Intratumoural immune heterogeneity as a hallmark of tumour evolution and progression in hepatocellular carcinoma

Differentiating Glioma Recurrence and Pseudoprogression by APTw CEST MRI

OBJECTIVES

Recurrent glioma is highly treatment resistant due to its metabolic, cellular, and molecular heterogeneity and invasiveness. Tumor monitoring by conventional MRI has shortcomings to assess these key glioma characteristics. Recent studies introduced chemical exchange saturation transfer for metabolic imaging in oncology and assessed its diagnostic value for newly diagnosed glioma. This prospective study investigates amide proton transfer-weighted (APTw) MRI at 3 T as an imaging biomarker to elucidate the molecular heterogeneity and invasion patterns of recurrent glioma in comparison to pseudoprogression (PsPD).

MATERIALS AND METHODS

We performed a monocenter, prospective trial and screened 371 glioma patients who received tumor monitoring between August 2021 and March 2024 at our institution. The study included IDH wildtype astrocytoma and IDH mutant astrocytoma and oligodendroglioma, graded according to the WHO 2021 classification. Patients had received clinical standard of care treatment including surgical resection and radiochemotherapy prior to study inclusion. Patients were monitored by 3 monthly MRI follow-up imaging, and response assessment was performed according to the RANO criteria. Within this cohort, we identified 30 patients who presented with recurrent glioma and 12 patients with PsPD. In addition to standard anatomical sequences (FLAIR and T1-w Gd-enhanced sequences), MRI included APTw imaging. After sequence co-registration, semiautomated segmentation was performed of the FLAIR lesion, CE lesion, resection cavity, and the contralateral normal-appearing white matter, and APTw signals were quantified in these regions of interest.

RESULTS

APTw values were highest in solid, Gd-enhancing tumor parts as compared with the nonenhancing FLAIR lesion (APTw: 1.99% vs 1.36%, P = 0.001), whereas there were no detectable APTw alterations in the normal-appearing white matter (APTw: 0.005%, P < 0.001 compared with FLAIR). Patients with progressive disease had higher APTw levels compared with patients with PsPD (APTw: 1.99% vs 1.26%, P = 0.008). Chemical exchange saturation transfer identified heterogeneity within the FLAIR lesion that was not detectable by conventional sequences. There were also focal APTw signal peaks within contrast enhancing lesions as putative metabolic hotspots within recurrent glioma. The resection cavity developed an APTw increase at recurrence that was not detectable prior to recurrence nor in patients with PsPD (APTw before recurrence: 0.6% vs 2.68% at recurrence, P = 0.03).

CONCLUSIONS

Our study shows that APTw imaging can differentiate PD and PsPD. We identify previously undetectable imaging patterns during glioma recurrence, which include alterations within resection cavity associated with disease progression. Our work highlights the clinical potential of APTw imaging for glioma monitoring and further establishes it as an imaging biomarker in neuro-oncology.

Bridging Immune Evasion and Vascular Dynamics for Novel Therapeutic Frontiers in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers globally, driven by chronic liver disease and a complex tumor microenvironment (TME). Recent advances in spatial omics, single-cell analyses, and AI-driven digital pathology have shed light on the intricate heterogeneity of HCC, highlighting key roles for immune suppression, angiogenesis, and fibrosis in tumor progression. This review synthesizes current epidemiological trends, noting a shift from viral hepatitis to metabolic syndrome as a primary etiology in Western populations, and elucidates how TME components-such as tumor-associated macrophages, cancer-associated fibroblasts, vascular endothelial cells, and immunosuppressive cytokines-contribute to resistance against conventional therapies. We detail the evolution of immunotherapeutic strategies from monotherapy to combination regimens, including dual immune checkpoint blockade and the integration of antiangiogenic agents. Emerging circulating and tissue-based biomarkers offer promise for enhanced patient stratification and real-time monitoring of treatment responses. Collectively, these innovations herald a paradigm shift toward TME-directed precision oncology in HCC, emphasizing the need for multi-targeted approaches to synergistically modulate interacting cellular constituents and ultimately improve clinical outcomes.

Dimethyl fumarate abrogates hepatocellular carcinoma growth by inhibiting Nrf2/Bcl-xL axis and enhances sorafenib's efficacy

Hepatocellular carcinoma (HCC) is characterized by poor prognosis and remains a leading cause of cancer mortality worldwide. Advanced HCC is managed with several first-line therapies, including tyrosine kinase inhibitors (TKI) and immunotherapy (mAb-PD-1 and mAb-VEGF). However, the efficacy of HCC therapeutics is often short-lived. Recent studies have demonstrated that the activation of the Nrf2-Bcl-xL pathway contributes to poor prognosis in a subset of HCC patients. Here, we found that dimethyl fumarate (DMF), a drug used for treating psoriasis and multiple sclerosis, regulates the Nrf2-Bcl-xL signaling axis to inhibit HCC growth in a mice xenograft model. Mechanistically, the downregulation of the Nrf2-Bcl-xL axis led to mitochondria stress and apoptosis in vitro and in vivo. Enforced Nrf2 or Bcl-xL expression in HCC cells markedly reversed the antitumor effects of DMF in HCC cells. Importantly, DMF enhanced sorafenib's antitumor effects. Collectively, our results demonstrate new mechanism insights into the antitumor effects of DMF and that Nrf2-targeted therapy might improve HCC treatment outcomes.

NAE1 protein: a prognostic, immunomodulatory, and therapeutic biomarker associated with neddylation in hepatocellular carcinoma

Current predictive biomarkers for clinical outcomes and treatment in hepatocellular carcinoma (HCC) are not reliable enough. Neddylation, a novel post-translational modification, plays a crucial role in the immunomodulation, metabolism, and pathogenesis of HCC. However, whether it can function as a powerful predictive biomarker for HCC remains unknown. In current research, we first identified NAE1 as the most significant neddylation-related gene affecting the prognosis of HCC patients mainly through weighted gene co-expression network (WGCNA) and machine learning. Subsequently, we determined NAE1 expression as an independent risk factor for HCC using univariate and multivariate Cox regression and constructed a nomogram integrating NAE1 expression with clinical characteristics to predict survival probabilities in HCC patients. Bulk and single-cell RNA sequencing analyses revealed that NAE1 expression was primarily positively connected with immune cell infiltration in HCC, as assessed by the six latest immune algorithms. In addition, drug sensitivity and molecular docking collectively revealed the influence of NAE1 expression on the IC50 values of the four agents and the binding interactions between NAE1 protein and these drugs. Furthermore, we found that NAE1 depletion suppressed proliferation, migration, and invasion of HCC cells in vitro experiments. In conclusion, NAE1 protein holds considerable potential as a valuable biomarker for predicting clinical outcomes, immune landscapes, and drug sensitivity in HCC, as well as a promising therapeutic target.

Cancer immune evasion, immunoediting and intratumour heterogeneity

Cancers can avoid immune-mediated elimination by acquiring traits that disrupt antitumour immunity. These mechanisms of immune evasion are selected and reinforced during tumour evolution under immune pressure. Some immunogenic subclones are effectively eliminated by antitumour T cell responses (a process known as immunoediting), which results in a clonally selected tumour. Other cancer cells arise to resist immunoediting, which leads to a tumour that includes several distinct cancer cell populations (referred to as intratumour heterogeneity (ITH)). Tumours with high ITH are associated with poor patient outcomes and a lack of responsiveness to immune checkpoint blockade therapy. In this Review, we discuss the different ways that cancer cells evade the immune system and how these mechanisms impact immunoediting and tumour evolution. We also describe how subclonal antigen presentation in tumours with high ITH can result in immune evasion.

Advancing precision oncology with AI-powered genomic analysis

Multiomics data integration approaches offer a comprehensive functional understanding of biological systems, with significant applications in disease therapeutics. However, the quantitative integration of multiomics data presents a complex challenge, requiring highly specialized computational methods. By providing deep insights into disease-associated molecular mechanisms, multiomics facilitates precision medicine by accounting for individual omics profiles, enabling early disease detection and prevention, aiding biomarker discovery for diagnosis, prognosis, and treatment monitoring, and identifying molecular targets for innovative drug development or the repurposing of existing therapies. AI-driven bioinformatics plays a crucial role in multiomics by computing scores to prioritize available drugs, assisting clinicians in selecting optimal treatments. This review will explain the potential of AI and multiomics data integration for disease understanding and therapeutics. It highlight the challenges in quantitative integration of diverse omics data and clinical workflows involving AI in cancer genomics, addressing the ethical and privacy concerns related to AI-driven applications in oncology. The scope of this text is broad yet focused, providing readers with a comprehensive overview of how AI-powered bioinformatics and integrative multiomics approaches are transforming precision oncology. Understanding bioinformatics in Genomics, it explore the integrative multiomics strategies for drug selection, genome profiling and tumor clonality analysis with clinical application of drug prioritization tools, addressing the technical, ethical, and practical hurdles in deploying AI-driven genomics tools.

Tumor Heterogeneity and the Immune Response in Non-Small Cell Lung Cancer: Emerging Insights and Implications for Immunotherapy

Resistance to immune checkpoint inhibitors (ICIs) represents a major challenge for the effective treatment of non-small cell lung cancer (NSCLC). Tumor heterogeneity has been identified as an important mechanism of treatment resistance in cancer and has been increasingly implicated in ICI resistance. The diversity and clonality of tumor neoantigens, which represent the target epitopes for tumor-specific immune cells, have been shown to impact the efficacy of immunotherapy. Advances in genomic techniques have further enhanced our understanding of clonal landscapes within NSCLC and their evolution in response to therapy. In this review, we examine the role of tumor heterogeneity during immune surveillance in NSCLC and highlight its spatial and temporal evolution as revealed by modern technologies. We explore additional sources of heterogeneity, including epigenetic and metabolic factors, that have come under greater scrutiny as potential mediators of the immune response. We finally discuss the implications of tumor heterogeneity on the efficacy of ICIs and highlight potential strategies for overcoming therapeutic resistance.

Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma

OBJECTIVES

The potential of medical imaging to non-invasively assess intratumoral heterogeneity (ITH) is increasingly being recognized. This study aimed to investigate the value of the ITH-based deep learning model for preoperative prediction of histopathologic grade in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

A total of 858 patients from primary cohort and two external cohorts were included. 3.0T or 1.5T axial portal venous phase MRI images were collected. We conducted radiomics feature-driven K-means clustering for automatic partition to reveal ITH. 2.5D and 3D deep learning models based on ResNet architecture were trained to extract deep learning hidden features of each subregion. The selected features were used to train Random Forest classifier, which constructed the feature-fusion model.

RESULTS

The extracted voxel-level radiomics features were unsupervised clustered by K-means to generate three subregions. In the 2.5D deep learning, the feature-fusion model based on ITH had superior predictive efficacy than the whole-tumor model (AUC: 0.82 vs. 0.72; p = 0.004). Even in the validation and external test sets, this model maintained a high AUC of 0.78-0.83, and net reclassification indices indicated that it could improve prediction by 25-28%. Regarding the prognostic value, overall survival (OS) and recurrence-free survival (RFS) could be significantly stratified by the 2.5D feature-fusion model, and multivariable Cox regressions indicated its signature was identified as a risk predictor for OS and RFS (p < 0.05).

CONCLUSION

The ITH-based feature-fusion model provided a non-invasive method for classifying tumor differentiation in HCC, which may serve as a promising strategy for stratification management.

Hepatic Nuclear Receptors in Cholestasis-to-Cholangiocarcinoma Pathology

Cholestasis, characterized by impaired bile flow, is associated with an increased risk of cholangiocarcinoma (CCA), a malignancy originating from the biliary epithelium and hepatocytes. Hepatic nuclear receptors (NRs) are pivotal in regulating bile acid and metabolic homeostasis, and their dysregulation is implicated in cholestatic liver diseases and the progression of liver cancer. This review elucidates the role of various hepatic NRs in the pathogenesis of cholestasis-to-CCA progression. It explores their impact on bile acid metabolism as well as their interactions with other signaling pathways implicated in CCA development. Additionally, it introduces available murine models of cholestasis/primary sclerosing cholangitis leading to CCA and discusses the clinical potential of targeting hepatic NRs as a promising approach for the prevention and treatment of cholestatic liver diseases and CCA. Understanding the complex interplay between hepatic NRs and cholestasis-to-CCA pathology holds promise for the development of novel preventive and therapeutic strategies for this devastating disease.

Molecular and immune landscape of hepatocellular carcinoma to guide therapeutic decision-making

Liver cancer, primarily HCC, exhibits highly heterogeneous histological and molecular aberrations across tumors and within individual tumor nodules. Such intertumor and intratumor heterogeneities may lead to diversity in the natural history of disease progression and various clinical disparities across the patients. Recently developed multimodality, single-cell, and spatial omics profiling technologies have enabled interrogation of the intertumor/intratumor heterogeneity in the cancer cells and the tumor immune microenvironment. These features may influence the natural history and efficacy of emerging therapies targeting novel molecular and immune pathways, some of which had been deemed undruggable. Thus, comprehensive characterization of the heterogeneities at various levels may facilitate the discovery of biomarkers that enable personalized and rational treatment decisions, and optimize treatment efficacy while minimizing the risk of adverse effects. Such companion biomarkers will also refine HCC treatment algorithms across disease stages for cost-effective patient management by optimizing the allocation of limited medical resources. Despite this promise, the complexity of the intertumor/intratumor heterogeneity and ever-expanding inventory of therapeutic agents and regimens have made clinical evaluation and translation of biomarkers increasingly challenging. To address this issue, novel clinical trial designs have been proposed and incorporated into recent studies. In this review, we discuss the latest findings in the molecular and immune landscape of HCC for their potential and utility as biomarkers, the framework of evaluation and clinical application of predictive/prognostic biomarkers, and ongoing biomarker-guided therapeutic clinical trials. These new developments may revolutionize patient care and substantially impact the still dismal HCC mortality.

Precision models in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents a global health challenge, and ranks among one of the most prevalent and deadliest cancers worldwide. Therapeutic advances have expanded the treatment armamentarium for patients with advanced HCC, but obstacles remain. Precision oncology, which aims to match specific therapies to patients who have tumours with particular features, holds great promise. However, its implementation has been hindered by the existence of numerous 'HCC influencers' that contribute to the high inter-patient heterogeneity. HCC influencers include tumour-related characteristics, such as genetic alterations, immune infiltration, stromal composition and aetiology, and patient-specific factors, such as sex, age, germline variants and the microbiome. This Review delves into the intricate world of HCC, describing the most innovative model systems that can be harnessed to identify precision and/or personalized therapies. We provide examples of how different models have been used to nominate candidate biomarkers, their limitations and strategies to optimize such models. We also highlight the importance of reproducing distinct HCC influencers in a flexible and modular way, with the aim of dissecting their relative contribution to therapy response. Next-generation HCC models will pave the way for faster discovery of precision therapies for patients with advanced HCC.

Cationizable transcytosis manganese nano-oxygenator for enhanced chemo-dynamic immunotherapy in deep tumour tissue

Effective delivery of therapeutic agents for solid tumour treatment is impeded by multiple obstacles, such as aberrant interstitial fluid pressure and high density of the extracellular matrix, which causes impaired penetration to deep avascular tumour tissue that exists in a hypoxic immune cold environment. Only limited tumoricidal effects have been achieved with traditional nanomedicine due to its inefficient penetration and the multiple resistant effects that exist in the tumour microenvironment. Herein, a new chemo-dynamic immunotherapy (CDIT) is proposed based on a transcytosis tumour oxygenator (MnPO2/MC3) with effective chemo-dynamic effects. As a CDIT agent, MnPO2/MC3 is designed and synthesized to enhance deep tumour tissue penetration as well as provide relief of hypoxia to decrease immunosuppression. MnPO2/MC3 is orchestrated by an inner manganese core and double lipid outer layer. The outer layer is constructed by a tumour pH-cationizable outer lipid (D-Lin-MC3-DMA, MC3) layer and O2-loading inner layer. The MC3 lipid endows MnPO2/MC3 with tumour-responsive transcytosis potential, which instead delivers oxygen and Mn deep into tumour tissues. MnPO2/MC3 catalyses hydrogen peroxide to hydroxyl radicals by Mn2+ and increases CD8+ T cell infiltration. The oxygenation and ROS burst by MnPO2/MC3 effectively altered the tumour cold immune microenvironment so that adaptive anti-tumoral immunity was enhanced. MnPO2/MC3-mediated CDIT serving as an effective tumour oxygenator and ROS initiator, effectively suppressed tumour growth while enhancing adaptive anti-tumour immunity.

A prognostic model of 8-T/B cell receptor-related signatures for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is the second leading cause of cancer-related death worldwide. The T cell receptor (TCR) and B cell receptor (BCR) are the receptors on the surface of T or B cell, which are crucial for recognizing tumor antigens. It is profound to establish a practical TCR/BCR-related gene signature prognostic model for the further diagnosis and treatment among HCC patients.

METHODS

In this study, we categorized gene expression data of HCC patients from The Cancer Genome Altas and identified TCR related genes by the Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analysis. Both the CIBERSORT algorithm and the TB tools were used to analyze the features and heterogeneity of the tumor microenvironment.

RESULTS

Finally, an 8-gene prognostic model was successfully established and achieved the validation in both the International Cancer Genome Consortium and Nanfang Hospital cohorts. Patients were divided into high-risk and low-risk groups based on the median of the risk scores. We observed that tumor differentiation was worse while the fibrinogen concentration was higher in the high-risk group of patients. Both the number of unique TCR and BCR clonotypes and the expanded clones were higher in the low-risk group than in the high-risk group.

CONCLUSIONS

Together, our study screened a TCR/BCR-related signature prognostic model, which might turn into a beneficial and practical tool to solve the perplexities of the treatment, prognosis prediction and management for HCC patients.

Evolutionary View of Liver Pathology

Evolutionary medicine emerged in the late twentieth century, integrating principles of natural selection and adaptation with the health sciences. Today, with a rapidly widening gap between the biology of Homo sapiens and its environment, maladaptation or maladaptive disorders can be detected in almost all diseases, including liver dysfunction. However, in hepatology, as in most medical specialties, evolutionary considerations are neglected because the majority of the medical community is not familiar with evolutionary principles. The aim of this brief review is to highlight an evolutionary approach that may facilitate understanding various liver diseases.

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.

Single-cell and bulk transcriptome analysis reveals tumor cell heterogeneity and underlying molecular program in uveal melanoma

BACKGROUND

Uveal melanoma (UM) is a rare and deadly eye cancer with high metastatic potential. Despite the predominance of malignant cells within the tumor microenvironment, the heterogeneity and underlying molecular features remain to be fully characterized.

METHODS

We analyzed single-cell transcriptomic profiling of 37,660 malignant cells from 17 UM tumors. A consensus non-negative factorization algorithm was used to decipher transcriptional programs underlying tumor cell-intrinsic heterogeneity. Tumor-infiltrated immune cells were computationally estimated from bulk transcriptomes and bioinformatics methods. A gene signature was derived for subtyping and prognostic stratification and validated in multicenter cohorts.

RESULTS

ScRNA-seq analysis revealed the existence of diverse subpopulations and transcriptional variability among malignant cells within and between tumors. Furthermore, we observed that the heterogeneity of malignant cell states and compositions correlated with genomic, immunological, and clinical characteristics. By identifying gene expression programs associated with malignant cell heterogeneity at the single cell level, UM samples were classified into two distinct intra-tumoral subtypes (ITMHlo and ITMHhi) with different prognoses and immune microenvironments. Finally, a machine learning-derived 9-gene signature was developed to translate single-cell information into bulk tissue transcriptomes for patient stratification and was validated in multicenter cohorts.

CONCLUSIONS

Our study provides insight into understanding the intra-tumoral heterogeneity of UM and its potential impact on patient stratification.

Deciphering the multifaceted role of microRNAs in hepatocellular carcinoma: Integrating literature review and bioinformatics analysis for therapeutic insights

Hepatocellular carcinoma (HCC) poses a significant global health challenge, necessitating innovative therapeutic strategies. MicroRNAs (miRNAs) have emerged as pivotal regulators of HCC pathogenesis, influencing key processes such as self-renewal, angiogenesis, glycolysis, autophagy, and metastasis. This article integrates findings from a comprehensive literature review and bioinformatics analysis to elucidate the role of miRNAs in HCC. We discuss how dysregulation of miRNAs can drive HCC initiation, progression, and metastasis by modulating various signaling pathways and target genes. Moreover, leveraging high-throughput technology and bioinformatics tools, we identify key miRNAs involved in multiple cancer hallmarks, offering insights into potential combinatorial therapeutic strategies. Through our analysis considering p-values and signaling pathways associated with key features, we unveil miRNAs with simultaneous roles across critical cancer characteristics, providing a basis for the development of high-performance biomarkers. The microRNAs, miR-34a-5p, miR-373-3p, miR-21-5p, miR-214-5p, miR-195-5p, miR-139-5p were identified to be shared microRNAs in stemness, angiogenesis, glycolysis, autophagy, EMT, and metastasis of HCC. However, challenges such as miRNA stability and delivery hinder the translation of miRNA-based therapeutics into clinical practice. This review underscores the importance of further research to overcome existing barriers and realize the full potential of miRNA-based interventions for HCC management.

Supercontinuum-tailoring multicolor imaging reveals spatiotemporal dynamics of heterogeneous tumor evolution

Tumor heterogeneity and tumor evolution contribute to cancer treatment failure. To understand how selective pressures drive heterogeneous tumor evolution, it would be useful to image multiple important components and tumor subclones in vivo. We propose a supercontinuum-tailoring two-photon microscope (SCT-TPM) and realize simultaneous observation of nine fluorophores with a single light beam, breaking through the 'color barrier' of intravital two-photon fluorescence imaging. It achieves excitation multiplexing only by modulating the phase of fiber supercontinuum (SC), allowing to capture rapid events of multiple targets with maintaining precise spatial alignment. We employ SCT-TPM to visualize the spatiotemporal dynamics of heterogeneous tumor evolution under host immune surveillance, particularly the behaviors and interactions of six tumor subclones, immune cells and vascular network, and thus infer the trajectories of tumor progression and clonal competition. SCT-TPM opens up the possibility of tumor lineage tracking and mechanism exploration in living biological systems.

Enhancing spatial domain detection in spatial transcriptomics with EnSDD

Advancements in spatial transcriptomics have transformed our understanding of organ function and tissue microenvironment. However, accurately identifying spatial domains to depict genome heterogeneity and cellular interactions remains a challenge. In this study, we propose EnSDD (Ensemble-learning for Spatial Domain Detection), a method that ingeniously integrates eight state-of-the-art spatial domain detection methods to automatically identify spatial domains. A key innovation of EnSDD is its dynamic weighting mechanism within the ensemble learning process, which optimizes the contribution of each base model and provides a performance evaluation metric without the need for ground truth data. By leveraging the spatial domains identified through EnSDD, we incorporate the detection of domain-specific spatially variable genes and the spatial distribution of cell types, thereby providing deeper insights into tissue heterogeneity. We validate EnSDD across diverse spatial transcriptomics datasets from various tissue organizational structures. Our results demonstrate that EnSDD significantly enhances spatial domain identification accuracy, identifies genes with spatial expression patterns, and reveals domain-specific cell type enrichment patterns, offering invaluable insights into tissue spatial heterogeneity and regionalization.

A quartet of cancer stem cell niches in hepatocellular carcinoma

Hepatocellular Carcinoma (HCC), the most prevalent type of primary liver cancer, is known for its aggressive behavior and poor prognosis. The Cancer Stem Cell theory, which postulates the presence of a small population of self-renewing cells called Cancer Stem Cells (CSCs), provides insights into various clinical and molecular features of HCC such as tumor heterogeneity, metabolic adaptability, therapy resistance, and recurrence. These CSCs are nurtured in the tumor microenvironment (TME), where a mix of internal and external factors creates a tumor-supportive niche that is continuously evolving both spatially and temporally, thus enhancing the tumor's complexity. This review details the origins of hepatic CSCs (HCSCs) and the factors influencing their stem-like qualities. It highlights the reciprocal crosstalk between HCSCs and the TME (hypoxic, vascular, invasive, and immune niches), exploring the signaling pathways involved and how these interactions control the malignant traits of CSCs. Additionally, it discusses potential therapeutic approaches targeting the HCSC niche and their possible uses in clinical practice.

GATA4 downregulation enhances CCL20-mediated immunosuppression in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a deadly cancer with a high global mortality rate, and the downregulation of GATA binding protein 4 (GATA4) has been implicated in HCC progression. In this study, we investigated the role of GATA4 in shaping the immune landscape of HCC.

METHODS

HCC tumor samples were classified into "low" or "normal/high" based on GATA4 RNA expression relative to adjacent non-tumor liver tissues. The immune landscapes of GATA4-low and GATA4-normal/high tumors were analyzed using cytometry by time-of-flight, bulk/spatial transcriptomic analyses and validated by multiplex immunofluorescence.

RESULTS

GATA4-low tumors displayed enrichment in exhausted programmed cell death protein 1+ T cells, immunosuppressive regulatory T cells, myeloid-derived suppressor cells, and macrophages, highlighting the impact of GATA4 downregulation on immunosuppression. Spatial and bulk transcriptomic analyses revealed a negative correlation between GATA4 and C-C Motif Chemokine Ligand 20 (CCL20) expression in HCC. Overexpressing GATA4 confirmed CCL20 as a downstream target, contributing to an immunosuppressive tumor microenvironment, as evidenced by increased regulatory T cells and myeloid-derived suppressor cells in CCL20-high tumors. Lastly, the reduced expression of GATA4 and higher expression of CCL20 were associated with poorer overall survival in patients with HCC, implicating their roles in tumor progression.

CONCLUSIONS

Our study reveals that GATA4 downregulation contributes to an immunosuppressive microenvironment, driven by CCL20-mediated enrichment of regulatory T cells and myeloid-derived suppressor cells in HCC. These findings underscore the critical role of GATA4 reduction in promoting immunosuppression and HCC progression.

Recent developments in molecular targeted therapies for hepatocellular carcinoma in the genomic era

INTRODUCTION

Primary liver cancer is a major health problem being the sixth most frequent cancer in the world and the third cause of cancer-related death in the world. The most common histological type of liver cancer is hepatocellular carcinoma (HCC, 75-80%).

AREAS COVERED

Based on primary literature, this review provides an updated analysis of studies of genetic characterization of HCC at the level of gene mutation profiling, copy number alterations, and gene expression, with the definition of molecular subgroups and the identification of some molecular biomarkers and therapeutic targets. Recent therapeutic developments are also highlighted.

EXPERT OPINION

Deepening the understanding of the molecular complexity of HCC is progressively paving the way for the development of more personalized treatment approaches. Two important strategies involve the definition and validation of molecularly defined therapeutic targets in a subset of HCC patients and the identification of suitable biomarkers for approved systematic therapies (multikinase inhibitors and immunotherapies). The extensive molecular characterization of patients at the genomic and transcriptomic levels and the inclusion of detailed and relevant translational studies in clinical trials will represent a fundamental tool for improving the benefit of systemic therapies in HCC.

Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma

BACKGROUND

The current study's objective is to evaluate the molecular genetic mechanisms influencing the biological behavior of hepatocellular carcinoma (HCC) by analyzing the transcriptomic and epigenetic signatures of the tumors.

METHODS

Transcriptomic data were downloaded from the NCBI GEO database. We investigated the expression differences between the GSE46444 (48 cirrhotic tissues versus 88 HCC tissues) and GSE63898 (168 cirrhotic tissues versus 228 HCC tissues) data sets using GEO2R. Differentially expressed genes were evaluated using GO and KEGG metabolic pathway analysis websites. Whole genome bisulfite sequencing (WGBS) and Methylated DNA Immunoprecipitation Sequencing (MeDIP-Seq) data sets (26 HCC tissues versus 26 adjacent non-tumoral tissues) were also downloaded from the NCBI SRA database. These data sets were analyzed using Bismark and QSEA, respectively. The methylation differences between the groups were assessed using functional enrichment analysis.

RESULTS

In the GSE46444 data set, 80 genes were upregulated, and 315 genes were downregulated in the tumor tissue (HCC tissue) compared to the non-tumor cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were downregulated in the cirrhotic tissue compared to the tumor tissues. WGBS revealed that 20 protein-coding loci were hypermethylated. while the hypomethylated regions were non-protein-coding. The methylated residues of the tumor tissue, non-tumorous cirrhotic tissue, and healthy tissue were comparable. MeDIP-Seq, conducted on tumoral and non-tumoral tissues, identified hypermethylated or hypomethylated areas as protein-coding regions. The functional enrichment analysis indicated that these genes were related to pathways including peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras, and PI3K/AKT signal transduction.

CONCLUSIONS

The investigation of transcriptomic and epigenetic mechanisms identified several genes significant in the biological behavior of HCC. These genes present potential targets for the development of targeted therapy.

Spatial Transcriptome-Wide Profiling of Small Cell Lung Cancer Reveals Intra-Tumoral Molecular and Subtype Heterogeneity

Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid growth and early metastasis and is susceptible to treatment resistance and recurrence. Understanding the intra-tumoral spatial heterogeneity in SCLC is crucial for improving patient outcomes and clinically relevant subtyping. In this study, a spatial whole transcriptome-wide analysis of 25 SCLC patients at sub-histological resolution using GeoMx Digital Spatial Profiling technology is performed. This analysis deciphered intra-tumoral multi-regional heterogeneity, characterized by distinct molecular profiles, biological functions, immune features, and molecular subtypes within spatially localized histological regions. Connections between different transcript-defined intra-tumoral phenotypes and their impact on patient survival and therapeutic response are also established. Finally, a gene signature, termed ITHtyper, based on the prevalence of intra-tumoral heterogeneity levels, which enables patient risk stratification from bulk RNA-seq profiles is identified. The prognostic value of ITHtyper is rigorously validated in independent multicenter patient cohorts. This study introduces a preliminary tumor-centric, regionally targeted spatial transcriptome resource that sheds light on previously unexplored intra-tumoral spatial heterogeneity in SCLC. These findings hold promise to improve tumor reclassification and facilitate the development of personalized treatments for SCLC patients.

The association between tumour heterogeneity and immune evasion mechanisms in hepatocellular carcinoma and its clinical implications

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality worldwide. The emergence of combination therapy, atezolizumab (anti-PDL1, immune checkpoint inhibitor) and bevacizumab (anti-VEGF) has revolutionised the management of HCC. Despite this breakthrough, the best overall response rate with first-line systemic therapy is only about 30%, owing to intra-tumoural heterogeneity, complex tumour microenvironment and the lack of predictive biomarkers. Many groups have attempted to classify HCC based on the immune microenvironment and have consistently observed better outcomes in immunologically "hot" HCC. We summarised possible mechanisms of tumour immune evasion based on the latest literature and the rationale for combination/sequential therapy to improve treatment response. Lastly, we proposed future strategies and therapies to overcome HCC immune evasion to further improve treatment outcomes of HCC.

The role of tissue-derived extracellular vesicles in tumor microenvironment

The tumor microenvironment (TME) is a highly heterogeneous ecosystem that plays critical roles in the initiation, progression, invasion, and metastasis of cancers. Extracellular vesicles (EVs), as emerging components of the host-tumor communication, are lipid-bilayer membrane structures that are secreted by most cell types into TEM and increasingly recognized as critical elements that regulate the interaction between tumor cells and their surroundings. They contain a variety of bioactive molecules, such as proteins, nucleic acids, and lipids, and participate in various pathophysiological processes while regulating intercellular communication. While many studies have focused on the EVs derived from different body fluids or cell culture supernatants, the direct isolation of tissue-derived EVs (Ti-EVs) has garnered more attention due to the advantages of tissue specificity and accurate reflection of tissue microenvironment. In this review, we summarize the protocol for isolating Ti-EVs from different tissue interstitium, discuss the role of tumor-derived and adipose tissue-derived Ti-EVs in regulating TME. In addition, we sum up the latest application of Ti-EVs as potential biomarkers for cancer diseases.

Immune features are associated with response to neoadjuvant chemo-immunotherapy for muscle-invasive bladder cancer

Neoadjuvant cisplatin-based chemotherapy is standard of care for muscle-invasive bladder cancer (MIBC). Immune checkpoint inhibition (ICI) alone, and ICI in combination with chemotherapy, have demonstrated promising pathologic response ( Collapse

Key Words

• predictive markers
• bladder cancer
• immunotherapy
• tumour immunology

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MESH Headings

• Humans
• Urinary Bladder Neoplasms/drug therapy
• Urinary Bladder Neoplasms/immunology
• Urinary Bladder Neoplasms/therapy
• Urinary Bladder Neoplasms/pathology
• Neoadjuvant Therapy/methods
• Deoxycytidine/analogs & derivatives
• Deoxycytidine/therapeutic use
• Deoxycytidine/administration & dosage
• Gemcitabine
• Immunotherapy/methods
• Male
• Cisplatin/therapeutic use
• Cisplatin/administration & dosage
• Female
• Antibodies, Monoclonal, Humanized/therapeutic use
• Antineoplastic Combined Chemotherapy Protocols/therapeutic use
• Immune Checkpoint Inhibitors/therapeutic use
• Aged
• Middle Aged
• Neoplasm Invasiveness
• Interleukin-9/metabolism
• B7-H1 Antigen/metabolism
• Biomarkers, Tumor/blood
• Treatment Outcome

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Grants

• F30 CA278317 NCI NIH HHS
• 1F30CA278317-01A1 U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)
• Merck & Co., Inc. | Merck Sharp and Dohme (Merck Sharp & Dohme)
• U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI)

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Affiliation(s)

Wolfgang Beckabir Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Mi Zhou Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jin Seok Lee Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA
Steven P Vensko Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Mark G Woodcock Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Hsing-Hui Wang Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Sara E Wobker Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Gatphan Atassi Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Alec D Wilkinson Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Kenneth Fowler Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Leah M Flick Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jeffrey S Damrauer Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Michael R Harrison Division of Medical Oncology, Department of Medicine, Duke Cancer Institute, Duke University, Durham, NC, USA
Karen P McKinnon Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA
Tracy L Rose Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Matthew I Milowsky Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Jonathan S Serody Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Microbiology and Immunology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
William Y Kim Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Division of Hematology, Department of Medicine, UNC School of Medicine, Chapel Hill, NC, USA.
Benjamin G Vincent Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Curriculum in Bioinformatics and Computational Biology, UNC School of Medicine, Chapel Hill, NC, USA. Division of Oncology, Department of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Computational Medicine Program, UNC School of Medicine, Chapel Hill, NC, USA.

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Heterogeneity and molecular landscape of melanoma: implications for targeted therapy

Uveal cancer (UM) offers a complex molecular landscape characterized by substantial heterogeneity, both on the genetic and epigenetic levels. This heterogeneity plays a critical position in shaping the behavior and response to therapy for this uncommon ocular malignancy. Targeted treatments with gene-specific therapeutic molecules may prove useful in overcoming radiation resistance, however, the diverse molecular makeups of UM call for a patient-specific approach in therapy procedures. We need to understand the intricate molecular landscape of UM to develop targeted treatments customized to each patient's specific genetic mutations. One of the promising approaches is using liquid biopsies, such as circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA), for detecting and monitoring the disease at the early stages. These non-invasive methods can help us identify the most effective treatment strategies for each patient. Single-cellular is a brand-new analysis platform that gives treasured insights into diagnosis, prognosis, and remedy. The incorporation of this data with known clinical and genomics information will give a better understanding of the complicated molecular mechanisms that UM diseases exploit. In this review, we focused on the heterogeneity and molecular panorama of UM, and to achieve this goal, the authors conducted an exhaustive literature evaluation spanning 1998 to 2023, using keywords like "uveal melanoma, "heterogeneity". "Targeted therapies"," "CTCs," and "single-cellular analysis".

New roles of tumor-derived exosomes in tumor microenvironment

Throughout tumorigenesis, the co-evolution of tumor cells and their surrounding microenvironment leads to the development of malignant phenotypes. Cellular communication within the tumor microenvironment (TME) plays a critical role in influencing various aspects of tumor progression, including invasion and metastasis. The release of exosomes, a type of extracellular vesicle, by most cell types in the body, is an essential mediator of intercellular communication. A growing body of research indicates that tumor-derived exosomes (TDEs) significantly expedite tumor progression through multiple mechanisms, inducing epithelial-mesenchymal transition and macrophage polarization, enhancing angiogenesis, and aiding in the immune evasion of tumor cells. Herein, we describe the formation and characteristics of the TME, and summarize the contents of TDEs and their diverse functions in modulating tumor development. Furthermore, we explore potential applications of TDEs in tumor diagnosis and treatment.

Versican Associates with Tumor Immune Phenotype and Limits T-cell Trafficking via Chondroitin Sulfate

Immunotherapies for cancers of epithelial origin have limited efficacy, and a growing body of evidence links the composition of extracellular matrix (ECM) with the likelihood of a favorable response to treatment. The ECM may be considered an immunologic barrier, restricting the localization of cytotoxic immune cells to stromal areas and inhibiting their contact with tumor cells. Identifying ECM components of this immunologic barrier could provide targets that whether degraded in situ may support antitumor immunity and improve immunotherapy response. Using a library of primary triple-negative breast cancer tissues, we correlated CD8+ T-cell tumor contact with ECM composition and identified a proteoglycan, versican (VCAN), as a putative member of the immunologic barrier. Our analysis reveals that CD8+ T-cell contact with tumor associates with the location of VCAN expression, the specific glycovariant of VCAN [defined through the pattern of posttranslational attachments of glycosaminoglycans (GAG)], and the cell types that produce the variant. In functional studies, the isomers of chondroitin sulfate presented on VCAN have opposing roles being either supportive or inhibiting of T-cell trafficking, and removal of the GAGs ameliorates these effects on T-cell trafficking. Overall, we conclude that VCAN can either support or inhibit T-cell trafficking within the tumor microenvironment depending on the pattern of GAGs present, and that VCAN is a major component of the ECM immunologic barrier that defines the type of response to immunotherapy.

SIGNIFICANCE

The response to immunotherapy has been poor toward solid tumors despite immune cells infiltrating into the tumor. The ECM has been associated with impacting T-cell infiltration toward the tumor and in this article we have identified VCAN and its structural modification, chondroitin sulfate as having a key role in T-cell invasion.

Mechanisms of drug resistance in HCC

HCC comprises ∼80% of primary liver cancer. HCC is the only major cancer for which death rates have not improved over the last 10 years. Most patients are diagnosed with advanced disease when surgical and locoregional treatments are not feasible or effective. Sorafenib, a multikinase inhibitor targeting cell growth and angiogenesis, was approved for advanced unresectable HCC in 2007. Since then, other multikinase inhibitors have been approved. Lenvatinib was found to be noninferior to sorafenib as a first-line agent. Regorafenib, cabozantinib, and ramucirumab were shown to prolong survival as second-line agents. Advances in immunotherapy for HCC have also added hope for patients, but their efficacy remains limited. A large proportion of patients with advanced HCC gain no long-term benefit from systemic therapy due to primary and acquired drug resistance, which, combined with its rising incidence, keeps HCC a highly fatal disease. This review summarizes mechanisms of primary and acquired resistance to therapy and includes methods for bypassing resistance. It addresses recent advancements in immunotherapy, provides new perspectives on the linkage between drug resistance and molecular etiology of HCC, and evaluates the role of the microbiome in drug resistance. It also discusses alterations in signaling pathways, dysregulation of apoptosis, modulations in the tumor microenvironment, involvement of cancer stem cells, changes in drug metabolism/transport, tumor hypoxia, DNA repair, and the role of microRNAs in drug resistance. Understanding the interplay among these factors will provide guidance on the development of new therapeutic strategies capable of improving patient outcomes.

Immune checkpoints between epithelial-mesenchymal transition and autophagy: A conflicting triangle

Inhibitory immune checkpoint (ICP) molecules are pivotal in inhibiting innate and acquired antitumor immune responses, a mechanism frequently exploited by cancer cells to evade host immunity. These evasion strategies contribute to the complexity of cancer progression and therapeutic resistance. For this reason, ICP molecules have become targets for antitumor drugs, particularly monoclonal antibodies, collectively referred to as immune checkpoint inhibitors (ICI), that counteract such cancer-associated immune suppression and restore antitumor immune responses. Over the last decade, however, it has become clear that tumor cell-associated ICPs can also induce tumor cell-intrinsic effects, in particular epithelial-mesenchymal transition (EMT) and macroautophagy (hereafter autophagy). Both of these processes have profound implications for cancer metastasis and drug responsiveness. This article reviews the positive or negative cross-talk that tumor cell-associated ICPs undergo with autophagy and EMT. We discuss that tumor cell-associated ICPs are upregulated in response to the same stimuli that induce EMT. Moreover, ICPs themselves, when overexpressed, become an EMT-inducing stimulus. As regards the cross-talk with autophagy, ICPs have been shown to either stimulate or inhibit autophagy, while autophagy itself can either up- or downregulate the expression of ICPs. This dynamic equilibrium also extends to the autophagy-apoptosis axis, further emphasizing the complexities of cellular responses. Eventually, we delve into the intricate balance between autophagy and apoptosis, elucidating its role in the broader interplay of cellular dynamics influenced by ICPs. In the final part of this article, we speculate about the driving forces underlying the contradictory outcomes of the reciprocal, inhibitory, or stimulatory effects between ICPs, EMT, and autophagy. A conclusive identification of these driving forces may allow to achieve improved antitumor effects when using combinations of ICIs and compounds acting on EMT and/or autophagy. Prospectively, this may translate into increased and/or broadened therapeutic efficacy compared to what is currently achieved with ICI-based clinical protocols.

Identification of Two Distinct Immune Subtypes in Hepatitis B Virus (HBV)-Associated Hepatocellular Carcinoma (HCC)

HBV is the most common risk factor for HCC development, accounting for almost 50% of cases worldwide. Despite significant advances in immunotherapy, there is limited information on the HBV-HCC tumor microenvironment (TME), which may influence the response to checkpoint inhibitors. Here, we characterize the TME in a unique series of liver specimens from HBV-HCC patients to identify who might benefit from immunotherapy. By combining an extensive immunohistochemistry analysis with the transcriptomic profile of paired liver samples (tumor vs. nontumorous tissue) from 12 well-characterized Caucasian patients with HBV-HCC, we identified two distinct tumor subtypes that we defined immune-high and immune-low. The immune-high subtype, seen in half of the patients, is characterized by a high number of infiltrating B and T cells in association with stromal activation and a transcriptomic profile featuring inhibition of antigen presentation and CTL activation. All the immune-high tumors expressed high levels of CTLA-4 and low levels of PD-1, while PD-L1 was present only in four of six cases. In contrast, the immune-low subtype shows significantly lower lymphocyte infiltration and stromal activation. By whole exome sequencing, we documented that four out of six individuals with the immune-low subtype had missense mutations in the CTNNB1 gene, while only one patient had mutations in this gene in the immune-high subtype. Outside the tumor, there were no differences between the two subtypes. This study identifies two distinctive immune subtypes in HBV-associated HCC, regardless of the microenvironment observed in the surrounding nontumorous tissue, providing new insights into pathogenesis. These findings may be instrumental in the identification of patients who might benefit from immunotherapy.

AMPKα2 promotes tumor immune escape by inducing CD8+ T-cell exhaustion and CD4+ Treg cell formation in liver hepatocellular carcinoma

BACKGROUND

Adenosine monophosphate-activated protein kinase (AMPK) is associated with the development of liver hepatocellular carcinoma (LIHC). AMPKα2, an α2 subunit of AMPK, is encoded by PRKAA2, and functions as the catalytic core of AMPK. However, the role of AMPKα2 in the LIHC tumor immune environment is unclear.

METHODS

RNA-seq data were obtained from the Cancer Genome Atlas and Genotype-Tissue Expression databases. Using the single-cell RNA-sequencing dataset for LIHC obtained from the China National Genebank Database, the communication between malignant cells and T cells in response to different PRKAA2 expression patterns was evaluated. In addition, the association between PRKAA2 expression and T-cell evolution during tumor progression was explored using Pseudotime analysis, and the role of PRKAA2 in metabolic reprogramming was explored using the R "scMetabolis" package. Functional experiments were performed in LIHC HepG2 cells.

RESULTS

AMPK subunits were expressed in tissue-specific and substrate-specific patterns. PRKAA2 was highly expressed in LIHC tissues and was associated with poor patient prognosis. Tumors with high PRKAA2 expression displayed an immune cold phenotype. High PRKAA2 expression significantly promoted LIHC immune escape. This result is supported by the following evidence: 1) the inhibition of major histocompatibility complex class I (MHC-I) expression through the regulation of interferon-gamma activity in malignant cells; 2) the promotion of CD8+ T-cell exhaustion and the formation of CD4+ Treg cells in T cells; 3) altered interactions between malignant cells and T cells in the tumor immune environment; and 4) induction of metabolic reprogramming in malignant cells.

CONCLUSIONS

Our study indicate that PRKAA2 may contribute to LIHC progression by promoting metabolic reprogramming and tumor immune escape through theoretical analysis, which offers a theoretical foundation for developing PRKAA2-based strategies for personalized LIHC treatment.

A whirl of radiomics-based biomarkers in cancer immunotherapy, why is large scale validation still lacking?

The search for understanding immunotherapy response has sparked interest in diverse areas of oncology, with artificial intelligence (AI) and radiomics emerging as promising tools, capable of gathering large amounts of information to identify suitable patients for treatment. The application of AI in radiology has grown, driven by the hypothesis that radiology images capture tumor phenotypes and thus could provide valuable insights into immunotherapy response likelihood. However, despite the rapid growth of studies, no algorithms in the field have reached clinical implementation, mainly due to the lack of standardized methods, hampering study comparisons and reproducibility across different datasets. In this review, we performed a comprehensive assessment of published data to identify sources of variability in radiomics study design that hinder the comparison of the different model performance and, therefore, clinical implementation. Subsequently, we conducted a use-case meta-analysis using homogenous studies to assess the overall performance of radiomics in estimating programmed death-ligand 1 (PD-L1) expression. Our findings indicate that, despite numerous attempts to predict immunotherapy response, only a limited number of studies share comparable methodologies and report sufficient data about cohorts and methods to be suitable for meta-analysis. Nevertheless, although only a few studies meet these criteria, their promising results underscore the importance of ongoing standardization and benchmarking efforts. This review highlights the importance of uniformity in study design and reporting. Such standardization is crucial to enable meaningful comparisons and demonstrate the validity of biomarkers across diverse populations, facilitating their implementation into the immunotherapy patient selection process.

Immune landscape of hepatocellular carcinoma tumor microenvironment identifies a prognostic relevant model

Background

Various studies highlighted that immune cell-mediated inflammatory processes play crucial roles in the progression and treatment of hepatocellular carcinoma (HCC). However, the immune microenvironment of HCC is still poorly characterized. Exploring the role of immune-related genes (IRGs) and describing the immune landscape in HCC would provide insights into tumor-immune co-evolution along HCC progression.

Methods

We integrated the datasets with complete prognostic information from the Cancer Genome Atlas (TCGA) database and GEO DataSets (GSE14520, GSE76427, and GSE54236) to construct a novel immune landscape based on the Cibersort algorithm and reveal the prognostic signature in HCC patients.

Results

To describe the tumor microenvironment (TME) in HCC, immune infiltration patterns were defined using the CIBERSORT method, and a prognostic signature contains 5 types of immune cells, including 3 high-risk immune cells (T.cells. CD4. memory. resting, Macrophages.M0, Macrophages.M2) and 2 low-risk immune cells (Plasma. cells, T.cells.CD8), were finally constructed. A novel prognostic index, based on prognostic immune risk score (pIRG), was developed using the univariate Cox regression analyses and LASSO Cox regression algorithm. Furthermore, the ROC curve and KM curve showed that the TME signatures had a stable value in predicting the prognosis of HCC patients in the internal training cohort, internal validation, and external validation cohort. Differential genes analysis and qPCR experiment showed that the expression levels of AKR1B10, LAPTM4B, MMP9, and SPP1 were significantly increased in high-risk patients, while the expression of CD5L was lower. Further analysis found that AKR1B10 and MMP9 were associated with higher M0 macrophage infiltration, while CD5L was associated with higher plasma cell infiltration.

Conclusions

Taken together, we performed a comprehensive evaluation of the immune landscape of HCC and constructed a novel and robust prognostic prediction model. AKR1B10, LAPTM4B, MMP9, SPP1, and CD5L were involved in important processes in the HCC tumor microenvironment and were expected to become HCC prediction markers and potential targets of treatment.

A robust primary liver cancer subtype related to prognosis and drug response based on a multiple combined classifying strategy

The recurrence or resistance to treatment of primary liver cancer (PLL) is significantly related to the heterogeneity present within the tumor. In this study, we integrated prognosis risk score, mRNAsi index, and immune characteristics clustering to classify patients. The four subtypes obtained from the combined classification are associated with PLC's prognosis and drug response. In these subtypes, we observed mRNAsiH_ICCA subtype, the intersection between high mRNAsi and immune characteristics clustering A, had the worst prognosis. Specifically, immune characteristics clustering B (ICC_B) had high drug sensitivity in most drugs regardless of the value of mRNAsi. On the other hand, patients with low mRNAsi responded better to ten drugs including KU-55933 and NU7441, while patients with high mRNAsi might benefit from drugs like Leflunomide. By matching the specific characteristics of each combined subtype with the drug-induced cell line expression profile, we identified a group of potential therapeutic drugs that might regulate the expression of disease signature genes. We developed a feasible multiple combined typing strategy, hoping to guide therapeutic selection and promote the development of precision medicine.

γδ T Cells: A Game Changer in the Future of Hepatocellular Carcinoma Immunotherapy

Hepatocellular carcinoma (HCC) remains a global health challenge with limited treatment options and a poor prognosis for advanced-stage patients. Recent advancements in cancer immunotherapy have generated significant interest in exploring novel approaches to combat HCC. One such approach involves the unique and versatile subset of T cells known as γδ T cells. γδ T cells represent a distinct subset of T lymphocytes that differ from conventional αβ T cells in terms of antigen recognition and effector functions. They play a crucial role in immunosurveillance against various malignancies, including HCC. Recent studies have demonstrated that γδ T cells can directly recognize and target HCC cells, making them an attractive candidate for immunotherapy. In this article, we aimed to explore the role exerted by γδ T cells in the context of HCC. We investigate strategies designed to maximize the therapeutic effectiveness of these cells and examine the challenges and opportunities inherent in applying these research findings to clinical practice. The potential to bring about a revolutionary shift in HCC immunotherapy by capitalizing on the unique attributes of γδ T cells offers considerable promise for enhancing patient outcomes, warranting further investigation.

Unsupervised spatially embedded deep representation of spatial transcriptomics

Optimal integration of transcriptomics data and associated spatial information is essential towards fully exploiting spatial transcriptomics to dissect tissue heterogeneity and map out inter-cellular communications. We present SEDR, which uses a deep autoencoder coupled with a masked self-supervised learning mechanism to construct a low-dimensional latent representation of gene expression, which is then simultaneously embedded with the corresponding spatial information through a variational graph autoencoder. SEDR achieved higher clustering performance on manually annotated 10 × Visium datasets and better scalability on high-resolution spatial transcriptomics datasets than existing methods. Additionally, we show SEDR's ability to impute and denoise gene expression (URL: https://github.com/JinmiaoChenLab/SEDR/ ).

Integrated multi-omics profiling to dissect the spatiotemporal evolution of metastatic hepatocellular carcinoma

Comprehensive molecular analyses of metastatic hepatocellular carcinoma (HCC) are lacking. Here, we generate multi-omic profiling of 257 primary and 176 metastatic regions from 182 HCC patients. Primary tumors rich in hypoxia signatures facilitated polyclonal dissemination. Genomic divergence between primary and metastatic HCC is high, and early dissemination is prevalent. The remarkable neoantigen intratumor heterogeneity observed in metastases is associated with decreased T cell reactivity, resulting from disruptions to neoantigen presentation. We identify somatic copy number alterations as highly selected events driving metastasis. Subclones without Wnt mutations show a stronger selective advantage for metastasis than those with Wnt mutations and are characterized by a microenvironment rich in activated fibroblasts favoring a pro-metastatic phenotype. Finally, metastases without Wnt mutations exhibit higher enrichment of immunosuppressive B cells that mediate terminal exhaustion of CD8+ T cells via HLA-E:CD94-NKG2A checkpoint axis. Collectively, our results provide a multi-dimensional dissection of the complex evolutionary process of metastasis.

Mechanism-guided fine-tuned microbiome potentiates anti-tumor immunity in HCC

Microbiome, including bacteria, fungi, and viruses, plays a crucial role in shaping distal and proximal anti-tumor immunity. Mounting evidence showed that commensal microbiome critically modulates immunophenotyping of hepatocellular carcinoma (HCC), a leading cause of cancer-related death. However, their role in anti-tumor surveillance of HCC is still poorly understood. Herein, we spotlighted growing interests in how the microbiome influences the progression and immunotherapeutic responses of HCC via changing local tumor microenvironment (TME) upon translocating to the sites of HCC through different "cell-type niches". Moreover, we summarized not only the associations but also the deep insight into the mechanisms of how the extrinsic microbiomes interplay with hosts to shape immune surveillance and regulate TME and immunotherapeutic responses. Collectively, we provided a rationale for a mechanism-guided fine-tuned microbiome to be neoadjuvant immunotherapy in the near future.

EMT-induced immune evasion: connecting the dots from mechanisms to therapy

Epithelial-mesenchymal transition (EMT) is a dynamic program crucial for organismal development and tissue regeneration. Unfortunately, this program is often hijacked by epithelial tumors to facilitate metastasis. Beyond its role in cancer spread, EMT increases cancer cell survival by activating stem cell programs and bypassing apoptotic programs. Importantly, the capacity of EMT to enforce tumor progression by altering the tumor cell phenotype without triggering immune responses opens the intriguing possibility of a mechanistic link between EMT-driven cancers and immune evasion. Indeed, EMT has been acknowledged as a of driver immune evasion, but the mechanisms are still evolving. Here, we review recent insights into the influence of EMT on tumor immune evasion. Specifically, we focus on the mechanistic roles of EMT in immune escape as the basis that may provide a platform for innovative therapeutic approaches in advanced tumors. We summarize promising therapeutic approaches currently in clinical trials and trending preclinical studies aimed at reinvigorating the tumor microenvironment to create immune-permissive conditions that facilitates immune-mediated tumor clearance. We anticipate that this will assist researchers and pharmaceutical companies in understanding how EMT compromises the immune response, potentially paving the way for effective cancer therapies.

CDK1-SRC Interaction-Dependent Transcriptional Activation of HSP90AB1 Promotes Antitumor Immunity in Hepatocellular Carcinoma

This study aimed to analyze multiomics data and construct a regulatory network involving kinases, transcription factors, and immune genes in hepatocellular carcinoma (HCC) prognosis. The researchers used transcriptomic, proteomic, and clinical data from TCGA and GEO databases to identify immune genes associated with HCC. Statistical analysis, meta-analysis, and protein-protein interaction analyses were performed to identify key immune genes and their relationships. In vitro and in vivo experiments validated the CDK1-SRC-HSP90AB1 network's effects on HCC progression and antitumor immunity. A prognostic risk model was developed using clinicopathological features and immune infiltration. The immune genes LPA, BIRC5, HSP90AB1, ROBO1, and CCL20 were identified as the key prognostic factors. The CDK1-SRC-HSP90AB1 network promoted HCC cell proliferation and migration, with HSP90AB1 being transcriptionally activated by the CDK1-SRC interaction. Manipulating SRC or HSP90AB1 reversed the effects of CDK1 and SRC on HCC. The CDK1-SRC-HSP90AB1 network also influenced HCC tumor formation and antitumor immunity. Overall, this study highlights the importance of the CDK1-SRC-HSP90AB1 network as a crucial immune-regulatory network in the HCC prognosis.

Integrative pan-cancer analysis of cuproplasia-associated genes for the genomic and clinical characterization of 33 tumors

BACKGROUND

The molecular mechanisms driving tumorigenesis have continually been the focus of researchers. Cuproplasia is defined as copper-dependent cell growth and proliferation, including its primary and secondary roles in tumor formation and proliferation through signaling pathways. In this study, we analyzed the differences in the expression of cuproplasia-associated genes (CAGs) in pan-cancerous tissues and investigated their role in immune-regulation and tumor prognostication.

METHODS

Raw data from 11,057 cancer samples were acquired from multiple databases. Pan-cancer analysis was conducted to analyze the CAG expression, single-nucleotide variants, copy number variants, methylation signatures, and genomic signatures of micro RNA (miRNA)-messenger RNA (mRNA) interactions. The Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal databases were used to evaluate drug sensitivity and resistance against CAGs. Using single-sample Gene Set Enrichment Analysis (ssGSEA) and Immune Cell Abundance Identifier database, immune cell infiltration was analyzed with the ssGSEA score as the standard.

RESULTS

Aberrantly expressed CAGs were found in multiple cancers. The frequency of single-nucleotide variations in CAGs ranged from 1% to 54% among different cancers. Furthermore, the correlation between CAG expression in the tumor microenvironment and immune cell infiltration varied among different cancers. ATP7A and ATP7B were negatively correlated with macrophages in 16 tumors including breast invasive carcinoma and esophageal carcinoma, while the converse was true for MT1A and MT2A . In addition, we established cuproplasia scores and demonstrated their strong correlation with patient prognosis, immunotherapy responsiveness, and disease progression ( P  <0.05). Finally, we identified potential candidate drugs by matching gene targets with existing drugs.

CONCLUSIONS

This study reports the genomic characterization and clinical features of CAGs in pan-cancers. It helps clarify the relationship between CAGs and tumorigenesis, and may be helpful in the development of biomarkers and new therapeutic agents.

Biomarkers for immunotherapy of hepatocellular carcinoma

Immune-checkpoint inhibitors (ICIs) are now widely used for the treatment of patients with advanced-stage hepatocellular carcinoma (HCC). Two different ICI-containing regimens, atezolizumab plus bevacizumab and tremelimumab plus durvalumab, are now approved standard-of-care first-line therapies in this setting. However, and despite substantial improvements in survival outcomes relative to sorafenib, most patients with advanced-stage HCC do not derive durable benefit from these regimens. Advances in genome sequencing including the use of single-cell RNA sequencing (both of tumour material and blood samples), as well as immune cell identification strategies and other techniques such as radiomics and analysis of the microbiota, have created considerable potential for the identification of novel predictive biomarkers enabling the accurate selection of patients who are most likely to derive benefit from ICIs. In this Review, we summarize data on the immunology of HCC and the outcomes in patients receiving ICIs for the treatment of this disease. We then provide an overview of current biomarker use and developments in the past 5 years, including gene signatures, circulating tumour cells, high-dimensional flow cytometry, single-cell RNA sequencing as well as approaches involving the microbiome, radiomics and clinical markers. Novel concepts for further biomarker development in HCC are then discussed including biomarker-driven trials, spatial transcriptomics and integrated 'big data' analysis approaches. These concepts all have the potential to better identify patients who are most likely to benefit from ICIs and to promote the development of new treatment approaches.

Immune-active tumor-adjacent tissues are associated with favorable prognosis in stage I lung squamous cell carcinoma

The immunogenomic features of tumor-adjacent lungs (TALs) in stage I lung squamous cell carcinoma (LUSC) are not clear. Multiomics analyses of tumor tissues and paired TALs from 59 stage I LUSC patients were performed. Compared to tumors, TALs exhibited a better-preserved immune contexture indicated by upregulation of immune pathways, increased immune infiltration, and higher expression of immune effector molecules. Notably, TALs had no mutations in PTEN and KEAP1, a lower incidence of human leukocyte antigen (HLA) loss and higher expression of HLA class I genes, major histocompatibility complex (MHC) I chaperones, and interferon (IFN)-γ-associated genes. Digital spatial profiling validated the generally higher immune infiltration in TALs and revealed a higher level of immune heterogeneity in LUSC tumors. Importantly, patients with higher immune infiltration in TALs had significantly longer survival, while high immune heterogeneity was associated with inferior patient survival. Our work can be considered in the selection of patients for adjuvant therapy, especially immunotherapy.

Targeted Molecular Profiling of Circulating Cell-Free DNA in Patients With Advanced Hepatocellular Carcinoma

PURPOSE

Next-generation sequencing (NGS) of tumor-derived, circulating cell-free DNA (cfDNA) may aid in diagnosis, prognostication, and treatment of patients with hepatocellular carcinoma (HCC). The operating characteristics of cfDNA mutational profiling must be determined before routine clinical implementation.

METHODS

This was a single-center, retrospective study with the primary objective of defining genomic alterations in circulating cfDNA along with plasma-tissue genotype agreement between NGS of matched tumor samples in patients with advanced HCC. cfDNA was analyzed using a clinically validated 129-gene NGS assay; matched tissue-based NGS was analyzed with a US Food and Drug Administration-authorized NGS tumor assay.

RESULTS

Fifty-three plasma samples from 51 patients with histologically confirmed HCC underwent NGS-based cfDNA analysis. Genomic alterations were detected in 92.2% of patients, with the most commonly mutated genes including TERT promoter (57%), TP53 (47%), CTNNB1 (37%), ARID1A (18%), and TSC2 (14%). In total, 37 (73%) patients underwent paired tumor NGS, and concordance was high for mutations observed in patient-matched plasma samples: TERT (83%), TP53 (94%), CTNNB1 (92%), ARID1A (100%), and TSC2 (71%). In 10 (27%) of 37 tumor-plasma samples, alterations were detected by cfDNA analysis that were not detected in the patient-matched tumors. Potentially actionable mutations were identified in 37% of all cases including oncogenic/likely oncogenic alterations in TSC1/2 (18%), BRCA1/2 (8%), and PIK3CA (8%). Higher average variant allele fraction was associated with elevated alpha-fetoprotein, increased tumor volume, and no previous systemic therapy, but did not correlate with overall survival in treatment-naïve patients.

CONCLUSION

Tumor mutation profiling of cfDNA in HCC represents an alternative to tissue-based genomic profiling, given the high degree of tumor-plasma NGS concordance; however, genotyping of both blood and tumor may be required to detect all clinically actionable genomic alterations.

Computational Quantification of Cancer Immunoediting

The remarkable success of cancer immunotherapy has revolutionized cancer treatment, emphasizing the importance of tumor-immune interactions in cancer evolution and treatment. Cancer immunoediting describes the dual effect of tumor-immune interactions: inhibiting tumor growth by destroying tumor cells and facilitating tumor escape by shaping tumor immunogenicity. To better understand tumor-immune interactions, it is critical to develop computational methods to measure the extent of cancer immunoediting. In this review, we provide a comprehensive overview of the computational methods for quantifying cancer immunoediting. We focus on describing the basic ideas, computational processes, advantages, limitations, and influential factors. We also summarize recent advances in quantifying cancer immunoediting studies and highlight future research directions. As the methods for quantifying cancer immunoediting are continuously improved, future research will further help define the role of immunity in tumorigenesis and hopefully provide a basis for the design of new personalized cancer immunotherapy strategies.

Development of a novel tumor microenvironment-related radiogenomics model for prognosis prediction in hepatocellular carcinoma

Background

The tumour microenvironment (TME) has occupied a potent position in the tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Radiogenomics is an emerging field that integrates imaging and genetic information, thus offering a novel class of non-invasive biomarkers with diagnostic, prognostic, and treatment response. However, optimal evaluation methodologies for radiogenomics in patients with HCC have not been well established. Therefore, this study aims to develop a radiogenomics models, associating contrast-enhanced computed tomography (CECT) based radiomics features and transcriptomics data with TME, to increase predictive precision for overall survival (OS) in patients with HCC.

Methods

Transcriptome profiles of 365 patients with HCC from The Cancer Genome Atlas (TCGA)-HCC cohort were used to obtain TME-related genes by differential expression analysis. TME-related radiomics features of 53 patients with HCC from The Cancer Imaging Archive (TCIA)-HCC cohort matched with the TCGA-HCC cohort were screened via correlation analysis. Furthermore, a radiogenomics score-based prognostic model was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCIA-HCC cohort. Finally, the ability to predict prognosis and the value of the model in identifying the abundance of immune cell infiltration were investigated.

Results

A radiogenomics prognostic model was developed, which incorporated 1 radiomics feature [original_gray-level co-occurrence matrix (glcm)_inverse difference normalized (Idn)] and 3 genes [spen paralogue and orthologue C‑terminal domain containing 1 (SPOCD1); killer cell lectin like receptor B1 (KLRB1); G protein-coupled receptor 182 (GPR182)]. The model performed satisfactorily in the training and test sets [1-year, 2-year, 3-year area under the curve (AUC) of 0.81, 0.85 and 0.87 in the training set, respectively; and 0.73, 0.83, and 0.84 in the test set, respectively]. Moreover, the model showed that higher radiogenomics scores were associated with worse OS and lower levels of immune infiltration.

Conclusions

The novel CECT-based radiogenomics model may provide valuable insights for prognostic stratification and TME assessment of patients with HCC.