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Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen

Functional Genomics Unit, Jules Bordet Institute, Brussels, Belgium.
BMC Genomics (Impact Factor: 4.04). 02/2008; 9:239. DOI: 10.1186/1471-2164-9-239
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ABSTRACT Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings.
We developed a gene classifier consisting of 181 genes belonging to 13 biological clusters. In the independent set of adjuvantly-treated samples, it was able to define two distinct prognostic groups (HR 2.01 95%CI: 1.29-3.13; p = 0.002). Six of the 13 gene clusters represented pathways involved in cell cycle and proliferation. In 112 metastatic breast cancer patients treated with tamoxifen, one of the classifier components suggesting a cellular inflammatory mechanism was significantly predictive of response.
We have developed a gene classifier that can predict clinical outcome in tamoxifen-treated ER+ BC patients. Whilst our study emphasizes the important role of proliferation genes in prognosis, our approach proposes other genes and pathways that may elucidate further mechanisms that influence clinical outcome and prediction of response to tamoxifen.

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Available from: Els M J J Berns, Aug 28, 2015
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    • "The data revealed three gene signatures consisting of a total of 14 genes (AKT1, BCAR3, BCL2, CDKN1A, CGA, EGFR, ESR1, IGF1R, NAT1, NCOA1, NRG1, PRKCD, PRKCE and TFF1). The strongest prediction of outcome (75% accuracy) was obtained with a 2-gene combination of BCL2 and CDKN1A and confirmed by independent examination of 4 previouslyreported microarray datasets of tamoxifen-treated patient samples in the adjuvant setting (n ¼ 503)(Chanrion et al., 2008; Loi et al., 2008; Ma et al., 2004; Zhang et al., 2009). The predictive value was further validated by comparing the ability of the genes to predict recurrence in an additional, previouslypublished , cohort (Loi et al., 2007) consisting of both adjuvant tamoxifen-treated and untreated patients (Lyng et al., 2013). "
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    • "Although the results of the genetic association should be replicated, they are consistent with the anticipated functional role of VAV3 and with the observations made in gene expression analyses. In our present study, we identified an association between the rs10494071 minor allele and better tamoxifen response, and, in turn, we found in our analysis of a tumor data set that low VAV3 expression correlates with better tamoxifen response [45]. Additionally, these observations seem to be coherent with the role of the rs10494071 variant as an expression quantitative trait locus for VAV3, with the minor allele being associated with significantly lower gene expression in monocytes [44]. "
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