About the lab

Vision: Develop Personalized Genomic Medicine Technology
to help cancer, obesity, and acne patients based on genome, microbiome and immunologic data

Main Research Area :
1. Genomic Medicine
2. Cancer Genetics
3. Cancer Immunology
4. Microbiome

Ongoing Projects :
1. Onco-probiotics project for Cancer Immunotherapy
2. Mouse Avatar and Humanized Mouse Program for Cancer
3. Microbiome Project for Obesity/Diabetes and Acne

Featured research (3)

The human body contains a variety of microbes. The distribution of microbes varies from organ to organ. Sequencing and bioinformatics techniques have revolutionized microbial research. Although previously considered to be sterile, the urinary bladder contains various microbes. Several studies have used urine and bladder tissues to reveal the microbiome of the urinary bladder. Lactic acid-producing bacteria, such as Bifidobacterium, Lactobacillus, and Lactococcus, are particularly beneficial for human health and are linked to bladder cancer. This review highlights the analysis protocols for microbiome research, the studies undertaken to date, and the microbes with therapeutic potential in bladder cancer.
Breast cancer (BC) is the second most common solid malignant tumor that metastasizes to the brain. Despite emerging therapies such as immunotherapy, whether the tumor microenvironment (TME) in breast cancer brain metastasis (BCBM) has potential as a target of new treatments is unclear. Expression profiling of 770 genes in 12 pairs of primary BC and matched brain metastasis (BM) samples was performed using the NanoString nCounter PanCancer IO360TM Panel. Immune cell profiles were validated by immunohistochemistry (IHC) in samples from 50 patients with BCBM. Pathway analysis revealed that immune-related pathways were downregulated. Immune cell profiling showed that CD8+ T cells and M1 macrophages were significantly decreased, and M2 macrophages were significantly increased, in BM compared to primary BC samples (p = 0.001, p = 0.021 and p = 0.007, respectively). CCL19 and CCL21, the top differentially expressed genes, were decreased significantly in BM compared to primary BC (p < 0.001, both). IHC showed that the CD8+ count was significantly lower (p = 0.027), and the CD163+ and CD206+ counts were higher, in BM than primary BC (p < 0.001, both). A low CD8+ T cell count, low CD86+ M1 macrophage count, and high M2/M1 macrophage ratio were related to unfavorable clinical outcomes. BC exhibits an immunosuppressive characteristic after metastasis to the brain. These findings will facilitate establishment of a treatment strategy for BCBM based on the TME of metastatic cancer.
The identification of predictive biomarkers or models is necessary for the selection of patients who might benefit the most from immunotherapy. Seven histological features (signet ring cell [SRC], fibrous stroma, myxoid stroma, tumor-infiltrating lymphocytes [TILs], necrosis, tertiary lymphoid follicles, and ulceration) detected in surgically resected tissues ( N = 44) were used to train a model. The presence of SRC became an optimal decision parameter for pathology alone (AUC = 0.78). Analysis of differentially expressed genes (DEGs) for the prediction of genomic markers showed that C-X-C motif chemokine ligand 11 ( CXCL11 ) was high in responders ( P < 0.001). Immunohistochemistry (IHC) was performed to verify its potential as a biomarker. IHC revealed that the expression of CXCL11 was associated with responsiveness ( P = 0.003). The response prediction model was trained by integrating the results of the analysis of pathological factors and RNA sequencing (RNA-seq). When trained with the C5.0 decision tree model, the categorical level of the expression of CXCL11 , a single variable, was shown to be the best model (AUC = 0.812). The AUC of the model trained with the random forest was 0.944. Survival analysis revealed that the C5.0-trained model (log-rank P = 0.01 for progression-free survival [PFS]; log-rank P = 0.012 for overall survival [OS]) and the random forest-trained model (log-rank P < 0.001 for PFS; log-rank P = 0.001 for OS) predicted prognosis more accurately than the PD-L1 test (log-rank P = 0.031 for PFS; log-rank P = 0.107 for OS).

Lab head

Hansoo Park
  • Department of Biomedical Science and Engineering

Members (5)

Charles Lee
  • The Jackson Laboratory for Genomic Medicine, Farmington, CT., United States
Shinyoung Park
  • Genome & Company
Jee Yun Han
  • University of California, San Diego
Kyungchan Min
  • Gwangju Institute of Science and Technology
Lee Eulgi
  • Gwangju Institute of Science and Technology
Jong-Il Kim
Jong-Il Kim
  • Not confirmed yet
Eulgi Lee
Eulgi Lee
  • Not confirmed yet

Alumni (1)

Myung-Giun Noh
  • Chonnam National University