Prediction of Response to Aromatase Inhibitors in Breast Cancer

Abstract

Aromatase inhibitors (AIs) are major treatment options for the management of patients with breast cancer. The drugs are effective and response rates can be high. However, resistance, either primary or acquired during treatment, may occur. Optimal clinical management requires accurate predictors of response to identify those tumours, which are most likely to respond (so sparing patients with resistant tumours needless side-effects of ineffective therapy). Currently, oestrogen receptor (ER) status is the only factor used routinely to select for treatment with aromatase inhibitors, but a substantial proportion of ER-positive tumours fails treatment. There is, therefore, an urgent need to identify additional markers by which accurately to predict clinical response on an individual basis. Whilst other markers (such as progesterone receptors or HER2) have some predictive powers, individually they have limited utility for routine use. The hope is that discovery strategies based on genome-wide searches will identify novel markers that can be used as predictive indices. Molecular phenotyping of individual tumours could then be used to decide not only which patients should be treated with AIs but whether AIs should be used alone or in combination or in sequence with other targeted agents in order that clinical benefits are maximized. This chapter will focus on utility of routinely assessed biomarkers, multi-component indices and gene signatures and outline the future perspectives in studies aimed to AI response prediction.

The Path to Implementation of Personalized Medicine of Aromatase Inhibitors in Patients with Breast Cancer

“…PgR, HER2 and Ki67 capture important aspects of breast tumor biology and are routinely measured. The presence of PgR has generally been regarded as an indicator of a functional ER pathway [5], HER2 positive (HER2 + ) implies possible estrogenindependent tumor growth [6], and Ki67 is a marker of proliferation. The roles of these biomarkers in predicting response to treatment with AIs have been widely studied.…”

“…The roles of these biomarkers in predicting response to treatment with AIs have been widely studied. However, the conclusions of these studies have been inconclusive, and these markers have only limited utility for predicting response to AIs [6].…”