High-resolution comparative genomic hybridization of inflammatory breast cancer and identification of candidate genes

Decoding the pathological and genomic profile of epithelial ovarian cancer

Ovarian cancer (OC) is one of the most common cancers in women, with a high mortality rate. Most of published studies have been focused on Caucasian populations, with the need to explore biological features and clinical outcomes of patients from other ethnicities. We described clinical outcome (progression-free survival and overall survival) and biomarkers associated with survival in a cohort of patients with OC from Tunisia. Using immunohistochemistry, we assessed the expression of 14 proteins known to be altered in OC in a cohort of 198 patients. We explored the correlation between protein expression and copy number alteration (CNA) profiles. FIGO stage, menopausal status and mismatch repair deficiency were associated with survival. ERBB2 amplification was correlated with high ERBB2 expression (OR = 69.32, p = 4.03 E-09), and high PDL1 expression was associated to CD274 amplification (OR = 4.97, p = 5.79 E-2). We identified a correlation between survival and exposure to two CNA signatures (MAPK pathway and BRCA-related homologous recombination deficiency). Moreover, Gama-H2AX protein expression was correlated with exposure to a genomic signature associated with homologous recombination deficiency. We observed that OC clinical and pathological characteristics of these patients from Tunisia were similar to those of Caucasian patients. We identified frequent CNA in this population that need to be confirmed in other sets from Africa.

TGIRT-seq of Inflammatory Breast Cancer Tumor and Blood Samples Reveals Widespread Enhanced Transcription Impacting RNA Splicing and Intronic RNAs in Plasma

Inflammatory breast cancer (IBC) is the most aggressive and lethal breast cancer subtype but lacks unequivocal genomic differences or robust biomarkers that differentiate it from non-IBC. Here, Thermostable Group II intron Reverse Transcriptase RNA-sequencing (TGIRT-seq) revealed myriad differences in tumor samples, Peripheral Blood Mononuclear Cells (PBMCs), and plasma that distinguished IBC from non-IBC patients and healthy donors across all tested receptor-based subtypes. These included numerous differentially expressed protein-coding gene and non-coding RNAs in all three sample types, a granulocytic immune response in IBC PBMCs, and over-expression of antisense RNAs, suggesting wide-spread enhanced transcription in both IBC tumors and PBMCs. By using TGIRT-seq to quantitate Intron-exon Depth Ratios (IDRs) and mapping reads to both genome and transcriptome reference sequences, we developed methods for parallel analysis of transcriptional and post-transcriptional gene regulation. This analysis identified numerous differentially and non-differentially expressed protein-coding genes in IBC tumors and PBMCs with high IDRs, the latter reflecting rate-limiting RNA splicing that negatively impacts mRNA production. Mirroring gene expression differences in tumors and PBMCs, over-represented protein-coding gene RNAs in IBC patient plasma were largely intronic RNAs, while those in non-IBC patients and healthy donor plasma were largely mRNA fragments. Potential IBC biomarkers in plasma included T-cell receptor pre-mRNAs and intronic, LINE-1, and antisense RNAs. Our findings provide new insights into IBC and set the stage for monitoring disease progression and response to treatment by liquid biopsy. The methods developed for parallel transcriptional and post-transcriptional gene regulation analysis have potentially broad RNA-seq and clinical applications.

Mutational landscape of inflammatory breast cancer

BACKGROUND

Inflammatory breast cancer (IBC) is the most pro-metastatic form of BC. Better understanding of its enigmatic pathophysiology is crucial. We report here the largest whole-exome sequencing (WES) study of clinical IBC samples.

METHODS

We retrospectively applied WES to 54 untreated IBC primary tumor samples and matched normal DNA. The comparator samples were 102 stage-matched non-IBC samples from TCGA. We compared the somatic mutational profiles, spectra and signatures, copy number alterations (CNAs), HRD and heterogeneity scores, and frequencies of actionable genomic alterations (AGAs) between IBCs and non-IBCs. The comparisons were adjusted for the molecular subtypes.

RESULTS

The number of somatic mutations, TMB, and mutational spectra were not different between IBCs and non-IBCs, and no gene was differentially mutated or showed differential frequency of CNAs. Among the COSMIC signatures, only the age-related signature was more frequent in non-IBCs than in IBCs. We also identified in IBCs two new mutational signatures not associated with any environmental exposure, one of them having been previously related to HIF pathway activation. Overall, the HRD score was not different between both groups, but was higher in TN IBCs than TN non-IBCs. IBCs were less frequently classified as heterogeneous according to heterogeneity H-index than non-IBCs (21% vs 33%), and clonal mutations were more frequent and subclonal mutations less frequent in IBCs. More than 50% of patients with IBC harbored at least one high-level of evidence (LOE) AGA (OncoKB LOE 1-2, ESCAT LOE I-II), similarly to patients with non-IBC.

CONCLUSIONS

We provide the largest mutational landscape of IBC. Only a few subtle differences were identified with non-IBCs. The most clinically relevant one was the higher HRD score in TN IBCs than in TN non-IBCs, whereas the most intriguing one was the smaller intratumor heterogeneity of IBCs.

Deciphering the molecular biology of inflammatory breast cancer through molecular characterization of patient samples and preclinical models

Inflammatory breast cancer is an aggressive subtype of breast cancer with dismal patient prognosis and a unique clinical presentation. In the past two decades, molecular profiling technologies have been used in order to gain insight into the molecular biology of IBC and to search for possible targets for treatment. Although a gene signature that accurately discriminates between IBC and nIBC patient samples and preclinical models was identified, the overall genomic and transcriptomic differences are small and ambiguous, mainly due to the limited sample sizes of the evaluated patient series and the failure to correct for confounding effects of the molecular subtypes. Nevertheless, data collected over the past 20 years by independent research groups increasingly support the existence of several IBC-specific biological characteristics. In this review, these features are classified as established, emerging and conceptual hallmarks based on the level of evidence reported in the literature. In addition, a synoptic model is proposed that integrates all hallmarks and that can explain how cancer cell intrinsic mechanisms (i.e. NF-κB activation, genomic instability, MYC-addiction, TGF-β resistance, adaptive stress response, chromatin remodeling, epithelial-to-mesenchymal transition) can contribute to the establishment of the dynamic immune microenvironment associated with IBC. It stands to reason that future research projects are needed to further refine (parts of) this model and to investigate its clinical translatability.

Inflammatory breast cancer: Epidemiologic data and therapeutic results

Since the early description more than a century ago, inflammatory breast cancer (IBC) remains an aggressive disease, with a different geographic repartition, with the highest ones incidence reported in the North of Africa (Tunisia, Algeria, Morocco, and Egypt), and the lowest incidence in Western countries (USA, Europe…). In this study, we reviewed the literature using the Surveillance, Epidemiology, and End Results (SEER) database compared to other published series. We observed that in the high incidence areas (North of Africa) when compared to "classical" breast cancer, IBC was associated to younger age (less than 50 years) with rapid evolution of signs and symptoms (in less than 3 up to 6 months), and more aggressive clinical and histopathological-molecular parameters, due to the predominance of triple-negative and HER2+ subtypes in around 60% of cases. An epidemiologic trend was observed in both high and low incidence areas since the eighties are towards reduction of IBC prevalence. Concerning Tunisia, in comparison with the historical series of the 1980s, the incidence decreased in part by applying more stringent diagnostic criteria but also probably due to a slight improvement of the socio-economic level (SEL). This trend was also observed in the US, due to the efforts of collaborative IBC groups from MD Anderson Cancer Center (MDACC), Duke and IBC patient advocacy groups. Therapeutic results are slightly better due to the standardization of a multidisciplinary approach and the use of combined primary chemotherapy and/or targeted therapies (especially in HER2 positive patients), followed by mastectomy plus radiotherapy. The 5-year overall and disease-free survival is at more than 60%, related to an IBC mortality decrease observed in the cohorts of patients treated in the last decade.

Obesity-Associated Breast Cancer: Analysis of Risk Factors and Current Clinical Evaluation

Several studies show that a significantly stronger association is obvious between increased body mass index (BMI) and higher breast cancer incidence. Additionally, obese and postmenopausal women are at higher risk of all-cause and breast cancer-specific mortality compared with non-obese women with breast cancer. In this context, increased levels of estrogens, excessive aromatization activity of the adipose tissue, overexpression of pro-inflammatory cytokines, insulin resistance, adipocyte-derived adipokines, hypercholesterolemia, and excessive oxidative stress contribute to the development of breast cancer in obese women. Genetic evaluation is an integral part of diagnosis and treatment for patients with breast cancer. Despite trimodality therapy, the four-year cumulative incidence of regional recurrence is significantly higher. Axillary lymph nodes as well as primary lesions have diagnostic, prognostic, and therapeutic significance for the management of breast cancer. In clinical setting, because of the obese population primary lesions and enlarged lymph nodes could be less palpable, the diagnosis may be challenging due to misinterpretation of physical findings. Thereby, a nomogram has been created as the "Breast Imaging Reporting and Data System" (BI-RADS) to increase agreement and decision-making consistency between mammography and ultrasonography (USG) experts. Additionally, the "breast density classification system," "artificial intelligence risk scores," ligand-targeted receptor probes," "digital breast tomosynthesis," "diffusion-weighted imaging," "18F-fluoro-2-deoxy-D-glucose positron emission tomography," and "dynamic contrast-enhanced magnetic resonance imaging (MRI)" are important techniques for the earlier detection of breast cancers and to reduce false-positive results. A high concordance between estrogen receptor (ER) and progesterone receptor (PR) status evaluated in preoperative percutaneous core needle biopsy and surgical specimens is demonstrated. Breast cancer surgery has become increasingly conservative; however, mastectomy may be combined with any axillary procedures, such as sentinel lymph node biopsy (SLNB) and/or axillary lymph node dissection whenever is required. As a rule, SLNB-guided axillary dissection in breast cancer patients who have clinically axillary lymph node-positive to node-negative conversion following neoadjuvant chemotherapy is recommended, because lymphedema is the most debilitating complication after any axillary surgery. There is no clear consensus on the optimal treatment of occult breast cancer, which is much discussed today. Similarly, the current trend in metastatic breast cancer is that the main palliative treatment option is systemic therapy.

Copy Number Variation in Inflammatory Breast Cancer

Identification of a unique genomic biomarker in de novo inflammatory breast cancer (IBC) may provide an insight into the biology of this aggressive disease. The goal of our study was to elucidate biomarkers associated with IBC. We examined breast biopsies collected from Dana–Farber Cancer Institute patients with IBC prior to initiating preoperative systemic treatment (30 samples were examined, of which 14 were eligible). Patients without available biopsies (n = 1), with insufficient tumor epithelial cells (n = 10), or insufficient DNA yield (n = 5) were excluded from the analysis. Molecular subtype and tumor grade were abstracted from a medical records’ review. Ten IBC tumors were estrogen-receptor-positive (ER+) and human epidermal growth factor receptor 2 (HER2)-negative (n = 10 out of 14). Sufficient RNA and DNA were simultaneously extracted from 14 biopsy specimens using the Qiagen AllPrep Kit. RNA was amplified using the Sensation kit and profiled using the Affymetrix Human Transcriptome Array 2.0. DNA was profiled for genome-wide copy number variation (CNV) using the Affymetrix OncoScan Array and analyzed using the Nexus Chromosome Analysis Suite. Among the 14 eligible samples, we first confirmed biological concordance and quality control metrics using replicates and gene expression data. Second, we examined CNVs and gene expression change by IBC subtype. We identified significant CNVs in IBC patients after adjusting for multiple comparisons. Next, to assess whether the CNVs were unique to IBC, we compared the IBC CNV data to fresh-frozen non-IBC CNV data from The Cancer Genome Atlas (n = 388). On chromosome 7p11.2, we identified significant CN gain located at position 58,019,983-58,025,423 in 8 ER+ IBC samples compared to 338 non-IBC ER+ samples (region length: 5440 bp gain and 69,039 bp, False Discovery Rate (FDR) p-value = 3.12 × 10−10) and at position 57,950,944–58,025,423 in 3 TN-IBC samples compared to 50 non-IBC TN samples (74,479 base pair, gain, FDR p-value = 4.27 × 10−5; near the EGFR gene). We also observed significant CN loss on chromosome 21, located at position 9,648,315–9,764,385 (p-value = 4.27 × 10−5). Secondarily, differential gene expression in IBC patients with 7p11.2 CN gain compared to SUM149 were explored after FDR correction for multiple testing (p-value = 0.0016), but the results should be interpreted with caution due to the small sample size. Finally, the data presented are hypothesis-generating. Validation of CNVs that contribute to the unique presentation and biological features associated with IBC in larger datasets may lead to the optimization of treatment strategies.

CASP9 As a Prognostic Biomarker and Promising Drug Target Plays a Pivotal Role in Inflammatory Breast Cancer

Background

Inflammatory breast cancer (IBC) is one of the most rare and aggressive subtypes of primary breast cancer (BC). Our study aimed to explore hub genes related to the pathogenesis of IBC, which could be considered as novel molecular biomarkers for IBC diagnosis and prognosis. Material and Methods. Two datasets from gene expression omnibus database (GEO) were selected. Enrichment analysis and protein-protein interaction (PPI) network for the DEGs were performed. We analyzed the prognostic values of hub genes in the Kaplan-Meier Plotter. Connectivity Map (CMap) and Comparative Toxicogenomics Database (CTD) was used to find candidate small molecules capable to reverse the gene status of IBC.

Results

157 DEGs were selected in total. We constructed the PPI network with 154 nodes interconnected by 128 interactions. The KEGG pathway analysis indicated that the DEGs were enriched in apoptosis, pathways in cancer and insulin signaling pathway. PTEN, PSMF1, PSMC6, AURKB, FZR1, CASP9, CASP6, CASP8, BAD, AKR7A2, ZNF24, SSX2IP, SIGLEC1, MS4A4A, and VSIG4 were selected as hub genes based on the high degree of connectivity. Six hub genes (PSMC6, AURKB, CASP9, BAD, ZNF24, and SSX2IP) that were significantly associated with the prognosis of breast cancer. The expression of CASP9 protein was associated with prognosis and immune cells infiltration of breast cancer. CASP9- naringenin (NGE) is expected to be the most promising candidate gene-compound interaction for the treatment of IBC.

Conclusion

Taken together, CASP9 can be used as a prognostic biomarker and a novel therapeutic target in IBC.

Comparative transcriptional analyses of preclinical models and patient samples reveal MYC and RELA driven expression patterns that define the molecular landscape of IBC

Inflammatory breast cancer (IBC) is an aggressive disease for which the spectrum of preclinical models was rather limited in the past. More recently, novel cell lines and xenografts have been developed. This study evaluates the transcriptome of an extended series of IBC preclinical models and performed a comparative analysis with patient samples to determine the extent to which the current models recapitulate the molecular characteristics of IBC observed clinically. We demonstrate that the IBC preclinical models are exclusively estrogen receptor (ER)-negative and of the basal-like subtype, which reflects to some extent the predominance of these subtypes in patient samples. The IBC-specific 79-signature we previously reported was retrained and discriminated between IBC and non-IBC preclinical models, but with a relatively high rate of false positive predictions. Further analyses of gene expression profiles revealed important roles for cell proliferation, MYC transcriptional activity, and TNFɑ/NFκB in the biology of IBC. Patterns of MYC expression and transcriptional activity were further explored in patient samples, which revealed interactions with ESR1 expression that are contrasting in IBC and nIBC and notable given the comparatively poor outcomes of ER+ IBC. Our analyses also suggest important roles for NMYC, MXD3, MAX, and MLX in shaping MYC signaling in IBC. Overall, we demonstrate that the IBC preclinical models can be used to unravel cancer cell intrinsic molecular features, and thus constitute valuable research tools. Nevertheless, the current lack of ER-positive IBC models remains a major hurdle, particularly since interactions with the ER pathway appear to be relevant for IBC.

Inflammatory Breast Cancer: Clinical Implications of Genomic Alterations and Mutational Profiling

Inflammatory breast cancer (IBC) is a rare and aggressive type of breast cancer whose molecular basis is poorly understood. We performed a comprehensive molecular analysis of 24 IBC biopsies naïve of treatment, using a high-resolution microarray platform and targeted next-generation sequencing (105 cancer-related genes). The genes more frequently affected by gains were MYC (75%) and MDM4 (71%), while frequent losses encompassed TP53 (71%) and RB1 (58%). Increased MYC and MDM4 protein expression levels were detected in 18 cases. These genes have been related to IBC aggressiveness, and MDM4 is a potential therapeutic target in IBC. Functional enrichment analysis revealed genes associated with inflammatory regulation and immune response. High homologous recombination (HR) deficiency scores were detected in triple-negative and metastatic IBC cases. A high telomeric allelic imbalance score was found in patients having worse overall survival (OS). The mutational profiling was compared with non-IBC (TCGA, n = 250) and IBC (n = 118) from four datasets, validating our findings. Higher frequency of TP53 and BRCA2 variants were detected compared to non-IBC, while PIKC3A showed similar frequency. Variants in mismatch repair and HR genes were associated with worse OS. Our study provided a framework for improved diagnosis and therapeutic alternatives for this aggressive tumor type.

Prognostic Impact of Stromal Immune Infiltration before and after Neoadjuvant Chemotherapy (NAC) in Triple Negative Inflammatory Breast Cancers (TNIBC) Treated with Dose-Dense Dose-Intense NAC

Inflammatory breast cancers are very aggressive, and among them, triple negative breast cancer (TNBC) has the worst prognosis. While many studies have investigated the association between tumor-infiltrating lymphocytes (TIL) before neoadjuvant chemotherapy (NAC) and outcome in TNBC, the impact of post-NAC TIL and TIL variation in triple negative inflammatory breast cancer (TNIBC) outcome is unknown. Between January 2010 to December 2018, all patients with TNIBC seen at the breast disease unit (Saint-Louis Hospital) were treated with dose-dense dose-intense NAC. The main objective of the study was to determine factors associated with event-free survival (EFS), particularly pathological complete response (pCR), pre- and post-NAC TIL, delta TIL and post-NAC lymphovascular invasion (LVI). After univariate analysis, post-NAC LVI (HR 2.06; CI 1.13-3.74; p = 0.02), high post-NAC TIL (HR 1.81; CI 1.07-3.06; p = 0.03) and positive delta TIL (HR 2.20; CI 1.36-3.52; p = 0.001) were significantly associated with impaired EFS. After multivariate analysis, only a positive TIL variation remained negatively associated with EFS (HR 1.88; CI 1.05-3.35; p = 0.01). TNIBC patients treated with intensive NAC who present TIL enrichment after NAC have a high risk of relapse, which could be used as a prognostic marker in TNIBC and could help to choose adjuvant post-NAC treatment.

Hemoglobin overexpression and splice signature as new features of inflammatory breast cancer?

Introduction

Inflammatory Breast Cancer (IBC) is the most aggressive form of breast carcinoma characterized by rapid onset of inflammatory signs and its molecular fingerprint has not yet been elucidated.

Objectives

The objective of this study was to detect both gene expression levels and alternate RNA splice variants specific for IBC.

Methods

W e performed splice-sensitive array profiling using Affymetrix Exon Array and quantitative RT-PCR analyses in 177 IBC compared to 183 non-IBC. We also assessed the prognostic value of the identified candidate genes and splice variants.

Results

A 5-splice signature (HSPA8, RPL10, RPL4, DIDO1 and EVL) was able to distinguish IBC from non-IBC tumors (p<10^-7). This splice signature was associated with poor metastasis-free survival in hormone receptor-negative non-IBC (p=0.02), but had no prognostic value in IBC. PAM analysis of dysregulated genes in IBC compared to non-IBC identified a 10-gene signature highly predictive of IBC phenotype and conferring a poor prognosis in non-IBC. The genes most commonly upregulated in IBC were 3 hemoglobin genes able to reliably discriminate IBC from non-IBC (p<10^-4). Hb protein expression in epithelial breast tumor cells was confirmed by immunohistochemistry.

Conclusion

IBC has a specific spliced transcript profile and is characterized by hemoglobin gene overexpression that should be investigated in further functional studies.

Screening and Identification of Key Biomarkers in Inflammatory Breast Cancer Through Integrated Bioinformatic Analyses

Background: Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognoses, moreover its pathogenesis is not entirely clear. The aim of this study was to identify key genes of IBC, which might serve as diagnostic biomarkers and/or therapeutic targets. Methods: Two microarray datasets, GSE23720 and GSE45581, were obtained from the Gene Expression Omnibus database, and a differential expression analysis was performed. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential biological functions of the differentially expressed genes (DEGs). Next, a protein-protein interaction (PPI) network was constructed and visualized by Cytoscape. Functional modules and hub genes were screened using MCODE and cytohubba plug-ins, and the Cancer Genome Atlas survival analysis along with quantitative reverse transcriptional polymerase chain reactions of clinical samples was used to validate the effect that the hub genes have on IBC. Results: A total of 215 DEGs were identified, consisting of 105 upregulated and 110 downregulated genes. GO and KEGG analyses showed that the enriched terms and pathways were mainly associated with cell cycle, proliferation, drug metabolism, and oncogenesis. From the PPI network, we identified six hub genes, including Cell Division Cycle 45 (CDC45), Polo Like Kinase 1 (PLK1), BUB1 Mitotic Checkpoint Serine/Threonine Kinase B (BUB1B), Cell Division Cycle 20 (CDC20), Aurora Kinase A (AURKA), and Mitotic Arrest Deficient 2 Like 1 (MAD2L1). The survival analyses and expression validation studies verified the robustness of these hub genes. Conclusion: This study provides new insights into the understanding of the molecular mechanisms of IBC; in addition, the identified hub genes may serve as potential targets for diagnosis and treatment.

Identification of Potential Key Genes and Pathways for Inflammatory Breast Cancer Based on GEO and TCGA Databases

Introduction

Inflammatory breast cancer (IBC) is a rare type of breast cancer with poor prognosis, and the pathogenesis of this life-threatening disease is yet to be fully elucidated. This study aims to identify key genes of IBC, which could be potential diagnostic or therapeutic targets.

Methods

Four datasets GSE5847, GSE22597, GSE23720, and GSE45581 were downloaded from the Gene Expression Omnibus (GEO) and differential expression analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to understand the potential bio-functions of the differentially expressed genes (DEGs). Protein-protein interaction (PPI) network was constructed for functional modules analysis and hub genes identification, and TCGA survival analysis and qRT-PCR of clinical samples were used to further explore and validate the effect of hub genes on IBC.

Results

A total of 114 DEGs were identified from the GEO datasets. GO and KEGG analyses showed that the DEGs were mainly enriched in oncogenesis and cell adhesion. From the PPI network, we screened out five hub genes, including PTPRC, IL6, SELL, CD40, and SPN. Survival analysis and expression validation verified the robustness of the hub genes.

Discussion

The present study provides new insight into the understanding of IBC pathogenesis and the identified hub genes may serve as potential targets for diagnosis and treatment.

TRPM4 is overexpressed in breast cancer associated with estrogen response and epithelial-mesenchymal transition gene sets

Ion channels form an important class of drug targets in malignancies. Transient receptor potential cation channel subfamily M member 4 (TRPM4) plays oncological roles in various solid tumors. Herein, we examined TRPM4 protein expression profile by immunohistochemistry (IHC) in breast cancer cases compared with normal breast ducts, its association with clinico-demographical parameters, and its potential function in breast cancers by Gene Set Enrichment Analysis (GSEA). Data-mining demonstrated that TRPM4 transcript levels were significantly higher in The Cancer Genome Atlas series of breast cancer cases (n = 1,085) compared with normal breast tissues (n = 112) (p = 1.03 x 10⁻¹¹). Our IHC findings in tissue microarrays showed that TRPM4 protein was overexpressed in breast cancers (n = 83/99 TRPM4⁺; 83.8%) compared with normal breast ducts (n = 5/10 TRPM4⁺; 50%) (p = 0.022). Higher TRPM4 expression (median frequency cut-off) was significantly associated with higher lymph node status (N1-N2 vs N0; p = 0.024) and higher stage (IIb-IIIb vs I-IIa; p = 0.005). GSEA evaluation in three independent gene expression profiling (GEP) datasets of breast cancer cases (GSE54002, n = 417; GSE20685, n = 327; GSE23720, n = 197) demonstrated significant association of TRPM4 transcript expression with estrogen response and epithelial-mesenchymal transition (EMT) gene sets (p<0.01 and false discovery rate<0.05). These gene sets were not enriched in GEP datasets of normal breast epithelium cases (GSE10797, n = 5; GSE9574, n = 15; GSE20437, n = 18). In conclusion, TRPM4 protein expression is upregulated in breast cancers associated with worse clinico-demographical parameters, and TRPM4 potentially regulates estrogen receptor signaling and EMT progression in breast cancer.

Epigenetics in Inflammatory Breast Cancer: Biological Features and Therapeutic Perspectives

Evidence has emerged implicating epigenetic alterations in inflammatory breast cancer (IBC) origin and progression. IBC is a rare and rapidly progressing disease, considered the most aggressive type of breast cancer (BC). At clinical presentation, IBC is characterized by diffuse erythema, skin ridging, dermal lymphatic invasion, and peau d'orange aspect. The widespread distribution of the tumor as emboli throughout the breast and intra- and intertumor heterogeneity is associated with its poor prognosis. In this review, we highlighted studies documenting the essential roles of epigenetic mechanisms in remodeling chromatin and modulating gene expression during mammary gland differentiation and the development of IBC. Compiling evidence has emerged implicating epigenetic changes as a common denominator linking the main risk factors (socioeconomic status, environmental exposure to endocrine disruptors, racial disparities, and obesity) with IBC development. DNA methylation changes and their impact on the diagnosis, prognosis, and treatment of IBC are also described. Recent studies are focusing on the use of histone deacetylase inhibitors as promising epigenetic drugs for treating IBC. All efforts must be undertaken to unravel the epigenetic marks that drive this disease and how this knowledge could impact strategies to reduce the risk of IBC development and progression.

NOTCH and DNA repair pathways are more frequently targeted by genomic alterations in inflammatory than in non-inflammatory breast cancers

Inflammatory breast cancer (IBC) is the most pro‐metastatic form of breast cancer. Better understanding of its pathophysiology and identification of actionable genetic alterations (AGAs) are crucial to improve systemic treatment. We aimed to define the DNA profiles of IBC vs noninflammatory breast cancer (non‐IBC) clinical samples in terms of copy number alterations (CNAs), mutations, and AGAs. We applied targeted next‐generation sequencing (tNGS) and array‐comparative genomic hybridization (aCGH) to 57 IBC and 50 non‐IBC samples and pooled these data with four public datasets profiled using NGS and aCGH, leading to a total of 101 IBC and 2351 non‐IBC untreated primary tumors. The respective percentages of each molecular subtype [hormone receptor‐positive (HR+)/HER2−, HER2+, and triple‐negative] were 68%, 15%, and 17% in non‐IBC vs 25%, 35%, and 40% in IBC. The comparisons were adjusted for both the molecular subtypes and the American Joint Committee on Cancer (AJCC) stage. The 10 most frequently altered genes in IBCs were TP53 (63%), HER2/ERBB2 (30%), MYC (27%), PIK3CA (21%), BRCA2 (14%), CCND1 (13%), GATA3 (13%), NOTCH1 (12%), FGFR1 (11%), and ARID1A (10%). The tumor mutational burden was higher in IBC than in non‐IBC. We identified 96 genes with an alteration frequency (p < 5% and q < 20%) different between IBC and non‐IBC, independently from the molecular subtypes and AJCC stage; 95 were more frequently altered in IBC, including TP53, genes involved in the DNA repair (BRCA2) and NOTCH pathways, and one (PIK3CA) was more frequently altered in non‐IBC. Ninety‐seven percent of IBCs displayed at least one AGA. This percentage was higher than in non‐IBC (87%), notably for drugs targeting DNA repair, NOTCH signaling, and CDK4/6, whose pathways were more frequently altered (DNA repair) or activated (NOTCH and CDK4/6) in IBC than in non‐IBC. The genomic landscape of IBC is different from that of non‐IBC. Enriched AGAs in IBC may explain its aggressiveness and provide clinically relevant targets.

A meta-analysis of multiple matched aCGH/expression cancer datasets reveals regulatory relationships and pathway enrichment of potential oncogenes

The copy numbers of genes in cancer samples are often highly disrupted and form a natural amplification/deletion experiment encompassing multiple genes. Matched array comparative genomics and transcriptomics datasets from such samples can be used to predict inter-chromosomal gene regulatory relationships. Previously we published the database METAMATCHED, comprising the results from such an analysis of a large number of publically available cancer datasets. Here we investigate genes in the database which are unusual in that their copy number exhibits consistent heterogeneous disruption in a high proportion of the cancer datasets. We assess the potential relevance of these genes to the pathology of the cancer samples, in light of their predicted regulatory relationships and enriched biological pathways. A network-based method was used to identify enriched pathways from the genes’ inferred targets. The analysis predicts both known and new regulator-target interactions and pathway memberships. We examine examples in detail, in particular the gene POGZ, which is disrupted in many of the cancer datasets and has an unusually large number of predicted targets, from which the network analysis predicts membership of cancer related pathways. The results suggest close involvement in known cancer pathways of genes exhibiting consistent heterogeneous copy number disruption. Further experimental work would clarify their relevance to tumor biology. The results of the analysis presented in the database METAMATCHED, and included here as an R archive file, constitute a large number of predicted regulatory relationships and pathway memberships which we anticipate will be useful in informing such experiments.

Expression signatures and roles of microRNAs in inflammatory breast cancer

Inflammatory breast cancer (IBC) is an infrequent but aggressive manifestation of breast cancer, which accounts for 2-4% of all breast cancer cases but responsible for 7-10% of breast cancer-related deaths, and with a 20-30% 10-year overall survival compared with 80% for patients with non-IBC with an unordinary phenotype, whose molecular mechanisms are still largely unknown to date. Discovering and identifying novel bio-markers responsible for diagnosis and therapeutic targets is a pressing need. MicroRNAs are a class of small non-coding RNAs that are capable to post-transcriptionally regulate gene expression of genes by targeting mRNAs, exerting vital and tremendous affects in numerous malignancy-related biological processes, including cell apoptosis, metabolism, proliferation and differentiation. In this study, we review present and high-quality evidences regarding the potential applications of inflammatory breast cancer associated microRNAs for diagnosis and prognosis of this lethal disease.

Clinical Response to Induction Chemotherapy Predicts Outcome after Combined-Modality Therapy in Inflammatory Breast Cancer

PURPOSE

To assess predictors of outcome in a cohort of Inflammatory Breast Cancer (IBC) patients receiving induction chemotherapy followed by local treatment.

METHODS

We retrospectively reviewed 95 non-metastatic IBC patient files.

RESULTS

Complete clinical response (cCR) was obtained in 15 (16%) patients. Median follow up was 13.4 years (IC95%: 10.4-14.6). Loco-regional control (LC), disease-free survival (DFS), and overall survival (OS) at 5 years were 85%, 41%, and 55%, respectively; cCR was associated with better DFS and OS in multivariate analyses adjusted for age (p = 0.02).

CONCLUSIONS

Clinical response to upfront chemotherapy predicts the outcome of patients affected by IBC.

Agrin has a pathological role in the progression of oral cancer

Background

The extracellular matrix modulates the hallmarks of cancer. Here we examined the role of agrin—a member of this matrix—in progression of oral squamous cell carcinoma (OSCC).

Methods

We evaluated the immunohistochemical expression of agrin in OSCC and dysplasias. Benign lesions were used as control. In subsequent experiments, we investigated whether the silencing of agrin interferes with tumour expansion both in vitro as well as in vivo. To gain insights into the role of agrin, we identified its protein network (interactome) using mass spectrometry-based proteomics and bioinformatics. Finally, we evaluated the clinical relevance of agrin interactome.

Results

Agrin was elevated in malignant and premalignant lesions. Further, we show that agrin silencing interferes with cancer cell motility, proliferation, invasion, colony and tumour spheroid formation, and it also reduces the phosphorylation of FAK, ERK and cyclin D1 proteins in OSCC cells. In orthotopic model, agrin silencing reduces tumour aggressiveness, like vascular and neural invasion. From a clinical perspective, agrin contextual hubs predict a poor clinical prognosis related with overall survival.

Conclusions

Altogether, our results demonstrate that agrin is a histological marker for the progression of oral cancer and is a strong therapeutic target candidate for both premalignant and OSCC lesions.

MARCKS protein overexpression in inflammatory breast cancer

Background

Inflammatory breast cancer (IBC) is the most aggressive form of locally-advanced breast cancer. Identification of new therapeutic targets is crucial. We previously reported MARCKS mRNA overexpression in IBC in the largest transcriptomics study reported to date. Here, we compared MARCKS protein expression in IBC and non-IBC samples, and searched for correlations between protein expression and clinicopathological features.

Results

Tumor samples showed heterogeneity with respect to MARCKS staining: 18% were scored as MARCKS-positive (stained cells ≥ 1%) and 82% as MARCKS-negative. MARCKS expression was more frequent in IBC (36%) than in non-IBC (11%; p = 1.4E−09), independently from molecular subtypes and other clinicopathological variables. We found a positive correlation between protein and mRNA expression in the 148/502 samples previously analyzed for MARCKS mRNA expression. MARCKS protein expression was associated with other poor-prognosis features in the whole series of samples such as clinical axillary lymph node or metastatic extension, high pathological grade, ER-negativity, PR-negativity, HER2-positivity, and triple-negative and HER2+ statutes. In IBC, MARCKS expression was the sole tested variable associated with poor MFS.

Materials and Methods

We retrospectively analyzed MARCKS protein expression by immunohistochemistry in 502 tumors, including 133 IBC and 369 non-IBC, from Tunisian and French patients. All samples were pre-therapeutic clinical samples. We searched for correlations between MARCKS expression and clinicopathological features including the IBC versus non-IBC phenotype and metastasis-free survival (MFS).

Conclusions

MARCKS overexpression might in part explain the poor prognosis of IBC. As an oncogene associated with poor MFS, MARCKS might represent a new potential therapeutic target in IBC.

Targeted NGS, array-CGH, and patient-derived tumor xenografts for precision medicine in advanced breast cancer: a single-center prospective study

Background

Routine feasibility and clinical impact of genomics-based tumor profiling in advanced breast cancer (aBC) remains to be determined. We conducted a pilot study to evaluate whether precision medicine could be prospectively implemented for aBC patients in a single center and to examine whether patient-derived tumor xenografts (PDX) could be obtained in this population.

Results

Thirty-four aBC patients were included. Actionable targets were found in 28 patients (82%). A targeted therapy could be proposed to 22 patients (64%), either through a clinical trial (n=15) and/or using already registered drugs (n=21). Ten patients (29%) eventually received targeted treatment, 2 of them deriving clinical benefit. Of 22 patients subjected to mouse implantation, 10 had successful xenografting (45%), mostly in triple-negative aBC.

Methods

aBC patients accessible to tumor biopsy were prospectively enrolled at the Institut Paoli-Calmettes in the BC-BIO study (ClinicalTrials.gov, NCT01521676). Genomic profiling was established by whole-genome array comparative genomic hybridization (aCGH) and targeted next-generation sequencing (NGS) of 365 candidate cancer genes. For a subset of patients, a sample of fresh tumor was orthotopically implanted in humanized cleared fat pads of NSG mice for establishing PDX.

Conclusions

Precision medicine can be implemented in a single center in the context of clinical practice and may allow genomic-driven treatment in approximately 30% of aBC patients. PDX may be obtained in a significant fraction of cases.

Comparative genomic analysis of primary tumors and metastases in breast cancer

Personalized medicine uses genomic information for selecting therapy in patients with metastatic cancer. An issue is the optimal tissue source (primary tumor or metastasis) for testing. We compared the DNA copy number and mutational profiles of primary breast cancers and paired metastases from 23 patients using whole-genome array-comparative genomic hybridization and next-generation sequencing of 365 “cancer-associated” genes. Primary tumors and metastases harbored copy number alterations (CNAs) and mutations common in breast cancer and showed concordant profiles. The global concordance regarding CNAs was shown by clustering and correlation matrix, which showed that each metastasis correlated more strongly with its paired tumor than with other samples. Genes with recurrent amplifications in breast cancer showed 100% (ERBB2, FGFR1), 96% (CCND1), and 88% (MYC) concordance for the amplified/non-amplified status. Among all samples, 499 mutations were identified, including 39 recurrent (AKT1, ERBB2, PIK3CA, TP53) and 460 non-recurrent variants. The tumors/metastases concordance of variants was 75%, higher for recurrent (92%) than for non-recurrent (73%) variants. Further mutational discordance came from very different variant allele frequencies for some variants. We showed that the chosen targeted therapy in two clinical trials of personalized medicine would be concordant in all but one patient (96%) when based on the molecular profiling of tumor and paired metastasis. Our results suggest that the genotyping of primary tumor may be acceptable to guide systemic treatment if the metastatic sample is not obtainable. However, given the rare but potentially relevant divergences for some actionable driver genes, the profiling of metastatic sample is recommended.

Determining the optimal number of independent components for reproducible transcriptomic data analysis

BACKGROUND

Independent Component Analysis (ICA) is a method that models gene expression data as an action of a set of statistically independent hidden factors. The output of ICA depends on a fundamental parameter: the number of components (factors) to compute. The optimal choice of this parameter, related to determining the effective data dimension, remains an open question in the application of blind source separation techniques to transcriptomic data.

RESULTS

Here we address the question of optimizing the number of statistically independent components in the analysis of transcriptomic data for reproducibility of the components in multiple runs of ICA (within the same or within varying effective dimensions) and in multiple independent datasets. To this end, we introduce ranking of independent components based on their stability in multiple ICA computation runs and define a distinguished number of components (Most Stable Transcriptome Dimension, MSTD) corresponding to the point of the qualitative change of the stability profile. Based on a large body of data, we demonstrate that a sufficient number of dimensions is required for biological interpretability of the ICA decomposition and that the most stable components with ranks below MSTD have more chances to be reproduced in independent studies compared to the less stable ones. At the same time, we show that a transcriptomics dataset can be reduced to a relatively high number of dimensions without losing the interpretability of ICA, even though higher dimensions give rise to components driven by small gene sets.

CONCLUSIONS

We suggest a protocol of ICA application to transcriptomics data with a possibility of prioritizing components with respect to their reproducibility that strengthens the biological interpretation. Computing too few components (much less than MSTD) is not optimal for interpretability of the results. The components ranked within MSTD range have more chances to be reproduced in independent studies.

Developmental therapeutics for inflammatory breast cancer: Biology and translational directions

Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer, which accounts for approximately 3% of cases of breast malignancies. Diagnosis relies largely on its clinical presentation, and despite a characteristic phenotype, underlying molecular mechanisms are poorly understood. Unique clinical presentation indicates that IBC is a distinct clinical and biological entity when compared to non-IBC. Biological understanding of non-IBC has been extrapolated into IBC and targeted therapies for HER2 positive (HER2+) and hormonal receptor positive non-IBC led to improved patient outcomes in the recent years. This manuscript reviews recent discoveries related to the underlying biology of IBC, clinical progress to date and suggests rational approaches for investigational therapies.

Lack of Breastfeeding History in Parous Women with Inflammatory Breast Cancer Predicts Poor Disease-Free Survival

Purpose: Breastfeeding alters the breast microenvironment, and several lines of evidence suggest the breast microenvironment contributes to the clinical phenotype of inflammatory breast cancer. We investigated breastfeeding history as a modifier of locoregional recurrence (LRR), distant metastasis (DM), disease-free survival (DFS), and overall survival (OS) in parous women with inflammatory breast cancer.

Methods: Parous women with inflammatory breast cancer were identified from a prospective registry at The University of Texas MD Anderson Cancer Center. We compared patient and tumor characteristics, LRR, DM, DFS, and OS patients with (BF+) and without (BF-) a history of breastfeeding.

Results: Eighty-two patients were included. At a median follow-up of 50 months, BF+ patients had significantly lower risk of LRR (9.0% vs. 23.6%; p=0.01), a lower risk of DM (26.8% vs. 53.8%; p=0.008), and better DFS (73.1% vs. 48.1%; p=0.006) than BF- patients. On multivariate analysis, BF+ history was associated with significantly lower risk of DM (hazard ratio 0.38, 95% confidence interval 0.15-0.97; p=0.04) and better DFS (hazard ratio 0.37, 95% confidence interval 0.15-0.93; p=0.04) after adjusting for established predictive and prognostic variables. The prognostic significance of breastfeeding may be most pronounced in women with triple-negative IBC.

Conclusion: A lack of breastfeeding history in parous women with inflammatory breast cancer may predict worse prognosis. We speculate that breastfeeding-induced alterations in the breast microenvironment may alter the aggressiveness of inflammatory breast cancer.

Inflammatory breast cancer: unique biological and therapeutic considerations

Through the concerted efforts of many patients, health-care providers, legislators, and other supporters, the past decade has seen the development of the first clinics dedicated to the care of patients with inflammatory breast cancer in the USA and other countries. Together with social networking, advocacy, and education, a few specialised centres have had substantial increases in patient numbers (in some cases ten times higher), which has further expanded the community of science and advocacy and increased the understanding of the disease process. Although inflammatory breast cancer is considered rare, constituting only 2-4% of breast cancer cases, poor prognosis means that patients with the disease account for roughly 10% of breast cancer mortality annually in the USA. I propose that the unique presentation of inflammatory breast cancer might require specific, identifiable changes in the breast parenchyma that occur before the tumour-initiating event. This would make the breast tissue itself a tumour-promoting medium that should be treated as a component of the pathology in multidisciplinary treatment and should be further studied for complementary targets to inhibit the pathobiology that is specific to inflammatory breast cancer.

Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations

BACKGROUND

Copy number variation (CNV) analysis has become one of the most important research areas for understanding complex disease. With increasing resolution of array-based comparative genomic hybridization (aCGH) arrays, more and more raw copy number data are collected for multiple arrays. It is natural to realize the co-existence of both recurrent and individual-specific CNVs, together with the possible data contamination during the data generation process. Therefore, there is a great need for an efficient and robust statistical model for simultaneous recovery of both recurrent and individual-specific CNVs.

RESULT

We develop a penalized weighted low-rank approximation method (WPLA) for robust recovery of recurrent CNVs. In particular, we formulate multiple aCGH arrays into a realization of a hidden low-rank matrix with some random noises and let an additional weight matrix account for those individual-specific effects. Thus, we do not restrict the random noise to be normally distributed, or even homogeneous. We show its performance through three real datasets and twelve synthetic datasets from different types of recurrent CNV regions associated with either normal random errors or heavily contaminated errors.

CONCLUSION

Our numerical experiments have demonstrated that the WPLA can successfully recover the recurrent CNV patterns from raw data under different scenarios. Compared with two other recent methods, it performs the best regarding its ability to simultaneously detect both recurrent and individual-specific CNVs under normal random errors. More importantly, the WPLA is the only method which can effectively recover the recurrent CNVs region when the data is heavily contaminated.

HDAC6 activity is a non-oncogene addiction hub for inflammatory breast cancers

INTRODUCTION

Inflammatory breast cancer (IBC) is the most lethal form of breast cancers with a 5-year survival rate of only 40 %. Despite its lethality, IBC remains poorly understood which has greatly limited its therapeutic management. We thus decided to utilize an integrative functional genomic strategy to identify the Achilles' heel of IBC cells.

METHODS

We have pioneered the development of genetic tools as well as experimental and analytical strategies to perform RNAi-based loss-of-function studies at a genome-wide level. Importantly, we and others have demonstrated that these functional screens are able to identify essential functions linked to certain cancer phenotypes. Thus, we decided to use this approach to identify IBC specific sensitivities.

RESULTS

We identified and validated HDAC6 as a functionally necessary gene to maintain IBC cell viability, while being non-essential for other breast cancer subtypes. Importantly, small molecule inhibitors for HDAC6 already exist and are in clinical trials for other tumor types. We thus demonstrated that Ricolinostat (ACY1215), a leading HDAC6 inhibitor, efficiently controls IBC cell proliferation both in vitro and in vivo. Critically, functional HDAC6 dependency is not associated with genomic alterations at its locus and thus represents a non-oncogene addiction. Despite HDAC6 not being overexpressed, we found that its activity is significantly higher in IBC compared to non-IBC cells, suggesting a possible rationale supporting the observed dependency.

CONCLUSION

Our finding that IBC cells are sensitive to HDAC6 inhibition provides a foundation to rapidly develop novel, efficient, and well-tolerated targeted therapy strategies for IBC patients.

Investigating inter-chromosomal regulatory relationships through a comprehensive meta-analysis of matched copy number and transcriptomics data sets

Background

Gene regulatory relationships can be inferred using matched array comparative genomics and transcriptomics data sets from cancer samples. The way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment. There are now a large number of such data sets publicly available making a meta-analysis of the data possible.

Results

We infer inter-chromosomal acting gene regulatory relationships from a meta-analysis of 31 publicly available matched array comparative genomics and transcriptomics data sets in humans. We obtained statistically significant predictions of target genes for 1430 potential regulatory genes. The regulatory relationships being inferred are either direct relationships, of a transcription factor on its target, or indirect ones, through pathways containing intermediate steps. We analyse the predictions in terms of cocitations, both publications which cite a regulator with any of its inferred targets and cocitations of any genes in a target list.

Conclusions

The most striking observation from the results is the greater number of inter-chromosomal regulatory relationships involving repression compared to those involving activation. The complete results of the meta-analysis are presented in the database METAMATCHED. We anticipate that the predictions contained in the database will be useful in informing experiments and in helping to construct networks of regulatory relationships.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-2100-5) contains supplementary material, which is available to authorized users.

LINC00472 expression is regulated by promoter methylation and associated with disease-free survival in patients with grade 2 breast cancer

Long non-coding RNAs (lncRNAs) are a class of newly recognized DNA transcripts that have diverse biological activities. Dysregulation of lncRNAs may be involved in many pathogenic processes including cancer. Recently, we found an intergenic lncRNA, LINC00472, whose expression was correlated with breast cancer progression and patient survival. Our findings were consistent across multiple clinical datasets and supported by results from in vitro experiments. To evaluate further the role of LINC00472 in breast cancer, we used various online databases to investigate possible mechanisms that might affect LINC00472 expression in breast cancer. We also analyzed associations of LINC00472 with estrogen receptor, tumor grade, and molecular subtypes in additional online datasets generated by microarray platforms different from the one we investigated previously. We found that LINC00472 expression in breast cancer was regulated more possibly by promoter methylation than by the alteration of gene copy number. Analysis of additional datasets confirmed our previous findings of high expression of LINC00472 associated with ER-positive and low-grade tumors and favorable molecular subtypes. Finally, in nine datasets, we examined the association of LINC00472 expression with disease-free survival in patients with grade 2 tumors. Meta-analysis of the datasets showed that LINC00472 expression in breast tumors predicted the recurrence of breast cancer in patients with grade 2 tumors. In summary, our analyses confirm that LINC00472 is functionally a tumor suppressor, and that assessing its expression in breast tumors may have clinical implications in breast cancer management.

Systems biology analysis reveals NFAT5 as a novel biomarker and master regulator of inflammatory breast cancer

Background

Inflammatory breast cancer (IBC) is the most rare and aggressive variant of breast cancer (BC); however, only a limited number of specific gene signatures with low generalization abilities are available and few reliable biomarkers are helpful to improve IBC classification into a molecularly distinct phenotype. We applied a network-based strategy to gain insight into master regulators (MRs) linked to IBC pathogenesis.

Methods

In-silico modeling and Algorithm for the Reconstruction of Accurate Cellular Networks (ARACNe) on IBC/non-IBC (nIBC) gene expression data (n = 197) was employed to identify novel master regulators connected to the IBC phenotype. Pathway enrichment analysis was used to characterize predicted targets of candidate genes. The expression pattern of the most significant MRs was then evaluated by immunohistochemistry (IHC) in two independent cohorts of IBCs (n = 39) and nIBCs (n = 82) and normal breast tissues (n = 15) spotted on tissue microarrays. The staining pattern of non-neoplastic mammary epithelial cells was used as a normal control.

Results

Using in-silico modeling of network-based strategy, we identified three top enriched MRs (NFAT5, CTNNB1 or β-catenin, and MGA) strongly linked to the IBC phenotype. By IHC assays, we found that IBC patients displayed a higher number of NFAT5-positive cases than nIBC (69.2% vs. 19.5%; p-value = 2.79 10^-7). Accordingly, the majority of NFAT5-positive IBC samples revealed an aberrant nuclear expression in comparison with nIBC samples (70% vs. 12.5%; p-value = 0.000797). NFAT5 nuclear accumulation occurs regardless of WNT/β-catenin activated signaling in a substantial portion of IBCs, suggesting that NFAT5 pathway activation may have a relevant role in IBC pathogenesis. Accordingly, cytoplasmic NFAT5 and membranous β-catenin expression were preferentially linked to nIBC, accounting for the better prognosis of this phenotype.

Conclusions

We provide evidence that NFAT-signaling pathway activation could help to identify aggressive forms of BC and potentially be a guide to assignment of phenotype-specific therapeutic agents. The NFAT5 transcription factor might be developed into routine clinical practice as a putative biomarker of IBC phenotype.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-015-0492-2) contains supplementary material, which is available to authorized users.

Decreased expression of ABAT and STC2 hallmarks ER-positive inflammatory breast cancer and endocrine therapy resistance in advanced disease

BACKGROUND

Patients with Estrogen Receptor α-positive (ER+) Inflammatory Breast Cancer (IBC) are less responsive to endocrine therapy compared with ER+ non-IBC (nIBC) patients. The study of ER+ IBC samples might reveal biomarkers for endocrine resistant breast cancer.

MATERIALS & METHODS

Gene expression profiles of ER+ samples from 201 patients were explored for genes that discriminated between IBC and nIBC. Classifier genes were applied onto clinically annotated expression data from 947 patients with ER+ breast cancer and validated with RT-qPCR for 231 patients treated with first-line tamoxifen. Relationships with metastasis-free survival (MFS) and progression-free survival (PFS) following adjuvant and first-line endocrine treatment, respectively, were investigated using Cox regression analysis.

RESULTS

A metagene of six genes including the genes encoding for 4-aminobutyrate aminotransferase (ABAT) and Stanniocalcin-2 (STC2) were identified to distinguish 22 ER+ IBC from 43 ER+ nIBC patients and remained discriminatory in an independent series of 136 patients. The metagene and two genes were not prognostic in 517 (neo)adjuvant untreated lymph node-negative ER+ nIBC breast cancer patients. Only ABAT was related to outcome in 250 patients treated with adjuvant tamoxifen. Three independent series of in total 411 patients with advanced disease showed increased metagene scores and decreased expression of ABAT and STC2 to be correlated with poor first-line endocrine therapy outcome. The biomarkers remained predictive for first-line tamoxifen treatment outcome in multivariate analysis including traditional factors or published signatures. In an exploratory analysis, ABAT and STC2 protein expression levels had no relation with PFS after first-line tamoxifen.

CONCLUSIONS

This study utilized ER+ IBC to identify a metagene including ABAT and STC2 as predictive biomarkers for endocrine therapy resistance.

miRNAs in Sera of Tunisian patients discriminate between inflammatory breast cancer and non-inflammatory breast cancer

In recent years, circulating miRNAs have attracted interest as stable, non-invasive biomarkers for various pathological conditions. Here, we investigated their potential to serve as minimally invasive, early detection markers for inflammatory breast cancer (IBC) and non-inflammatory breast cancer (non-IBC) in serum. miRNA profiling was performed on serum from 20 patients with non-IBC, 20 with IBC, and 20 normal control subjects. Real-time reverse transcription-polymerase chain reaction (qRT-PCR) was applied to measure the level of 12 candidate miRNAs previously identified in other research(miR-342-5p, miR-342--3p, miR-320, miR-30b, miR-29a, miR-24, miR-15a, miR-548d-5p, miR-486-3p, miR-451, miR-337-5p, miR-335).We found that 4 miRNAs (miR-24, miR-342-3p, miR-337-5p and miR-451) were differentially expressed in serum of IBC patients compared to non-IBC, and 3 miRNAs (miR-337-5p ,miR-451and miR-30b) were differentially expressed in IBC and non-IBC patients combined compared to healthy controls. miR-24, miR-342-3p, miR-337-5p and miR-451 were found to be significantly down-regulated in IBC patients compared to non-IBC. Likewise, the expression level of mir-451 showed significant down-regulation in IBC serum, while mir-30b and miR-337-5p were up-regulated in non-IBC serum comparatively to normal controls. Using receiver operational curve (ROC) analysis, we show that dysregulated miRNAs can discriminate patients with IBC and non-IBC from healthy controls with sensitivity ranging from 76 to 81% and specificity from 66 to 80%, for three separate miRNAs. In conclusion, our data suggest that circulating miRNAs are potential biomarkers for classifying IBC and non-IBC, and may also be candidates for early detection of breast cancer.

A meta-analysis of multiple matched copy number and transcriptomics data sets for inferring gene regulatory relationships

Inferring gene regulatory relationships from observational data is challenging. Manipulation and intervention is often required to unravel causal relationships unambiguously. However, gene copy number changes, as they frequently occur in cancer cells, might be considered natural manipulation experiments on gene expression. An increasing number of data sets on matched array comparative genomic hybridisation and transcriptomics experiments from a variety of cancer pathologies are becoming publicly available. Here we explore the potential of a meta-analysis of thirty such data sets. The aim of our analysis was to assess the potential of in silico inference of trans-acting gene regulatory relationships from this type of data. We found sufficient correlation signal in the data to infer gene regulatory relationships, with interesting similarities between data sets. A number of genes had highly correlated copy number and expression changes in many of the data sets and we present predicted potential trans-acted regulatory relationships for each of these genes. The study also investigates to what extent heterogeneity between cell types and between pathologies determines the number of statistically significant predictions available from a meta-analysis of experiments.

Expression profile of inflammatory breast cancer

Inflammatory breast cancer is characterized by high malignancy, early and rapid lymphogenic and hematogenic metastasizing, and high mortality. The diagnosis of this form of cancer is based fully on the clinical criteria. Whole transcriptome analysis of tumor tissue specimens from patients with inflammatory breast cancer detected 137 differentially expressed mRNA (17 genes with low expression and 120 with high expression). Genes involved in the organization of inflammatory process, chemotaxis, and transcription regulation were active in the process of pathogenesis of inflammatory breast cancer.

Genomic profiling of inflammatory breast cancer: a review

Inflammatory breast cancer (IBC) is a rare but aggressive form of breast cancer. Despite efforts in the past decade to delineate the molecular biology of IBC by applying high-throughput molecular profiling technologies to clinical samples, IBC remains insufficiently characterized. The reasons for that include limited sizes of the study population, heterogeneity with respect to the composition of the IBC and non-IBC control groups and technological differences across studies. In 2008, the World IBC Consortium was founded to foster collaboration between research groups focusing on IBC. One of the initial projects was to redefine the molecular profile of IBC using an unprecedented number of samples and search for gene signatures associated with survival and response to neo-adjuvant chemotherapy. Here, we provide an overview of all the molecular profiling studies that have been performed on IBC clinical samples to date.

Piecewise-constant and low-rank approximation for identification of recurrent copy number variations

MOTIVATION

The post-genome era sees urgent need for more novel approaches to extracting useful information from the huge amount of genetic data. The identification of recurrent copy number variations (CNVs) from array-based comparative genomic hybridization (aCGH) data can help understand complex diseases, such as cancer. Most of the previous computational methods focused on single-sample analysis or statistical testing based on the results of single-sample analysis. Finding recurrent CNVs from multi-sample data remains a challenging topic worth further study.

RESULTS

We present a general and robust method to identify recurrent CNVs from multi-sample aCGH profiles. We express the raw dataset as a matrix and demonstrate that recurrent CNVs will form a low-rank matrix. Hence, we formulate the problem as a matrix recovering problem, where we aim to find a piecewise-constant and low-rank approximation (PLA) to the input matrix. We propose a convex formulation for matrix recovery and an efficient algorithm to globally solve the problem. We demonstrate the advantages of PLA compared with alternative methods using synthesized datasets and two breast cancer datasets. The experimental results show that PLA can successfully reconstruct the recurrent CNV patterns from raw data and achieve better performance compared with alternative methods under a wide range of scenarios.

AVAILABILITY AND IMPLEMENTATION

The MATLAB code is available at http://bioinformatics.ust.hk/pla.zip.

Candidate luminal B breast cancer genes identified by genome, gene expression and DNA methylation profiling

Breast cancers (BCs) of the luminal B subtype are estrogen receptor-positive (ER+), highly proliferative, resistant to standard therapies and have a poor prognosis. To better understand this subtype we compared DNA copy number aberrations (CNAs), DNA promoter methylation, gene expression profiles, and somatic mutations in nine selected genes, in 32 luminal B tumors with those observed in 156 BCs of the other molecular subtypes. Frequent CNAs included 8p11-p12 and 11q13.1-q13.2 amplifications, 7q11.22-q34, 8q21.12-q24.23, 12p12.3-p13.1, 12q13.11-q24.11, 14q21.1-q23.1, 17q11.1-q25.1, 20q11.23-q13.33 gains and 6q14.1-q24.2, 9p21.3-p24,3, 9q21.2, 18p11.31-p11.32 losses. A total of 237 and 101 luminal B-specific candidate oncogenes and tumor suppressor genes (TSGs) presented a deregulated expression in relation with their CNAs, including 11 genes previously reported associated with endocrine resistance. Interestingly, 88% of the potential TSGs are located within chromosome arm 6q, and seven candidate oncogenes are potential therapeutic targets. A total of 100 candidate oncogenes were validated in a public series of 5,765 BCs and the overexpression of 67 of these was associated with poor survival in luminal tumors. Twenty-four genes presented a deregulated expression in relation with a high DNA methylation level. FOXO3, PIK3CA and TP53 were the most frequent mutated genes among the nine tested. In a meta-analysis of next-generation sequencing data in 875 BCs, KCNB2 mutations were associated with luminal B cases while candidate TSGs MDN1 (6q15) and UTRN (6q24), were mutated in this subtype. In conclusion, we have reported luminal B candidate genes that may play a role in the development and/or hormone resistance of this aggressive subtype.

Gene expression profiles of inflammatory breast cancer: correlation with response to neoadjuvant chemotherapy and metastasis-free survival

BACKGROUND

Inflammatory breast cancer (IBC) is an aggressive disease. To date, no molecular feature reliably predicts either the response to chemotherapy (CT) or the survival. Using DNA microarrays, we searched for multigene predictors.

PATIENTS AND METHODS

The World IBC Consortium generated whole-genome expression profiles of 137 IBC and 252 non-IBC (nIBC) samples. We searched for transcriptional profiles associated with pathological complete response (pCR) to neoadjuvant anthracycline-based CT and distant metastasis-free survival (DMFS) in respective subsets of 87 and 106 informative IBC samples. Correlations were investigated with predictive and prognostic gene expression signatures published in nIBC (nIBC-GES). Supervised analyses tested genes and activation signatures of 19 biological pathways and 234 transcription factors.

RESULTS

Three of five tested prognostic nIBC-GES and the two tested predictive nIBC-GES discriminated between IBC with and without pCR, as well as two interferon activation signatures. We identified a 107-gene signature enriched for immunity-related genes that distinguished between responders and nonresponders in IBC. Its robustness was demonstrated by external validation in three independent sets including two IBC sets and one nIBC set, with independent significant predictive value in IBC and nIBC validation sets in multivariate analysis. We found no robust signature associated with DMFS in patients with IBC, and neither of the tested prognostic GES, nor the molecular subtypes were informative, whereas they were in our nIBC series (220 stage I-III informative samples).

CONCLUSION

Despite the relatively small sample size, we show that response to neoadjuvant CT in IBC is, as in nIBC, associated with immunity-related processes, suggesting that similar mechanisms responsible for pCR exist. Analysis of a larger IBC series is warranted regarding the correlation of gene expression profiles and DMFS.

Integrated genomic analysis of breast cancers

Breast cancer is the most frequent and the most deadly cancer in women in Western countries. Different classifications of disease (anatomoclinical, pathological, prognostic, genetic) are used for guiding the management of patients. Unfortunately, they fail to reflect the whole clinical heterogeneity of the disease. Consequently, molecularly distinct diseases are grouped in similar clinical classes, likely explaining the different clinical outcome between patients in a given class, and the fact that selection of the most appropriate diagnostic or therapeutic strategy for each patient is not done accurately. Today, treatment is efficient in only 70.0–75.0% of cases overall. Our repertoire of efficient drugs is limited but is being expanded with the discovery of new molecular targets for new drugs, based on the identification of candidate oncogenes and tumor suppressor genes (TSG) functionally relevant in disease. Development of new drugs makes therapeutical decisions even more demanding of reliable classifiers and prognostic/predictive tests. Breast cancer is a complex, heterogeneous disease at the molecular level. The combinatorial molecular origin and the heterogeneity of malignant cells, and the variability of the host background, create distinct subgroups of tumors endowed with different phenotypic features such as response to therapy and clinical outcome. Cellular and molecular analyses can identify new classes biologically and clinically relevant, as well as provide new clinically relevant markers and targets.

The various stages of mammary tumorigenesis are not clearly defined and the genetic and epigenetic events critical to the development and aggressiveness of breast cancer are not precisely known. Because the phenotype of tumors is dependent on many genes, a large-scale and integrated molecular characterization of the genetic and epigenetic alterations and gene expression deregulation should allow the identification of new molecular classes clinically relevant, as well as among the altered genes and/or pathways, the identification of more accurate molecular diagnostic, prognostic/predictive factors, and for some of them, after functional validation, the identification of new therapeutic targets.

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

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

Inflammatory breast cancer (IBC): clues for targeted therapies

Inflammatory breast cancer (IBC) is the most aggressive type of advanced breast cancer characterized by rapid proliferation, early metastatic development and poor prognosis. Since there are few preclinical models of IBC, there is a general lack of understanding of the complexity of the disease. Recently, we have developed a new model of IBC derived from the pleural effusion of a woman with metastatic secondary IBC. FC-IBC02 cells are triple negative and form clusters (mammospheres) in suspension that are strongly positive for E-cadherin, β-catenin and TSPAN24, all adhesion molecules that play an important role in cell migration and invasion. FC-IBC02 cells expressed stem cell markers and some, but not all of the characteristics of cells undergoing epithelial mesenchymal transition (EMT). Breast tumor FC-IBC02 xenografts developed quickly in SCID mice with the presence of tumor emboli and the development of lymph node and lung metastases. Remarkably, FC-IBC02 cells were able to produce brain metastasis in mice on intracardiac or intraperitoneal injections. Genomic studies of FC-IBC02 and other IBC cell lines showed that IBC cells had important amplification of 8q24 where MYC, ATAD2 and the focal adhesion kinase FAK1 are located. MYC and ATAD2 showed between 2.5 and 7 copies in IBC cells. FAK1, which plays important roles in anoikis resistance and tumor metastasis, showed 6–4 copies in IBC cells. Also, CD44 was amplified in triple-negative IBC cells (10–3 copies). Additionally, FC-IBC02 showed amplification of ALK and NOTCH3. These results indicate that MYC, ATAD2, CD44, NOTCH3, ALK and/or FAK1 may be used as potential targeted therapies against IBC.

A method for finding consensus breakpoints in the cancer genome from copy number data

MOTIVATION

Recurrent DNA breakpoints in cancer genomes indicate the presence of critical functional elements for tumor development. Identifying them can help determine new therapeutic targets. High-dimensional DNA microarray experiments like arrayCGH afford the identification of DNA copy number breakpoints with high precision, offering a solid basis for computational estimation of recurrent breakpoint locations.

RESULTS

We introduce a method for identification of recurrent breakpoints (consensus breakpoints) from copy number aberration datasets. The method is based on weighted kernel counting of breakpoints around genomic locations. Counts larger than expected by chance are considered significant. We show that the consensus breakpoints facilitate consensus segmentation of the samples. We apply our method to three arrayCGH datasets and show that by using consensus segmentation we achieve significant dimension reduction, which is useful for the task of prediction of tumor phenotype based on copy number data. We use our approach for classification of neuroblastoma tumors from different age groups and confirm the recent recommendation for the choice of age cut-off for differential treatment of 18 months. We also investigate the (epi)genetic properties at consensus breakpoint locations for seven datasets and show enrichment in overlap with important functional genomic regions.

AVAILABILITY

Implementation in R of our approach can be found at http://www.mpi-inf.mpg.de/ ∼laura/FeatureGrouping.html.

CONTACT

laura@mpi-inf.mpg.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Coexpression and coregulation analysis of time-series gene expression data in estrogen-induced breast cancer cell

BACKGROUND

Estrogen is a chemical messenger that has an influence on many breast cancers as it helps cells to grow and divide. These cancers are often known as estrogen responsive cancers in which estrogen receptor occupies the surface of the cells. The successful treatment of breast cancers requires understanding gene expression, identifying of tumor markers, acquiring knowledge of cellular pathways, etc. In this paper we introduce our proposed triclustering algorithm δ-TRIMAX that aims to find genes that are coexpressed over subset of samples across a subset of time points. Here we introduce a novel mean-squared residue for such 3D dataset. Our proposed algorithm yields triclusters that have a mean-squared residue score below a threshold δ.

RESULTS

We have applied our algorithm on one simulated dataset and one real-life dataset. The real-life dataset is a time-series dataset in estrogen induced breast cancer cell line. To establish the biological significance of genes belonging to resultant triclusters we have performed gene ontology, KEGG pathway and transcription factor binding site enrichment analysis. Additionally, we represent each resultant tricluster by computing its eigengene and verify whether its eigengene is also differentially expressed at early, middle and late estrogen responsive stages. We also identified hub-genes for each resultant triclusters and verified whether the hub-genes are found to be associated with breast cancer. Through our analysis CCL2, CD47, NFIB, BRD4, HPGD, CSNK1E, NPC1L1, PTEN, PTPN2 and ADAM9 are identified as hub-genes which are already known to be associated with breast cancer. The other genes that have also been identified as hub-genes might be associated with breast cancer or estrogen responsive elements. The TFBS enrichment analysis also reveals that transcription factor POU2F1 binds to the promoter region of ESR1 that encodes estrogen receptor α. Transcription factor E2F1 binds to the promoter regions of coexpressed genes MCM7, ANAPC1 and WEE1.

CONCLUSIONS

Thus our integrative approach provides insights into breast cancer prognosis.