Combined treatment for advanced head and neck cancer is more efficacious and not more toxic than hyperfractionated irradiation alone.

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BackgroundOptimizing treatment through microarray-based molecular subtyping is a promising method to address the problem of heterogeneity in breast cancer; however, current application is restricted to prediction of distant recurrence risk. This study investigated whether breast cancer molecular subtyping according to its global intrinsic biology could be used for treatment customization.MethodsGene expression profiling was conducted on fresh frozen breast cancer tissue collected from 327 patients in conjunction with thoroughly documented clinical data. A method of molecular subtyping based on 783 probe-sets was established and validated. Statistical analysis was performed to correlate molecular subtypes with survival outcome and adjuvant chemotherapy regimens. Heterogeneity of molecular subtypes within groups sharing the same distant recurrence risk predicted by genes of the Oncotype and MammaPrint predictors was studied.ResultsWe identified six molecular subtypes of breast cancer demonstrating distinctive molecular and clinical characteristics. These six subtypes showed similarities and significant differences from the Perou-Sørlie intrinsic types. Subtype I breast cancer was in concordance with chemosensitive basal-like intrinsic type. Adjuvant chemotherapy of lower intensity with CMF yielded survival outcome similar to those of CAF in this subtype. Subtype IV breast cancer was positive for ER with a full-range expression of HER2, responding poorly to CMF; however, this subtype showed excellent survival when treated with CAF. Reduced expression of a gene associated with methotrexate sensitivity in subtype IV was the likely reason for poor response to methotrexate. All subtype V breast cancer was positive for ER and had excellent long-term survival with hormonal therapy alone following surgery and/or radiation therapy. Adjuvant chemotherapy did not provide any survival benefit in early stages of subtype V patients. Subtype V was consistent with a unique subset of luminal A intrinsic type. When molecular subtypes were correlated with recurrence risk predicted by genes of Oncotype and MammaPrint predictors, a significant degree of heterogeneity within the same risk group was noted. This heterogeneity was distributed over several subtypes, suggesting that patients in the same risk groups require different treatment approaches.ConclusionsOur results indicate that the molecular subtypes established in this study can be utilized for customization of breast cancer treatment.

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 im...