We describe a comprehensive modeling approach to combining genomic and clinical data for personalized prediction in disease outcome studies. This integrated clinicogenomic modeling framework is based on statistical classification tree models that evaluate the contributions of multiple forms of data, both clinical and genomic, to define interactions of multiple risk factors that associate with the clinical outcome and derive predictions customized to the individual patient level. Gene expression data from DNA microarrays is represented by multiple, summary measures that we term metagenes; each metagene characterizes the dominant common expression pattern within a cluster of genes. A case study of primary breast cancer recurrence demonstrates that models using multiple metagenes combined with traditional clinical risk factors improve prediction accuracy at the individual patient level, delivering predictions more accurate than those made by using a single genomic predictor or clinical data alone. The analysis also highlights issues of communicating uncertainty in prediction and identifies combinations of clinical and genomic risk factors playing predictive roles. Implicated metagenes identify gene subsets with the potential to aid biological interpretation. This framework will extend to incorporate any form of data, including emerging forms of genomic data, and provides a platform for development of models for personalized prognosis. G enomic information, in the form of gene expression patterns, has an established capacity to define clinically relevant risk factors in disease prognosis. Recent studies have generated such patterns related to lymph node metastasis and disease recurrence in breast cancer (1-8), as well as in other cancers and disease contexts (9-16). The challenge now is the integration of such genomic information into prognostic models that can be applied in a clinical setting to improve the accuracy of treatment decisions. Achievement of this goal requires modeling approaches that focus on the generation of predictions for the individual patient and that can evaluate and combine multiple risk factors to produce informed predictions. Gene expression profiles may indeed prove to be powerful individual indicators of tumor behavior, but analysis should not force a choice of one form of data over the other; rather, analysis should evaluate and combine all forms of potentially relevant information. This integrative view underlies our development of clinicogenomic models and should underlie prognostic systems in support of personalized health planning. Consistent with this view, the example of breast cancer recurrence presented here highlights the predictive value of multiple genomic patterns in models defining accurate predictions at the individual patient level. This analysis uses integrative models that combine clinical and genomic factors, such as multiple gene expression patterns, clinical risk factors, and treatment information, and that predict recurrence for individual patients. The example shows improved recurrence prediction accuracy at the individual patient level based on multiple risk factors in combination and the relevance of multiple summary measures of gene expression. Prediction accuracy in the combined clinicogenomic models exceeds that achieved by using either clinical data or single genomic predictors alone, and the analysis highlights the importance of representing and communicating uncertainties in prediction. The analysis also identifies gene candidates that can now be studied to shed light on potential regulatory pathways. The example study involves 158 breast cancer patients at the Koo Foundation Sun Yat-Sen Cancer Center in Taipei Table 1 , which is published as supporting information on the PNAS web site. Gene expression assays were performed with RNA extracted from the banked tissue. Total RNA was extracted with Qiagen RNeasy kits and assessed for quality with an Agilent Lab-on-a-Chip 2100 Bioanalyzer. Probes for hybridization were then prepared according to standard Affymetrix protocols on the Human U95Av2 GeneChip. Affymetrix GeneChip scanning and analysis produced the Affymetrix MAS VERSION 5.0 expression signal intensity estimates. The core methodology uses statistical classification and prediction tree models, and the gene expression data enter into these models in the form of metagenes. As previously described (7, 17, 18) , metagenes represent the aggregate patterns of variation of subsets of potentially related genes. In this example, metagenes were constructed as the first principal components (singular factors) of clusters of genes created by using k-means clustering. Bayesian methods of analysis were used to fit multiple candidate classification tree models, each candidate model based on varying the selection of predictor variables, and trees were individually generated by using a forward selection process. Predictions were based on weighted averages across multiple candidate tree models, and the combinations of genomic and clinical predictor variables appearing in highly weighted tree models provide insights on the interactions of risk factors determining the predictions. Full details of the statistical approach appear in the supporting information. Combining Multiple Metagene Signatures Improves Accuracy of Recurrence Prediction. Data summaries in terms of raw survival curve and relative risk estimates illustrate the traditional view of stratiAbbreviation: ER, estrogen receptor.
REF combine metagenes with clinical risk factors to improve prediction.
Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes
Proceedings of the National Academy of Sciences of the United States of America
Proceedings of the National Academy of Sciences of the United States of America
Medicine Biology