Doctor of Philosophy, Tongji University (2022)
Bachelor of Science, Harbin Medical University (2017)
Ph.D., Tongji University, Shanghai, China, Bioinformatics (2022)
B.S., Harbin Medical University, Harbin, China, Bioinformatics (2017)
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.
High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE.
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
Addressing Tumor Heterogeneity by Sensitizing Resistant Cancer Cells to T cell-secreted Cytokines.
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.
2022; 8 (41): eabm8564
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
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
2022; 20 (5): 882-898
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
2022; 50 (D1): D1391-D1397
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
2021; 11 (6): 1524-1541
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
2019; 14 (3): 756-780
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
2016; 11 (10): e0164542
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