Professional Education


  • Ph.D., Tongji University, Shanghai, China, Bioinformatics (2022)
  • B.S., Harbin Medical University, Harbin, China, Bioinformatics (2017)

Stanford Advisors


Current Research and Scholarly Interests


I'm interested in developing innovative methods and integrating multi-omics data to understand tumor-immune regulation and identify potential targets for cancer therapy.

All Publications


  • 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

  • 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