The promising application of cell-cell interaction analysis in cancer from single-cell and spatial transcriptomics

My talk will present three computational frameworks we developed to study cytokine signaling activities and cell-cell communications during the antitumor immune response, using tumor single-cell and spatial transcriptomics. The basic immunology tool to study cytokine signaling mostly measures cytokine release, which is transient and does not represent downstream target activities. Therefore, we first developed the CytoSig platform, providing a database of target genes modulated by cytokines and a predictive model of cytokine signaling cascades from transcriptomic profiles. We collected 20,591 transcriptome profiles for human cytokine, chemokine, and growth factor responses. This atlas of transcriptional patterns induced by cytokines enabled the reliable prediction of signaling activities in distinct cell populations in infectious diseases, chronic inflammation, and cancer using bulk and single-cell transcriptomic data. CytoSig revealed previously unidentified roles of many cytokines, such as BMP6 as an anti-inflammatory factor. Then, based on CytoSig, we developed Tres, a computational model utilizing single-cell transcriptomic data to identify signatures of T cells that are resilient to immunosuppressive signals, such as TGF-β1, TRAIL, and prostaglandin E2. Tres reliably predicts clinical responses to immunotherapy in multiple cancer types using bulk T cell transcriptomic data from pre-treatment patient tumors or infusion/pre-manufacture samples for cellular immunotherapies. Further, Tres identified FIBP as a candidate immunotherapy target to potentiate adoptive cell therapy efficacies. FIBP knockout in T cells enhanced adoptive cell therapy by down-regulating T cells' cholesterol metabolism. Last, I will briefly show our SpaCET framework for deconvolving cell fractions and identifying cell-cell interactions in tumor spatial transcriptomics data. SpaCET resolved several challenges in spatial transcriptomics analysis that previous methods did not address sufficiently. Through coupling cell fractions with ligand-receptor co-expression analysis, SpaCET reveals that intercellular interactions tend to be located at the tumor-immune boundaries. Citation Format: Peng Jiang. Inference of intercellular signaling activities in tumor spatial and single-cell transcriptomics, with applications in identifying cancer immunotherapy targets [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr IA002.

Pancreatic Cancer Ductal Adenocarcinoma (PDAC) is one of the deadliest cancers with 5-year survival of 11%. Understanding the intratumor heterogeneity is a pivotal piece to unravel the complexity of PDAC. While single cell RNASeq identifies the heterogeneous cell populations within the tumor tissue, spatially characterizing the transcriptomic profile of neoplastic and pre-neoplastic populations within the tissue remains a challenge, as the spatial dimension is usually lost upon tissue dissociation. We identified an approach to integrate spatial transcriptomics data with single cell RNASeq data. To characterize the cell populations within the tissue we performed single cell RNASeq on disease pathology-free pancreas tissue and primary PDAC samples. We profiled the transcriptomic profile of Acinar, Ductal, Acinar-to-Ductal (ADM), and Pancreatic Intraepithelial Neoplasia (PanINs) regions of interest (ROIs) across the tissue using the Nanostring GeoMx platform. Differential gene expression analysis using linear mixed-effect models of the cell-type specific ROIs defined pan-marker gene sets for each cell type, which were mapped to UMAP projections of single cell RNA sequencing data using AUCell scoring. As expected, the acinar pan-markers gene set derived from the spatial transcriptomics mapped to the manually annotated acinar population in the single cell data. On the other hand, Ductal, ADM, and PanIN pan-marker gene sets were mapped to distinct clusters that previously were not well-defined by single cell sequencing. The analysis coupled with orthogonal validation using RNAScope revealed gene signatures uniquely specific to ADM lesions and PanINs, respectively. Interestingly, our list included known markers as well as novel findings, supporting the validity of the findings. Furthermore, RNA velocity analysis using scVelo revealed a trajectory of cell evolution originating from acinar cells passing through the newly-defined ADM population and ending towards the ductal population derived from tumor samples. Overall, this integration approach of spatial and single cell transcriptomics can further define the characteristics that differentiate neoplastic and pre-neoplastic populations, as well as the potential drivers for tumorigenesis that could be therapeutically targeted. Citation Format: Ahmed M. Elhossiny, Eileen Carpenter, Padma Kadiyala, Yaqing Zhang, Filip Bednar, Arvind Rao, Timothy Frankel, Marina Pasca Di Magliano. Integrating single cell and spatial transcriptomics define gene signature for pancreatic ductal adenocarcinoma pre-neoplastic lesion [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer; 2022 Sep 13-16; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2022;82(22 Suppl):Abstract nr A006.

In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.

Artificial intelligence (AI) has become a common tool for bioinformatics, with hundreds of methods published in recent years. Due to the training data demands of deep-learning algorithms, high-throughput single-cell and spatial transcriptomics is one of the most popular areas for these applications. Here we review how AI is being used for single-cell and spatial transcriptomics analysis, and how these approaches compare to alternative statistical or heuristic-based methods. We explored 10 common analysis tasks: dimensionality reduction, cross-dataset integration, data denoising, data augmentation, deconvolution, cell-cell interactions, transcriptional velocity, transcriptomic-chromatin accessibility integration, and integrating single-cell and spatial transcriptomics modalities. We highlight which algorithms are likely to be useful for discovery researchers, and which are not yet ready for general research use.

<p dir="ltr">Early diagnosis and effective treatment of complex and malignant diseases are critical challenges in modern medicine. These diseases exhibit significant heterogeneity, manifesting differently across patients and involving diverse genes and pathways. This variability complicates the identification of universal biomarkers, necessitating innovative, system-level approaches to uncover robust and predictive biomarkers across various biological contexts.</p><p dir="ltr">This dissertation addresses these challenges by leveraging both knowledge- based and data-driven methods to analyze multi-omics data, including spatial and single-cell transcriptomics, as well as bulk data. The goal is to prioritize potential biomarkers that can provide system-level insights into disease mechanisms, ultimately facilitating personalized medicine. The findings of this thesis promise to transform biomarker discovery, leading to earlier diagnoses, improved treatment outcomes, and enhanced patient care.</p><p dir="ltr">The thesis encompasses four comprehensive studies:</p><p dir="ltr">1. Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases: We analyzed scRNA-seq data from mice and humans with immune-mediated inflammatory diseases (IMIDs). We identified upstream regulators acting as on/off switches, which collectively serve as candidate biomarkers and potential drug targets. These findings were validated in a study involving sera from nearly 300 systemic lupus erythematosus patients.</p><p dir="ltr">2. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases: Using multiomics data from UK Biobank (UKBB), we identified reliable biomarkers for nine complex diseases and constructed an interactive atlas to explore their performance. Proteomics outperformed other omics types in most of the diseases.</p><p dir="ltr">3. Multiomics biomarkers were not superior to clinical variables for pan- cancer screening: We analyzed multiomics data to find early diagnostic biomarkers for cancers. While proteomics data from peripheral blood did not outperform routine clinical variables for most cancers, promising biomarkers were identified for cancers in highly vascularized organs.</p><p dir="ltr">4. Combining spatial transcriptomics and pseudotime to find urine biomarkers for prostate cancer: We analyzed spatial transcriptomics data of prostate cancer to identify transcripts correlated with malignant transformation. Using pseudotime and machine learning, we validated the diagnostic accuracy of these transcripts in various samples from over 2,000 patients and controls.</p><p dir="ltr">In conclusion, this thesis demonstrates the potential of systems-level approaches in biomarker discovery, providing insights into disease mechanisms and paving the way for personalized medicine. The studies highlight the importance of considering disease heterogeneity and the need for tailored biomarker strategies, ultimately contributing to earlier diagnoses and improved patient outcomes.</p><h3>List of scientific papers</h3><p dir="ltr">I. Sandra Lilja*, Xinxiu Li*, <b>Martin Smelik</b>*, Eun Jung Lee, Joseph Loscalzo, Pratheek Bellur Marthanda, Lang Hu, Mattias Magnusson, Oleg Sysoev, Huan Zhang, Yelin Zhao, Christopher Sjöwall, Danuta Gawel, Hui Wang, Mikael Benson. Multi-organ single-cell analysis reveals an on/off switch system with potential for personalized treatment of immunological diseases. Cell Rep Med. 2023 Mar 21;4(3):100956. <a href="https://doi.org/10.1016/j.xcrm.2023.100956" rel="noreferrer" target="_blank">https://doi.org/10.1016/j.xcrm.2023.100956</a></p><p dir="ltr">II. <b>Martin Smelik</b>*, Yelin Zhao*, Xinxiu Li, Joseph Loscalzo, Oleg Sysoev, Firoj Mahmud, Dina Mansour Aly, Mikael Benson. An interactive atlas of genomic, proteomic, and metabolomic biomarkers promotes the potential of proteins to predict complex diseases. Sci Rep. 14, 12710 (2024). <a href="https://doi.org/10.1038/s41598-024-63399-9" rel="noreferrer" target="_blank">https://doi.org/10.1038/s41598-024-63399-9</a></p><p dir="ltr">III. <b>Martin Smelik</b>, Yelin Zhao, Dina Mansour Aly, AKM Firoj Mahmud, Oleg Sysoev, Xinxiu Li, Mikael Benson. Multiomics biomarkers were not superior to clinical variables for pan-cancer screening. Communications Medicine. 4, 234 (2024). <a href="https://doi.org/10.1038/s43856-024-00671-z" rel="noreferrer" target="_blank">https://doi.org/10.1038/s43856-024-00671-z</a></p><p dir="ltr">IV. <b>Martin Smelik</b>*, Daniel Diaz-Roncero Gonzalez*, Xiaojing An, Rakesh Heer, Lars Henningsohn, Xinxiu Li, Hui Wang, Yelin Zhao, Mikael Benson. Combining spatial transcriptomics, pseudotime and machine learning to find biomarkers for prostate cancer. [Manuscript]</p><p dir="ltr">* Shared first-author</p>

The recent advances in spatial transcriptomics have brought unprecedented opportunities to understand the cellular heterogeneity in the spatial context. However, the current limitations of spatial technologies hamper the exploration of cellular localizations and interactions at single-cell level. Here, we present spatial transcriptomics deconvolution by topic modeling (STRIDE), a computational method to decompose cell types from spatial mixtures by leveraging topic profiles trained from single-cell transcriptomics. STRIDE accurately estimated the cell-type proportions and showed balanced specificity and sensitivity compared to existing methods. We demonstrated STRIDE’s utility by applying it to different spatial platforms and biological systems. Deconvolution by STRIDE not only mapped rare cell types to spatial locations but also improved the identification of spatially localized genes and domains. Moreover, topics discovered by STRIDE were associated with cell-type-specific functions and could be further used to integrate successive sections and reconstruct the three-dimensional architecture of tissues. Taken together, STRIDE is a versatile and extensible tool for integrated analysis of spatial and single-cell transcriptomics and is publicly available at https://github.com/wanglabtongji/STRIDE.

Cancer-associated fibroblasts (CAFs) are a diverse group of cells that significantly contribute to reshaping the tumor microenvironment (TME), and no research has systematically explored the molecular landscapes of senescence related CAFs (senes CAF) in NB. We utilized pan-cancer single cell and spatial transcriptomics analysis to identify the subpopulation of senes CAFs via senescence related genes, exploring its spatial distribution characteristics. Harnessing the maker genes with prognostic significance, we delineated the molecular landscapes of senes CAFs in bulk-seq data. We established the senes CAFs related signature (SCRS) by amalgamating 12 and 10 distinct machine learning (ML) algorithms to precisely diagnose stage 4 NB and to predict prognosis in NB. Based on risk scores calculated by prognostic SCRS, patients were categorized into high and low risk groups according to median risk score. We conducted comprehensive analysis between two risk groups, in terms of clinical applications, immune microenvironment, somatic mutations, immunotherapy, chemotherapy and single cell level. Ultimately, we explore the biological function of the hub gene JAK1 in pan-cancer multi-omics landscape. Through integrated analysis of pan-cancer spatial and single-cell transcriptomics data, we identified distinct functional subgroups of CAFs and characterized their spatial distribution patterns. With marker genes of senes CAF and leave-one-out cross-validation, we selected RF algorithm to establish diagnostic SCRS, and SuperPC algorithm to develop prognostic SCRS. SCRS demonstrated a stable predictive capability, outperforming the previously published NB signatures and clinic variables. We stratified NB patients into high and low risk group, which showed the low-risk group with a superior survival outcome, an abundant immune infiltration, a different mutation landscape, and an enhanced sensitivity to immunotherapy. Single cell analysis reveals biologically cellular variations underlying model genes of SCRS. Spatial transcriptomics delineated the molecular variant expressions of hub gene JAK1 in malignant cells across cancers, while immunohistochemistry validated the differential protein levels of JAK1 in NB. Based on multi-omics analysis and ML algorithms, we successfully developed the SCRS to enable accurate diagnosis and prognostic stratification in NB, which shed light on molecular landscapes of senes CAF and clinical utilization of SCRS.

Biomaterials serve as bridges between synthetic structures and biological systems, facilitating interactions with bodily tissues and regulating cellular responses. However, the intricate mechanisms governing cell-material interactions within the body are not fully understood. To address these challenges, single-cell transcriptomics and spatial technologies have emerged as promising tools. This review introduces the concept of the 'Biomaterial-mediated Cell Atlas' and summarizes its recent advances in biomaterials research. Building upon the framework of the Human Cell Atlas, it can extend its scope to include biomaterial-mediated cellular responses. The principles and workflows of single-cell and spatial transcriptomics are then presented, emphasizing their potential to reveal cellular diversity and spatial arrangements affecting material-cell interactions. Furthermore, transcriptomics-driven approaches are highlighted for their potential in mapping biomaterial-cell communication pathways, which are essential for elucidating biological mechanisms and guiding material design. Challenges inherent to current methodologies and emerging frontiers in this interdisciplinary field are discussed.

Lung metastasis is responsible for nearly all deaths caused by osteosarcoma, the most common pediatric bone tumor. How malignant bone cells coerce the lung microenvironment to support metastatic growth is unclear. The purpose of this study is to identify metastasis-specific therapeutic vulnerabilities by delineating the cellular and molecular mechanisms underlying osteosarcoma lung metastatic niche formation. Using single-cell transcriptomics (scRNA-seq), we characterized genome- and tissue-wide molecular changes induced within lung tissues by disseminated osteosarcoma cells in both immunocompetent murine models of metastasis and patient samples. We confirmed transcriptomic findings at the protein level and determined spatial relationships with multi-parameter immunofluorescence and spatial transcriptomics. Based on these findings, we evaluated the ability of nintedanib, a kinase inhibitor used to treat patients with pulmonary fibrosis, to impair metastasis progression in both immunocompetent murine osteosarcoma and immunodeficient human xenograft models. Single-nucleus and spatial transcriptomics was used to perform molecular pharmacodynamic studies that define the effects of nintedanib on tumor and non-tumor cells within the metastatic microenvironment. Osteosarcoma cells induced acute alveolar epithelial injury upon lung dissemination. scRNA-seq demonstrated that the surrounding lung stroma adopts a chronic, non-resolving wound-healing phenotype similar to that seen in other models of lung injury. Accordingly, metastasis-associated lung demonstrated marked fibrosis, likely due to the accumulation of pathogenic, pro-fibrotic, partially differentiated epithelial intermediates and macrophages. Our data demonstrated that nintedanib prevented metastatic progression in multiple murine and human xenograft models by inhibiting osteosarcoma-induced fibrosis. Fibrosis represents a targetable vulnerability to block the progression of osteosarcoma lung metastasis. Our data support a model wherein interactions between osteosarcoma cells and epithelial cells create a pro-metastatic niche by inducing tumor deposition of extracellular matrix proteins such as fibronectin that is disrupted by the anti-fibrotic TKI nintedanib. Our data shed light on the non-cell autonomous effects of TKIs on metastasis and provide a roadmap for using single-cell and spatial transcriptomics to define the mechanism of action of TKI on metastases in animal models.

The landscape of cell–cell communication through single-cell transcriptomics

Hepatocellular carcinoma (HCC) is one of the most common types of cancer globally. However, HCC features poor prognosis due to complex pathogenesis and limitations of therapeutic approaches. To improve the prognosis of HCC patients, we analyzed single-cell transcriptome and spatial transcriptome on liver tissues from HCC patients. We applied Non-negative Matrix Factorization (NMF) method on spatial transcriptome and found a critical pathway associated with HCC through GO over-representation analysis. According to the pathway activity score, the cells in single-cell transcriptome were divided into three groups. Then we conducted hdWGCNA and selected HCC-related co-expressed gene modules which showed significant intergroup differences. Integrative machine learning algorithms on bulk transcriptome were employed for core genes selection. We respectively evaluated their AUC values as independent diagnostic markers and retained a core gene. We conducted a survival analysis using this gene. Next, we performed immune infiltration analysis, tumor microenvironmental analysis and drug sensitivity analysis. Finally, pseudotime analysis, differentiation potential analysis and cell-cell communication analysis were applied to decipher the mechanisms underlying tumor initiation and progression. Single-cell transcriptome analysis revealed the high heterogeneity of cells in patients with HCC. The GO over-representation analysis based on NMF indicated that cytoplasmic translation path was enriched in most NMF components. The SSR2 gene passed the integrative machine learning algorithms and possessed excellent diagnosis (AUC > 0.8) and prognostic abilities (K-M curve, P = 0.007). Further analysis identified SSR2 as the top-ranked gene by average expression level in NK cells compared to all other immune cell clusters. Its expression showed a consistent downward trend during NK cell maturation.NK cells with high expression of SSR2 exhibited stronger cell-cell communication than those with low expression of SSR2. NK cells mainly communicated with other cells through MIF-mediated signaling pathway. In hepatocellular carcinoma (HCC), the high expression of the SSR2 gene in NK cells is accompanied by enhanced cytoplasmic translation pathway, which promotes MIF secretion and activates its downstream signaling pathways, thereby driving the disruption of the tumor immune microenvironment and the progression of the disease.

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by the absence of estrogen receptor, progesterone receptor, and HER2 expression, which limits the availability of targeted therapies and results in poor prognosis. Immune checkpoint blockade (ICB) therapies have emerged as promising treatments by enhancing anti-tumor immunity; however, a substantial proportion of patients with TNBC exhibit primary or acquired resistance. This resistance is largely influenced by the tumor microenvironment (TME). This study uses integrated single-cell and spatial transcriptomics to elucidate key cellular mechanisms of resistance, with particular emphasis on lipid-mediated stromal-immune interactions within the TNBC TME. This investigation encompassed analysis of single-cell RNA sequencing (scRNA-seq) data from three TNBC datasets and spatial transcriptomic data from 43 TNBC samples. Spatial niches and cell-cell interactions were identified using the Multimodal Intersection Analysis (MIA) algorithm. Experimentally, adipose-derived mesenchymal stem cells (AD-SCs) were co-cultured with MDA-MB-231 TNBC cells to generate lipid-processing CAFs (lpCAFs) and subsequently co-cultured with THP-1 macrophages. Lipid metabolism and M2 polarization of macrophages were assessed using BODIPY staining, Oil Red O, qPCR, flow cytometry and Western blotting techniques. ABCA8+ lpCAFs and APOE+ lipid-associated macrophages (LAMs) exhibited significant enrichment in ICB-resistant TNBC, with co-localization at the immune-stromal junction. lpCAFs facilitated M2 macrophage polarization through lipid metabolism reprogramming, establishing an immunosuppressive TME. High ABCA8 expression demonstrated correlation with enhanced M2 macrophage infiltration, decreased cytotoxic immune cells, and poorer prognosis. Experimental validation demonstrated that lpCAFs increased expression of lipid metabolism and M2 polarization marker in macrophages, substantiating their immunosuppressive function. ABCA8+ lpCAFs and APOE+ LAMs contribute to ICB resistance in TNBC through the establishment of an immunosuppressive TME via lipid metabolism reprogramming. Therapeutic intervention targeting the ABCA8-lipid axis presents a promising strategy to enhance ICB efficacy, potentially advancing TNBC treatment outcomes and improving patient survival.

BackgroundHepatocellular carcinoma (HCC) is a highly heterogeneous malignancy characterized by marked cellular and spatial diversity within the tumor microenvironment (TME). The transforming growth factor-β (TGF-β) signaling pathway plays a dual role in the initiation and progression of HCC. However, the spatial distribution characteristics and key regulatory mechanisms of TGF-β signaling within HCC tissues remain inadequately elucidated.MethodsThis study integrated spatial transcriptomics (ST), single-cell RNA sequencing (scRNA-seq), and bulk RNA-seq data to systematically characterize the spatial heterogeneity of the TGF-β signaling pathway in HCC. By combining non-negative matrix factorization (NMF), CellChat-based cell–cell communication analysis, and multi-algorithm machine learning approaches, we identified key driver genes closely associated with TGF-β activity. Subsequently, CCK-8 assays, colony formation, wound-healing, and Western blot experiments were performed in HCC cell lines to validate the biological functions of the identified gene.ResultsThe results revealed that the TGF-β signaling pathway exhibited the highest activity at the tumor–stroma interface, which was enriched with cancer-associated fibroblasts (CAFs), immunosuppressive cells, and genes related to extracellular matrix (ECM) remodeling. CellChat analysis showed that TGF-β–TGFBR ligand–receptor interactions between tumor cells, CAFs, and immune cells were markedly enhanced, contributing to the formation of a localized immunosuppressive microenvironment. Machine learning analysis identified SLC20A1 as a key regulatory factor. Functional assays demonstrated that SLC20A1 enhances the proliferation, migration, and epithelial–mesenchymal transition (EMT) of HCC cells, whereas its knockout significantly suppresses these malignant phenotypes.ConclusionThis study represents the first comprehensive integration of spatial and single-cell transcriptomics to uncover the spatial organization of TGF-β signaling in HCC and to identify the TGF-β–SLC20A1 axis as a critical driver of tumor invasion at the tumor–stroma interface. Our findings provide new mechanistic insights into tumor–stroma interactions and suggest a potential therapeutic target for precision treatment of HCC.

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.

R-loop-driven molecular subtypes reveal prognostic and immunogenomic features in uterine corpus endometrial carcinoma.