An invasive zone in human liver cancer identified by Stereo-seq promotes hepatocyte-tumor cell crosstalk, local immunosuppression and tumor progression

Right eye metastasis of small‑cell lung carcinoma: A case report

The incidence of eye metastasis from primary malignant tumors is low. Predominantly, these primary malignant tumors consist of breast and lung carcinoma. Ocular metastatic carcinoma is often clinically overlooked. In clinical practice, it is rare for small-cell lung carcinoma (SCLC) to metastasize to the right eye. Early detection and treatment via the monitoring of clinical symptoms and auxiliary examinations of the eye are of great significance in preserving the patient's vision and improving their quality of life. Such treatments include radiotherapy or enucleation of the eyeball. A 54-year-old male patient with SCLC experienced a decline in vision and blurred vision during his systemic treatment using combined enverolumab and etoposide and cisplatin. Upon examination, including fundus photography, ocular B-scan and magnetic resonance imaging, a right eye metastasis was suspected. Within a short period of time, the patient experienced significant pain and blindness in the right eye, which required surgical removal of the right eyeball. Postoperative pathology confirmed metastasis. After six cycles of treatment, the primary lesion in the lung reduced in size. By reporting this case of SCLC metastasis to the right eye, we aim to provide a reference for the clinical diagnosis and treatment of ocular metastatic carcinoma.

A genetic variant in the TAPBP gene enhances cervical cancer susceptibility by increasing m6A modification

Genetic variants can affect gene expression by altering the level of N6-methyladenosine (m6A) modifications. A better understanding of the association of these genetic variants with susceptibility to cervical cancer (CC) can promote advances in disease screening and treatment. Genome-wide identification of m6A-associated functional SNPs for CC was performed using the TCGA and JENGER databases, incorporating the data from RNA-seq and MeRIP-seq. The screened risk-associated SNP rs1059288 (A>G), which is located in the 3' UTR of TAPBP, was further validated in a case-control study involving 921 cases and 1077 controls. The results revealed a significant association between rs1059288 and the risk of CC (OR 1.48, 95% CI 1.13-1.92). Mechanistically, the presence of the risk G allele of rs1059288 was associated with increased m6A modification of TAPBP compared with the A allele. This modification was facilitated by the m6A methyltransferase METTL14 and the reading protein YTHDF2. Immunohistochemical staining of tissue microarrays containing 61 CC and 45 normal tissues showed an overexpression of TAPBP in CC. Furthermore, the upregulation of TAPBP promoted the growth and migration of CC cells as well as tumor-forming ability, inhibited apoptosis, and conferred increased resistance to commonly used chemotherapeutic drugs such as bleomycin, cisplatin, and doxorubicin. Knockdown of TAPBP inhibited the JAK/STAT/MICB signaling pathway in CC cells and upregulated certain immune genes including ISG15, IRF3, PTPN6, and HLA-A. These findings offer insights into the involvement of genetic variations in TAPBP in the development and progression of CC.

Open-ST: High-resolution spatial transcriptomics in 3D

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

Unveiling contact-mediated cellular crosstalk

Cell-cell interactions orchestrate complex functions in multicellular organisms, forming a regulatory network for diverse biological processes. Their disruption leads to disease states. Recent advancements - including single-cell sequencing and spatial transcriptomics, coupled with powerful bioengineering and molecular tools - have revolutionized our understanding of how cells respond to each other. Notably, spatial transcriptomics allows us to analyze gene expression changes based on cell proximity, offering a unique window into the impact of cell-cell contact. Additionally, computational approaches are being developed to decipher how cell contact governs the symphony of cellular responses. This review explores these cutting-edge approaches, providing valuable insights into deciphering the intricate cellular changes influenced by cell-cell communication.

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

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.

Plasma proteomics analysis of Chinese HIV-1 infected individuals focusing on the immune and inflammatory factors afford insight into the viral control mechanism

Background

Long-term non-progressors (LTNPs) with HIV infection can naturally control viral replication for up to a decade without antiretroviral therapy (ART), but the underlying mechanisms of this phenomenon remain elusive.

Methods

To investigate the relevant immune and inflammatory factors associated with this natural control mechanism, we collected plasma samples from 16 LTNPs, 14 untreated viral progressors (VPs), 17 successfully ART-treated patients (TPs), and 16 healthy controls (HCs). The OLINK immune response panel and inflammation panel were employed to detect critical proteins, and the plasma neutralizing activity against a global panel of pseudoviruses was assessed using TZM-bl cells.

Results

The combination of IL17C, IL18, DDX58, and NF2 contributed to discriminating LTNPs and VPs. IL18 and CCL25 were positively associated with CD4+ T cell counts but negatively correlated with viral load. Furthermore, CXCL9 and CXCL10 emerged as potential supplementary diagnostic markers for assessing the efficacy of antiretroviral therapy (ART). Finally, TNFRSF9 displayed positive correlations with neutralization breadth and Geometry Median Titer (GMT) despite the lack of significant differences between LTNPs and VPs.

Conclusion

In summary, this study identified a set of biomarkers in HIV-infected individuals at different disease stages. These markers constitute a potential network for immune balance regulation in HIV infection, which is related to the long-term control of HIV by LTNPs. It provides important clues for further exploring the immune regulatory mechanism of HIV.

Digital pathology and spatial omics in steatohepatitis: Clinical applications and discovery potentials

Steatohepatitis with diverse etiologies is the most common histological manifestation in patients with liver disease. However, there are currently no specific histopathological features pathognomonic for metabolic dysfunction-associated steatotic liver disease, alcohol-associated liver disease, or metabolic dysfunction-associated steatotic liver disease with increased alcohol intake. Digitizing traditional pathology slides has created an emerging field of digital pathology, allowing for easier access, storage, sharing, and analysis of whole-slide images. Artificial intelligence (AI) algorithms have been developed for whole-slide images to enhance the accuracy and speed of the histological interpretation of steatohepatitis and are currently employed in biomarker development. Spatial biology is a novel field that enables investigators to map gene and protein expression within a specific region of interest on liver histological sections, examine disease heterogeneity within tissues, and understand the relationship between molecular changes and distinct tissue morphology. Here, we review the utility of digital pathology (using linear and nonlinear microscopy) augmented with AI analysis to improve the accuracy of histological interpretation. We will also discuss the spatial omics landscape with special emphasis on the strengths and limitations of established spatial transcriptomics and proteomics technologies and their application in steatohepatitis. We then highlight the power of multimodal integration of digital pathology augmented by machine learning (ML)algorithms with spatial biology. The review concludes with a discussion of the current gaps in knowledge, the limitations and premises of these tools and technologies, and the areas of future research.

Spaco: A comprehensive tool for coloring spatial data at single-cell resolution

Understanding tissue architecture and niche-specific microenvironments in spatially resolved transcriptomics (SRT) requires in situ annotation and labeling of cells. Effective spatial visualization of these data demands appropriate colorization of numerous cell types. However, current colorization frameworks often inadequately account for the spatial relationships between cell types. This results in perceptual ambiguity in neighboring cells of biological distinct types, particularly in complex environments such as brain or tumor. To address this, we introduce Spaco, a potent tool for spatially aware colorization. Spaco utilizes the Degree of Interlacement metric to construct a weighted graph that evaluates the spatial relationships among different cell types, refining color assignments. Furthermore, Spaco incorporates an adaptive palette selection approach to amplify chromatic distinctions. When benchmarked on four diverse datasets, Spaco outperforms existing solutions, capturing complex spatial relationships and boosting visual clarity. Spaco ensures broad accessibility by accommodating color vision deficiency and offering open-accessible code in both Python and R.

Artificial tumor matrices and bioengineered tools for tumoroid generation

The tumor microenvironment (TME) is critical for tumor growth and metastasis. The TME contains cancer-associated cells, tumor matrix, and tumor secretory factors. The fabrication of artificial tumors, so-called tumoroids, is of great significance for the understanding of tumorigenesis and clinical cancer therapy. The assembly of multiple tumor cells and matrix components through interdisciplinary techniques is necessary for the preparation of various tumoroids. This article discusses current methods for constructing tumoroids (tumor tissue slices and tumor cell co-culture) for pre-clinical use. This article focuses on the artificial matrix materials (natural and synthetic materials) and biofabrication techniques (cell assembly, bioengineered tools, bioprinting, and microfluidic devices) used in tumoroids. This article also points out the shortcomings of current tumoroids and potential solutions. This article aims to promotes the next-generation tumoroids and the potential of them in basic research and clinical application.

Heterogeneity and tumoral origin of medulloblastoma in the single-cell era

Medulloblastoma is one of the most common malignant pediatric brain tumors derived from posterior fossa. The current treatment includes maximal safe surgical resection, radiotherapy, whole cranio-spinal radiation and adjuvant with chemotherapy. However, it can only limitedly prolong the survival time with severe side effects and relapse. Defining the intratumoral heterogeneity, cellular origin and identifying the interaction network within tumor microenvironment are helpful for understanding the mechanisms of medulloblastoma tumorigenesis and relapse. Due to technological limitations, the mechanisms of cellular heterogeneity and tumor origin have not been fully understood. Recently, the emergence of single-cell technology has provided a powerful tool for achieving the goal of understanding the mechanisms of tumorigenesis. Several studies have demonstrated the intratumoral heterogeneity and tumor origin for each subtype of medulloblastoma utilizing the single-cell RNA-seq, which has not been uncovered before using conventional technologies. In this review, we present an overview of the current progress in understanding of cellular heterogeneity and tumor origin of medulloblastoma and discuss novel findings in the age of single-cell technologies.

Serum amyloid A promotes glycolysis of neutrophils during PD-1 blockade resistance in hepatocellular carcinoma

The response to programmed death-1 (PD-1) blockade varies in hepatocellular carcinoma (HCC). We utilize a panel of 16 serum factors to show that a circulating level of serum amyloid A (SAA) > 20.0 mg/L has the highest accuracy in predicting anti-PD-1 resistance in HCC. Further experiments show a correlation between peritumoral SAA expression and circulating SAA levels in patients with progressive disease after PD-1 inhibition. In vitro experiments demonstrate that SAA induces neutrophils to express PD-L1 through glycolytic activation via an LDHA/STAT3 pathway and to release oncostatin M, thereby attenuating cytotoxic T cell function. In vivo, genetic or pharmacological inhibition of STAT3 or SAA eliminates neutrophil-mediated immunosuppression and enhances antitumor efficacy of anti-PD-1 treatment. This study indicates that SAA may be a critical inflammatory cytokine implicated in anti-PD-1 resistance in HCC. Targeting SAA-induced PD-L1+ neutrophils through STAT3 or SAA inhibition may present a potential approach for overcoming anti-PD1 resistance.

Precision treatment in advanced hepatocellular carcinoma

The past decade has witnessed significant advances in the systemic treatment of advanced hepatocellular carcinoma (HCC). Nevertheless, the newly developed treatment strategies have not achieved universal success and HCC patients frequently exhibit therapeutic resistance to these therapies. Precision treatment represents a paradigm shift in cancer treatment in recent years. This approach utilizes the unique molecular characteristics of individual patient to personalize treatment modalities, aiming to maximize therapeutic efficacy while minimizing side effects. Although precision treatment has shown significant success in multiple cancer types, its application in HCC remains in its infancy. In this review, we discuss key aspects of precision treatment in HCC, including therapeutic biomarkers, molecular classifications, and the heterogeneity of the tumor microenvironment. We also propose future directions, ranging from revolutionizing current treatment methodologies to personalizing therapy through functional assays, which will accelerate the next phase of advancements in this area.

The oncogenic mechanisms of the Janus kinase-signal transducer and activator of transcription pathway in digestive tract tumors

Digestive tract tumors are heterogeneous and involve the dysregulation of multiple signaling pathways. The Janus kinase-signal transducer and activator of transcription (JAK-STAT) pathway plays a notable role in the oncogenesis of digestive tract tumors. Typically activated by pro-inflammatory cytokines, it regulates important biological processes, such as cell growth, differentiation, apoptosis, immune responses, and inflammation. The aberrant activation of this pathway manifests in different forms, including mutations in JAKs, overexpression of cytokine receptors, and sustained STAT activation, and contributes to promoting the malignant characteristics of cancer cells, including uncontrolled proliferation, resistance to apoptosis, enhanced invasion and metastasis, angiogenesis, acquisition of stem-like properties, and drug resistance. Numerous studies have shown that aberrant activation of the JAK-STAT pathway is closely related to the development and progression of digestive tract tumors, contributing to tumor survival, angiogenesis, changes in the tumor microenvironment, and even immune escape processes. In addition, this signaling pathway also affects the sensitivity of digestive tract tumors to chemotherapy and targeted therapy. Therefore, it is crucial to comprehensively understand the oncogenic mechanisms underlying the JAK-STAT pathway in order to develop effective therapeutic strategies against digestive tract tumors. Currently, several JAK-STAT inhibitors are undergoing clinical and preclinical trials as potential treatments for various human diseases. However, further investigation is required to determine the role of this pathway, as well as the effectiveness and safety of its inhibitors, especially in the context of digestive tract tumors. In this review, we provide an overview of the structure, classic activation, and negative regulation of the JAK-STAT pathway. Furthermore, we discuss the pathogenic mechanisms of JAK-STAT signaling in different digestive tract tumors, with the aim of identifying potential novel therapeutic targets. Video Abstract.

Deciphering spatial domains from spatially resolved transcriptomics with Siamese graph autoencoder

BACKGROUND

Cell clustering is a pivotal aspect of spatial transcriptomics (ST) data analysis as it forms the foundation for subsequent data mining. Recent advances in spatial domain identification have leveraged graph neural network (GNN) approaches in conjunction with spatial transcriptomics data. However, such GNN-based methods suffer from representation collapse, wherein all spatial spots are projected onto a singular representation. Consequently, the discriminative capability of individual representation feature is limited, leading to suboptimal clustering performance.

RESULTS

To address this issue, we proposed SGAE, a novel framework for spatial domain identification, incorporating the power of the Siamese graph autoencoder. SGAE mitigates the information correlation at both sample and feature levels, thus improving the representation discrimination. We adapted this framework to ST analysis by constructing a graph based on both gene expression and spatial information. SGAE outperformed alternative methods by its effectiveness in capturing spatial patterns and generating high-quality clusters, as evaluated by the Adjusted Rand Index, Normalized Mutual Information, and Fowlkes-Mallows Index. Moreover, the clustering results derived from SGAE can be further utilized in the identification of 3-dimensional (3D) Drosophila embryonic structure with enhanced accuracy.

CONCLUSIONS

Benchmarking results from various ST datasets generated by diverse platforms demonstrate compelling evidence for the effectiveness of SGAE against other ST clustering methods. Specifically, SGAE exhibits potential for extension and application on multislice 3D reconstruction and tissue structure investigation. The source code and a collection of spatial clustering results can be accessed at https://github.com/STOmics/SGAE/.

Interorgan communication with the liver: novel mechanisms and therapeutic targets

The liver is a multifunctional organ that plays crucial roles in numerous physiological processes, such as production of bile and proteins for blood plasma, regulation of blood levels of amino acids, processing of hemoglobin, clearance of metabolic waste, maintenance of glucose, etc. Therefore, the liver is essential for the homeostasis of organisms. With the development of research on the liver, there is growing concern about its effect on immune cells of innate and adaptive immunity. For example, the liver regulates the proliferation, differentiation, and effector functions of immune cells through various secreted proteins (also known as "hepatokines"). As a result, the liver is identified as an important regulator of the immune system. Furthermore, many diseases resulting from immune disorders are thought to be related to the dysfunction of the liver, including systemic lupus erythematosus, multiple sclerosis, and heart failure. Thus, the liver plays a role in remote immune regulation and is intricately linked with systemic immunity. This review provides a comprehensive overview of the liver remote regulation of the body's innate and adaptive immunity regarding to main areas: immune-related molecules secreted by the liver and the liver-resident cells. Additionally, we assessed the influence of the liver on various facets of systemic immune-related diseases, offering insights into the clinical application of target therapies for liver immune regulation, as well as future developmental trends.

Targeting the mechano-microenvironment and liver cancer stem cells: a promising therapeutic strategy for liver cancer

Over the past 2 decades, cancer stem cells (CSCs) have been identified as the root cause of cancer occurrence, progression, chemoradioresistance, recurrence, and metastasis. Targeting CSCs is a novel therapeutic strategy for cancer management and treatment. Liver cancer (LC) is a malignant disease that can endanger human health. Studies are increasingly suggesting that changes in the liver mechanical microenvironment are a primary driver triggering the occurrence and development of liver cancer. In this review, we summarize current understanding of the roles of the liver mechano-microenvironment and liver cancer stem cells (LCSCs) in liver cancer progression. We also discuss the relationship between the mechanical heterogeneity of liver cancer tissues and LCSC recruitment and metastasis. Finally, we highlight potential mechanosensitive molecules in LCSCs and mechanotherapy in liver cancer. Understanding the roles and regulatory mechanisms of the mechano-microenvironment and LCSCs may provide fundamental insights into liver cancer progression and aid in further development of novel therapeutic strategies.

Unveiling the Role of Tumor-Infiltrating T Cells and Immunotherapy in Hepatocellular Carcinoma: A Comprehensive Review

Hepatocellular carcinoma (HCC) is a rapidly rising global health concern, ranking as the third-leading cause of cancer-related mortality. Despite medical advancements, the five-year survival rate remains a dismal 18%, with a daunting 70% recurrence rate within a five-year period. Current systematic treatments, including first-line sorafenib, yield an overall response rate (ORR) below 10%. In contrast, immunotherapies have shown promise by improving ORR to approximately 30%. The IMbravel150 clinical trial demonstrates that combining atezolizumab and bevacizumab surpasses sorafenib in terms of median progression-free survival (PFS) and overall survival (OS). However, the therapeutic efficacy for HCC patients remains unsatisfactory, highlighting the urgent need for a comprehensive understanding of antitumor responses and immune evasion mechanisms in HCC. In this context, understanding the immune landscape of HCC is of paramount importance. Tumor-infiltrating T cells, including cytotoxic T cells, regulatory T cells, and natural killer T cells, are key components in the antitumor immune response. This review aims to shed light on their intricate interactions within the immunosuppressive tumor microenvironment and explores potential strategies for revitalizing dysfunctional T cells. Additionally, current immune checkpoint inhibitor (ICI)-based trials, ICI-based combination therapies, and CAR-T- or TCR-T-cell therapies for HCC are summarized, which might further improve OS and transform the management of HCC in the future.

Increased co-expression of PD1 and TIM3 is associated with poor prognosis and immune microenvironment heterogeneity in gallbladder cancer

BACKGROUND

The effectiveness of immune checkpoint inhibitors in treating gallbladder cancer (GBC) remains unsatisfactory. Recently, several new immune checkpoints have been identified. However, investigations exploring these immune checkpoints in GBC are limited. In this study, we aim to investigate the expression patterns and clinical implications of various immune checkpoints, and further characterize the spatial and quantitative heterogeneity of immune components in GBC.

METHODS

We employed single and multiplex immunohistochemistry to evaluate the expression of five immune checkpoint markers and four immune cell markers in the primary tumor core, hepatic invasion margin, and liver metastasis. Subsequently, we analyzed their interrelationships and their prognostic significance.

RESULTS

We observed a robust positive correlation between PD1/TIM3 expression in GBC (R = 0.614, P < 0.001). The co-expression of PD1/TIM3 exhibited a synergistic effect in predicting poor prognosis among postoperative GBC patients. Further analysis revealed that the prognostic significance of PD1/TIM3 was prominent in the subgroup with high infiltration of CD8 + T cells (P < 0.001). Multiplex immunohistochemistry reveals that PD1 + TIM3 + FOXP3 + cells constitute a significant proportion of FOXP3 + TILs in GBC tissue. Moreover, the co-high expression of PD1 and TIM3 is positively correlated with the accumulation of CD8 + TILs at the hepatic invasion margin. Lastly, our findings indicated reduced expression levels of immune checkpoints and diminished immune cell infiltration in liver metastases compared to primary tumors.

CONCLUSIONS

Increased co-expression of PD1/TIM3 is associated with poor prognosis in GBC patients and is related to the heterogeneity of immune microenvironment between GBC primary tumor and its hepatic invasion margin or liver metastases, which may be a potential target for future immunotherapy of GBC.

Spatial transcriptomics in development and disease

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.