Breast Cancer Microarray Datasets Research Articles

Targeting FOS attenuates malignant phenotypes of breast cancer: Evidence from in silico and in vitro studies.

Data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases can reveal important information behind molecular biomarkers and their associated oncogenesis. Therefore, this study was based on in silico predictions and in vitro experiments to explore regulatory network associated with breast carcinogenesis. The breast cancer (BC)-related data sets were retrieved from GEO database, followed by differential analysis and protein-protein interaction (PPI) analysis. Then, Fos proto-oncogene, AP-1 transcription factor subunit (FOS)-associated gene network was constructed, and the key gene-related genes in BC were screened by LinkedOmics. Finally, FOS expression was determined in BC tissues and cells, and gain-of-function assays were performed to define the role of FOS in BC cells. It was noted that seven differentially expressed genes (EGR1, RASSF9, FOSB, CDC20, KLF4, PTGS2, and FOS) were obtained from BC microarray data sets. FOS was the gene with the most nodes in PPI analysis. Poor FOS mRNA expression was identified in BC patients. Furthermore, FOS was mainly located in the extracellular matrix and was involved in cell processes. FOS was downregulated in BC tissues and cells, and FOS overexpression restrained the malignant phenotypes of BC cells. Collectively, ectopic expression of FOS curtails the development of BC.

CUP-AI-Dx: A tool for inferring cancer tissue of origin and molecular subtype using RNA gene-expression data and artificial intelligence.

BackgroundCancer of unknown primary (CUP), representing approximately 3-5% of all malignancies, is defined as metastatic cancer where a primary site of origin cannot be found despite a standard diagnostic workup. Because knowledge of a patient's primary cancer remains fundamental to their treatment, CUP patients are significantly disadvantaged and most have a poor survival outcome. Developing robust and accessible diagnostic methods for resolving cancer tissue of origin, therefore, has significant value for CUP patients.MethodsWe developed an RNA-based classifier called CUP-AI-Dx that utilizes a 1D Inception convolutional neural network (1D-Inception) model to infer a tumor's primary tissue of origin. CUP-AI-Dx was trained using the transcriptional profiles of 18,217 primary tumours representing 32 cancer types from The Cancer Genome Atlas project (TCGA) and International Cancer Genome Consortium (ICGC). Gene expression data was ordered by gene chromosomal coordinates as input to the 1D-CNN model, and the model utilizes multiple convolutional kernels with different configurations simultaneously to improve generality. The model was optimized through extensive hyperparameter tuning, including different max-pooling layers and dropout settings. For 11 tumour types, we also developed a random forest model that can classify the tumour's molecular subtype according to prior TCGA studies. The optimised CUP-AI-Dx tissue of origin classifier was tested on 394 metastatic samples from 11 tumour types from TCGA and 92 formalin-fixed paraffin-embedded (FFPE) samples representing 18 cancer types from two clinical laboratories. The CUP-AI-Dx molecular subtype was also independently tested on independent ovarian and breast cancer microarray datasetsFindingsCUP-AI-Dx identifies the primary site with an overall top-1-accuracy of 98.54% in cross-validation and 96.70% on a test dataset. When applied to two independent clinical-grade RNA-seq datasets generated from two different institutes from the US and Australia, our model predicted the primary site with a top-1-accuracy of 86.96% and 72.46% respectively.InterpretationThe CUP-AI-Dx predicts tumour primary site and molecular subtype with high accuracy and therefore can be used to assist the diagnostic work-up of cancers of unknown primary or uncertain origin using a common and accessible genomics platform.FundingNIH R35 GM133562, NCI P30 CA034196, Victorian Cancer Agency Australia.

Systems biology comprehensive analysis on breast cancer for identification of key gene modules and genes associated with TNM-based clinical stages

Breast cancer (BC), as one of the leading causes of death among women, comprises several subtypes with controversial and poor prognosis. Considering the TNM (tumor, lymph node, metastasis) based classification for staging of breast cancer, it is essential to diagnose the disease at early stages. The present study aims to take advantage of the systems biology approach on genome wide gene expression profiling datasets to identify the potential biomarkers involved at stage I, stage II, stage III, and stage IV as well as in the integrated group. Three HER2-negative breast cancer microarray datasets were retrieved from the GEO database, including normal, stage I, stage II, stage III, and stage IV samples. Additionally, one dataset was also extracted to test the developed predictive models trained on the three datasets. The analysis of gene expression profiles to identify differentially expressed genes (DEGs) was performed after preprocessing and normalization of data. Then, statistically significant prioritized DEGs were used to construct protein–protein interaction networks for the stages for module analysis and biomarker identification. Furthermore, the prioritized DEGs were used to determine the involved GO enrichment and KEGG signaling pathways at various stages of the breast cancer. The recurrence survival rate analysis of the identified gene biomarkers was conducted based on Kaplan–Meier methodology. Furthermore, the identified genes were validated not only by using several classification models but also through screening the experimental literature reports on the target genes. Fourteen (21 genes), nine (17 genes), eight (10 genes), four (7 genes), and six (8 genes) gene modules (total of 53 unique genes out of 63 genes with involving those with the same connectivity degree) were identified for stage I, stage II, stage III, stage IV, and the integrated group. Moreover, SMC4, FN1, FOS, JUN, and KIF11 and RACGAP1 genes with the highest connectivity degrees were in module 1 for abovementioned stages, respectively. The biological processes, cellular components, and molecular functions were demonstrated for outcomes of GO analysis and KEGG pathway assessment. Additionally, the Kaplan–Meier analysis revealed that 33 genes were found to be significant while considering the recurrence-free survival rate as an alternative to overall survival rate. Furthermore, the machine learning calcification models show good performance on the determined biomarkers. Moreover, the literature reports have confirmed all of the identified gene biomarkers for breast cancer. According to the literature evidence, the identified hub genes are highly correlated with HER2-negative breast cancer. The 53-mRNA signature might be a potential gene set for TNM based stages as well as possible therapeutics with potentially good performance in predicting and managing recurrence-free survival rates at stages I, II, III, and IV as well as in the integrated group. Moreover, the identified genes for the TNM-based stages can also be used as mRNA profile signatures to determine the current stage of the breast cancer.

A two-step method for estimating high-dimensional Gaussian graphical models

The problem of estimating high-dimensional Gaussian graphical models has gained much attention in recent years. Most existing methods can be considered as one-step approaches, being either regression-based or likelihood-based. In this paper, we propose a two-step method for estimating the high-dimensional Gaussian graphical model. Specifically, the first step serves as a screening step, in which many entries of the concentration matrix are identified as zeros and thus removed from further consideration. Then in the second step, we focus on the remaining entries of the concentration matrix and perform selection and estimation for nonzero entries of the concentration matrix. Since the dimension of the parameter space is effectively reduced by the screening step, the estimation accuracy of the estimated concentration matrix can be potentially improved. We show that the proposed method enjoys desirable asymptotic properties. Numerical comparisons of the proposed method with several existing methods indicate that the proposed method works well. We also apply the proposed method to a breast cancer microarray data set and obtain some biologically meaningful results.

Diagnosis of breast cancer with Stacked autoencoder and Subspace kNN

Breast cancer is one of the most common and deadliest cancer types in women worldwide. Research on this disease has become very important because early diagnosis stages, clinical applications and the speed of response to treatment are facilitated in diseases such as cancer. In this study, an approach is proposed in which a Subspace kNN algorithm is used together with Stacked autoencoder (SAE) for diagnosis of disease on the breast cancer microarray dataset for the first time. Such hybrid approaches can provide better results when classifying data sets with high-dimensional and uncertainty. The data set used in the study was taken from Kent Ridge-2 database. It consists of 97 samples (51 benign, 46 malicious) and 24482 attributes. The performance of the proposed method was evaluated and the results were compared with other well-known methods of dimension reduction and machine learning. As a result of the comparison, the data set was reduced to 100 attributes by using SAE and Subspace kNN and 91.24% accuracy was achieved. The result obtained provides important classification accuracy, especially in high-dimensional data sets. The importance of this study is that the models that were created by using various classifiers to increase the success rate of the stacked autoencoder-softmax classifier model in the breast cancer microarray data set were applied for the first time. In this regard, it is considered that automation-based studies will provide diagnostic decision support system a solution using the proposed method in future works.

Prognostic and Therapeutic Significance of Adhesion-regulating Molecule 1 in Estrogen Receptor-positive Breast Cancer

IntroductionAdhesion-regulating molecule 1 (ADRM1) is a polyubiquitin receptor on the 26S proteasome. ADRM1 is upregulated in many cancers. In this study, we evaluated the potential prognostic and predictive value of ADRM1 in breast cancer. Materials and MethodsIndividual and pooled survival analyses were performed on 19 independent breast cancer microarray datasets. Gene signatures enriched by ADRM1 were also analyzed in pooled datasets. ResultsGene set enrichment analysis revealed that high expression of ADRM1 was significantly associated with aggressive breast cancer. Our findings revealed that ADRM1 mRNA levels were significantly associated with estrogen receptor (ER) status, progesterone receptor status, tumor size, lymph node status, histologic grade, and molecular subtypes. We also found that higher mRNA ADRM1 expression was significantly correlated with poor survival in patients with breast cancer. The prognostic power of ADRM1 mRNA was similar to the 70-gene wound response genes and 21 gene recurrence score; it was superior to TNM staging. The prognostic value of ADRM1 was better in ER-positive (ER+) breast cancer cases than in ER-negative breast cancer cases. In cases involving stage II breast cancer, radiotherapy significantly reduced the relative risk of OS in the ADRM1-low subgroup. ConclusionADRM1 mRNA levels were significantly related to poor outcome in our breast cancer sample population. It could serve as a prognostic biomarker, especially in ER+ breast cancer and Luminal A breast cancer cases, as well as a predictive biomarker for ER+ breast cancer.

Advances in gene ontology utilization improve statistical power of annotation enrichment.

Gene-annotation enrichment is a common method for utilizing ontology-based annotations in gene and gene-product centric knowledgebases. Effective utilization of these annotations requires inferring semantic linkages by tracing paths through edges in the ontological graph, referred to as relations. However, some relations are semantically problematic with respect to scope, necessitating their omission or modification lest erroneous term mappings occur. To address these issues, we created the Gene Ontology Categorization Suite, or GOcats—a novel tool that organizes the Gene Ontology into subgraphs representing user-defined concepts, while ensuring that all appropriate relations are congruent with respect to scoping semantics. Here, we demonstrate the improvements in annotation enrichment by re-interpreting edges that would otherwise be omitted by traditional ancestor path-tracing methods. Specifically, we show that GOcats’ unique handling of relations improves enrichment over conventional methods in the analysis of two different gene-expression datasets: a breast cancer microarray dataset and several horse cartilage development RNAseq datasets. With the breast cancer microarray dataset, we observed significant improvement (one-sided binomial test p-value = 1.86E-25) in 182 of 217 significantly enriched GO terms identified from the conventional path traversal method when GOcats’ path traversal was used. We also found new significantly enriched terms using GOcats, whose biological relevancy has been experimentally demonstrated elsewhere. Likewise, on the horse RNAseq datasets, we observed a significant improvement in GO term enrichment when using GOcat’s path traversal: one-sided binomial test p-values range from 1.32E-03 to 2.58E-44.

Screening of differentially expressed genes and identification of NUF2 as a prognostic marker in breast cancer.

The aims of the present study were to screen differentially expressed genes (DEGs) in breast cancer (BC) and investigate NDC80 kinetochore complex component (NUF2) as a prognostic marker of BC in detail. A total of four BC microarray datasets, downloaded from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases, were used to screen DEGs. A total of 190 DEGs with the same expression trends were identified in the 4 data-sets, including 65 upregulated and 125 downregulated DEGs. Functional and pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery. The upregulated DEGs were enriched for 10 Gene Ontology (GO) terms and 7 pathways, and the downregulated DEGs were enriched for 10 GO terms and 10 pathways. A protein-protein interaction network containing 149 nodes and 930 edges was constructed using the Search Tool for the Retrieval of Interacting Genes, and 2 functional modules were identified using the MCODE plugin of Cytoscape. Based on an in-depth analysis of module 1 and literature mining, NUF2 was selected for further research. Oncomine database analysis and reverse transcription-quantitative PCR showed that NUF2 is significantly upregulated in BC tissues. In analyses of correlations between NUF2 and clinical pathological characteristics, NUF2 was significantly associated with the malignant features of BC. Using 5 additional datasets from GEO, it was demonstrated that NUF2 has a significant prognostic role in both ER-positive and ER-negative BC. A Gene Set Enrichment Analysis indicated that NUF2 may regulate breast carcinogenesis and progression via cell cycle-related pathways. The results of the present study demonstrated that NUF2 is overexpressed in BC and is significantly associated with its multiple pathological features and prognosis.

Aberrant fatty acid profile and FFAR4 signaling confer endocrine resistance in breast cancer

BackgroundEvidence suggests that fatty acid receptor FFAR4 plays a tumor-promoting role in adipose tissue-adjacent malignancies, but its clinical relevance remains unexplored. Here, we investigated the clinical significance and underlying mechanisms of FFAR4 in hormone receptor-positive breast cancer (HRPBC).MethodsFFAR4 expression was assessed by immunohistochemistry in an exploration cohort of 307 breast cancer cases collected from two independent institutes. Two public breast cancer microarray datasets served as validation cohorts. Gas chromatography-mass spectrometry was employed to identify FFAR4 ligands in normal and cancerous breast tissues. Survival analyses were performed in all cohorts and designated molecular subgroups. Mechanistic studies were performed in vitro in hormone receptor-positive breast cancer cell lines MCF-7 and T-47D.ResultsAberrant FFAR4 expression and endogenous FFAR4 ligands were identified in breast cancer tissues, five FFAR4 ligands showed significantly elevated proportions in cancerous versus normal tissues. In the exploration cohort, FFAR4 was demonstrated as an independent prognostic factor for recurrences (HR: 2.183, 95% CI: 1.360–3.504, P = 0.001) and breast cancer-specific deaths (HR: 2.102, 95% CI: 1.173–3.766, P = 0.013) in HRPBC cases. In contrast, FFAR4 expression was not associated with prognosis in hormone receptor-negative cases. In the validation cohorts, FFAR4 mRNA levels were also observed to be associated with disease recurrence in estrogen receptor-positive cases, but not so in estrogen receptor-negative cases. FFAR4 activation by endogenous ligands and a synthetic ligand TUG891 significantly dampened tamoxifen’s efficacy on HRPBC cells, whereas FFAR4 knockdown or antagonist AH7614 abrogated this effect. Furthermore, FFAR4-induced tamoxifen resistance was dependent on ERK and AKT pathways in HRPBC.ConclusionsOur results establish a novel role of FFAR4 and its ligands in the complicated interactions between tissue lipid profile and cancer biology. FFAR4 signaling confers tamoxifen resistance in HRPBC cell line and FFAR4 expression can serve as a prognostic biomarker for tamoxifen-treated HRPBC patients. FFAR4 may serve as a potential target for anti-breast cancer therapies, especially in endocrine resistant cases.

Breast cancer is associated with methylation and expression of the a disintegrin and metalloproteinase domain33 (ADAM33) gene affected by endocrine‑disrupting chemicals.

A disintegrin and metalloproteinase domain33 (ADAM33) gene is a transmembrane glycoprotein that mediates changes in cell adhesion and plays an important role in cancer progression. Since bisphenolA (BPA) and phthalates are epigenetically toxic, the purpose of this study was to examine whether BPA and phthalate metabolites, including monoethyl phthalate(MEP), mono‑n‑butyl phthalate (MBP), mono‑isobutyl phthalate(MIBP), mono(2‑ethylhexyl) phthalate(MEHP), mono(2‑ethyl‑5‑hydroxyhexyl) phthalate (MEHHP), mono(2‑ethyl‑5‑carboxypentyl) phthalate (MECPP), and mono(2‑ethyl‑5‑oxohexyl) phthalate (MEOHP), have an epigenetic impact on ADAM33 and the incidence of breast cancer. CpG islands of breast cancer microarraydatasets obtained from the Gene Expression Omnibus (GEO) were used to assess the ADAM33 methylation profile. We designed a case‑control study including 44cases and 22age‑matched controls to detect the methylation status of intron1 in ADAM33 from peripheral blood mononuclear cells (PBMCs) in blood, using BSP, nested PCR, and bisulfite sequencing, and measured the in vivo gene expression of ADAM33 and the urinary concentrations of endocrine‑disrupting chemicals(EDCs), using real‑time PCR, high‑performance liquid chromatography(HPLC) and liquid chromatography-mass spectrometry(LC‑MS). Only one dataset, GSE32393, reached significance(P=0.016). ADAM33 expression and methylation frequencies at CpG site3 in intron1 were higher in the control group. We found a positive association between intron1 methylation level and ADAM33 expression as well as urinary concentrations of MEHHP, MECPP, MEOHP and Σ4MEHP (the sum of MEHP, MECPP, MEHHP, and MEOHP) in the cases. This study suggests that metabolites of phthalate such as MEHHP, MECPP, MEOHP and Σ4MEHP may increase the intron1 methylation level to elevate ADAM33 gene expression and have a protective effect on reducing the risk of breast cancer.

An integrated bioinformatical analysis to evaluate the role of KIF4A as a prognostic biomarker for breast cancer.

PurposeThe aim of this study was to investigate the diagnostic and prognostic value of human kinesin family member 4A (KIF4A) as an effective biomarker for breast cancer.Materials and methodsCancer Genome Atlas data and 12 independent public breast cancer microarray data sets were downloaded and analyzed using individual and pooled approaches.ResultsThe results of our study revealed a strong and positive correlation between KIF4A expression and malignant features of breast cancer. KIF4A had a strong prognostic value in both ER-positive and ER-negative breast cancers comparable to or even better than tumor size, lymph node invasion, and Elston grade. We also found that KIF4A might be the target gene of microRNA-335, which can suppress KIF4A expression by targeting the 3′-untranslated region of its mRNA.ConclusionKIF4A might serve as a robust prognostic predictor for breast cancer. Targeting KIF4A activity could be a promising therapeutic option in breast cancer treatment.

Abstract 3497: Network-based systematic inference of lncRNA activity in breast cancer

Abstract The production of lncRNA is widespread across the human genome, yet the functions of lncRNAs and the phenotypes resulting from their activation have remained largely unstudied. In cancer, several lncRNAs have been found to associate with disease development and poor patient prognosis including HOTAIR and MALAT1. Recent studies have been performed using lncRNA microarrays or re-annotated probes from coding gene microarrays to systematically investigate their roles in cancer. However, datasets from lncRNA microarrays are typically not accompanied by comprehensive clinical information that can be used to draw associations between lncRNA expression and clinical phenotypes. Furthermore, lncRNA stability and cellular localization is highly variable suggesting that expression-based inference of lncRNA activity may not always be an accurate measure of its activity. To address this issue, we introduce an activity-based approach whereby we analyze the expression of lncRNA target genes to infer lncRNA activity. Specifically, we construct a breast cancer specific lncRNA-target gene network from RNA-seq data using a network reconstruction algorithm. We then apply this network to several breast cancer microarray datasets to systematically investigate lncRNA activity and their association with clinical phenotypes. Notably, we find that certain lncRNAs are active in specific molecular subtypes of breast cancer even though their expression levels do not reveal significant differences. Lastly, we performed survival analysis to systematically screen for novel lncRNAs whose activity associate with patient prognosis. Our results suggest that activity-based inference of lncRNAs can identify novel lncRNAs that may serve as therapeutic targets or biomarkers in breast cancer. Citation Format: Matthew Ung, Daniel Mattox, George Wang, Chao Cheng. Network-based systematic inference of lncRNA activity in breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3497. doi:10.1158/1538-7445.AM2017-3497

Improving information retrieval in functional analysis

Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities.

Prognostic and therapeutic value of mitochondrial serine hydroxyl-methyltransferase 2 as a breast cancer biomarker.

Mitochondrial serine hydroxylmethyltransferase 2 (SHMT2) is a key enzyme in the serine/glycine synthesis pathway. SHMT2 has been implicated as a critical component for tumor cell survival. The aim of the present study was to evaluate the prognostic value and efficiency of SHMT2 as a biomarker in patients with breast cancer. Individual and pooled survival analyses were performed on five independent breast cancer microarray datasets. Gene signatures enriched by SHMT2 were also analyzed in these datasets. SHMT2 protein expression was detected using immunohistochemistry (IHC) assay in 128 breast cancer cases. Gene set enrichment analysis revealed that SHMT2 was significantly associated with gene signatures of mitochondrial module, cancer invasion, metastasis and poor survival among breast cancer patients (p<0.05). The clinical relevance of SHMT2 was validated on IHC data. The mitochondrial localization of SHMT2 protein was visualized on IHC staining. Independent and pooled analysis confirmed that SHMT2 expression was associated with breast cancer tumor aggressiveness (TNM staging and Elson grade) in a dose-dependent manner (p<0.05). The prognostic performance of SHMT2 mRNA was comparable to other gene signatures and proved superior to TNM staging. Further analysis results indicated that SHMT2 had better prognostic value for estrogen receptor (ER)-negative breast cancer patients, compared to ER-positive patients. In cases involving stage IIb breast cancer, chemotherapy significantly extended survival time among patients with high SHMT2 expression. These results indicate that SHMT2 may be a valuable prognostic biomarker in ER-negative breast cancer cases. Furthermore, SHMT2 may be a potential target for breast cancer treatment and drug discovery.

Ranking, selecting, and prioritising genes with desirability functions

In functional genomics experiments, researchers often select genes to follow-up or validate from a long list of differentially expressed genes. Typically, sharp thresholds are used to bin genes into groups such as significant/non-significant or fold change above/below a cut-off value, and ad hoc criteria are also used such as favouring well-known genes. Binning, however, is inefficient and does not take the uncertainty of the measurements into account. Furthermore, p-values, fold-changes, and other outcomes are treated as equally important, and relevant genes may be overlooked with such an approach. Desirability functions are proposed as a way to integrate multiple selection criteria for ranking, selecting, and prioritising genes. These functions map any variable to a continuous 0–1 scale, where one is maximally desirable and zero is unacceptable. Multiple selection criteria are then combined to provide an overall desirability that is used to rank genes. In addition to p-values and fold-changes, further experimental results and information contained in databases can be easily included as criteria. The approach is demonstrated with a breast cancer microarray data set. The functions and an example data set can be found in the desiR package on CRAN (https://cran.r-project.org/web/packages/desiR/) and the development version is available on GitHub (https://github.com/stanlazic/desiR).

Abstract 1182: Metformin affects breast cancer cell growth and disturbs an IGF1/insulin related gene network that correlates with breast cancer progression

Abstract Background Obesity and the insulin resistance syndrome are risk factors for breast cancer and might also affect breast cancer progression. The anti-diabetic drug Metformin (METF) reduces the breast cancer risk in diabetic women. Insulin like growth factor 1 (IGF1) and insulin are involved in breast cancer tumorigenesis and progression. We tested the effect of METF on the IGF1/insulin pathway and its involvement in breast cancer progression. Methods We developed a prognostic signature based on IGF1/insulin pathway genes using the Stockholm breast cancer microarray dataset of 149 cases for training and primary validation and the Uppsala dataset of 249 for external validation. The effect of METF on the prognostic gene set identified was tested in vitro on a panel of breast cancer cell lines. METF effects on proliferation and glucose metabolism were analyzed in vitro and in vivo. The insulin receptor substrate 2 (IRS2) was silenced by transfection with shRNA-lentiviral vectors. Xenograft growth, in the presence and absence of METF, was studied and 18FDG-uptake was measured in vitro and in vivo. Results A 15-gene signature (Insulin sensitivity score, ISS) was developed and predicted breast cancer metastasis with an accuracy similar to the Recurrence Score. ISS genes were expressed at variable levels in a breast cancer cell line panel and showed variable responsiveness to METF. The high expression correlation among the ISS genes observed in untreated breast cancer cell lines was lost upon treatment with METF. METF reduced breast cancer cell growth in vitro with IC50 values ranging from 1mM to 25mM. Growth of MDA-MB-231 cells and hyper-invasive subpopulations derived therefrom was reduced in vivo by oral administration of METF to xenografted nude mice. Response to METF in terms of IC50 values correlated with basal expression of the 15 ISS genes with the strongest inverse correlation observed for IRS2. Stable silencing of IRS2 reduced the MDA-231 cell responsiveness to METF in vitro. Discussion METF acts on the insulin/IGF1 axis by disturbing a network of breast cancer progression related genes and appears to depend in its action on the expression of IRS2 that inversely correlates with the sensitivity of cell lines to the drug. The disruption of the ISS gene network is expected to correlate with an effect on breast cancer growth and progression and in fact, mouse xenografts show reduced growth upon treatment with METF. IRS2 appears to be a major mediator of METF effects. Citation Format: Alessia I. Esposito, Adriana Amaro, Giovanna Angelini, Laura Emionite, Alessandra Gennari, Stefano Indraccolo, Davide Maggi, Cecilia Marini, Barabara Salani, Gianmario Sambuceti, Maria Pia Sormani, Ulrich Pfeffer. Metformin affects breast cancer cell growth and disturbs an IGF1/insulin related gene network that correlates with breast cancer progression. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1182. doi:10.1158/1538-7445.AM2015-1182

DECODE: an integrated differential co-expression and differential expression analysis of gene expression data.

BackgroundBoth differential expression (DE) and differential co-expression (DC) analyses are appreciated as useful tools in understanding gene regulation related to complex diseases. The performance of integrating DE and DC, however, remains unexplored.ResultsIn this study, we proposed a novel analytical approach called DECODE (Differential Co-expression and Differential Expression) to integrate DC and DE analyses of gene expression data. DECODE allows one to study the combined features of DC and DE of each transcript between two conditions. By incorporating information of the dependency between DC and DE variables, two optimal thresholds for defining substantial change in expression and co-expression are systematically defined for each gene based on chi-square maximization. By using these thresholds, genes can be categorized into four groups with either high or low DC and DE characteristics. In this study, DECODE was applied to a large breast cancer microarray data set consisted of two thousand tumor samples. By identifying genes with high DE and high DC, we demonstrated that DECODE could improve the detection of some functional gene sets such as those related to immune system, metastasis, lipid and glucose metabolism. Further investigation on the identified genes and the associated functional pathways would provide an additional level of understanding of complex disease mechanism.ConclusionsBy complementing the recent DC and the traditional DE analyses, DECODE is a valuable methodology for investigating biological functions of genes exhibiting disease-associated DE and DC combined characteristics, which may not be easily revealed through DC or DE approach alone.DECODE is available at the Comprehensive R Archive Network (CRAN): http://cran.r-project.org/web/packages/decode/index.html.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0582-4) contains supplementary material, which is available to authorized users.

Prognostic value of a 92-probe signature in breast cancer.

Clinical applications of gene expression signatures in breast cancer prognosis still remain limited due to poor predictive strength of single training datasets and appropriate invariable platforms. We proposed a gene expression signature by reducing baseline differences and analyzing common probes among three recent Affymetrix U133 plus 2 microarray data sets. Using a newly developed supervised method, a 92-probe signature found in this study was associated with overall survival. It was robustly validated in four independent data sets and then repeated on three subgroups by incorporating 17 breast cancer microarray datasets. The signature was an independent predictor of patients' survival in univariate analysis [(HR) 1.927, 95% CI (1.237-3.002); p < 0.01] as well as multivariate analysis after adjustment of clinical variables [(HR) 7.125, 95% CI (2.462-20.618); p < 0.001]. Consistent predictive performance was found in different multivariate models in increased patient population (p = 0.002). The survival signature predicted a late metastatic feature through 5-year disease free survival (p = 0.006). We identified subtypes within the lymph node positive (p < 0.001) and ER positive (p = 0.01) patients that best reflected the invasive breast cancer biology. In conclusion using the Common Probe Approach, we present a novel prognostic signature as a predictor in breast cancer late recurrences.

Prognostic and therapeutic significance of ribonucleotide reductase small subunit M2 in estrogen-negative breast cancers.

BackgroundRibonucleotide reductase (RR) is an essential enzyme involved in DNA synthesis. We hypothesized that RR subunit M2 (RRM2) might be a novel prognostic and predictive biomarker for estrogen receptor (ER)-negative breast cancers.MethodsIndividual and pooled survival analyses were conducted on six independent large-scale breast cancer microarray data sets; and findings were validated on a human breast tissue set (ZJU set).ResultsGene set enrichment analysis revealed that RRM2-high breast cancers were significantly enriched for expression of gene sets that increased in proliferation, invasiveness, undifferentiation, embryonic stem/progenitor-like phenotypes, and poor patient survival (p < 0.01). Independent and pooled analyses verified that increased RRM2 mRNA levels were associated with poor patient outcome in a dose-dependent manner. The prognostic power of RRM2 mRNA was comparable to multiple gene signatures, and it was superior to TNM stage. In ER-negative breast cancers, RRM2 showed more prognostic power than that in ER-positive breast cancers. Further analysis indicated that RRM2 was a more accurate prognostic biomarker for ER-negative breast cancers than the pathoclinical indicators and uPA. A new RR inhibitor, COH29, could significantly enhance the chemosensitivity to doxorubicin in ER-negative MDA-MB-231 cells, but not in ER-positive MCF-7 cells.ConclusionRRM2 appears to be a promising prognostic biomarker and therapeutic target for ER-negative breast cancer patients.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2407-14-664) contains supplementary material, which is available to authorized users.

On the identification of circulating tumor cells in breast cancer.

Breast cancer is a highly heterogeneous disease and very common among western women. The main cause of death is not the primary tumor but its metastases at distant sites, such as lymph nodes and other organs (preferentially lung, liver, and bones). The study of circulating tumor cells (CTCs) in peripheral blood resulting from tumor cell invasion and intravascular filtration highlights their crucial role concerning tumor aggressiveness and metastasis. Genomic research regarding CTCs monitoring for breast cancer is limited due to the lack of indicative genes for their detection and isolation. Instead of direct CTC detection, in our study, we focus on the identification of factors in peripheral blood that can indirectly reveal the presence of such cells. Using selected publicly available breast cancer and peripheral blood microarray datasets, we follow a two-step elimination procedure for the identification of several discriminant factors. Our procedure facilitates the identification of major genes involved in breast cancer pathology, which are also indicative of CTCs presence.