Transcriptional Spatial Profiling of Cancer Tissues in the Era of Immunotherapy: The Potential and Promise

Spatial omics techniques and data analysis for cancer immunotherapy applications

In-depth profiling of cancer cells/tissues is expanding our understanding of the genomic, epigenomic, transcriptomic, and proteomic landscape of cancer. However, the complexity of the cancer microenvironment, particularly its immune regulation, has made it difficult to exploit the potential of cancer immunotherapy. High-throughput spatial omics technologies and analysis pipelines have emerged as powerful tools for tackling this challenge. As a result, a potential revolution in cancer diagnosis, prognosis, and treatment is on the horizon. In this review, we discuss the technological advances in spatial profiling of cancer around and beyond the central dogma to harness the full benefits of immunotherapy. We also discuss the promise and challenges of spatial data analysis and interpretation and provide an outlook for the future.

Edge-relational window-attentional graph neural network for gene expression prediction in spatial transcriptomics analysis

Spatial transcriptomics (ST), containing gene expression with fine-grained (i.e., different windows) spatial location within tissue samples, has become vital in developing innovative treatments. Traditional ST technology, however, rely on costly specialized commercial equipment. Addressing this, our article aims to creates a cost-effective, virtual ST approach using standard tissue images for gene expression prediction, eliminating the need for expensive equipment. Conventional approaches in this field often overlook the long-distance spatial dependencies between different sample windows or need prior gene expression data. To overcome these limitations, we propose the Edge-Relational Window-Attentional Network (ErwaNet), enhancing gene prediction by capturing both local interactions and global structural information from tissue images, without prior gene expression data. ErwaNet innovatively constructs heterogeneous graphs to model local window interactions and incorporates an attention mechanism for global information analysis. This dual framework not only provides a cost-effective solution for gene expression predictions but also obviates the necessity of prior knowledge gene expression information, a significant advantage in the field of cancer research where it enables a more efficient and accessible analytical paradigm. ErwaNet stands out as a prior-free and easy-to-implement Graph Convolution Network (GCN) method for predicting gene expression from tissue images. Evaluation of the two public breast cancer datasets shows that ErwaNet, without additional information, outperforms the state-of-the-art (SOTA) methods. Code is available at https://github.com/biyecc/ErwaNet.

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.

Just how transformative will AI/ML be for immuno-oncology?

Immuno-oncology involves the study of approaches which harness the patient's immune system to fight malignancies. Immuno-oncology, as with every other biomedical and clinical research field as well as clinical operations, is in the midst of technological revolutions, which vastly increase the amount of available data. Recent advances in artificial intelligence and machine learning (AI/ML) have received much attention in terms of their potential to harness available data to improve insights and outcomes in many areas including immuno-oncology. In this review, we discuss important aspects to consider when evaluating the potential impact of AI/ML applications in the clinic. We highlight four clinical/biomedical challenges relevant to immuno-oncology and how they may be able to be addressed by the latest advancements in AI/ML. These challenges include (1) efficiency in clinical workflows, (2) curation of high-quality image data, (3) finding, extracting and synthesizing text knowledge as well as addressing, and (4) small cohort size in immunotherapeutic evaluation cohorts. Finally, we outline how advancements in reinforcement and federated learning, as well as the development of best practices for ethical and unbiased data generation, are likely to drive future innovations.

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.

Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate

The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.

Comprehensive Single-Cell Immune Profiling Defines the Patient Multiple Myeloma Microenvironment Following Oncolytic Virus Therapy in a Phase Ib Trial

PURPOSE

Our preclinical studies showed that the oncolytic reovirus formulation pelareorep (PELA) has significant immunomodulatory anti-myeloma activity. We conducted an investigator-initiated clinical trial to evaluate PELA in combination with dexamethasone (Dex) and bortezomib (BZ) and define the tumor immune microenvironment (TiME) in patients with multiple myeloma treated with this regimen.

PATIENTS AND METHODS

Patients with relapsed/refractory multiple myeloma (n = 14) were enrolled in a phase Ib clinical trial (ClinicalTrials.gov: NCT02514382) of three escalating PELA doses administered on Days 1, 2, 8, 9, 15, and 16. Patients received 40 mg Dex and 1.5 mg/m2 BZ on Days 1, 8, and 15. Cycles were repeated every 28 days. Pre- and posttreatment bone marrow specimens (IHC, n = 9; imaging mass cytometry, n = 6) and peripheral blood samples were collected for analysis (flow cytometry, n = 5; T-cell receptor clonality, n = 7; cytokine assay, n = 7).

RESULTS

PELA/BZ/Dex was well-tolerated in all patients. Treatment-emergent toxicities were transient, and no dose-limiting toxicities occurred. Six (55%) of 11 response-evaluable patients showed decreased paraprotein. Treatment increased T and natural killer cell activation, inflammatory cytokine release, and programmed death-ligand 1 expression in bone marrow. Compared with nonresponders, responders had higher reovirus protein levels, increased cytotoxic T-cell infiltration posttreatment, cytotoxic T cells in significantly closer proximity to multiple myeloma cells, and larger populations of a novel immune-primed multiple myeloma phenotype (CD138+ IDO1+HLA-ABCHigh), indicating immunomodulation.

CONCLUSIONS

PELA/BZ/Dex is well-tolerated and associated with anti-multiple myeloma activity in a subset of responding patients, characterized by immune reprogramming and TiME changes, warranting further investigation of PELA as an immunomodulator.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain in male mice. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Application of spatial transcriptomics analysis using the Visium system for the mouse nasal cavity after intranasal vaccination

Intranasal vaccines that elicit mucosal immunity are deemed effective against respiratory tract infections such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), but their ability to induce humoral immunity characterized by immunoglobulin A (IgA) and IgG production is low. It has been reported that vaccination with a mixture of a viscous base carboxyvinyl polymer (CVP) and viral antigens induced robust systemic and mucosal immune responses. In this study, we analyzed the behavior of immunocompetent cells in the nasal cavity over time by spatial transcriptome profiling induced immediately after antigen vaccination using CVP. We established a method for performing spatial transcriptomics using the Visium system in the mouse nasal cavity and analyzed gene expression profiles within the nasal cavity after intranasal vaccination. Glycoprotein 2 (Gp2)-, SRY-box transcription factor 8 (Sox8)-, or Spi-B transcription factor (Spib)-expressing cells were increased in the nasal passage (NP) region at 3-6 hr after SARS-CoV-2 spike protein and CVP (S-CVP) vaccination. The results suggested that microfold (M) cells are activated within a short period of time (3-6 hr). Subsequent cluster analysis of cells in the nasal cavity showed an increase in Cluster 9 at 3-6 hr after intranasal vaccination with the S-CVP. We found that Il6 in Cluster 9 had the highest log2 fold values within the NP at 3-6 hr. A search for gene expression patterns similar to that of Il6 revealed that the log2 fold values of Edn2, Ccl20, and Hk2 also increased in the nasal cavity after 3-6 hr. The results showed that the early response of immune cells occurred immediately after intranasal vaccination. In this study, we identified changes in gene expression that contribute to the activation of M cells and immunocompetent cells after intranasal vaccination of mice with antigen-CVP using a time-series analysis of spatial transcriptomics data. The results facilitated the identification of the cell types that are activated during the initial induction of nasal mucosal immunity.

Spatial mapping of the DNA adducts in cancer

DNA adducts and strand breaks are induced by various exogenous and endogenous agents. Accumulation of DNA damage is implicated in many disease processes, including cancer, aging, and neurodegeneration. The continuous acquisition of DNA damage from exogenous and endogenous stressors coupled with defects in DNA repair pathways contribute to the accumulation of DNA damage within the genome and genomic instability. While mutational burden offers some insight into the level of DNA damage a cell may have experienced and subsequently repaired, it does not quantify DNA adducts and strand breaks. Mutational burden also infers the identity of the DNA damage. With advances in DNA adduct detection and quantification methods, there is an opportunity to identify DNA adducts driving mutagenesis and correlate with a known exposome. However, most DNA adduct detection methods require isolation or separation of the DNA and its adducts from the context of the nuclei. Mass spectrometry, comet assays, and other techniques precisely quantify lesion types but lose the nuclear context and even tissue context of the DNA damage. The growth in spatial analysis technologies offers a novel opportunity to leverage DNA damage detection with nuclear and tissue context. However, we lack a wealth of techniques capable of detecting DNA damage in situ. Here, we review the limited existing in situ DNA damage detection methods and examine their potential to offer spatial analysis of DNA adducts in tumors or other tissues. We also offer a perspective on the need for spatial analysis of DNA damage in situ and highlight Repair Assisted Damage Detection (RADD) as an in situ DNA adduct technique with the potential to integrate with spatial analysis and the challenges to be addressed.

More than Meets the Eye: Integration of Radiomics with Transcriptomics for Reconstructing the Tumor Microenvironment and Predicting Response to Therapy

For over a decade, large cancer-related datasets (big data) have continuously been produced and made publicly available to the scientific community [...]

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Spatial transcriptomics reveals unique gene expression changes in different brain regions after sleep deprivation

Sleep deprivation has far-reaching consequences on the brain and behavior, impacting memory, attention, and metabolism. Previous research has focused on gene expression changes in individual brain regions, such as the hippocampus or cortex. Therefore, it is unclear how uniformly or heterogeneously sleep loss affects the brain. Here, we use spatial transcriptomics to define the impact of a brief period of sleep deprivation across the brain. We find that sleep deprivation induced pronounced differences in gene expression across the brain, with the greatest changes in the hippocampus, neocortex, hypothalamus, and thalamus. Both the differentially expressed genes and the direction of regulation differed markedly across regions. Importantly, we developed bioinformatic tools to register tissue sections and gene expression data into a common anatomical space, allowing a brain-wide comparison of gene expression patterns between samples. Our results suggest that distinct molecular mechanisms acting in discrete brain regions underlie the biological effects of sleep deprivation.

Integrated spatial analysis of gene mutation and gene expression for understanding tumor diversity in formalin-fixed paraffin-embedded lung adenocarcinoma

Introduction

A deeper understanding of intratumoral heterogeneity is essential for prognosis prediction or accurate treatment plan decisions in clinical practice. However, due to the cross-links and degradation of biomolecules within formalin-fixed paraffin-embedded (FFPE) specimens, it is challenging to analyze them. In this study, we aimed to optimize the simultaneous extraction of mRNA and DNA from microdissected FFPE tissues (φ = 100 µm) and apply the method to analyze tumor diversity in lung adenocarcinoma before and after erlotinib administration.

Method

Two magnetic beads were used for the simultaneous extraction of mRNA and DNA. The decross-linking conditions were evaluated for gene mutation and gene expression analyses of microdissected FFPE tissues. Lung lymph nodes before treatment and lung adenocarcinoma after erlotinib administration were collected from the same patient and were preserved as FFPE specimens for 4 years. Gene expression and gene mutations between histologically classified regions of lung adenocarcinoma (pre-treatment tumor in lung lymph node biopsies and post-treatment tumor, normal lung, tumor stroma, and remission stroma, in resected lung tissue) were compared in a microdissection-based approach.

Results

Using the optimized simultaneous extraction of DNA and mRNA and whole-genome amplification, we detected approximately 4,000-10,000 expressed genes and the epidermal growth factor receptor (EGFR) driver gene mutations from microdissected FFPE tissues. We found the differences in the highly expressed cancer-associated genes and the positive rate of EGFR exon 19 deletions among the tumor before and after treatment and tumor stroma, even though they were collected from tumors of the same patient or close regions of the same specimen.

Conclusion

Our integrated spatial analysis method would be applied to various FFPE pathology specimens providing area-specific gene expression and gene mutation information.

A review of spatial profiling technologies for characterizing the tumor microenvironment in immuno-oncology

Interpreting the mechanisms and principles that govern gene activity and how these genes work according to -their cellular distribution in organisms has profound implications for cancer research. The latest technological advancements, such as imaging-based approaches and next-generation single-cell sequencing technologies, have established a platform for spatial transcriptomics to systematically quantify the expression of all or most genes in the entire tumor microenvironment and explore an array of disease milieus, particularly in tumors. Spatial profiling technologies permit the study of transcriptional activity at the spatial or single-cell level. This multidimensional classification of the transcriptomic and proteomic signatures of tumors, especially the associated immune and stromal cells, facilitates evaluation of tumor heterogeneity, details of the evolutionary trajectory of each tumor, and multifaceted interactions between each tumor cell and its microenvironment. Therefore, spatial profiling technologies may provide abundant and high-resolution information required for the description of clinical-related features in immuno-oncology. From this perspective, the present review will highlight the importance of spatial transcriptomic and spatial proteomics analysis along with the joint use of other sequencing technologies and their implications in cancers and immune-oncology. In the near future, advances in spatial profiling technologies will undoubtedly expand our understanding of tumor biology and highlight possible precision therapeutic targets for cancer patients.

Multiplexed Digital Spatial Protein Profiling Reveals Distinct Phenotypes of Mononuclear Phagocytes in Livers with Advanced Fibrosis

Background and Aims: Intrahepatic mononuclear phagocytes (MPs) are critical for the initiation and progression of liver fibrosis. In this study, using multiplexed digital spatial protein profiling, we aimed to derive a unique protein signature predicting advanced liver fibrosis. Methods: Snap-frozen liver tissues from various chronic liver diseases were subjected to spatially defined protein-based multiplexed profiling (Nanostring GeoMX^TM). A single-cell RNA sequencing analysis was performed using Gene Expression Omnibus (GEO) datasets from normal and cirrhotic livers. Results: Sixty-four portal regions of interest (ROIs) were selected for the spatial profiling. Using the results from the CD68⁺ area, a highly sensitive and specific immune-related protein signature (CD68, HLA-DR, OX40L, phospho-c-RAF, STING, and TIM3) was developed to predict advanced (F3 and F4) fibrosis. A combined analysis of single-cell RNA sequencing data from GEO datasets (GSE136103) and spatially-defined, protein-based multiplexed profiling revealed that most proteins upregulated in F0-F2 livers in portal CD68⁺ cells were specifically marked in tissue monocytes, whereas proteins upregulated in F3 and F4 livers were marked in scar-associated macrophages (SAMacs) and tissue monocytes. Internal validation using mRNA expression data with the same cohort tissues demonstrated that mRNA levels for TREM2, CD9, and CD68 are significantly higher in livers with advanced fibrosis. Conclusions: In patients with advanced liver fibrosis, portal MPs comprise of heterogeneous populations composed of SAMacs, Kupffer cells, and tissue monocytes. This is the first study that used spatially defined protein-based multiplexed profiling, and we have demonstrated the critical difference in the phenotypes of portal MPs between livers with early- or late-stage fibrosis.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

Gut microbiome of multiple sclerosis patients and paired household healthy controls reveal associations with disease risk and course

Changes in gut microbiota have been associated with several diseases. Here, the International Multiple Sclerosis Microbiome Study (iMSMS) studied the gut microbiome of 576 MS patients (36% untreated) and genetically unrelated household healthy controls (1,152 total subjects). We observed a significantly increased proportion of Akkermansia muciniphila, Ruthenibacterium lactatiformans, Hungatella hathewayi, and Eisenbergiella tayi and decreased Faecalibacterium prausnitzii and Blautia species. The phytate degradation pathway was over-represented in untreated MS, while pyruvate-producing carbohydrate metabolism pathways were significantly reduced. Microbiome composition, function, and derived metabolites also differed in response to disease-modifying treatments. The therapeutic activity of interferon-β may in part be associated with upregulation of short-chain fatty acid transporters. Distinct microbial networks were observed in untreated MS and healthy controls. These results strongly support specific gut microbiome associations with MS risk, course and progression, and functional changes in response to treatment.

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Spatial Transcriptomics for Tumor Heterogeneity Analysis

The molecular heterogeneity of cancer is one of the major causes of drug resistance that leads to treatment failure. Thus, better understanding the heterogeneity of cancer will contribute to more precise diagnosis and improved patient outcomes. Although single-cell sequencing has become an important tool for investigating tumor heterogeneity recently, it lacks the spatial information of analyzed cells. In this regard, spatial transcriptomics holds great promise in deciphering the complex heterogeneity of cancer by providing localization-indexed gene expression information. This study reviews the applications of spatial transcriptomics in the study of tumor heterogeneity, discovery of novel spatial-dependent mechanisms, tumor immune microenvironment, and matrix microenvironment, as well as the pathological classification and prognosis of cancer. Finally, future challenges and opportunities for spatial transcriptomics technology’s applications in cancer are also discussed.

Precision Medicine in Head and Neck Cancers: Genomic and Preclinical Approaches

Head and neck cancers (HNCs) represent the sixth most widespread malignancy worldwide. Surgery, radiotherapy, chemotherapeutic and immunotherapeutic drugs represent the main clinical approaches for HNC patients. Moreover, HNCs are characterised by an elevated mutational load; however, specific genetic mutations or biomarkers have not yet been found. In this scenario, personalised medicine is showing its efficacy. To study the reliability and the effects of personalised treatments, preclinical research can take advantage of next-generation sequencing and innovative technologies that have been developed to obtain genomic and multi-omic profiles to drive personalised treatments. The crosstalk between malignant and healthy components, as well as interactions with extracellular matrices, are important features which are responsible for treatment failure. Preclinical research has constantly implemented in vitro and in vivo models to mimic the natural tumour microenvironment. Among them, 3D systems have been developed to reproduce the tumour mass architecture, such as biomimetic scaffolds and organoids. In addition, in vivo models have been changed over the last decades to overcome problems such as animal management complexity and time-consuming experiments. In this review, we will explore the new approaches aimed to improve preclinical tools to study and apply precision medicine as a therapeutic option for patients affected by HNCs.

Integrating single-cell RNA sequencing with spatial transcriptomics reveals immune landscape for interstitial cystitis

Interstitial cystitis (IC) is a severely debilitating and chronic disorder with unclear etiology and pathophysiology, which makes the diagnosis difficult and treatment challenging. To investigate the role of immunity in IC bladders, we sequenced 135,091 CD45⁺ immune cells from 15 female patients with IC and 9 controls with stress urinary incontinence using single-cell RNA sequencing (scRNA-seq). 22 immune subpopulations were identified in the constructed landscape. Among them, M2-like macrophages, inflammatory CD14⁺ macrophages, and conventional dendritic cells had the most communications with other immune cells. Then, a significant increase of central memory CD4⁺ T cells, regulatory T cells, GZMK⁺CD8⁺ T cells, activated B cells, un-switched memory B cells, and neutrophils, and a significant decrease of CD8⁺ effector T cells, Th17 cells, follicular helper T cells, switched memory B cells, transitional B cells, and macrophages were noted in IC bladders. The enrichment analysis identified a virus-related response during the dynamic change of cell proportion, furthermore, the human polyomavirus-2 was detected with a positive rate of 95% in urine of patients with IC. By integrating the results of scRNA-seq with spatial transcriptomics, we found nearly all immune subpopulations were enriched in the urothelial region or located close to fibroblasts in IC bladders, but they were discovered around urothelium and smooth muscle cells in control bladders. These findings depict the immune landscape for IC and might provide valuable insights into the pathophysiology of IC.

Single-cell immunology: Past, present, and future

The immune system is a complex, dynamic, and plastic ecosystem composed of multiple cell types that constantly sense and interact with their local microenvironment to protect from infection and maintain homeostasis. For over a century, great efforts and ingenuity have been applied to the characterization of immune cells and their microenvironments, but traditional marker-based and bulk technologies left key questions unanswered. In the past decade, the advent of single-cell genomic approaches has revolutionized our knowledge of the cellular and molecular makeup of the immune system. In this perspective, we outline the past, present, and future applications of single-cell genomics in immunology and discuss how the integration of multiomics at the single-cell level will pave the way for future advances in immunology research and clinical translation.

NanoString Digital Molecular Profiling of Protein and microRNA in Rhabdomyosarcoma

Simple Summary

NanoString digital profiling methods are novel techniques to identify biologic markers from human formalin-fixed, paraffin-embedded cancer tissue. We have applied NanoString Digital spatial profiling and microRNA profiling methods in non-alveolar rhabdomyosarcoma, a common soft tissue tumor in young adults and children with variable prognosis. Our results have highlighted aberrant miRNA expression and over-expression of several members of PI3-AKT, MAPK and apoptosis signaling pathways in fusion-negative rhabdomyosarcoma, particularly in tumors with unfavorable prognosis. INPP4B, an entry molecule in the PI3/AKT pathway, was significantly over-expressed in tumors with poor prognosis, confirmed by traditional immunohistochemistry. Several microRNAs had increased expression in association with poor patient prognosis. These results highlight the utility of NanoString digital profiling as a screening method to identify prognostic biomarkers of interest in rhabdomyosarcoma from formalin-fixed paraffin-embedded tissue.

Abstract

Purpose: Rhabdomyosarcoma (RMS) exhibits a complex prognostic algorithm based on histologic, biologic and clinical parameters. The embryonal (ERMS) and spindle cell-sclerosing RMS (SRMS) histologic subtypes warrant further studies due to their heterogenous genetic background and variable clinical behavior. NanoString digital profiling methods have been previously highlighted as robust novel methods to detect protein and microRNA expression in several cancers but not in RMS. Methods/Patients: To identify prognostic biomarkers, we categorized 12 ERMS and SRMS tumor cases into adverse (n = 5) or favorable (n = 7) prognosis groups and analyzed their signaling pathways and microRNA profiles. The digital spatial profiling of protein and microRNA analysis was performed on formalin-fixed, paraffin-embedded (FFPE) tumor tissue using NanoString technology. Results: The detectable expression of several component members of the PI3K/AKT, MAPK and apoptosis signaling pathways was highlighted in RMS, including INPP4B, Pan-AKT, MET, Pan-RAS, EGFR, phospho-p90 RSK, p44/42 ERK1/2, BAD, BCL-XL, cleaved caspase-9, NF1, PARP and p53. Compared to cases with favorable prognosis, the adverse-prognosis tumor samples had significantly increased expression of INPP4B, which was confirmed with traditional immunohistochemistry. The analysis of microRNA profiles revealed that, out of 798 microRNAs assessed, 228 were overexpressed and 134 downregulated in the adverse prognosis group. Significant over-expression of oncogenic/tumor suppressor miR-3144-3p, miR-612, miR-302d-3p, miR-421, miR-548ar-5p and miR-548y (p < 0.05) was noted in the adverse prognosis group. Conclusion: This study highlights the utility of NanoString digital profiling methods in RMS, where it can detect distinct molecular signatures with the expression of signaling pathways and microRNAs from FFPE tumor tissue that may help identify prognostic biomarkers of interest. The overexpression of INPP4B and miR-3144-3p, miR-612, miR-302d-3p, miR-421, miR-548y and miR-548ar-5p may be associated with worse overall survival in ERMS and SRMS.

The transcriptomic revolution and radiation biology

PURPOSE

This article will briefly review the origins and evolution of functional genomics, first describing the experimental technology, and then some of the approaches applied to data analysis and visualization. It will emphasize application of functional genomics to radiation biology, using examples from the author's work to illustrate several key types of analysis. It concludes with a look at non-coding RNA, alternative reading of the genome, and single-cell transcriptomics, some of the innovative areas that may help to shape future research in radiation biology and oncology.

CONCLUSIONS

Transcriptomic approaches have provided insight into many areas of radiation biology and medicine, and innovations in technology and data analysis approaches promise continued contributions to radiation science in the future.

Clinical challenges of tissue preparation for spatial transcriptome

Spatial transcriptomics is considered as an important part of spatiotemporal molecular images to bridge molecular information with clinical images. Of those potentials and opportunities, the excellent quality of human sample preparation and handling will ensure the precise and reliable information generated from clinical spatial transcriptome. The present study aims at defining potential factors that might influence the quality of spatial transcriptomics in lung cancer, para-cancer, or normal tissues, pathological images of sections and the RNA integrity before spatial transcriptome sequencing. We categorised potential influencing factors from clinical aspects, including patient selection, pathological definition, surgical types, sample harvest, temporary preservation conditions and solutions, frozen approaches, transport and storage conditions and duration. We emphasis on the relationship between the combination of histological scores with RNA integrity number (RIN) and the unique molecular identifier (UMI), which is determines the quality of of spatial transcriptomics; however, we did not find significantly relevance between them. Our results showed that isolated times and dry conditions of sample are critical for the UMI and the quality of spatial transcriptomic samples. Thus, clinical procedures of sample preparation should be furthermore optimised and standardised as new standards of operation performance for clinical spatial transcriptome. Our data suggested that the temporary preservation time and condition of samples at operation room should be within 30 min and in 'dry' status. The direct cryo-preservation within OCT media for human lung sample is recommended. Thus, we believe that clinical spatial transcriptome will be a decisive approach and bridge in the development of spatiotemporal molecular images and provide new insights for understanding molecular mechanisms of diseases at multi-orientations.

Delineating the breast cancer immune microenvironment in the era of multiplex immunohistochemistry/immunofluorescence

Breast cancer is the most common malignancy and the leading cause of cancer death in females worldwide. Treatment is challenging, especially for those who are triple-negative. Increasing evidence suggests that diverse immune populations are present in the breast tumour microenvironment, which opens up avenues for personalised drug targets. Historically, our investigations into the immune constitution of breast tumours have been restricted to analyses of one or two markers at a given time. Recent technological advances have allowed simultaneous labelling of more than 35 markers and detailed profiling of tumour-immune infiltrates at the single-cell level, as well as determining the cellular composition and spatial analysis of the entire tumour architecture. In this review, we describe emerging technologies that have contributed to the field of breast cancer diagnosis, and discuss how to interpret the vast data sets obtained in order to effectively translate them for clinically relevant use.

High-Plex and High-Throughput Digital Spatial Profiling of Non-Small-Cell Lung Cancer (NSCLC)

Profiling the tumour microenvironment (TME) has been informative in understanding the underlying tumour-immune interactions. Multiplex immunohistochemistry (mIHC) coupled with molecular barcoding technologies have revealed greater insights into the TME. In this study, we utilised the Nanostring GeoMX Digital Spatial Profiler (DSP) platform to profile a non-small-cell lung cancer (NSCLC) tissue microarray for protein markers across immune cell profiling, immuno-oncology (IO) drug targets, immune activation status, immune cell typing, and pan-tumour protein modules. Regions of interest (ROIs) were selected that described tumour, TME, and normal adjacent tissue (NAT) compartments. Our data revealed that paired analysis (n = 18) of matched patient compartments indicate that the TME was significantly enriched in CD27, CD3, CD4, CD44, CD45, CD45RO, CD68, CD163, and VISTA relative to the tumour. Unmatched analysis indicated that the NAT (n = 19) was significantly enriched in CD34, fibronectin, IDO1, LAG3, ARG1, and PTEN when compared to the TME (n = 32). Univariate Cox proportional hazards indicated that the presence of cells expressing CD3 (hazard ratio (HR): 0.5, p = 0.018), CD34 (HR: 0.53, p = 0.004), and ICOS (HR: 0.6, p = 0.047) in tumour compartments were significantly associated with improved overall survival (OS). We implemented both high-plex and high-throughput methodologies to the discovery of protein biomarkers and molecular phenotypes within biopsy samples, and demonstrate the power of such tools for a new generation of pathology research.