Accelerating the understanding of cancer biology through the lens of genomics

Genomics in Cancer of Unknown Primary: Utility in Modern Clinical Practice

Cancer of unknown primary (CUP) is defined as metastatic cancer where the primary tumour responsible for metastatic spread cannot be identified despite thorough diagnostics. It has a very poor prognosis, is rapidly progressive, and has limited treatment options beyond empirical chemotherapy. Modern genomic advances play a role in identifying the primary tissue of origin (TOO) and in allowing molecular targeted therapies and immunotherapies to be used in the treatment of CUP patients. Whole genome and whole transcriptome sequencing produce vast amounts of data, and predictive algorithms and artificial intelligence can be used to make this data clinically actionable. Recent trials have shown that using genomic data in clinical decision-making improves outcomes for CUP patients. Liquid biopsies are an exciting development that allow for repeated genomic analysis throughout treatment or when tissue is difficult to obtain. Genomics should be used routinely in the care of CUP patients, at diagnosis and to aid treatment decisions.

Advances and applications of multiomics technologies in precision diagnosis and treatment for gastric cancer

Gastric cancer (GC), one of the most prevalent malignancies worldwide, is distinguished by extensive genetic and phenotypic heterogeneity, posing persistent challenges to conventional diagnostic and therapeutic strategies. The significant global burden of GC highlights an urgent need to unravel its complex underlying mechanisms, discover novel diagnostic and prognostic biomarkers, and develop more effective therapeutic interventions. In this context, this review comprehensively examines the transformative roles of cutting-edge technologies, including radiomics, pathomics, genomics, transcriptomics, epigenomics, proteomics, and metabolomics, in advancing precision diagnosis and treatment for GC. Multiomics data analysis not only deepens our understanding of GC pathogenesis and molecular subtypes but also identifies promising biomarkers, facilitating the creation of tailored therapeutic approaches. Additionally, integrating multiomics approaches holds immense potential for elucidating drug resistance mechanisms, predicting patient outcomes, and uncovering novel therapeutic targets, thereby laying a robust foundation for precision medicine in the comprehensive management of GC.

Neoadjuvant therapy for colorectal cancer from 2015 to 2024: a visual analysis and bibliometric analysis

Background

Colorectal cancer (CRC) imposes a substantial burden on global health., but research trends and hotspots in this field are still not clear. The purpose of this research is to create a visual knowledge map based on bibliometric analysis, identify research hotspots and predict future research trends.

Method

Utilizing the Web of Science Core Collection (WoSCC) as data source and integrating the visualization capabilities of the Bibliometrix R software package, CiteSpace, and VOSviewer, analyze the authors, research institutions, countries, cited documents, publishing journals, abstracts, and keyword information of literature pertaining to neoadjuvant therapy for colorectal cancer spanning from January 2015 to December 2024.

Result

The analysis included 1,587 articles from 1,464 institutions, 385 journals, and 61 countries or regions. China has the largest number of publications (449) and the largest number of citations (5,035). The United States occupies the leading position with an average of 21.6. "Annals of Surgical Oncology" is the most published journal with 51 articles, and "Journal of Clinical Oncology" is the journal with the most references (4,465 references). Highly cited references focus on clinical trials and guidelines for neoadjuvant therapy for colorectal cancer. In recent years, the most important keywords in the research on colorectal cancer and neoadjuvant therapy have been "artificial intelligence", "total neoadjuvant therapy" and "immunotherapy".

Conclusion

This article provided a review of the research on neoadjuvant therapy for colorectal cancer, can provide reference for subsequent research on neoadjuvant therapy for colorectal cancer. The results offered valuable insights and data that informed the direction of future advancements.

Spatial relationships and interactions of immune cell niches are linked to the pathologic response of muscle-invasive bladder cancer to neoadjuvant therapy

BACKGROUND

The identification of the complex spatial architecture of immune cell infiltration and its interaction mechanisms within tumor ecosystems provides crucial insights into therapeutic responses to neoadjuvant therapy in muscle-invasive bladder cancer (MIBC). This study aims to characterize the spatial features of distinct cell-type niches within the tumor microenvironment (TME) of patients with varying responses to neoadjuvant therapy.

METHODS

We performed spatial transcriptomic profiling on six MIBC specimens obtained from a registered clinical trial (ChiCTR2000032359), generating whole-transcriptome spatial atlases to map the TME architecture. High-throughput analytical frameworks were employed to deconstruct the TME, and key findings were validated through immunohistochemistry and mouse model experiments.

RESULTS

Our analysis revealed that tissues from complete responders exhibited greater infiltration of T and B cells, with the formation of tertiary lymphoid structure (TLS). Trajectory analysis identified CCL19/CCL21 as the key signaling molecules driving TLS formation in MIBC. Mouse experiments demonstrated that recombinant CCL19/CCL21 protein injections promoted intratumoral TLS formation and enhance the efficacy of immunotherapy. Furthermore, we observed significant intrinsic heterogeneity within individual tumors, which may contribute to the lack of therapeutic efficacy in MIBC.

CONCLUSIONS

This study underscores the critical role of TLS formation in the response to neoadjuvant therapy in MIBC. We identified CCL19/CCL21 as key drivers of TLS formation within MIBC tumors and potential immune-sensitizing agents. Additionally, the intrinsic heterogeneity of tumor should be considered a significant factor influencing therapeutic efficacy.

A single-cell atlas reveals immune heterogeneity in anti-PD-1-treated non-small cell lung cancer

Anti-PD-(L)1 treatment is standard for non-small cell lung cancer (NSCLC), but patients show variable responses to the same regimen. The tumor immune microenvironment (TIME) is associated with immunotherapy response, yet the heterogeneous underlying therapeutic outcomes remain underexplored. We applied single-cell RNA and TCR sequencing (scRNA/TCR-seq) to analyze surgical tumor samples from 234 NSCLC patients post-neoadjuvant chemo-immunotherapy. Analyses revealed five distinct TIME subtypes with varying major pathological response (MPR) rates. MPR patients had elevated levels of FGFBP2+ NK/NK-like T cells, memory B cells, or effector T cells, while non-MPR patients showed higher CCR8+ Tregs. T cell clonal expansion analyses unveiled heterogeneity in non-MPR patients, marked by varying expansions of Tex-relevant cells and CCR8+ Tregs. Precursor exhausted T cells (Texp cells) correlated with recurrence-free survival, identifying a patient subgroup with reduced recurrence risk despite lack of MPR. Our study dissects TIME heterogeneity in response to chemoimmunotherapy, offering insights for NSCLC management.

Advances in multi-omics studies of microvascular invasion in hepatocellular carcinoma

Microvascular invasion (MVI) represents a pivotal independent prognostic factor for the recurrence of hepatocellular carcinoma (HCC) after surgery. It contributes to early intervention for potentially recurrent HCC to enhance patient outcomes and increase survival rates. Traditionally, the diagnosis of MVI has relied on postoperative pathological analysis, and accurate preoperative detection methodologies are lacking. Recent research suggests that multi-omics strategies play a role in definitively diagnosing MVI before surgery and offering personalized selection for clinical decision-making in HCC management. This review meticulously examines a multi-omics approach for the preoperative prediction of MVI in HCC patients, aiming to innovate diagnostic paradigms to anticipate postsurgical recurrence, thereby facilitating earlier and more personalized therapeutic strategies.

Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data

Accurate cancer prognosis is essential for personalized clinical management, guiding treatment strategies and predicting patient survival. Conventional methods, which depend on the subjective evaluation of histopathological features, exhibit significant inter-observer variability and limited predictive power. To overcome these limitations, we developed cross-attention transformer-based multimodal fusion network (CATfusion), a deep learning framework that integrates multimodal histology-genomic data for comprehensive cancer survival prediction. By employing self-supervised learning strategy with TabAE for feature extraction and utilizing cross-attention mechanisms to fuse diverse data types, including mRNA-seq, miRNA-seq, copy number variation, DNA methylation variation, mutation data, and histopathological images. By successfully integrating this multi-tiered patient information, CATfusion has become an advanced survival prediction model to utilize the most diverse data types across various cancer types. CATfusion's architecture, which includes a bidirectional multimodal attention mechanism and self-attention block, is adept at synchronizing the learning and integration of representations from various modalities. CATfusion achieves superior predictive performance over traditional and unimodal models, as demonstrated by enhanced C-index and survival area under the curve scores. The model's high accuracy in stratifying patients into distinct risk groups is a boon for personalized medicine, enabling tailored treatment plans. Moreover, CATfusion's interpretability, enabled by attention-based visualization, offers insights into the biological underpinnings of cancer prognosis, underscoring its potential as a transformative tool in oncology.

Finding a needle in a haystack: functional screening for novel targets in cancer immunology and immunotherapies

Genome-wide functional genetic screening has been widely used in the biomedicine field, which makes it possible to find a needle in a haystack at the genetic level. In cancer research, gene mutations are closely related to tumor development, metastasis, and recurrence, and the use of state-of-the-art powerful screening technologies, such as clustered regularly interspaced short palindromic repeat (CRISPR), to search for the most critical genes or coding products provides us with a new possibility to further refine the cancer mapping and provide new possibilities for the treatment of cancer patients. The use of CRISPR screening for the most critical genes or coding products has further refined the cancer atlas and provided new possibilities for the treatment of cancer patients. Immunotherapy, as a highly promising cancer treatment method, has been widely validated in the clinic, but it could only meet the needs of a small proportion of cancer patients. Finding new immunotherapy targets is the key to the future of tumor immunotherapy. Here, we revisit the application of functional screening in cancer immunology from different perspectives, from the selection of diverse in vitro and in vivo screening models to the screening of potential immune checkpoints and potentiating genes for CAR-T cells. The data will offer fresh therapeutic clues for cancer patients.

Synthesis and Application of a Novel Multifunctional Nanoprodrug for Synergistic Chemotherapy and Phototherapy with Hydrogen Sulfide Gas

With the dilemma of limited efficacy of individual therapies, it is crucial to develop innovative combination therapy systems to target the complex pathogenesis of cancer. In this study, we designed a nanoprodrug ISL@MIL-101-ADT to facilitate synergistic delivery of hydrogen sulfide (H2S) and prodrug ISL for specific eradication of tumor cells with minimal toxicity and maximal efficacy. The nanoprodrug passively targeted tumors through enhanced permeation and retention effects, followed by disintegration and release of IR780, lonidamine (LND), and H2S. IR780 localizes LND to mitochondria to enhance therapeutic effects and turn on the phototherapy and chemotherapy when exposed to a laser; H2S inhibits procancer signaling pathways and mitochondrial function. In vivo experiments have demonstrated that ISL@MIL-101-ADT exhibits excellent pharmacokinetic properties and significant tumor inhibitory effects. Additionally, this nanoprodrug possesses outstanding photothermal and fluorescence imaging capabilities. Therefore, we strongly believe that the nanoprodrug present herein holds great potential for application in cancer therapy.

From Images to Genes: Radiogenomics Based on Artificial Intelligence to Achieve Non-Invasive Precision Medicine in Cancer Patients

With the increasing demand for precision medicine in cancer patients, radiogenomics emerges as a promising frontier. Radiogenomics is originally defined as a methodology for associating gene expression information from high-throughput technologies with imaging phenotypes. However, with advancements in medical imaging, high-throughput omics technologies, and artificial intelligence, both the concept and application of radiogenomics have significantly broadened. In this review, the history of radiogenomics is enumerated, related omics technologies, the five basic workflows and their applications across tumors, the role of AI in radiogenomics, the opportunities and challenges from tumor heterogeneity, and the applications of radiogenomics in tumor immune microenvironment. The application of radiogenomics in positron emission tomography and the role of radiogenomics in multi-omics studies is also discussed. Finally, the challenges faced by clinical transformation, along with future trends in this field is discussed.

Metastatic brain tumors: from development to cutting-edge treatment

Metastatic brain tumors, also called brain metastasis (BM), represent a challenging complication of advanced tumors. Tumors that commonly metastasize to the brain include lung cancer and breast cancer. In recent years, the prognosis for BM patients has improved, and significant advancements have been made in both clinical and preclinical research. This review focuses on BM originating from lung cancer and breast cancer. We briefly overview the history and epidemiology of BM, as well as the current diagnostic and treatment paradigms. Additionally, we summarize multiomics evidence on the mechanisms of tumor occurrence and development in the era of artificial intelligence and discuss the role of the tumor microenvironment. Preclinically, we introduce the establishment of BM models, detailed molecular mechanisms, and cutting-edge treatment methods. BM is primarily treated with a comprehensive approach, including local treatments such as surgery and radiotherapy. For lung cancer, targeted therapy and immunotherapy have shown efficacy, while in breast cancer, monoclonal antibodies, tyrosine kinase inhibitors, and antibody-drug conjugates are effective in BM. Multiomics approaches assist in clinical diagnosis and treatment, revealing the complex mechanisms of BM. Moreover, preclinical agents often need to cross the blood-brain barrier to achieve high intracranial concentrations, including small-molecule inhibitors, nanoparticles, and peptide drugs. Addressing BM is imperative.

Patient-derived tumor organoids: A preclinical platform for personalized cancer therapy

Patient-derived tumor organoids (PDTOs) represent a significant advancement in cancer research and personalized medicine. These organoids, derived from various cancer types, have shown the ability to retain the genetic and molecular characteristics of the original tumors, allowing for the detailed study of tumor biology and drug responses on an individual basis. The success rates of establishing PDTOs vary widely and are influenced by factors such as cancer type, tissue quality, and media composition. Furthermore, the dynamic nature of organoid cultures may also lead to unique molecular characteristics that deviate from the original tumors, affecting their interpretation in clinical settings without the implementation of rigorous validation and establishment of standardized protocols. Recent studies have supported the correlation between PDTOs and the corresponding patient response. Although these studies involved a small number of patients, they promoted the integration of PDTOs in observational and interventional clinical trials to advance translational cancer therapies.

Collateral lethality: A unique type of synthetic lethality in cancers

Genetic interactions play crucial roles in cell-essential functions. Intrinsic genetic defects in tumors typically involve gain-of- and loss-of-function mutations in tumor suppressor genes (TSGs) and oncogenes, respectively, providing potential antitumor vulnerabilities. Moreover, tumor cells with TSG deficiencies exhibit heightened sensitivity to the inhibition of compensatory pathways. Synthetic and collateral lethality are two strategies used for exploiting novel drug targets in multiple types of cancer. Collateral lethality is a unique type of synthetic lethality that occurs when passenger genes are co-deleted in neighboring TSGs. Although synthetic lethality has already been successfully demonstrated in clinical practice, antitumor therapeutics based on collateral lethality are predominantly still in the preclinical phase. Therefore, screening for potential genetic interactions within the cancer genome has emerged as a promising approach for drug development. Here, the two conceptual therapeutic strategies that involve the deletion or inactivation of cancer-specific TSGs are discussed. Moreover, existing approaches for screening and identifying potential gene partners are also discussed. Particularly, this review highlights the current advances of "collateral lethality" in the preclinical phase and addresses the challenges involved in translating them into therapeutic applications. This review provides insights into these strategies as new opportunities for the development of personalized antitumor therapies.

Enhancing immuno-oncology investigations through multidimensional decoding of tumor microenvironment with IOBR 2.0

The use of large transcriptome datasets has greatly improved our understanding of the tumor microenvironment (TME) and helped develop precise immunotherapies. The growing application of multi-omics, single-cell RNA sequencing (scRNA-seq), and spatial transcriptome sequencing has led to many new insights, yet these findings still require clinical validation in large cohorts. To advance multi-omics integration in TME research, we have upgraded the Immuno-Oncology Biological Research (IOBR) package to IOBR 2.0, restructuring and standardizing its analytical workflow. IOBR 2.0 offers six modules for TME analysis based on multi-omics data, including data preprocessing, TME estimation, TME infiltration pattern identification, cellular interaction analysis, genome and TME interaction, and feature visualization, as well as modeling. Additionally, IOBR 2.0 enables constructing gene signatures and reference matrices from scRNA-seq data for TME deconvolution. The user-friendly pipeline provides comprehensive insights into tumor-immune interactions, and a detailed GitBook(https://iobr.github.io/book/) offers a complete manual and analysis guide for each module.

Long-read RNA sequencing of archival tissues reveals novel genes and transcripts associated with clear cell renal cell carcinoma recurrence and immune evasion

The use of long-read direct RNA sequencing (DRS) and PCR cDNA sequencing (PCS) in clinical oncology remains limited, with no direct comparison between the two methods. We used DRS and PCS to study clear cell renal cell carcinoma (ccRCC), focusing on new transcript and gene discovery. Twelve primary ccRCC archival tumors, six from patients who went on to relapse, were analyzed. Results were validated in an independent cohort of 20 patients by qRT-PCR and compared to DRS analysis of RCC4 cells. In archival clinical samples and due to the long-term storage, the average read length was lower (400-500 nt) than that achieved through DRS of RCC4 cells (>1100 nt). Still, deconvolution analysis showed a loss of immune infiltrate in primary tumors of patients who relapse as reported by others. Differentially expressed genes in patients who went on to relapse were determined with good overlap between DRS and PCS, identifying LINC04216 and the T-cell exhaustion marker TOX as novel candidate recurrence-associated genes. Novel transcript analysis revealed over 10,000 candidate novel transcripts detected by both methods and in ccRCC cells in vitro, including a novel CD274 (PD-L1) transcript encoding for the soluble version of the protein with a longer 3' UTR and lower stability than the annotated transcript. Both methods identified 414 novel genes, also detected in RCC4 cells, including a novel noncoding gene overexpressed in patients who relapse. Overall, we showcase the use of PCS and DRS in archival tumor samples to uncover unmapped features of cancer transcriptomes, linked to disease progression and immune evasion.

GLN2 as a key biomarker and therapeutic target: evidence from a comprehensive pan-cancer study using molecular, functional, and bioinformatic analyses

BACKGROUND

GNL2, a nuclear protein, is involved in ribosome production and cell cycle regulation. However, its expression and function in different types of tumors are not well understood. Comprehensive studies across multiple cancer types are needed to assess the potential of GNL2 as a diagnostic, prognostic, and immunological marker.

METHODS

mRNA expression data, copy number alteration threshold data, masked copy number segmentation data, and DNA methylation 450 K data from The Cancer Genome Atlas (TCGA) pan-cancer cohort were obtained from the Firehose database. Additional data, including miRNA, The Cancer Proteome Atlas (TCPA), mutation data, and clinical information, were sourced from the University of California Santa Cruz (UCSC) Xena database. The cBioPortal database facilitates the examination of GNL2 mutation frequency, location, and 3D structure in the TCGA database. Gene Expression Omnibus (GEO) data verified the transcriptome level expression in the TCGA cohort. Protein expression levels were analyzed via the Human Protein Atlas (HPA) database and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Gene set enrichment analysis (GSEA) was employed to investigate the biological role of GNL2 across cancers. Multiple immune infiltration algorithms from the TIMER2.0 database were utilized to examine the correlation between GNL2 expression and the tumor immune microenvironment. The transcriptome-wide immune infiltration results were validated using 72 single-cell datasets from the Tumor Immune Single-cell Hub (TISCH) database. Pan-cancer survival maps were constructed, and GNL2 expression in different molecular subtypes across cancers was examined. The relationship between GNL2 and drug resistance was investigated using data from CellMiner, GDSC, and CTRP. The Comparative Toxicogenomics Database (CTD) was used to identify chemicals affecting GNL2 expression.

RESULTS

GNL2 is located primarily in the nucleus, and its expression is regulated mainly through somatic copy number alteration (SCNA) and aberrant DNA methylation, according to TCGA data. Database analysis and immunohistochemical results from clinical samples revealed high GNL2 expression in most tumors, which was correlated with diagnostic significance. High GNL2 expression often indicates a poor prognosis with pan-cancer prognostic value. Gene set enrichment analysis (GSEA) suggested that GNL2 is involved in tumor development through cell proliferation-related pathways. GNL2 expression is correlated with the expression of immune-related genes and the infiltration levels of multiple immune cells. The relationships between GNL2 and various drugs and chemicals were examined, revealing its influence on drug sensitivity and identifying five chemicals countering GNL2-mediated pro-cancer effects.

CONCLUSION

Comprehensive bioinformatics analysis of GNL2 in pan-cancer tissues, combined with experimental validation, elucidated the pan-cancer expression pattern of GNL2, determined its diagnostic and prognostic value, and explored the biological functions of GNL2. GNL2 may be involved in the regulation of cell cycle progression and remodeling of the tumor microenvironment and is associated with poor prognosis as a risk factor in most tumors. The potential of GNL2-based cancer therapies is emphasized, assisting in predicting the response to chemotherapy.

Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data

Introduction

The combination of next-generation sequencing technology and Cancer Genome Atlas (TCGA) data provides unprecedented opportunities for the discovery of cancer subtypes. Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.

Methods

In this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. SMMSN can not only fuse multi-level data representations of single omics data by Graph Convolutional Network (GCN) and Stacked Autoencoder Network (SAE), but also achieve the organic fusion of multi- -omics data through multiple fusion strategies. In response to the problem of lack label information in multi-omics data, SMMSN propose to use dual self-supervise method to cluster cancer subtypes from the integrated data.

Results

We conducted experiments on three labeled and five unlabeled multi-omics datasets to distinguish potential cancer subtypes. Kaplan Meier survival curves and other results showed that SMMSN can obtain cancer subtypes with significant differences.

Discussion

In the case analysis of Glioblastoma Multiforme (GBM) and Breast Invasive Carcinoma (BIC), we conducted survival time and age distribution analysis, drug response analysis, differential expression analysis, functional enrichment analysis on the predicted cancer subtypes. The research results showed that SMMSN can discover clinically meaningful cancer subtypes.

Overcoming tyrosine kinase inhibitor resistance in lung cancer brain metastasis with CTLA4 blockade

Lung cancer brain metastasis (LCBM) poses a significant clinical challenge due to acquired resistance to tyrosine kinase inhibitor (TKI) treatment. To elucidate its underlying mechanisms, we employed single-cell RNA sequencing analysis on surgically obtained LCBM samples with diverse genetic backgrounds and TKI treatment histories. Our study uncovers that TKI treatment elevates the immune checkpoint CTLA4 expression in T cells, promoting an immune-suppressive microenvironment. This immunomodulation is initiated by tumor-derived HMGB1 in response to TKIs. In LCBM syngeneic murine models with TKI-sensitive or TKI-resistant EGFR mutations, combining CTLA4 blockade with TKIs demonstrates enhanced efficacy over TKI monotherapy or TKIs with PD1 blockade. These findings provide insights into the TKI resistance mechanisms and highlight the potential of CTLA4 blockade in effectively overcoming TKI resistance in LCBM.

Machine learning unveils key Redox signatures for enhanced breast Cancer therapy

BACKGROUND

Breast cancer remains a leading cause of mortality among women worldwide, necessitating innovative prognostic models to enhance treatment strategies.

METHODS

Our study retrospectively enrolled 9,439 breast cancer patients from 12 independent datasets and single-cell data from 12 patients (64,308 cells). Moverover, 30 in-house clinical cohort were collected for validation. We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. To affirm the efficacy of our developed model, immunohistochemistry assays were performed. Additionally, we investigated various potential immunotherapeutic and chemotherapeutic interventions.

RESULTS

This study introduces an Artificial Intelligence-aided Redox Signature (AIARS) as a novel prognostic tool, leveraging machine learning to identify critical redox-related gene signatures in breast cancer. Our results demonstrate that AIARS significantly outperforms existing prognostic models in predicting breast cancer outcomes, offering a robust tool for personalized treatment planning. Validation through immunohistochemistry assays on samples from 30 patients corroborated our results, underscoring the model's applicability on a wider scale. Furthermore, the analysis revealed that patients with low AIARS expression levels are more responsive to immunotherapy. Conversely, those exhibiting high AIARS were found to be more susceptible to certain chemotherapeutic agents, including vincristine.

CONCLUSIONS

Our study underscores the importance of redox biology in breast cancer prognosis and introduces a powerful machine learning-based tool, the AIARS, for personalized treatment strategies. By providing a more nuanced understanding of the redox landscape in breast cancer, the AIARS paves the way for the development of redox-targeted therapies, promising to enhance patient outcomes significantly. Future work will focus on clinical validation and exploring the mechanistic roles of identified genes in cancer biology.

Photoimmunotherapy for cancer treatment based on organic small molecules: Recent strategies and future directions

Photodynamic therapy (PDT) is considered as a promising anticancer approach, owning to its high efficiency and spatiotemporal selectivity. Ample evidence indicated that PDT can trigger immunogenic cell death by releasing antigens that activate immune cells to promote anti-tumor immunity. Nevertheless, the inherent nature of tumors and their complex heterogeneity often limits the efficiency of PDT, which can be overcome with a novel strategy of photo-immunotherapy (PIT) strategy. By exploring the principles of PDT induction and ICD enhancement, combined with other therapies such as chemotherapy or immune checkpoint blockade, the tailored solutions can be designed to address specific challenges of drug resistance, hypoxic conditions, and tumor immunosuppressive microenvironments (TIMEs), which enables targeted enhancement of systemic immunity to address most distant and recurrent cancers. The present article summarizes the specific strategies of PIT and discusses recent existing limitations. More importantly, we anticipate that the perspectives presented herein will help address the clinical translation challenges associated with PIT.

High-resolution and programmable RNA-IN and RNA-OUT genetic circuit in living mammalian cells

RNAs and their encoded proteins intricately regulate diverse cell types and states within the human body. Dysregulated RNA expressions or mutations can lead to various diseased cell states, including tumorigenesis. Detecting and manipulating these endogenous RNAs offers significant promise for restoring healthy cell states and targeting tumors both in research and clinical contexts. This study presents an RNA-IN and RNA-OUT genetic circuit capable dynamically sensing and manipulating any RNA target in a programmable manner. The RNA-IN module employes a programmable CRISPR-associated protease (CASP) complex for RNA detection, while the RNA-OUT module utilizes an engineered protease-responsive dCas9-VPR activator. Additionally, the CASP module can detect point mutations by harnessing an uncovered dual-nucleotide synergistic switching effect within the CASP complex, resulting in the amplification of point-mutation signals from initially undetectable levels (1.5-fold) to a remarkable 94-fold. We successfully showcase the circuit's ability to rewire endogenous RNA-IN signals to activate endogenous progesterone biosynthesis pathway, dynamically monitor adipogenic differentiation of mesenchymal stem cells (MSCs) and the epithelial-to-mesenchmal trans-differentiation, as well as selective killing of tumor cells. The programmable RNA-IN and RNA-OUT circuit exhibits tremendous potential for applications in gene therapy, biosensing and design of synthetic regulatory networks.

Construction of a Novel Disulfidptosis-Related lncRNA Prognostic Signature in Pancreatic Cancer

Pancreatic cancer is a lethal, extremely aggressive gastrointestinal tumor with a poor prognosis and limited treatment alternatives. Disulfidptosis is a newly defined type of cell death with potential influence on cancer. Research on the association between disulfidptosis and pancreatic cancer is scarce. The expression data of disulfidptosis-related genes were downloaded from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA). Disulfidptosis-related lncRNA signature (DRLS) was developed through the Cox and the least absolute shrinkage and selection operator (LASSO) analysis. Differences in enrichment functions, mutational landscape, immune microenvironment, and predicted therapeutic efficacy between high- and low-risk groups were assessed. Consensus clustering analysis was applied to identify the DRLS-related subtypes. Among 98 disulfidptosis-related lncRNAs, 5 lncRNAs were screened thus constructing a prognostic DRLS. DRLS showed high predictive accuracy and was an independent prognostic factor for pancreatic cancer. According to the risk scores calculated from the signature, samples were categorized into high- and low- risk groups. Overall, low-risk patients had a better prognosis, lower mutational occurrences, higher immune cell infiltration and more sensitivity to anti-tumor agents. The DRLS performed well in predicting prognosis and revealed intimate correlation with biological function, mutation status and immune infiltration landscape of pancreatic cancer, providing some insights for future research on the relationship between disulfidptosis and pancreatic cancer.

Pan-cancer integrative analyses dissect the remodeling of endothelial cells in human cancers

Therapeutics targeting tumor endothelial cells (TECs) have been explored for decades, with only suboptimal efficacy achieved, partly due to an insufficient understanding of the TEC heterogeneity across cancer patients. We integrated single-cell RNA-seq data of 575 cancer patients from 19 solid tumor types, comprehensively charting the TEC phenotypic diversities. Our analyses uncovered underappreciated compositional and functional heterogeneity in TECs from a pan-cancer perspective. Two subsets, CXCR4 + tip cells and SELE + veins, represented the prominent angiogenic and proinflammatory phenotypes of TECs, respectively. They exhibited distinct spatial organization patterns, and compared to adjacent non-tumor tissues, tumor tissue showed an increased prevalence of CXCR4 + tip cells, yet with SELE + veins depleted. Such functional and spatial characteristics underlie their differential associations with the response of anti-angiogenic therapies and immunotherapies. Our integrative resources and findings open new avenues to understand and clinically intervene in the tumor vasculature.

HMGB2 drives tumor progression and shapes the immunosuppressive microenvironment in hepatocellular carcinoma: insights from multi-omics analysis

Background

Hepatocellular carcinoma (HCC) poses a significant health burden globally, with high mortality rates despite various treatment options. Immunotherapy, particularly immune-checkpoint inhibitors (ICIs), has shown promise, but resistance and metastasis remain major challenges. Understanding the intricacies of the tumor microenvironment (TME) is imperative for optimizing HCC management strategies and enhancing patient prognosis.

Methods

This study employed a comprehensive approach integrating multi-omics approaches, including single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (Bulk RNA-seq), and validation in clinical samples using spatial transcriptomics (ST) and multiplex immunohistochemistry (mIHC). The analysis aimed to identify key factors influencing the immunosuppressive microenvironment associated with HCC metastasis and immunotherapy resistance.

Results

HMGB2 is significantly upregulated in HCCTrans, a transitional subgroup associated with aggressive metastasis. Furthermore, HMGB2 expression positively correlates with an immunosuppressive microenvironment, particularly evident in exhausted T cells. Notably, HMGB2 expression correlated positively with immunosuppressive markers and poor prognosis in HCC patients across multiple cohorts. ST combined with mIHC validated the spatial expression patterns of HMGB2 within the TME, providing additional evidence of its role in HCC progression and immune evasion.

Conclusion

HMGB2 emerges as a critical player of HCC progression, metastasis, and immunosuppression. Its elevated expression correlates with aggressive tumor behavior and poor patient outcomes, suggesting its potential as both a therapeutic target and a prognostic indicator in HCC management.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

Spatiotemporal single-cell analysis decodes cellular dynamics underlying different responses to immunotherapy in colorectal cancer

Expanding the efficacy of immune checkpoint blockade (ICB) in colorectal cancer (CRC) presses for a comprehensive understanding of treatment responsiveness. Here, we analyze multiple sequential single-cell samples from 22 patients undergoing PD-1 blockade to map the evolution of local and systemic immunity of CRC patients. In tumors, we identify coordinated cellular programs exhibiting distinct response associations. Specifically, exhausted T (Tex) or tumor-reactive-like CD8+ T (Ttr-like) cells are closely related to treatment efficacy, and Tex cells show correlated proportion changes with multiple other tumor-enriched cell types following PD-1 blockade. In addition, we reveal the less-exhausted phenotype of blood-associated Ttr-like cells in tumors and find that their higher abundance suggests better treatment outcomes. Finally, a higher major histocompatibility complex (MHC) II-related signature in circulating CD8+ T cells at baseline is linked to superior responses. Our study provides insights into the spatiotemporal cellular dynamics following neoadjuvant PD-1 blockade in CRC.

Unveiling the therapeutic promise: exploring Lysophosphatidic Acid (LPA) signaling in malignant bone tumors for novel cancer treatments

Malignant bone tumors, including primary bone cancer and metastatic bone tumors, are a significant clinical challenge due to their high frequency of presentation, poor prognosis and lack of effective treatments and therapies. Bone tumors are often accompanied by skeletal complications such as bone destruction and cancer-induced bone pain. However, the mechanisms involved in bone cancer progression, bone metastasis and skeletal complications remain unclear. Lysophosphatidic acid (LPA), an intercellular lipid signaling molecule that exerts a wide range of biological effects mainly through specifically binding to LPA receptors (LPARs), has been found to be present at high levels in the ascites of bone tumor patients. Numerous studies have suggested that LPA plays a role in primary malignant bone tumors, bone metastasis, and skeletal complications. In this review, we summarize the role of LPA signaling in primary bone cancer, bone metastasis and skeletal complications. Modulating LPA signaling may represent a novel avenue for future therapeutic treatments for bone cancer, potentially improving patient prognosis and quality of life.

Dissecting tumor microenvironment from spatially resolved transcriptomics data by heterogeneous graph learning

Spatially resolved transcriptomics (SRT) has enabled precise dissection of tumor-microenvironment (TME) by analyzing its intracellular molecular networks and intercellular cell-cell communication (CCC). However, lacking computational exploration of complicated relations between cells, genes, and histological regions, severely limits the ability to interpret the complex structure of TME. Here, we introduce stKeep, a heterogeneous graph (HG) learning method that integrates multimodality and gene-gene interactions, in unraveling TME from SRT data. stKeep leverages HG to learn both cell-modules and gene-modules by incorporating features of diverse nodes including genes, cells, and histological regions, allows for identifying finer cell-states within TME and cell-state-specific gene-gene relations, respectively. Furthermore, stKeep employs HG to infer CCC for each cell, while ensuring that learned CCC patterns are comparable across different cell-states through contrastive learning. In various cancer samples, stKeep outperforms other tools in dissecting TME such as detecting bi-potent basal populations, neoplastic myoepithelial cells, and metastatic cells distributed within the tumor or leading-edge regions. Notably, stKeep identifies key transcription factors, ligands, and receptors relevant to disease progression, which are further validated by the functional and survival analysis of independent clinical data, thereby highlighting its clinical prognostic and immunotherapy applications.

Generalizable transcriptome-based tumor malignant level evaluation and molecular subtyping towards precision oncology

In cancer treatment, therapeutic strategies that integrate tumor-specific characteristics (i.e., precision oncology) are widely implemented to provide clinical benefits for cancer patients. Here, through in-depth integration of tumor transcriptome and patients' prognoses across cancers, we investigated dysregulated and prognosis-associated genes and catalogued such important genes in a cancer type-dependent manner. Utilizing the expression matrices of these genes, we built models to quantitatively evaluate the malignant levels of tumors across cancers, which could add value to the clinical staging system for improved prediction of patients' survival. Furthermore, we performed a transcriptome-based molecular subtyping on hepatocellular carcinoma, which revealed three subtypes with significantly diversified clinical outcomes, mutation landscapes, immune microenvironment, and dysregulated pathways. As tumor transcriptome was commonly profiled in clinical practice with low experimental complexity and cost, this work proposed easy-to-perform approaches for practical clinical promotion towards better healthcare and precision oncology of cancer patients.

Design, docking optimization, and evaluation of biotin-PEG4-1,8-naphthalimide as a potent and safe antitumor agent with dual targeting of ferroptosis and DNA

A set of biotin-polyethylene glycol (PEG)-naphthalimide derivatives 4a-4h with dual targeting of ferroptosis and DNA were designed and optimized using docking simulation as antitumor agents. Docking simulation optimization results indicated that biotin-PEG4-piperazine-1,8-naphthalimide 4d should be the best candidate among these designed compounds 4a-4h, and therefore, we synthesized and evaluated it as a novel antitumor agent. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay and MGC-803 and U251 xenograft models identified 4d as a good candidate antitumor agent with potent efficacy and safety profiles, compared with amonafide and temozolomide. The findings of the docking simulations, fluorescence intercalator displacement (FID), western blot, comet, 5-ethynyl-2'-deoxyuridine (EdU), flow cytometry, transmission electron microscopy, and BODIPY-581/591-C11, FerroOrange, and dihydroethidium (DHE) fluorescent probe assays revealed that 4d could induce DNA damage, affect DNA synthesis, and cause cell cycle arrest in the S phase in MGC-803 cells. Also, it could induce lipid peroxidation and thus lead to ferroptosis in MGC-803 cells, indicating that it mainly exerted antitumor effects through dual targeting of ferroptosis and DNA. These results suggested that it was feasible to design, optimize using docking simulation, and evaluate the potency and safety of biotin-PEG-1,8-naphthalimide as a antitumor agent with dual targeting of ferroptosis and DNA, based on a multi-target drug strategy.

Identification of hypoxic macrophages in glioblastoma with therapeutic potential for vasculature normalization

Monocyte-derived tumor-associated macrophages (Mo-TAMs) intensively infiltrate diffuse gliomas with remarkable heterogeneity. Using single-cell transcriptomics, we chart a spatially resolved transcriptional landscape of Mo-TAMs across 51 patients with isocitrate dehydrogenase (IDH)-wild-type glioblastomas or IDH-mutant gliomas. We characterize a Mo-TAM subset that is localized to the peri-necrotic niche and skewed by hypoxic niche cues to acquire a hypoxia response signature. Hypoxia-TAM destabilizes endothelial adherens junctions by activating adrenomedullin paracrine signaling, thereby stimulating a hyperpermeable neovasculature that hampers drug delivery in glioblastoma xenografts. Accordingly, genetic ablation or pharmacological blockade of adrenomedullin produced by Hypoxia-TAM restores vascular integrity, improves intratumoral concentration of the anti-tumor agent dabrafenib, and achieves combinatorial therapeutic benefits. Increased proportion of Hypoxia-TAM or adrenomedullin expression is predictive of tumor vessel hyperpermeability and a worse prognosis of glioblastoma. Our findings highlight Mo-TAM diversity and spatial niche-steered Mo-TAM reprogramming in diffuse gliomas and indicate potential therapeutics targeting Hypoxia-TAM to normalize tumor vasculature.

Genetic interactions reveal distinct biological and therapeutic implications in breast cancer

Co-occurrence and mutual exclusivity of genomic alterations may reflect the existence of genetic interactions, potentially shaping distinct biological phenotypes and impacting therapeutic response in breast cancer. However, our understanding of them remains limited. Herein, we investigate a large-scale multi-omics cohort (n = 873) and a real-world clinical sequencing cohort (n = 4,405) including several clinical trials with detailed treatment outcomes and perform functional validation in patient-derived organoids, tumor fragments, and in vivo models. Through this comprehensive approach, we construct a network comprising co-alterations and mutually exclusive events and characterize their therapeutic potential and underlying biological basis. Notably, we identify associations between TP53mut-AURKAamp and endocrine therapy resistance, germline BRCA1mut-MYCamp and improved sensitivity to PARP inhibitors, and TP53mut-MYBamp and immunotherapy resistance. Furthermore, we reveal that precision treatment strategies informed by co-alterations hold promise to improve patient outcomes. Our study highlights the significance of genetic interactions in guiding genome-informed treatment decisions beyond single driver alterations.

Design, synthesis, and anticancer evaluation of N-sulfonylpiperidines as potential VEGFR-2 inhibitors, apoptotic inducers

A new panel of N-sulfonylpiperidine derivatives has been designed and synthesized as vascular endothelial growth factor receptor-2 (VEGFR-2) inhibitors. Anti-proliferative activities of the synthesized members were tested against colorectal carcinoma (HCT-116), hepatocellular carcinoma (HepG-2), and breast cancer (MCF-7) cell lines. Compounds 3a, 4, 8, and 9 showed the highest activities against the tested cell lines. In particular, compound 8 showed excellent activities against HCT-116, HepG-2, and MCF-7 with IC50 values of 3.94, 3.76, and 4.43 μM, respectively. Such IC50 values are comparable to vinblastine (IC50 = 3.21, 7.35, 5.83 μM, respectively) and doxorubicin (IC50 = 6.74, 7.52, 8.19 μM, respectively). In vitro VEGFR-2 inhibitory activity of the most promising molecules (3a, 4, 8, and 9) indicated that compound 8 is the highest VEGFR-2 inhibitor with an IC50 of 0.0554 μM, compared to sorafenib (IC50 = 0.0416 μM). The most promising candidates (3a, 4, 8, and 9) were subjected to flow cytometry analyses to assess their effects on the cell cycle behavior and the apoptotic power against the three tested cell lines (HCT-116, HepG-2, and MCF-7). The tested compound arrested the tumor cells at both the G2/M and Pre-G1 phases. In addition, compound 9 was proved as the most effective apoptotic inducer among the tested compounds against the tested cells. Molecular docking studies against VEGFR-2 (PDB ID: 2OH4) revealed good binding modes of the synthesized compound similar to that of sorafenib. Computational investigation of ADMET parameters revealed the drug-likeness of the synthesized compounds.

Discovery of 4-(N-dithiobenzyl piperazine)-1,8-naphthalimide as a potent multi-target antitumor agent with good efficacy, limited toxicity, and low resistance

A series of 4-(N-dithiobenzyl piperazine)-1,8-naphthalimide derivatives 4-6 were designed, synthesized, and evaluated as novel multi-target antitumor agents. 3-(4,5-Dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) results showed that compounds 5j, 5k, and 6j exhibited superior in vitro antiproliferative activity in MGC-803, HepG-2, SKOV-3, and T24 cancer cell lines and the cisplatin-resistant cell line A549/DDP. HepG-2, SKOV-3, and T24 xenograft assay results revealed that compounds 5j, 5k, and 6j exhibited good antitumor effects compared with amonafide. The pathology results indicated that compound 5j exhibited the least comprehensive toxicity among the three compounds, identifying compound 5j as a good candidate antitumor agent with good efficacy, limited toxicity, and low resistance. Compound 5j was thus chose for further antitumor mechanism investigation. Results from the omics research, confocal immunofluorescence, Western blot, transmission electron microscopy, and flow cytometry indicated that compound 5j exerted antitumor effects through multiple mechanisms, including ferroptosis, autophagy, apoptosis, and cell cycle arrest. These results suggest that screening novel 1,8-naphthalimide-based antitumor agents for good efficacy, limited toxicity, and low resistance based on a multi-target drug strategy is feasible.

Multiorgan locked-state model of chronic diseases and systems pharmacology opportunities

With increasing human life expectancy, the global medical burden of chronic diseases is growing. Hence, chronic diseases are a pressing health concern and will continue to be in decades to come. Chronic diseases often involve multiple malfunctioning organs in the body. An imminent question is how interorgan crosstalk contributes to the etiology of chronic diseases. We conceived the locked-state model (LoSM), which illustrates how interorgan communication can give rise to body-wide memory-like properties that 'lock' healthy or pathological conditions. Next, we propose cutting-edge systems biology and artificial intelligence strategies to decipher chronic multiorgan locked states. Finally, we discuss the clinical implications of the LoSM and assess the power of systems-based therapies to dismantle pathological multiorgan locked states while improving treatments for chronic diseases.

Identification and Interpretation of A-to-I RNA Editing Events in Insect Transcriptomes

Adenosine-to-inosine (A-to-I) RNA editing is the most prevalent RNA modification in the nervous systems of metazoans. To study the biological significance of RNA editing, we first have to accurately identify these editing events from the transcriptome. The genome-wide identification of RNA editing sites remains a challenging task. In this review, we will first introduce the occurrence, regulation, and importance of A-to-I RNA editing and then describe the established bioinformatic procedures and difficulties in the accurate identification of these sit esespecially in small sized non-model insects. In brief, (1) to obtain an accurate profile of RNA editing sites, a transcriptome coupled with the DNA resequencing of a matched sample is favorable; (2) the single-cell sequencing technique is ready to be applied to RNA editing studies, but there are a few limitations to overcome; (3) during mapping and variant calling steps, various issues, like mapping and base quality, soft-clipping, and the positions of mismatches on reads, should be carefully considered; (4) Sanger sequencing of both RNA and the matched DNA is the best verification of RNA editing sites, but other auxiliary evidence, like the nonsynonymous-to-synonymous ratio or the linkage information, is also helpful for judging the reliability of editing sites. We have systematically reviewed the understanding of the biological significance of RNA editing and summarized the methodology for identifying such editing events. We also raised several promising aspects and challenges in this field. With insightful perspectives on both scientific and technical issues, our review will benefit the researchers in the broader RNA editing community.

E3 ligase TRIM28 promotes anti-PD-1 resistance in non-small cell lung cancer by enhancing the recruitment of myeloid-derived suppressor cells

BACKGROUND

Alterations in several tripartite motif-containing (TRIM) family proteins have been implicated in the pathogenesis of lung cancer. TRIM28, a member of the TRIM E3 ligase family, has been associated with tumorigenesis, cell proliferation, and inflammation. However, little is known about TRIM28 expression and its role in the immune microenvironment of non-small cell lung cancer (NSCLC).

METHODS

We assessed the clinical significance of TRIM28 in tissue microarrays and TCGA cohorts. We investigated the function of TRIM28 in syngeneic mouse tumor models, the KrasLSL-G12D/+; Tp53fl/fl (KP) mouse model, and humanized mice. Immune cell composition was analyzed using flow cytometry and immunohistochemistry.

RESULTS

Our findings revealed a positive correlation between TRIM28 expression and the infiltration of suppressive myeloid-derived suppressor cells (MDSCs) in NSCLC. Moreover, silencing TRIM28 enhanced the efficacy of anti-PD-1 immunotherapy by reshaping the inflamed tumor microenvironment. Mechanistically, we demonstrated that TRIM28 could physically interact with receptor-interacting protein kinase 1 (RIPK1) and promote K63-linked ubiquitination of RIPK1, which is crucial for sustaining activation of the NF-κB pathway. Mutagenesis of the E3 ligase domain corroborated the essential role of E3 ligase activity in TRIM28-mediated NF-κB activation. Further experiments revealed that TRIM28 could upregulate the expression of CXCL1 by activating NF-κB signaling. CXCL1 could bind to CXCR2 on MDSCs and promote their migration to the tumor microenvironment. TRIM28 knockdown increased responsiveness to anti-PD-1 therapy in immunocompetent mice, characterized by increased CD8+T tumor-infiltrating lymphocytes and decreased MDSCs.

CONCLUSION

The present study identified TRIM28 as a promoter of chemokine-driven recruitment of MDSCs through RIPK1-mediated NF-κB activation, leading to the suppression of infiltrating activated CD8+T cells and the development of anti-PD-1 resistance. Understanding the regulation of MDSC recruitment and function by TRIM28 provides crucial insights into the association between TRIM28 signaling and the development of an immunosuppressive tumor microenvironment. These insights may inform the development of combination therapies to enhance the effectiveness of immune checkpoint blockade therapy in NSCLC.

Dynamic single-cell mapping unveils Epstein‒Barr virus-imprinted T-cell exhaustion and on-treatment response

Epstein‒Barr virus (EBV)-associated gastric cancer (GC) manifests an intriguing immunotherapy response. However, the cellular basis for EBV-imprinted tumour immunity and on-treatment response remains undefined. This study aimed to finely characterize the dynamic tumour immune contexture of human EBV (+) GC treated with immunochemotherapy by longitudinal scRNA-seq and paired scTCR/BCR-seq. EBV (+) GC exhibits an inflamed-immune phenotype with increased T-cell and B-cell infiltration. Immunochemotherapy triggers clonal revival and reinvigoration of effector T cells which step to determine treatment response. Typically, an antigen-specific ISG-15+CD8+ T-cell population is highly enriched in EBV (+) GC patients, which represents a transitory exhaustion state. Importantly, baseline intratumoural ISG-15+CD8+ T cells predict immunotherapy responsiveness among GC patients. Re-emerged clonotypes of pre-existing ISG-15+CD8+ T cells could be found after treatment, which gives rise to a CXCL13-expressing effector population in responsive EBV (+) tumours. However, LAG-3 retention may render the ISG-15+CD8+ T cells into a terminal exhaustion state in non-responsive EBV (+) tumours. In accordance, anti-LAG-3 therapy could effectively reduce tumour burden in refractory EBV (+) GC patients. Our results delineate a distinct implication of EBV-imprinted on-treatment T-cell immunity in GC, which could be leveraged to optimize the rational design of precision immunotherapy.