Junha Cha

All Publications

• Secretory IgA dysfunction underlies poor prognosis in Fusobacterium-infected colorectal cancer. Gut microbes Choi, I., Kim, K. A., Kim, S. C., Park, D., Nam, K. T., Cha, J. H., Baek, S., Cha, J., Jo, H. Y., Jung, M., Zeng, M. Y., Matei, I., Bullman, S., Ahn, J. B., Han, Y. D., Kim, H. S., Lee, I. 2025; 17 (1): 2528428

  Abstract

  Fusobacterium nucleatum (Fn) is commonly enriched in colorectal cancer (CRC) and associated with poor outcomes, though its mechanisms remain unclear. Our study investigated how Fn affects the tumor microenvironment through single-cell transcriptomic analyses of 42 CRC patient tissues, comparing Fn-positive and Fn-negative tumors. We discovered that Fn impairs IgA plasma cell development and secretory IgA (sIgA) production by disrupting communication with tumor-associated macrophages. Additional experiments in germ-free mice, together with our re-analysis of a publicly available single-cell RNA-seq data set from a CRC mouse model with an intact gut microbiome-both models having been orally gavaged with Fn-jointly validated the causal role of Fn in impairing sIgA induction. We identified a dysregulated IgA maturation (IGAM) module in Fn-positive patients, indicating compromised mucosal immunity and increased bacterial infiltration. This IGAM signature effectively stratified Fn-positive patients, suggesting potential for targeted therapeutic approaches. Our findings reveal that Fn disrupts sIgA production, increasing tumor microbial burden and worsening prognosis through chronic inflammation in Fn-positive CRC.

  View details for DOI 10.1080/19490976.2025.2528428

  View details for PubMedID 40667611

  View details for PubMedCentralID PMC12269704

• 4-1BB+ Tregs and inhibitory progenitor exhausted T cells confer resistance to anti-PD-L1 and anti-CTLA-4 combination therapy. Cell reports. Medicine Cha, J., Kim, C. G., Sim, N. S., Kim, G., Son, W., Kim, D., Jung, Y., Hong, H. J., Lee, H. B., Kim, J., Kim, J., Yoon, S. O., Go, S., Kim, J., Seong, E., Baek, S., Kim, K. H., Hong, M. H., Koh, Y. W., Lee, I., Kim, H. R. 2025: 102408

  Abstract

  Predictors of immune checkpoint inhibitor response in cancer remain elusive. From a previous phase 2 neoadjuvant immunotherapy window-of-opportunity study, we present the single-cell RNA and T cell receptor (TCR) sequencing analysis of 57 pre- and post-treatment tumor biopsies from head and neck cancer patients treated with durvalumab (anti-PD-L1) alone or with tremelimumab (anti-CTLA-4), identifying key cellular and molecular predictors of immune checkpoint inhibitor (ICI) response. Malignant cells and neutrophil senescence promote ICI response. While CXCL13+ exhausted T (Tex) cells enhance response through 4-1BB signaling, anti-CTLA-4 induces 4-1BB+ regulatory T cells (Tregs) restricting ICI efficacy. These opposing roles of 4-1BB in different cellular contexts may explain the limited benefit of combinatorial immunotherapy observed in clinical trials. We identify two subsets of tumor-reactive progenitor Tex (Tpex): ICI-responsive Tpex1 and ICI-resistant Tpex2, a subset characterized by KLRB1 and IL17R. The balance of Tpex1 and Tpex2 associates with ICI response across multiple cancers, offering insights into sustaining response. This study was registered at ClinicalTrials.gov (NCT03737968).

  View details for DOI 10.1016/j.xcrm.2025.102408

  View details for PubMedID 41045934

• Comparative single-cell analysis of esophageal cancer subtypes reveals tumor microenvironment distinctions explaining varied immunotherapy responses CANCER COMMUNICATIONS Baek, S., Cha, J., Hong, M., Kim, G., Koh, Y., Kim, D., Son, W., Ock, C., Lee, S., Hemberg, M., Park, S., Kim, H., Lee, I. 2025

  View details for DOI 10.1002/cac2.70046

  View details for Web of Science ID 001518984300001

  View details for PubMedID 40581834

• Single-cell network biology enabling cell-type-resolved disease genetics. Genomics & informatics Cha, J., Lee, I. 2025; 23 (1): 10

  Abstract

  Gene network models provide a foundation for graph theory approaches, aiding in the novel discovery of drug targets, disease genes, and genetic mechanisms for various biological functions. Disease genetics must be interpreted within the cellular context of disease-associated cell types, which cannot be achieved with datasets consisting solely of organism-level samples. Single-cell RNA sequencing (scRNA-seq) technology allows computational distinction of cell states which provides a unique opportunity to understand cellular biology that drives disease processes. Importantly, the abundance of cell samples with their transcriptome-wide profile allows the modeling of systemic cell-type-specific gene networks (CGNs), offering insights into gene-cell-disease relationships. In this review, we present reference-based and de novo inference of gene functional interaction networks that we have recently developed using scRNA-seq datasets. We also introduce a compendium of CGNs as a useful resource for cell-type-resolved disease genetics. By leveraging these advances, we envision single-cell network biology as the key approach for mapping the gene-cell-disease axis.

  View details for DOI 10.1186/s44342-025-00042-7

  View details for PubMedID 40148916

  View details for PubMedCentralID PMC11951680

• Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas. Animal cells and systems Sung, E., Cha, J., Baek, S., Lee, I. 2025; 29 (1): 11-20

  Abstract

  Gene co-expression network inference from bulk tissue samples often misses cell-type-specific interactions, which can be detected through single-cell gene expression data. However, the noise and sparsity of single-cell data challenge the inference of these networks. We developed scNET, a framework for integrative cell-type-specific co-expression network inference from single-cell transcriptome data, demonstrating its utility in augmenting the human interactome for more accurate disease gene prediction. We address the limitations of de novo network inference from single-cell expression data through dropout imputation, metacell formation, and data transformation. Employing this data preprocessing pipeline, we inferred cell-type-specific co-expression links from single-cell atlas data, covering various cell types and tissues, and integrated over 850K of these inferred links into a preexisting human interactome, HumanNet, resulting in HumanNet-plus. This integration notably enhanced the accuracy of network-based disease gene prediction. These findings suggest that with proper data preprocessing, network inference from single-cell gene expression data can be highly effective, potentially enriching the human interactome and advancing the field of network medicine.

  View details for DOI 10.1080/19768354.2025.2472002

  View details for PubMedID 40066175

  View details for PubMedCentralID PMC11892045

• HCNetlas: A reference database of human cell type-specific gene networks to aid disease genetic analyses. PLoS biology Yu, J., Cha, J., Koh, G., Lee, I. 2025; 23 (2): e3002702

  Abstract

  Cell type-specific actions of disease genes add a significant layer of complexity to the genetic architecture underlying diseases, obscuring our understanding of disease mechanisms. Single-cell omics have revealed the functional roles of genes at the cellular level, identifying cell types critical for disease progression. Often, a gene impact on disease through its altered network within specific cell types, rather than mere changes in expression levels. To explore the cell type-specific roles of disease genes, we developed HCNetlas (human cell network atlas), a resource cataloging cell type-specific gene networks (CGNs) for various healthy tissue cells. We also devised 3 network analysis methods to investigate cell type-specific functions of disease genes. These methods involve comparing HCNetlas CGNs with those derived from disease-affected tissue samples. These methods find that systemic lupus erythematosus genes predominantly function in myeloid cells, and Alzheimer's disease genes mainly play roles in inhibitory and excitatory neurons. Additionally, they suggest that many lung cancer-related genes may exert their roles in immune cells. These findings suggest that HCNetlas has the potential to link disease-associated genes to cell types of action, facilitating development of cell type-resolved diagnostics and therapeutic strategies for complex human diseases.

  View details for DOI 10.1371/journal.pbio.3002702

  View details for PubMedID 39908239

  View details for PubMedCentralID PMC11798474

• Single-cell analysis reveals cellular and molecular factors counteracting HPV-positive oropharyngeal cancer immunotherapy outcomes. Journal for immunotherapy of cancer Cha, J., Kim, D. H., Kim, G., Cho, J. W., Sung, E., Baek, S., Hong, M. H., Kim, C. G., Sim, N. S., Hong, H. J., Lee, J. E., Hemberg, M., Park, S., Yoon, S. O., Ha, S. J., Koh, Y. W., Kim, H. R., Lee, I. 2024; 12 (6)

  Abstract

  Oropharyngeal squamous cell carcinoma (OPSCC) induced by human papillomavirus (HPV-positive) is associated with better clinical outcomes than HPV-negative OPSCC. However, the clinical benefits of immunotherapy in patients with HPV-positive OPSCC remain unclear.To identify the cellular and molecular factors that limited the benefits associated with HPV in OPSCC immunotherapy, we performed single-cell RNA (n=20) and T-cell receptor sequencing (n=10) analyses of tonsil or base of tongue tumor biopsies prior to immunotherapy. Primary findings from our single-cell analysis were confirmed through immunofluorescence experiments, and secondary validation analysis were performed via publicly available transcriptomics data sets.We found significantly higher transcriptional diversity of malignant cells among non-responders to immunotherapy, regardless of HPV infection status. We also observed a significantly larger proportion of CD4+ follicular helper T cells (Tfh) in HPV-positive tumors, potentially due to enhanced Tfh differentiation. Most importantly, CD8+ resident memory T cells (Trm) with elevated KLRB1 (encoding CD161) expression showed an association with dampened antitumor activity in patients with HPV-positive OPSCC, which may explain their heterogeneous clinical outcomes. Notably, all HPV-positive patients, whose Trm presented elevated KLRB1 levels, showed low expression of CLEC2D (encoding the CD161 ligand) in B cells, which may reduce tertiary lymphoid structure activity. Immunofluorescence of HPV-positive tumors treated with immune checkpoint blockade showed an inverse correlation between the density of CD161+ Trm and changes in tumor size.We found that CD161+ Trm counteracts clinical benefits associated with HPV in OPSCC immunotherapy. This suggests that targeted inhibition of CD161 in Trm could enhance the efficacy of immunotherapy in HPV-positive oropharyngeal cancers.NCT03737968.

  View details for DOI 10.1136/jitc-2023-008667

  View details for PubMedID 38857913

  View details for PubMedCentralID PMC11168198

• A Phase II Open-Label Randomized Clinical Trial of Preoperative Durvalumab or Durvalumab plus Tremelimumab in Resectable Head and Neck Squamous Cell Carcinoma. Clinical cancer research : an official journal of the American Association for Cancer Research Kim, C. G., Hong, M. H., Kim, D., Lee, B. H., Kim, H., Ock, C. Y., Kelly, G., Bang, Y. J., Kim, G., Lee, J. E., Kim, C., Kim, S. H., Hong, H. J., Park, Y. M., Sim, N. S., Park, H., Park, J. W., Lee, C. G., Kim, K. H., Park, G., Jung, I., Han, D., Kim, J. H., Cha, J., Lee, I., Kang, M., Song, H., Oum, C., Kim, S., Kim, S., Lim, Y., Kim-Schulze, S., Merad, M., Yoon, S. O., Kim, H. J., Koh, Y. W., Kim, H. R. 2024; 30 (10): 2097-2110

  Abstract

  Clinical implications of neoadjuvant immunotherapy in patients with locally advanced but resectable head and neck squamous cell carcinoma (HNSCC) remain largely unexplored.Patients with resectable HNSCC were randomized to receive a single dose of preoperative durvalumab (D) with or without tremelimumab (T) before resection, followed by postoperative (chemo)radiotherapy based on multidisciplinary discretion and 1-year D treatment. Artificial intelligence (AI)-powered spatial distribution analysis of tumor-infiltrating lymphocytes and high-dimensional profiling of circulating immune cells tracked dynamic intratumoral and systemic immune responses.Of the 48 patients enrolled (D, 24 patients; D+T, 24 patients), 45 underwent surgical resection per protocol (D, 21 patients; D+T, 24 patients). D±T had a favorable safety profile and did not delay surgery. Distant recurrence-free survival (DRFS) was significantly better in patients treated with D+T than in those treated with D monotherapy. AI-powered whole-slide image analysis demonstrated that D+T significantly reshaped the tumor microenvironment toward immune-inflamed phenotypes, in contrast with the D monotherapy or cytotoxic chemotherapy. High-dimensional profiling of circulating immune cells revealed a significant expansion of T-cell subsets characterized by proliferation and activation in response to D+T therapy, which was rare following D monotherapy. Importantly, expansion of specific clusters in CD8+ T cells and non-regulatory CD4+ T cells with activation and exhaustion programs was associated with prolonged DRFS in patients treated with D+T.Preoperative D±T is feasible and may benefit patients with resectable HNSCC. Distinct changes in the tumor microenvironment and circulating immune cells were induced by each treatment regimen, warranting further investigation.

  View details for DOI 10.1158/1078-0432.CCR-23-3249

  View details for PubMedID 38457288

• Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis. Computational and structural biotechnology journal Cha, J., Lavi, M., Kim, J., Shomron, N., Lee, I. 2023; 21: 2296-2304

  Abstract

  Single-cell transcriptome data provide a unique opportunity to explore the gene networks of a particular cell type. However, insufficient capture rate and high dimensionality of single-cell RNA sequencing (scRNA-seq) data challenge cell-type-specific gene network (CGN) reconstruction. Here, we demonstrated that the imputation of scRNA-seq data enables reconstruction of CGNs by effective retrieval of gene functional associations. We reconstructed CGNs for seven primary and nine metastatic breast cancer cell lines using scRNA-seq data with imputation. Key genes for primary or metastatic cell lines were prioritized based on network centrality measures and CGN hub genes that were presumed to be the major determinant of cell type characteristics. To identify novel genes in breast cancer metastasis, we used the average rank difference of centrality between the primary and metastatic cell lines. Genes predicted using CGN centrality analysis were more enriched for known breast cancer metastatic genes than those predicted using differential expression. The molecular chaperone CCT2 was identified as a novel gene for breast metastasis during knockdown assays of several candidate genes. Overall, our study demonstrated an effective CGN reconstruction technique with imputation of scRNA-seq data and the feasibility of identifying key genes for particular cell subsets using single-cell network analysis.

  View details for DOI 10.1016/j.csbj.2023.03.036

  View details for PubMedID 37035549

  View details for PubMedCentralID PMC10073994

• scHumanNet: a single-cell network analysis platform for the study of cell-type specificity of disease genes. Nucleic acids research Cha, J., Yu, J., Cho, J. W., Hemberg, M., Lee, I. 2023; 51 (2): e8

  Abstract

  A major challenge in single-cell biology is identifying cell-type-specific gene functions, which may substantially improve precision medicine. Differential expression analysis of genes is a popular, yet insufficient approach, and complementary methods that associate function with cell type are required. Here, we describe scHumanNet (https://github.com/netbiolab/scHumanNet), a single-cell network analysis platform for resolving cellular heterogeneity across gene functions in humans. Based on cell-type-specific gene networks (CGNs) constructed under the guidance of the HumanNet reference interactome, scHumanNet displayed higher functional relevance to the cellular context than CGNs built by other methods on single-cell transcriptome data. Cellular deconvolution of gene signatures based on network compactness across cell types revealed breast cancer prognostic markers associated with T cells. scHumanNet could also prioritize genes associated with particular cell types using CGN centrality and identified the differential hubness of CGNs between disease and healthy conditions. We demonstrated the usefulness of scHumanNet by uncovering T-cell-specific functional effects of GITR, a prognostic gene for breast cancer, and functional defects in autism spectrum disorder genes specific for inhibitory neurons. These results suggest that scHumanNet will advance our understanding of cell-type specificity across human disease genes.

  View details for DOI 10.1093/nar/gkac1042

  View details for PubMedID 36350625

  View details for PubMedCentralID PMC9881140

• HumanNet v3: an improved database of human gene networks for disease research. Nucleic acids research Kim, C. Y., Baek, S., Cha, J., Yang, S., Kim, E., Marcotte, E. M., Hart, T., Lee, I. 2022; 50 (D1): D632-D639

  Abstract

  Network medicine has proven useful for dissecting genetic organization of complex human diseases. We have previously published HumanNet, an integrated network of human genes for disease studies. Since the release of the last version of HumanNet, many large-scale protein-protein interaction datasets have accumulated in public depositories. Additionally, the numbers of research papers and functional annotations for gene-phenotype associations have increased significantly. Therefore, updating HumanNet is a timely task for further improvement of network-based research into diseases. Here, we present HumanNet v3 (https://www.inetbio.org/humannet/, covering 99.8% of human protein coding genes) constructed by means of the expanded data with improved network inference algorithms. HumanNet v3 supports a three-tier model: HumanNet-PI (a protein-protein physical interaction network), HumanNet-FN (a functional gene network), and HumanNet-XC (a functional network extended by co-citation). Users can select a suitable tier of HumanNet for their study purpose. We showed that on disease gene predictions, HumanNet v3 outperforms both the previous HumanNet version and other integrated human gene networks. Furthermore, we demonstrated that HumanNet provides a feasible approach for selecting host genes likely to be associated with COVID-19.

  View details for DOI 10.1093/nar/gkab1048

  View details for PubMedID 34747468

  View details for PubMedCentralID PMC8728227

• Single-cell network biology for resolving cellular heterogeneity in human diseases. Experimental & molecular medicine Cha, J., Lee, I. 2020; 52 (11): 1798-1808

  Abstract

  Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future.

  View details for DOI 10.1038/s12276-020-00528-0

  View details for PubMedID 33244151

  View details for PubMedCentralID PMC8080824