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开发一种预后遗传标志物,以预测与皮肤黑色素瘤相关的转移风险。

Development of a prognostic genetic signature to predict the metastatic risk associated with cutaneous melanoma.

机构信息

Northwestern University School of Medicine, Chicago, Illinois.

Castle Biosciences, Inc., Friendswood, Texas.

出版信息

Clin Cancer Res. 2015 Jan 1;21(1):175-83. doi: 10.1158/1078-0432.CCR-13-3316.

Abstract

PURPOSE

The development of a genetic signature for the identification of high-risk cutaneous melanoma tumors would provide a valuable prognostic tool with value for stage I and II patients who represent a remarkably heterogeneous group with a 3% to 55% chance of disease progression and death 5 years from diagnosis.

EXPERIMENTAL DESIGN

A prognostic 28-gene signature was identified by analysis of microarray expression data. Primary cutaneous melanoma tumor tissue was evaluated by RT-PCR for expression of the signature, and radial basis machine (RBM) modeling was performed to predict risk of metastasis.

RESULTS

RBM analysis of cutaneous melanoma tumor gene expression reports low risk (class 1) or high risk (class 2) of metastasis. Metastatic risk was predicted with high accuracy in development (ROC = 0.93) and validation (ROC = 0.91) cohorts of primary cutaneous melanoma tumor tissue. Kaplan-Meier analysis indicated that the 5-year disease-free survival (DFS) rates in the development set were 100% and 38% for predicted classes 1 and 2 cases, respectively (P < 0.0001). DFS rates for the validation set were 97% and 31% for predicted classes 1 and 2 cases, respectively (P < 0.0001). Gene expression profile (GEP), American Joint Committee on Cancer stage, Breslow thickness, ulceration, and age were independent predictors of metastatic risk according to Cox regression analysis.

CONCLUSIONS

The GEP signature accurately predicts metastasis risk in a multicenter cohort of primary cutaneous melanoma tumors. Preliminary Cox regression analysis indicates that the signature is an independent predictor of metastasis risk in the cohort presented.

摘要

目的

开发一种用于识别高危皮肤黑色素瘤肿瘤的遗传特征,将为 I 期和 II 期患者提供有价值的预后工具,这些患者是一组异质性显著的患者,他们在诊断后 5 年内疾病进展和死亡的风险为 3%至 55%。

实验设计

通过分析微阵列表达数据,确定了一个预后 28 基因特征。通过 RT-PCR 评估原发性皮肤黑色素瘤肿瘤组织中特征的表达,并进行径向基机器 (RBM) 建模以预测转移风险。

结果

RBM 分析皮肤黑色素瘤肿瘤基因表达报告低风险(1 类)或高风险(2 类)转移。在原发性皮肤黑色素瘤肿瘤组织的开发和验证队列中,转移风险的预测具有很高的准确性(ROC = 0.93 和 ROC = 0.91)。Kaplan-Meier 分析表明,在开发组中,预测为 1 类和 2 类病例的 5 年无病生存率 (DFS) 分别为 100%和 38%(P < 0.0001)。验证组中,预测为 1 类和 2 类病例的 5 年无病生存率分别为 97%和 31%(P < 0.0001)。根据 Cox 回归分析,基因表达谱 (GEP)、美国癌症联合委员会分期、Breslow 厚度、溃疡和年龄是转移风险的独立预测因素。

结论

GEP 特征可准确预测原发性皮肤黑色素瘤肿瘤多中心队列的转移风险。初步 Cox 回归分析表明,该特征是队列中转移风险的独立预测因素。

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