Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value.
Evaluation of public cancer datasets and signatures identifies TP53 mutant signatures with robust prognostic and predictive value.
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DOI：
10.1186/s12885-015-1102-7
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发表时间：
2015-03-26
期刊：
影响因子：
3.8
通讯作者：
Yi Y
中科院分区：
文献类型：
--
作者：
Lehmann BD;Ding Y;Viox DJ;Jiang M;Zheng Y;Liao W;Chen X;Xiang W;Yi Y
使用高通量转录谱分析技术对癌症基因表达模式进行系统分析，已经发现并发表了数百个基因表达特征。然而，很少有公共签名值在多项研究中交叉验证，用于预测新辅助治疗环境中的癌症预后和化疗敏感性。为了分析公开可用签名的预后和预测价值，我们实施了一种系统方法，用于高通量和有效验证大量数据集和基因表达签名。使用这种方法，我们进行了一项荟萃分析，包括351个公开的签名，37，000个随机签名和31个乳腺癌数据集。生存分析和病理反应被用来评估预后预测，化疗反应性和化疗药物敏感性。在31个乳腺癌数据集和351个公共签名中，我们确定了22个验证数据集，其中两个是乳腺癌的稳健预后签名（BRmet 50和PMID 18271932 Sig 33），一个是ER阴性肿瘤患者预后预测的特异性签名（PMID 20813035 Sig 137）。22个验证数据集显示了区分癌症基因谱与随机基因谱的增强能力。这两个预后标志都由与TP 53突变相关的基因组成，并且能够分别在22个验证数据集中的82%和68%中成功地对良好和不良预后组进行分层。然后，我们评估了这两个特征预测乳腺癌患者常用化疗方案治疗反应的能力。BRmet 50和PMID 18271932 Sig 33均回顾性确定了对新辅助化疗反应不敏感的患者（平均阳性预测值为85%-88%）。在预测为治疗敏感的患者中，远端无复发生存率（DRFS）得到改善（阴性预测值87%-88%）。BRmet 50进一步显示可前瞻性预测HER 2阴性（HER 2-）乳腺癌患者的紫杉烷-蒽环类药物敏感性。我们已经开发并应用了一种用于公共癌症签名验证的高通量筛选方法。使用这种方法，我们确定了用于交叉验证的适当数据集和区分TP 53突变状态并对乳腺癌患者具有预后和预测价值的两个稳健特征。本文的在线版本（doi：10.1186/s12885-015-1102-7）包含补充材料，可供授权用户使用。
Systematic analysis of cancer gene-expression patterns using high-throughput transcriptional profiling technologies has led to the discovery and publication of hundreds of gene-expression signatures. However, few public signature values have been cross-validated over multiple studies for the prediction of cancer prognosis and chemosensitivity in the neoadjuvant setting. To analyze the prognostic and predictive values of publicly available signatures, we have implemented a systematic method for high-throughput and efficient validation of a large number of datasets and gene-expression signatures. Using this method, we performed a meta-analysis including 351 publicly available signatures, 37,000 random signatures, and 31 breast cancer datasets. Survival analyses and pathologic responses were used to assess prediction of prognosis, chemoresponsiveness, and chemo-drug sensitivity. Among 31 breast cancer datasets and 351 public signatures, we identified 22 validation datasets, two robust prognostic signatures (BRmet50 and PMID18271932Sig33) in breast cancer and one signature (PMID20813035Sig137) specific for prognosis prediction in patients with ER-negative tumors. The 22 validation datasets demonstrated enhanced ability to distinguish cancer gene profiles from random gene profiles. Both prognostic signatures are composed of genes associated with TP53 mutations and were able to stratify the good and poor prognostic groups successfully in 82%and 68% of the 22 validation datasets, respectively. We then assessed the abilities of the two signatures to predict treatment responses of breast cancer patients treated with commonly used chemotherapeutic regimens. Both BRmet50 and PMID18271932Sig33 retrospectively identified those patients with an insensitive response to neoadjuvant chemotherapy (mean positive predictive values 85%-88%). Among those patients predicted to be treatment sensitive, distant relapse-free survival (DRFS) was improved (negative predictive values 87%-88%). BRmet50 was further shown to prospectively predict taxane-anthracycline sensitivity in patients with HER2-negative (HER2-) breast cancer. We have developed and applied a high-throughput screening method for public cancer signature validation. Using this method, we identified appropriate datasets for cross-validation and two robust signatures that differentiate TP53 mutation status and have prognostic and predictive value for breast cancer patients. The online version of this article (doi:10.1186/s12885-015-1102-7) contains supplementary material, which is available to authorized users.
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DOI：
10.1001/jama.2011.593
发表时间：
2011-05-11
期刊：
JAMA
影响因子：
--
作者：
Hatzis C;Pusztai L;Valero V;Booser DJ;Esserman L;Lluch A;Vidaurre T;Holmes F;Souchon E;Wang H;Martin M;Cotrina J;Gomez H;Hubbard R;Chacón JI;Ferrer-Lozano J;Dyer R;Buxton M;Gong Y;Wu Y;Ibrahim N;Andreopoulou E;Ueno NT;Hunt K;Yang W;Nazario A;DeMichele A;O'Shaughnessy J;Hortobagyi GN;Symmans WF
通讯作者：
Symmans WF
影响因子：
2.7
作者：
Fan C;Prat A;Parker JS;Liu Y;Carey LA;Troester MA;Perou CM
通讯作者：
Perou CM
影响因子：
4.7
作者：
Chen, Miao-Fen;Lee, Kuan-Der;Chen, Wen-Cheng
通讯作者：
Chen, Wen-Cheng
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
影响因子：
64.8
作者：
Garnett, Mathew J.;Edelman, Elena J.;Heidorn, Sonja J.;Greenman, Chris D.;Dastur, Anahita;Lau, King Wai;Greninger, Patricia;Thompson, I. Richard;Luo, Xi;Soares, Jorge;Liu, Qingsong;Iorio, Francesco;Surdez, Didier;Chen, Li;Milano, Randy J.;Bignell, Graham R.;Tam, Ah T.;Davies, Helen;Stevenson, Jesse A.;Barthorpe, Syd;Lutz, Stephen R.;Kogera, Fiona;Lawrence, Karl;McLaren-Douglas, Anne;Mitropoulos, Xeni;Mironenko, Tatiana;Thi, Helen;Richardson, Laura;Zhou, Wenjun;Jewitt, Frances;Zhang, Tinghu;O'Brien, Patrick;Boisvert, Jessica L.;Price, Stacey;Hur, Wooyoung;Yang, Wanjuan;Deng, Xianming;Butler, Adam;Choi, Hwan Geun;Chang, JaeWon;Baselga, Jose;Stamenkovic, Ivan;Engelman, Jeffrey A.;Sharma, Sreenath V.;Delattre, Olivier;Saez-Rodriguez, Julio;Gray, Nathanael S.;Settleman, Jeffrey;Futreal, P. Andrew;Haber, Daniel A.;Stratton, Michael R.;Ramaswamy, Sridhar;McDermott, Ultan;Benes, Cyril H.
通讯作者：
Benes, Cyril H.