Impact of recurrent copy number alterations and cancer gene mutations on the predictive accuracy of prognostic models in clear cell renal cell carcinoma.
Impact of recurrent copy number alterations and cancer gene mutations on the predictive accuracy of prognostic models in clear cell renal cell carcinoma.
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DOI：
10.1016/j.juro.2014.01.088
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发表时间：
2014-07
影响因子：
6.6
通讯作者：
Ostrovnaya, Irina
中科院分区：
文献类型：
--
作者：
Hakimi, A. Ari;Mano, Roy;Ciriello, Giovanni;Gonen, Mithat;Mikkilineni, Nina;Sfakianos, John P.;Kim, Philip H.;Motzer, Robert J.;Russo, Paul;Reuter, Victor E.;Hsieh, James J.;Ostrovnaya, Irina
最近报道的透明细胞肾细胞癌（ccRCC）中的几种复发性基因组改变与病理和临床结果有关。我们确定癌症基因组图谱（TCGA）ccRCC数据集中发现的任何复发性癌症基因突变或拷贝数改变是否可以增加当前预后模型的预测准确性。对413名接受肾切除术/肾部分切除术的患者进行了全外显子组、拷贝数阵列分析和临床变量的询问。根据患病率确定了65个复发性基因组变异，并将其合并为35个变异，包括12个癌症基因突变。使用术前变量（肿瘤大小+年龄）的弹性网络算法对基因组标志物进行建模，并在术后环境中使用外部验证的马约诊所分期、大小、分级和坏死（SSIGN）预后评分系统进行建模。使用bootstrap对这些模型进行内部验证。幸存者的中位随访时间为45个月。在单变量分析中，几种标志物与不良癌症特异性生存期（CSS）和复发时间（TTR）相关。然而，当在术前模型中控制肿瘤大小+/-年龄或在术后设置中控制SSIGN评分时，大多数失去了意义。通过弹性网络算法选择的多个基因组标记的添加未能实质上增加CSS或TTR的任何术前或术后模型的预测准确性。虽然复发性拷贝数改变和癌症基因突变具有生物学意义，但它们似乎并不能提高ccRCC中现有临床CSS或TTR模型的预测准确性。
Several recently reported recurrent genomic alterations in clear cell renal cell carcinoma (ccRCC) are linked to pathological and clinical outcomes. We determined if any of the recurrent cancer gene mutations or copy number alterations identified in the Cancer Genome Atlas (TCGA) ccRCC dataset could add to the predictive accuracy of the current prognostic models. 413 patients who underwent nephrectomy/partial nephrectomy with whole exome, copy number array analyses, and clinical variables were interrogated. Sixty-five recurrent genomic alterations were identified based on prevalence and combined into 35 alterations including 12 cancer gene mutations. The genomic markers were modeled using the elastic-net algorithm with preoperative variables (tumor size + age) and in the postoperative setting using the externally validated Mayo Clinic stage, size, grade, and necrosis (SSIGN) prognostic scoring system. These models were subjected to internal validation using bootstrap. The median follow up for survivors was 45 months. Several markers correlated with adverse cancer-specific survival (CSS) and time to recurrence (TTR) on univariate analysis. However, most lost significance when controlling for tumor size +/- age in the preoperative models or SSIGN score in the postoperative setting. The addition of multiple genomic markers selected by the elastic-net algorithm failed to substantially add to the predictive accuracy of any of the preoperative or postoperative models for CSS or TTR. While recurrent copy number alterations and cancer gene mutations are biologically significant, they do not appear to improve the predictive accuracy of existing clinical CSS or TTR models in ccRCC.
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DOI：
10.1158/1078-0432.ccr-12-3886
发表时间：
2013-06-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Hakimi AA;Ostrovnaya I;Reva B;Schultz N;Chen YB;Gonen M;Liu H;Takeda S;Voss MH;Tickoo SK;Reuter VE;Russo P;Cheng EH;Sander C;Motzer RJ;Hsieh JJ;ccRCC Cancer Genome Atlas (KIRC TCGA) Research Network investigators
通讯作者：
ccRCC Cancer Genome Atlas (KIRC TCGA) Research Network investigators
影响因子：
64.8
作者：
Varela, Ignacio;Tarpey, Patrick;Raine, Keiran;Huang, Dachuan;Ong, Choon Kiat;Stephens, Philip;Davies, Helen;Jones, David;Lin, Meng-Lay;Teague, Jon;Bignell, Graham;Butler, Adam;Cho, Juok;Dalgliesh, Gillian L.;Galappaththige, Danushka;Greenman, Chris;Hardy, Claire;Jia, Mingming;Latimer, Calli;Lau, King Wai;Marshall, John;McLaren, Stuart;Menzies, Andrew;Mudie, Laura;Stebbings, Lucy;Largaespada, David A.;Wessels, L. F. A.;Richard, Stephane;Kahnoski, Richard J.;Anema, John;Tuveson, David A.;Perez-Mancera, Pedro A.;Mustonen, Ville;Fischer, Andrej;Adams, David J.;Rust, Alistair;Chan-on, Waraporn;Subimerb, Chutima;Dykema, Karl;Furge, Kyle;Campbell, Peter J.;Teh, Bin Tean;Stratton, Michael R.;Futreal, P. Andrew
通讯作者：
Futreal, P. Andrew
影响因子：
6.6
作者：
Kattan, MW;Reuter, V;Russo, P
通讯作者：
Russo, P
影响因子：
7.5
作者：
Brunelli, Matteo;Eccher, Albino;Martignoni, Guido
通讯作者：
Martignoni, Guido
影响因子：
6.2
作者：
Tan, Min-Han;Li, Huihua;Cheng, Christopher
通讯作者：
Cheng, Christopher