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Originally published as JCO Early Release 10.1200/JCO.2005.05.3520 on February 27 2006 © 2006 American Society of Clinical Oncology.
Functional Analysis of the Breast Cancer GenomeDivision of Oncology, Department of Internal Medicine, Washington University in St Louis, St Louis, MO 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.1 It took the weight of a meta-analysis to resolve this issue,2 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,3 and the introduction of the HER2-targeting monoclonal antibody trastuzumab into the adjuvant setting improves outcomes for patients with ER+ HER2+ tumors.4 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,5 would therefore have been very interested in the paper by Oh et al6 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.7 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% false-discovery 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 snap-frozen tumor biopsy whose quality and tumor content have been assessed by histopathology. On the other hand, the measurement of 822 genes by quantitative polymerase chain reaction from RNA extracted from a paraffin block is impractical at this stage, and a step to reduce the gene number closer to 100 or so without loss of predictive abilities would be required for the clinical implementation of Oh et al's signature. We may have to face the fact that, like our colleagues who treat lymphoma, breast cancer physicians will have to insist on frozen tumor acquisition in the near future. We achieved fresh tissue acquisition for biochemical ER testing before and I predict that, despite the barriers, we will be doing it again. An obvious move in this direction is the prospective evaluation of the Amsterdam 70-gene prognostic signature that requires frozen tissue RNA analysis.8 One of the most interesting aspects of the Oh et al article concerns the glimpses of the molecular complexities of the biologic system that we are trying to understand. Certain genes, such as STK6, keep surfacing in breast cancer profiling studies. This gene is present in the National Surgical Adjuvant Breast and Bowel Project (NSABP) recurrence score published by Paik et al.7 As a side note, gene nomenclature is extremely frustrating: STK6 is actually assigned to the mouse gene, whereas the human homolog is Aurora-A/AURKA/STK15/BTAK. STK15 is deregulated in a wide spectrum of poor-prognosis cancers9-11 and of interest, Aurora kinase inhibitors are in development.12 The power of the HER2-trastuzumab paradigm is that we have successfully paired a somatic mutation with a targeted therapy. It seems likely that the final formulation of a biologically based classification of breast cancer will have a strong bias toward genetic abnormalities that create opportunities for targeted treatments. The functional annotation of the breast cancer genome required for this vision has only just begun, and the excitement in the breast cancer research community is palpable. Author's Disclosures of Potential Conflicts of Interest The author indicated no potential conflicts of interest. Author Contributions
ACKNOWLEDGMENTS Supported by National Cancer Institute (Bethesda, MD) Grants No. U01 CA114722 and R01 CA095614. REFERENCES 1. Chamness GC, Mercer WD, McGuire WL: Are histochemical methods for estrogen receptor valid? J Histochem Cytochem 28:792-798, 1980[Abstract] 2. Tamoxifen for early breast cancer: An overview of the randomised trials—Early Breast Cancer Trialists' Collaborative Group. Lancet 351:1451-1467, 1998 3. De Laurentiis M, Arpino G, Massarelli E, et al: A meta-analysis on the interaction between HER-2 expression and response to endocrine treatment in advanced breast cancer. Clin Cancer Res 11:4741-4748, 2005 4. Piccart-Gebhart MJ, Procter M, Leyland-Jones B, et al: Trastuzumab after adjuvant chemotherapy in HER2-positive breast cancer. N Engl J Med 353:1659-1672, 2005 5. McGuire WL, Horwitz KB, Pearson OH, et al: Current status of estrogen and progesterone receptors in breast cancer. Cancer 39:2934-2947, 1977[CrossRef][Medline] 6. Oh DS, Troester MA, Usary J, et al: Estrogen-regulated genes predict survival in hormone receptor-positive breast cancers. J Clin Oncol 24:10.1200/JCO.2005.03.2755 7. Paik S, Shak S, Tang G, et al: A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N Engl J Med 351:2817-2826, 2004 8. van de Vijver MJ, He YD, van't Veer LJ, et al: A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999-2009, 2002 9. Zhou H, Kuang J, Zhong L, et al: Tumour amplified kinase STK15/BTAK induces centrosome amplification, aneuploidy and transformation. Nat Genet 20:189-193, 1998[CrossRef][Medline] 10. Forozan F, Mahlamaki EH, Monni O, et al: Comparative genomic hybridization analysis of 38 breast cancer cell lines: A basis for interpreting complementary DNA microarray data. Cancer Res 60:4519-4525, 2000 11. Tanner MM, Grenman S, Koul A, et al: Frequent amplification of chromosomal region 20q12-q13 in ovarian cancer. Clin Cancer Res 6:1833-1839, 2000 12. Gadea BB, Ruderman JV: Aurora kinase inhibitor ZM447439 blocks chromosome-induced spindle assembly, the completion of chromosome condensation, and the establishment of the spindle integrity checkpoint in Xenopus egg extracts. Mol Biol Cell 16:1305-1318, 2005
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Copyright © 2006 by the American Society of Clinical Oncology, Online ISSN: 1527-7755. Print ISSN: 0732-183X
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