Spatial omics: applications and utility in profiling the tumor microenvironment

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.

*These authors contributed to the devlopment and review of this manuscript but are unable to endorse the request for NIH funding. On Sept. 20, 2011, the American Association for Cancer Research (AACR) released its inaugural AACR Cancer Progress Report to commemorate the

e15076 Background: Precision oncology aims to first break diagnoses into biologically distinct subtypes, then pursue personalized therapies for each group of patients ( Garraway et al, J Clin Onc, 2013). This strategy is enabled by recent advances in technologies for medical imaging, molecular profiling, and artificial intelligence. Spatially resolved molecular profiling (“spatial omics”) is an emerging technology that harnesses all three of these advances ( Navarro et al, Science, 2016; Rao et al, Nature, 2021). Cancer is driven by localized interactions between tumor cells, immune and non-immune stromal components. Methods: Spatial omics studies have revealed specific cell types and pathways that control those tumor-microenvironment crosstalks ( Hunter et al, Nat Comm, 2021; Alon et al, Science, 2021), involving both innate and adaptive immunity ( Binnewies et al, Nat Med, 2018). Identifying new cancer subtypes based on the spatially localized tumor-host interactions across many patients have the potential to revolutionize immuno-oncology. The advances in utilizing AI-based methods have so far been unattainable due to a small number of patients in spatial omics studies. Results: Here we present the MOSAIC project - a collaborative initiative across industry and top oncology hospitals to build the largest collection of spatial omics data in cancer. We combine comprehensive clinical annotation with new deep profiling methods to both discover cancer subtypes and identify drug targets and biomarkers within them. MOSAIC aims to generate multimodal data for a total of 7,000 patients in seven cancer indications. Data modalities will include spatial and single cell transcriptomics and proteomics, bulk molecular profiling, pathology images, and curated clinical information. This collaboration harnesses the strengths of academia and industry to provide patient samples, generate high-quality data, develop AI-based analytical tools, and compile a resource that will eventually be made widely available for medical research. Conclusions: Here we present the workflow of the MOSAIC study, demonstrate the data processing pipelines and show spatial distributions of transcripts within the tumor microenvironment. Building upon these initial datasets, we discuss how the MOSAIC parties will collaborate to build an unprecedented database for research and discovery of new immuno-oncology therapeutic approaches.

In high-grade serous carcinoma (HGSC) tumors, the immune composition of the tumor microenvironment (TME) is linked to patient outcomes, with the presence of tumor-infiltrating T and B cells correlating with better outcomes, while increased tumor-associated macrophages are associated with a poor prognosis. As nearly all patients with HGSC are treated in a similar fashion, the TME and survival associations can be extended to treatment response and resistance. Although there are reported mechanisms of therapy resistance, none have been translated into FDA-approved therapies. Few studies have assessed the utility of targeting non-DNA damage repair vulnerabilities or improving tumor immune surveillance. Semaphorin 7A (SEMA7A)—a neuroimmune signaling molecule—is a membrane-bound and shed signaling molecule that activates integrin-β1 signaling. In other cancers, SEMA7A is associated with drug resistance, invasion, immune evasion, and promotion of pro-tumor inflammation. SEMA7A confers resistance to detachment-induced apoptosis and immunotherapy. Uncovering the SEMA7A-dependent mechanisms of therapy resistance represents an opportunity to define a novel therapeutic target and improve treatment responses and patient outcomes. Thus, we hypothesize that SEMA7A signaling activates anti-apoptotic and immunosuppressive signaling to promote therapy resistance. Publicly available data shows that high SEMA7A expression correlates with worse progression-free survival (HR: 1.48, Log-rank p=0.00087) and overall survival (HR: 1.41, Log-rank p=0.025) in patients treated with carboplatin and taxol. Moreover, high expression of both SEMA7A and PD-L1 (an immunosuppressive signaling protein) is associated with worse progression-free survival and overall survival compared to high SEMA7A with low PD-L1 (HR: 1.63, Log-rank p=0.00048; HR: 1.62, Log-rank p=0.0076, respectively). SEMA7A expression is also correlated with increased macrophage and regulatory T cell infiltration. In vitro assays revealed that SEMA7A expression increases greater than threefold in chemotherapy- and PARPi-resistant HGSC cells. SEMA7A was knocked down (KD) in isogenic therapy-sensitive vs -resistant pairs of human and murine HGSC cell lines to determine how tumor cell expression of SEMA7A mediates cell survival in suspension and in the presence of chemotherapy and/or PARPi. In clonogenic and drug response assays, loss of SEMA7A alone inhibited the growth of therapy-resistant HGSC cells and restored sensitivity to treatment. Immunoblotting was used to dissect SEMA7A-dependent survival signaling and resistance mechanisms. In vivo studies using an immunocompetent murine model of HGSC compared SEMA7A KD to control tumors and revealed that the loss of SEMA7A increased mouse survival (HR: 0.246, Log-rank p=0.011). Flow cytometry for used for immune profiling of the control and SEMA7A KD tumors. Our data highlight that SEMA7A is associated with poorer outcomes, therapy resistance, and an immune-suppressed TME. Thus, SEMA7A represents an attractive therapeutic target against therapy-resistant HGSC tumors. Citation Format: Alan Elder, Benjamin Bitler. Discerning mechanisms of semaphorin 7A-mediated therapy resistance and tumor progression in high-grade serous carcinoma of tubo-ovarian origin [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Ovarian Cancer Research; 2025 Sep 19-21; Denver, CO. Philadelphia (PA): AACR; Cancer Res 2025;85(18_Suppl):Abstract nr B049.

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Background: The SCRUM-Japan MONSTAR-SCREEN and CIRCULATE-Japan platforms have successfully implemented organ-agnostic liquid biopsy-based precision oncology and molecular residual disease (MRD)-guided therapeutic development, leading to multiple regulatory approvals of therapeutic agents and diagnostics through collection of quality-assured clinical data. Understanding tumor heterogeneity and microenvironment dynamics remains fundamental to advancing precision medicine. Here, we present MONSTAR-SCREEN-3, an innovative large-scale prospective study platform designed to integrate high-resolution spatial transcriptomics with multi-omics analysis across diverse cancer types. In our preliminary analyses, spatial transcriptomic analysis of HER2-amplified metastatic colorectal cancer revealed that treatment responses to anti-HER2 therapy are closely associated with tumor microenvironment characteristics, including increased T and B cell infiltration and interactions between cancer stem-like cells and HER2-expressing cells. Methods: MONSTAR-SCREEN-3 (UMIN000053975) is a large-scale multi-institutional study involving 55 centers across Japan, planning to enroll 3,200 patients stratified into three distinct cohorts: advanced solid tumors undergoing systemic therapy (n=1,700), resectable solid tumors receiving definitive treatment (n=1,100), and hematologic malignancies (n=400). The study implements systematic multi-sampling at strategically defined timepoints. Our comprehensive molecular characterization utilizes 5000Plex spatial transcriptomics integrated with circulating tumor DNA/RNA sequencing, bulk tissue WES/WTS, plasma proteomics, and microbiome analyses. Standardized longitudinal monitoring with WGS-based MRD analysis is implemented in resectable cases, while disease-specific MRD assessment approaches are applied to hematologic malignancies. Following SCRUM-Japan's established quality assurance system, standardized monitoring collects regulatory-grade clinical data, including objective response rate, progression-free survival, and overall survival. All molecular and clinical data will be integrated into our VAPOR CONE supercomputing infrastructure, creating an AI-driven ecosystem. This large-scale implementation of spatial transcriptomics analysis across multiple cancer types represents an unprecedented opportunity to comprehensively map the tumor microenvironment architecture and its dynamics during treatment. The integration of this spatial information with molecular and clinical data will provide unprecedented insights into tumor-immune interactions and resistance mechanisms, establishing an evidence-based framework for next-generation precision oncology. Citation Format: Mitsuho Imai, Shingo Sakashita, Junichiro Yuda, Shin Kobayashi, Riu Yamashita, Tadayoshi Hashimoto, Takao Fujisawa, Taro Shibuki, Shun-Ichiro Kageyama, Yoshikatsu Koga, Michiko Nagamine, Takeshi Kuwata, Hideaki Bando, Yoshiaki Nakamura, Woong-Yang Park, Takayuki Yoshino. SCRUM-Japan MONSTAR-SCREEN-3: A large-scale multi-institutional prospective study platform integrating spatial transcriptomics and multi-omics analysis for comprehensive understanding of tumor microenvironment dynamics [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 1154.

Cancer is a group of highly heterogeneous malignant diseases, and early diagnosis plays a crucial role in improving patient outcomes. Nucleoporins, including Nucleoporin (NUP)153, are involved in key cellular processes such as nucleocytoplasmic transport and cell cycle regulation. However, the role of NUP153 in cancer, especially its expression patterns, mutations, diagnostic value, and relationship with the tumour immune microenvironment, remains insufficiently explored. This study analysed NUP153 expression data from public databases such as TCGA and GTEx. Expression differences between tumour and normal tissues were assessed using Wilcoxon signed-rank tests. Gene set enrichment analysis (GSEA) was used to identify the biological functions and pathways related to NUP153. The relationship between NUP153 expression and immune cell infiltration was evaluated using the TIMER database, while drug sensitivity data were obtained from the GDSC and CTRP databases. Additionally, NUP153 expression in gastric cancer tissues was validated using immunohistochemistry and RT-qPCR. NUP153 showed significant expression variation across cancers, with high levels in cholangiocarcinoma, colorectal cancer, and head and neck squamous cell carcinoma. In gastric cancer, NUP153 was markedly upregulated compared to adjacent non-cancerous tissues. High NUP153 expression was linked to tumour-associated macrophage infiltration and correlated with poor prognosis in some cancers like Kidney Renal Papillary Cell Carcinoma and Sarcoma. Drug genomics analysis revealed that NUP153 expression predicted chemotherapy resistance, with imatinib and 4.5-dianilinophthalimide showing potential for inhibition in multiple cancers. Single-cell analysis and spatial transcriptomics further revealed that NUP153 expression drives proliferative states in mucus-producing cells in gastric cancer, and its expression was associated with immune cell infiltration patterns, particularly neutrophil and macrophage distribution in the tumour microenvironment. Our findings indicate that NUP153 is a critical factor in multiple cancers, especially gastric cancer, where its elevated expression holds promise as a diagnostic and prognostic biomarker. The results indicate that NUP153 plays a key role in modulating the immune microenvironment and driving tumour progression, positioning it as a potential target for future therapeutic interventions. However, additional studies are required to elucidate the specific molecular mechanisms underlying NUP153's function in cancer and to explore its clinical applicability.

Background: Colorectal cancer is a heterogeneous disease, harboring tremendous differences in genetic mutations, gene expression pathways, and the tumor microenvironment. While there has been recent progress in understanding immune infiltration patterns in colorectal cancer, the majority of patients are thus far largely refractory to immunotherapy, and the underlying mechanism remains poorly understood. Here, we present a large-scale multi-omics analyses on 161 colorectal cancer tumors. More specifically, we aim to characterize the spatial and phenotypic profiles as well as the relationships between the different cell populations in the tumor microenvironment using multiplex immunohistochemistry. Methods: Tissue microarrays were constructed, comprising the tumor core, close-to-edge, tumor edge, and adjacent normal tissues for each tumor. H&E and multiplex immunohistochemistry were performed by staining 7 markers (cytokeratin [CK], CD3, CD8, FOXP3, CD68, PD-L1 and DAPI). This allows for simultaneous detection of different cell populations, including CK+ epithelial cells, CD68+ macrophages, CD3+ T cells, CD8+ T cells, and FOXP3 regulatory T cells. Further complementing the study were whole genome sequencing to describe genetic mutations and tumor mutation burden, along with whole transcriptome sequencing to investigate differential gene expression and pathway enrichment. MSI status was determined from whole genome sequencing data, and molecular subtypes were called from RNA-sequencing data. Results: Based upon spatial patterns of immune cell infiltration, we qualitatively and quantitatively describe several immune infiltration subtypes which were further correlated with tumor mutation burden and neoantigen burden, activation of major oncogenic pathways and inflammatory signatures. The density of immune cells varies across the different tumor sites, from the adjacent normal tissue into the tumor core. We have also uncovered interactions between the different cell populations within the tumor microenvironment. Overall, the integration of data from multiple platforms represents a comprehensive interrogation into the complex tumor microenvironment in colorectal cancer. Conclusion: There exists a substantial variability in the transcriptomic landscape and the immune infiltration patterns across different patients. This demonstrates the complex interplay between the different cell populations in the tumor microenvironment, representing the fine balance between immune activation and suppression. Citation Format: Christine L. Eng, Joe P. Yeong, Andy Nguyen, Amanda Y. Guo, Brenda Tay, Mei Mei Chang, Sherlly Lim, Clarinda W. Chua, Harini Srinivasan, Lindsay H. Kua, Fiona Y. Lee, Dominique C. Macalinao, ThinZar Aung, Anders J. Skanderup, Tony K. Lim, Iain B. Tan, Si-Lin Koo. Spatial and multi-omics characterization of the tumor microenvironment in colorectal cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3872.

Spatial omics profiling technologies have been recognized recently for its ability to decipher the genetic molecules that are structurally relevant in pathology. Especially, in tumor biology, tumor is not the group of malignant tumor cells, but rather group of various cells such as tumor cells, immune cells, fibroblasts, etc. gathers together, constructing the tumor microenvironments. Technologies to analyze such microstructures have evolved from bulk sequencing, single cell sequencing to spatial omics profiling technologies. Spatial omics profiling technologies have highly influenced in decoding cancerous mechanisms by questioning the tumor heterogeneity, tumor microenvironment and spatial biomarkers. Most of the spatial omics technologies focus on mapping the spatial omics landscape in a large scale. They rather introduces the spatially-barcoded capture probes or fluorescence labeled target probes to spatially locate the genetic molecules. The information depth and the scalability of the techniques varies according to the purpose of the spatial assay techniques. Such technologies are capable of discovering the spatial heterogeneity and the spatial landscape of the consisting cell types due to relatively low depth of the omics information. To effectively address the target molecules for therapeutics or diagnostics, higher depth of the omics information are required. To meet the needs, region of interest (ROI)-based spatial technologies isolated the target regions and applies chemistries for higher coverage omics data. Conventional cell sorters utilizes microfluidic channels to sort cells of interest which requires cell dissociation in a solution phase. For instance, Fluorescence activated cell sorter (FACS) or Magnetic-activated cell sorting (MACS) uses fluorescence or magnetic particles, respectively, to designate the cells of interest in dissociated cell solutions. Spatially isolating techniques such as laser capture microdissection (LCM) is able to sort out the ROIs while preserving the spatial context, but it approximately takes an hour for isolating the targets. Also, it uses rather UV laser to dissect out cells or IR-activated melting of polymers to stick out cells which might cause damage to cells. Here, I developed the automated spatially resolved laser activated cell sorter that isolates the cells in target per second while preserving the spatial context of the cells. Specific region of indium tin oxide (ITO) coated slide glass evaporates when illuminated by IR laser pulse, plunging the cells into the desired reservoir. The applicability of the suggested cell sorter are demonstrated in omics profiling chemistries such as DNA sequencing, RNA sequencing, mass spectrometry, etc. Citation Format: Sumin Lee, Amos C. Lee, Sunghoon Kwon. High throughput spatially resolved laser-activated cell sorting links the genomic molecules with its spatial information. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5639.

The tumor microenvironment (TME) actively contribute to tumor development and treatment response. The interplay between tumor cells, immune cells, fibroblasts and blood vessels contribute to immune escape and drug resistance. Prior to treatment, higher tumor infiltrating lymphocytes correlate with better survival, while greater stromal content is linked to poor survival. Studying the composition and dynamics of the TME is essential for improving patient stratification, however a scalable tool for addressing this question is still lacking. Spatially resolved omics technologies allow for charting tissue architecture at the individual cell level, though large-scale studies remain challenging due to high expenses. In contrast, hematoxylin and eosin (H&E) slides are a cost-effective modality that provide rich morphological information for studying spatial biology. However, their use relies on pathologist interpretation. A key area of research in digital pathology has been automating cell (type) identification, predicting nuclei location and cell type in H&E slides. Existing deep learning models are limited by the quantity and diversity of training data, which requires pathologists to carefully annotate the location and identity of large volume of cells. To date, the largest dataset comprises approximately 200,000 cells, annotated with four cell types across 19 cancer types. We propose a novel approach of automated “molecular annotation”, where cell types and location on H&E slides are annotated with the aid of spatial proteomics, in place of pathologist annotation. Specifically, samples were profiled with both modalities. From the spatial omics modality, pixels were first segmented into cells, followed by cell clustering and cluster annotation based on molecular features of the cells. The location and identity of all cells on the tissue were then identified. These information were subsequently transferred to the H&E image by alignment at single-cell resolution, forming a dataset annotated with molecular ground truth. With a spatial omics dataset of two spatial proteomics slides from colorectal cancer patients, we obtained 160,000 annotated cells including 50,000 immune cells, 25,000 tumor cells, 12,600 stroma, 7,900 endothelial cells, among others. The size of this dataset is close to the largest annotated dataset publicly available to date. We then use this dataset to benchmark existing state-of-the-art deep-learning based cell type predictions models, as well as to fine-tune existing models for predicting cells in the colorectal tumor microenvironment. This proof-of-concept study aims to demonstrate the feasibility of molecular annotation approach. By including more spatial omics data, this approach can boost the performance of existing pre-trained models and enhance generalizability to specific tumor types. It opens up the opportunity to harness millions of cells for deep learning models to predict cell types on H&E slides, make AI models a cost-effective option for studying the TME. Citation Format: Siao-Han Wong, Benedikt Brors, Sonja Loges. Empowering AI-driven prediction of the tumor microenvironment from histopathology images via molecular annotation [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B053.

Current risk stratification and treatment decision-making for bladder cancer informed by histopathology as well as molecular diagnostics face limitations. This review summarizes recent advancements in single-cell and spatial omics methodologies for understanding bladder cancer biology and their potential impact on development of novel therapeutic strategies. Single-cell RNA sequencing and spatial omics techniques offer unprecedented insights into various aspects of tumor microenvironment (TME), bladder cancer heterogeneity, cancer stemness, and cellular plasticity. Studies have identified multiple malignant cell subpopulations within tumors, revealing diverse transcriptional states and clonal evolution. Additionally, intratumor heterogeneity has been linked to tumor progression and therapeutic response. Immune cell composition analysis has revealed immunosuppressive features in the TME, impacting treatment response. Furthermore, studies have elucidated the role of cancer-associated fibroblasts and endothelial cells in shaping the tumor immune landscape and response to therapy. Single-cell and spatial omics technologies have revolutionized our understanding of bladder cancer biology, uncovering previously unseen complexities. These methodologies provide valuable insights into tumor heterogeneity and microenvironmental interactions, with implications for therapeutic development. However, challenges remain in translating research findings into clinical practice and implementing personalized treatment strategies. Continued interdisciplinary collaboration and innovation are essential for overcoming these challenges and leveraging the full potential of single-cell and spatial omics in improving bladder cancer diagnosis and treatment.

Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell-cell interactions within their native environments. Despite challenges in data complexity and integration, advancements in multi-omics pipelines and computational tools are enhancing data accuracy and biological interpretation. This review provides a comprehensive overview of key spatial omics technologies, their analytical methods, validation strategies, and clinical applications. By integrating spatially resolved molecular data with traditional omics, spatial omics is transforming precision medicine, biomarker discovery, and personalized therapy. Future research should focus on improving standardization, reproducibility, and multimodal data integration to fully realize the potential of spatial omics in clinical and translational research.

Background: Invasive lobular carcinoma (ILC) is the second most common histological breast cancer subtype; however, little is known about its tumor microenvironment (TME). Here, we aimed to identify and to characterise ILC subgroups based on TME heterogeneity by combining spatial transcriptomics (ST) and single cell RNA-sequencing (scRNA-seq). Methods: We performed ST (Visium 10x Genomics) on frozen tumor samples from 41 primary hormone receptor positive (HR+), HER2-negative (HER2-) ILCs. Information coming from the morphological annotation of ST slides and the gene expression-based clustering of ST spots was integrated and used as input for a patient level classification (using intNMF algorithm). Subgroups of patients were annotated using morphology, gene set enrichment analysis and ST deconvolution based on scRNA-seq data (CARD software). SCAN-B bulk RNA-seq dataset was used as validation set (HR+, HER2- ILC samples, n = 853). Results: Four subgroups of patients were identified: proliferative (P, n = 12, enriched in tumor cells and proliferation-related pathways), normal-stroma enriched (NSE, n = 10, enriched in fibroblasts, stroma-related pathways and showing an higher level of colocalization between invasive and in situ carcinoma), metabolic (M, n = 9, enriched in metabolic related-pathways) and metabolic-immune enriched (MIE, n = 10, enriched in adipocytes, metabolic and immune-related pathways). Deconvolution of ST spots with single cell data revealed matching results with morphology (in terms of cell types). We observed an enrichment of myofibroblasts in NSE, an enrichment of cancer epithelial in P and, interestingly, an enrichment of endothelial cells in M subgroup (fdr < 0.25). MIE was characterized by perivascular like-endothelial cells and myeloid cells (monocytes and macrophages, fdr < 0.25). Of note, at the level of the ST slide, myeloid cells in MIE subgroup were more present in the adipose tissue compartment (fdr < 0.25) compared to the rest of the slide. NSE subgroup showed a trend for association with better prognosis. Gene signature-based assignment identified the same four subgroups in SCAN-B: GSEA analysis and comparisons among the four subgroups showed matching results between our dataset and SCAN-B. Of note, in SCAN-B, we observed differences in prognosis among the subgroups, with NSE showing better, M and MIE intermediate and P worse prognosis for overall survival and relapse free interval (p < 0.01). Conclusions: Four subgroups of ILC based on TME heterogeneity and showing differences in prognosis were identified and validated in external cohort. Of note, two of these groups were related to metabolism, highlighting the value of such process in ILC biology. Importantly, myeloid cells were the predominant immune cell types in ILC, and they showed a specific compartmentalization inside the TME. Further validation is needed. Citation Format: Matteo Serra, Andreas Papagiannis, Mattia Rediti, Frederic Lifrange, Nicola Occelli, Laetitia Collet, Delphine Vincent, Ghizlane Rouas, Danai Fimereli, Ligia Craciun, Denis Larsimont, David Venet, Miikka Vikkula, Francois P. Duhoux, Françoise Rothé, Christos Sotiriou. Integrating spatial and single cell transcriptomics to identify and characterise biologically driven subgroups in invasive lobular carcinoma [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 5066.

Cell annotation is a crucial methodological component to interpreting single cell and spatial omics data. These approaches were developed for single cell analysis but are often biased, manually curated and yet unproven in spatial omics. Here we apply a stemness model for assessing oncogenic states to single cell and spatial omic cancer datasets. This one-class logistic regression machine learning algorithm is used to extract transcriptomic features from non-transformed stem cells to identify dedifferentiated cell states in tumors. We found this method identifies single cell states in metastatic tumor cell populations without the requirement of cell annotation. This machine learning model identified stem-like cell populations not identified in single cell or spatial transcriptomic analysis using existing methods. For the first time, we demonstrate the application of a ML tool across five emerging spatial transcriptomic and proteomic technologies to identify oncogenic stem-like cell types in the tumor microenvironment.

Recent advances in self-supervised foundation models for computational cancer histopathology have shown great promise for predicting clinical phenotypes from routine hematoxylin and eosin (H&E)-stained images. However, application of these models in the context of discovery, for example for identification of novel biomarkers or drug targets, remains challenging as this typically requires linking whole slide imaging (WSI) data to molecular data, which has historically been scarce. Recently, spatial omics (SO) has emerged as a powerful technology for high-throughput profiling of RNAs and proteins directly within cancer tissues. This presents new and exciting opportunities to improve histopathology models for making molecular predictions about the tumor microenvironment. In this work, we develop machine learning models that can generate transcriptomic profiles at two different spatial scales: (1) cellular-level resolution, i.e. SO, and (2) slide-level resolution, i.e. bulk RNA-sequencing. We present a preprocessing and benchmarking framework to enable rigorous comparison of models on an in-house SO dataset, as well as a public bulk RNA-seq dataset (TCGA-COAD/READ), of colorectal cancer. We fine-tune recently published digital pathology foundation models for these tasks, and find that they outperform both general pathology-naïve image foundation models, as well as specialized digital pathology deep learning models. We also find that existing models cannot be simultaneously trained using supervision from multiple spatial resolutions. To address this, we propose a graph neural network (GNN) modeling approach that can model both local interactions between cells within tumor niches, as well as global interactions across tumor regions. We show that our GNN model outperforms existing models for both cell-level and slide-level tasks. This work presents a novel multi-scale framework for predicting transcriptome profiles from WSIs. Citation Format: Tianyi Zhang, Malay Singh, Dwivedi Naman, Timothy Antoni, Ju Can, Firas Elbayoumi, Iain Bee Huat Tan, Tiantian He, Hwee Kuan Lee, Shyam Prabhakar, Grace Hui Ting Yeo. Multi-Scale Modeling for Prediction of Spatial Single-Cell and Bulk Transcriptomic Profiles from Whole-Slide Images of Colorectal Cancer [abstract]. In: Proceedings of Frontiers in Cancer Science 2024; 2024 Nov 13-15; Singapore. Philadelphia (PA): AACR; Cancer Res 2025;85(15_Suppl):Abstract nr P24.