Popliteal lymph node metastasis of tibial osteoblastic osteosarcoma

A clinical prediction model for lung metastasis risk in osteosarcoma: A multicenter retrospective study

Background

Lung metastases (LM) have a poor prognosis of osteosarcoma. This study aimed to predict the risk of LM using the nomogram in patients with osteosarcoma.

Methods

A total of 1100 patients who were diagnosed as osteosarcoma between 2010 and 2019 in the Surveillance, Epidemiology and End Results (SEER) database were selected as the training cohort. Univariate and multivariate logistic regression analyses were used to identify independent prognostic factors of osteosarcoma lung metastases. 108 osteosarcoma patients from a multicentre dataset was as valiation data. The predictive power of the nomogram model was assessed by receiver operating characteristic curves (ROC) and calibration plots, and decision curve analysis (DCA) was utilized to interpret the accurate validity in clinical practice.

Results

A total of 1208 patients with osteosarcoma from both the SEER database(n=1100) and the multicentre database (n=108) were analyzed. Univariate and multivariate logistic regression analyses showed that Survival time, Sex, T-stage, N-stage, Surgery, Radiation, and Bone metastases were independent risk factors for lung metastasis. We combined these factors to construct a nomogram for estimating the risk of lung metastasis. Internal and external validation showed significant predictive differences (AUC 0.779, 0.792 respectively). Calibration plots showed good performance of the nomogram model.

Conclusions

In this study, a nomogram model for predicting the risk of lung metastases in osteosarcoma patients was constructed and turned out to be accurate and reliable through internal and external validation. Moreover we built a webpage calculator (https://drliwenle.shinyapps.io/OSLM/) taken into account nomogram model to help clinicians make more accurate and personalized predictions.

Interpretable clinical visualization model for prediction of prognosis in osteosarcoma: a large cohort data study

Background

Currently, the clinical prediction model for patients with osteosarcoma was almost developed from single-center data, lacking external validation. Due to their low reliability and low predictive power, there were few clinical applications. Our study aimed to set up a clinical prediction model with stronger predictive ability, credibility, and clinical application value for osteosarcoma.

Methods

Clinical information related to osteosarcoma patients from 2010 to 2016 was collected in the SEER database and four different Chinese medical centers. Factors were screened using three models (full subset regression, univariate Cox, and LASSO) via minimum AIC and maximum AUC values in the SEER database. The model was selected by the strongest predictive power and visualized by three statistical methods: nomogram, web calculator, and decision tree. The model was further externally validated and evaluated for its clinical utility in data from four medical centers.

Results

Eight predicting factors, namely, age, grade, laterality, stage M, surgery, bone metastases, lung metastases, and tumor size, were selected from the model based on the minimum AIC and maximum AUC value. The internal and external validation results showed that the model possessed good consistency. ROC curves revealed good predictive ability (AUC > 0.8 in both internal and external validation). The DCA results demonstrated that the model had an excellent clinical predicted utility in 3 years and 5 years for North American and Chinese patients.

Conclusions

The clinical prediction model was built and visualized in this study, including a nomogram and a web calculator (https://dr-lee.shinyapps.io/osteosarcoma/), which indicated very good consistency, predictive power, and clinical application value.

The Construction and Development of a Clinical Prediction Model to Assess Lymph Node Metastases in Osteosarcoma

Background: This study aimed to construct a clinical prediction model for osteosarcoma patients to evaluate the influence factors for the occurrence of lymph node metastasis (LNM).

Methods: In our retrospective study, a total of 1,256 patients diagnosed with chondrosarcoma were enrolled from the SEER (Surveillance, Epidemiology, and End Results) database (training cohort, n = 1,144) and multicenter dataset (validation cohort, n = 112). Both the univariate and multivariable logistic regression analysis were performed to identify the potential risk factors of LNM in osteosarcoma patients. According to the results of multivariable logistic regression analysis, A nomogram were established and the predictive ability was assessed by calibration plots, receiver operating characteristics (ROCs) curve, and decision curve analysis (DCA). Moreover, Kaplan-Meier plot of overall survival (OS) was plot and a web calculator visualized the nomogram.

Results: Five independent risk factors [chemotherapy, surgery, lung metastases, lymphatic metastases (M-stage) and tumor size (T-stage)] were identified by multivariable logistic regression analysis. What's more, calibration plots displayed great power both in training and validation group. DCA presented great clinical utility. ROCs curve provided the predictive ability in the training cohort (AUC = 0.805) and the validation cohort (AUC = 0.808). Moreover, patients in LNN group had significantly better survival than that in LNP group both in training and validation group.

Conclusion: In this study, we constructed and developed a nomogram with risk factors, which performed well in predicting risk factors of LNM in osteosarcoma patients. It may give a guide for surgeons and oncologists to optimize individual treatment and make a better clinical decision.

Pigment Epithelium Derived Factor Is Involved in the Late Phase of Osteosarcoma Metastasis by Increasing Extravasation and Cell-Cell Adhesion

Organ tropism of metastatic cells is not well understood. To determine the key factors involved in the selection of a specific organ upon metastasis, we established metastatic cell lines and analyzed their homing to specific tissues. Toward this, 143B osteosarcoma cells were injected intracardially until the kidney-metastasizing sub-cell line Bkid was established, which significantly differed from the parental 143B cells. The candidate genes responsible for kidney metastasis were validated, and SerpinF1/Pigment epithelium derived factor (PEDF) was identified as the primary target. Bkid cells with PEDF knockdown injected intracardially did not metastasize to the kidneys. In contrast, PEDF overexpressing 143B cells injected into femur metastasized to the lungs and kidneys. PEDF triggered mesenchymal-to-epithelial transition (MET) in vitro as well as in vivo. Based on these results, we hypothesized that the MET might be a potential barrier to extravasation. PEDF overexpression in various osteosarcoma cell lines increased their extravasation to the kidneys and lungs. Moreover, when cultured close to the renal endothelial cell line TKD2, Bkid cells disturbed the TKD2 layer and hindered wound healing via the PEDF-laminin receptor (lamR) axis. Furthermore, novel interactions were observed among PEDF, lamR, lysyl oxidase-like 1 (Loxl1), and SNAI3 (Snail-like transcription factor) during endothelial-to-mesenchymal transition (EndoMT). Collectively, our results show that PEDF induces cancer cell extravasation by increasing the permeability of kidney and lung vasculature acting via lamR and its downstream genes. We also speculate that PEDF promotes extravasation via inhibiting EndoMT, and this warrants investigation in future studies.

Popliteal lymph nodes in patients with osteosarcoma: are they metastatic?

PURPOSE

To evaluate the prevalence, imaging appearance, and significance of popliteal lymph nodes (PLN) on magnetic resonance imaging (MRI) in patients with distal femoral or proximal tibial osteosarcoma (OS).

METHOD AND MATERIALS

This study included consecutive patients with OS presenting from May 2016 to March 2018. Inclusion criteria were patients with distal femoral or proximal tibial OS with MRI and pathology at our institution. On MRI, two radiologists recorded primary tumor features (size, location, signal, extra-compartmental extension), and PLN characteristics (mean size, presence/absence of fatty hilum, mineralization, PET/bone scintigraphy avidity, contrast enhancement, and diffusion restriction). Tumor histology, stage, and clinical follow-up were recorded. Descriptive statistics were provided.

RESULTS

Sixteen patients with OS (age 20 ± 10 (range10-40) years, 10/16 male) were included. Although 81% (13/16) of the patients had PLNs at presentation (size range 0.3-3.6 cm), fewer patients had extra-compartmental spread: intra-articular extension 50% (8/16), skip lesions 19% (3/16), lung metastases 31% (5/16), and osseous metastases 12% (2/16). Four (25% (4/16)) patients had PLN ≥ 1 cm; two were histologically proven reactive. One was presumed metastatic due to rapid development, mineralization, and FDG-avidity on PET/CT. The other ≥ 1 cm PLNs along with all twelve (75% (12/16)) that were < 1 cm in mean diameter were presumed non-metastatic with documented stability for at least 12 months of follow-up.

CONCLUSION

PLNs are frequently visible on MRI in patients with OS but are rarely (prevalence = 6%) metastatic. Features on MRI which may suggest metastatic PLNs include large size > 1 cm and loss of a fatty hilum.

SOX6 is downregulated in osteosarcoma and suppresses the migration, invasion and epithelial-mesenchymal transition via TWIST1 regulation

Transcription factor SOX6 (SOX6) has been reported to serve essential roles in numerous types of cancers. However, the expression and functions of SOX6 in osteosarcoma (OS) have not been analyzed. In the present study, the patterns of SOX6 expression in OS cell lines and tissues were investigated by reverse transcription‑quantitative polymerase chain reaction and western blotting. The results of the present study revealed that SOX6 was notably downregulated in OS tissues and cell lines. Subsequently, gain‑ and loss‑of‑function studies demonstrated that SOX6 inhibited OS cell migration and invasion. In addition, SOX6 may have suppressed epithelial‑mesenchymal transition via twist‑related protein 1 (TWIST1) modulation. Chromatin immunoprecipitation (ChIP), quantitative ChIP and dual luciferase activity assays were used to confirm the binding of SOX6 to the promoter region of TWIST1. Additionally, colony formation assays and Cell Counting Kit‑8 assays demonstrated that SOX6 suppressed cell proliferation. The findings of the present study indicated that SOX6 serves as a tumor suppressor in OS and may be a potential therapeutic target for OS.

[Lymph node metastasis of osteosarcomas]

Lymph node metastasis of osteosarcomas is a rather rare phenomenon; according to different authors, the incidence of lymph node metastasis is 4 to 11%. The detection of lymph node metastases in osteosarcoma is associated with a significant reduction in the 5-year survival of patients and allows its classification as clinical stage IV tumor. The risk factors for lymph node metastases in patients with bone sarcomas are age (≥64 years), gender (female), nosological entity (undifferentiated pleomorphic sarcoma, osteosarcoma, chondrosarcoma), tumor depth (muscle, bone), and the size of primary tumor (>5 сm). The mechanism of lymph node metastasis of osteosarcomas seems to be related to mesenchymal-to-epithelial transition.

Understanding the Osteosarcoma Pathobiology: A Comparative Oncology Approach

Osteosarcoma is an aggressive primary bone tumor in humans and is among the most common cancer afflicting dogs. Despite surgical advancements and intensification of chemo- and targeted therapies, the survival outcome for osteosarcoma patients is, as of yet, suboptimal. The presence of metastatic disease at diagnosis or its recurrence after initial therapy is a major factor for the poor outcomes. It is thought that most human and canine patients have at least microscopic metastatic lesions at diagnosis. Osteosarcoma in dogs occurs naturally with greater frequency and shares many biological and clinical similarities with osteosarcoma in humans. From a genetic perspective, osteosarcoma in both humans and dogs is characterized by complex karyotypes with highly variable structural and numerical chromosomal aberrations. Similar molecular abnormalities have been observed in human and canine osteosarcoma. For instance, loss of TP53 and RB regulated pathways are common. While there are several oncogenes that are commonly amplified in both humans and dogs, such as MYC and RAS, no commonly activated proto-oncogene has been identified that could form the basis for targeted therapies. It remains possible that recurrent aberrant gene expression changes due to gene amplification or epigenetic alterations could be uncovered and these could be used for developing new, targeted therapies. However, the remarkably high genomic complexity of osteosarcoma has precluded their definitive identification. Several advantageous murine models of osteosarcoma have been generated. These include spontaneous and genetically engineered mouse models, including a model based on forward genetics and transposon mutagenesis allowing new genes and genetic pathways to be implicated in osteosarcoma development. The proposition of this review is that careful comparative genomic studies between human, canine and mouse models of osteosarcoma may help identify commonly affected and targetable pathways for alternative therapies for osteosarcoma patients. Translational research may be found through a path that begins in mouse models, and then moves through canine patients, and then human patients.