Spatial transcriptomics technology in cancer research.

BackgroundTumor infiltrating lymphocytes (TILs) decisively influence the clinical outcomes of immunotherapy. IL-7 is known for its regulatory role in the development and homeostasis of various immune cells, such as T...

BackgroundTertiary lymphoid structures (TLS) are lymphoid aggregates that often form locally in tissues with chronic infection, autoimmune disease, and cancer. As such, TLS correlate with favorable prognosis in patients with...

Tertiary lymphoid structures (TLS) play an important role in the tumor microenvironment (TME) of Head and Neck Cancer (HNC) and have been linked to improved response to immunotherapy. Moreover, HNC patients with human papillomavirus (HPV)+ tumors have better survival compared to those with HPV- tumors, although both groups have better clinical outcomes when the tumors are TLS-rich. How TLS composition, characteristics, and signatures differ between response to treatment in relation to HPV status is still unclear. To investigate this, whole-tissue samples from 55 HNC patients were collected across two separate sites in Brisbane, Australia (The Princess Alexandra Hospital and The Royal Brisbane and Women’s Hospital) prior to immune checkpoint therapy (ICI). Using a multi-pathway panel, covering the ‘hallmarks of cancer’ and ‘TLS-associated proteins,’ we profiled 53 spatially resolved biomarkers using both a PhenoCycler-Fusion panel and 7-plex PSP panel (Akoya Biosciences). Using spatial proteomics, the TLS were assessed for their composition, activity, maturity, and distance metrics from the tumor. Using spatial transcriptomics, we profiled 96 TLS using whole transcriptome profiling (Bruker Spatial Biology). In addition, in patient samples abundant with TLS, we profiled 50 serial sections to obtain and profile the volumetric 3D data. Our study identified various degrees of TLS maturity, proteomic, and transcriptomic profiles associated with TLS localisation within the TME and proximity to the tumor. In addition, we identified TLS signatures associated with clinical endpoints such as response and resistance to ICI therapy. Taken together, this research highlights the multifaceted role of TLS in modulating the immunogenic landscape of HNC, influencing immunotherapy efficacy, and potentially serving as a biomarker for patient stratification and therapeutic strategies. Citation Format: Meg L. Donovan, Habib Sadeghirad, Naomi Berrell, James Monkman, Vahid Yaghoubi Naei, Chin Wee Tan, Joseph Yunis, Zoe West, Majid E. Warkiani, Rahul Ladwa, Brett G. Hughes, Jessica Da Gama Duarte, Arutha Kulasinghe. 2D and 3D spatial characterisation of tertiary lymphoid structures in head and neck cancer patients receiving immune checkpoint blockade therapy [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 5252.

For over 100 years, the traditional tools of pathology, such as tissue-marking dyes (e.g. the H&E stain) have been used to study the disorganization and dysfunction of cells within tissues. This has represented a principal diagnostic and prognostic tool in cancer. However, in the last 5 years, new technologies have promised to revolutionize histopathology, with Spatial Transcriptomics technologies allowing us to measure gene expression directly in pathology-stained tissue sections. In parallel with these developments, Artificial Intelligence (AI) applied to histopathology tissue images now approaches pathologist level performance in cell type identification. However, these new technologies still have severe limitations, with Spatial Transcriptomics suffering difficulties distinguishing transcriptionally similar cell types, and AI-based pathology tools often performing poorly on real world out-of-batch test datasets. Thus, century-old techniques still represent standard-of-care in most areas of clinical cancer diagnostics and prognostics. Here, we present a new frontier in digital pathology: describing a conceptually novel computational methodology, based on Bayesian probabilistic modelling, that allows Spatial Transcriptomics data to be leveraged together with the output of deep learning-based AI used to computationally annotate H&E-stained sections of the same tumor. By leveraging cell-type annotations from multiple independent pathologists, we show that this integrated methodology achieves better performance than any given pathologist’s manual tissue annotation in the task of identifying regions of immune cell infiltration in breast cancer, and easily outperforms either technology alone. We also show that on a subset of histopathology slides examined, the methodology can identify regions of clinically relevant immune cell infiltration that were missed entirely by an initial pathologist’s manual annotation. While this use case has clear diagnostic and prognostic value in cancer (e.g. predicting response to immunotherapy), our methodology is generalizable to any type of pathology images and also has broad applications in spatial transcriptomics data analytics, where most applications (such as identifying cell-cell interactions) rely on correct cell type annotations having been established a priori. We anticipate that this work will spur many follow-up studies, including new computational innovations building on the approach. The work sets the stage for better-than-pathologist performance in other cell-type annotation tasks, with relevant applications in diagnostics and prognostics across almost all cancers. Citation Format: Asif Zubair, Rich Chapple, Sivaraman Natarajan, William C. Wright, Min Pan, Hyeong-Min Lee, Heather Tillman, John Easton, Paul Geeleher. Jointly leveraging spatial transcriptomics and deep learning models for image annotation achieves better-than-pathologist performance in cell type identification in tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 456.

Spatial transcriptomics (ST) has emerged as a powerful tool for analyzing cell-cell communication (CCC) across various biological processes, ranging from embryonic development to cancer progression. However, its limited resolution and high data sparsity hinder the detailed characterization of CCC patterns within complex tissues. Here, we introduce FineST, a deep contrastive learning model that leverages a histology foundation model to fuse ST and histology images, enabling Fine-grained Spatial Transcriptomics analysis. This approach facilitates precise nuclei segmentation, high-resolution RNA expression imputation, and the identification of intricate ligand-receptor interactions. Using both colorectal cancer VisiumHD and breast cancer Xenium datasets, we demonstrate that FineST significantly outperforms existing methods in high-resolution RNA imputation, cell type prediction, and CCC pattern discovery. With focused application to the Visium platform, FineST reveals novel biological insights into tumor-immune interactions across multiple cancer types, including invasive fronts in breast cancer, tertiary lymphoid structures in nasopharyngeal carcinoma, and PD-1 therapy resistance barriers in hepatocellular carcinoma. These findings highlight a new paradigm in ST analysis through the integration of readily available histology images.

The application of spatial transcriptomics technologies to the interrogation of kidney tissue is a burgeoning effort. These technologies share a common purpose in mapping both the expression of individual molecules and entire transcriptomic signatures of kidney cell types and structures. Such information is often superimposed upon a histologic image. The resulting datasets are readily merged with other imaging and transcriptomic techniques to establish a spatially anchored atlas of the kidney. This review provides an overview of the various spatial transcriptomic technologies and recent studies in kidney disease. Potential applications gleaned from the interrogation of other organ systems, but relative to the kidney, are also discussed. Spatial transcriptomic technologies have enabled localization of whole transcriptome mRNA expression, correlation of mRNA to histology, measurement of in situ changes in expression across time, and even subcellular localization of transcripts within the kidney. These innovations continue to aid in the development of human cellular atlases of the kidney, the reclassification of disease, and the identification of important therapeutic targets. Spatial localization of gene expression will complement our current understanding of disease derived from single cell RNA sequencing, histopathology, protein immunofluorescence, and electron microscopy. Although spatial technologies continue to evolve rapidly, their importance in the localization of disease signatures is already apparent. Further efforts are required to integrate whole transcriptome and subcellular expression signatures into the individualized assessment of human kidney disease.

Spatial transcriptomics technologies have been widely applied to decode cellular distribution by resolving gene expression profiles in tissue. However, sequencing techniques still limit the ability to create a fine-resolved spatial cell-type map. To this end, we develop a novel deep-learning-based approach, STASCAN, to predict the spatial cellular distribution of captured or uncharted areas where only histology images are available by cell feature learning integrating gene expression profiles and histology images. STASCAN is successfully applied across diverse datasets from different spatial transcriptomics technologies and displays significant advantages in deciphering higher-resolution cellular distribution and resolving enhanced organizational structures.

Spatial transcriptomics technologies are used to profile transcriptomes while preserving spatial information, which enables high-resolution characterization of transcriptional patterns and reconstruction of tissue architecture. Due to the existence of low-resolution spots in recent spatial transcriptomics technologies, uncovering cellular heterogeneity is crucial for disentangling the spatial patterns of cell types, and many related methods have been proposed. Here, we benchmark 18 existing methods resolving a cellular deconvolution task with 50 real-world and simulated datasets by evaluating the accuracy, robustness, and usability of the methods. We compare these methods comprehensively using different metrics, resolutions, spatial transcriptomics technologies, spot numbers, and gene numbers. In terms of performance, CARD, Cell2location, and Tangram are the best methods for conducting the cellular deconvolution task. To refine our comparative results, we provide decision-tree-style guidelines and recommendations for method selection and their additional features, which will help users easily choose the best method for fulfilling their concerns.

The prognosis for muscle-invasive bladder cancer (MIBC) remains poor, and reliable prognostic markers have yet to be identified. Tertiary lymphoid structures (TLS) have been associated with favorable outcomes in certain cancers. However, the relationship between TLS and MIBC remains unclear. A multi-omics approach is utilized, leveraging single-cell RNA sequencing, spatial transcriptomics, bulk RNA sequencing, and immunohistochemistry, to investigate the roles of B cells and TLS in MIBC. These findings indicate that elevated levels of B cells and TLS correlate with improved prognoses in patients with MIBC, aligning with the robust antitumor immune responses observed in the TLS region. From a mechanistic perspective, CXCL13 serves as a critical cytokine for TLS formation in MIBC, primarily secreted by clonally expanded CXCL13+ T cells. This cytokine interacts with the CXCR5 receptor on NR4A2+ B cells, promoting TLS development. Plasma cells arising within the TLS microenvironment predominantly produce the IGHG antibody, potentially enhancing the phagocytic capabilities of C1QC+ macrophages. From an application standpoint, a TLS-specific gene signature is developed that effectively predicts outcomes in MIBC and other cancers. This study highlights the prognostic potential of TLS in MIBC and reveals immune mechanisms, offering insights for personalized treatment strategies.

Background: In pancreatic ductal adenocarcinoma (PDAC), rare long-term survivors correlate with high intratumoral tertiary lymphoid structure (TLS) density. This finding prompted our investigation of clinically viable strategies to induce TLS in patients with immune-excluded tumors. We previously reported the induction of intratumoral TLS following administration of a neoadjuvant GM-CSF-secreting allogeneic vaccine (GVAX) to PDAC patients (NCT00727441). However, no clinical benefit was observed, likely owing to immune tolerance mechanisms governing the PDAC tumor microenvironment (TME). We previously observed upregulation of both the PD-1 and 4-1BB pathways with GVAX, and thus in a subsequent neoadjuvant trial combined GVAX with PD-1 blockade and 4-1BB agonism (NCT02451982) which was associated with pathologic responses. We hypothesized this combination strategy induced TLS of higher maturity and anti-tumor activity compared to GVAX alone. Methods: To explore how this therapeutic strategy affected TLS morphology and intercellular crosstalk, we leveraged the Visium spatial transcriptomics platform and a 35-marker customized TLS panel for imaging mass cytometry. We generated cellular and molecular maps of the TME after neoadjuvant treatment in 26 PDAC patients (GVAX n=19, GVAX+aPD1 n=2, GVAX+aPD1+a41BB n=5). To compare TLS maturation in parallel with secondary lymphoid organ-mediated tumor immunity, we also profiled tumor-adjacent lymph nodes. We applied unsupervised learning with non-negative matrix factorization (NMF) and trained AI-enabled image classification models to characterize cellular states within tissue structures of interest. Results: We identified spatial gene expression NMF patterns in PDAC TLS spanning across distinct morphologies and neoadjuvant treatment arms. Intratumoral TLS after GVAX were found to propagate activated B cells expressing immunoglobulins that infiltrated into malignant cellular niches. TLS NMF patterns were also associated with autoimmune disease signatures, such as diabetes, in a subset of patients. We scored TLS using tumor-draining lymph nodes as a reference and found increased maturation of TLS after PD-1 blockade, while addition of 4-1BB agonism significantly boosted the cytotoxic NK/T cell compartment compared to GVAX alone. Conclusions: We present machine learning approaches for spatial multi-omics analysis to characterize the TLS-enriched TME. We mined genome-wide spatial TLS gene expression patterns elucidating the spatial dynamics of humoral immunity of rare immunotherapy pathologic responders in PDAC. Altogether our findings shed light on the plasticity of TLS in neoadjuvant immunotherapy and suggest future immunotherapy approaches should target both humoral and cytotoxic NK/T cell compartments to augment responses in solid tumors. Citation Format: Dimitrios N. Sidiropoulos, Sarah M. Shin, Alexander Girgis, Daniel H. Shu, Janelle Montagne, Atul Deshpande, Jeanette A. Johnson, Lucie Dequiedt, Victoria Jacobs, Aleksandra Ogurtsova, Guanglan Mo, Xuan Yuan, Genevieve Stein-O’Brien, Mark Yarchoan, Qingfeng Zhu, Ashley Kiemen, Elizabeth M. Jaffee, Lei Zheng, Won Jin Ho, Robert Anders, Elana J. Fertig, Luciane T. Kagohara. Machine learning integrating spatial omics uncovers humoral immunity patterns in intratumoral tertiary lymphoid structures in pancreatic cancer pathologic responders [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 1159.

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.

Global deaths attributed to gastric cancer (GC) are increasing, yet our understanding of it remains limited. Recently, single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics have provided important insights into the dissection of metastasis-related biological processes in subpopulations of cells in the GC tumor microenvironment, especially intratumoral cellular heterogeneity and cell-cell interactions. In this review, we discuss the mechanisms underlying GC metastasis and potential strategies for developing upcoming immunotherapies by combining advances in scRNA-seq 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.

Over the past century, studying the human brain has been one of the most complex and enduring biological challenges. Initial approaches, ranging from gross neural anatomy to cellular subtype organization, have significantly advanced our understanding of the intricate structure of the human brain. Recent innovations in spatial transcriptomic technologies offer high-resolution insights into mRNA expression at single-cell or even subcellular resolution. Developing a greater understanding of the spatial expression of genes in specific cell types in the human brain can provide additional insights into their functions and underlying mechanisms that influence neurological disease states. Although these tools have been highly successful in rodent and non-human primate brains, analysis of the human brain has several specific challenges. In this review, we initially provide a comparison of spatial transcriptomic tools, followed by a summary of studies using these tools in human brains, and finally, we discuss the associated challenges and opportunities. The guidelines should enable researchers to address the challenges of using new spatial transcriptomic technologies to analyse complex organs, such as the human brain.

Macrophages are central players of inflammation, lipid metabolism, and remodeling in atherosclerotic plaques. Historically simplified into "M1" and "M2" polarization states, their biology has been fundamentally redefined by single-cell and spatial transcriptomic technologies. Over the past decade, these approaches have identified multiple macrophage subsets within human atheromas, each driven by distinct metabolic and cytokine signatures and occupying discrete spatial niches. Human single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multimodal omic profiling collectively demonstrate that macrophage subsets extend far beyond fixed polarization states to engage their long-recognized functions in the atheroma, including inflammation, lipid handling and repair. These findings now link macrophage identity to microenvironmental cues, vascular location, and disease stage. Importantly, these data demonstrate that these macrophages do not exist in mutually exclusive states and can transition between these subtypes in response to these aforementioned factors. Here we synthesize these advances, focusing on human data describing macrophage diversity, spatial organization, and metabolic function, and discuss how this knowledge is reshaping mechanistic models of atherosclerosis and the potential therapeutic targeting of macrophage-mediated pathology.