Trends in Tumor Site-Specific Survival of Bone Sarcomas from 1980 to 2018: A Surveillance, Epidemiology and End Results-Based Study

The relationship between metabolite mediated immune regulatory imbalance and the occurrence of malignant tumors of bone and articular cartilage: a Mendelian randomization study

Background

This study aims to assess the causal relationship between immune cell characteristics and malignant tumors of bone and articular cartilage, focusing on the mediating role of metabolites. Using Mendelian randomization, we evaluated these relationships based on genetic variations to identify potential biomarkers and therapeutic targets.

Methods

A two-sample Mendelian randomization analysis was conducted using GWAS data for immune cell features and 1,400 metabolites to investigate direct and mediating effects. Effective instrumental variables (IVs) were selected, and statistical analyses-including inverse variance weighting (IVW), weighted median, and mode-based methods-were performed using R software. This approach enabled the assessment of direct causal relationships as well as the potential mediating role of metabolites in the association between immune cell features and malignancies.

Results

Significant causal relationships were identified between 26 immune phenotypes and the risk of malignant tumors of bone and articular cartilage. Notably, the HLA DR+ NK cell phenotype SSC-A showed a positive correlation with the risk of these malignancies. Further analysis revealed causal relationships with 67 metabolites, 38 of which were positively correlated and 29 negatively correlated. Mediation analysis highlighted the role of immune surveillance and metabolic dysregulation in tumor development, as evidenced by the association between the immune phenotype SSC-A on HLA DR+ NK cells and the metabolite 5-hydroxyhexanoate.

Conclusion

The findings suggest significant causal relationships between immune phenotypes and malignant tumors of bone and articular cartilage, with metabolites potentially mediating these relationships. These insights lay the groundwork for further research and could contribute to the development of new biomarkers and treatment strategies.

A Novel Clinical Tool to Predict Cancer-specific Survival in Postoperative Patients With Primary Spinal and Pelvic Sarcomas: A Large Population-Based Retrospective Cohort Study

STUDY DESIGN

Retrospective cohort study.

OBJECTIVE

Primary osseous sarcomas originating from the spine and pelvis are rare and usually portend inferior prognoses. Currently, the standard treatment for spinal and pelvic sarcomas is surgical resection, but the poor prognosis limits the benefits to postoperative patients. This study aims to identify the independent prognostic factors of cancer-specific survival (CSS) in postoperative patients with primary spinal and pelvic sarcomas and construct a nomogram for predicting these patients' 3-, 5-, and 10-year CSS probability.

METHODS

A total of 452 patients were enrolled from the Surveillance, Epidemiology, and End Results (SEER) database. They were divided into a training cohort and a validation cohort. Univariate and multivariate Cox regression analyses were used to identify these patients' CSS-related independent prognostic factors. Then, those factors were used to construct a prognostic nomogram for predicting the 3-, 5-, and 10-year CSS probability, whose predictive performance and clinical value were verified by the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was constructed.

RESULTS

Sex, histological type, tumor stage, and tumor grade were identified as CSS-related independent prognostic factors. A nomogram with high predictive performance and good clinical value to predict the 3-, 5-, and 10-year CSS probability was constructed, on which a mortality risk stratification system was constructed based to divide these patients into 3 mortality risk subgroups effectively.

CONCLUSIONS

This study constructed and validated a clinical nomogram to predict CSS in postoperative patients with primary spinal and pelvic sarcomas. It could assist clinicians in classifying these patients into different mortality risk subgroups and realize sarcoma-specific management.

EphA2-specific microvesicles derived from tumor cells facilitate the targeted delivery of chemotherapeutic drugs for osteosarcoma therapy

Despite advances in surgery and chemotherapy, the survival of patients with osteosarcoma (OS) has not been fundamentally improved over the last two decades. Microvesicles (MVs) have a high cargo-loading capacity and are emerging as a promising drug delivery nanoplatform. The aim of this study was to develop MVs as specifically designed vehicles to enable OS-specific targeting and efficient treatment of OS. Herein, we designed and constructed a nanoplatform (YSA-SPION-MV/MTX) consisting of methotrexate (MTX)-loaded MVs coated with surface-carboxyl Fe3O4 superparamagnetic nanoparticles (SPIONs) conjugated with ephrin alpha 2 (EphA2)-targeted peptides (YSAYPDSVPMMS, YSA). YSA-SPION-MV/MTX showed an effective targeting effect on OS cells, which was depended on the binding of the YSA peptide to EphA2. In the orthotopic OS mouse model, YSA-SPION-MV/MTX effectively delivered drugs to tumor sites with specific targeting, resulting in superior anti-tumor activity compared to MTX or MV/MTX. And YSA-SPION-MV/MTX also reduced the side effects of high-dose MTX. Taken together, this strategy opens up a new avenue for OS therapy. And we expect this MV-based therapy to serve as a promising platform for the next generation of precision cancer nanomedicines.

A deep learning model for accurately predicting cancer-specific survival in patients with primary bone sarcoma of the extremity: a population-based study

PURPOSE

Primary bone and joint sarcomas of the long bone are relatively rare neoplasms with poor prognosis. An efficient clinical tool that can accurately predict patient prognosis is not available. The current study aimed to use deep learning algorithms to develop a prediction model for the prognosis of patients with long bone sarcoma.

METHODS

Data of patients with long bone sarcoma in the extremities was collected from the Surveillance, Epidemiology, and End Results Program database from 2004 to 2014. Univariate and multivariate analyses were performed to select possible prediction features. DeepSurv, a deep learning model, was constructed for predicting cancer-specific survival rates. In addition, the classical cox proportional hazards model was established for comparison. The predictive accuracy of our models was assessed using the C-index, Integrated Brier Score, receiver operating characteristic curve, and calibration curve.

RESULTS

Age, tumor extension, histological grade, tumor size, surgery, and distant metastasis were associated with cancer-specific survival in patients with long bone sarcoma. According to loss function values, our models converged successfully and effectively learned the survival data of the training cohort. Based on the C-index, area under the curve, calibration curve, and Integrated Brier Score, the deep learning model was more accurate and flexible in predicting survival rates than the cox proportional hazards model.

CONCLUSION

A deep learning model for predicting the survival probability of patients with long bone sarcoma was constructed and validated. It is more accurate and flexible in predicting prognosis than the classical CoxPH model.

Does primary tumor resection improve survival for patients with sarcomas of pelvic bones, sacrum, and coccyx who have metastasis at diagnosis ?

BACKGROUND

Recent studies demonstrated that primary tumor resection (PTR) improves survival of patients with metastatic bone sarcomas. However, it remains quite unclear regarding the role of PTR in the treatment of sarcomas of pelvic bones with synchronous metastasis at diagnosis.

METHODS

Using the Surveillance, Epidemiology, and End Results Program, we enrolled a total of 385 patients with sarcomas of pelvic bones, sacrum, and coccyx who have metastasis at initial diagnosis, including 139 patients with osteosarcoma, 176 with Ewing sarcoma, and 70 with chondrosarcoma. Association between PTR and disease-specific survival (DSS) were investigated using the univariable and multivariable Cox regression models. Hazard ratio (HR) and 95% confidence interval (CI) were reported. Representative institutional PTR strategies and clinical outcomes for patients with metastatic pelvic sarcomas from our cancer center were displayed.

RESULTS

The usage rate of PTR was 28.1% (39/139) in osteosarcoma, 13.6% (24/176) in Ewing sarcoma, and 41.4% (29/70) in chondrosarcoma with synchronous metastatic lesions. PTR was not associated with an improved DSS for metastatic pelvic osteosarcoma (HR = 0.686, 95% CI = 0.430 ~ 1.094, P = 0.113) and Ewing sarcoma (HR = 0.580, 95% CI = 0.291 ~ 1.154, P = 0.121). The use of PTR was associated with an improved DSS for metastatic pelvic chondrosarcoma (HR = 0.464, 95% CI = 0.225 ~ 0.954, P = 0.037).

CONCLUSION

Primary lesion resection may provide a survival benefit for metastatic chondrosarcoma, but not for osteosarcoma and Ewing sarcoma of pelvic bones, sacrum, and coccyx. This population-based study recommends an active surgical intervention for metastatic chondrosarcoma while non-surgical treatment for metastatic osteosarcoma and Ewing sarcoma of the pelvis in terms of survival improvement.

Ewing sarcoma from molecular biology to the clinic

In Europe, with an incidence of 7.5 cases per million, Ewing sarcoma (ES) is the second most common primary malignant bone tumor in children, adolescents and young adults, after osteosarcoma. Since the 1980s, conventional treatment has been based on the use of neoadjuvant and adjuvant chemotherapeutic agents combined with surgical resection of the tumor when possible. These treatments have increased the patient survival rate to 70% for localized forms, which drops drastically to less than 30% when patients are resistant to chemotherapy or when pulmonary metastases are present at diagnosis. However, the lack of improvement in these survival rates over the last decades points to the urgent need for new therapies. Genetically, ES is characterized by a chromosomal translocation between a member of the FET family and a member of the ETS family. In 85% of cases, the chromosomal translocation found is (11; 22) (q24; q12), between the EWS RNA-binding protein and the FLI1 transcription factor, leading to the EWS-FLI1 fusion protein. This chimeric protein acts as an oncogenic factor playing a crucial role in the development of ES. This review provides a non-exhaustive overview of ES from a clinical and biological point of view, describing its main clinical, cellular and molecular aspects.

Machine learning for predicting the survival in osteosarcoma patients: Analysis based on American and Hebei Province cohort

Osteosarcoma, a rare malignant tumor, has a poor prognosis. This study aimed to find the best prognostic model for osteosarcoma. There were 2912 patients included from the SEER database and 225 patients from Hebei Province. Patients from the SEER database (2008-2015) were included in the development dataset. Patients from the SEER database (2004-2007) and Hebei Province cohort were included in the external test datasets. The Cox model and three tree-based machine learning algorithms (survival tree [ST], random survival forest [RSF] and gradient boosting machine [GBM]) were used to develop the prognostic models by 10-fold cross-validation with 200 iterations. Additionally, performance of models in the multivariable group was compared with the TNM group. The 3-year and 5-year cancer specific survival (CSS) were 72.71% and 65.92% in the development dataset, respectively. The predictive ability in the multivariable group was superior to that in the TNM group. The calibration curves and consistency in the multivariable group were superior to those in the TNM group. The Cox and RSF models performed better than the ST and GBM models. A nomogram was constructed to predict the 3-year and 5-year CSS of osteosarcoma patients. The RSF model can be used as a nonparametric alternative to the Cox model. The constructed nomogram based on the Cox model can provide reference for clinicians to formulate specific therapeutic decisions both in America and China.

Decision tree-based data mining approach for the evaluation of survival in primary malignant bone tumors: A surveillance, epidemiology and end results database study

PURPOSE

This study aimed to conduct a large-scale population-based study to understand the epidemiological characteristics of Primary Malignant Bone Tumors (PMBTs) and determine the prognostic factors by concurrently using the classical statistical method and data mining methods.

METHODS

Patients included in this study were extracted from the National Cancer Institute's Surveillance, Epidemiology and End Results (SEER) database: "Incidence-SEER Research Data, 18 Registries, Nov 2020 Sub". Patients with unclassified and incomplete information were excluded. This search algorithm resulted in a dataset comprising 6234 cases. Survival analyses were performed with Kaplan-Meier curves and the Log-rank test. Multivariate Cox regression analysis determined the independent prognostic factors of PMBT. A decision tree-based data mining technique was used in this study to confirm the prognostic factors.

RESULTS

5-years survival rate was 63.6% and 10-years survival rate was 55.3% in the patients with PMBT. Sex, age, median household income, histology, primary site, grade, stage, metastasis, and the total number of malignant tumors were determined as independent risk factors associated with overall survival (OS) in the multivariate COX regression analysis. The prognostic factors resulting in five terminal nodes in the decision tree (DT) included stage, age, and grade. The stage was the most important determining factor for vital status. The terminal node with the shortest number of surviving patients included 801 (72.3%) deaths in 1102 patients with distant stage, and hazard ratio was calculated as 5.4 (95% CI: 4.9-5.9; p < .001). These patients had a median survival of only 17 months.

CONCLUSIONS

Rules extracted from DTs provide information about risk factors in specific patient groups and can be used by clinicians making decisions on individual patients. We recommend using DTs in combination with COX regression analysis to determine risk factors and the effect of these factors on survival.

Subtype Classification and Prognosis Signature Construction of Osteosarcoma Based on Cellular Senescence-Related Genes

Background. Cellular senescence (CS) is an alternative procedure that replaces or reinforces inadequate apoptotic responses and is used as an influencing factor for a variety of cancers. The value of CS gene in evaluating the immunotherapy response and clinical outcome of osteosarcoma (OS) has not been reported, and an accurate risk model based on CS gene has not been developed for OS patients. Methods. 279 CS genes were obtained from CellAge. Univariate Cox regression analysis was used to screen the CS gene which was significantly related to the prognosis of OS samples in TARGET data set. The prognosis, clinicopathological features, immune infiltration, gene expression at immune checkpoints, tumor immune dysfunction and exclusion (TIDE) score, and chemotherapy resistance of OS were analyzed among clusters. Least absolute shrinkage and selection operator (Lasso) Cox regression analysis to build cellular senescence-related gene signature (CSRS). Univariate and multivariate Cox regression analysis of CSRS and clinical parameters were carried out, and the parameters with independent prognostic value were used to construct nomogram. Results. Based on 30 CS genes related to OS prognosis, OS samples were divided into three clusters: C1, C2, and C3. C3 showed the lowest survival rate and metastasis rate and the highest immune score and stromal score and was more likely to respond to immune checkpoint blockade (ICB) treatment. A CSRS scoring system including four CS genes (MYC, DLX2, EPHA3, and LIMK1) was constructed, which could distinguish the survival outcome, tumor microenvironment (TME) status, and ICB treatment response of patients with different CSRS score. Nomogram constructed by CSRS score and metastatic has a high prognostic value for OS. Conclusions. Our study identified a molecular classification determined by CS-related genes and developed a new CSRS that has potential value in OS immunotherapy response and clinical outcome prediction.

Computer-Aided Design and 3D Printing of Hemipelvic Endoprosthesis for Personalized Limb-Salvage Reconstruction after Periacetabular Tumor Resection

3D-printed hemipelvic endoprosthesis is an emerging solution for personalized limb-salvage reconstruction after periacetabular tumor resection. Further clinical studies are still required to report its surgical characteristics, outcomes, benefits and drawbacks. Sixteen consecutive patients underwent periacetabular tumor wide resection and pelvic reconstruction with a 3D-printed hemipelvic endoprosthesis from 2018 to 2021. The surgical characteristics and outcomes are described. The mean follow-up duration was 17.75 months (range, 6 to 46 months). Five patients underwent surgery for type I + II resection and reconstruction, seven for type II + III resection and reconstruction, three for type II resection and reconstruction, and one for type I + II + IV resection and reconstruction. The incidence of postoperative complication was 12.5% (2/16) for deep venous thrombosis (DVT), 12.5% (2/16) for pneumonia, and 12.5% (2/16) for would deep or superficial infection. During follow-up, two patients (12.5%) suffered hip dislocation and underwent revision surgery. CT demonstrated an obvious prosthetic porous structure–bone fusion after follow-up of at least 6 months. At the final follow-up, 12 lived with no evidence of disease while four lived with disease; no patients experienced pain; and 15 had independent ambulation, with a mean Musculoskeletal Tumor Society (MSTS) score of 85.8% (range, 26.7% to 100%). 3D-printed hemipelvic endoprosthesis facilitates wide resection of periacetabular tumor and limb-salvage reconstruction, thus resulting in good oncological and functional outcomes. The custom-made nature is able to well mimic the skeletal anatomy and microstructure and promote osseointegration. Perioperative complications and rehabilitation exercise still need to be stressed for this engineering technology-assisted major orthopedic surgery.

A novel clinical tool to predict cancer-specific survival in patients with primary pelvic sarcomas: A large population-based retrospective cohort study

BACKGROUND

Primary osseous sarcoma of the pelvis is rare and has a particularly sinister outcome. This study aims to identify independent prognostic factors of cancer-specific survival (CSS) in patients with primary pelvic sarcoma (PS) and develop a nomogram to predict 3-, 5-, and 10-year probability of CSS in these patients.

METHODS

The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 416 patients with primary PS, who were divided into two groups: a training cohort and a validation cohort. Univariate and multivariate Cox analyses were used to screen independent prognostic factors in patients with primary PS. Based on these independent prognostic factors, a prognostic nomogram was developed to predict 3-, 5-, and 10-year probability of CSS. The nomogram's predictive performance and clinical value were evaluated using the calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Finally, a mortality risk stratification system was developed.

RESULTS

Tumor size, tumor stage, histological type, surgery, and chemotherapy were identified as independent prognostic factors for the CSS of primary PS patients. Based on these factors, a nomogram was created to predict the 3-, 5-, and 10-year probability of CSS in these patients. The calibration curve, ROC, and DCA indicated that the nomogram performed well and was appropriate for clinical use, with 3-, 5-, and 10-year areas under ROC curve all higher than 0.800. Furthermore, the nomogram-based mortality risk stratification system could effectively divide these patients into three risk subgroups.

CONCLUSIONS

The nomogram constructed in this study could accurately predict 3-, 5-, and 10-year probability of CSS in patients with primary PS. Clinicians can use the nomogram to categorize these patients into risk subgroups and provide personalized treatment plans.

Challenges of Systemic Therapy Investigations for Bone Sarcomas

Bone sarcoma is a rare component of malignant solid tumors that accounts for only ~0.2% of malignancies. Bone sarcomas present various histological types, and genomic mutations differ markedly by the histological types. Although there are vast mutations in various bone sarcomas, most of them are non-actionable, and even potential targetable mutations that are actionable targets in other malignancies have not shown the appropriate responses in clinical trials for bone sarcomas. Investigations of new systemic therapy, including molecular targeted therapies for bone sarcomas, have thus not progressed like those for other solid tumors. Another problem is that high rates of pediatric/adolescent and young adult patients have bone sarcomas such as osteosarcoma, and patient recruitment for clinical trials (especially randomized trials) is challenging. For pediatric patients, evaluations of tolerability and appropriate dose modifications of new drugs are needed, as their findings could provide the threshold for investigating new drugs for bone sarcomas. To solve these problems, improvements in registry systems, real world data, and pediatric extrapolation have been attempted. We review the issues regarding targeted drug investigations for bone sarcomas, focusing on the current clinical evidence and efforts to resolve these issues.