Wubing Zhang

All Publications

• Integrated computational analysis identifies therapeutic targets with dual action in cancer cells and T cells. Immunity Luo, C., Zhang, R., Guo, R., Wu, L., Xue, T., He, Y., Jin, Y., Zhao, Y., Zhang, Z., Zhang, P., Ye, S., Li, X., Li, D., Zhang, W., Wang, C., Lai, L., Pan-Hammarström, Q., Wucherpfennig, K. W., Gao, Z., Pan, D., Zeng, Z. 2025

  Abstract

  Many cancer drugs that target cancer cell pathways also impair the immune system. We developed a computational target discovery platform to enable examination of both cancer and immune cells so as to identify pathways that restrain tumor progression and potentiate anti-tumor immunity. Immune-related CRISPR screen analyzer of functional targets (ICRAFT) integrates immune-related CRISPR screen datasets, single-cell RNA sequencing (scRNA-seq) data, and pre-treatment RNA-seq data from clinical trials, enabling a systems-level approach to therapeutic target discovery. Using ICRAFT, we identified numerous targets that enhance both cancer cell susceptibility to immune attack and T cell activation, including tumor necrosis factor (TNF) alpha-induced protein 3 (TNFAIP3), protein tyrosine phosphatase non-receptor type 2 (PTPN2), and suppressor of cytokine signaling 1 (SOCS1). In cancer cells, Tnfaip3 (A20) deletion activated the TNF-nuclear factor kappa-B (NF-κB) pathway, promoting chemokine expression and T cell recruitment to the tumor. T cell-mediated elimination of Tnaifp3-null cancer cells was primarily driven by TNF-induced apoptosis. Inactivation of Tnfaip3 in T cells enhanced anti-tumor efficacy. By integrating diverse functional genomics and clinical datasets, ICRAFT provides an interactive resource toward a deeper understanding of anti-tumor immunity and immuno-oncology drug development.

  View details for DOI 10.1016/j.immuni.2025.02.007

  View details for PubMedID 40023158

• CD4 T cells and toxicity from immune checkpoint blockade. Immunological reviews Earland, N., Zhang, W., Usmani, A., Nene, A., Bacchiocchi, A., Chen, D. Y., Sznol, M., Halaban, R., Chaudhuri, A. A., Newman, A. M. 2023

  Abstract

  Immune-related toxicities, otherwise known as immune-related adverse events (irAEs), occur in a substantial fraction of cancer patients treated with immune checkpoint inhibitors (ICIs). Ranging from asymptomatic to life-threatening, ICI-induced irAEs can result in hospital admission, high-dose corticosteroid treatment, ICI discontinuation, and in some cases, death. A deeper understanding of the factors underpinning severe irAE development will be essential for improved irAE prediction and prevention, toward maximizing the benefits and safety profiles of ICIs. In recent work, we applied mass cytometry, single-cell RNA sequencing, single-cell V(D)J sequencing, bulk RNA sequencing, and bulk T-cell receptor (TCR) sequencing to identify pretreatment determinants of severe irAE development in patients with advanced melanoma. Across 71 patients separated into three cohorts, we found that two baseline features in circulation-elevated activated CD4 effector memory T-cell abundance and TCR diversity-are associated with severe irAE development, independent of the affected organ system within 3 months of ICI treatment initiation. Here, we provide an extended perspective on this work, synthesize and discuss related literature, and summarize practical considerations for clinical translation. Collectively, these findings lay a foundation for data-driven and mechanistic insights into irAE development, with the potential to reduce ICI morbidity and mortality in the future.

  View details for DOI 10.1111/imr.13248

  View details for PubMedID 37491734

• High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nature biotechnology Vahid, M. R., Brown, E. L., Steen, C. B., Zhang, W., Jeon, H. S., Kang, M., Gentles, A. J., Newman, A. M. 2023

  Abstract

  Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.

  View details for DOI 10.1038/s41587-023-01697-9

  View details for PubMedID 36879008

  View details for PubMedCentralID 6132072

• Cancer Cell Resistance to IFNg Can Occur via Enhanced Double-Strand Break Repair Pathway Activity CANCER IMMUNOLOGY RESEARCH Han, T., Wang, X., Shi, S., Zhang, W., Wang, J., Wu, Q., Li, Z., Fu, J., Zheng, R., Zhang, J., Tang, Q., Zhang, P., Wang, C. 2023; 11 (3): 381-398

  Abstract

  The pleiotropic cytokine interferon-gamma (IFNγ) is associated with cytostatic, antiproliferation, and proapoptotic functions in cancer cells. However, resistance to IFNγ occurs in many cancer cells, and the underlying mechanism is not fully understood. To investigate potential IFNγ-resistance mechanisms, we performed IFNγ-sensitivity screens in more than 40 cancer cell lines and characterized the sensitive and resistant cell lines. By applying CRISPR screening and transcriptomic profiling in both IFNγ-sensitive and IFNγ-resistant cells, we discovered that activation of double-strand break (DSB) repair genes could result in IFNγ resistance in cancer cells. Suppression of single-strand break (SSB) repair genes increased the dependency on DSB repair genes after IFNγ treatment. Furthermore, inhibition of the DSB repair pathway exhibited a synergistic effect with IFNγ treatment both in vitro and in vivo. The relationship between the activation of DSB repair genes and IFNγ resistance was further confirmed in clinical tumor profiles from The Cancer Genome Atlas (TCGA) and immune checkpoint blockade (ICB) cohorts. Our study provides comprehensive resources and evidence to elucidate a mechanism of IFNγ resistance in cancer and has the potential to inform combination therapies to overcome immunotherapy resistance.

  View details for DOI 10.1158/2326-6066.CIR-22-0056

  View details for Web of Science ID 000943098200001

  View details for PubMedID 36629846

• Addressing Tumor Heterogeneity by Sensitizing Resistant Cancer Cells to T cell-secreted Cytokines. Cancer discovery Ito, Y., Pan, D., Zhang, W., Zhang, X., Juan, T. Y., Pyrdol, J. W., Kyrysyuk, O., Doench, J. G., Liu, X. S., Wucherpfennig, K. W. 2023

  Abstract

  Tumor heterogeneity is a major barrier to cancer therapy, including immunotherapy. Activated T cells can efficiently kill tumor cells following recognition of MHC class I (MHC-I) bound peptides, but this selection pressure favors outgrowth of MHC-I deficient tumor cells. We performed a genome-scale screen to discover alternative pathways for T cell-mediated killing of MHC-I deficient tumor cells. Autophagy and TNF signaling emerged as top pathways, and inactivation of Rnf31 (TNF signaling) and Atg5 (autophagy) sensitized MHC-I deficient tumor cells to apoptosis by T cell-derived cytokines. Mechanistic studies demonstrated that inhibition of autophagy amplified pro-apoptotic effects of cytokines in tumor cells. Antigens from apoptotic MHC-I deficient tumor cells were efficiently cross-presented by dendritic cells, resulting in heightened tumor infiltration by IFNa and TNFg-producing T cells. Tumors with a substantial population of MHC-I deficient cancer cells could be controlled by T cells when both pathways were targeted using genetic or pharmacological approaches.

  View details for DOI 10.1158/2159-8290.CD-22-1125

  View details for PubMedID 36811466

• Machine learning on syngeneic mouse tumor profiles to model clinical immunotherapy response. Science advances Zeng, Z., Gu, S. S., Wong, C. J., Yang, L., Ouardaoui, N., Li, D., Zhang, W., Brown, M., Liu, X. S. 2022; 8 (41): eabm8564

  Abstract

  Most patients with cancer are refractory to immune checkpoint blockade (ICB) therapy, and proper patient stratification remains an open question. Primary patient data suffer from high heterogeneity, low accessibility, and lack of proper controls. In contrast, syngeneic mouse tumor models enable controlled experiments with ICB treatments. Using transcriptomic and experimental variables from >700 ICB-treated/control syngeneic mouse tumors, we developed a machine learning framework to model tumor immunity and identify factors influencing ICB response. Projected on human immunotherapy trial data, we found that the model can predict clinical ICB response. We further applied the model to predicting ICB-responsive/resistant cancer types in The Cancer Genome Atlas, which agreed well with existing clinical reports. Last, feature analysis implicated factors associated with ICB response. In summary, our computational framework based on mouse tumor data reliably stratified patients regarding ICB response, informed resistance mechanisms, and has the potential for wide applications in disease treatment studies.

  View details for DOI 10.1126/sciadv.abm8564

  View details for PubMedID 36240281

• Hippo signaling pathway regulates cancer cell-intrinsic MHC-II expression. Cancer immunology research Zeng, Z., Gu, S. S., Ouardaoui, N., Tymm, C., Yang, L., Wong, C. J., Li, D., Zhang, W., Wang, X., Weirather, J. L., Rodig, S. J., Hodi, F. S., Brown, M., Liu, X. S. 2022

  Abstract

  MHC-II is known to be mainly expressed on the surface of antigen-presenting cells. Evidence suggests MHC-II is also expressed by cancer cells and may be associated with better immunotherapy responses. However, the role and regulation of MHC-II in cancer cells remain unclear. In this study, we leveraged data mining and experimental validation to elucidate the regulation of MHC-II in cancer cells and its role in modulating the response to immunotherapy. We collated an extensive collection of omics data to examine cancer cell-intrinsic MHC-II expression and its association with immunotherapy outcomes. We then tested the functional relevance of cancer cell-intrinsic MHC-II expression using a syngeneic transplantation model. Lastly, we performed data mining to identify pathways potentially involved in the regulation of MHC-II expression, and experimentally validated candidate regulators. Analyses of pre-immunotherapy clinical samples in the CheckMate 064 trial revealed that cancer cell-intrinsic MHC-II protein was positively correlated with more favorable immunotherapy outcomes. Comprehensive meta-analyses of multiomics data from an exhaustive collection of data revealed that MHC-II is heterogeneously expressed in various solid tumors, and its expression is particularly high in melanoma. Using a syngeneic transplantation model, we further established that melanoma cells with high MHC-II responded better to anti-PD-1 treatment. Data mining followed by experimental validation revealed the Hippo signaling pathway as a potential regulator of melanoma MHC-II expression. In summary, we identified the Hippo signaling pathway as a novel regulator of cancer cell-intrinsic MHC-II expression. These findings suggest modulation of MHC-II in melanoma could potentially improve immunotherapy response.

  View details for DOI 10.1158/2326-6066.CIR-22-0227

  View details for PubMedID 36219700

• Machine Learning Modeling of Protein-intrinsic Features Predicts Tractability of Targeted Protein Degradation GENOMICS PROTEOMICS & BIOINFORMATICS Zhang, W., Burman, S., Chen, J., Donovan, K. A., Cao, Y., Shu, C., Zhang, B., Zeng, Z., Gu, S., Zhang, Y., Li, D., Fischer, E. S., Tokheim, C., Liu, X. 2022; 20 (5): 882-898

  Abstract

  Targeted protein degradation (TPD) has rapidly emerged as a therapeutic modality to eliminate previously undruggable proteins by repurposing the cell's endogenous protein degradation machinery. However, the susceptibility of proteins for targeting by TPD approaches, termed "degradability", is largely unknown. Here, we developed a machine learning model, model-free analysis of protein degradability (MAPD), to predict degradability from features intrinsic to protein targets. MAPD shows accurate performance in predicting kinases that are degradable by TPD compounds [with an area under the precision-recall curve (AUPRC) of 0.759 and an area under the receiver operating characteristic curve (AUROC) of 0.775] and is likely generalizable to independent non-kinase proteins. We found five features with statistical significance to achieve optimal prediction, with ubiquitination potential being the most predictive. By structural modeling, we found that E2-accessible ubiquitination sites, but not lysine residues in general, are particularly associated with kinase degradability. Finally, we extended MAPD predictions to the entire proteome to find 964 disease-causing proteins (including proteins encoded by 278 cancer genes) that may be tractable to TPD drug development.

  View details for DOI 10.1016/j.gpb.2022.11.008

  View details for Web of Science ID 000962003300006

  View details for PubMedID 36494034

  View details for PubMedCentralID PMC10025769

• TISMO: syngeneic mouse tumor database to model tumor immunity and immunotherapy response NUCLEIC ACIDS RESEARCH Zeng, Z., Wong, C. J., Yang, L., Ouardaoui, N., Li, D., Zhang, W., Gu, S., Zhang, Y., Liu, Y., Wang, X., Fu, J., Zhou, L., Zhang, B., Kim, S., Yates, K. B., Brown, M., Freeman, G. J., Uppaluri, R., Manguso, R., Liu, X. 2022; 50 (D1): D1391-D1397

  Abstract

  Syngeneic mouse models are tumors derived from murine cancer cells engrafted on genetically identical mouse strains. They are widely used tools for studying tumor immunity and immunotherapy response in the context of a fully functional murine immune system. Large volumes of syngeneic mouse tumor expression profiles under different immunotherapy treatments have been generated, although a lack of systematic collection and analysis makes data reuse challenging. We present Tumor Immune Syngeneic MOuse (TISMO), a database with an extensive collection of syngeneic mouse model profiles with interactive visualization features. TISMO contains 605 in vitro RNA-seq samples from 49 syngeneic cancer cell lines across 23 cancer types, of which 195 underwent cytokine treatment. TISMO also includes 1518 in vivo RNA-seq samples from 68 syngeneic mouse tumor models across 19 cancer types, of which 832 were from immune checkpoint blockade (ICB) studies. We manually annotated the sample metadata, such as cell line, mouse strain, transplantation site, treatment, and response status, and uniformly processed and quality-controlled the RNA-seq data. Besides data download, TISMO provides interactive web interfaces to investigate whether specific gene expression, pathway enrichment, or immune infiltration level is associated with differential immunotherapy response. TISMO is available at http://tismo.cistrome.org.

  View details for DOI 10.1093/nar/gkab804

  View details for Web of Science ID 000743496700168

  View details for PubMedID 34534350

  View details for PubMedCentralID PMC8728303

• Therapeutically Increasing MHC-I Expression Potentiates Immune Checkpoint Blockade CANCER DISCOVERY Gu, S., Zhang, W., Wang, X., Jiang, P., Traugh, N., Li, Z., Meyer, C., Stewig, B., Xie, Y., Bu, X., Manos, M. P., Font-Tello, A., Gjini, E., Lako, A., Lim, K., Conway, J., Tewari, A. K., Zeng, Z., Das Sahu, A., Tokheim, C., Weirather, J. L., Fu, J., Zhang, Y., Kroger, B., Liang, J., Cejas, P., Freeman, G. J., Rodig, S., Long, H. W., Gewurz, B. E., Hodi, F., Brown, M., Liu, X. 2021; 11 (6): 1524-1541

  Abstract

  Immune checkpoint blockade (ICB) therapy revolutionized cancer treatment, but many patients with impaired MHC-I expression remain refractory. Here, we combined FACS-based genome-wide CRISPR screens with a data-mining approach to identify drugs that can upregulate MHC-I without inducing PD-L1. CRISPR screening identified TRAF3, a suppressor of the NFκB pathway, as a negative regulator of MHC-I but not PD-L1. The Traf3-knockout gene expression signature is associated with better survival in ICB-naïve patients with cancer and better ICB response. We then screened for drugs with similar transcriptional effects as this signature and identified Second Mitochondria-derived Activator of Caspase (SMAC) mimetics. We experimentally validated that the SMAC mimetic birinapant upregulates MHC-I, sensitizes cancer cells to T cell-dependent killing, and adds to ICB efficacy. Our findings provide preclinical rationale for treating tumors expressing low MHC-I expression with SMAC mimetics to enhance sensitivity to immunotherapy. The approach used in this study can be generalized to identify other drugs that enhance immunotherapy efficacy. SIGNIFICANCE: MHC-I loss or downregulation in cancer cells is a major mechanism of resistance to T cell-based immunotherapies. Our study reveals that birinapant may be used for patients with low baseline MHC-I to enhance ICB response. This represents promising immunotherapy opportunities given the biosafety profile of birinapant from multiple clinical trials.This article is highlighted in the In This Issue feature, p. 1307.

  View details for DOI 10.1158/2159-8290.CD-20-0812

  View details for Web of Science ID 000659290300034

  View details for PubMedID 33589424

  View details for PubMedCentralID PMC8543117

• Inhibition of MAN2A1 Enhances the Immune Response to Anti-PD-L1 in Human Tumors CLINICAL CANCER RESEARCH Shi, S., Gu, S., Han, T., Zhang, W., Huang, L., Li, Z., Pan, D., Fu, J., Ge, J., Brown, M., Zhang, P., Jiang, P., Wucherpfennig, K. W., Liu, X. 2020; 26 (22): 5990-6002

  Abstract

  Immune checkpoint blockade has shown remarkable efficacy, but in only a minority of patients with cancer, suggesting the need to develop additional treatment strategies. Aberrant glycosylation in tumors, resulting from the dysregulated expression of key enzymes in glycan biosynthesis, modulates the immune response. However, the role of glycan biosynthesis enzymes in antitumor immunity is poorly understood. We aimed to study the immunomodulatory effects of these enzymes.We integrated transcriptional profiles of treatment-naïve human tumors and functional CRISPR screens to identify glycometabolism genes with immunomodulatory effects. We further validated our findings using in vitro coculture and in vivo syngeneic tumor growth assays.We identified MAN2A1, encoding an enzyme in N-glycan maturation, as a key immunomodulatory gene. Analyses of public immune checkpoint blockade trial data also suggested a synergy between MAN2A1 inhibition and anti-PD-L1 treatment. Loss of Man2a1 in cancer cells increased their sensitivity to T-cell-mediated killing. Man2a1 knockout enhanced response to anti-PD-L1 treatment and facilitated higher cytotoxic T-cell infiltration in tumors under anti-PD-L1 treatment. Furthermore, a pharmacologic inhibitor of MAN2A1, swainsonine, synergized with anti-PD-L1 in syngeneic melanoma and lung cancer models, whereas each treatment alone had little effect.Man2a1 loss renders cancer cells more susceptible to T-cell-mediated killing. Swainsonine synergizes with anti-PD-L1 in suppressing tumor growth. In light of the limited efficacy of anti-PD-L1 and failed phase II clinical trial on swainsonine, our study reveals a potential therapy combining the two to overcome tumor immune evasion.See related commentary by Bhat and Kabelitz, p. 5778.

  View details for DOI 10.1158/1078-0432.CCR-20-0778

  View details for Web of Science ID 000592798200025

  View details for PubMedID 32723834

  View details for PubMedCentralID PMC8500537

• Clonal tracing reveals diverse patterns of response to immune checkpoint blockade GENOME BIOLOGY Gu, S., Wang, X., Hu, X., Jiang, P., Li, Z., Traugh, N., Bu, X., Tang, Q., Wang, C., Zeng, Z., Fu, J., Meyer, C., Zhang, Y., Cejas, P., Lim, K., Wang, J., Zhang, W., Tokheim, C., Sahu, A., Xing, X., Kroger, B., Ouyang, Z., Long, H., Freeman, G. J., Brown, M., Liu, X. 2020; 21 (1): 263

  Abstract

  Immune checkpoint blockade (ICB) therapy has improved patient survival in a variety of cancers, but only a minority of cancer patients respond. Multiple studies have sought to identify general biomarkers of ICB response, but elucidating the molecular and cellular drivers of resistance for individual tumors remains challenging. We sought to determine whether a tumor with defined genetic background exhibits a stereotypic or heterogeneous response to ICB treatment.We establish a unique mouse system that utilizes clonal tracing and mathematical modeling to monitor the growth of each cancer clone, as well as the bulk tumor, in response to ICB. We find that tumors derived from the same clonal populations showed heterogeneous ICB response and diverse response patterns. Primary response is associated with higher immune infiltration and leads to enrichment of pre-existing ICB-resistant cancer clones. We further identify several cancer cell-intrinsic gene expression signatures associated with ICB resistance, including increased interferon response genes and glucocorticoid response genes. These findings are supported by clinical data from ICB treatment cohorts.Our study demonstrates diverse response patterns from the same ancestor cancer cells in response to ICB. This suggests the value of monitoring clonal constitution and tumor microenvironment over time to optimize ICB response and to design new combination therapies. Furthermore, as ICB response may enrich for cancer cell-intrinsic resistance signatures, this can affect interpretations of tumor RNA-seq data for response-signature association studies.

  View details for DOI 10.1186/s13059-020-02166-1

  View details for Web of Science ID 000582752800002

  View details for PubMedID 33059736

  View details for PubMedCentralID PMC7559192

• Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute NATURE PROTOCOLS Wang, B., Wang, M., Zhang, W., Xiao, T., Chen, C., Wu, A., Wu, F., Traugh, N., Wang, X., Li, Z., Mei, S., Cui, Y., Shi, S., Lipp, J., Hinterndorfer, M., Zuber, J., Brown, M., Li, W., Liu, X. 2019; 14 (3): 756-780

  Abstract

  Genome-wide screening using CRISPR coupled with nuclease Cas9 (CRISPR-Cas9) is a powerful technology for the systematic evaluation of gene function. Statistically principled analysis is needed for the accurate identification of gene hits and associated pathways. Here, we describe how to perform computational analysis of CRISPR screens using the MAGeCKFlute pipeline. MAGeCKFlute combines the MAGeCK and MAGeCK-VISPR algorithms and incorporates additional downstream analysis functionalities. MAGeCKFlute is distinguished from other currently available tools by its comprehensive pipeline, which contains a series of functions for analyzing CRISPR screen data. This protocol explains how to use MAGeCKFlute to perform quality control (QC), normalization, batch effect removal, copy-number bias correction, gene hit identification and downstream functional enrichment analysis for CRISPR screens. We also describe gene identification and data analysis in CRISPR screens involving drug treatment. Completing the entire MAGeCKFlute pipeline requires ~3 h on a desktop computer running Linux or Mac OS with R support.

  View details for DOI 10.1038/s41596-018-0113-7

  View details for Web of Science ID 000459890700004

  View details for PubMedID 30710114

  View details for PubMedCentralID PMC6862721

• IGSA: Individual Gene Sets Analysis, including Enrichment and Clustering PLOS ONE Wu, L., Chen, X., Zhang, D., Zhang, W., Liu, L., Ma, H., Yang, J., Xie, H., Liu, B., Jin, Q. 2016; 11 (10): e0164542

  Abstract

  Analysis of gene sets has been widely applied in various high-throughput biological studies. One weakness in the traditional methods is that they neglect the heterogeneity of genes expressions in samples which may lead to the omission of some specific and important gene sets. It is also difficult for them to reflect the severities of disease and provide expression profiles of gene sets for individuals. We developed an application software called IGSA that leverages a powerful analytical capacity in gene sets enrichment and samples clustering. IGSA calculates gene sets expression scores for each sample and takes an accumulating clustering strategy to let the samples gather into the set according to the progress of disease from mild to severe. We focus on gastric, pancreatic and ovarian cancer data sets for the performance of IGSA. We also compared the results of IGSA in KEGG pathways enrichment with David, GSEA, SPIA, ssGSEA and analyzed the results of IGSA clustering and different similarity measurement methods. Notably, IGSA is proved to be more sensitive and specific in finding significant pathways, and can indicate related changes in pathways with the severity of disease. In addition, IGSA provides with significant gene sets profile for each sample.

  View details for DOI 10.1371/journal.pone.0164542

  View details for Web of Science ID 000386204500041

  View details for PubMedID 27764138

  View details for PubMedCentralID PMC5072653