Comparison of molecular subtype distribution in triple-negative inflammatory and non-inflammatory breast cancers

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Abstract

Introduction

Because of its high rate of metastasis, inflammatory breast cancer (IBC) has a poor prognosis compared with non-inflammatory types of breast cancer (non-IBC). In a recent study, Lehmann and colleagues identified seven subtypes of triple-negative breast cancer (TNBC). We hypothesized that the distribution of TNBC subtypes differs between TN-IBC and TN-non-IBC. We determined the subtypes and compared clinical outcomes by subtype in TN-IBC and TN-non-IBC patients.

Methods

We determined TNBC subtypes in a TNBC cohort from the World IBC Consortium for which IBC status was known (39 cases of TN-IBC; 49 cases of TN-non-IBC). We then determined the associations between TNBC subtypes and IBC status and compared clinical outcomes between TNBC subtypes.

Results

We found the seven subtypes exist in both TN-IBC and TN-non-IBC. We found no association between TNBC subtype and IBC status ( P = 0.47). TNBC subtype did not predict recurrence-free survival. IBC status was not a significant predictor of recurrence-free or overall survival in the TNBC cohort.

Conclusions

Our data show that, like TN-non-IBC, TN-IBC is a heterogeneous disease. Although clinical characteristics differ significantly between IBC and non-IBC, no unique IBC-specific TNBC subtypes were identified by mRNA gene-expression profiles of the tumor. Studies are needed to identify the subtle molecular or microenvironmental differences that contribute to the differing clinical behaviors between TN-IBC and TN-non-IBC.

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Most cited references 25

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Molecular profiling of inflammatory breast cancer: identification of a poor-prognosis gene expression signature.

Rosette Lidereau, Michel Marty, Sengül Tozlu-Kara … (2004)

Inflammatory breast cancer (IBC) is a rare but particularly aggressive form of primary breast cancer. The molecular mechanisms responsible for IBC are largely unknown. To obtain further insight into the molecular pathogenesis of IBC, we used real-time quantitative reverse transcription (RT)-PCR to quantify the mRNA expression of 538 selected genes in IBC relative to non-IBC. Twenty-seven (5.0%) of the 538 genes were significantly up-regulated in IBC compared with non-IBC. None were down-regulated. The 27 up-regulated genes mainly encoded transcription factors (JUN, EGR1, JUNB, FOS, FOSB, MYCN, and SNAIL1), growth factors (VEGF, DTR/HB-EGF, IGFBP7, IL6, ANGPT2, EREG, CCL3/MIP1A, and CCL5/RANTES) and growth factor receptors (TBXA2R, TNFRSF10A/TRAILR1, and ROBO2). We also identified a gene expression profile, based on MYCN, EREG, and SHH, which discriminated subgroups of IBC patients with good, intermediate, and poor outcome. Our study has identified a limited number of signaling pathways that require inappropriate activation for IBC development. Some of the up-regulated genes identified here could offer useful diagnostic or prognostic markers and could form the basis of novel therapeutic strategies.

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Uncovering the molecular secrets of inflammatory breast cancer biology: an integrated analysis of three distinct affymetrix gene expression datasets.

Steven Van Laere, Naoto Ueno, Pascal Finetti … (2013)

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. 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. 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-β signaling in IBC. 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-β signaling, potentially explaining the metastatic potential of IBC tumor cells in an unexpected manner. ©2013 AACR.

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Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.

Catherine Nguyen, Nathalie Cervera, Patrice Viens … (2005)

Breast cancer is a heterogeneous disease. Comprehensive gene expression profiles obtained using DNA microarrays have revealed previously indistinguishable subtypes of noninflammatory breast cancer (NIBC) related to different features of mammary epithelial biology and significantly associated with survival. Inflammatory breast cancer (IBC) is a rare, particular, and aggressive form of disease. Here we have investigated whether the five molecular subtypes described for NIBC (luminal A and B, basal, ERBB2 overexpressing, and normal breast-like) were also present in IBC. We monitored the RNA expression of approximately 8,000 genes in 83 breast tissue samples including 37 IBC, 44 NIBC, and 2 normal breast samples. Hierarchical clustering identified the five subtypes of breast cancer in both NIBC and IBC samples. These subtypes were highly similar to those defined in previous studies and associated with similar histoclinical features. The robustness of this classification was confirmed by the use of both alternative gene set and analysis method, and the results were corroborated at the protein level. Furthermore, we show that the differences in gene expression between NIBC and IBC and between IBC with and without pathologic complete response that we have recently reported persist in each subtype. Our results show that the expression signatures defining molecular subtypes of NIBC are also present in IBC. Obtained using different patient series and different microarray platforms, they reinforce confidence in the expression-based molecular taxonomy but also give evidence for its universality in breast cancer, independently of a specific clinical form.

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Author and article information

Contributors

Hiroko Masuda

Keith A Baggerly

Ying Wang

Takayuki Iwamoto

Takae Brewer

Lajos Pusztai

Kazuharu Kai

Takahiro Kogawa

Pascal Finetti

Daniel Birnbaum

Luc Dirix

Wendy A Woodward

James M Reuben

Savitri Krishnamurthy

W Fraser Symmans

Steven J Van Laere

François Bertucci

Gabriel N Hortobagyi

Naoto T Ueno

Journal

Journal ID (nlm-ta): Breast Cancer Res

Journal ID (iso-abbrev): Breast Cancer Res

Title: Breast Cancer Research : BCR

Publisher: BioMed Central

ISSN (Print): 1465-5411

ISSN (Electronic): 1465-542X

Publication date (Print): 2013

Publication date (Electronic): 25 November 2013

Volume: 15

Issue: 6

Page: R112

Affiliations

[1 ]Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[2 ]Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[3 ]Department of Bioinformatics and Computer Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[4 ]Department of Breast and Endocrine Surgery, Okayama University Hospital, Okayama, Okayama, Japan

[5 ]Institut Paoli-Calmettes, Marseille, France

[6 ]Translational Cancer Research Unit-Antwerp, Oncology Center, General Hospital Sint-Augustinus, Wilrijk, Belgium

[7 ]Laboratory of Gynecological Oncology, Department of Oncology, Catholic University Leuven, Leuven, Belgium

[8 ]Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[9 ]Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[10 ]Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

[11 ]Morgan Welch Inflammatory Breast Cancer Research Program and Clinic, Section of Translational Breast Cancer Research, Department of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, 1515 Holcombe, Unit 1354, Houston, TX 77030, USA

Article

Publisher ID: bcr3579

DOI: 10.1186/bcr3579

PMC ID: 3978878

PubMed ID: 24274653

SO-VID: 4a0d22ec-5ad9-48f5-897f-b890ca811246

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Comparison of molecular subtype distribution in triple-negative inflammatory and non-inflammatory breast cancers

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Abstract

Introduction

Methods

Results

Conclusions

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Karger: Oncology

Most cited references 25

Molecular profiling of inflammatory breast cancer: identification of a poor-prognosis gene expression signature.

Uncovering the molecular secrets of inflammatory breast cancer biology: an integrated analysis of three distinct affymetrix gene expression datasets.

Gene expression profiling identifies molecular subtypes of inflammatory breast cancer.

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