Comprehensive analysis of spatial architecture in primary liver cancer

Habitat radiomics based on CT images to predict survival and immune status in hepatocellular carcinoma, a multi-cohort validation study

BACKGROUND AND OBJECTIVE

Though several clinicopathological features are identified as prognostic indicators, potentially prognostic radiomic models are expected to preoperatively and noninvasively predict survival for HCC. Traditional radiomic models are lacking in a consideration for intratumoral regional heterogeneity. The study aimed to establish and validate the predictive power of multiple habitat radiomic models in predicting prognosis of hepatocellular carcinoma (HCC).

METHODS

A total of 232 HCC patients were retrospectively included, including a training/validation cohort and two external testing cohorts from 4 centers. For habitat radiomics, intratumoral habitat partitioning based on CT images was first performed by using Otsu thresholding method. Second, a total of 350 habitat radiomic models were constructed to select the optimal model. Then, both ROC curve analyses and Kaplan-Meier survival curve analyses were applied to assess the predictive performances. Ultimately, an immune status profiling was conducted based on bioinformatic analyses and multiplex immunohistochemistry (mIHC) assays to reveal the potential mechanisms.

RESULTS

A total of 4 habitats were segmented, and the corresponding habitat radiomic models were constructed based on each habitat and an integration of all the four habitats. Generally, habitat radiomic models outperformed traditional radiomic models in stratifying prognosis for HCC. The habitat radiomic model based on the segmented habitat 4 involving decision tree (DT) screening and random forest (RF) classifier was identified as the optimal model with an AUCmean of 0.806. Distinct resting natural killer (NK) cell infiltrations significantly contributed to the prognosis stratification of HCC by the optimal habitat radiomic model.

CONCLUSIONS

The habitat radiomic model based on CT images was potentially predictive of overall survival for HCC, with a superiority over the traditional radiomic model. The prognostic power of the habitat radiomic model was partly attributed to the distinct immune status captured in the CT images.

RASGRF2 as a potential pathogenic gene mediating the progression of alcoholic hepatitis to alcohol-related cirrhosis and hepatocellular carcinoma

BACKGROUND AND AIMS

Alcoholic hepatitis (AH) and hepatocellular carcinoma (HCC) are common liver diseases. Chronic inflammation caused by AH can progress to alcoholic cirrhosis (AC) and eventually HCC.

METHODS

This study sought to ascertain potential shared genes between AH and HCC through the utilization of multiple transcriptome databases. Employing an immune infiltration analysis, and calculating the correlation between shared genes and immune infiltration results, in conjunction with independent bulk transcriptome validation sets, led to the identification of core shared genes. Subsequently, single-cell transcriptome data, clinical sample immunohistochemistry experiments, and overexpressed core shared genes in HepG2 cells were employed to validate the core shared genes of AH and HCC.

RESULTS

Through the bulk transcriptome discovery sets of AH and HCC, 206 potential shared genes were identified. After screening with two machine learning algorithms, five shared genes remained. Combining the results of the immune infiltration and bulk transcriptome results from an independent validation cohort, the core shared gene was determined to be RASGRF2. Single-cell data further demonstrated that RASGRF2 and its downstream genes were highly expressed in AH, AC, and HCC tissues. Spatial transcriptome data indicated that RASGRF2 was highly expressed in HCC tumor tissues. Compared with the paracancerous tissues, the RASGRF2 gene was significantly overexpressed in HCC tissues. Overexpression of RASGRF2 in HepG2 cells resulted in significantly enhanced migration, invasion, and proliferation abilities.

CONCLUSION

RASGRF2 serve as a pathogenic gene that mediates the progression of AH to AC and potentially to HCC.

The spatial landscape of cancer hallmarks reveals patterns of tumor ecological dynamics and drug sensitivity

Tumors are complex ecosystems of interacting cell types. The concept of cancer hallmarks distills this complexity into underlying principles that govern tumor growth. Here, we explore the spatial distribution of cancer hallmarks across 63 primary untreated tumors from 10 cancer types using spatial transcriptomics. We show that hallmark activity is spatially organized, with the cancer compartment contributing to the activity of seven out of 13 hallmarks, while the tumor microenvironment (TME) contributes to the activity of the rest. Additionally, we discover that genomic distance between tumor subclones correlates with differences in hallmark activity, even leading to clone-hallmark specialization. Finally, we demonstrate interdependent relationships between hallmarks at the junctions of TME and cancer compartments and how they relate to sensitivity to different neoadjuvant treatments in 33 bladder cancer patients from the DUTRENEO trial. In conclusion, our findings may improve our understanding of tumor ecology and help identify new drug biomarkers.

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

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

SpatialDeX Is a Reference-Free Method for Cell-Type Deconvolution of Spatial Transcriptomics Data in Solid Tumors

The rapid development of spatial transcriptomics (ST) technologies has enabled transcriptome-wide profiling of gene expression in tissue sections. Despite the emergence of single-cell resolution platforms, most ST sequencing studies still operate at a multicell resolution. Consequently, deconvolution of cell identities within the spatial spots has become imperative for characterizing cell-type-specific spatial organization. To this end, we developed Spatial Deconvolution Explorer (SpatialDeX), a regression model-based method for estimating cell-type proportions in tumor ST spots. SpatialDeX exhibited comparable performance to reference-based methods and outperformed other reference-free methods with simulated ST data. Using experimental ST data, SpatialDeX demonstrated superior performance compared with both reference-based and reference-free approaches. Additionally, a pan-cancer clustering analysis on tumor spots identified by SpatialDeX unveiled distinct tumor progression mechanisms both within and across diverse cancer types. Overall, SpatialDeX is a valuable tool for unraveling the spatial cellular organization of tissues from ST data without requiring single-cell RNA-seq references. Significance: The development of a reference-free method for deconvolving the identity of cells in spatial transcriptomics datasets enables exploration of tumor architecture to gain deeper insights into the dynamics of the tumor microenvironment.

Single-cell omics: experimental workflow, data analyses and applications

Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.

Multi-omics analysis unveils the role of inflammatory cancer-associated fibroblasts in chordoma progression

Cancer-associated fibroblasts (CAFs) constitute the primary cellular component of the stroma in chordomas, characterized by an abundance of mucinous stromal elements, potentially facilitating their initiation and progression; however, this inference has yet to be fully confirmed. In this study, single-cell RNA sequencing (scRNA-seq), spatial transcriptomics (ST), bulk RNA-seq, multiplexed quantitative immunofluorescence (QIF), and in vivo and in vitro experiments were performed to determine the heterogeneity, spatial distribution, and clinical significance of CAFs in chordoma. ScRNA-seq was performed on 87,693 single cells derived from seven tumor samples and four control nucleus pulposus samples. A distinct CAF cluster distinguished by the upregulated expression of inflammatory genes and enriched functionality in activating inflammation-associated cells was identified. Pseudotime trajectory and cell communication analyses suggested that this inflammatory CAF (iCAF) subset originated from normal fibroblasts and interacted extensively with tumors and various other cell types. By integrating the scRNA-seq results with ST, the presence of iCAF in chordoma tissue was further confirmed, indicating their positioning at a distance from the tumor cells. Bulk RNA-seq data analysis from 126 patients revealed a correlation between iCAF signature scores, chordoma invasiveness, and poor prognosis. QIF validation involving an additional 116 patients found that although iCAFs were not in close proximity to tumor cells compared with other CAF subsets, their density correlated with malignant tumor phenotypes and adverse outcomes. In vivo and in vitro experiments further confirmed that iCAFs accelerate the malignant progression of chordomas. These findings could provide insights into the development of novel therapeutic strategies. © 2024 The Pathological Society of Great Britain and Ireland.

The heterogeneity of cellular metabolism in the tumour microenvironment of hepatocellular carcinoma with portal vein tumour thrombus

Given the growing interest in the metabolic heterogeneity of hepatocellular carcinoma (HCC) and portal vein tumour thrombus (PVTT). This study comprehensively analysed the metabolic heterogeneity of HCC, PVTT, and normal liver samples using multi-omics combinations. A single-cell RNA sequencing dataset encompassing six major cell types was obtained for integrated analysis. The optimal subtypes were identified using cluster stratification and validated using spatial transcriptomics and fluorescent multiplex immunohistochemistry. Then, a combined index based meta-cluster was calculated to verify its prognostic significance using multi-omics data from public cohorts. Our study first depicted the metabolic heterogeneity landscape of non-malignant cells in HCC and PVTT at multiomics levels. The optimal subtypes interpret the metabolic characteristics of PVTT formation and development. The combined index provided effective predictions of prognosis and immunotherapy responses. Patients with a higher combined index had a relatively poor prognosis (p <0.001). We also found metabolism of polyamines was a key metabolic pathway involved in conversion of metabolic heterogeneity in HCC and PVTT, and identified ODC1 was significantly higher expressed in PVTT compared to normal tissue (p =0.03). Our findings revealed both consistency and heterogeneity in the metabolism of non-malignant cells in HCC and PVTT. The risk stratification based on cancer-associated fibroblasts and myeloid cells conduce to predict prognosis and guide treatment. This offers new directions for understanding disease development and immunotherapy responses.

Application of Spatial Transcriptomics in Digestive System Tumors

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

Spatially-resolved analyses of muscle invasive bladder cancer microenvironment unveil a distinct fibroblast cluster associated with prognosis

Background

Muscle-invasive bladder cancer (MIBC) is a prevalent cancer characterized by molecular and clinical heterogeneity. Assessing the spatial heterogeneity of the MIBC microenvironment is crucial to understand its clinical significance.

Methods

In this study, we used imaging mass cytometry (IMC) to assess the spatial heterogeneity of MIBC microenvironment across 185 regions of interest in 40 tissue samples. We focused on three primary parameters: tumor (T), leading-edge (L), and nontumor (N). Cell gating was performed using the Cytobank platform. We calculated the Euclidean distances between cells to determine cellular interactions and performed single-cell RNA sequencing (scRNA-seq) to explore the molecular characteristics and mechanisms underlying specific fibroblast (FB) clusters. scRNA-seq combined with spatial transcriptomics (ST) facilitated the identification of ligand-receptor (L-R) pairs that mediate interactions between specific FB clusters and endothelial cells. Machine learning algorithms were used to construct a prognostic gene signature.

Results

The microenvironments in the N, L, and T regions of MIBC exhibited spatial heterogeneity and regional diversity in their components. A distinct FB cluster located in the L region-identified as S3-is strongly associated with poor prognosis. IMC analyses demonstrated a close spatial association between S3 and endothelial cells, with S3-positive tumors exhibiting increased blood vessel density and altered vascular morphology. The expression of vascular endothelial growth factor receptor and active vascular sprouting were significant in S3-positive tumors. scRNA-seq and ST analyses indicated that the genes upregulated in S3 were associated with angiogenesis. NOTCH1-JAG2 signaling pathway was identified as a significant L-R pair specific to S3 and endothelial cell interactions. Further analysis indicated that YAP1 was a potential regulator of S3. Machine learning algorithms and Gene Set Variation Analysis were used to establish an S3-related gene signature that was associated with the poor prognosis of tumors including MIBC, mesothelioma, glioblastoma multiforme, lower-grade glioma, stomach adenocarcinoma, uveal melanoma, kidney renal clear cell carcinoma, kidney renal papillary cell carcinoma, and lung squamous cell carcinoma.

Conclusions

We assessed the spatial landscape of the MIBC microenvironment and revealed a specific FB cluster with prognostic potential. These findings offer novel insights into the spatial heterogeneity of the MIBC microenvironment and highlight its clinical significance.

Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study

BACKGROUND

Tertiary lymphoid structures (TLS) within the tumor microenvironment have been associated with cancer prognosis and therapeutic response. However, the immunological pattern of a high peritumoral TLS (pTLS) density and its clinical potential in hepatocellular carcinoma (HCC) remain poor. This study aimed to elucidate biological differences related to pTLS density and develop a radiomic classifier for predicting pTLS density in HCC, offering new insights for clinical diagnosis and treatment.

METHODS

Spatial transcriptomics (n=4) and RNA sequencing data (n=952) were used to identify critical regulators of pTLS density and evaluate their prognostic significance in HCC. Baseline MRI images from 660 patients with HCC who had undergone surgery treatment between October 2015 and January 2023 were retrospectively recruited for model development and validation. This included training (n=307) and temporal validation (n=76) cohorts from Xiangya Hospital, and external validation cohorts from three independent hospitals (n=277). Radiomic features were extracted from intratumoral and peritumoral regions of interest and analyzed using machine learning algorithms to develop a predictive classifier. The classifier's performance was evaluated using the area under the curve (AUC), with prognostic and predictive value assessed across four independent cohorts and in a dual-center outcome cohort of 41 patients who received immunotherapy.

RESULTS

Patients with HCC and a high pTLS density experienced prolonged median overall survival (p<0.05) and favorable immunotherapy response (p=0.03). Moreover, immune infiltration by mature B cells was observed in the high pTLS density region. Spatial pseudotime analysis and immunohistochemistry staining revealed that expansion of pTLS in HCC was associated with elevated CXCL9 and CXCL10 co-expression. We developed an optimal radiomic-based classifier with excellent discrimination for predicting pTLS density, achieving an AUC of 0.91 (95% CI 0.87, 0.94) in the external validation cohort. This classifier also exhibited promising stratification ability in terms of overall survival (p<0.01), relapse-free survival (p<0.05), and immunotherapy response (p<0.05).

CONCLUSION

We identified key regulators of pTLS density in patients with HCC and proposed a non-invasive radiomic classifier capable of assisting in stratification for prognosis and treatment.

Targeting the immune privilege of tumor-initiating cells to enhance cancer immunotherapy

Tumor-initiating cells (TICs) possess the ability to evade anti-tumor immunity, potentially explaining many failures of cancer immunotherapy. Here, we identify CD49f as a prominent marker for discerning TICs in hepatocellular carcinoma (HCC), outperforming other commonly used TIC markers. CD49f-high TICs specifically recruit tumor-promoting neutrophils via the CXCL2-CXCR2 axis and create an immunosuppressive milieu in the tumor microenvironment (TME). Reciprocally, the neutrophils reprogram nearby tumor cells toward a TIC phenotype via secreting CCL4. These cells can evade CD8+ T cell-mediated killing through CCL4/STAT3-induced and CD49f-stabilized CD155 expression. Notably, while aberrant CD155 expression contributes to immune suppression, it also represents a TIC-specific vulnerability. We demonstrate that either CD155 deletion or antibody blockade significantly enhances sensitivity to anti-PD-1 therapy in preclinical HCC models. Our findings reveal a new mechanism of tumor immune evasion and provide a rationale for combining CD155 blockade with anti-PD-1/PD-L1 therapy in HCC.

Integrated Single-Cell and Spatial Transcriptome Reveal Metabolic Gene SLC16A3 as a Key Regulator of Immune Suppression in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal cancers, usually diagnosed at an advanced stage. Metabolic reprogramming plays a significant role in HCC progression, probably related to immune evasion, yet the key gene is unclear. In this study, six metabolism-related genes with prognostic implications were screened. Correlation analysis between the key genes and immune cell subtypes was conducted, and a prominent gene strongly associated with immunosuppression, SLC16A3, was identified. Overexpression of SLC16A3 is associated with the loss of T-cell function and might lead to the upregulation of several immunosuppressive proteins. Gene function enrichment analysis showed genes correlated with SLC16A3 primarily involved in cell adhesion. Single-cell analysis showed that the SLC16A3 gene was mainly expressed in macrophages, especially some tumour-promoting macrophages. Further analysis of spatial transcriptome data indicated that SLC16A3 was enriched at the tumour invasion front. The mIHC revealed that patients with high SLC16A3 expression exhibited significantly reduced infiltration of GZMB+ cells. And SLC16A3 inhibitors significantly suppressed the proliferation of HCC, while simultaneously enhancing T-cell cytotoxicity and reducing exhaustion. These results reveal the phenomenon of immune escape mediated by metabolic reprogramming and suggest that SLC16A3 may serve as a novel target for intervention.

Spatial Transcriptomics Reveals the Transcriptomic Signatures in a Mouse Model of Pediatric Metabolic Dysfunction-Associated Steatohepatitis

Metabolic dysfunction-associated steatohepatitis (MASH) is considered the progressive form of metabolic dysfunction-associated steatotic liver disease, which is the leading cause of chronic liver disease in children. However, the pathogenesis of pediatric MASH remains poorly understood because of the lack of animal models. In this study, a mouse model of pediatric MASH was developed and its hepatic transcriptomic profile was characterized using spatial transcriptomics technology. C57BL/6J mice were fed a Western diet (WD) along with weekly injections of carbon tetrachloride (CCl4) from the age of 3 weeks and lasting up to 8 weeks. After 5 weeks of feeding, WD + CCl4-treated mice showed significant liver injury without the development of insulin resistance. Histologically, WD + CCl4 induced key features of type 2 MASH, the most common type observed in children, characterized by liver steatosis, portal inflammation, and portal fibrosis. Spatial transcriptomics analysis of liver tissues indicated that cluster 0 in the mouse from the WD + CCl4 group was enriched in pathways associated with lipid metabolism. Further investigation revealed that cytochrome p450 2E1 was the top marker gene of cluster 0, and its expression was increased in the periportal area of mice from the WD + CCl4 group. These findings suggest that this mouse model of pediatric MASH mirrors the histologic features of human MASH, and the up-regulation of cytochrome p450 2E1 may be linked to the disease pathogenesis.

Development of mutated β-catenin gene signature to identify CTNNB1 mutations from whole and spatial transcriptomic data in patients with HCC

Background & Aims

Patients with β-catenin (encoded by CTNNB1)-mutated hepatocellular carcinoma (HCC) demonstrate heterogenous responses to first-line immune checkpoint inhibitors (ICIs). Precision-medicine based treatments for this subclass are currently in clinical development. Here, we report derivation of the Mutated β-catenin Gene Signature (MBGS) to predict CTNNB1-mutational status in patients with HCC for future application in personalized medicine treatment regimens.

Methods

Co-expression of mutant-Nrf2 and hMet ± mutant-β-catenin in murine livers in mice led to HCC development. The MBGS was derived using bulk RNA-seq and intersectional transcriptomic analysis of β-catenin-mutated and non-mutated HCC models. Integrated RNA/whole-exome-sequencing and spatial transcriptomic data from multiple cohorts of patients with HCC was assessed to address the ability of MBGS to detect CTNNB1 mutation, the tumor immune microenvironment, and/or predict therapeutic responses.

Results

Bulk RNA-seq comparing HCC specimens in mutant β-catenin-Nrf2, β-catenin-Met and β-catenin-Nrf2-Met to Nrf2-Met HCC model yielded 95 common upregulated genes. In The Cancer Genome Atlas (TCGA)-LIHC dataset, differential gene expression analysis with false discovery rate (FDR) = 0.05 and log2(fold change) >1.5 on the 95 common genes comparing CTNNB1-mutated vs. wild-type patients narrowed the gene panel to a 13-gene MBGS. MBGS predicted CTNNB1-mutations in TCGA (n = 374) and French (n = 398) patient cohorts with AUCs of 0.90 and 0.94, respectively. Additionally, a higher MBGS expression score was associated with lack of significant improvement in overall survival or progression-free survival in the atezolizumab-bevacizumab arm vs. the sorafenib arm in the IMbrave150 cohort. MBGS performed comparable or superior to other CTNNB1-mutant classifiers. MBGS overlapped with Hoshida S3, Boyault G5/G6, and Chiang CTNNB1 subclass tumors in TCGA and in HCC spatial transcriptomic datasets visually depicting these tumors to be situated in an immune excluded tumor microenvironment.

Conclusions

MBGS will aid in patient stratification to guide precision medicine therapeutics for CTNNB1-mutated HCC subclass as a companion diagnostic, as anti-β-catenin therapies become available.

Impact and implications

As precision medicine for liver cancer treatment becomes a reality, diagnostic tools are needed to help classify patients into groups for the best treatment choices. We have developed a molecular signature that could serve as a companion diagnostic and uses bulk or spatial transcriptomic data to identify a unique subclass of liver tumors. This subgroup of liver cancer patients derive limited benefit from the current standard of care and are expected to benefit from specialized directed therapies that are on the horizon.

Multi-omics-driven discovery of invasive patterns and treatment strategies in CA19-9 positive intrahepatic cholangiocarcinoma

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor with a poor prognosis, predominantly CA19-9 positive. High CA19-9 levels correlate with increased aggressiveness and worse outcomes. This study employs multi-omics analysis to reveal molecular features and identify therapeutic targets of CA19-9 positive ICC, aiming to support individualized treatment.

METHODS

Data from seven clinical cohorts, two whole-exome sequencing cohorts, six RNA sequencing/microarray cohorts, one proteomic cohort, 20 single-cell RNA sequencing samples, and one spatial transcriptome sample were analyzed. Key findings were validated on tissue microarrays from 52 ICC samples.

RESULTS

CA19-9 positive ICC exhibited poorer OS (median 24.1 v.s. 51.5 months) and RFS (median 11.7 v.s. 28.2 months) compared to negative group (all P < 0.05). Genomic analysis revealed a higher KRAS mutation frequency in the positive group and a greater prevalence of IDH1/2 mutations in the negative group (all P < 0.05). Transcriptomic analysis indicated upregulated glycolysis pathways in CA19-9 positive ICC. Single-cell analysis identified specific glycolysis-related cell subclusters associated with poor prognosis, including Epi_SLC2A1, CAF_VEGFA, and Mph_SPP1. Higher hypoxia in the CA19-9 positive group led to metabolic reprogramming and promoted these cells' formation. These cells formed interactive communities promoting epithelial-mesenchymal transition (EMT) and angiogenesis. Drug sensitivity analysis identified six potential therapeutic drugs.

CONCLUSIONS

This study systematically elucidated the clinical, genomic, transcriptomic, and immune features of CA19-9 positive ICC. It reveals glycolysis-associated cellular communities and their cancer-promoting mechanisms, enhancing our understanding of ICC and laying the groundwork for individualized therapeutic strategies.

Single cell-spatial transcriptomics and bulk multi-omics analysis of heterogeneity and ecosystems in hepatocellular carcinoma

This study profiled global single cell-spatial-bulk transcriptome landscapes of hepatocellular carcinoma (HCC) ecosystem from six HCC cases and a non-carcinoma liver control donor. We discovered that intratumoral heterogeneity mainly derived from HCC cells diversity and pervaded the genome-transcriptome-proteome-metabolome network. HCC cells are the core driving force of taming tumor-associated macrophages (TAMs) with pro-tumorigenic phenotypes for favor its dominant growth. Remarkably, M1-types TAMs had been characterized by disturbance of metabolism, poor antigen-presentation and immune-killing abilities. Besides, we found simultaneous cirrhotic and HCC lesions in an individual patient shared common origin and displayed parallel clone evolution via driving disparate immune reprograms for better environmental adaptation. Moreover, endothelial cells exhibited phenotypically conserved but executed differential functions in a space-dependent manner. Further, the spatiotemporal traits of rapid recurrence niche genes were identified and validated by immunohistochemistry. Our data unravels the great significance of HCC cells in shaping vibrant tumor ecosystems corresponding to clinical scenarios.

Integrated analysis of single-cell, spatial and bulk RNA-sequencing identifies a cell-death signature for predicting the outcomes of head and neck cancer

Background

Cell death plays an essential role in carcinogenesis, but its function in the recurrence and postoperative prognosis of head and neck cancer (HNC), which ranks as the 7th most common malignancy globally, remains unclear.

Methods

Data from five main subtypes of HNC related single-cell RNA sequencing (scRNA-seq) were recruited to establish a single-cell atlas, and the distribution of cell death models (CDMs) across different tissues as well as cell subtypes were analyzed. Bulk RNA-seq from the Cancer Genome Atlas Program (TCGA) dataset was subjected to a machine learning-based integrative procedure for constructing a consensus cell death-related signature risk score (CDRscore) model and validated by external data. The biofunctions including different expression analysis, immune cell infiltration, genomic mutations, enrichment analysis as well as cellchat analysis were compared between the high- and low- risk score groups categorized by this CDRscore model. Finally, samples from laryngeal squamous cell cancer (LSCC) were conducted by spatial transcriptomics (ST) to further validate the results of CDRscore model.

Results

T cells from HNC patients manifested the highest levels of cell death while HPV infection attenuates malignant cell death based on single-cell atlas. CDMs are positively correlated with the tumor-cell stemness, immune-related score and T cells are infiltrated. A CDRscore model was established based on the transcription of ten cell death prognostic genes (MRPL10, DDX19A, NDFIP1, PCMT1, HPRT1, SLC2A3, EFNB2, HK1, BTG3 and MAP2K7). It functions as an independent prognostic factor for overall survival in HNC and displays stable and powerful performance validated by GSE41613 and GSE65858 datasets. Patients in high CDRscore manifested worse overall survival, more active of epithelial mesenchymal transition, TGF-β-related pathways and hypoxia, higher transcription of T cell exhausted markers, and stronger TP53 mutation. ST from LSCC showed that spots with high-risk scores were colocalized with TGF-β and the proliferating malignant cells, additionally, the risk scores have a negative correlation with TCR signaling but positive association with LAG3 transcription.

Conclusion

The CDRscore model could be utilized as a powerful prognostic indicator for HNC.

Integration of single-cell and spatial transcriptome sequencing identifies CDKN2A as a senescent biomarker in endothelial cells implicating hepatocellular carcinoma malignancy

PURPOSE

Highly complex tumor microenvironment makes hepatocellular carcinoma (HCC) as one of the most malignant tumors worldwide. The role of cellular senescence in HCC has been gradually recognized. The present study aimed to comprehensively elucidate the senescence-related features of HCC in single-cell and spatial dimension.

METHODS

Single-cell RNA sequencing (scRNA-Seq) data was used to clarify the heterogeneity of senescence-related genes (SRGs) among multiple cell types within HCC. Spatial transcriptome RNA sequencing (stRNA-Seq) data was used for depicting SRGs features in spatial dimension. A prognostic model based on SRGs was constructed by using of bulk sequencing (bulk-Seq) data of HCC. The cell-cell interaction of senescent endothelial cells (ECs) in tumor microenvironment was analyzed. Then, the role of senescent ECs was verified through in vitro and in vivo experiments.

RESULTS

The level of senescence demonstrated substantial heterogeneity among different cell types within tumor microenvironment of HCC, where ECs exhibited the most prominent senescent phenotype. Senescent ECs activated specific regulatory pathways through communicating with other cell types, with a potential impact on tumor progression. Spatial analysis revealed senescent ECs mainly located in the core region of HCC. The interaction of senescent ECs and immune cells implicated their role in tumor progression and immunotherapeutic response. In addition, CDKN2A was identified as an independent risk factor for HCC prognosis by constructing a prognostic model. Patients with high risk displayed an even worse outcome. The experimental verification indicated senescence of ECs determined by CDKN2A exhibited a secretory phenotype. Furthermore, senescent ECs with CDKN2A overexpression promote the proliferation and migration of HCC.

CONCLUSION

The present study recognizes the critical effect of senescent ECs defined by CDKN2A in the promotion of tumor progression, which sheds new light on the investigation of ECs senescence in HCC.

Transcriptome analysis revealed the genes and major pathways involved in prunetrin treated hepatocellular carcinoma cells

Liver cancer represents a complex and severe ailment that poses tough challenges to global healthcare. Transcriptome sequencing plays a crucial role in enhancing our understanding of cancer biology and accelerating the development of more effective methods for cancer diagnosis and treatment. In the course of our current investigation, we identified a total of 1,149 differentially expressed genes (DEGs), encompassing 499 upregulated and 650 downregulated genes, subsequent to prunetrin (PUR) treatment. Our methodology encompassed gene and pathway enrichment analysis, functional annotation, KEGG pathway assessments, and protein-protein interaction (PPI) analysis of the DEGs. The preeminent genes within the DEGs were found to be associated with apoptotic processes, cell cycle regulation, the PI3k/Akt pathway, the MAPK pathway, and the mTOR pathway. Furthermore, key apoptotic-related genes exhibited close interconnections and cluster analysis found three interacting hub genes namely, TP53, TGFB1 and CASP8. Validation of these genes was achieved through GEPIA and western blotting. Collectively, our findings provide insights into the functional landscape of liver cancer-related genes, shedding light on the molecular mechanisms driving disease progression and highlighting potential targets for therapeutic intervention.

Gene interactions analysis of brain spatial transcriptome for Alzheimer's disease

Recent studies have explored the spatial transcriptomics patterns of Alzheimer's disease (AD) brain by spatial sequencing in mouse models, enabling the identification of unique genome-wide transcriptomic features associated with different spatial regions and pathological status. However, the dynamics of gene interactions that occur during amyloid-β accumulation remain largely unknown. In this study, we performed analyses on ligand-receptor communication, transcription factor regulatory network, and spot-specific network to reveal the dependence and the dynamics of gene associations/interactions on spatial regions and pathological status with mouse and human brains. We first used a spatial transcriptomics dataset of the App NL-G-F knock-in AD and wild-type mouse model. We revealed 17 ligand-receptor pairs with opposite tendencies throughout the amyloid-β accumulation process and showed the specific ligand-receptor interactions across the hippocampus layers at different extents of pathological changes. We then identified nerve function related transcription factors in the hippocampus and entorhinal cortex, as well as genes with different transcriptomic association degrees in AD versus wild-type mice. Finally, another independent spatial transcriptomics dataset from different AD mouse models and human single-nuclei RNA-seq data/AlzData database were used for validation. This is the first study to identify various gene associations throughout amyloid-β accumulation based on spatial transcriptomics, establishing the foundations to reveal advanced and in-depth AD etiology from a novel perspective based on the comprehensive analyses of gene interactions that are spatio-temporal dependent.

Decoder-seq enhances mRNA capture efficiency in spatial RNA sequencing

Spatial transcriptomics technologies with high resolution often lack high sensitivity in mRNA detection. Here we report a dendrimeric DNA coordinate barcoding design for spatial RNA sequencing (Decoder-seq), which offers both high sensitivity and high resolution. Decoder-seq combines dendrimeric nanosubstrates with microfluidic coordinate barcoding to generate spatial arrays with a DNA density approximately ten times higher than previously reported methods while maintaining flexibility in resolution. We show that the high RNA capture efficiency of Decoder-seq improved the detection of lowly expressed olfactory receptor (Olfr) genes in mouse olfactory bulbs and contributed to the discovery of a unique layer enrichment pattern for two Olfr genes. The near-cellular resolution provided by Decoder-seq has enabled the construction of a spatial single-cell atlas of the mouse hippocampus, revealing dendrite-enriched mRNAs in neurons. When applying Decoder-seq to human renal cell carcinomas, we dissected the heterogeneous tumor microenvironment across different cancer subtypes and identified spatial gradient-expressed genes related to epithelial-mesenchymal transition with the potential to predict tumor prognosis and progression.

Spatial mapping of the HCC landscape identifies unique intratumoral perivascular-immune neighborhoods

BACKGROUND

HCC develops in the context of chronic inflammation; however, the opposing roles the immune system plays in both the development and control of tumors are not fully understood. Mapping immune cell interactions across the distinct tissue regions could provide greater insight into the role individual immune populations have within tumors.

METHODS

A 39-parameter imaging mass cytometry panel was optimized with markers targeting immune cells, stromal cells, endothelial cells, hepatocytes, and tumor cells. We mapped the immune landscape of tumor, invasive margin, and adjacent nontumor regions across 16 resected tumors comprising 144 regions of interest. X-shift clustering and manual gating were used to characterize cell subsets, and Spectre quantified the spatial environment to identify cellular neighborhoods. Ligand-receptor communication was quantified on 2 single-cell RNA-sequencing data sets and 1 spatial transcriptomic data set.

RESULTS

We show immune cell densities remain largely consistent across these 3 regions, except for subsets of monocyte-derived macrophages, which are enriched within the tumors. Mapping cellular interactions across these regions in an unbiased manner identifies immune neighborhoods comprised of tissue-resident T cells, dendritic cells, and various macrophage populations around perivascular spaces. Importantly, we identify multiple immune cells within these neighborhoods interacting with VEGFA+ perivascular macrophages. VEGFA was further identified as a ligand for communication between perivascular macrophages and CD34+ endothelial cells.

CONCLUSIONS

Immune cell neighborhood interactions, but not cell densities, differ between intratumoral and adjacent nontumor regions in HCC. Unique intratumoral immune neighborhoods around the perivascular space point to an altered landscape within tumors. Enrichment of VEGFA+ perivascular macrophages within these tumors could play a key role in angiogenesis and vascular permeability.

Cold and hot tumors: from molecular mechanisms to targeted therapy

Immunotherapy has made significant strides in cancer treatment, particularly through immune checkpoint blockade (ICB), which has shown notable clinical benefits across various tumor types. Despite the transformative impact of ICB treatment in cancer therapy, only a minority of patients exhibit a positive response to it. In patients with solid tumors, those who respond well to ICB treatment typically demonstrate an active immune profile referred to as the "hot" (immune-inflamed) phenotype. On the other hand, non-responsive patients may exhibit a distinct "cold" (immune-desert) phenotype, differing from the features of "hot" tumors. Additionally, there is a more nuanced "excluded" immune phenotype, positioned between the "cold" and "hot" categories, known as the immune "excluded" type. Effective differentiation between "cold" and "hot" tumors, and understanding tumor intrinsic factors, immune characteristics, TME, and external factors are critical for predicting tumor response and treatment results. It is widely accepted that ICB therapy exerts a more profound effect on "hot" tumors, with limited efficacy against "cold" or "altered" tumors, necessitating combinations with other therapeutic modalities to enhance immune cell infiltration into tumor tissue and convert "cold" or "altered" tumors into "hot" ones. Therefore, aligning with the traits of "cold" and "hot" tumors, this review systematically delineates the respective immune characteristics, influencing factors, and extensively discusses varied treatment approaches and drug targets based on "cold" and "hot" tumors to assess clinical efficacy.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Tertiary lymphoid structures and their therapeutic implications in cancer

Tertiary lymphoid structures (TLSs) are ectopic lymphoid aggregates formed by the structured accumulation of immune cells such as B cells and T cells in non-lymphoid tissues induced by infection, inflammation, and tumors. They play a crucial role in the immune response, particularly in association with tumor development, where they primarily exert anti-tumor immune functions during tumorigenesis. Current research suggests that TLSs inhibit tumor growth by facilitating immune cell infiltration and are correlated with favorable prognosis in various solid tumors, serving as an indicator of immunotherapy effectiveness to some extent. Therefore, TLSs hold great promise as a valuable biomarker. Most importantly, immunotherapies aimed to prompting TLSs formation are anticipated to be potent adjuncts to current cancer treatment. This review focuses on the formation process of TLSs and their potential applications in cancer therapy.

Single-cell and spatial transcriptomics reveal apelin/APJ pathway's role in microvessel formation and tumour progression in hepatocellular carcinoma

The apelin receptor (APJ) is a key player in tumour angiogenesis, but its role in hepatocellular carcinoma (HCC) remains unclear. This study aims to elucidate the function of the apelin/APJ pathway in HCC using a multi-omics approach and identify potential therapeutic biomarkers. Differentially expressed genes related to the apelin/APJ axis were identified from bulk transcriptomics to reveal HCC-associated disparities. Single-cell and spatial transcriptomics were used to localize and analyse the function of these genes. Machine learning models were constructed to predict outcomes based on apelin/APJ expression, and experimental validation was conducted to explore the pathway's impact on HCC angiogenesis. Single cell analysis revealed an overexpression of APJ/Aplin in the endothelium. The stemness of endothelial cell (EC) with high apelin/APJ was enhanced, as well as the expression of TGFb, oxidative stresses and PI3K/AKT pathway genes. Spatial transcriptomics confirmed that EC populations with high APJ scores were enriched within the tumour. Machine learning models showed high prognostic accuracy. High APJ expression was linked to worse outcomes (p = 0.001), and AUC values were high (1 year, 3 year, 5 year) (0.95, 0.97, 0.98). Immune suppression and non-responsiveness of immune therapy were also seen in high-risk groups. The experimental validation showed that silencing apelin reduced angiogenesis (p  < 0.05), endothelial proliferation, decreased expression of ANG2, KLF2, VEGFA and lower ERK1/2 phosphorylation. Apelin may serve as a potential therapeutic target in HCC, given its role in promoting tumour angiogenesis and poor patient outcomes.

Spatial single-cell protein landscape reveals vimentinhigh macrophages as immune-suppressive in the microenvironment of hepatocellular carcinoma

Tumor microenvironment heterogeneity in hepatocellular carcinoma (HCC) on a spatial single-cell resolution is unclear. Here, we conducted co-detection by indexing to profile the spatial heterogeneity of 401 HCC samples with 36 biomarkers. By parsing the spatial tumor ecosystem of liver cancer, we identified spatial patterns with distinct prognosis and genomic and molecular features, and unveiled the progressive role of vimentin (VIM)high macrophages. Integration analysis with eight independent cohorts demonstrated that the spatial co-occurrence of VIMhigh macrophages and regulatory T cells promotes tumor progression and favors immunotherapy. Functional studies further demonstrated that VIMhigh macrophages enhance the immune-suppressive activity of regulatory T cells by mechanistically increasing the secretion of interleukin-1β. Our data provide deep insights into the heterogeneity of tumor microenvironment architecture and unveil the critical role of VIMhigh macrophages during HCC progression, which holds potential for personalized cancer prevention and drug discovery and reinforces the need to resolve spatial-informed features for cancer treatment.

Single-cell analysis reveals hypoxia-induced immunosuppressive microenvironment in intrahepatic cholangiocarcinoma

The role of hypoxia in the tumor microenvironment of intrahepatic cholangiocarcinoma (iCCA) remains unclear. Here, we generated a comprehensive atlas of the entire tumor microenvironment and delineated the multifaceted cell-cell interactions to decipher hypoxia-induced pro-tumor immune suppression. We discovered hypoxia is significantly associated with iCCA progression via the activation of HIF1A expression. Moreover, hypoxia-dependent PPARγ-mediated fatty acid oxidation in APOE+ TAMs promoted M2 macrophage polarization by activating the HIF1A-PPARG-CD36 axis. These polarized APOE+ TAMs recruited Treg cell infiltration via the CCL3-CCR5 pair to form an immunosuppressive microenvironment. APOE+ TAMs tended to co-localize spatially with Treg cells in the malignant tissue based on spatial transcriptome data and immunofluorescence analysis results. We identified tumor-reactive CXCL13+ CD8-PreTex with specific high expression of ENTPD1 and ITGAE, which acted as precursors of CD8-Tex and had higher cytotoxicity, lower exhaustion, and more vigorous proliferation. Consequently, CXCL13+ CD8-PreTex functioned as a positive regulator of antitumor immunity by expressing the pro-inflammatory cytokines IFNG and TNF, associated with a better survival outcome. Our study reveals the mechanisms involved in hypoxia-induced immunosuppression and suggests that targeting precursor-exhausted CXCL13+CD8+ T cells might provide a pratical immunotherapeutic approach.

Tertiary Lymphoid Structure in Tumor Microenvironment and Immunotherapy of Lung Cancer

Immune checkpoint inhibitors have opened an era of lung cancer therapy. However, a notable disparity exists in the efficacy of immunotherapy among individual patients. The tertiary lymphoid structure (TLS) is an ectopic lymphocyte aggregation that appears under pathological conditions and is the primary site of action for anti-tumor immunity. It is commonly reported that the presence of TLS within the tumor microenvironment (TME) relates to a favorable clinical prognosis and an excellent response to immunotherapy in lung cancer patients. A thorough understanding of TLS and its dynamic changes in TME has become an attractive focus for optimizing immunotherapy strategies for lung cancer. In this review, we comprehensively generalize the composition, formation, mechanism, detection methods of TLS, and summarize the role of TLS in lung cancer immunotherapy. Finally, induction of TLS is also discussed, which may provide more effective therapeutic strategies for lung cancer therapy.

HCC spatial transcriptomic profiling reveals significant and potentially targetable cancer-endothelial interactions

BACKGROUND

HCC is a highly vascular tumor, and many effective drug regimens target the tumor blood vessels. Prior bulk HCC subtyping data used bulk transcriptomes, which contained a mixture of parenchymal and stromal contributions.

METHODS

We utilized computational deconvolution and cell-cell interaction analyses to cell type-specific (tumor-enriched and vessel-enriched) spatial transcriptomic data collected from 41 resected HCC tissue specimens.

RESULTS

We report that the prior Hoshida bulk transcriptional subtyping schema is driven largely by an endothelial fraction, show an alternative tumor-specific schema has potential prognostic value, and use spatially paired ligand-receptor analyses to identify known and novel (LGALS9 tumor-HAVCR2 vessel) signaling relationships that drive HCC biology in a subtype-specific and potentially targetable manner.

CONCLUSIONS

Our study leverages spatial gene expression profiling technologies to dissect HCC heterogeneity and identify heterogeneous signaling relationships between cancer cells and their endothelial cells. Future validation and expansion of these findings may validate novel cancer-endothelial cell interactions and related drug targets.

Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis

Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.

Defining and modeling dynamic spatial heterogeneity within tumor microenvironments

Many solid tumors exhibit significant genetic, cellular, and biophysical heterogeneity which dynamically evolves during disease progression and after treatment. This constant flux in cell composition, phenotype, spatial relationships, and tissue properties poses significant challenges in accurately diagnosing and treating patients. Much of the complexity lies in unraveling the molecular changes in different tumor compartments, how they influence one another in space and time and where vulnerabilities exist that might be appropriate to target therapeutically. Recent advances in spatial profiling tools and technologies are enabling new insight into the underlying biology of complex tumors, creating a greater understanding of the intricate relationship between cell types, states, and the microenvironment. Here we reflect on some recent discoveries in this area, where the key knowledge and technology gaps lie, and the advancements in spatial measurements and in vitro models for the study of spatial intratumoral heterogeneity.

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data

Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

Spatial transcriptomics reveals tumor-derived SPP1 induces fibroblast chemotaxis and activation in the hepatocellular carcinoma microenvironment

BACKGROUND

The tumor microenvironment (TME) exerts profound effects on tumor progression and therapeutic efficacy. In hepatocellular carcinoma (HCC), the TME is enriched with cancer-associated fibroblasts (CAFs), which secrete a plethora of cytokines, chemokines, and growth factors that facilitate tumor cell proliferation and invasion. However, the intricate architecture of the TME in HCC, as well as the mechanisms driving interactions between tumor cells and CAFs, remains largely enigmatic.

METHODS

We analyzed 10 spatial transcriptomics and 12 single-cell transcriptomics samples sourced from public databases, complemented by 20 tumor tissue samples from liver cancer patients obtained in a clinical setting.

RESULTS

Our findings reveal that tumor cells exhibiting high levels of SPP1 are preferentially localized adjacent to hepatic stellate cells (HSCs). The SPP1 secreted by these tumor cells interacts with the CD44 receptor on HSCs, thereby activating the PI3K/AKT signaling pathway, which promotes the differentiation of HSCs into CAFs. Notably, blockade of the CD44 receptor effectively abrogates this interaction. Furthermore, in vivo studies demonstrate that silencing SPP1 expression in tumor cells significantly impairs HSC differentiation into CAFs, leading to a reduction in tumor volume and collagen deposition within the tumor stroma.

CONCLUSIONS

This study delineates the SPP1-CD44 signaling axis as a pivotal mechanism underpinning the interaction between tumor cells and CAFs. Targeting this pathway holds potential to mitigate liver fibrosis and offers novel therapeutic perspectives for liver cancer management.

Intratumoral CXCL13+ CD160+ CD8+ T cells promote the formation of tertiary lymphoid structures to enhance the efficacy of immunotherapy in advanced gastric cancer

BACKGROUND

Stage IV gastric cancer is a highly heterogeneous and lethal tumor with few therapeutic strategies. The combination of programmed cell death protein 1 inhibitors and chemotherapy is currently the standard frontline treatment regimen for advanced gastric cancer. Nevertheless, it remains a great challenge to screen the beneficiaries of immunochemotherapy and expand indications for this treatment regimen.

METHODS

We conducted a pathological assessment to ascertain the importance of tertiary lymphoid structures based on the tissue samples collected from patients with stage IV gastric cancer (n=15) both prior to and following immunochemotherapy treatment. Additionally, we used spatial (n=10) and single-cell transcriptional analysis (n=97) to investigate the key regulators of tertiary lymphoid structures (TLSs). Multiplex immunofluorescence and image analysis (n=34) were performed to explore the association between tumor-infiltrating CXCL13+ CD160+ CD8+ T cells and TLSs. The relationship between CXCL13+ CD160+ CD8+ T cells and the responsiveness to immunotherapy was also evaluated by multiplex immunofluorescence and image analysis approaches (n=15). Furthermore, we explored the intrinsic characteristics of CXCL13+ CD160+ CD8+ T cells through various experimental techniques, including quantitative reverse transcription-PCR, western blot, and flow cytometry.

RESULTS

We found that responders exhibited higher levels of TLSs and CXCL13+ CD160+ CD8+ T cells in biopsy tissues prior to immunochemotherapy compared with non-responders. Following conversion therapy, responders also had a higher percentage of mature TLSs and a higher number of CXCL13+ CD160+ CD8+ T cells in surgical resections. Moreover, we discovered that vitamin B6 in CD160+ CD8+ T cells could reduce the ubiquitination modification of HIF-1α by MDM2, thereby attenuating the degradation of HIF-1α. Consequently, this led to the transcriptional upregulation of CXCL13 expression, facilitating the recruitment of CXCR5+ B cells and the formation of TLSs.

CONCLUSION

The number and maturity of TLSs, along with the extent of CXCL13+ CD160+ CD8+ T-cell infiltration, might function as potential indicators for assessing the effectiveness of immunotherapy in treating gastric malignancies. Furthermore, our research suggests that vitamin B6 could enhance the secretion of CXCL13 by CD160+ CD8+ T cells by reducing the degradation of HIF-1α. Additionally, we demonstrate that vitamin B6 supplementation or targeting pyridoxal kinase could substantially improve the efficacy of immunotherapies for gastric cancer.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

DGAT2 Plays a Crucial Role to Control ESRRA-PROX1 Transcriptional Network to Maintain Hepatic Mitochondrial Sustainability

BACKGRUOUND

Diacylglycerol O-acyltransferase 2 (DGAT2) synthesizes triacylglycerol (TG) from diacylglycerol; therefore, DGAT2 is considered as a therapeutic target for steatosis. However, the consequence of inhibiting DGAT2 is not fully investigated due to side effects including lethality and lipotoxicity. In this article, we observed the role of DGAT2 in hepatocarcinoma.

METHODS

The role of DGAT2 is analyzed via loss-of-function assay. DGAT2 knockdown (KD) and inhibitor treatment on HepG2 cell line was analyzed. Cumulative analysis of cell metabolism with bioinformatic data were assessed, and further compared with different cohorts of liver cancer patients and non-alcoholic fatty liver disease (NAFLD) patients to elucidate how DGAT2 is regulating cancer metabolism.

RESULTS

Mitochondrial function is suppressed in DGAT2 KD HepG2 cell along with the decreased lipid droplets. In the aspect of the cancer, DGAT2 KD upregulates cell proliferation. Analyzing transcriptome of NAFLD and hepatocellular carcinoma (HCC) patients highlights negatively correlating expression patterns of 73 lipid-associated genes including DGAT2. Cancer patients with the lower DGAT2 expression face lower survival rate. DGAT2 KD cell and patients' transcriptome show downregulation in estrogen- related receptor alpha (ESRRA) via integrated system for motif activity response analysis (ISMARA), with increased dimerization with corepressor prospero homeobox 1 (PROX1).

CONCLUSION

DGAT2 sustains the stability of mitochondria in hepatoma via suppressing ESRRA-PROX1 transcriptional network and hinders HCC from shifting towards glycolytic metabolism, which lowers cell proliferation.

Heterogeneity of hepatocellular carcinoma that responds differently to combination therapy with TACE and Sorafenib as determined by digital spatial gene expression profiling

BACKGROUND

The combination of Sorafenib and transcatheter arterial chemoembolization (TACE) exhibits limited efficacy in the treatment of certain advanced hepatocellular carcinomas (HCC), and the molecular mechanisms underlying resistance to this combination remain unclear.

OBJECTIVE

This study aims to underscore the distinctive contribution of GeoMx DSP technology in elucidating the molecular intricacies of HCC resistance to the Sorafenib and TACE combination.

METHODS

Patients with advanced HCC during the waiting period before liver transplantation were classified into sensitive and resistant groups based on their response to Sorafenib and TACE combination therapy. Employing GeoMx DSP technology for comprehensive gene expression profiling, we identified pivotal molecular targets linked to resistance against combination therapy.

RESULTS

The investigation scrutinized intra-tumoral and inter-individual variances, unveiling a spectrum of crucial molecular targets, such as PLG, PLVAP, immunoglobulin genes, ORM1, and NR4A1, among others. Additionally, we explored signaling pathways associated with treatment responsiveness, including the PPAR signaling pathway. Notably, we emphasized the significance of the immune microenvironment characterized by heightened SPP1 expression in HCC resistance to combination therapy. In the resistant group, SPP1+ tumor-associated macrophage (TAM) infiltration was notably pronounced (p = 0.037), while T-cell depletion showed a mitigated presence (p = 0.013).

CONCLUSION

The study reveals intra- and inter-individual heterogeneity in HCC that is differentially responsive to the combination of Sorafenib and TACE, highlighting multiple key molecular targets associated with treatment resistance. The immune microenvironment is important, and in particular, SPP1+ TAM infiltration may play a key role. Meanwhile, the introduction of immunotherapy in patients resistant to combination therapy may lead to positive results.

Spatial multiomics reveals a subpopulation of fibroblasts associated with cancer stemness in human hepatocellular carcinoma

BACKGROUND

Cancer-associated fibroblasts (CAFs) are the prominent cell type in the tumor microenvironment (TME), and CAF subsets have been identified in various tumors. However, how CAFs spatially coordinate other cell populations within the liver TME to promote cancer progression remains unclear.

METHODS

We combined multi-region proteomics (6 patients, 24 samples), 10X Genomics Visium spatial transcriptomics (11 patients, 25 samples), and multiplexed imaging (92 patients, 264 samples) technologies to decipher the expression heterogeneity, functional diversity, spatial distribution, colocalization, and interaction of fibroblasts. The newly identified CAF subpopulation was validated by cells isolated from 5 liver cancer patients and in vitro functional assays.

RESULTS

We identified a liver CAF subpopulation, marked by the expression of COL1A2, COL4A1, COL4A2, CTGF, and FSTL1, and named F5-CAF. F5-CAF is preferentially located within and around tumor nests and colocalizes with cancer cells with higher stemness in hepatocellular carcinoma (HCC). Multiplexed staining of 92 patients and the bulk transcriptome of 371 patients demonstrated that the abundance of F5-CAFs in HCC was associated with a worse prognosis. Further in vitro experiments showed that F5-CAFs isolated from liver cancer patients can promote the proliferation and stemness of HCC cells.

CONCLUSIONS

We identified a CAF subpopulation F5-CAF in liver cancer, which is associated with cancer stemness and unfavorable prognosis. Our results provide potential mechanisms by which the CAF subset in the TME promotes the development of liver cancer by supporting the survival of cancer stem cells.

DAB2 + macrophages support FAP + fibroblasts in shaping tumor barrier and inducing poor clinical outcomes in liver cancer

Background: Cancer-associated fibroblasts (CAFs) are the key components of the immune barrier in liver cancer. Therefore, gaining a deeper understanding of the heterogeneity and intercellular communication of CAFs holds utmost importance in boosting immunotherapy effectiveness and improving clinical outcomes. Methods: A comprehensive analysis by combing single-cell, bulk, and spatial transcriptome profiling with multiplexed immunofluorescence was conducted to unravel the complexities of CAFs in liver cancer. Results: Through an integrated approach involving 235 liver cancer scRNA-seq samples encompassing over 1.2 million cells, we found that CAFs were particularly increased in hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). FAP + fibroblasts were identified as the dominant subtype of CAFs, and which were mainly involved in extracellular matrix organization and angiogenesis. These CAFs were enriched in the tumor boundary of HCC, but diffusely scattered within ICC. The DAB2 + and SPP1 + tumor-associated macrophages (TAMs) reinforce the function of FAP + CAFs through signals such as TGF-β, PDGF, and ADM. Notably, the interaction between DAB2 + TAMs and FAP + CAFs promoted the formation of immune barrier and correlated with poorer patient survival, non-response to immunotherapy in HCC. High FAP and DAB2 immunohistochemical scores predicted shorter survival and higher serum AFP concentration in a local clinical cohort of 90 HCC patients. Furthermore, this communication pattern might be applicable to other solid malignancies as well. Conclusions: The interaction between DAB2 + TAMs and FAP + CAFs appears crucial in shaping the immune barrier. Strategies aimed at disrupting this communication or inhibiting the functions of FAP + CAFs could potentially enhance immunotherapy effectiveness and improve clinical outcomes.

Tumor-associated macrophage clusters linked to immunotherapy in a pan-cancer census

Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.

Multi-transcriptomics analysis of microvascular invasion-related malignant cells and development of a machine learning-based prognostic model in hepatocellular carcinoma

Background

Microvascular invasion (MVI) stands as a pivotal pathological hallmark of hepatocellular carcinoma (HCC), closely linked to unfavorable prognosis, early recurrence, and metastatic progression. However, the precise mechanistic underpinnings governing its onset and advancement remain elusive.

Methods

In this research, we downloaded bulk RNA-seq data from the TCGA and HCCDB repositories, single-cell RNA-seq data from the GEO database, and spatial transcriptomics data from the CNCB database. Leveraging the Scissor algorithm, we delineated prognosis-related cell subpopulations and discerned a distinct MVI-related malignant cell subtype. A comprehensive exploration of these malignant cell subpopulations was undertaken through pseudotime analysis and cell-cell communication scrutiny. Furthermore, we engineered a prognostic model grounded in MVI-related genes, employing 101 algorithm combinations integrated by 10 machine-learning algorithms on the TCGA training set. Rigorous evaluation ensued on internal testing sets and external validation sets, employing C-index, calibration curves, and decision curve analysis (DCA).

Results

Pseudotime analysis indicated that malignant cells, showing a positive correlation with MVI, were primarily concentrated in the early to middle stages of differentiation, correlating with an unfavorable prognosis. Importantly, these cells showed significant enrichment in the MYC pathway and were involved in extensive interactions with diverse cell types via the MIF signaling pathway. The association of malignant cells with the MVI phenotype was corroborated through validation in spatial transcriptomics data. The prognostic model we devised demonstrated exceptional sensitivity and specificity, surpassing the performance of most previously published models. Calibration curves and DCA underscored the clinical utility of this model.

Conclusions

Through integrated multi-transcriptomics analysis, we delineated MVI-related malignant cells and elucidated their biological functions. This study provided novel insights for managing HCC, with the constructed prognostic model offering valuable support for clinical decision-making.

Identification and validation of inflammatory subtypes in intrahepatic cholangiocellular carcinoma

BACKGROUND

Inflammation plays a critical role in tumor development. Inflammatory cell infiltration and inflammatory mediator synthesis cause changes in the tumor microenvironment (TME) in several cancers, especially in intrahepatic cholangiocellular carcinoma (ICC). However, methods to ascertain the inflammatory state of patients using reliable biomarkers are still being explored.

METHOD

We retrieved the RNA sequencing and somatic mutation analyses results and the clinical characteristics of 244 patients with ICC from published studies. We performed consensus clustering to identify the molecular subtypes associated with inflammation. We compared the prognostic patterns, clinical characteristics, somatic mutation profiles, and immune cell infiltration patterns across inflammatory subtypes. We performed quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC) to confirm gene expression. We performed logistic regression analyses to construct a nomogram predicting the inflammatory status of patients with ICC.

RESULTS

Our results confirmed that ICC can be categorized into an inflammation-high subtype (IHS) and an inflammation-low subtype (ILS). Patients from each group had distinct prognosis, clinical characteristics, and TME composition. Patients with ICC in the IHS group showed poorer prognosis owing to the immunosuppressive microenvironment and high frequency of KRAS and TP53 mutations. Cancer-associated fibroblast (CAF)-derived COLEC11 reduced myeloid inflammatory cell infiltration and attenuated inflammatory responses. The results of qRT-PCR and IHC experiments confirmed that COLEC11 expression levels were significantly reduced in tumor tissues compared to those in paracancerous tissues. Patients with ICC in the IHS group were more likely to respond to treatment with immune checkpoint inhibitors (ICIs) owing to their higher tumor mutational burden (TMB) scores, tumor neoantigen burden (TNB) scores, neoantigen counts, and immune checkpoint expression levels. Finally, we developed a nomogram to effectively predict the inflammatory status of patients with ICC based on their clinical characteristics and inflammatory gene expression levels. We evaluated the calibration, discrimination potential, and clinical utility of the nomogram.

CONCLUSION

The inflammatory response in IHS is primarily induced by myeloid cells. COLEC11 can reduce the infiltration level of this group of cells, and myeloid inflammatory cells may be a novel target for ICC treatment. We developed a novel nomogram that could effectively predict the inflammatory state of patients with ICC, which will be useful for guiding individualized treatment plans.

Single-cell tumor heterogeneity landscape of hepatocellular carcinoma: unraveling the pro-metastatic subtype and its interaction loop with fibroblasts

BACKGROUND

Tumor heterogeneity presents a formidable challenge in understanding the mechanisms driving tumor progression and metastasis. The heterogeneity of hepatocellular carcinoma (HCC) in cellular level is not clear.

METHODS

Integration analysis of single-cell RNA sequencing data and spatial transcriptomics data was performed. Multiple methods were applied to investigate the subtype of HCC tumor cells. The functional characteristics, translation factors, clinical implications and microenvironment associations of different subtypes of tumor cells were analyzed. The interaction of subtype and fibroblasts were analyzed.

RESULTS

We established a heterogeneity landscape of HCC malignant cells by integrated 52 single-cell RNA sequencing data and 5 spatial transcriptomics data. We identified three subtypes in tumor cells, including ARG1+ metabolism subtype (Metab-subtype), TOP2A+ proliferation phenotype (Prol-phenotype), and S100A6+ pro-metastatic subtype (EMT-subtype). Enrichment analysis found that the three subtypes harbored different features, that is metabolism, proliferating, and epithelial-mesenchymal transition. Trajectory analysis revealed that both Metab-subtype and EMT-subtype originated from the Prol-phenotype. Translation factor analysis found that EMT-subtype showed exclusive activation of SMAD3 and TGF-β signaling pathway. HCC dominated by EMT-subtype cells harbored an unfavorable prognosis and a deserted microenvironment. We uncovered a positive loop between tumor cells and fibroblasts mediated by SPP1-CD44 and CCN2/TGF-β-TGFBR1 interaction pairs. Inhibiting CCN2 disrupted the loop, mitigated the transformation to EMT-subtype, and suppressed metastasis.

CONCLUSION

By establishing a heterogeneity landscape of malignant cells, we identified a three-subtype classification in HCC. Among them, S100A6+ tumor cells play a crucial role in metastasis. Targeting the feedback loop between tumor cells and fibroblasts is a promising anti-metastatic strategy.

Immunosuppressive tumor microenvironment in the progression, metastasis, and therapy of hepatocellular carcinoma: from bench to bedside

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy with high incidence, recurrence, and metastasis rates. The emergence of immunotherapy has improved the treatment of advanced HCC, but problems such as drug resistance and immune-related adverse events still exist in clinical practice. The immunosuppressive tumor microenvironment (TME) of HCC restricts the efficacy of immunotherapy and is essential for HCC progression and metastasis. Therefore, it is necessary to elucidate the mechanisms behind immunosuppressive TME to develop and apply immunotherapy. This review systematically summarizes the pathogenesis of HCC, the formation of the highly heterogeneous TME, and the mechanisms by which the immunosuppressive TME accelerates HCC progression and metastasis. We also review the status of HCC immunotherapy and further discuss the existing challenges and potential therapeutic strategies targeting immunosuppressive TME. We hope to inspire optimizing and innovating immunotherapeutic strategies by comprehensively understanding the structure and function of immunosuppressive TME in HCC.

POSTN+ cancer-associated fibroblasts determine the efficacy of immunotherapy in hepatocellular carcinoma

OBJECTIVE

Hepatocellular carcinoma (HCC) poses a significant clinical challenge because the long-term benefits of immune checkpoint blockade therapy are limited. A comprehensive understanding of the mechanisms underlying immunotherapy resistance in HCC is imperative for improving patient prognosis.

DESIGN

In this study, to systematically investigate the characteristics of cancer-associated fibroblast (CAF) subsets and the dynamic communication among the tumor microenvironment (TME) components regulated by CAF subsets, we generated an HCC atlas by compiling single-cell RNA sequencing (scRNA-seq) datasets on 220 samples from six datasets. We combined spatial transcriptomics with scRNA-seq and multiplexed immunofluorescence to identify the specific CAF subsets in the TME that determine the efficacy of immunotherapy in HCC patients.

RESULTS

Our findings highlight the pivotal role of POSTN+ CAFs as potent immune response barriers at specific tumor locations, as they hinder effective T-cell infiltration and decrease the efficacy of immunotherapy. Additionally, we elucidated the interplay between POSTN+ CAFs and SPP1+ macrophages, whereby the former recruits the latter and triggers increased SPP1 expression via the IL-6/STAT3 signaling pathway. Moreover, we demonstrated a spatial correlation between POSTN+ CAFs and SPP1+ macrophages, revealing an immunosuppressive microenvironment that limits the immunotherapy response. Notably, we found that patients with elevated expression levels of both POSTN+ CAFs and SPP1+ macrophages achieved less therapeutic benefit in an immunotherapy cohort.

CONCLUSION

Our research elucidates light on the role of a particular subset of CAFs in immunotherapy resistance, emphasizing the potential benefits of targeting specific CAF subpopulations to improve clinical responses to immunotherapy.