Background/Objectives: Spinal metastatic disease is a life-altering problem for individuals with cancer. Prognostication is key for tailored treatment of spinal metastases. This manuscript provides a comprehensive overview of the genomic profiles of metastatic spine tumors and investigates the potential of mutational data to stratify overall survival (OS) across various histologies.Methods: This is a cohort study of consecutive patients with spine metastatic disease whose tumors were sequenced on a next generation sequencing platform; a machine learning (ML) algorithm was used to stratify OS risk.Results: Targeted sequencing and stratification of OS risk of 282 spine metastases (breast (84), non-small cell lung (56), prostate (49), other (93)) was performed.TP53(HR 1.80; 95% CI 1.26, 2.56) andKEAP1(HR 3.95, 95% CI 2.24, 6.98) mutations were associated with poor survival across the entire cohort in univariate Cox proportional hazards models. The ML algorithm categorized breast cancer metastasis into low- and high-risk groups, revealing a median OS of 71 compared to 22 months (HR 3.3,p< 0.001).TP53mutations andESR1mutations conferred poor prognosis. In lung cancer, low- and high-risk groups with median OS of 30 and 6 months (HR 8.3,p< 0.001), respectively, were identified with poor prognosis linked toMETamplification. No significant prognostic associations were identified for spinal prostate metastases.Conclusions: Metastatic spine tumor molecular data allows for the identification of prognostic groups. We present an open-source machine learning algorithm utilizing genomic mutational data that may aid in prognostication and tailored decision making.
背景/目的：脊柱转移性疾病是癌症患者生命质量发生重大改变的问题。预后评估是脊柱转移瘤个体化治疗的关键。本文全面综述了脊柱转移瘤的基因组特征，并探讨了突变数据在不同组织学类型中分层预测总生存期（OS）的潜力。 方法：本研究为队列研究，纳入连续收治的脊柱转移瘤患者，其肿瘤组织采用新一代测序平台进行测序；应用机器学习（ML）算法对OS风险进行分层。 结果：对282例脊柱转移瘤（乳腺癌84例，非小细胞肺癌56例，前列腺癌49例，其他类型93例）进行了靶向测序和OS风险分层。单变量Cox比例风险模型显示，在整个队列中，TP53（风险比HR 1.80；95%置信区间CI 1.26, 2.56）和KEAP1（HR 3.95，95% CI 2.24, 6.98）基因突变与不良生存相关。ML算法将乳腺癌脊柱转移瘤分为低风险组和高风险组，其中位OS分别为71个月和22个月（HR 3.3，p < 0.001）。TP53突变和ESR1突变提示预后不良。在肺癌脊柱转移瘤中，识别出低风险组和高风险组，其中位OS分别为30个月和6个月（HR 8.3，p < 0.001），预后不良与MET基因扩增相关。未发现前列腺癌脊柱转移瘤存在显著的预后相关分子标志物。 结论：脊柱转移瘤的分子数据可用于识别预后分组。我们提出了一种利用基因组突变数据的开源机器学习算法，该算法可能有助于预后判断和个体化治疗决策。
肿瘤(癌症)患者之家