues. Our approach provides critical insights into the tumor microenvironment and cellular interactions, with significant implications for both research and clinical applications in oncology. Citation Format: Xiaofei Song, Xiaoqing Yu, Carlos M. Moran-Segura, G Daniel Grass, Roger Li, Xuefeng Wang. Missed opportunity at the subcellular level: Enhancing the utility of cellular imaging modality in spatial transcriptomic profiling of tumor tissues [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6245.