Hongkyung Kim

Hongkyung Kim, M.D., Ph.D., Oh Joo Kweon, M.D., Ph.D., Sumi Yoon, M.D., Ph.D., Yong Kwan Lim, M.D., Ph.D., and Bohyun Kim, M.D., Ph.D.. Ann Lab Med -0001;0:. https://doi.org/10.3343/alm.2026.0097

Jin Ju Kim, M.D., Yu Ri Kim, M.D., Hongkyung Kim, M.D., Hye Won Kook, M.D., Haerim Chung, M.D., Doh Yu Hwang, M.D., Ji Eun Jang, M.D., June-Won Cheong, M.D., Seung-Tae Lee, M.D., Jong Rak Choi, M.D., Saeam Shin, M.D. , and Jin Seok Kim, M.D.. Ann Lab Med -0001;0:. https://doi.org/10.3343/alm.2026.0122

Background: Gene fusions play a pivotal role in the pathogenesis and classification of hematologic malignancies. RNA sequencing (RNA-seq) has emerged as a powerful tool for detecting gene fusions; however, many clinical studies have focused on targeted RNA-seq, and optimal parameters for whole transcriptome RNA-seq remain uncertain. Methods: We retrospectively analyzed whole RNA-seq data from 301 patients diagnosed with acute leukemia between October 2022 and May 2025 to characterize the landscape of pathogenic gene fusions. Fusions were identified using the Arriba algorithm, and subsampling analyses were performed on cases with recurrent fusions to determine the minimum sequencing output required for reliable detection. Results: Pathogenic gene fusions were identified in 113 of 301 patients (37.5%). Whole RNA-seq detected fusions that were not identifiable by conventional assays, including UBTF::ATXN7L3, and highlighted frequent fusion events, such as ZNF384 rearrangements. Subsampling analysis demonstrated that a sequencing output ≥ 100 million reads (moderate confidence) or ≥300 million reads (high confidence) was sufficient for 100% detection of recurrent fusions. Conclusions: Whole RNA-seq reliably detects clinically relevant gene fusions in acute leukemia, aligns well with conventional karyotyping results, and surpasses targeted RNA-seq in comprehensiveness. A sequencing output of at least 100 million reads is recommended for clinical fusion detection.

Background/Objectives: Patients in long-term care hospitals (LTCHs) are at increased risk of harboring antimicrobial-resistant organisms due to frequent healthcare exposure and multiple comorbidities. This retrospective observational study aimed to compare the antimicrobial susceptibility of bacterial isolates from LTCH-onset infections (LTCHIs) with those from community-acquired infections (CAIs) in elderly patients. Methods: This study was conducted at a 700-bed urban tertiary university hospital and included patients aged ≥65 years with positive cultures for bacteremia, lower respiratory tract infections (LRTIs), or urinary tract infections (UTIs) within 48 h of admission. Medical records, including antimicrobial susceptibility test results, were reviewed for a total of 1780 patients and their isolates. Antimicrobial susceptibility patterns were compared between LTCHI and CAI patients. Results: Patients with LTCHI exhibited significantly higher antimicrobial non-susceptibility than those with CAIs across multiple pathogens and antimicrobial classes (p < 0.05). In bacteremia, Staphylococcus aureus, Escherichia coli, and Klebsiella pneumoniae from LTCHI cases showed increased non-susceptibility to β-lactams and fluoroquinolones. In LRTIs, Pseudomonas aeruginosa and Acinetobacter baumannii demonstrated high non-susceptibility to carbapenems (52.9% and 90%, respectively) and aminoglycosides. In UTIs, LTCHI isolates exhibited broader resistance among Enterobacterales and P. aeruginosa. Notably, the proportion of multidrug-resistant organisms, including carbapenem-resistant Enterobacterales (15.4–50.0%) and carbapenem-resistant Acinetobacter baumannii (90.5%), was substantially higher in the LTCHI group across all infection sites. Conclusions: Elderly patients with LTCHI are more likely to harbor antimicrobial-resistant pathogens than those with CAIs. Careful consideration of LTCHI origin is therefore essential for empirical antibiotic selection and for strategies aimed at limiting further resistance.

Sumi Yoon, M.D., Ph.D., Kye Won Choe, M.D., Hongkyung Kim, M.D., Ph.D., Yong Kwan Lim, M.D., Ph.D., and Oh Joo Kweon, M.D., Ph.D.. Ann Lab Med -0001;0:. https://doi.org/10.3343/alm.2025.0495

Objectives: This study aimed to evaluate the performance of PBIA (UIMD, Seoul, Republic of Korea), an automated digital morphology analyzer using deep learning, for white blood cell (WBC) classification in peripheral blood smears and compare it with the widely used DI-60 (Sysmex, Kobe, Japan). Methods: A total of 461 slides were analyzed using PBIA and DI-60. For each instrument, pre-classification performance was evaluated on the basis of post-classification results verified by users. Pre- and post-classification results were compared with manual WBC differentials, and the ability to identify abnormal cells was assessed. Results: The pre-classification performance of PBIA was better than that of DI-60 for most cell classes. PBIA had an accuracy of 90.0 % and Cohen’s kappa of 0.934, higher than DI-60 (45.5 % accuracy and 0.629 kappa) across all cell classes. The pre-classification performance of both instruments decreased when abnormal cells were observed in manual counts, but PBIA still performed better. PBIA also appeared to show better correlation with manual WBC differential counts, particularly in pre-classification (Pearson’s correlation coefficient: 0.696–0.944 vs. 0.230–0.882 for neutrophils, lymphocytes, monocytes, eosinophils, basophils, and blasts), although the mean differences varied by cell class. For abnormal cells identified in manual counts, PBIA exhibited more false positives for blasts (30.5 vs. 2.3 %), while DI-60 had a higher rate of false negatives (42.1 vs. 6.1 %). Both instruments exhibited high false negative rates for atypical lymphocytes. Conclusions: PBIA demonstrated better performance than DI-60, highlighting its clinical utility. Further multicenter studies are required for full validation.

Hongkyung Kim, M.D., Ph.D., Jinho Lee, M.D., Mi Ri Park, B.S., Zisun Choi, M.S., Seung Jung Han, Ph.D., Dongha Kim, M.S., Saeam Shin, M.D., Ph.D., Seung-Tae Lee, M.D., Ph.D., Jong Rak Choi, M.D., Ph.D., and Seung Woo Park, M.D., Ph.D.. Ann Lab Med 2025;45:199-208. https://doi.org/10.3343/alm.2024.0345

Yong Kwan Lim, M.D., Oh Joo Kweon, M.D., Ae Ja Park, M.D., Hongkyung Kim, M.D., Sumi Yoon, M.D., Tae-Hyoung Kim, M.D., and Mi-Kyung Lee, M.D.. Ann Lab Med 2025;45:218-22. https://doi.org/10.3343/alm.2024.0118

Most patients diagnosed with clear cell renal cell carcinoma (ccRCC) are also detected with small and organ‐confined tumors, and the majority of these are classified as clinical tumor stage 1a (cT1a). A considerable proportion of patients with cT1 RCC shows tumor upstaging to pathological stage 3a (pT3a), and these patients have worse oncological outcomes. The role of circulating tumor DNA (ctDNA) in RCC has been limited to monitoring treatment response and resistance. Therefore, the present study aimed to evaluate the potential of ctDNA in predicting pT3a upstaging in cT1a ccRCC. We sequenced plasma samples preoperatively collected from 48 patients who had undergone partial nephrectomy for cT1a ccRCC using data from a prospective cohort RCC. The ctDNA were profiled and compared with clinicopathological ccRCC features to predict pT3a upstaging. Associations between ctDNA, tumor complexity, and pT3a upstaging were evaluated. Tumor complexity was assessed using the anatomical classification system. Univariate analysis used chi‐squared and Student's t‐tests; multivariate analysis considered significant factors from univariate analyses. Of the 48 patients with cT1a ccRCC, 12 (25%) were upstaged to pT3a, with ctDNA detected in 10 (20.8%), predominantly in patients with renal sinus fat invasion (SFI; n = 8). Among the pT3a group, ctDNA was detected in 75%, contrasting with only 2.8% in patients with pT1a (1/36). Detection of ctDNA was the only significant preoperative predictor of pT3a upstaging, especially in SFI. This study is the first to suggest ctDNA as a preoperative predictor of pT3a RCC upstaging from cT1a based on preoperative radiological images.