Prognostic Factors for Survival in Patients with Malignant Giant Cell Tumor of Bone: A Risk Nomogram Analysis Based on the Population

Genetic characterization of a novel organoid from human malignant giant-cell tumor

Malignant giant-cell tumors are extremely rare bone sarcomas that transform from conventional giant-cell tumors during long periods of treatment. Owing to their rarity, no further analysis of their molecular pathogenesis exists, and thus, no standard treatment has been established. Recently, organoid culture methods have been highlighted for recapturing the tumor microenvironment, and we have applied the culture methods and succeeded in establishing patient-derived organoids (PDO) of rare sarcomas. This study aimed to investigate the genomic characteristics of our established novel organoids from human malignant giant-cell tumors. At our institute, we treated a patient with malignant giant-cell tumor. The remaining sarcoma specimens after surgical resection were cultured according to the air-liquid interface organoid-culture method. Organoids were xenografted into NOD-scid IL2Rgnull mice. The developed tumors were histologically and genomically analyzed to compare their characteristics with those of the original tumors. Genetic changes over time throughout treatment were analyzed, and the genomic status of the established organoid was confirmed. Organoids from malignant giant-cell tumors could be serially maintained using air-liquid interface organoid-culture methods. The tumors developed in xenografted NOD-scid IL2Rgnull mice. After several repetitions of the process, a patient-derived organoid line from the malignant giant-cell tumor was established. Immunohistochemical analyses and next-generation sequencing revealed that the established organoids lacked the H3-3A G34W mutation. The xenografted organoids of the malignant giant-cell tumor had phenotypes histologically and genetically similar to those of the original tumor. The established organoids were confirmed to be derived from human malignant giant-cell tumors. In summary, the present study demonstrated a novel organoid model of a malignant giant-cell tumor that was genetically confirmed to be a malignant transformed tumor. Our organoid model could be used to elucidate the molecular pathogenesis of a malignant giant-cell tumor and develop novel treatment modalities.

Nomogram-Based Prediction of Overall and Cancer-Specific Survival in Patients with Primary Bone Diffuse Large B-Cell Lymphoma: A Population-Based Study

Background

Primary bone diffuse large B-cell lymphoma (PD-DLBCL) accounts for more than 80% of primary bone lymphoma. We created two nomograms to predict overall survival (OS) and cancer-specific survival (CSS) in patients with PD-DLBCL for this rare disease.

Methods

In total, 891 patients diagnosed with PB-DLBCL between 2007 and 2016 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore independent prognostic factors and create nomograms for OS and CSS. The area under the curve (AUC), the calibration curve, decision curve analysis (DCA), and Kaplan-Meier (K-M) curve analysis were used to evaluate the nomograms.

Results

Four variables were identified as independent prognostic factors for OS, and three variables were identified as independent prognostic factors for CSS. The receiver operating characteristic (ROC) curves demonstrated the strong discriminatory power of the nomograms. The calibration and DCA curves showed that the nomograms had a satisfactory ability to predict OS and CSS. The K-M curves showed that age, gender, primary site, chemotherapy, and tumor stage affected patient survival.

Conclusions

In patients with PD-DLBCL, age, race, primary site, and chemotherapy affected OS, while age, race, and chemotherapy affected CSS. The two nomograms created based on the aforementioned variables provided more accurate individual survival predictions for PD-DLBCL patients and can help physicians make appropriate clinical decisions.

Nomogram to Predict Overall and Cancer-Specific Survival in Patients with Synovial Sarcoma in the Extremities: A Population-Based Study

Background

Synovial sarcoma is a rare disease, and synovial sarcoma that first appears in the extremities accounts for more than 80% of cases. We established two nomograms to predict the overall survival (OS) and cancer-specific survival (CSS) rates of patients with synovial sarcoma.

Methods

A total of 227 patients diagnosed with synovial sarcoma in the extremities between 2010 and 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Univariate and multivariate Cox analyses were performed to explore independent prognostic factors and to create two separate nomograms for OS and CSS. The C-index, the area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan-Meier (KM) curve were used to evaluate the column line graphs and analyze prognostic factors.

Results

Age, Stage M, and surgery were identified as independent prognostic factors for OS and CSS. The ROC curve showed good discriminative power for the nomogram. Calibration curves and DCA curves showed that the nomogram had a satisfactory ability to predict OS and CSS. The KM curve showed that chemotherapy alone did not affect patient survival.

Conclusion

Age, Stage M, and surgery are variables that affect OS and CSS in patients with synovial sarcoma in the extremities. Two nomograms were established based on the above variables to provide patients with more accurate individual survival predictions and to help physicians make appropriate clinical decisions.

Development and Validation of Prognostic Nomograms for Elderly Patients with Osteosarcoma

Background

The aim of the current study was to construct prognostic nomograms for individual risk prediction in elderly patients with osteosarcoma.

Methods

Data for 816 elderly patients (≥40 years old) with osteosarcoma between 2004 and 2016 from the Surveillance, Epidemiology, and End Results (SEER) database were randomly assigned to training (N=573) and internal validation (N=243) sets. The essential clinical predictors were identified based on least absolute shrinkage and selection operator (Lasso) Cox regression. Nomograms were constructed to predict the 1-, 3-, and 5-year cancer-specific survival (CSS) and overall survival (OS).

Results

Our LASSO regression analyses of the training set yielded five clinicopathological features (age, chemotherapy, surgery, AJCC stage, and summary stage) in the training cohort for the prognosis of elderly patients with osteosarcoma, while grade was only associated with OS and M stage was only associated with CSS. Construction of nomograms based on these predictors was performed to evaluate the prognosis of elderly patients with osteosarcoma. The C-index, calibration and decision curve analysis also showed the satisfactory performance of these nomograms for prognosis prediction.

Conclusion

The constructed nomograms are helpful tools for exactly predicting the prognosis of elderly patients with osteosarcoma, which could enable patients to be more accurately managed in clinical practice.