Karski Erin E, McIlvaine Elizabeth, Segal Mark R, Krailo Mark, Grier Holcombe E, Granowetter Linda, Womer Richard B, Meyers Paul A, Felgenhauer Judy, Marina Neyssa, DuBois Steven G
Department of Pediatrics, University of California, San Francisco School of Medicine, San Francisco, California.
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.
Pediatr Blood Cancer. 2016 Jan;63(1):47-53. doi: 10.1002/pbc.25709. Epub 2015 Aug 10.
Although multiple prognostic variables have been proposed for Ewing sarcoma (EWS), little work has been done to further categorize these variables into prognostic groups for risk classification.
We derived initial prognostic groups from 2,124 patients with EWS in the SEER database. We constructed a multivariable recursive partitioning model of overall survival using the following covariates: age; stage; race/ethnicity; sex; axial primary; pelvic primary; and bone or soft tissue primary. Based on this model, we identified risk groups and estimated 5-year overall survival for each group using Kaplan-Meier methods. We then applied these groups to 1,680 patients enrolled on COG clinical trials.
A multivariable model identified five prognostic groups with significantly different overall survival: (i) localized, age <18 years, non-pelvic primary; (ii) localized, age <18, pelvic primary or localized, age ≥18, white, non-Hispanic; (iii) localized, age ≥18, all races/ethnicities other than white, non-Hispanic; (iv) metastatic, age <18; and (v) metastatic, age ≥18. These five groups were applied to the COG dataset and showed significantly different overall and event-free survival based upon this classification system (P < 0.0001). A sub-analysis of COG patients treated with ifosfamide and etoposide as a component of therapy evaluated these findings in patients receiving contemporary therapy.
Recursive partitioning analysis yields discrete prognostic groups in EWS that provide valuable information for patients and clinicians in determining an individual patient's risk of death. These groups may enable future clinical trials to adjust EWS treatment according to individualized risk.
尽管已针对尤因肉瘤（EWS）提出了多个预后变量，但在将这些变量进一步分类为用于风险分类的预后组方面所做的工作很少。
我们从监测、流行病学和最终结果（SEER）数据库中的2124例EWS患者中得出初始预后组。我们使用以下协变量构建了总生存的多变量递归划分模型：年龄；分期；种族/民族；性别；轴向原发部位；骨盆原发部位；以及骨或软组织原发部位。基于该模型，我们确定了风险组，并使用Kaplan-Meier方法估计了每组的5年总生存率。然后我们将这些组应用于参加儿童肿瘤协作组（COG）临床试验的1680例患者。
一个多变量模型确定了五个总生存率有显著差异的预后组：（i）局限性，年龄<18岁，非骨盆原发部位；（ii）局限性，年龄<18岁，骨盆原发部位或局限性，年龄≥18岁，白人，非西班牙裔；（iii）局限性，年龄≥18岁，除白人、非西班牙裔以外的所有种族/民族；（iv）转移性，年龄<18岁；以及（v）转移性，年龄≥18岁。这五个组应用于COG数据集，并基于该分类系统显示出显著不同的总生存率和无事件生存率（P < 0.0001）。对接受异环磷酰胺和依托泊苷作为治疗组成部分的COG患者进行的亚分析评估了接受当代治疗患者的这些结果。
递归划分分析在EWS中产生了离散的预后组，为患者和临床医生确定个体患者的死亡风险提供了有价值的信息。这些组可能使未来的临床试验能够根据个体风险调整EWS治疗。
