No abstract

Purpose The clinical relevancy of the 7-subtype classification of triple-negative breast cancer (TNBC) reported by Lehmann and Bauer et al is unknown. We investigated the clinical relevancy of TNBC heterogeneity by determining pathological complete response (pCR) rates after neoadjuvant chemotherapy, based on TNBC subtypes. Experimental Design We revalidated the Lehmann and Bauer et al. experiments using Affymetrix CEL files from public datasets. We applied these methods to 146 TNBC patients with gene expression microarrays obtained from June 2000 to March 2010 at our institution. Of those, 130 had received standard neoadjuvant chemotherapy and had evaluable pathological response data. We classified the TNBC samples by subtype, then correlated subtype and pCR status using Fisher’s exact test and a logistic regression model. We also assessed survival and compared the subtypes to PAM50 intrinsic subtypes and residual cancer burden (RCB) index. Results TNBC subtype and pCR status were significantly associated (P=0.04379). The basal-like 1 (BL1) subtype had the highest pCR rate (52%); basal-like 2 (BL2) and luminal androgen receptor (LAR) had the lowest (0% and 10%, respectively). TNBC subtype was an independent predictor of pCR status (P=0.022) by a likelihood ratio test. The subtypes better predicted pCR status than did the PAM50 intrinsic subtypes (basal-like vs non basal-like). Conclusions Classifying TNBC by 7 subtypes predicts high vs. low pCR rate. We confirm the clinical relevancy of the 7 subtypes of TNBC. We need to prospectively validate whether the pCR rate differences translate into long-term outcome differences. The 7-subtype classification may spur innovative personalized medicine strategies for TNBC patients.

Decades of research in molecular oncology have brought about promising new therapies that are designed to target specific molecules that promote tumor growth and survival. The epidermal growth factor receptor (EGFR) is one of the first identified important targets of these novel antitumor agents. Approximately half of cases of triple-negative breast cancer (TNBC) and inflammatory breast cancer (IBC) overexpress EGFR. Thus, EGFR inhibitors for treatment of breast cancer have been evaluated in several studies. However, results so far have been disappointing. One of the reasons for these unexpected results is the lack of biomarkers for predicting which patients are most likely to respond to EGFR inhibitors. Recent studies have shown that EGFR and its downstream pathway regulate epithelial-mesenchymal transition, migration, and tumor invasion and that high EGFR expression is an independent predictor of poor prognosis in IBC. Further, recent studies have shown that targeting EGFR enhances the chemosensitivity of TNBC cells by rewiring apoptotic signaling networks in TNBC. These studies indicate that EGFR-targeted therapy might have a promising role in TNBC and IBC. Further studies of the role of EGFR in TNBC and IBC are needed to better understand the best way to use EGFR-targeted therapy—e.g., as a chemosensitizer or to prevent metastases—to treat these aggressive diseases.

Background: Inflammatory breast cancer (IBC) is a poorly characterized form of breast cancer. So far, the results of expression profiling in IBC are inconclusive due to various reasons including limited sample size. Here, we present the integration of three Affymetrix expression datasets collected through the World IBC Consortium allowing us to interrogate the molecular profile of IBC using the largest series of IBC samples ever reported.Experimental Design: Affymetrix profiles (HGU133-series) from 137 patients with IBC and 252 patients with non-IBC (nIBC) were analyzed using unsupervised and supervised techniques. Samples were classified according to the molecular subtypes using the PAM50-algorithm. Regression models were used to delineate IBC-specific and molecular subtype-independent changes in gene expression, pathway, and transcription factor activation.Results: Four robust IBC-sample clusters were identified, associated with the different molecular subtypes (P < 0.001), all of which were identified in IBC with a similar prevalence as in nIBC, except for the luminal A subtype (19% vs. 42%; P < 0.001) and the HER2-enriched subtype (22% vs. 9%; P < 0.001). Supervised analysis identified and validated an IBC-specific, molecular subtype-independent 79-gene signature, which held independent prognostic value in a series of 871 nIBCs. Functional analysis revealed attenuated TGF-b signaling in IBC.Conclusion: We show that IBC is transcriptionally heterogeneous and that all molecular subtypes described in nIBC are detectable in IBC, albeit with a different frequency. The molecular profile of IBC, bearing molecular traits of aggressive breast tumor biology, shows attenuation of TGF-b signaling, potentially explaining the metastatic potential of IBC tumor cells in an unexpected manner. Clin Cancer Res; 19(17); 4685-96. Ó2013 AACR.