Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort.
Combined value of validated clinical and genomic risk stratification tools for predicting prostate cancer mortality in a high-risk prostatectomy cohort.
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DOI：
10.1016/j.eururo.2014.05.039
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发表时间：
2015-02
期刊：
影响因子：
23.4
通讯作者：
Karnes, R. Jeffrey
中科院分区：
文献类型：
--
作者：
Cooperberg, Matthew R.;Davicioni, Elai;Crisan, Anamaria;Jenkins, Robert B.;Ghadessi, Mercedeh;Karnes, R. Jeffrey
结合生物标志物和临床病理变量的风险预测模型可用于改善前列腺癌根治术(RP)后的决策。我们比较了两个先前验证的前列腺癌术后风险评估分类器(CAPRA-S)和解密基因组分类器(GC)，以预测当代RP患者的前列腺癌特异性死亡率(CSM)。目的：评价卡普拉-S和GC联合预测脊髓型颈椎病的能力。2000年至2006年间，1,010名高危患者在梅奥诊所接受了RP术后复发的治疗。高危以以下任何一项为标准：术前PSA和GT；20 ng/mL，病理Gleason评分≥8或分期pT3b。病例队列随机抽样确定225例患者(定义为经历过CSM的患者)，其中185例可确定卡普拉-S和GC。使用一致性(C)指数、决策曲线分析、再分类、累积发病率和COX回归对CSM的预测进行单独和联合评估。在185名男性中，有28人经历过CSM。CAPRA-S和GC的C指数分别为0.75(95%CI为0.65~0.84)和0.78(95%CI为0.68~0.88)。GC在决策曲线分析中显示出较高的净收益，但结合卡普拉-S和GC的评分在乐观调整自举后并没有改善AUC。在82例根据Capra-S评分≥6分级为高危的患者中，有33例GC评分同样为高危，其中17例有脊髓型颈椎病事件。GC将其余49名男性重新归类为低至中风险；在这些男性中，观察到3个CSM事件。在多变量分析中，GC和卡普拉-S作为连续变量是CSM的独立预后变量，其危险比分别为1.81(p<；0.001，每单位分数变化)和1.36(p=0.001，每单位分数变化)。当归入危险组时，高卡普拉-S评分(≥6)的多变量HR为2.36(p=0.0 4)，高GC评分(≥0.6)的多变量HR为11.2 6(p<；0.001)。对于同时具有高GC和Capra-S评分的患者，10年后CSM的累积发生率为45%。这项研究受限于其回溯性设计。GC和CAPRA-S均为CSM的显著独立预测因子。仅根据卡普拉-S≥6，GC就被证明对许多被归类为高危男性的人进行了重新分层。同时具有高GC和卡普拉-S风险评分的患者在RP治疗后患致命性前列腺癌的风险显著增加。如果前瞻性验证，这些发现表明，基因组-临床分类器的整合可能能够更好地识别那些应该考虑进行更积极的二级治疗和临床试验的RP术后患者。
Risk prediction models that incorporate biomarkers and clinicopathologic variables may be used to improve decision-making post radical prostatectomy (RP). We compared two previously validated post-RP classifiers—the Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) and the Decipher genomic classifier (GC)—to predict prostate cancer-specific mortality (CSM) in a contemporary cohort of RP patients. To evaluate the combined prognostic ability of CAPRA-S and GC to predict CSM. A cohort of 1,010 patients at high risk of recurrence post-RP was treated at Mayo Clinic between 2000–06. High-risk was defined by any of: pre-operative PSA >20ng/mL, pathological Gleason score ≥8 or stage pT3b. A case-cohort random sample identified 225 patients (cases defined as patients who experienced CSM), among whom CAPRA-S and GC could be determined for 185. The scores were evaluated individually and in combination using concordance (c)-index, decision curve analysis, re-classification, cumulative incidence, and Cox regression for prediction of CSM. Among 185 men, 28 experienced CSM. The c-index for CAPRA-S and GC were 0.75 (95% CI 0.65–0.84) and 0.78 (95% CI 0.68–0.88), respectively. GC showed higher net-benefit on decision curve analysis but a score combining CAPRA-S and GC did not improve AUC after optimism-adjusted bootstrapping. In 82 patients stratified to high-risk based on CAPRA-S score ≥6, GC scores were likewise high-risk for 33, among whom 17 had CSM events. GC reclassified the remaining 49 men as low to intermediate-risk; among these men 3 CSM events were observed. In multivariable analysis, GC and CAPRA-S as continuous variables were independently prognostic of CSM, with hazard ratios of 1.81 (p<0.001, per 0.1 unit change in score) and 1.36 (p=0.05, per one unit change in score). When categorized into risk groups, the multivariable HR for high CAPRA-S scores (≥6) was 2.36 (p=0.04), and 11.26 (p<0.001) for high GC scores (≥0.6). For patients with both high GC and CAPRA-S scores, cumulative incidence of CSM was 45% at 10 years. The study is limited by its retrospective design. Both GC and CAPRA-S were significant independent predictors of CSM. GC was shown to re-stratify many men classified as high-risk based on CAPRA-S ≥6 alone. Patients with both high GC and CAPRA-S risk scores were at markedly elevated post-RP risk for lethal prostate cancer. If validated prospectively, these findings suggest that integration of a genomic-clinical classifier may enable better identification of those post-RP patients who should be considered for more aggressive secondary therapies and clinical trials.
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影响因子：
2
作者：
KORN, EL
通讯作者：
KORN, EL
影响因子：
1.3
作者：
Therneau, TM;Li, HZ
通讯作者：
Li, HZ
影响因子：
2.7
作者：
FIRTH, D
通讯作者：
FIRTH, D
关键评估用于分类或预测的生物标志物的准确性：研究设计标准。
影响因子：
10.3
作者：
Pepe, Margaret S.;Feng, Ziding;Janes, Holly;Bossuyt, Patrick M.;Potter, John D.
通讯作者：
Potter, John D.
影响因子：
3.7
作者：
Nakagawa T;Kollmeyer TM;Morlan BW;Anderson SK;Bergstralh EJ;Davis BJ;Asmann YW;Klee GG;Ballman KV;Jenkins RB
通讯作者：
Jenkins RB