Functional analysis of the breast cancer genome.

Clinicians have long recognized that a diagnosis of breast cancer encompasses different tumor types with very different clinical outcomes. The first critical step toward a biomarker-based subclassification schema to aid disease management occurred with the widespread introduction of tumor estrogen-receptor (ER) measurement three decades ago. It is worth revisiting the literature of the time, because the introduction of ER testing was very controversial. In particular, investigators worried that a benefit from endocrine therapy in ER– disease could not be excluded. It took the weight of a meta-analysis to resolve this issue, but even today we worry about excluding a patient from appropriate endocrine therapy because of false-negative ER results. The single-sample prediction problem continues to be central to the current debate regarding new and more complex biomarker approaches. Apparently robust group predictions typical of these analyses does not necessarily require great measurement accuracy. Prospective clinical testing, on the other hand, needs to be highly precise to avoid the potentially tragic consequences of tumor misclassification. Further subclassification for ER disease to prospectively identify endocrine therapy–resistant ER tumors is of longstanding interest. The introduction of HER2 testing has already taken us some way down this road. It is clear that the activation of HER2 by gene amplification drives endocrine therapy resistance, and the introduction of the HER2-targeting monoclonal antibody trastuzumab into the adjuvant setting improves outcomes for patients with ER HER2 tumors. HER2 gene amplification does not, however, explain all or even most cases of endocrine therapy failure, and new insights and predictive models are needed. One longstanding idea is to consider the ER in the context of an entire pathway; as our insight into the role of estrogen in tumorigenesis deepens, we will be able to move beyond reliance on progesterone receptor (PgR) measurement for ER activity assessment. The late William McGuire, who championed progesterone receptor measurement in breast cancer, would therefore have been very interested in the paper by Oh et al in this issue of the Journal of Clinical Oncology. The University of North Carolina–Chapel Hill (Chapel Hill, NC) team assessed the biomarker potential of hundreds of estrogen-regulated genes (including PgR) encoding proteins with a wide variety of cellular functions. To briefly summarize their findings, Oh et al identified an extensive list of genes that were found to be regulated by estradiol at multiple time points in the MCF7 breast cancer cell line. Although they found both estrogen-repressed genes and estrogen-induced genes, their subsequent work in the paper focused on only upregulated genes. As an interesting side issue in the article, Oh et al compared this list of genes with a list induced by transfection of the GATA3 transcription factor into a GATA3 null cell line and found considerable overlap. Thus GATA3 and ER must work in concert to regulate this large gene repertoire. There was also a good deal of overlap with genes found to be expressed in ER breast cancers in earlier studies (referred to as “luminal” in keeping with Oh et al’s earlier work on breast cancer classification) as well as genes represented in the only clinically available multigene test that predicts tamoxifen resistance. To ascertain the role these estrogen-induced genes might play in clinical outcomes, Oh et al first performed hierarchical clustering of the estrogen-induced genes in a 64 –luminal tumor training set and identified two major groups, groups I and II. All of the gene information available from these cases was subsequently compared between groups I and II (a supervised analysis), with a 1% falsediscovery rate, to produce an 822-gene list. The tumors were then reclustered with the 822 differentially expressed genes to reproduce two groups (called IE and IIE). Information on as many of the 822 genes as possible was then sought from three publicly available, clinically annotated breast cancer microarray databases. After data manipulation to remove “platform and source systemic bias” an average expression index or “centroid” was used to classify clinically ER and/or PgR tumors into two groups with significant differences in survival. From a functional genomics standpoint, it is interesting to note that the favorable-prognosis group IE gene list was largely populated by transcription factors XBP1, FOXA1, and PgR and ribosomal genes. Unfavorable Group IIE tumors were rich in proliferation genes (Ki-67, MYBL2, Survivin, STK6, and CCNB2), as well as apoptosis regulators FLIP/CFLAR, AVEN, and BCL2A1. These gene signatures imply, not surprisingly, that endocrine therapy resistance is associated with a deregulated cell cycle and resistance to programmed cell death. So what are we to make of these data? The first issue, of course, is that Oh et al’s extensive gene list is far from a clinical test. Although the signature is robust, and survives the data averaging algorithms necessary for cross-platform assessment, clinical testing needs to be formulated into a reproducible technology that can be repeated thousands of times in a cost-effective manner. Here, we are faced with a dilemma that is becoming more acute every day. Although the systematic evaluation of expression from 822 genes in a clinical environment sounds expensive, actually, by using smaller customized microarrays, it could be achieved for a couple hundred dollars with a turnaround of 1 or 2 days. However, the starting material for RNA extraction (at least currently) must be a JOURNAL OF CLINICAL ONCOLOGY E D I T O R I A L VOLUME 24 NUMBER 11 APRIL 1