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首页> 外文期刊>BMC Medical Genomics >Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures
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Building prognostic models for breast cancer patients using clinical variables and hundreds of gene expression signatures

机译：使用临床变量和数百种基因表达特征为乳腺癌患者建立预后模型

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Background 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. Methods 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. Results 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. Conclusions 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.
机译：背景技术已经开发出多种乳腺癌基因表达谱，这些谱图谱提供了相似的预测结果的能力，并且可能优于临床病理标准。但是，似乎相异的概况提供附加的预后信息的程度尚不清楚，我们也不知道在临床定义的乳腺癌亚型中预后概况是否表现相同。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结合起来是否可以改善我们预测患者预后的能力。方法使用临床病理变量和323个基因表达“模块”（包括115个以前发表的签名）的集合，我们使用550例淋巴结阴性，全身未治疗的乳腺癌患者的数据集，建立了多元Cox比例风险模型。使用这种方法还可以预测对新辅助化疗的病理完全反应（pCR）的模型。结果我们确定了整个人群以及ER阳性或荧光瘤患者亚组7年无复发生存（RFS）的统计学上显着的预后模型。此外，我们发现与仅具有临床或基因组变量的模型相比，包括临床和基因组参数的组合模型可改善预后。最后，我们能够建立统计上有意义的组合模型，以预测整个人群的病理完全缓解（pCR）。结论整合基因表达特征和临床病理因素是相对于单独的任一可变类型的改进方法。当使用所有患者以及淋巴结阴性和ER阳性乳腺癌的患者子集时，可以创建高度预后的模型。除了基因表达和临床病理变量外，还需要其他变量，例如基因突变状态或DNA拷贝数变化，以为ER阴性乳腺癌患者建立可靠的预后模型。这种结合了临床和基因组模型的方法还可以用于建立治疗反应性的预测指标，并且最终可以应用于其他类型的肿瘤。

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