As a high-grade soft-tissue sarcoma (STS), undifferentiated pleomorphic sarcoma (UPS) is highly recurrent and malignant. UPS is categorized as a tumor of uncertain differentiation and has few options for treatment due to its lack of targetable genetic alterations. There are also few cell lines that provide a representative model for UPS, leading to a dearth of experimental research. Here, we established and characterized new cell lines derived from two recurrent UPS tissues. Cells were obtained from UPS tissues by mincing, followed by extraction or dissociation using enzymes and culture in a standard culture environment. Cells were maintained for several months without artificial treatment, and some cell clones were found to be tumorigenic in an immunodeficient mouse model. Interestingly, some cells formed tumors in vivo when injected after aggregation in a non-adherent culture system for 24 h. The tissues from in vivo study and tissues from patients shared common histological characteristics. Pathways related to the cell cycle, such as DNA replication, were enriched in both cell clones. Pathways related to cell-cell adhesion and cell-cell signaling were also enriched, suggesting a role of the mesenchymal-to-epithelial transition for tumorigenicity in vivo. These new UPS cell lines may facilitate research to identify therapeutic strategies for UPS. [BMB Reports 2023; 56(4): 258-264].
Synovial sarcoma is a rare disease with diverse progression characteristics. We developed a novel deep-learning-based prediction algorithm for survival rates of synovial sarcoma patients. The purpose of this study is to evaluate the performance of the proposed prediction model and demonstrate its clinical usage. The study involved 242 patients who were diagnosed with synovial sarcoma in three institutions between March 2001 and February 2013. The patients were randomly divided into a training set (80%) and a testing set (20%). Fivefold cross validation was performed utilizing the training set. The test set was retained for the final testing. A Cox proportional hazard model, simple neural network, and the proposed survival neural network were all trained utilizing the same training set, and fivefold cross validation was performed. The final testing was performed utilizing the isolated test data to determine the best prediction model. The multivariate Cox proportional hazard regression analysis revealed that size, initial metastasis, and margin were independent prognostic factors. In fivefold cross validation, the median value of the receiver-operating characteristic curve (area under the curve) was 0.87 in the survival neural network, which is significantly higher compared to the area under the curve of 0.792 for the simple neural network (p = 0.043). In the final test, survival neural network model showed the better performance (area under the curve: 0.814) compared to the Cox proportional hazard model (area under the curve: 0.629; p = 0.0001). The survival neural network model predicted survival of synovial sarcoma patients more accurately compared to Cox proportional hazard model.
Background and Objectives The three‐dimensional (3D)‐printed bone tumor resection guide can be personalized for a specific patient and utilized for bone tumor surgery. It is noninvasive, eidetic, and easy to use. We aimed to categorize the use of the 3D‐printed guide and establish in vivo accuracy data. Methods We retrospectively reviewed 12 patients, who underwent limb salvage surgery using the 3D‐printed guide at a single institution. To confirm the achievement of a safe bone margin, we compared the actual and planned distances between the cutting surface and tumor, which were reported in the final pathological report and measured from the same virtual cutting plane using graphical data of the cutting guide design, respectively. Results The use of the 3D‐printed guide was categorized as follows: (a) wide excision only, (b) wide excision and biological reconstruction with a structural bone allograft shaped in accordance with the 3D‐printed guide, and (c) wide excision and reconstruction with a 3D‐printed personalized implant. The maximal cutting error was 3 mm. Conclusions The 3D‐printed resection guide is easy to use and shows promise in the field of orthopedic oncology, with its application in bone tumor resection and reconstruction with a structural bone allograft or 3D‐printed implant.
BACKGROUND Rapid technological advances have increased the diversity of omics data. In addition, technological advances and improvements in information-processing capacity have made integrated analyses (multi-omics) of different omics data types possible. Although multi-omics models are more advantageous for target discovery and clinical diagnosis than single-omics models, efficiency and performance are limited by data-handling steps, such as the need to change the model structure when a new omics data type is added and variation in available data among samples. OBJECTIVE This study proposes a novel artificial intelligence (AI) model and learning strategies for the use of incomplete datasets, which are common in omics research. The following goals were set: 1) to design a single AI model that can analyze both complete and incomplete data, 2) to design an AI model that can infer missing omics data by learning from partial omics data as input, and 3) to compare the proposed novel approach with previous methods that do not allow the use of incomplete data. METHODS The proposed model consists of two key components: (1) a multi-omics generative model based on the variational auto-encoder (VAE) that can learn genetic patterns in tumors based on different omics data types, and (2) an expanded classification model that can predict cancer phenotypes. In addition, padding was applied to replace missing omics data in each sample and generate virtual data. RESULTS The embedding data generated by the proposed model has three characteristics. First, its accuracy for classifying various cancer phenotypes (cancer type, sample type, and primary site) was high, addressing the class imbalance problem (cancer type weighted F1 score > 95%). Second, the virtual omics data generated by the model resembled the actual omics data (mean absolute error < 0.09). Third, the performance for classifying cancer phenotypes was higher for the model that could learn from incomplete data and complete data than for the model that could learn from complete data alone(primary site weighted F1 score: 0.0113 improvement). CONCLUSIONS The proposed novel model was capable of utilizing incomplete omics data. This showed good classification performance for cancer phenotypes and effective data construction in cases of missing omics data. Thus, overcoming data limitations, generating omics data through deep learning, and contributing to the realization of precision medicine. The model can be expanded to any omics data type for cancer, in addition to the data types evaluated in this study, suggesting that model performance can be further improved.
The epidemiology of osteosarcoma in adolescents and young adults (AYA) remains unclear. We aimed to assess and compare the clinical features of osteosarcoma between AYA and other age groups. We retrieved osteosarcoma cases diagnosed between 1999 and 2017 from the Korea Central Cancer Registry. We compared survival trends and clinical characteristics between AYA and other age groups. AYA comprised 43.3% (1309/3022) of the osteosarcoma cases. Compared to other age groups, the male-to-female ratio was highest in AYA (1.61:1). The proportion of tumors located in an extremity was 80.3% in AYA, which was lower than in young children (92.5%) or pubertal children (93.8%) but higher than in adults (55.7%) or the elderly (47.5%). As for treatments, 71.2% of AYA received local treatment and systemic chemotherapy, and 28.8% received only local treatment (surgery: 261, radiotherapy: 9, surgery and radiotherapy: 5). The 5-year overall survival (OS) was lower in AYA (68%) than in young children (78%) or pubertal children (73%) but higher than in adults (47%) or the elderly (25%). When AYA were divided into five subgroups by age, patients aged 15–19 years constituted the largest proportion (45.4%, n = 594). Additionally, the proportion of patients with a non-extremity tumor increased in an age-dependent manner, from 10.3% in AYA aged 15–19 years to 35.3% in AYA aged 35–39 years. OS did not significantly differ among the different age subgroups of AYA. The clinical characteristics and OS of the AYA were more similar to those of children than to those of adults. There is a need for cooperation between pediatric and adult oncologists for effective osteosarcoma treatment in AYA.
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