The prognostic value of androgen receptors in breast cancer subtypes

Introduction

We hypothesized that breast tissue not involved by tumor in inflammatory breast cancer (IBC) patients contains intrinsic differences, including increased mammary stem cells and macrophage infiltration, which may promote the IBC phenotype.

Materials and methods

Normal breast parenchyma?≥?5 cm away from primary tumors was obtained from mastectomy specimens. This included an initial cohort of 8 IBC patients and 60 non-IBC patients followed by a validation cohort of 19 IBC patients and 25 non-IBC patients. Samples were immunostained for either CD44⁺CD49f⁺CD133/2⁺ mammary stem cell markers or the CD68 macrophage marker and correlated with IBC status. Quantitation of positive cells was determined using inForm software from PerkinElmer. We also examined the association between IBC status and previously published tumorigenic stem cell and IBC tumor signatures in the validation cohort samples.

Results

8 of 8 IBC samples expressed isolated CD44⁺CD49f⁺CD133/2⁺ stem cell marked cells in the initial cohort as opposed to 0/60 non-IBC samples (p?=?0.001). Similarly, the median number of CD44⁺CD49f⁺CD133/2⁺ cells was significantly higher in the IBC validation cohort as opposed to the non-IBC validation cohort (25.7 vs. 14.2, p?=?0.007). 7 of 8 IBC samples expressed CD68?+?histologically confirmed macrophages in initial cohort as opposed to 12/48 non-IBC samples (p?=?0.001). In the validation cohort, the median number of CD68?+?cells in IBC was 3.7 versus 1.0 in the non-IBC cohort (p?=?0.06). IBC normal tissue was positively associated with a tumorigenic stem cell signature (p?=?0.02) and with a 79-gene IBC signature (p?<?0.001).

Conclusions

Normal tissue from IBC patients is enriched for both mammary stem cells and macrophages and has higher association with both a tumorigenic stem cell signature and IBC-specific tumor signature. Collectively, these data suggest that IBC normal tissue differs from non-IBC tissue. Whether these changes occur before the tumor develops or is induced by tumor warrants further investigation.

     

Gene expression profiling for molecular characterization of inflammatory breast cancer and prediction of response to chemotherapy

Inflammatory breast cancer (IBC) is a rare but aggressive form of breast cancer with a 5-year survival limited to approximately 40%. Diagnosis, based on clinical and/or pathological criteria, may be difficult. Optimal systemic neoadjuvant therapy and accurate predictors of pathological response have yet to be defined for increasing response rate and survival. Using DNA microarrrays containing approximately 8,000 genes, we profiled breast cancer samples from 81 patients, including 37 with IBC and 44 with noninflammatory breast cancer (NIBC). Global unsupervised hierarchical clustering was able to some extent to distinguish IBC and NIBC cases and revealed subclasses of IBC. Supervised analysis identified a 109-gene set the expression of which discriminated IBC from NIBC samples. This molecular signature was validated in an independent series of 26 samples, with an overall performance accuracy of 85%. Discriminator genes were associated with various cellular processes possibly related to the aggressiveness of IBC, including signal transduction, cell motility, adhesion, and angiogenesis. A similar approach, with leave-one-out cross-validation, identified an 85-gene set that divided IBC patients with significantly different pathological complete response rate (70% in one group and 0% in the other group). These results show the potential of gene expression profiling to contribute to a better understanding of IBC, and to provide new diagnostic and predictive factors for IBC, as well as for potential therapeutic targets.     

Distinct Molecular Signature of Inflammatory Breast Cancer by cDNA Microarray Analysis

Summary Inflammatory breast cancer (IBC) is a clinically distinct and aggressive form of locally advanced breast cancer with largely unknown genetic determinants. Overexpression of the RhoC GTPase and of HER2, and decreased ER-expression are involved in IBC. Multimodality treatment has increased survival but prognosis is still poor. Novel molecular targets for improved neoadjuvant treatment are necessary. Using cDNA microarrays, we performed genome-wide expression profiling of pre-treatment tumour samples of 16 patients with IBC and 18 patients with non-stage-matched non-IBC. Rigid clinical diagnostic criteria according to the TNM classification of␣the American Joint Committee on Cancer were adopted. Unsupervised hierarchical clustering accurately distinguished IBC and non-IBC samples. A set of 50 discriminator genes was identified in a learning group of tumour samples and was successful in diagnosing IBC in a validation group of samples (accuracy of 88%). Exclusion of ER-related or HER2-related genes did not alter this discriminatory accuracy, indicating that the expression of other genes in addition to ER and HER2 characterize the IBC phenotype. The molecular signature of IBC revealed the overexpression of a large number of NF-κB target genes, explaining at least part of the aggressive nature of IBC. Successful validation of some of the overexpressed genes by immunohistochemistry or real-time quantitative PCR demonstrated the robustness of the cDNA microarray experiments. The results of our study provide potential targets for the treatment of patients with IBC.     

New Therapeutic Targets in Inflammatory Breast Cancer

Inflammatory breast cancer (IBC) is the most lethal variant of locally advanced breast cancer. Although recognized as a distinct clinical entity, there have been few advances in the development of pre-clinical models of IBC, and a lack of IBC-specific therapeutic targets translated into clinical utility to increase overall survival, which is currently 40?% at three years. By use of newly developed pre-clinical models of IBC and patient tumor tissues, E-cadherin, anaplastic lymphoma kinase (ALK), and HSP90 have been identified as targets relevant to IBC that are matched by therapeutics that are either currently in clinical trials or will be tested in clinical trials within the next year. These exciting results illustrate the advances that have been made in recent years in defining the molecular basis of IBC as a distinct disease and the significant strides made in identifying more effective strategies for treatment of patients with IBC.     

miRNA expression profiling of inflammatory breast cancer identifies a 5‐miRNA signature predictive of breast tumor aggressiveness

IBC (inflammatory breast cancer) is a rare but very aggressive form of breast cancer with a particular phenotype. The molecular mechanisms responsible for IBC remain largely unknown. In particular, genetic and epigenetic alterations specific to IBC remain to be identified. MicroRNAs, a class of small noncoding RNAs able to regulate gene expression, are deregulated in breast cancer and may therefore serve as tools for diagnosis and prediction. This study was designed to determine miRNA expression profiling (microRNAome) in IBC. Quantitative RT‐PCR was used to determine expression levels of 804 miRNAs in a screening series of 12 IBC compared to 31 non‐stage‐matched non‐IBC and 8 normal breast samples. The differentially expressed miRNAs were then validated in a series of 65 IBC and 95 non‐IBC. From a set of 18 miRNAs of interest selected from the screening series, 13 were differentially expressed with statistical significance in the validation series of IBC compared to non‐IBC. Among these, a 5‐miRNA signature comprising miR‐421, miR‐486, miR‐503, miR‐720 and miR‐1303 was shown to be predictive for IBC phenotype with an overall accuracy of 89%. Moreover, multivariate analysis showed that this signature was an independent predictor of poor Metastasis‐Free Survival in non‐IBC patients.     

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

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.     

A gene expression signature that predicts the therapeutic response of the basal-like breast cancer to neoadjuvant chemotherapy

Several gene expression profiles have been reported to predict breast cancer response to neoadjuvant chemotherapy. These studies often consider breast cancer as a homogeneous entity, although higher rates of pathologic complete response (pCR) are known to occur within the basal-like subclass. We postulated that profiles with higher predictive accuracy could be derived from a subset analysis of basal-like tumors in isolation. Using a previously described “intrinsic” signature to differentiate breast tumor subclasses, we identified 50 basal-like tumors from two independent clinical trials associated with gene expression profile data. 24 tumor data sets were derived from a 119-patient neoadjuvant trial at our institution and an additional 26 tumor data sets were identified from a published data set (Hess et al. J Clin Oncol 24:4236–4244, 2006). The combined 50 basal-like tumors were partitioned to form a 37 sample training set with 13 sequestered for validation. Clinical surveillance occurred for a mean of 26 months. We identified a 23-gene profile which predicted pCR in basal-like breast cancers with 92% predictive accuracy in the sequestered validation data set. Furthermore, distinct cluster of patients with high rates of cancer recurrence was observed based on cluster analysis with the 23-gene signature. Disease-free survival analysis of these three clusters revealed significantly reduced survival in the patients of this high recurrence cluster. We identified a 23-gene signature which predicts response of basal-like breast cancer to neoadjuvant chemotherapy as well as disease-free survival. This signature is independent of tissue collection method and chemotherapeutic regimen.     

Different gene expressions are associated with the different molecular subtypes of inflammatory breast cancer

The goal of this study was to determine whether gene expression differences exist between inflammatory breast cancers (IBC) and T stage-matched non-IBC patients stratified by hormone receptor and HER2 status. We used Affymetrix GeneChips to analyze 82 tumor samples (25 T4d patients, and 57 T4a-c patients) of newly diagnosed breast cancers. Genes that were differentially expressed between the IBC and non-IBC specimens were identified using the t test, and differential expression of gene sets was assessed using gene set analysis. Three distinct clinical subtypes of IBC and non-IBC were compared: ER-positive/HER2-normal, HER2-amplified, and ER-negative/HER2-normal. When we compared expression data from all IBC with all non-IBC, we found no significant differences after adjusting for multiple testing. When IBC and non-IBC tumors were compared by clinical subtype, however, significant differences emerged. Complement and immune system-related pathways were overexpressed in ER-positive/HER2-normal IBC. Protein translation and mTOR signaling were overexpressed in HER2-amplified IBC. Apoptosis-, neural-, and lipid metabolism-related pathways were overexpressed in ER-negative/HER2-normal IBC compared with non-IBC of the same receptor phenotype. In this T stage-matched case-control study, the survival curves of patients with IBC and non-IBC were similar for all three clinical subtypes. IBC tumors can be divided into molecular and clinical subtypes similar to those of non-IBC. Clinical subtypes of IBC show molecular differences compared with similar subtypes of non-IBC.     

Distinct molecular phenotype of inflammatory breast cancer compared to non-inflammatory breast cancer using Affymetrix-based genome-wide gene-expression analysis

The present study aims at a platform-independent confirmation of previously obtained cDNA microarray results on inflammatory breast cancer (IBC) using Affymetrix chips. Gene-expression data of 19 IBC and 40 non-IBC specimens were subjected to clustering and principal component analysis. The performance of a previously identified IBC signature was tested using clustering and gene set enrichment analysis. The presence of different cell-of-origin subtypes in IBC was investigated and confirmed using immunohistochemistry on a TMA. Differential gene expression was analysed using SAM and topGO was used to identify the fingerprints of a pro-metastatic-signalling pathway. IBC and non-IBC have distinct gene-expression profiles. The differences in gene expression between IBC and non-IBC are captured within an IBC signature, identified in a platform-independent manner. Part of the gene-expression differences between IBC and non-IBC are attributable to the differential presence of the cell-of-origin subtypes, since IBC primarily segregated into the basal-like or ErbB2-overexpressing group. Strikingly, IBC tumour samples more closely resemble the gene-expression profile of T1/T2 tumours than the gene-expression profile or T3/T4 tumours. We identified the insulin-like growth factor-signalling pathway, potentially contributing to the biology of IBC. Our previous results have been validated in a platform-independent manner. The distinct biological behaviour of IBC is reflected in a distinct gene-expression profile. The fact that IBC tumours are quickly arising tumours might explain the close resemblance of the IBC gene-expression profile to the expression profile of T1/T2 tumours.     

Gene expression profiling identifies molecular subtypes of inflammatory breast cancer

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.     

Identification of gene signatures for invasive colorectal tumor cells

BACKGROUND: Gene signatures of sporadic colorectal carcinoma tissues and microdissected colorectal tumor cells were analyzed to identify stromal and tumor cell-specific markers, respectively. METHODS: Serial sections of frozen colorectal tumors (n=29) were subjected to RNA isolation of (1) entire tissue sections with a various tumor cell content and of (2) microdissected invasive tumor cells. Three matching samples of microdissected normal colorectal epithelial and invasive tumor cells were similarly obtained. RNA samples were analyzed using the HG95A and HG95Av2 GeneChip microarrays (Affymetrix). The microarray data was evaluated by established methods and validated by Q-RT-PCR. RESULTS: Unsupervised hierarchical cluster analysis of 18 sample pairs (training set) clearly distinguished tumors from microdissected tumor cells. A 149-gene signature was identified using statistical methods, which was then validated by a hierarchical clustering analysis of 11 independent sample pairs (test set). Genes specifically associated with microdissected invasive tumor cells were for example CKS2 and NME1. In contrast, genes associated with stromal cells were for example MMP2, SDF1 and FBLN2. Finally, a 65-gene signature distinguished normal colorectal epithelial cells and invasive tumor cells, including down-regulation of BMP2 and ANPEP mRNA expression as well as up-regulation of TKT, SPARC, MCM5 mRNA expression. CONCLUSIONS: Our approach allowed precise evaluation of molecular signatures in morphologically defined cell populations and identified novel target genes related to stroma-tumor interactions in colorectal cancer. The approach enables further analysis of gene signatures in different tumor areas and cell types, such as within invasive margins to decipher molecular mechanisms of colorectal cancer invasion and metastasis.     

Identifying molecular features that distinguish fluvastatin-sensitive breast tumor cells

Statins, routinely used to treat hypercholesterolemia, selectively induce apoptosis in some tumor cells by inhibiting the mevalonate pathway. Recent clinical studies suggest that a subset of breast tumors is particularly susceptible to lipophilic statins, such as fluvastatin. To quickly advance statins as effective anticancer agents for breast cancer treatment, it is critical to identify the molecular features defining this sensitive subset. We have therefore characterized fluvastatin sensitivity by MTT assay in a panel of 19 breast cell lines that reflect the molecular diversity of breast cancer, and have evaluated the association of sensitivity with several clinicopathological and molecular features. A wide range of fluvastatin sensitivity was observed across breast tumor cell lines, with fluvastatin triggering cell death in a subset of sensitive cell lines. Fluvastatin sensitivity was associated with an estrogen receptor alpha (ERα)-negative, basal-like tumor subtype, features that can be scored with routine and/or strong preclinical diagnostics. To ascertain additional candidate sensitivity-associated molecular features, we mined publicly available gene expression datasets, identifying genes encoding regulators of mevalonate production, non-sterol lipid homeostasis, and global cellular metabolism, including the oncogene MYC. Further exploration of this data allowed us to generate a 10-gene mRNA abundance signature predictive of fluvastatin sensitivity, which showed preliminary validation in an independent set of breast tumor cell lines. Here, we have therefore identified several candidate predictors of sensitivity to fluvastatin treatment in breast cancer, which warrant further preclinical and clinical evaluation.     

A stromal gene signature associated with inflammatory breast cancer

The factors that determine whether a breast carcinoma will develop into inflammatory breast cancer (IBC) remain poorly understood. Recent evidence indicates that the tumor stroma influences cancer phenotypes. We tested the hypotheses that the gene expression signature of the tumor stroma is a distinctive feature of IBC. We used laser capture microdissection to obtain enriched populations of tumor epithelial cells and adjacent stromal cells from 15 patients with IBC and 35 patients with invasive, noninflammatory breast cancer (non-IBC). Their mRNA expression profiles were assessed using Affymetrix GeneChips. In addition, a previously established classifier for IBC was evaluated for the resulting data sets. The gene expression profile of the tumor stroma distinguished IBC from non-IBC, and a previously established IBC prediction signature performed better in classifying IBC using the gene expression profile of the tumor stroma than it did using the profile of the tumor epithelium. In a pathway analysis, the genes differentially expressed between IBC and non-IBC tumors clustered in distinct pathways. We identified multiple pathways related to the endoplasmic stress response that could be functionally significant in IBC. Our findings suggest that the gene expression in the tumor stroma may play a role in determining the IBC phenotype.     

Identification of cell-of-origin breast tumor subtypes in inflammatory breast cancer by gene expression profiling

Inflammatory breast cancer (IBC) is an aggressive form of locally advanced breast cancer with high metastatic potential. Most patients have lymph node involvement at the time of diagnosis and 1/3 of the patients have distant metastases. In a previous study, we demonstrated that IBC is a distinct form of breast cancer in comparison with non-IBC. The aim of this study was to investigate the presence of the different molecular subtypes in our data set of 16 IBC and 18 non-IBC specimen. Therefore, we selected an ‘intrinsic gene set’ of 144 genes, present on our cDNA chips and common to the ‘intrinsic gene set’ described by Sorlie et al. [PNAS, 2003]. This set of genes was tested for performance in the Norway/Stanford data set by unsupervised hierarchical clustering. Expression centroids were then calculated for the core members of each of the five subclasses in the Norway/Stanford data set and used to classify our own specimens by calculating Spearman correlations between each sample and each centroid. We identified the same cell-of-origin subtypes in IBC as those already described in non-IBC. The classification was in good agreement with immunohistochemical data for estrogen receptor protein expression and cytokeratin 5/6 protein expression. Confirmation was done by an alternative unsupervised hierarchical clustering method. The robustness of this classification was assessed by an unsupervised hierarchical clustering with an alternative gene set of 141 genes related to the cell-of-origin subtypes, selected using a discriminating score and iterative random permutation testing. The contribution of the different cell-of-origin subtypes to the IBC phenotype was investigated by principal component analysis. Generally, the combined ErbB2-overexpressing and basal-like cluster was more expressed in IBC compared to non-IBC, whereas the combined luminal A, luminal B and normal-like cluster was more pronounced in non-IBC compared to IBC. The presence of the same molecular cell-of-origin subtypes in IBC as in non-IBC does not exclude the specific molecular nature of IBC, since gene lists that characterize IBC and non-IBC are entirely different from gene lists that define the different cell-of-origin subtypes, as evidenced by principal component analysis.     

RhoC GTPase, a novel transforming oncogene for human mammary epithelial cells that partially recapitulates the inflammatory breast cancer phenotype

Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer and is phenotypically distinct from other forms of locally advanced breast cancer. In a previous study, we identified specific genetic alterations of IBC that could account for a highly invasive phenotype. RhoC GTPase was overexpressed in 90% of IBC archival tumor samples, but not in stage-matched, non-IBC tumors. To study the role of RhoC GTPase in contributing to an IBC-like phenotype, we generated stable transfectants of human mammary epithelial cells overexpressing the RhoC gene. The HME-RhoC transfectants formed large colonies under anchorage-independent growth conditions, were more motile, and were invasive. In conjunction with an increase in motility, overexpression of RhoC led to an increase in actin stress fiber and focal adhesion contact formation. Furthermore, orthotopic injection into immunocompromised mice led to tumor formation. Taken together, these data indicate that RhoC GTPase is a transforming oncogene in human mammary epithelial cells and can lead to a highly invasive phenotype, akin to that seen in IBC.     

Disulfiram (DSF) acts as a copper ionophore to induce copper‐dependent oxidative stress and mediate anti‐tumor efficacy in inflammatory breast cancer

Cancer cells often have increased levels of reactive oxygen species (ROS); however, acquisition of redox adaptive mechanisms allows for evasion of ROS‐mediated death. Inflammatory breast cancer (IBC) is a distinct, advanced BC subtype characterized by high rates of residual disease and recurrence despite advances in multimodality treatment. Using a cellular model of IBC, we identified an oxidative stress response (OSR) signature in surviving IBC cells after administration of an acute dose of an ROS inducer. Metagene analysis of patient samples revealed significantly higher OSR scores in IBC tumor samples compared to normal or non‐IBC tissues, which may contribute to the poor response of IBC tumors to common treatment strategies, which often rely heavily on ROS induction. To combat this adaptation, we utilized a potent redox modulator, the FDA‐approved small molecule Disulfiram (DSF), alone and in combination with copper. DSF forms a complex with copper (DSF‐Cu) increasing intracellular copper concentration both in vitro and in vivo, bypassing the need for membrane transporters. DSF‐Cu antagonized NFκB signaling, aldehyde dehydrogenase activity and antioxidant levels, inducing oxidative stress‐mediated apoptosis in multiple IBC cellular models. In vivo, DSF‐Cu significantly inhibited tumor growth without significant toxicity, causing apoptosis only in tumor cells. These results indicate that IBC tumors are highly redox adapted, which may render them resistant to ROS‐inducing therapies. DSF, through redox modulation, may be a useful approach to enhance chemo‐ and/or radio‐sensitivity for advanced BC subtypes where therapeutic resistance is an impediment to durable responses to current standard of care.     

Colon Cancer Tumor Location Defined by Gene Expression May Disagree With Anatomic Tumor Location

BackgroundCancers of the right colon have been shown to differ from left-side colon cancers in prognosis, response to epithelial growth factor receptor inhibitors, microsatellite instability and BRAF mutation status, and other molecular characteristics. Clinical application of these differences will benefit from a deeper understanding of how tumor location defines and is defined by gene expression.Materials and MethodsThis study was carried out using Affymetrix microarray datasets (Cohort A: training set, n = 352; validation set, n = 519) and samples from The Cancer Genome Atlas Colon Adenocarcinoma database (Cohort B: n = 408), in which tumor location was reported. Gene expression patterns characteristic of tumor side were identified in a manner unbiased by statistical classification method.ResultsIn the Cohort A validation set, the anatomic locations of 75% of tumors agree with the locations predicted by gene expression (so-called genomic location), whereas 8% of tumors had genomic locations discordant with their anatomic locations, and 17% of tumors had ambiguous genomic locations. Genomic location was a better predictor of microsatellite instability, CpG island methylator phenotype status, and BRAF mutation status than anatomic location. Tumors with ambiguous genomic location were significantly (P = 1.3 × 10⁻⁷) more likely to have the mesenchymal consensus molecular subtype (40%) than those with a specific genomic location (18%). A genomic signature to predict genomic location was defined.ConclusionTumor location is increasingly considered in deciding treatment of a colon tumor. We showed that genomic location was superior to anatomic location as a predictor of molecular characteristics, suggesting that it may be a more accurate predictor of response.     

Lipocalin 2 promotes inflammatory breast cancer tumorigenesis and skin invasion

Inflammatory breast cancer (IBC) is an aggressive form of primary breast cancer characterized by rapid onset and high risk of metastasis and poor clinical outcomes. The biological basis for the aggressiveness of IBC is still not well understood and no IBC‐specific targeted therapies exist. In this study, we report that lipocalin 2 (LCN2), a small secreted glycoprotein belonging to the lipocalin superfamily, is expressed at significantly higher levels in IBC vs non‐IBC tumors, independently of molecular subtype. LCN2 levels were also significantly higher in IBC cell lines and in their culture media than in non‐IBC cell lines. High expression was associated with poor‐prognosis features and shorter overall survival in IBC patients. Depletion of LCN2 in IBC cell lines reduced colony formation, migration, and cancer stem cell populations in vitro and inhibited tumor growth, skin invasion, and brain metastasis in mouse models of IBC. Analysis of our proteomics data showed reduced expression of proteins involved in cell cycle and DNA repair in LCN2‐silenced IBC cells. Our findings support that LCN2 promotes IBC tumor aggressiveness and offer a new potential therapeutic target for IBC.     

Pathologic aspects of inflammatory breast carcinoma: part 1. Histomorphology and differential diagnosis

Inflammatory breast carcinoma (IBC) is rare, but it is the most aggressive variant of breast cancer. Because a diagnosis of IBC is usually based on the presence of typical clinical symptoms that are not always associated with certain pathologic characteristics, the diagnosis often presents a great challenge even to the most experienced clinicians. Even more, IBC has not been associated with a specific histologic tumor type. With recent epidemiologic and molecular evidence now suggesting that IBC is a distinct disease entity rather than a subtype of locally advanced breast cancer, the accurate differentiation of this disease has never been more crucial to ensuring appropriate treatment. The focus of this chapter is on the histomorphologic characteristics of IBC and its broad differential diagnosis.