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Comparative Study
. 2009;11(4):R55.
doi: 10.1186/bcr2344. Epub 2009 Jul 28.

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

Affiliations
Comparative Study

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

Andrea H Bild et al. Breast Cancer Res. 2009.

Erratum in

  • Breast Cancer Res. 2011;13(4):406

Abstract

Introduction: Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation.

Methods: We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies.

Results: We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient.

Conclusions: Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.

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Figures

Figure 1
Figure 1
Dissection of breast cancer heterogeneity by using intrinsic subtypes and pathway patterns. (a) Hierarchic cluster analysis from a combined dataset comprising 537 samples. The centroid subtype predictions are shown immediately below the dendrogram with red for Basal-like, green for Normal-like, Luminal A as dark blue, Luminal B as light blue, and pink as HER2-enriched. (b) Kaplan-Meier survival plot for relapse-free survival by using the centroid predictions. (c) Cluster analysis of pathway-activation status predictions with red indicating active status, black, average pathway status, and green, low to absent pathway-activation status. (d) Kaplan-Meier survival plot based on the clustering of pathways from (c). (e) Correlations between tumor subtype (a) and groupings based on pathway status (c).
Figure 2
Figure 2
Pathway-activation status patterns are characteristic of intrinsic subtypes. (a) Heat maps of scaled pathway-activation scores in which the subjects are ordered according to their predicted subtype. The pathway-activation status of different pathways is displayed for the combined dataset, with strong pathway activation displayed by red, average status by black, and low to absent pathway activation by green. (b) Box-and-whisker plot showing pathway activation as a function of subtype displayed for the combined dataset. (c) Comparison of pathway activation as a function of subtype in the two datasets.
Figure 3
Figure 3
Inverse relations in pathway activation in Basal-like and Luminal B subtypes. Pathway-activation patterns are shown for (a, b) Basal-like, and (c, d) Luminal B subtypes. Relative pathway-activation status is shown on the y-axis, whereas each location on the x-axis represents an individual tumor.
Figure 4
Figure 4
Chemotherapeutic sensitivity profiles for Luminal B and Basal-like subtypes. Heat maps of scaled chemotherapeutic-sensitivity predictions in the combined dataset for (a) Luminal B and (b) Basal–like subtypes are shown. Samples were clustered, based on the predicted sensitivities.
Figure 5
Figure 5
Schematic diagram of a potential genomics-guided trial based on intrinsic subtype and pathway signatures.

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