Spratt Daniel E, Yousefi Kasra, Deheshi Samineh, Ross Ashley E, Den Robert B, Schaeffer Edward M, Trock Bruce J, Zhang Jingbin, Glass Andrew G, Dicker Adam P, Abdollah Firas, Zhao Shuang G, Lam Lucia L C, du Plessis Marguerite, Choeurng Voleak, Haddad Zaid, Buerki Christine, Davicioni Elai, Weinmann Sheila, Freedland Stephen J, Klein Eric A, Karnes R Jeffrey, Feng Felix Y
Daniel E. Spratt and Shuang G. Zhao, University of Michigan, Ann Arbor; Firas Abdollah, Henry Ford Health System, Detroit, MI; Kasra Yousefi, Samineh Deheshi, Jingbin Zhang, Lucia L.C. Lam, Marguerite du Plessis, Voleak Choeurng, Zaid Haddad, Christine Buerki, and Elai Davicioni, GenomeDx Biosciences, Vancouver, British Columbia, Canada; Ashley E. Ross and Bruce J. Trock, Johns Hopkins Hospital, Baltimore, MD; Robert B. Den and Adam P. Dicker, Thomas Jefferson University, Philadelphia, PA; Edward M. Schaeffer, Northwestern University, Evanston, IL; Andrew G. Glass and Sheila Weinmann, Center for Health Research, Kaiser Permanente Northwest, Portland, OR; Stephen J. Freedland, Cedars-Sinai Medical Center, Los Angeles; Felix Y. Feng, University of California, San Francisco, CA; Eric A. Klein, Cleveland Clinic, Cleveland, OH; and R. Jeffrey Karnes, Mayo Clinic, Rochester, MN.
J Clin Oncol. 2017 Jun 20;35(18):1991-1998. doi: 10.1200/JCO.2016.70.2811. Epub 2017 Mar 30.
Purpose To perform the first meta-analysis of the performance of the genomic classifier test, Decipher, in men with prostate cancer postprostatectomy. Methods MEDLINE, EMBASE, and the Decipher genomic resource information database were searched for published reports between 2011 and 2016 of men treated by prostatectomy that assessed the benefit of the Decipher test. Multivariable Cox proportional hazards models fit to individual patient data were performed; meta-analyses were conducted by pooling the study-specific hazard ratios (HRs) using random-effects modeling. Extent of heterogeneity between studies was determined with the I test. Results Five studies (975 total patients, and 855 patients with individual patient-level data) were eligible for analysis, with a median follow-up of 8 years. Of the total cohort, 60.9%, 22.6%, and 16.5% of patients were classified by Decipher as low, intermediate, and high risk, respectively. The 10-year cumulative incidence metastases rates were 5.5%, 15.0%, and 26.7% ( P < .001), respectively, for the three risk classifications. Pooling the study-specific Decipher HRs across the five studies resulted in an HR of 1.52 (95% CI, 1.39 to 1.67; I = 0%) per 0.1 unit. In multivariable analysis of individual patient data, adjusting for clinicopathologic variables, Decipher remained a statistically significant predictor of metastasis (HR, 1.30; 95% CI, 1.14 to 1.47; P < .001) per 0.1 unit. The C-index for 10-year distant metastasis of the clinical model alone was 0.76; this increased to 0.81 with inclusion of Decipher. Conclusion The genomic classifier test, Decipher, can independently improve prognostication of patients postprostatectomy, as well as within nearly all clinicopathologic, demographic, and treatment subgroups. Future study of how to best incorporate genomic testing in clinical decision-making and subsequent treatment recommendations is warranted.
目的 对基因组分类器检测Decipher在前列腺癌前列腺切除术后男性患者中的性能进行首次荟萃分析。方法 检索MEDLINE、EMBASE和Decipher基因组资源信息数据库,查找2011年至2016年期间关于接受前列腺切除术治疗的男性患者的已发表报告,这些报告评估了Decipher检测的益处。对个体患者数据进行多变量Cox比例风险模型拟合;采用随机效应模型合并各研究的风险比(HR)进行荟萃分析。用I²检验确定研究间的异质性程度。结果 五项研究(共975例患者,855例有个体患者水平数据)符合分析条件,中位随访时间为8年。在整个队列中,分别有60.9%、22.6%和16.5%的患者被Decipher分类为低、中、高风险。三种风险分类的10年累积转移发生率分别为5.5%、15.0%和26.7%(P <.001)。五项研究中合并各研究的Decipher HR,每0.1单位的HR为1.52(95%CI,1.39至1.67;I² = 0%)。在个体患者数据的多变量分析中,校正临床病理变量后,Decipher每0.1单位仍是转移的统计学显著预测因子(HR,1.30;95%CI,1.14至1.47;P <.001)。仅临床模型的10年远处转移C指数为0.76;纳入Decipher后增至0.81。结论 基因组分类器检测Decipher可独立改善前列腺切除术后患者的预后,以及几乎所有临床病理、人口统计学和治疗亚组患者的预后。有必要进一步研究如何在临床决策和后续治疗建议中最佳地纳入基因组检测。