Zexian Zeng’s research while affiliated with Peking University and other places

The formation of immune synapses (ISs) between cytotoxic T cells and tumor cells is crucial for effective tumor elimination. However, the role of ISs in immune evasion and resistance to immune checkpoint blockades (ICBs) remains unclear. We demonstrate that ICAM-1, a key IS molecule activating LFA-1 signaling in T and natural killer (NK) cells, is often expressed at low levels in cancers. The absence of ICAM-1 leads to significant resistance to T and NK cell-mediated anti-tumor immunity. Using a CRISPR screen, we show that ICAM-1 is epigenetically regulated by the DNA methylation pathway involving UHRF1 and DNMT1. Furthermore, we engineer an antibody-based therapeutic agent, “LFA-1 engager,” to enhance T cell-mediated anti-tumor immunity by reconstituting LFA-1 signaling. Treatment with LFA-1 engagers substantially enhances immune-mediated cytotoxicity, potentiates anti-tumor immunity, and synergizes with ICB in mouse models of ICAM-1-deficient tumors. Our data provide promising therapeutic strategies for re-engaging immune stimulatory signals in cancer immunotherapy.

Recent advancements in spatial transcriptomics technologies have significantly enhanced resolution and throughput, underscoring an urgent need for systematic benchmarking. To address this, we collected clinical samples from three cancer types - colon adenocarcinoma, hepatocellular carcinoma, and ovarian cancer - and generated serial tissue sections for systematic evaluation. Using these uniformly processed samples, we generated spatial transcriptomics data across five high-throughput platforms with subcellular resolution: Stereo-seq v1.3, Visium HD FFPE, Visium HD FF, CosMx 6K, and Xenium 5K. To establish ground truth datasets, we profiled proteins from adjacent tissue sections corresponding to all five platforms using CODEX and performed single-cell RNA sequencing on the same samples. Leveraging manual cell segmentation and detailed annotations, we systematically assessed each platform's performance across key metrics, including capture sensitivity, specificity, diffusion control, cell segmentation, cell annotation, spatial clustering, and transcript-protein alignment with adjacent CODEX. The uniformly generated, processed, and annotated multi-omics dataset is valuable for advancing computational method development and biological discoveries. The dataset is accessible via SPATCH, a user-friendly web server for visualization and download (http://spatch.pku-genomics.org/).

Background: Despite breakthroughs in treatment, ovarian cancer (OC) remains one of the most lethal gynecological malignancies, with an increasing age-standardized mortality rate. This underscores an urgent need for novel biomarkers and therapeutic targets. Although growth factor receptor-bound protein 7 (GRB7) is implicated in cell signaling and tumorigenesis, its expression pattern and clinical implications in OC remain poorly characterized. Methods: To systematically investigate GRB7’s expression in OC, our study utilized extensive datasets from TCGA, GTEx, CCLE, and GEO. The prognostic significance of GRB7 was evaluated by means of Kaplan–Meier and Cox regression analyses. Using a correlation analysis and gene set enrichment analysis, relationships between GRB7’s expression and gene networks, immune cell infiltration and immunotherapy response were investigated. In vitro experiments were conducted to confirm GRB7’s function in the biology of OC. Results: Compared to normal tissues, OC tissues exhibited a substantial upregulation of GRB7. Reduced overall survival, disease-specific survival, and disease-free interval were all connected with high GRB7 mRNA levels. The network study demonstrated that GRB7 is involved in pathways relevant to the course of OC and has a positive connection with several key driver genes. Notably, GRB7’s expression was linked to the infiltration of M2 macrophage and altered response to immunotherapy. Data from single-cell RNA sequencing data across multiple cancer types indicated GRB7’s predominant expression in malignant cells. Moreover, OC cells with GRB7 deletion showed decreased proliferation and migration, as well as increased susceptibility to T cell-mediated cytotoxicity. Conclusion: With respect to OC, our results validated GRB7 as a viable prognostic biomarker and a promising therapeutic target, providing information about its function in tumorigenesis and immune modulation. GRB7’s preferential expression in malignant cells highlights its significance in the biology of cancer and bolsters the possibility that it could be useful in enhancing the effectiveness of immunotherapy.

Background Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti–PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC. Methods Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials. Results A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression–based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1. Conclusions This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy. Trial registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015.

Colorectal cancer (CRC) continues to be a prevalent malignancy, posing a significant risk to human health. The involvement of alpha/beta hydrolase domain 6 (ABHD6), a serine hydrolase family member, in CRC development was suggested by our analysis of clinical data. However, the role of ABHD6 in CRC remains unclear. This study seeks to elucidate the clinical relevance, biological function, and potential molecular mechanisms of ABHD6 in CRC. We investigated the role of ABHD6 in clinical settings, conducting proliferation, migration, and cell cycle assays. To determine the influence of ABHD6 expression levels on Oxaliplatin sensitivity, we also performed apoptosis assays. RNA sequencing and KEGG analysis were utilized to uncover the potential molecular mechanisms of ABHD6. Furthermore, we validated its expression levels using Western blot and reactive oxygen species (ROS) detection assays. Our results demonstrated that ABHD6 expression in CRC tissues was notably lower compared to adjacent normal tissues. This low expression correlated with a poorer prognosis for CRC patients. Moreover, ABHD6 overexpression impeded CRC cell proliferation and migration while inducing G0/G1 cell cycle arrest. In vivo experiments revealed that downregulation of ABHD6 resulted in an increase in tumor weight and volume. Mechanistically, ABHD6 overexpression inhibited the activation of the AKT signaling pathway and decreased ROS levels in CRC cells, suggesting the role of ABHD6 in CRC progression via the AKT signaling pathway. Our findings demonstrate that ABHD6 functions as a tumor suppressor, primarily by inhibiting the AKT signaling pathway. This role establishes ABHD6 as a promising prognostic biomarker and a potential therapeutic target for CRC patients.

Metabolic reprogramming toward glycolysis is a hallmark of cancer malignancy. The molecular mechanisms by which the tumor glycolysis pathway promotes immune evasion remain to be elucidated. Here, by performing genome-wide CRISPR screens in murine tumor cells co-cultured with cytotoxic T cells (CTLs), we identified that deficiency of two important glycolysis enzymes, Glut1 (glucose transporter 1) and Gpi1 (glucose-6-phosphate isomerase 1), resulted in enhanced killing of tumor cells by CTLs. Mechanistically, Glut1 inactivation causes metabolic rewiring toward oxidative phosphorylation, which generates an excessive amount of reactive oxygen species (ROS). Accumulated ROS potentiate tumor cell death mediated by tumor necrosis factor alpha (TNF-α) in a caspase-8- and Fadd-dependent manner. Genetic and pharmacological inactivation of Glut1 sensitizes tumors to anti-tumor immunity and synergizes with anti-PD-1 therapy through the TNF-α pathway. The mechanistic interplay between tumor-intrinsic glycolysis and TNF-α-induced killing provides new therapeutic strategies to enhance anti-tumor immunity.

RNA-sequencing (RNA-seq) has become an increasingly cost-effective technique for molecular profiling and immune characterization of tumors. In the past decade, many computational tools have been developed to characterize tumor immunity from gene expression data. However, the analysis of large-scale RNA-seq data requires bioinformatics proficiency, large computational resources and cancer genomics and immunology knowledge. In this tutorial, we provide an overview of computational analysis of bulk RNA-seq data for immune characterization of tumors and introduce commonly used computational tools with relevance to cancer immunology and immunotherapy. These tools have diverse functions such as evaluation of expression signatures, estimation of immune infiltration, inference of the immune repertoire, prediction of immunotherapy response, neoantigen detection and microbiome quantification. We describe the RNA-seq IMmune Analysis (RIMA) pipeline integrating many of these tools to streamline RNA-seq analysis. We also developed a comprehensive and user-friendly guide in the form of a GitBook with text and video demos to assist users in analyzing bulk RNA-seq data for immune characterization at both individual sample and cohort levels by using RIMA.

Mapping gene networks requires large amounts of transcriptomic data to learn the connections between genes, which impedes discoveries in settings with limited data, including rare diseases and diseases affecting clinically inaccessible tissues. Recently, transfer learning has revolutionized fields such as natural language understanding1,2 and computer vision³ by leveraging deep learning models pretrained on large-scale general datasets that can then be fine-tuned towards a vast array of downstream tasks with limited task-specific data. Here, we developed a context-aware, attention-based deep learning model, Geneformer, pretrained on a large-scale corpus of about 30 million single-cell transcriptomes to enable context-specific predictions in settings with limited data in network biology. During pretraining, Geneformer gained a fundamental understanding of network dynamics, encoding network hierarchy in the attention weights of the model in a completely self-supervised manner. Fine-tuning towards a diverse panel of downstream tasks relevant to chromatin and network dynamics using limited task-specific data demonstrated that Geneformer consistently boosted predictive accuracy. Applied to disease modelling with limited patient data, Geneformer identified candidate therapeutic targets for cardiomyopathy. Overall, Geneformer represents a pretrained deep learning model from which fine-tuning towards a broad range of downstream applications can be pursued to accelerate discovery of key network regulators and candidate therapeutic targets.

Recent advances in single-cell RNA sequencing have shown heterogeneous cell types and gene expression states in the non-cancerous cells in tumors. The integration of multiple scRNA-seq datasets across tumors can indicate common cell types and states in the tumor microenvironment (TME). We develop a data driven framework, MetaTiME, to overcome the limitations in resolution and consistency that result from manual labelling using known gene markers. Using millions of TME single cells, MetaTiME learns meta-components that encode independent components of gene expression observed across cancer types. The meta-components are biologically interpretable as cell types, cell states, and signaling activities. By projecting onto the MetaTiME space, we provide a tool to annotate cell states and signature continuums for TME scRNA-seq data. Leveraging epigenetics data, MetaTiME reveals critical transcriptional regulators for the cell states. Overall, MetaTiME learns data-driven meta-components that depict cellular states and gene regulators for tumor immunity and cancer immunotherapy.