Spatial transcriptomics technology in cancer research

Tumor-infiltrating myeloid cells; mechanisms, functional significance, and targeting in cancer therapy

Tumor-infiltrating myeloid cells (TIMs), which encompass tumor-associated macrophages (TAMs), tumor-associated neutrophils (TANs), myeloid-derived suppressor cells (MDSCs), and tumor-associated dendritic cells (TADCs), are of great importance in tumor microenvironment (TME) and are integral to both pro- and anti-tumor immunity. Nevertheless, the phenotypic heterogeneity and functional plasticity of TIMs have posed challenges in fully understanding their complexity roles within the TME. Emerging evidence suggested that the presence of TIMs is frequently linked to prevention of cancer treatment and improvement of patient outcomes and survival. Given their pivotal function in the TME, TIMs have recently been recognized as critical targets for therapeutic approaches aimed at augmenting immunostimulatory myeloid cell populations while depleting or modifying those that are immunosuppressive. This review will explore the important properties of TIMs related to immunity, angiogenesis, and metastasis. We will also document the latest therapeutic strategies targeting TIMs in preclinical and clinical settings. Our objective is to illustrate the potential of TIMs as immunological targets that may improve the outcomes of existing cancer treatments.

GlycoRNA: A new player in cellular communication

The discovery of glycosylated RNA molecules, known as glycoRNAs, introduces a novel dimension to cellular biology. This commentary explores the transformative findings surrounding glycoRNAs, emphasizing their unique roles in cancer progression and the therapeutic opportunities they present. GlycoRNAs, through interactions with lectins and immune receptors, may contribute to tumor immune evasion. Moreover, the therapeutic potential of this emerging knowledge includes interventions targeting glycoRNA synthesis and modulation of associated signaling pathways. By highlighting these critical insights, this commentary aims to encourage the development of innovative strategies that could improve cancer prognosis and treatment.

Flexible integration of spatial and expression information for precise spot embedding via ZINB-based graph-enhanced autoencoder

Domain identification is a critical problem in spatially resolved transcriptomics data analysis, which aims to identify distinct spatial domains within a tissue that maintain both spatial continuity and expression consistency. The degree of coupling between expression data and spatial information in different datasets often varies significantly. Some regions have intact and clear boundaries, while others exhibit blurred boundaries with high intra-domain expression similarity. However, most domain identification methods do not adequately integrate expression and spatial information to flexibly identify different types of domains. To address these issues, we introduce Spot2vector, a computational framework that leverages a graph-enhanced autoencoder integrating zero-inflated negative binomial distribution modeling, combining both graph convolutional networks and graph attention networks to extract the latent embeddings of spots. Spot2vector encodes and integrates spatial and expression information, enabling effective identification of domains with diverse spatial patterns across spatially resolved transcriptomics data generated by different platforms. The decoders enable us to decipher the distribution and generation mechanisms of data while improving expression quality through denoising. Extensive validation and analyses demonstrate that Spot2vector excels in enhancing domain identification accuracy, effectively reducing data dimensionality, improving expression recovery and denoising, and precisely capturing spatial gene expression patterns.

Single-cell sequencing and spatial transcriptomics reveal the evolution of glucose metabolism in hepatocellular carcinoma and identify G6PD as a potential therapeutic target

Background

Glucose metabolism reprogramming provides significant insights into the development and progression of malignant tumors. This study aims to explore the temporal-spatial evolution of the glucose metabolism in HCC using single-cell sequencing and spatial transcriptomics (ST), and validates G6PD as a potential therapeutic target for HCC.

Methods

We collected single-cell sequencing data from 7 HCC and adjacent non-cancerous tissues from the GSE149614 database, and ST data from 4 HCC tissues from the HRA000437 database. Pseudotime analysis was performed on the single-cell data, while ST data was used to analyze spatial metabolic activity. High-throughput sequencing and experiments, including wound healing, CCK-8, and transwell assays, were conducted to validate the role and regulatory mechanisms of G6PD in HCC.

Results

Our study identified a progressive upregulation of PPP-related genes during tumorigenesis. ST analysis revealed elevated PPP metabolic scores in the central and intermediate tumor regions compared to the peripheral zones. High-throughput sequencing and experimental validation further suggested that G6PD-mediated regulation of HCC cell proliferation, migration, and invasion is likely associated with glutathione metabolism and ROS production. Finally, Cox regression analysis cofirmed G6PD as an independent prognostic factor for overall survival in HCC patients.

Conclusion

Our study provides novel insights into the changes in glucose metabolism in HCC from both temporal and spatial perspectives. We experimentally demonstrated that G6PD regulates proliferation, migration, and invasion in HCC and propose G6PD as a prognostic marker and therapeutic metabolic target for the HCC.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

Spatial genomics reveals cholesterol metabolism as a key factor in colorectal cancer immunotherapy resistance

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape across multiple cancer types achieving durable responses for a significant number of patients. Despite their success, many patients still fail to respond to ICIs or develop resistance soon after treatment. We sought to identify early treatment features associated with ICI outcome. We leveraged the MC38 syngeneic tumor model because it has variable response to ICI therapy driven by tumor intrinsic heterogeneity. ICI response was assessed based on the level of immune cell infiltration into the tumor - a well-established clinical hallmark of ICI response. We generated a spatial atlas of 48,636 transcriptome-wide spots across 16 tumors using spatial transcriptomics; given the tumors were difficult to profile, we developed an enhanced transcriptome capture protocol yielding high quality spatial data. In total, we identified 8 tumor cell subsets (e.g., proliferative, inflamed, and vascularized) and 4 stroma subsets (e.g., immune and fibroblast). Each tumor had orthogonal histology and bulk-RNA sequencing data, which served to validate and benchmark observations from the spatial data. Our spatial atlas revealed that increased tumor cell cholesterol regulation, synthesis, and transport were associated with a lack of ICI response. Conversely, inflammation and T cell infiltration were associated with response. We further leveraged spatially aware gene expression analysis, to demonstrate that high cholesterol synthesis by tumor cells was associated with cytotoxic CD8 T cell exclusion. Finally, we demonstrate that bulk RNA-sequencing was able to detect immune correlates of response but lacked the sensitivity to detect cholesterol synthesis as a feature of resistance.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

Precision oncology: Using cancer genomics for targeted therapy advancements

Cancer genomics plays a crucial role in oncology by enhancing our understanding of how genes drive cancer and facilitating the development of improved treatments. This field meticulously examines various cancers' genetic makeup through various methodologies, leading to groundbreaking discoveries. Innovative tools such as rapid gene sequencing, single-cell studies, spatial gene mapping, epigenetic analysis, liquid biopsies, and computational modeling have significantly progressed the field. These techniques uncover genetic alterations, tumor heterogeneity, and the evolutionary dynamics of cancers. Genetic abnormalities and molecular markers that initiate and propagate distinct cancer types are classified according to tumor type. The integration of precision medicine with cancer genomics emphasizes the significance of utilizing genetic data in treatment decision-making, enabling personalized care and enhancing patient outcomes. Critical topics in cancer genomics encompass tumor diversity, alterations in non-coding DNA, epigenetic modifications, cancer-specific proteins, metabolic changes, and the impact of inherited genes on cancer risk.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Application of Spatial Transcriptomics in Digestive System Tumors

In the field of digestive system tumor research, spatial transcriptomics technologies are used to delve into the spatial structure and the spatial heterogeneity of tumors and to analyze the tumor microenvironment (TME) and the inter-cellular interactions within it by revealing gene expression in tumors. These technologies are also instrumental in the diagnosis, prognosis, and treatment of digestive system tumors. This review provides a concise introduction to spatial transcriptomics and summarizes recent advances, application prospects, and technical challenges of these technologies in digestive system tumor research. This review also discusses the importance of combining spatial transcriptomics with single-cell RNA sequencing (scRNA-seq), artificial intelligence, and machine learning in digestive system cancer research.

Predicting the tumor microenvironment composition and immunotherapy response in non-small cell lung cancer from digital histopathology images

Immune checkpoint inhibitors (ICI) have become integral to treatment of non-small cell lung cancer (NSCLC). However, reliable biomarkers predictive of immunotherapy efficacy are limited. Here, we introduce HistoTME, a novel weakly supervised deep learning approach to infer the tumor microenvironment (TME) composition directly from histopathology images of NSCLC patients. We show that HistoTME accurately predicts the expression of 30 distinct cell type-specific molecular signatures directly from whole slide images, achieving an average Pearson correlation of 0.5 with the ground truth on independent tumor cohorts. Furthermore, we find that HistoTME-predicted microenvironment signatures and their underlying interactions improve prognostication of lung cancer patients receiving immunotherapy, achieving an AUROC of 0.75 [95% CI: 0.61-0.88] for predicting treatment responses following first-line ICI treatment, utilizing an external clinical cohort of 652 patients. Collectively, HistoTME presents an effective approach for interrogating the TME and predicting ICI response, complementing PD-L1 expression, and bringing us closer to personalized immuno-oncology.

Current state and future prospects of spatial biology in colorectal cancer

Over the past century, colorectal cancer (CRC) has become one of the most devastating cancers impacting the human population. To gain a deeper understanding of the molecular mechanisms driving this solid tumor, researchers have increasingly turned their attention to the tumor microenvironment (TME). Spatial transcriptomics and proteomics have emerged as a particularly powerful technology for deciphering the complexity of CRC tumors, given that the TME and its spatial organization are critical determinants of disease progression and treatment response. Spatial transcriptomics enables high-resolution mapping of the whole transcriptome. While spatial proteomics maps protein expression and function across tissue sections. Together, they provide a detailed view of the molecular landscape and cellular interactions within the TME. In this review, we delve into recent advances in spatial biology technologies applied to CRC research, highlighting both the methodologies and the challenges associated with their use, such as the substantial tissue heterogeneity characteristic of CRC. We also discuss the limitations of current approaches and the need for novel computational tools to manage and interpret these complex datasets. To conclude, we emphasize the importance of further developing and integrating spatial transcriptomics into CRC precision medicine strategies to enhance therapeutic targeting and improve patient outcomes.

Deciphering the effects of radiopharmaceutical therapy in the tumor microenvironment of prostate cancer: an in-silico exploration with spatial transcriptomics

Radiopharmaceutical therapy (RPT) is an emerging prostate cancer treatment that delivers radiation to specific molecules within the tumor microenvironment (TME), causing DNA damage and cell death. Given TME heterogeneity, it's crucial to explore RPT dosimetry and biological impacts at the cellular level. We integrated spatial transcriptomics (ST) with computational modeling to investigate the effects of RPT targeting prostate-specific membrane antigen (PSMA), fibroblast activation protein (FAP), and gastrin-releasing peptide receptor (GRPR) each labelled with beta-emitting lutetium-177 (177Lu) and alpha-emitting actinium-225 (225Ac). Methods: Three ST datasets from primary tissue samples of two prostate cancer patients were obtained. From these datasets, we extracted gene expressions, including FOLH1, GRPR, FAP, and Harris Hypoxia, and estimated the proportions of different cell types-epithelial, endothelial, and prostate cancer (PC) cells-in the corresponding ST spots. We computed the spatiotemporal distribution of each RPT targeting PSMA, FAP, and GRPR at each ST spot by solving the partial differential equation (PDE) using a convection-reaction-diffusion (CRD) model, assuming similar pharmacokinetic parameters across all ligands. A well-established physiologically based pharmacokinetic (PBPK) model was used to simulate the input function in the prostate, carefully calibrated to deliver 10 Gy to the prostate tumor over 20 days. Dosimetry was estimated using the Medical Internal Radiation Dose (MIRD) formalism, applying the dose point kernels (DVK) method. The survival probability was estimated using the linear quadratic model, applied to both beta-emitting RPT labeled with 177Lu and 225Ac. A modified linear quadratic model was used to estimate the bioeffect of the alpha-emitting RPT. Results: The results demonstrate distinct dose-response and efficacy patterns across ST samples, with FAP-targeted RPT exhibiting limited effectiveness in tumor cell-rich areas compared to PSMA- and GRPR-targeted therapies. GRPR-targeted RPT showed higher resistance in hypoxic regions relative to the other therapies. Additionally, 225Ac-labeled RPT was more effective overall than 177Lu-labeled RPT, especially in areas with low cancer-cell fraction or high hypoxia. The findings suggest that a combination of 225Ac-labeled FAP- and PSMA-targeted RPT offers the best therapeutic strategy. Conclusion: The proposed method, which combines ST and computational modeling to determine the dosimetry and cell survival probability of RPT in the TME, holds promise for identifying optimal personalized RPT strategies.

Genome analysis through image processing with deep learning models

Genomic sequences are traditionally represented as strings of characters: A (adenine), C (cytosine), G (guanine), and T (thymine). However, an alternative approach involves depicting sequence-related information through image representations, such as Chaos Game Representation (CGR) and read pileup images. With rapid advancements in deep learning (DL) methods within computer vision and natural language processing, there is growing interest in applying image-based DL methods to genomic sequence analysis. These methods involve encoding genomic information as images or integrating spatial information from images into the analytical process. In this review, we summarize three typical applications that use image processing with DL models for genome analysis. We examine the utilization and advantages of these image-based approaches.

Cross-expression analysis reveals patterns of coordinated gene expression in spatial transcriptomics

Spatial transcriptomics promises to transform our understanding of tissue biology by molecularly profiling individual cells in situ. A fundamental question they allow us to ask is how nearby cells orchestrate their gene expression. To investigate this, we introduce cross-expression, a novel framework for discovering gene pairs that coordinate their expression across neighboring cells. Just as co-expression quantifies synchronized gene expression within the same cells, cross-expression measures coordinated gene expression between spatially adjacent cells, allowing us to understand tissue gene expression programs with single cell resolution. Using this framework, we recover ligand-receptor partners and discover gene combinations marking anatomical regions. More generally, we create cross-expression networks to find gene modules with orchestrated expression patterns. Finally, we provide an efficient R package to facilitate cross-expression analysis, quantify effect sizes, and generate novel visualizations to better understand spatial gene expression programs.

Spatial profiling of non-small cell lung cancer provides insights into tumorigenesis and immunotherapy response

Lung cancer is the second most common cancer worldwide and a leading cause of cancer-related deaths. Despite advances in targeted therapy and immunotherapy, the prognosis remains unfavorable, especially in metastatic cases. This study aims to identify molecular changes in non-small cell lung cancer (NSCLC) patients based on their response to treatment. Using tumor and matched immune cell rich peritumoral tissues, we perform a retrospective, comprehensive spatial transcriptomic analysis of a proven malignant NSCLC sample treated with immune checkpoint inhibitor (ICI). In addition to T cells, other immune cell types, such as B cells and macrophages, were also activated in responders to ICI treatment. In particular, B cells and B cell-mediated immunity pathways are consistently found to be activated. Analysis of the histologic subgroup (lung squamous cell carcinoma, LUSC; lung adenocarcinoma, LUAD) of NSCLC also confirms activation of B cell mediated immunity. Analysis of B cell subtypes shows that B cell subtypes were more activated in immune cell-rich tissues near tumor tissue. Furthermore, increased expression of B cell immunity-related genes is associated with better prognosis. These findings provide insight into predicting ICI treatment responses and identifying appropriate candidates for immunotherapy in NSCLC patients.

Crosstalk between tumor and microenvironment: Insights from spatial transcriptomics

Cancer is a dynamic disease, and clonal heterogeneity plays a fundamental role in tumor development, progression, and resistance to therapies. Single-cell and spatial multimodal technologies can provide a high-resolution molecular map of underlying genomic, epigenomic, and transcriptomic alterations involved in inter- and intra-tumor heterogeneity and interactions with the microenvironment. In this review, we provide a perspective on factors driving cancer heterogeneity, tumor evolution, and clonal states. We briefly describe spatial transcriptomic technologies and summarize recent literature that sheds light on the dynamical interactions between tumor states, cell-to-cell communication, and remodeling local microenvironment.

Applications of spatial transcriptomics and artificial intelligence to develop integrated management of pancreatic cancer

Cancer is a complex disease intrinsically associated with cellular processes and gene expression. With the development of techniques such as single-cell sequencing and sequential fluorescence in situ hybridization (seqFISH), it was possible to map the location of cells based on their gene expression with more precision. Moreover, in recent years, many tools have been developed to analyze these extensive datasets by integrating machine learning and artificial intelligence in a comprehensive manner. Since these tools analyze sequencing data, they offer the chance to analyze any tissue regardless of its origin. By applying this to cancer settings, spatial transcriptomic analysis based on artificial intelligence may help us understand cell-cell communications within the tumor microenvironment. Another advantage of this analysis is the identification of new biomarkers and therapeutic targets. The integration of such analysis with other omics data and with routine exams such as magnetic resonance imaging can help physicians with the earlier diagnosis of tumors as well as establish a more personalized treatment for pancreatic cancer patients. In this review, we give an overview description of pancreatic cancer, describe how spatial transcriptomics and artificial intelligence have been used to study pancreatic cancer and provide examples of how integrating these tools may help physicians manage pancreatic cancer in a more personalized approach.

Data enhancement in the age of spatial biology

Unveiling the intricate interplay of cells in their native environment lies at the heart of understanding fundamental biological processes and unraveling disease mechanisms, particularly in complex diseases like cancer. Spatial transcriptomics (ST) offers a revolutionary lens into the spatial organization of gene expression within tissues, empowering researchers to study both cell heterogeneity and microenvironments in health and disease. However, current ST technologies often face limitations in either resolution or the number of genes profiled simultaneously. Integrating ST data with complementary sources, such as single-cell transcriptomics and detailed tissue staining images, presents a powerful solution to overcome these limitations. This review delves into the computational approaches driving the integration of spatial transcriptomics with other data types. By illuminating the key challenges and outlining the current algorithmic solutions, we aim to highlight the immense potential of these methods to revolutionize our understanding of cancer biology.

Single-cell sequencing, spatial transcriptome ad periodontitis: Rethink pathogenesis and classification

OBJECTIVE

This narrative review illuminates on the application of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) in periodontitis and highlights the probability of relating cell population and gene signatures to the pathogenesis of the disease for a better diagnosis.

METHODS

An electronic search of the literature in the PubMed database for the keywords, "single cell sequencing" OR "spatial transcriptomics" and "periodontitis" OR "gingiva" OR "oral mucosa" yielded 486 research articles and reviews. After filtering duplicates and careful curation, 22 papers conducted in humans were retained.

RESULTS

The molecular mechanisms underlying periodontitis are complex and involve the interaction of multiple cells and various gene expressions. Most residing cells in periodontal tissues participate in maintaining homeostasis and health, while in addition to infiltrating immune cells contribute to the fight against the bacterial insult.

CONCLUSION

scRNA-seq and ST have provided new insights into the cellular and molecular changes associated with periodontitis for a better diagnosis and clinical outcome. New functions of cells and genes are revealed with these techniques; however, no cells or gene signatures are attributed to periodontitis so far.

From pre-clinical to translational brain metastasis research: current challenges and emerging opportunities

Brain metastasis, characterized by poor clinical outcomes, is a devastating disease. Despite significant mechanistic and therapeutic advances in recent years, pivotal improvements in clinical interventions have remained elusive. The heterogeneous nature of the primary tumor of origin, complications in drug delivery across the blood-brain barrier, and the distinct microenvironment collectively pose formidable clinical challenges in developing new treatments for patients with brain metastasis. Although current preclinical models have deepened our basic understanding of the disease, much of the existing research on brain metastasis has employed a reductionist approach. This approach, which often relies on either in vitro systems or in vivo injection models in young and treatment-naive mouse models, does not give sufficient consideration to the clinical context. Given the translational importance of brain metastasis research, we advocate for the design of preclinical experimental models that take into account these unique clinical challenges and align more closely with current clinical practices. We anticipate that aligning and simulating real-world patient conditions will facilitate the development of more translatable treatment regimens. This brief review outlines the most pressing clinical challenges, the current state of research in addressing them, and offers perspectives on innovative metastasis models and tools aimed at identifying novel strategies for more effective management of clinical brain metastasis.

Facts and Hopes in Using Omics to Advance Combined Immunotherapy Strategies

The field of oncology has been transformed by immune checkpoint inhibitors (ICI) and other immune-based agents; however, many patients do not receive a durable benefit. While biomarker assessments from pivotal ICI trials have uncovered certain mechanisms of resistance, results thus far have only scraped the surface. Mechanisms of resistance are as complex as the tumor microenvironment (TME) itself, and the development of effective therapeutic strategies will only be possible by building accurate models of the tumor-immune interface. With advancement of multi-omic technologies, high-resolution characterization of the TME is now possible. In addition to sequencing of bulk tumor, single-cell transcriptomic, proteomic, and epigenomic data as well as T-cell receptor profiling can now be simultaneously measured and compared between responders and nonresponders to ICI. Spatial sequencing and imaging platforms have further expanded the dimensionality of existing technologies. Rapid advancements in computation and data sharing strategies enable development of biologically interpretable machine learning models to integrate data from high-resolution, multi-omic platforms. These models catalyze the identification of resistance mechanisms and predictors of benefit in ICI-treated patients, providing scientific foundation for novel clinical trials. Moving forward, we propose a framework by which in silico screening, functional validation, and clinical trial biomarker assessment can be used for the advancement of combined immunotherapy strategies.

The glycosylation landscape of prostate cancer tissues and biofluids

An overview of the role of glycosylation in prostate cancer (PCa) development and progression is presented, focusing on recent advancements in defining the N-glycome through glycomic profiling and glycoproteomic methodologies. Glycosylation is a common post-translational modification typified by oligosaccharides attached N-linked to asparagine or O-linked to serine or threonine on carrier proteins. These attached sugars have crucial roles in protein folding and cellular recognition processes, such that altered glycosylation is a hallmark of cancer pathogenesis and progression. In the past decade, advancements in N-glycan profiling workflows using Matrix Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) technology have been applied to define the spatial distribution of glycans in PCa tissues. Multiple studies applying N-glycan MALDI-MSI to pathology-defined PCa tissues have identified significant alterations in N-glycan profiles associated with PCa progression. N-glycan compositions progressively increase in number, and structural complexity due to increased fucosylation and sialylation. Additionally, significant progress has been made in defining the glycan and glycopeptide compositions of prostatic-derived glycoproteins like prostate-specific antigen in tissues and biofluids. The glycosyltransferases involved in these changes are potential drug targets for PCa, and new approaches in this area are summarized. These advancements will be discussed in the context of the further development of clinical diagnostics and therapeutics targeting glycans and glycoproteins associated with PCa progression. Integration of large scale spatial glycomic data for PCa with other spatial-omic methodologies is now feasible at the tissue and single-cell levels.

SPADE: spatial deconvolution for domain specific cell-type estimation

Understanding gene expression in different cell types within their spatial context is a key goal in genomics research. SPADE (SPAtial DEconvolution), our proposed method, addresses this by integrating spatial patterns into the analysis of cell type composition. This approach uses a combination of single-cell RNA sequencing, spatial transcriptomics, and histological data to accurately estimate the proportions of cell types in various locations. Our analyses of synthetic data have demonstrated SPADE's capability to discern cell type-specific spatial patterns effectively. When applied to real-life datasets, SPADE provides insights into cellular dynamics and the composition of tumor tissues. This enhances our comprehension of complex biological systems and aids in exploring cellular diversity. SPADE represents a significant advancement in deciphering spatial gene expression patterns, offering a powerful tool for the detailed investigation of cell types in spatial transcriptomics.

Beyond genetics: driving cancer with the tumour microenvironment behind the wheel

Cancer has long been viewed as a genetic disease of cumulative mutations. This notion is fuelled by studies showing that ageing tissues are often riddled with clones of complex oncogenic backgrounds coexisting in seeming harmony with their normal tissue counterparts. Equally puzzling, however, is how cancer cells harbouring high mutational burden contribute to normal, tumour-free mice when allowed to develop within the confines of healthy embryos. Conversely, recent evidence suggests that adult tissue cells expressing only one or a few oncogenes can, in some contexts, generate tumours exhibiting many of the features of a malignant, invasive cancer. These disparate observations are difficult to reconcile without invoking environmental cues triggering epigenetic changes that can either dampen or drive malignant transformation. In this Review, we focus on how certain oncogenes can launch a two-way dialogue of miscommunication between a stem cell and its environment that can rewire downstream events non-genetically and skew the morphogenetic course of the tissue. We review the cells and molecules of and the physical forces acting in the resulting tumour microenvironments that can profoundly affect the behaviours of transformed cells. Finally, we discuss possible explanations for the remarkable diversity in the relative importance of mutational burden versus tumour microenvironment and its clinical relevance.

Applications of single‑cell omics and spatial transcriptomics technologies in gastric cancer (Review)

Gastric cancer (GC) is a prominent contributor to global cancer-related mortalities, and a deeper understanding of its molecular characteristics and tumor heterogeneity is required. Single-cell omics and spatial transcriptomics (ST) technologies have revolutionized cancer research by enabling the exploration of cellular heterogeneity and molecular landscapes at the single-cell level. In the present review, an overview of the advancements in single-cell omics and ST technologies and their applications in GC research is provided. Firstly, multiple single-cell omics and ST methods are discussed, highlighting their ability to offer unique insights into gene expression, genetic alterations, epigenomic modifications, protein expression patterns and cellular location in tissues. Furthermore, a summary is provided of key findings from previous research on single-cell omics and ST methods used in GC, which have provided valuable insights into genetic alterations, tumor diagnosis and prognosis, tumor microenvironment analysis, and treatment response. In summary, the application of single-cell omics and ST technologies has revealed the levels of cellular heterogeneity and the molecular characteristics of GC, and holds promise for improving diagnostics, personalized treatments and patient outcomes in GC.

Updates in Skin Cancer in Transplant Recipients and Immunosuppressed Patients: Review of the 2022-2023 Scientific Symposium of the International Immunosuppression and Transplant Skin Cancer Collaborative

The International Immunosuppression and Transplant Skin Cancer Collaborative (ITSCC) and its European counterpart, Skin Care in Organ Transplant Patients-Europe (SCOPE) are comprised of physicians, surgeons, and scientist who perform integrative collaborative research focused on cutaneous malignancies that arise in solid organ transplant recipients (SOTR) and patients with other forms of long-term immunosuppression. In October 2022, ITSCC held its biennial 4-day scientific symposium in Essex, Massachusetts. This meeting was attended by members of both ITSCC and SCOPE and consisted of specialists including Mohs micrographic and dermatologic oncology surgeons, medical dermatologists, transplant dermatologists, transplant surgeons, and transplant physicians. During this symposium scientific workshop groups focusing on consensus standards for case reporting of retrospective series for invasive squamous cell carcinoma (SCC), defining immunosuppressed patient status for cohort reporting, development of multi-institutional registry for reporting rare tumors, and development of a KERACON clinical trial of interventions after a SOTRs' first cutaneous SCC were developed. The majority of the symposium focused on presentation of the most up to date research in cutaneous malignancy in SOTR and immunosuppressed patients with specific focus on chemoprevention, immunosuppression regimens, immunotherapy in SOTRs, spatial transcriptomics, and the development of cutaneous tumor registries. Here, we present a summary of the most impactful scientific updates presented at the 2022 ITSCC symposium.

Review of Molecular Technologies for Investigating Canine Cancer

Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

Current Advances in the Use of Tissue Engineering for Cancer Metastasis Therapeutics

Cancer is a leading cause of death worldwide and results in nearly 10 million deaths each year. The global economic burden of cancer from 2020 to 2050 is estimated to be USD 25.2 trillion. The spread of cancer to distant organs through metastasis is the leading cause of death due to cancer. However, as of today, there is no cure for metastasis. Tissue engineering is a promising field for regenerative medicine that is likely to be able to provide rehabilitation procedures to patients who have undergone surgeries, such as mastectomy and other reconstructive procedures. Another important use of tissue engineering has emerged recently that involves the development of realistic and robust in vitro models of cancer metastasis, to aid in drug discovery and new metastasis therapeutics, as well as evaluate cancer biology at metastasis. This review covers the current studies in developing tissue-engineered metastasis structures. This article reports recent developments in in vitro models for breast, prostate, colon, and pancreatic cancer. The review also identifies challenges and opportunities in the use of tissue engineering toward new, clinically relevant therapies that aim to reduce the cancer burden.

Spatially Exploring RNA Biology in Archival Formalin-Fixed Paraffin-Embedded Tissues

Spatial transcriptomics has emerged as a powerful tool for dissecting spatial cellular heterogeneity but as of today is largely limited to gene expression analysis. Yet, the life of RNA molecules is multifaceted and dynamic, requiring spatial profiling of different RNA species throughout the life cycle to delve into the intricate RNA biology in complex tissues. Human disease-relevant tissues are commonly preserved as formalin-fixed and paraffin-embedded (FFPE) blocks, representing an important resource for human tissue specimens. The capability to spatially explore RNA biology in FFPE tissues holds transformative potential for human biology research and clinical histopathology. Here, we present Patho-DBiT combining in situ polyadenylation and deterministic barcoding for spatial full coverage transcriptome sequencing, tailored for probing the diverse landscape of RNA species even in clinically archived FFPE samples. It permits spatial co-profiling of gene expression and RNA processing, unveiling region-specific splicing isoforms, and high-sensitivity transcriptomic mapping of clinical tumor FFPE tissues stored for five years. Furthermore, genome-wide single nucleotide RNA variants can be captured to distinguish different malignant clones from non-malignant cells in human lymphomas. Patho-DBiT also maps microRNA-mRNA regulatory networks and RNA splicing dynamics, decoding their roles in spatial tumorigenesis trajectory. High resolution Patho-DBiT at the cellular level reveals a spatial neighborhood and traces the spatiotemporal kinetics driving tumor progression. Patho-DBiT stands poised as a valuable platform to unravel rich RNA biology in FFPE tissues to study human tissue biology and aid in clinical pathology evaluation.

Spatial Transcriptomic Profiling of Tetraspanins in Stage 4 Colon Cancer from Primary Tumor and Liver Metastasis

Stage 4 colon cancer (CC) presents a significant global health challenge due to its poor prognosis and limited treatment options. Tetraspanins, the transmembrane proteins involved in crucial cancer processes, have recently gained attention as diagnostic markers and therapeutic targets. However, their spatial expression and potential roles in stage 4 CC tissues remain unknown. Using the GeoMx digital spatial profiler, we profiled all 33 human tetraspanin genes in 48 areas within stage 4 CC tissues, segmented into immune, fibroblast, and tumor compartments. Our results unveiled diverse gene expression patterns across different primary tumor sub-regions. CD53 exhibited distinct overexpression in the immune compartment, hinting at a potential role in immune modulation. TSPAN9 was specifically overexpressed in the fibroblast compartment, suggesting involvement in tumor invasion and metastasis. CD9, CD151, TSPAN1, TSPAN3, TSPAN8, and TSPAN13 displayed specific overexpression in the tumor compartment, indicating potential roles in tumor growth. Furthermore, our differential analysis revealed significant spatial changes in tetraspanin expression between patient-matched stage 4 primary CC and metastatic liver tissues. These findings provide spatially resolved insights into the expression and potential roles of tetraspanins in stage 4 CC progression, proposing their utility as diagnostic markers and therapeutic targets. Understanding this landscape is beneficial for tailoring therapeutic strategies to specific sub-tumor regions in the context of stage 4 CC and liver metastasis.

Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling

In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics

Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.

Lineage Plasticity and Stemness Phenotypes in Prostate Cancer: Harnessing the Power of Integrated "Omics" Approaches to Explore Measurable Metrics

Prostate cancer (PCa), the most frequent and second most lethal cancer type in men in developed countries, is a highly heterogeneous disease. PCa heterogeneity, therapy resistance, stemness, and lethal progression have been attributed to lineage plasticity, which refers to the ability of neoplastic cells to undergo phenotypic changes under microenvironmental pressures by switching between developmental cell states. What remains to be elucidated is how to identify measurements of lineage plasticity, how to implement them to inform preclinical and clinical research, and, further, how to classify patients and inform therapeutic strategies in the clinic. Recent research has highlighted the crucial role of next-generation sequencing technologies in identifying potential biomarkers associated with lineage plasticity. Here, we review the genomic, transcriptomic, and epigenetic events that have been described in PCa and highlight those with significance for lineage plasticity. We further focus on their relevance in PCa research and their benefits in PCa patient classification. Finally, we explore ways in which bioinformatic analyses can be used to determine lineage plasticity based on large omics analyses and algorithms that can shed light on upstream and downstream events. Most importantly, an integrated multiomics approach may soon allow for the identification of a lineage plasticity signature, which would revolutionize the molecular classification of PCa patients.

Advances in single-cell RNA sequencing and its applications in cancer research

Cancers are a group of heterogeneous diseases characterized by the acquisition of functional capabilities during the transition from a normal to a neoplastic state. Powerful experimental and computational tools can be applied to elucidate the mechanisms of occurrence, progression, metastasis, and drug resistance; however, challenges remain. Bulk RNA sequencing techniques only reflect the average gene expression in a sample, making it difficult to understand tumor heterogeneity and the tumor microenvironment. The emergence and development of single-cell RNA sequencing (scRNA-seq) technologies have provided opportunities to understand subtle changes in tumor biology by identifying distinct cell subpopulations, dissecting the tumor microenvironment, and characterizing cellular genomic mutations. Recently, scRNA-seq technology has been increasingly used in cancer studies to explore tumor heterogeneity and the tumor microenvironment, which has increased the understanding of tumorigenesis and evolution. This review summarizes the basic processes and development of scRNA-seq technologies and their increasing applications in cancer research and clinical practice.

The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI

Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.

An omics approach to delineating the molecular mechanisms that underlie the biological effects of physical plasma

The use of physical plasma to treat cancer is an emerging field, and interest in its applications in oncology is increasing rapidly. Physical plasma can be used directly by aiming the plasma jet onto cells or tissue, or indirectly, where a plasma-treated solution is applied. A key scientific question is the mechanism by which physical plasma achieves selective killing of cancer over normal cells. Many studies have focused on specific pathways and mechanisms, such as apoptosis and oxidative stress, and the role of redox biology. However, over the past two decades, there has been a rise in omics, the systematic analysis of entire collections of molecules in a biological entity, enabling the discovery of the so-called "unknown unknowns." For example, transcriptomics, epigenomics, proteomics, and metabolomics have helped to uncover molecular mechanisms behind the action of physical plasma, revealing critical pathways beyond those traditionally associated with cancer treatments. This review showcases a selection of omics and then summarizes the insights gained from these studies toward understanding the biological pathways and molecular mechanisms implicated in physical plasma treatment. Omics studies have revealed how reactive species generated by plasma treatment preferentially affect several critical cellular pathways in cancer cells, resulting in epigenetic, transcriptional, and post-translational changes that promote cell death. Finally, this review considers the outlook for omics in uncovering both synergies and antagonisms with other common cancer therapies, as well as in overcoming challenges in the clinical translation of physical plasma.