Abstract
BackgroundMultiple 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.MethodsUsing 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.ResultsWe 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.ConclusionsIntegration 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.
Highlights
Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform across clinically defined breast cancer subtypes
We developed a prognostic model in systematically untreated node-negative breast cancer patients derived from multiple commonly used clinical variables and a large database of gene expression modules, and we confirmed that models that incorporate both clinical and genomic variables are the most accurate for outcome predictions for newly diagnosed patients with node-negative breast cancer
Using a Cox proportional hazards approach with Least Absolute Shrinkage and Selection Operator (LASSO) Regression [69], which is a method of model building that can handle large numbers of potentially co-linear variables, all patients and different patient subsets defined by clinical parameters or intrinsic molecular subtyping were tested for prognostic model building
Summary
Multiple breast cancer gene expression profiles have been developed that appear to provide similar abilities to predict outcome and may outperform clinical-pathologic criteria; the extent to which seemingly disparate profiles provide additive prognostic information is not known, nor do we know whether prognostic profiles perform 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. Many studies have examined the prognostic significance of genomic biomarkers along with clinicalpathological variables and often shown that both provide independent information [41,42,43,44]; very few studies have attempted to create integrated prognostic models that contain both genomic and clinical biomarkers [45]. We have recently shown that integration of one pathological variable (i.e. tumor size) with one genomic signature (i.e. intrinsic subtypes) outperforms either strategy alone in terms of outcome prediction [46], suggesting that both data types can provide independent prognostic power and be combined into a single model. With the explosion of signatures developed with distinct biologic processes in mind, it makes sense to take this approach one step further and develop models that include clinical and genomic information, but systematically examines inclusion of multiple genomic signatures in an effort to further hone prognostication beyond one profile versus another
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