Mapping the spatial dynamics of the CD4+ T cell spectrum in classical Hodgkin lymphoma.

As around 25-30% of classical Hodgkin Lymphoma (cHL) patients with advanced stages do not respond to standard therapies, the tumor microenvironment (TME) of cHL is one avenue that may be explored with the aim of improving risk stratification. CD4+ T cells are thought to be one of the main cell types in the TME. However, few immune signatures have been studied, and many of these lack related spatial data. Thus, our aim is to spatially resolve the CD4+ T cell subtypes that influence cHL outcome, depicting new immune signatures or transcriptional patterns that are in crosstalk with the tumor cells. This study was conducted using the Nanostring GeoMx DSP technology, based on the selection of distinct functional areas of patients' tissues followed by the gene-expression profiling. The goals were to assess the differences in CD4+ T cell populations between tumor-rich and immune-predominant areas defined by different CD30 and PD-L1 expression levels and to seek correlations with clinical metadata. Our results depict a complex map of CD4+ T cells with different functions and differentiation states that are enriched at distinct locations, the flux of cytokines and chemokines that could be related to these, and the specific relationships with the clinical outcome.

A Mesoporous Gold Sensor Unveils Phospho PD-L1 in Extracellular Vesicles as a Proxy for PD-L1 Expression in Lung Cancer Tissue.

Immune checkpoint inhibitors (ICIs) targeting programmed cell death ligand 1 (PD-L1), or its receptor, PD-1 have improved survival in patients with non-small-cell lung cancer (NSCLC). Assessment of PD-L1 expression requires tissue biopsy or fine needle aspiration that are currently used to identify patients most likely to respond to single agent anti-PD-1/PD-L1 therapy. However, obtaining sufficient tissue to generate a PD-L1 tissue proportion score (TPS) ≥ 50% using immunohistochemistry remains a challenge that potentially may be overcome by liquid biopsies. This study utilized a mesoporous gold sensor (MGS) assay to examine the phosphorylation status of PD-L1 in plasma extracellular vesicles (EV pPD-L1) and PD-L1 levels in plasma from NSCLC patient samples and their association with tumor PD-L1 TPS. The 3-dimensional mesoporous network of the electrodes provides a large surface area, high signal-to-noise ratio, and a superior electro-conductive framework, thereby significantly improving the detection sensitivity of PD-L1 nanosensing. Test (n = 20) (Pearson's r = 0.99) and validation (n = 45) (Pearson's r = 0.99) cohorts show that EV pPD-L1 status correlates linearly with the tumor PD-L1 TPS assessed by immunohistochemistry irrespective of the tumor stage, with 64% of patients overall showing detectable EV pPD-L1 levels in plasma. In contrast to the EV pPD-L1 results, plasma PD-L1 levels did not correlate with the tumor PD-L1 TPS score or EV pPD-L1 levels. These data demonstrate that EV pPD-L1 levels may be used to select patients for appropriate PD-1 and PD-L1 ICI therapy regimens in early, locally advanced, and advanced NSCLC and should be tested further in randomized controlled trials. Most importantly, the assay used has a less than 24h turnaround time, facilitating adoption of the test into the routine diagnostic evaluation of patients prior to therapy.

Characterisation of circulating tumor-associated and immune cells in patients with advanced-stage non-small cell lung cancer.

Objectives: Globally, non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and the leading cause of cancer-related deaths. Tumor-associated circulating cells in NSCLC can have a wide variety of morphological and phenotypic characteristics, including epithelial, immunological or hybrid subtypes. The distinctive characteristics and potential clinical significance of these cells in patients with NSCLC are explored in this study. Methods: We utilised a spiral microfluidic device to enrich large cells and cell aggregates from the peripheral blood samples of NSCLC patients. These cells were characterised through high-resolution immunofluorescent imaging and statistical analysis, correlating findings with clinical information from our patient cohort. Results: We have identified varied populations of heterotypic circulating tumor cell clusters with differing immune cell composition that included a distinct class of atypical tumor-associated macrophages that exhibits unique morphology and cell size. This subtype's prevalence is positively correlated with the tumor stage, progression and metastasis. Conclusions: Our study reveals a heterogeneous landscape of circulating tumor cells and their clusters, underscoring the complexity of NSCLC pathobiology. The identification of a unique subtype of atypical tumor-associatedmacrophages that simultaneously express both tumor and immune markers and whose presence correlates with late disease stages, poor clinical outcomes and metastatic risk infers  the potential of these cells as biomarkers for NSCLC staging and prognosis. Future studies should focus on the role of these cells in the tumor microenvironment and their potential as therapeutic targets. Additionally, longitudinal studies tracking these cell types through disease progression could provide further insights into their roles in NSCLC evolution and response to treatment.

Glycan Profiling in Small Extracellular Vesicles with a SERS Microfluidic Biosensor Identifies Early Malignant Development in Lung Cancer.

Glycosylation is the most common post-translational modification of proteins and regulates a myriad of fundamental biological processes under normal, and pathological conditions. Altered protein glycosylation is linked to malignant transformation, showing distinct glycopatterns that are associated with cancer initiation and progression by regulating tumor proliferation, invasion, metastasis, and therapeutic resistance. The glycopatterns of small extracellular vesicles (sEVs) released by cancer cells are promising candidates for cancer monitoring since they exhibit glycopatterns similar to their cell-of-origin. However, the clinical application of sEV glycans is challenging due to the limitations of current analytical technologies in tracking the trace amounts of sEVs specifically derived from tumors in circulation. Herein, a sEV GLYcan PHenotype (EV-GLYPH) assay that utilizes a microfluidic platform integrated with surface-enhanced Raman scattering for multiplex profiling of sEV glycans in non-small cell lung cancer is clinically validated. For the first time, the EV-GLYPH assay effectively identifies distinct sEV glycan signatures between non-transformed and malignantly transformed lung cells. In a clinical study evaluated on 40 patients, the EV-GLYPH assay successfully differentiates patients with early-stage malignant lung nodules from benign lung nodules. These results reveal the potential to profile sEV glycans for noninvasive diagnostics and prognostics, opening up promising avenues for clinical applications and understanding the role of sEV glycosylation in lung cancer.

Spatial insights into immunotherapy response in non-small cell lung cancer (NSCLC) by multiplexed tissue imaging.

The spatial localisation of immune cells within tumours are key to understand the intercellular communications that can dictate clinical outcomes. Here, we demonstrate an analysis pipeline for highly multiplexed CODEX data to phenotype and profile spatial features and interactions in NSCLC patients that subsequently received PD1 axis immunotherapy. We found that regulatory T cells (Tregs) are enriched in non-responding patients and this was consistent with their localization within stromal and peripheral tumour-margins. Proximity-based interactions between Tregs and both monocytes (p = 0.009) and CD8+ T cells (p = 0.009) were more frequently found in non-responding patients, while macrophages were more frequently located in proximity to HLADR+ tumour cells (p = 0.01) within responding patients. Cellular neighbourhoods analysis indicated that both macrophages (p = 0.003) and effector CD4+ T cells (p = 0.01) in mixed tumour neighbourhoods, as well as CD8+ T cells (p = 0.03) in HLADR+ tumour neighbourhoods were associated with favorable clinical response. Evaluation of the inferred regulatory functions between immune cells relative to the tumour suggested that macrophages exhibit an immunosuppressive phenotype against both CD4+ and CD8+ T cells, and that this association scores more highly in ICI refractory patients. These spatial patterns are associated with overall survival in addition to ICI response and may thus indicate features for the functional understanding of the tumour microenvironment.

Whole transcriptome profiling of placental pathobiology in SARS-CoV-2 pregnancies identifies placental dysfunction signatures.

Objectives: Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus infection in pregnancy is associated with higher incidence of placental dysfunction, referred to by a few studies as a 'preeclampsia-like syndrome'. However, the mechanisms underpinning SARS-CoV-2-induced placental malfunction are still unclear. Here, we investigated whether the transcriptional architecture of the placenta is altered in response to SARS-CoV-2 infection. Methods: We utilised whole-transcriptome, digital spatial profiling, to examine gene expression patterns in placental tissues from participants who contracted SARS-CoV-2 in the third trimester of their pregnancy (n = 7) and those collected prior to the start of the coronavirus disease 2019 (COVID-19) pandemic (n = 9). Results: Through comprehensive spatial transcriptomic analyses of the trophoblast and villous core stromal cell subpopulations in the placenta, we identified SARS-CoV-2 to promote signatures associated with hypoxia and placental dysfunction. Notably, genes associated with vasodilation (NOS3), oxidative stress (GDF15, CRH) and preeclampsia (FLT1, EGFR, KISS1, PAPPA2) were enriched with SARS-CoV-2. Pathways related to increased nutrient uptake, vascular tension, hypertension and inflammation were also enriched in SARS-CoV-2 samples compared to uninfected controls. Conclusions: Our findings demonstrate the utility of spatially resolved transcriptomic analysis in defining the underlying pathogenic mechanisms of SARS-CoV-2 in pregnancy, particularly its role in placental dysfunction. Furthermore, this study highlights the significance of digital spatial profiling in mapping the intricate crosstalk between trophoblasts and villous core stromal cells, thus shedding light on pathways associated with placental dysfunction in pregnancies with SARS-CoV-2 infection.

In situ characterization of the tumor microenvironment.

The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.

In situ single-cell profiling sheds light on IFI27 localisation during SARS-CoV-2 infection.

The utilization of single-cell resolved spatial transcriptomics to delineate immune responses during SARS-CoV-2 infection was able to identify M1 macrophages to have elevated expression of IFI27 in areas of infection.

Inhibition of Aurora B kinase (AURKB) enhances the effectiveness of 5-fluorouracil chemotherapy against colorectal cancer cells.

BACKGROUND: 5-Fluorouracil (5-FU) remains a core component of systemic therapy for colorectal cancer (CRC). However, response rates remain low, and development of therapy resistance is a primary issue. Combinatorial strategies employing a second agent to augment the therapeutic effect of chemotherapy is predicted to reduce the incidence of treatment resistance and increase the durability of response to therapy. METHODS: Here, we employed quantitative proteomics approaches to identify novel druggable proteins and molecular pathways that are deregulated in response to 5-FU, which might serve as targets to improve sensitivity to chemotherapy. Drug combinations were evaluated using 2D and 3D CRC cell line models and an ex vivo culture model of a patient-derived tumour. RESULTS: Quantitative proteomics identified upregulation of the mitosis-associated protein Aurora B (AURKB), within a network of upregulated proteins, in response to a 24 h 5-FU treatment. In CRC cell lines, AURKB inhibition with the dihydrogen phosphate prodrug AZD1152, markedly improved the potency of 5-FU in 2D and 3D in vitro CRC models. Sequential treatment with 5-FU then AZD1152 also enhanced the response of a patient-derived CRC cells to 5-FU in ex vivo cultures. CONCLUSIONS: AURKB inhibition may be a rational approach to augment the effectiveness of 5-FU chemotherapy in CRC.

Leveraging spatial omics for the development of precision sarcoma treatments.

Sarcomas are rare and heterogeneous cancers that arise from bone or soft tissue, and are the second most prevalent solid cancer in children and adolescents. Owing to the complex nature of pediatric sarcomas, the development of therapeutics for pediatric sarcoma has seen little progress in the past decades. Existing treatments are largely limited to chemotherapy, radiation, and surgery. Limited knowledge of the sarcoma tumor microenvironment (TME) and of well-defined target antigens in the different subtypes necessitates an alternative investigative approach to improve treatments. Recent advances in spatial omics technologies have enabled a more comprehensive study of the TME in multiple cancers. In this opinion article we discuss advances in our understanding of the TME of some cancers enabled by spatial omics technologies, and we explore how these technologies might advance the development of precision treatments for sarcoma, especially pediatric sarcoma.

Tissue biomarkers of immune checkpoint inhibitor therapy.

Cancer immunotherapy has been rejuvenated by the growing understanding of the immune system's role in tumor activity over the past two decades. During cancer initiation and progression, tumor cells employ various mechanisms that resemble peripheral immune tolerance to evade the antitumor responses of the immune system. Immune checkpoint molecules are the major mechanism of immune resistance that are exploited by tumor cells to inhibit T-cell activation and suppress immune responses. The targeting of immune checkpoint pathways has led to substantial improvements in survival rates in a number of solid cancers. However, a lack of understanding of the heterogeneity of the tumor microenvironment (TME) has resulted in inefficient therapy responses. A greater understanding of the TME is needed to identify patients likely to respond, and those that will have resistance to immune checkpoint inhibitors (ICIs). Advancement in spatial single-cell technologies has allowed deeper insight into the phenotypic and functional diversities of cells in the TME. In this review, we provide an overview of ICI biomarkers and highlight how high-dimensional spatially resolved, single-cell approaches provide deep molecular insights into the TME and allow for the discovery of biomarkers of clinical benefit.

The in situ transcriptomic landscape of breast tumour-associated and normal adjacent endothelial cells.

BACKGROUND AND AIMS: Triple Negative Breast Cancer (TNBC) is associated with increased angiogenesis, which is known to aid tumour growth and metastasis. Anti-angiogenic therapies that have been developed to target this feature have mostly generated disappointing clinical results. Further research into targeted approaches is limited by a lack of understanding of the in situ molecular profile of tumour-associated vasculature. In this study, we aimed to understand the differences in the molecular profiles of tumour endothelial cells vs normal-adjacent endothelial cells in TNBC tissues. METHOD: We have applied unbiased whole transcriptome spatial profiling of in situ gene expressions of endothelial cells localized in full-face patient TNBC tissues (n = 4) and normal-adjacent regions of the same patient breast tissues. RESULTS: Our comparative analysis revealed that 2412 genes were differentially expressed (padj < 0.05) between the tumour endothelial cells and normal-adjacent endothelial cells. Pathway enrichment showed the enrichment of gene sets related to cell-cell, cell-ECM adhesion, chromatin organization and remodeling, and protein-DNA complex subunit organization. CONCLUSION: Overall, the results revealed unique molecular profiles and signalling pathways of tumour-associated vasculature, which is a critical step towards larger cohort studies investigating potential targets for TNBC prognosis and anti-angiogenic treatments.

standR: spatial transcriptomic analysis for GeoMx DSP data.

To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations.

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Genomic communication via circulating extracellular vesicles and long-term health consequences of COVID-19.

COVID-19 continues to affect an unprecedented number of people with the emergence of new variants posing a serious challenge to global health. There is an expansion of knowledge in understanding the pathogenesis of Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the impact of the acute disease on multiple organs. In addition, growing evidence reports that the impact of COVID-19 on different organs persists long after the recovery phase of the disease, leading to long-term consequences of COVID-19. These long-term consequences involve pulmonary as well as extra-pulmonary sequelae of the disease. Noteably, recent research has shown a potential association between COVID-19 and change in the molecular cargo of extracellular vesicles (EVs). EVs are vesicles released by cells and play an important role in cell communication by transfer of bioactive molecules between cells. Emerging evidence shows a strong link between EVs and their molecular cargo, and regulation of metabolism in health and disease. This review focuses on current knowledge about EVs and their potential role in COVID-19 pathogenesis, their current and future implications as tools for biomarker and therapeutic development and their possible effects on long-term impact of COVID-19.

Advances in novel strategies for isolation, characterization, and analysis of CTCs and ctDNA.

Over the past decade, the detection and analysis of liquid biopsy biomarkers such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) have advanced significantly. They have received recognition for their clinical usefulness in detecting cancer at an early stage, monitoring disease, and evaluating treatment response. The emergence of liquid biopsy has been a helpful development, as it offers a minimally invasive, rapid, real-time monitoring, and possible alternative to traditional tissue biopsies. In resource-limited settings, the ideal platform for liquid biopsy should not only extract more CTCs or ctDNA from a minimal sample volume but also accurately represent the molecular heterogeneity of the patient's disease. This review covers novel strategies and advancements in CTC and ctDNA-based liquid biopsy platforms, including microfluidic applications and comprehensive analysis of molecular complexity. We discuss these systems' operational principles and performance efficiencies, as well as future opportunities and challenges for their implementation in clinical settings. In addition, we emphasize the importance of integrated platforms that incorporate machine learning and artificial intelligence in accurate liquid biopsy detection systems, which can greatly improve cancer management and enable precision diagnostics.

A robust platform for integrative spatial multi-omics analysis to map immune responses to SARS-CoV-2 infection in lung tissues.

The SARS-CoV-2 (COVID-19) virus has caused a devastating global pandemic of respiratory illness. To understand viral pathogenesis, methods are available for studying dissociated cells in blood, nasal samples, bronchoalveolar lavage fluid and similar, but a robust platform for deep tissue characterization of molecular and cellular responses to virus infection in the lungs is still lacking. We developed an innovative spatial multi-omics platform to investigate COVID-19-infected lung tissues. Five tissue-profiling technologies were combined by a novel computational mapping methodology to comprehensively characterize and compare the transcriptome and targeted proteome of virus infected and uninfected tissues. By integrating spatial transcriptomics data (Visium, GeoMx and RNAScope) and proteomics data (CODEX and PhenoImager HT) at different cellular resolutions across lung tissues, we found strong evidence for macrophage infiltration and defined the broader microenvironment surrounding these cells. By comparing infected and uninfected samples, we found an increase in cytokine signalling and interferon responses at different sites in the lung and showed spatial heterogeneity in the expression level of these pathways. These data demonstrate that integrative spatial multi-omics platforms can be broadly applied to gain a deeper understanding of viral effects on cellular environments at the site of infection and to increase our understanding of the impact of SARS-CoV-2 on the lungs.

The seductive allure of spatial biology: accelerating new discoveries in the life sciences.

Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic assay developments. Immediate applications are in precision medicine, such as being able to match patients to optimal therapies through better understanding the tumor microenvironment. However, it also has ramifications for many other disciplines (e.g. immunology, cancer, infectious disease and digital pathology). With increasingly massive data sets being generated, data storage, curation, analysis and sharing require more computational approaches and artificial intelligence-powered tools to fully utilize spatial tools. Here, we discuss spatial biology as an important convergent science approach to tackling complex global challenges in areas such as health.

Compartmentalized spatial profiling of the tumor microenvironment in head and neck squamous cell carcinoma identifies immune checkpoint molecules and tumor necrosis factor receptor superfamily members as biomarkers of response to immunotherapy.

Mucosal head and neck squamous cell carcinoma (HNSCC) are the seventh most common cancer, with approximately 50% of patients living beyond 5 years. Immune checkpoint inhibitors (ICIs) have shown promising results in patients with recurrent or metastatic (R/M) disease, however, only a subset of patients benefit from immunotherapy. Studies have implicated the tumor microenvironment (TME) of HNSCC as a major factor in therapy response, highlighting the need to better understand the TME, particularly by spatially resolved means to determine cellular and molecular components. Here, we employed targeted spatial profiling of proteins on a cohort of pre-treatment tissues from patients with R/M disease to identify novel biomarkers of response within the tumor and stromal margins. By grouping patient outcome categories into response or non-response, based on Response Evaluation Criteria in Solid Tumors (RECIST) we show that immune checkpoint molecules, including PD-L1, B7-H3, and VISTA, were differentially expressed. Patient responders possessed significantly higher tumor expression of PD-L1 and B7-H3, but lower expression of VISTA. Analysis of response subgroups indicated that tumor necrosis factor receptor (TNFR) superfamily members including OX40L, CD27, 4-1BB, CD40, and CD95/Fas, were associated with immunotherapy outcome. CD40 expression was higher in patient-responders than non responders, while CD95/Fas expression was lower in patients with partial response (PR) relative to those with stable disease (SD) and progressive disease (PD). Furthermore, we found that high 4-1BB expression in the tumor compartment, but not in the stroma, was associated with better overall survival (OS) (HR= 0.28, p-adjusted= 0.040). Moreover, high CD40 expression in tumor regions (HR= 0.27, p-adjusted= 0.035), and high CD27 expression in the stroma (HR= 0.2, p-adjusted=0.032) were associated with better survival outcomes. Taken together, this study supports the role of immune checkpoint molecules and implicates the TNFR superfamily as key players in immunotherapy response in our cohort of HNSCC. Validation of these findings in a prospective study is required to determine the robustness of these tissue signatures.

Liquid Biopsy Snapshots of Key Phosphoproteomic Pathways in Lung Cancer Patients for Diagnosis and Therapy Monitoring.

Phosphorylation is a post-translational modification in proteins that changes protein conformation and activity for regulating signal transduction pathways. This mechanism is frequently impaired in lung cancer, resulting in permanently active constitutive phosphorylation to initiate tumor growth and/or reactivate pathways in response to therapy. We developed a multiplexed phosphoprotein analyzer chip (MPAC) that enables rapid (detection time: 5 min) and sensitive (LOD: 2 pg/µL) detection of protein phosphorylation and presents phosphoproteomic profiling of major phosphorylation pathways in lung cancer. We monitored phosphorylated receptors and downstream proteins involved in mitogen-activated protein kinase (MAPK) and PI3K/AKT/mTOR pathways in lung cancer cell line models and patient-derived extracellular vesicles (EV). Using kinase inhibitor drugs in cell line models, we found that the drug can inhibit the phosphorylation and/or activation of the kinase pathway. We then generated a phosphorylation heatmap by EV phosphoproteomic profiling of plasma samples isolated from 36 lung cancer patients and 8 noncancer individuals. The heatmap showed a clear difference between the noncancer and cancer samples and identify the specific proteins that are activated in the cancer samples. Our data also showed that MPAC could monitor immunotherapy responses by assessment of the phosphorylation states of the proteins, particularly for PD-L1. Finally, with a longitudinal study, we found that the phosphorylation levels of the proteins were indicative of a positive response to therapy. We believe that this study will lead to personalized treatment by providing a better understanding of the active and resistant pathways and will provide a tool for selecting combined and targeted therapies for precision medicine.