Gene Expression Profiling to Predict Outcome of Chemotherapy for Early-Stage Breast Cancer

Abstract

Rationale Optimal adjuvant systemic treatment of patients with breast cancer following primary surgery is crucial to the achievement of successful outcomes. Although adjuvant chemotherapy and hormonal treatment both increase survival in patients with early-stage breast cancer, it remains difficult to accurately identify which patients require chemotherapy and to select the optimal chemotherapy for individual patients. In general, prognostication relies on broad clinical measures such as tumor size, lymph node status, and hormone receptor status to estimate risk of recurrence and to select treatment. Unfortunately, this leads to homogenous treatment of heterogeneous patients, with a proportion being overtreated and another group undertreated. • With the recent completion of the sequencing of the human genome comes a unique opportunity to advance the means by which clinical decision-making occurs. Through the use of microarray analysis, the expression of a large number of genes can be queried, and gene expression patterns can be used to classify breast cancers into subtypes with varying prognoses and different sensitivities to anticancer agents. • Perou and colleagues were the first group to show that gene expression profiles could be used to stratify patients with early-stage breast cancer into multiple subtypes with markedly different prognoses (P < 0.01).1,2 As an important proof of principle, distinct gene expression profiles have been correlated with varying survival times in different cancer types, including lymphoma3,4 and lung cancer.5 Microarray analysis has also been demonstrated to be more powerful in predicting outcome in patients with stage I or II breast cancer than standard clinicopathologic variables such as lymph node status. In an important pioneering study, the 10year overall survival probability was 55% for patients with the poor prognosis gene expression signature compared with 95% for those with the good prognosis signature.6 • The use of microarray analysis to identify predictive markers requires the ability to correlate differential gene expression with treatment outcome. In the context of neoadjuvant chemotherapy, both of these requirements can be met. Gene expression can be analyzed on the initial tumor biopsy and clinical and pathologic response to chemotherapy can be measured following chemotherapy. Both clinical and pathologic response to neoadjuvant chemotherapy have been shown to be valid predictors of outcome in patients with locally advanced and early-stage breast cancer. Therefore, clinical and pathologic responses can be correlated with breast cancer gene expression profiles in the search for gene expression patterns that predict benefit from chemotherapy. The hope is that gene expression profiles may aid in the determination of which patients will benefit from chemotherapy and which will not.