Dendritic cells (DCs) are the most professional antigen presenting cells that play important roles in connection between innate and adaptive immune responses. Numerous studies revealed that the functions of DCs are related with the capture and processing of antigen as well as the migration to lymphoid tissues for the presenting antigens to T cells. These unique features of DCs allow them to be considered as therapeutic vaccines that can induce immune responses and anti-tumor activity. Here, we discuss and understand the immunological basis of DCs and presume the possibilities of DC-based vaccines for the promising cancer therapy.
Antigen-Presenting Cells ; Cancer Vaccines ; Dendritic Cells ; Immunotherapy ; Lymphoid Tissue ; T-Lymphocytes ; Vaccines

Objective: The study’s aim was to evaluate the validity of the Korean version of the University of California San Diego Performance-based Skills Assessment, Validation of Intermediate Measures (K-UPSA-2-VIM) in patients with dementia (D), mild cognitive impairment (MCI), and cognitive normal control group (CN). Methods: Study participants were 25 patients with D, 43 patients with MCI, and 111 controls with CN group, respectively. For cognitive assessment, Mini Mental State Examination, Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery, and Clinical Dementia Rating were used. For functional assessment, Barthel-Activities of Daily Living, Instrumental Activities of Daily Living, Dementia Screening questionnaire, and K-UPSA-2-VIM were used. Results: Statistically significant differences were observed in all subdomains and total score of the K-UPSA-2-VIM among three cognitive groups. K-UPSA-2-VIM demonstrated 75.7% of sensitivity and 65.1% of specificity, with an area under the curve (AUC) of 0.731 (95% confidence interval [CI]: 0.641-0.821, p<0.001) in discriminating between CN and MCI groups. In discriminating between MCI and D groups, 76.7% of sensitivity and 64.0% of specificity, with an AUC of 0.706 (95% CI: 0.580-0.833, p=0.005) were demonstrated. Conclusion The K-UPSA-2-VIM is useful to evaluate activities of daily living function in Korean patients with D and MCI.

Purpose: Epidermal growth factor receptor kinase domain duplication (EGFR-KDD) is a rare and poorly understood oncogenic mutation in non–small cell lung cancer (NSCLC). We aimed to investigate the acquired resistance mechanism of EGFR-KDD against EGFR-TKIs. Materials and Methods: We identified EGFR-KDD in tumor tissue obtained from a patient with stage IV lung adenocarcinoma and established the patient-derived cell line SNU-4784. We also established several EGFR-KDD Ba/F3 cell lines: EGFR-KDD wild type (EGFR-KDDWT), EGFR-KDD domain 1 T790M (EGFR-KDDD1T), EGFR-KDD domain 2 T790M (EGFR-KDDD2T), and EGFR-KDD both domain T790M (EGFR-KDDBDT). We treated the cells with EGFR tyrosine kinase inhibitors (TKIs) and performed cell viability assays, immunoblot assays, and ENU (N-ethyl-N-nitrosourea) mutagenesis screening. Results: In cell viability assays, SNU-4784 cells and EGFR-KDDWT Ba/F3 cells were sensitive to 2nd generation and 3rd generation EGFR TKIs. In contrast, the T790M-positive EGFR-KDD Ba/F3 cell lines (EGFR-KDDT790M) were only sensitive to 3rd generation EGFR TKIs. In ENU mutagenesis screening, we identified the C797S mutation in kinase domain 2 of EGFR-KDDBDT Ba/F3 cells. Based on this finding, we established an EGFR-KDD domain 1 T790M/domain 2 cis-T790M+C797S (EGFR-KDDT/T+C) Ba/F3 model, which was resistant to EGFR TKIs and anti-EGFR monoclonal antibody combined with EGFR TKIs. Conclusion Our study reveals that the T790M mutation in EGFR-KDD confers resistance to 1st and 2nd generation EGFR TKIs, but is sensitive to 3rd generation EGFR TKIs. In addition, we identified that the C797S mutation in kinase domain 2 of EGFR-KDDT790M mediates a resistance mechanism against 3rd generation EGFR TKIs.

Background: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. Methods: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing’s syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. Results: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6β-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. Conclusion The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.