Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures.
Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures.
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DOI：
10.1186/1755-8794-4-3
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发表时间：
2011-01-09
影响因子：
2.7
通讯作者：
Perou CM
中科院分区：
文献类型：
--
作者：
Fan C;Prat A;Parker JS;Liu Y;Carey LA;Troester MA;Perou CM
已经开发出多种乳腺癌基因表达谱，它们似乎具有相似的预测结果的能力，并且可能优于临床病理标准；然而，看似不同的概况在多大程度上提供附加的预后信息尚不清楚，我们也不知道预后概况在临床定义的乳腺癌亚型中是否表现相同。我们评估了将标准乳腺癌临床变量的预后能力与大量基因表达特征相结合是否可以提高我们预测患者结果的能力。利用临床病理变量和 323 个基因表达“模块”的集合（包括 115 个先前发表的特征），我们使用 550 名淋巴结阴性且未经系统治疗的乳腺癌患者的数据集构建了多元 Cox 比例风险模型。还使用这种方法建立了预测新辅助化疗病理完全缓解（pCR）的模型。我们针对整个人群以及 ER 阳性或 Luminal 肿瘤患者亚组确定了具有统计学意义的 7 年无复发生存 (RFS) 预后模型。此外，我们发现，与单独包含临床或基因组变量的模型相比，包含临床和基因组参数的组合模型可以改善预后。最后，我们能够建立统计显着的组合模型，用于整个人群的病理完全缓解（pCR）预测。基因表达特征和临床病理因素的整合是比单独使用任一变量类型的改进方法。当使用所有患者以及淋巴结阴性和 ER 阳性乳腺癌患者的子集时，可以创建高度预后的模型。为 ER 阴性乳腺癌患者建立稳健的预后模型，需要基因表达和临床病理变量之外的其他变量，例如基因突变状态或 DNA 拷贝数变化。这种结合的临床和基因组学模型方法还可用于构建治疗反应性的预测因子，并最终可应用于其他肿瘤类型。
Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; however, the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform equally across clinically defined breast cancer subtypes. We evaluated whether combining the prognostic powers of standard breast cancer clinical variables with a large set of gene expression signatures could improve on our ability to predict patient outcomes. Using clinical-pathological variables and a collection of 323 gene expression "modules", including 115 previously published signatures, we build multivariate Cox proportional hazards models using a dataset of 550 node-negative systemically untreated breast cancer patients. Models predictive of pathological complete response (pCR) to neoadjuvant chemotherapy were also built using this approach. We identified statistically significant prognostic models for relapse-free survival (RFS) at 7 years for the entire population, and for the subgroups of patients with ER-positive, or Luminal tumors. Furthermore, we found that combined models that included both clinical and genomic parameters improved prognostication compared with models with either clinical or genomic variables alone. Finally, we were able to build statistically significant combined models for pathological complete response (pCR) predictions for the entire population. Integration of gene expression signatures and clinical-pathological factors is an improved method over either variable type alone. Highly prognostic models could be created when using all patients, and for the subset of patients with lymph node-negative and ER-positive breast cancers. Other variables beyond gene expression and clinical-pathological variables, like gene mutation status or DNA copy number changes, will be needed to build robust prognostic models for ER-negative breast cancer patients. This combined clinical and genomics model approach can also be used to build predictors of therapy responsiveness, and could ultimately be applied to other tumor types.
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影响因子：
45.3
作者：
Gonzalez-Angulo, Ana M.;Litton, Jennifer K.;Hortobagyi, Gabriel N.
通讯作者：
Hortobagyi, Gabriel N.
影响因子：
12.3
作者：
Herschkowitz JI;Simin K;Weigman VJ;Mikaelian I;Usary J;Hu Z;Rasmussen KE;Jones LP;Assefnia S;Chandrasekharan S;Backlund MG;Yin Y;Khramtsov AI;Bastein R;Quackenbush J;Glazer RI;Brown PH;Green JE;Kopelovich L;Furth PA;Palazzo JP;Olopade OI;Bernard PS;Churchill GA;Van Dyke T;Perou CM
通讯作者：
Perou CM
影响因子：
2
作者：
HARRELL, FE;LEE, KL;ROSATI, RA
通讯作者：
ROSATI, RA
影响因子：
15.9
作者：
Bosco, Emily E.;Wang, Ying;Knudsen, Erik S.
通讯作者：
Knudsen, Erik S.
影响因子：
5.8
作者：
Benito, M;Parker, J;Marron, JS
通讯作者：
Marron, JS