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Hongyoon Choi



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    OA08 - Advanced Models and "Omics" for Therapeutic Development (ID 133)

    • Event: WCLC 2019
    • Type: Oral Session
    • Track: Biology
    • Presentations: 1
    • Now Available
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      OA08.06 - Reciprocal Change in Glucose Metabolism of Cancer and Immune Cells Mediated by Different GLUT Predicts Immunotherapy Response (Now Available) (ID 642)

      11:00 - 12:30  |  Presenting Author(s): Hongyoon Choi

      • Abstract
      • Presentation
      • Slides

      Background

      Tumor metabolism represented by aerobic glycolysis is dynamically changed in tumor microenvironment (TME) to achieve immune escape. However, in vivo properties of glucose metabolism in cancer and immune cells are poorly understood and their clinical implications are still lacking. We scrutinized the association of tumor metabolism and immune properties of TME by comprehensive analyses using tissue RNA-seq, positron emission tomography (PET), and single cell RNA-seq data.

      Method

      Lung squamous cell carcinoma (LUSC) samples with both RNA-seq and 18F-deoxyglucose (FDG) PET (n = 63) were collected to examine the association of in vivo glucose metabolism, gene expression levels related to glucose metabolism, and immune cell enrichment. An overall enrichment score of TME (ImmuneScore) was estimated from tissue RNA-seq data. The gene expression levels of each cell component of TME were analyzed by single cell RNA-seq from lung cancer patients. The expression patterns of glucose transporters (GLUTs) were evaluated in patients who underwent immunotherapy to investigate whether it can predict immunotherapy response.

      Result

      Single cell RNA-seq showed that GLUT1 was mostly expressed in cancer cells while GLUT3 was mostly found in myeloid cells in TME. ImmuneScore showed a negative correlation with GLUT1 (r=-0.70, p<0.01) and a positive correlation with GLUT3 (r=0.39, p<0.01) in LUSC samples, and it was validated in TCGA cohort (r=-0.44, p<0.01 for GLUT1; r=0.26, p<0.01 for GLUT3). LUSC samples were divided into two distinct groups (immure-rich and immune-poor) by ImmuneScore. In immune-poor cluster, FDG uptake was positively correlated with GLUT1 (r=0.27, p=0.04), while not correlated with GLUT3. In immune-rich cluster, FDG uptake was positively correlated with GLUT3 (r=0.78, p=0.01), while not correlated with GLUT1. ImmuneScore was negatively correlated with FDG uptake in immune-poor cluster, while there was positive correlation in immune-rich cluster. We defined GLUT3-GLUT1 ratio (GLUTratio) as a metabolic biomarker representing immune status in TME. High GLUTratio indicates increased metabolic activity in immune cells and decreased metabolic activity in cancer cells in TME. For melanoma patients who underwent anti-PD-1 therapy, GLUTratio was significantly higher in responders than nonresponders (p=0.03).

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      Conclusion

      Our findings support a reciprocal change of glucose metabolism between cancer and immune cells within TME mediated by different GLUTs. A new glucose metabolism-based biomarker, GLUTratio, can reflect reciprocal metabolic activity of immune and cancer cells in TME, and be a feasible predictive biomarker for immunotherapy.

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