Wang Zhen, Wang Zhe, Wang Ruoyu, Wang Zumin, Cao Xiangyang, Chen Rui, Ma Zebing, Liang Shanshan, Tao Shuai
The Key Laboratory of Biomarker High-Throughput Screening and Target Translation of Breast and Gastrointestinal Tumors, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.
College of Information Engineering, Dalian University, Dalian, Liaoning, China.
J Transl Med. 2025 Jul 7;23(1):746. doi: 10.1186/s12967-025-06796-6.
Osteosarcoma is the most common primary malignant bone tumor, with high invasiveness and metastatic potential and a poor prognosis in patients with metastatic cancer. Despite the rapid advancements in genomics in recent years that provided new perspectives for studying the molecular mechanisms of osteosarcoma, the understanding of its tumor heterogeneity and evolutionary mutation process remains limited.
In this study, whole-exome evolutionary profiling was performed on data from the TARGET database representing 61 osteosarcoma cases. Subclonal architectures were reconstructed to characterize mutational trajectories. Differential mutation analysis was used to identify candidate metastasis-associated mutations. These features were used to build a metastasis-prediction classifier, which was cross-validated and tested on an independent external cohort. Finally, Suppes' probabilistic theory of causality was integrated with cohort data to infer high-frequency evolutionary paths linked to metastasis.
A linear evolutionary trajectory was observed in 62% of patients, indicating sequential clonal expansion. Eight key mutations were closely associated with metastatic progression. The classifier achieved 83% accuracy in cross-validation and maintained robust performance on the external validation set. Through causal inference, distinct evolutionary routes underpinning metastasis were uncovered, with ATRX mutations frequently occurring as early events that reshaped clonal dynamics and facilitated tumor spread.
In this study, the dynamic evolutionary landscape of osteosarcoma metastasis was delineated, an early metastasis classification model was constructed, and the impact of early clonal ATRX mutations on metastasis initiation were highlighted. These findings offer potential avenues for the early diagnosis and risk assessment of osteosarcoma.
骨肉瘤是最常见的原发性恶性骨肿瘤，具有高侵袭性和转移潜能，转移性癌症患者预后较差。尽管近年来基因组学迅速发展，为研究骨肉瘤的分子机制提供了新视角，但对其肿瘤异质性和进化突变过程的了解仍然有限。
在本研究中，对来自TARGET数据库的代表61例骨肉瘤病例的数据进行了全外显子组进化分析。重建亚克隆结构以表征突变轨迹。差异突变分析用于识别与转移相关的候选突变。这些特征被用于构建转移预测分类器，并在独立的外部队列中进行交叉验证和测试。最后，将Suppes因果概率理论与队列数据相结合，以推断与转移相关的高频进化路径。
在62%的患者中观察到线性进化轨迹，表明克隆的顺序性扩增。八个关键突变与转移进展密切相关。该分类器在交叉验证中准确率达到83%，并在外部验证集上保持了稳健的性能。通过因果推断，发现了支持转移的不同进化途径，其中ATRX突变经常作为早期事件发生，重塑克隆动态并促进肿瘤扩散。
在本研究中，描绘了骨肉瘤转移的动态进化格局，构建了早期转移分类模型，并强调了早期克隆性ATRX突变对转移起始的影响。这些发现为骨肉瘤的早期诊断和风险评估提供了潜在途径。
