June Hyuk Kim

Undifferentiated sarcomas (USs), including undifferentiated pleomorphic sarcoma (UPS), are aggressive mesenchymal malignancies with limited molecular biomarkers for prognostic assessment and therapeutic stratification. Expression-based markers may provide insight into tumor aggressiveness and clinical outcomes. Here, we performed integrative transcriptomic and spatial analyses to identify differentially expressed genes (DEGs). By comparing normal tissues with sarcoma tumors and sarcoma tumors with cell lines. Intersection and clustering analyses were conducted to define shared expression programs, which revealed a subset of DEGs enriched in epithelial-mesenchymal transition (EMT)-related pathways. CosMx spatial transcriptomics was applied to xenograft tumors derived from two UPS cell lines to resolve tumor-intrinsic signatures. The National Cancer Center Cohort samples were used for validation, and immunohistochemistry confirmed the expression in thirty US tissues. Spatial transcriptomic profiling identified mesenchymal tissue–driven gene expression programs in UPS xenografts. Across bulk RNA-seq and spatial data, epithelial membrane protein 3 (EMP3) consistently emerged as highly expressed in US tissues and cell lines. EMP3 is a robust mesenchymal-associated biomarker linked to EMT, tumor progression, and clinical outcomes in USs, supporting its potential utility as a prognostic indicator and therapeutic target.

Background and Objectives: This study aimed to describe the clinical characteristics and survival outcomes of three representative complex karyotype soft tissue sarcoma (STS) subtypes—undifferentiated sarcoma (US, primarily undifferentiated pleomorphic sarcoma (UPS)), myxofibrosarcoma (MFS), and leiomyosarcoma of soft tissue (LMS-ST)—using data from a single-institution cohort. Materials and Methods: A retrospective review of 124 patients treated at a single tertiary referral center between 2002 and 2024 was conducted. Clinicopathologic characteristics and survival outcomes were analyzed. Kaplan–Meier methods were used to estimate overall survival (OS). Cox proportional hazards regression models were applied to identify independent prognostic factors for survival, incorporating variables such as age, sex, tumor stage, and treatment modality. Results: The cohort comprised 36 cases of US, 64 of MFS, and 24 of LMS-ST. OS and survival after cohort enrollment (S-NCC) were evaluated both by subtype and across the entire cohort to assess potential differences across tumor subgroups. In both univariable and multivariable analyses, US subtypes showed poorer survival than MFS and LMS-ST. FNCLCC grade 3 emerged as a significant adverse prognostic factor for survival across all three subtypes. For FNCLCC grade 3 patients, the presence of benign prostatic hyperplasia (BPH) was significantly associated with an increased risk of death. Conclusions: Among the three subtypes, US demonstrated the most aggressive clinical course, MFS was notable for frequent local recurrence but relatively favorable survival, and LMS-ST showed intermediate outcomes. These findings highlight the clinical heterogeneity of complex karyotype STS and provide a foundation for future studies integrating molecular and multi-omics data to refine risk stratification and therapeutic strategies.

Lee S, et al. Clin Orthop Surg. 2026 Apr;18(2):366-374. https://doi.org/10.4055/cios25232

Background: Although radiographs are the first-line imaging modality, differentiating between neoplastic pathologic fractures and nonpathologic fractures on radiographs can sometimes be challenging. In this study, we aimed to develop and evaluate a deep learning model capable of distinguishing neoplastic pathologic fractures from nonpathologic fractures on hip radiographs to enhance diagnostic accuracy. Methods: This retrospective, multicenter study analyzed anteroposterior hip radiographs from patients who visited the emergency department at 4 different institutions. The deep learning model was trained on, and tested using, data from 338 patients at a single institution and externally validated on data from 488 patients across 3 additional institutions. Results: The model achieved an overall accuracy of 0.880, with a sensitivity of 0.882 and a specificity of 0.879, on the internal test set. It was then externally validated using the data of 488 patients (67 with neoplastic pathologic fracture and 421 with nonpathologic fracture) from institutions separate from where the model was developed. The model achieved an overall accuracy of 0.848, sensitivity of 0.910, and specificity of 0.786. Its performance was comparable with that of general orthopaedic surgeons. Conclusions: The developed deep learning model is a reliable and valid tool for distinguishing neoplastic pathologic fractures from nonpathologic fractures on hip radiographs. It has the potential to assist orthopaedic surgeons in resource-limited settings, where optimizing the interpretation of radiographs is critical for patient care. The model is publicly available at https://pathfxdx.org. Level of Evidence: Diagnostic Level III . See Instructions for Authors for a complete description of levels of evidence.

Sarcomas are a heterogeneous group of mesenchymal malignancies with poor prognosis and limited response to standard therapies, including immune checkpoint inhibitors (ICIs). Tumour‐intrinsic factors—such as telomere maintenance mechanisms (TMMs) and metabolic reprogramming—play central roles in driving immune evasion and therapeutic resistance. Telomerase activation and alternative lengthening of telomeres sustain replicative immortality while influencing the tumour immune microenvironment. In parallel, metabolic adaptations, including glutamine dependency and arginine auxotrophy, further suppress antitumour immunity. Together, TMMs and metabolism form an integrated axis that shapes immune modulation and treatment outcomes. Recent advances—ranging from telomerase‐based vaccines and TMM‐targeted immunotherapies to metabolic modulators combined with ICIs—demonstrate the translational promise of targeting this axis. This review synthesises current knowledge on telomere‒metabolism crosstalk in sarcomas, highlights its impact on immunotherapy response, and outlines future directions for biomarker‐driven, combinatorial strategies to overcome resistance and improve patient outcomes. Key points: Telomere maintenance mechanisms (telomerase reverse transcriptase and alternative lengthening of telomeres) reprogram metabolism and dampen innate immune sensing in sarcomas. Metabolic rewiring (glutamine addiction, glycolysis and fatty acid oxidation) fosters T‐cell dysfunction and myeloid‐derived suppressor cell accumulation. Targeting the telomere‒metabolism‒immunity axis offers strategies to overcome immunotherapy resistance.

CD47 is expressed on cell surfaces and acts as a “don’t eat me” signal by interacting with signal-regulatory protein-α on the macrophage surface. Some cancer cells express CD47 protein and can evade macrophage phagocytosis. Herein, we evaluated the feasibility of targeting CD47 for osteosarcoma by analyzing its expression patterns, clinicopathological correlations, and immunotherapeutic potential. We performed a retrospective analysis on 24 biopsy samples from patients with osteosarcoma to investigate correlations between CD47 protein positivity and clinicopathological characteristics. CD47 protein expression was detected in 20.8% of the biopsy samples. CD47 positivity correlated with metastasis at diagnosis. Patients with CD47-positive tumors were older than those with CD47-negative tumors. However, CD47 protein expression was not associated with sex, tumor size, or histologic response to preoperative chemotherapy. In vitro, CD47 antibody (B6H12) did not affect osteosarcoma cell viability or apoptosis. In a wound-healing assay, CD47 inhibited the migration of osteosarcoma cells. Differentiated macrophages exhibited higher phagocytic activity against osteosarcoma cells when pretreated with B6H12 compared with the isotype control. Our preliminary data suggest a possible interaction between CD47 protein and macrophage phagocytosis in osteosarcoma metastasis. A better understanding of the role of CD47 is necessary to develop an innovative immunotherapeutic approach against osteosarcoma.

Technological advances in information-processing capacity have enabled integrated analyses (multi-omics) of different omics data types, improving target discovery and clinical diagnosis. This study proposes novel artificial intelligence (AI) learning strategies for incomplete datasets, common in omics research. The model comprises (1) a multi-omics generative model based on a variational auto-encoder that learns tumor genetic patterns based on different omics data types and (2) an expanded classification model that predicts cancer phenotypes. Padding was applied to replace missing data with virtual data. The embedding data generated by the model accurately classified cancer phenotypes, addressing the class imbalance issue (weighted F1 score: cancer type > 0.95, primary site > 0.92, sample type > 0.97). The classification performance was maintained in the absence of omics data, and the virtual data resembled actual omics data (cosine similarity mRNA gene expression > 0.96, mRNA isoform expression > 0.95, DNA methylation > 0.96). Meanwhile, in the presence of omics data, high-quality, non-existent omics data were generated (cosine similarity mRNA gene expression: 0.9702, mRNA isoform expression: 0.9546, DNA methylation: 0.9687). This model can effectively classify cancer phenotypes based on incomplete omics data with data sparsity robustness, generating omics data through deep learning and enabling precision medicine.

Sehan Jeong, Sharmin Afroz, Donghyun Kang, Jeonghwan Noh, Jooyeon Suh, June Hyuk Kim, Hye Jin You, Hyun Guy Kang, Yi-Jun Kim, and Jin-Hong Kim. Mol. Cells 2023;46:579-88. https://doi.org/10.14348/molcells.2023.0079

Park JW, et al. Clin Orthop Surg. 2023 Jul;15:e41. https://doi.org/10.4055/cios22366