Breast cancer expression profiling: the impact of microarray testing on clinical decision making

Abstract

The available clinical prognostic tools show an obvious limitation in predicting the outcome of breast cancer patients, and pathological features cannot classify tumours accurately. Microarray-based molecular classification of breast tumours or selection of gene expression panels to improve risk prediction or treatment outcomes are thought to be theoretically superior to established clinical and pathological criteria, based on guidelines such as the St Gallen and National Institute of Health consensus, or which use specific prognostic tools, such as the Nottingham Prognostic Index or Adjuvant-Online algorithm. Although two diagnostic tests based on gene expression profiling of breast cancer are commercially available, a new molecular classification and molecular forecasting of breast cancer based on expression profiling cannot outperform the standard tumour diagnostic at present. This review focuses on some important problems in the practical application of molecular profiling of breast cancer for clinical purposes.

Bromodomain 4 activation predicts breast cancer survival

“…One of the main questions these trials seek to answer is whether tumor molecular profiling can be used prospectively to identify those node-negative/ER-positive patients who eventually relapse, to initiate adjuvant therapy that would not be administered under current treatment protocols. One of the criticisms leveled at microarray tumor gene expression analyses has been that it is a ''black-box'' methodology that provides results without a coherent biologic explanation and that dysregulated expression of any particular gene as a marker of poor prognosis does not indicate a clear-cut condition (26). The current study allows us to further characterize the origins of poor prognosis signatures in breast cancer by demonstrating that dysregulated expression of a single gene, Brd4, can drive the expression of many genes that are frequently observed as components of metastasis-predictive gene expression signatures.…”

The rapamycin-regulated gene expression signature determines prognosis for breast cancer

“…Technical differences among the studies contribute to the discrepancy in gene expression data, such as different microarray platforms, probes, RNA-labeling methods, and gene sets [ 35 ]. Microarray-based studies of breast cancer usually focus on three main uses of gene expression profiling [ 36 ]. First, gene expression profiling may can generate a molecular classification of breast cancer into different subsets according to clinical subtype, such as high versus low grade [ 37 - 41 ].…”

Discovery of candidate genes and pathways that may help explain fertility cycle stage dependent post-resection breast cancer outcome

“…The costs of systemic therapies are high and the side effects from such therapies are severe, so it is important to identify patients who are most and least likely to benefit from these treatments. To date, many gene expression profiling studies have been performed in breast cancer research with disappointingly small overlap among the prognostic signatures identified [28][29][30][31][32][33][34][35][36][37][38]. It has been demonstrated that the prognostic capacity of each of these signatures is better than the conventional outcome classifiers (stage and grade), and some of these signatures are useful enough to have been made commercially available.…”

“…Although many effective and reliable breast cancer prognostic gene signatures have been identified, there is little overlap among the identified prognostic genes across different studies [28][29][30][31][32][33][34][35][36][37][38]. This, in part, reflects the obvious heterogeneity of human breast cancers.…”