Novel insights from spatial transcriptome analysis in solid tumors.

Spatial transcriptomic technology enables resolving a tumor microenvironment heterogeneity of colorectal cancer (CRC) tissue samples. However, there is limited evidence regarding the relative strengths and weaknesses of different CRC sample types and preservation methods, including formalin-fixed paraffin-embedded (FFPE), fresh frozen (FF) and snap frozen (SF). Here, we examine gene expression profiles in 36 tissue areas, FFPE, FF and SF samples, derived from a patient with stage IV CRC, using spatial analysis of the whole transcriptome. The tissue samples obtained from surgical resection of the cancer were subjected to a detailed histological evaluation and spatial transcriptomics analysis to compare the quality of the sample between different preservation methods. Spatial transcriptomic analysis demonstrated that FFPE tissue samples offered improved resolution of cellular morphology in the tumor microenvironment, facilitating greater detail of gene expression and cell types. In contrast, gene expression analysis of FF and SF tissues embedded in optimal cutting temperature compound revealed a high level of CRC gene expression. FFPE, FF and SF samples shared a significant overlap among the top 300 genes that were strongly associated with key biological processes in CRC. In summary, the spatial analysis of the whole transcriptome across FFPE, FF and SF CRC tissues revealed intratumoral heterogeneity and highlighted key transcriptomic signatures, suggesting the specific value of different tissue preservation methods in the profiling of CRC.

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.

PD09-11 SPATIAL TRANSCRIPTOME ANALYSIS REVEALS THE TUMOR HETEROGENEITY AND NICHE FACTORS IN PROGRESSIVE PROSTATE CANCER

Spatial transcriptomics has gained popularity over the past decade due to its ability to evaluate transcriptome data while preserving spatial information. Cell segmentation is a crucial step in spatial transcriptomic analysis, as it enables the avoidance of unpredictable tissue disentanglement steps. Although high-quality cell segmentation algorithms can aid in the extraction of valuable data, traditional methods are frequently non-spatial, do not account for spatial information efficiently, and perform poorly when confronted with the problem of spatial transcriptome cell segmentation with varying shapes. In this study, we propose ST-CellSeg, an image-based machine learning method for spatial transcriptomics that uses manifold for cell segmentation and is novel in its consideration of multi-scale information. We first construct a fully connected graph which acts as a spatial transcriptomic manifold. Using multi-scale data, we then determine the low-dimensional spatial probability distribution representation for cell segmentation. Using the adjusted Rand index (ARI), normalized mutual information (NMI), and Silhouette coefficient (SC) as model performance measures, the proposed algorithm significantly outperforms baseline models in selected datasets and is efficient in computational complexity.

ObjectiveHead and neck squamous cell carcinoma (HNSC) is a common malignant tumor with a 5-year survival rate of less than 60% and lactylation modification plays a key role in its occurrence and progression. This study aims to identify key lactylation-related genes (LRGs) in HNSC and their prognostic impact by combining machine learning methods and spatial transcriptomics analysis.MethodsHigh-throughput gene expression data of HNSC were obtained from the TCGA database. The “Limma” R package was used to screen differentially expressed genes (DEGs), and their biological significance was investigated through GO and KEGG functional enrichment analyses. Subsequently, weighted gene co-expression network analysis (WGCNA) was employed to identify disease trait-associated modules. We used 12 machine learning methods to screen for key genes and validated them in the GSE6631 dataset. Survival curves were plotted to evaluate the prognostic ability of key genes. Transcriptional regulation analysis via the “RcisTarget” package identified transcription factors associated with motifs exceeding NES 3.5. Single-cell and spatial transcriptomic analysis was performed, which involved dimensionality reduction, clustering via UMAP, cell type annotation and the plotting of spatial expression distribution maps of key genes. Finally, for immune infiltration analysis, the CIBERSORT algorithm was used to calculate the proportions of 22 types of immune cells, and then the correlations between key genes and immune-related genes were analyzed.ResultsOur study identified three key LRGs (MSN, KIF2C, and RFC4), which exhibited strong diagnostic performance, with AUC values of 0.986 for KIF2C, 0.964 for RFC4, and 0.89 for MSN in the TCGA-HNSC cohort. These findings were validated in the GSE6631 dataset. Western blotting showed a significant upregulation of the expression of these genes in the HNSC group, which was consistent with the RT-qPCR results. Survival analysis showed that the high expression of key genes was closely associated with a poorer overall survival (p < 0.05). Spatial transcriptomics analysis revealed that KIF2C, RFC4, and MSN were preferentially expressed in tumor core regions, indicating their involvement in the malignant microenvironment. Additionally, we discovered that 10 types of immune cell infiltrations were significantly changed in HNSC and well correlated with key genes.ConclusionMSN, KIF2C and RFC4 may serve as potential biomarkers for diagnosing and predicting the prognosis of HNSC, providing new insights into targeted therapy.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12672-025-03849-6.

Spatial transcriptomic analysis has proposed valuable insights into the behavior of tongue cancer. However, the specific cell types involved in chemically-induced carcinogenesis and the process of tumor development remain elusive. We leveraged artificial intelligence (AI) algorithms and spatial transcriptomic sequencing to meticulously characterize the spatial and temporal evolution of 4-nitroquinoline-1-oxide (4NQO)-induced tongue carcinogenesis and intratumor heterogeneity. An AI classifier effectively categorized dysplastic tongue tissue into 13 distinct groups. Spatial transcriptomics identified 13 corresponding cellular subgroups with unique features within the lesion. Both methods successfully distinguished subtle muscle phenotype and genetic lineage variations induced by 4NQO, despite limited morphological differences. Evolutionary tree analysis revealed the dynamic appearance and disappearance of functionally and genetically diverse cell subgroups during the progression from epithelial dysplasia to in situ carcinoma and invasive cancer. Key findings include the identification of specific switch genes associated with tumor invasion and the revelation of significant intratumor heterogeneity. This spatial transcriptomic analysis of 4NQO-induced tongue cancer provides a detailed characterization of tumor evolution and heterogeneity. It elucidates critical aspects of tongue cancer cell behavior and identifies potential therapeutic targets (switch genes). These findings offer novel insights for improving the diagnosis and treatment of tongue cancer.

Prostate cancer is one of the most prevalent malignancies among men and remains a major clinical challenge due to the complex tumor microenvironment. Understanding gene expression dynamics at both cellular and spatial levels is essential for improving therapeutic strategies. In this study, we performed an integrated multi-omics analysis using single-cell RNA sequencing and spatial transcriptomics. scRNA-seq data from 15 prostate samples, including 8 normal and 7 tumor tissues, were analyzed to characterize distinct cellular populations. Spatial transcriptomic profiling was conducted on three FFPE prostate tissue sections, including adjacent normal tissue, acinar cell carcinoma, and invasive adenocarcinoma, using the standard 10x Genomics Visium FFPE platform (55 µm capture spots). Single-cell analysis revealed heterogeneity among epithelial, stromal, and immune cell populations, highlighting complex signaling networks in which myeloid cells may contribute to tumor progression through immune suppression and epithelial adaptability. Spatial transcriptomic analysis further identified region-specific expression patterns and spatially restricted tumor niches, including the regional establishment of TXNIP and BIRC3 as genes associated with metabolic stress and inflammatory survival pathways. The spatial colocalization of BIRC3 with tumor vasculature in invasive carcinoma tissue suggests a novel interaction. Our discoveries using an integrated single-cell and spatial transcriptomic approach reveal a high-resolution molecular map of prostate cancer with spatial features that may provide further therapeutic investigation.

Glomerular diseases encompass a group of kidney diseases that may share common gene expression pathways. Here, we analyzed glomerular-specific gene expression profiles across various glomerular diseases. We performed spatial transcriptomic profiling using formalin-fixed paraffin-embedded kidney biopsy specimens of controls and patients with five types of glomerular diseases using the GeoMx Digital Spatial Profiler. Disease-representative glomerular regions of interest (ROIs) were configured, probed with oligonucleotide barcodes linked with target complimentary sequence. The UV-cleaved barcodes were amplified to generate libraries and subsequently sequenced. Common differentially expressed genes across glomerular diseases were identified and Gene Ontology annotation was performed using the ToppGene suite. The mean age of patients with glomerular diseases and kidney donors was 49.5 ± 12.2 and 49.5 ± 9.8years, respectively. A total of 35 differentially expressed genes were consistently downregulated in glomeruli across the disease compared to the control, while none of the differentially expressed genes were consistently upregulated. Twelve of 35 downregulated differentially expressed genes, including the two hub genes JUN and FOS, were annotated with molecular function Gene Ontology terms related to DNA-binding transcription factor activity. The annotated biological process Gene Ontology terms included response to lipid-related (17/35 differentially expressed genes), response to steroid hormone (12/35 differentially expressed genes), or cell cycle regulation (10/35 differentially expressed genes). Xenium and immunofluorescence staining confirmed the reduced expression of JUN, ZFP36, and KLF9 in intraglomerular cells of glomerular diseases. Identifying common differentially expressed genes by spatial transcriptomic analysis provides insights into the underlying molecular mechanisms of glomerular diseases and may lead to novel assessment or therapeutic strategies.

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.

Immunoglobulin A nephropathy (IgA-N) is a primary glomerulonephritis that is characterized by mesangial cell proliferation and expansion. Although glomerular endothelial cells (GE) are implicated in the pathogenesis of IgA-N, their role remains poorly understood. We conduct single-cell and spatial transcriptomic analysis using human specimens with mild IgA-N compared with normal. We integrate single-cell and spatial transcriptome analyses in human samples of mild IgA-N compared to normal samples, revealing several novel findings: (1) identification of clusters of GE and their expressed gene profiles, (2) identification of novel inflammation-related molecules newly implicated in GE in mild IgA-N, (3) activation of inflammatory pathways characteristic of IgA-N. Therefore, we suggest the importance of GE in the mechanism of IgA-N, as GE initiates more active inflammatory response prior to mesangial cells in patients with mild IgA-N. These findings provide useful information for understanding the pathogenesis and choosing the best drug for IgA nephropathy.

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.

NG03: Spatial RNA-seq reveals intratumor heterogeneity and transcriptional substructure in high-grade ovarian cancers

Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

Single-cell technologies: From research to application

Residual cancer burden (RCB) based on pathologic characteristics is the most powerful prognostic factor in breast cancer (BC) which received neoadjuvant chemotherapy (NAC) followed by surgery. According to RCB classification, class III predicted short survival duration regardless of BC subtypes according to hormone receptor (HR) and human epidermal growth factor receptor-2 (HER-2) state. However, up to 20-40% of BCs with RCB class III did not experience BC recurrence and had long term survival without disease recurrence. In our study, we evaluated genomic characteristics of BC which had high RCB class. We collected 96 fresh frozen BC specimens which had undergone surgery after NAC between 2010 and 2018 from Samsung Medical Center Biobank. We performed whole genome or exome sequencing, RNA sequencing and high-plex single-cell spatial transcriptomics. Of 96 BCs, 65 BCs had experienced BC recurrence and 31 BCs have not been BC recurrence. Of 65 BCs with dismal prognosis, 12 were HR+HER2-, four in HR+HER2+, 39 in triple negative breast cancer (TNBC) and 10 in HR-HER2+ BC. Of 31 BCs without recurrence, 11 were HR+HER2-, two in HR+HER2+, 14 in TNBC and four in HR-HER2+ BC. In HR+HER2- BC, ten of 12 BCs with dismal prognosis were categorized into basal like intrinsic subtype and two were into luminal B subtype compared that six of luminal A and five of luminal B subtype categorization in good prognosis BC group. Age associated mutation signature were enriched in HR+HER2- BC with good prognosis (p=0.027). Tumor microenvironment (TME) analysis using Kassandra suggested that high monocytes were associated with BC with dismal prognosis (p=0.00031). In single-cell spatial transcriptome analysis, T-cell abundance in the tumoral niche is associated with dismal prognosis in HR+HER2- BC (p=0.039). In TNBC, almost BCs (88.7%) were categorized into basal-like intrinsic subtype regardless of their prognosis. TME analysis presented that immune cells including macrophages and lymphocytes with CD4 and CD8 T cells were significantly enriched in TNBC with good prognosis (ps &amp;lt;0.05, respectively). In HER2+ BC, intrinsic subtypes were heterogeneous in both groups. Focal amplification in HER2+BC patient harbored DNA segments from different chromosomes more frequently than in those with good prognosis (p=0.00091), which was prominent in oncogenic extrachromosomal DNA amplifications compared to other oncogenic amplifications. In TME analysis, there were no specific cell enrichments according to BC prognosis but sing-cell spatial transcriptome analysis revealed that higher T-cell abundance in the tumoral niche was associated with HR+HER2+ BC with good prognosis (p=0.047). In conclusion, each BC subtype had their own genomic characteristics to determine their prognosis. Our study also suggested that TME characteristics was common prognostic factor for survival outcome of BC with residual tumor after NAC. Citation Format: Ji-Yeon Kim, Sabin Park, Sepil Ahn, Eun Seop Seo, Soyeon Kim, Mark Gregory, Emily Killingbeck, Eun Yoon Cho, Jeong Eon Lee, Jin Seok Ahn, Yeon Hee Park, Woong-Yang Park, Hoon Kim, Semin Lee, Young-Hyuck Im. Prognostication of genomic characteristics of residual breast cancer after neoadjuvant chemotherapy [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 7644.