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Modeling Osteosarcoma Using Li-Fraumeni Syndrome Patient-derived Induced Pluripotent Stem Cells
Published on: June 13, 2018
Prediction of Patients With High-Risk Osteosarcoma on the Basis of XGBoost Algorithm Using Transcriptome and
Weisong Zhao1,2, Huanliang Meng1,2, Zhenwu Dai3,4,5
1Department of Orthopaedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Purpose:
Osteosarcoma (OS) is the most prevalent primary malignant bone sarcoma, characterized by its high rates of metastasis and mortality. In our previous multiomics analysis of the Shanghai General Hospital OS (SGH-OS) cohort, we identified four distinct OS subtypes, each with unique molecular characteristics and clinical outcomes. Of particular importance was the identification of the MYC-driven subtype, which exhibited the poorest prognosis and was referred to as high-risk OS. A diagnostic tool is needed for clinicians to identify high-risk OS in advance. The purpose of this study is to develop a classifier capable of accurately predicting the high-risk OS subtype using transcriptome and methylation data.
Methods:
In this study, using eXtreme Gradient Boosting (XGBoost) with Bayesian optimization, we developed a classification model by integrating transcriptome and methylation data from our internal SGH-OS cohort. We further validated the model's predictive performance with the external TARGET-OS cohort.
Results:
Using the XGBoost algorithm, we developed a classifier incorporating nine genes (ARHGAP9, CADM1, CPE, DUSP3, FGFR1, GALNT3, IGF2BP3, KIF26A, ZFP3). In our internal cohort, the classifier exhibited excellent predictive performance, with an area under the receiver operating characteristics curve (AUC) of 0.999 and an overall accuracy of 0.989. Furthermore, the classifier successfully stratified two groups with distinct survival outcomes in the external TARGET-OS cohort. Notably, our analysis revealed a positive correlation between IGF2BP3 and MYC signaling pathways, highlighting IGF2BP3 as a potential therapeutic target in high-risk OS.
Conclusion:
Our classifier demonstrated excellent predictive performance in identifying patients with high-risk OS, offering the potential to enhance treatment decision making and optimize patient management strategies.
