Implications of functional proteomics in breast cancer

Abstract

Breast cancer is one of the major public health problems of the Western world. Recent advances in genomics and gene expression-profiling approaches have enriched our understanding of this heterogeneous disease. However, progress in functional proteomics in breast cancer research has been relatively slow. Allied with genomics, the functional proteomics approach will be important in improving diagnosis through better classification of breast cancer and in predicting prognosis and response to different therapies, including chemotherapy, hormonal therapy, and targeted therapy. In this review, we will present functional proteomic approaches with a focus on the recent clinical implications of utilizing the reverse-phase protein array platform in breast cancer research.

Keywords: Breast cancer; Functional proteomics; Predictive marker; Prognostic marker.

Figure 1.

Supervised clustering of breast cancers with quantification data for 10 proteins derived using reverse-phase protein arrays. The 712 breast tumor samples (training set) (A) were clustered with the 10 markers using an “uncentered correlation” distance metric along with the Ward linkage rule. This analysis yielded six subgroups (BG1–6). The 168 breast tumor samples (test set) (B) were subgrouped into one of six groups (PG1–6) using the decision tree (C) that was derived from the training set. Patients in the six subgroups differed significantly in their recurrence-free survival in both training (D) and test (E) sets. Reprinted from Gonzalez-Angulo AM, Hennessy BT, Meric-Bernstam F et al. Functional proteomics can define prognosis and predict pathologic complete response in patients with breast cancer. Clinical Proteomics, 2011;8:11.

Figure 2.

Clustering into green and red groups depending on the expression levels of AKT, IGFBP2, LKB1, S6, and stathmin 54 residual triple-negative breast cancers (A). Multivariable Cox proportional hazard model and calculated risk score (RS) (B). Optimal cutoff point at 1.457 (sensitivity versus 1; specificity for the RS in all 54 cases) (C). Receiving operating curve of the RS model (area under the curve = 0.856) (D). Reprinted from Sohn J, Do KA, Liu S et al. Functional proteomics characterization of residual triple-negative breast cancer after standard neoadjuvant chemotherapy. Annals of Oncology 2013;24:2522–2526. Abbreviations: AUC, area under the curve; CI, confidence interval; HR, hazard ratio.