PD03-01: An Integrated Analysis of Three Distinct IBC/nIBC Affymetrix Gene Expression Data Sets Further Unveils the Molecular Biology of IBC.
Authors: SJ Van Laere, NT Ueno, P Finetti, PB Vermeulen, A Lucci, D Birnbaum, FM Robertson, PA van Dam, WA Woodward, P Viens, LY Dirix, JM Reuben, T Iwamoto, M Cristofanilli, F Bertucci
Journal: Cancer Research (2011)

Abstract Introduction. Several studies have applied gene expression profiling to inflammatory breast cancer (IBC). Most of these studies were underpowered. Here, we present an integrated analysis of 3 distinct gene expression data sets of IBC and non-IBC (nIBC) samples to further uncover the IBC-specific molecular biology with enhanced statistical power. Materials &amp; Methods. Three Affymetrix gene expression data sets were combined, resulting in a series of 137 IBC and 252 nIBC samples. IBC was diagnosed clinically. Each sample was classified according to several published gene signatures. Transcriptional heterogeneity was investigated using hierarchical clustering, coupled with silhouette score analysis. IBC-specific, molecular subtype-independent differences in gene expression were identified using linear regression modeling. Differentially expressed genes were translated into pathways using Ingenuity Pathway Analysis. Cox regression analysis was used to identify variables influencing distant metastasis-free survival (DMFS) in IBC. Finally, we focussed on the molecular aspects of pathological response to neodjuvant chemotherapy in patients with IBC. Results. In our series of IBC samples, 4 robust sample clusters were identified. These sample clusters were mainly associated with the different molecular subtypes (P&amp;lt;0.0001), all of which were identified in IBC with a similar prevalence in nIBC, except for the Luminal A subtype (9% vs. 40%; P&amp;lt;0.0001) and the ErbB2+ subtype (23% vs. 8%; P=0.0002). A total of 632 genes were differentially expressed. Analysis of this gene list identified an IBC-repressed network centered on TGFβ. Activated TGFβ-profiles and SMAD-profiles in the nIBC samples corroborated these findings. Consistent with published poor prognosis signatures, current survival analysis indicated that the molecular subtypes are significantly associated with prognosis in IBC. Surprisingly, in IBC, the Luminal A samples exhibited the shortest DMFS-interval (HR=4.02; P&amp;lt;0.05). Comparison of responders and non-responders to neoadjuvant chemotherapy suggests a prominent role for inflammation/immunity-related processes in determining the efficacy of neoadjuvant chemotherapy in IBC. Conclusions. IBC, like nIBC, is transcriptionally heterogeneous as exemplified by the identification of 4 robust sample clusters in the present series. This observation is further corroborated by the identification of all known molecular subtypes in IBC, albeit with a different distribution pattern characterized by a low frequency of Luminal A samples. Nevertheless, this phenotype is clinically relevant, as demonstrated by the poor prognosis profile. Our observations can be explained by the IBC-specific repression of TGFβ, which is a key molecule of epithelial-to-mesenchymal transition and is also known to prevent ER-expressing cells from proliferating. Finally, as in nIBC, inflammation- and immunity-related processes are important aspects of response to neoadjuvant chemotherapy in IBC. Citation Information: Cancer Res 2011;71(24 Suppl):Abstract nr PD03-01.