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基因组分类器增强了病理特征在识别前列腺癌患者辅助放疗最佳候选者中的作用:多变量预后模型的开发与内部验证

Genomic Classifier Augments the Role of Pathological Features in Identifying Optimal Candidates for Adjuvant Radiation Therapy in Patients With Prostate Cancer: Development and Internal Validation of a Multivariable Prognostic Model.

作者信息

Dalela Deepansh, Santiago-Jiménez María, Yousefi Kasra, Karnes R Jeffrey, Ross Ashley E, Den Robert B, Freedland Stephen J, Schaeffer Edward M, Dicker Adam P, Menon Mani, Briganti Alberto, Davicioni Elai, Abdollah Firas

机构信息

Deepansh Dalela, Mani Menon, and Firas Abdollah, Henry Ford Health System, Detroit, MI; María Santiago-Jiménez, Kasra Yousefi, and Elai Davicioni, GenomeDx Biosciences, Vancouver, British Columbia, Canada; R. Jeffrey Karnes, Mayo Clinic, Rochester, MN; Ashley E. Ross, Johns Hopkins Hospital, Baltimore, MD; Adam P. Dicker and Robert B. Den, Thomas Jefferson University, Philadelphia, PA; Stephen J. Freedland, Cedars-Sinai Medical Center, Los Angeles, CA; Edward M. Schaeffer, Northwestern University Feinberg School of Medicine, Chicago, IL; and Alberto Briganti, Vita Salute San Raffaele Hospital, Milan, Italy.

出版信息

J Clin Oncol. 2017 Jun 20;35(18):1982-1990. doi: 10.1200/JCO.2016.69.9918. Epub 2017 Mar 28.

Abstract

Purpose Despite documented oncologic benefit, use of postoperative adjuvant radiotherapy (aRT) in patients with prostate cancer is still limited in the United States. We aimed to develop and internally validate a risk-stratification tool incorporating the Decipher score, along with routinely available clinicopathologic features, to identify patients who would benefit the most from aRT. Patient and Methods Our cohort included 512 patients with prostate cancer treated with radical prostatectomy at one of four US academic centers between 1990 and 2010. All patients had ≥ pT3a disease, positive surgical margins, and/or pathologic lymph node invasion. Multivariable Cox regression analysis tested the relationship between available predictors (including Decipher score) and clinical recurrence (CR), which were then used to develop a novel risk-stratification tool. Our study adhered to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for development of prognostic models. Results Overall, 21.9% of patients received aRT. Median follow-up in censored patients was 8.3 years. The 10-year CR rate was 4.9% vs. 17.4% in patients treated with aRT versus initial observation ( P < .001). Pathologic T3b/T4 stage, Gleason score 8-10, lymph node invasion, and Decipher score > 0.6 were independent predictors of CR (all P < .01). The cumulative number of risk factors was 0, 1, 2, and 3 to 4 in 46.5%, 28.9%, 17.2%, and 7.4% of patients, respectively. aRT was associated with decreased CR rate in patients with two or more risk factors (10-year CR rate 10.1% in aRT v 42.1% in initial observation; P = .012), but not in those with fewer than two risk factors ( P = .18). Conclusion Using the new model to indicate aRT might reduce overtreatment, decrease unnecessary adverse effects, and reduce risk of CR in the subset of patients (approximately 25% of all patients with aggressive pathologic disease in our cohort) who benefit from this therapy.

摘要

目的 尽管有文献证明术后辅助放疗(aRT)对肿瘤治疗有益,但在美国,前列腺癌患者中术后辅助放疗的应用仍然有限。我们旨在开发并内部验证一种风险分层工具,该工具纳入Decipher评分以及常规可用的临床病理特征,以识别能从辅助放疗中获益最大的患者。

患者与方法 我们的队列包括1990年至2010年间在美国四个学术中心之一接受根治性前列腺切除术的512例前列腺癌患者。所有患者均患有≥pT3a疾病、手术切缘阳性和/或病理淋巴结侵犯。多变量Cox回归分析测试了可用预测因素(包括Decipher评分)与临床复发(CR)之间的关系,然后用于开发一种新的风险分层工具。我们的研究遵循了个体预后或诊断多变量预测模型的透明报告指南来开发预后模型。

结果 总体而言,21.9%的患者接受了辅助放疗。截尾患者的中位随访时间为8.3年。接受辅助放疗与初始观察的患者10年临床复发率分别为4.9%和17.4%(P < .001)。病理T3b/T4期、Gleason评分8 - 10、淋巴结侵犯和Decipher评分> 0.6是临床复发的独立预测因素(均P < .01)。分别有46.5%、28.9%、17.2%和7.4%的患者的风险因素累积数为0、1、2和3至4个。辅助放疗与两个或更多风险因素患者的临床复发率降低相关(辅助放疗组10年临床复发率为10.1%,初始观察组为42.1%;P = .012),但在风险因素少于两个的患者中并非如此(P = .18)。

结论 使用新模型来指示辅助放疗可能会减少过度治疗,减少不必要的不良反应,并降低在受益于该治疗的患者亚组(在我们的队列中约占所有侵袭性病理疾病患者的25%)中的临床复发风险。

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