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Background Biological networks are often used to describe the relationships between relevant entities, particularly genes and proteins, and are a powerful tool for functional genomics. Many important biological problems can be investigated by comparing biological networks between different conditions or networks obtained with different techniques. Findings We show that contrast subgraphs, a recently introduced technique to identify the most important structural differences between 2 networks, provide a versatile tool for comparing gene and protein networks of diverse origin. We demonstrate the use of contrast subgraphs in the comparison of coexpression networks derived from different subtypes of breast cancer, coexpression networks derived from transcriptomic and proteomic data, and protein–protein interaction networks assayed in different cell lines. Conclusions These examples demonstrate how contrast subgraphs can provide new insight in functional genomics by extracting the gene/protein modules whose connectivity is most altered between 2 conditions or experimental techniques. [ABSTRACT FROM AUTHOR]

Morgana/CHP-1 is a ubiquitously expressed protein able to inhibit ROCK II kinase activity. We have previously demonstrated that morgana haploinsufficiency leads to multiple centrosomes, genomic instability, and higher susceptibility to tumour development. While a large fraction of human cancers has shown morgana down-regulation, a small subset of tumours was shown to express high morgana levels. Here we demonstrate that high morgana expression in different breast cancer subtypes correlates with high tumour grade, mitosis number, and lymph node positivity. Moreover, morgana overexpression induces transformation in NIH-3T3 cells and strongly protects them from various apoptotic stimuli. From a mechanistic point of view, we demonstrate that morgana causes PTEN destabilization, by inhibiting ROCK activity, hence triggering the PI3K/AKT survival pathway. In turn, morgana down-regulation in breast cancer cells that express high morgana levels increases PTEN expression and leads to sensitization of cells to chemotherapy. [ABSTRACT FROM AUTHOR]

MicroRNAs are single-stranded non-coding RNAs that simultaneously down-modulate the expression of multiple genes post-transcriptionally by binding to the 3′UTRs of target mRNAs. Here we used computational methods to predict microRNAs relevant in breast cancer progression. Specifically, we applied different microRNA target prediction algorithms to various groups of differentially expressed protein-coding genes obtained from four breast cancer datasets. Six potential candidates were identified, among them miR-223, previously described to be highly expressed in the tumor microenvironment and known to be actively transferred into breast cancer cells. To investigate the function of miR-223 in tumorigenesis and to define its molecular mechanism, we overexpressed miR-223 in breast cancer cells in a transient or stable manner. Alternatively we overexpressed miR-223 in mouse embryonic fibroblasts or HEK293 cells and used their conditioned medium to treat tumor cells. With both approaches, we obtained elevated levels of miR-223 in tumor cells and observed decreased migration, increased cell death in anoikis conditions and augmented sensitivity to chemotherapy but no effect on adhesion and proliferation. The analysis of miR-223 predicted targets revealed enrichment in cell death and survival-related genes and in pathways frequently altered in breast cancer. Among these genes, we showed that protein levels for STAT5A, ITGA3 and NRAS were modulated by miR-223. In addition, we proved that STAT5A is a direct miR-223 target and highlighted a possible correlation between miR-223 and STAT5A in migration and chemotherapy response. Our investigation revealed that a computational analysis of cancer gene expression datasets can be a relevant tool to identify microRNAs involved in cancer progression and that miR-223 has a prominent role in breast malignancy that could potentially be exploited therapeutically. [ABSTRACT FROM AUTHOR]

Breast cancer is often fatal during its metastatic dissemination. To unravel the role of microRNAs (miRs) during malignancy, we analyzed mi expression in 77 primary breast carcinomas and identified 16 relapse-associated miRs that correlate with survival and/or distinguish tumor subtypes in different datasets. Among them, mi-148b, down-regulated in aggressive breast tumors, was found to be a major coordinator of malignancy. In fact, it is able to oppose various steps of tumor progression when overexpressed in cell lines by influencing invasion, survival to anoikis, extravasation, lung metastasis formation, and chemotherapy response. mi-148b controls malignancy by coordinating a novel pathway involving over 130 genes and, in particular, it directly targets players of the integrin signaling, such as ITGA5, ROCK1, PIK3CA/p110α, and NRAS, as well as CSF1, a growth factor for stroma cells. Our findings reveal the importance of the identified 16 miRs for disease outcome predictions and suggest a critical role for mi-148b in the control of breast cancer progression. [ABSTRACT FROM AUTHOR]

Primary tumor growth induces host tissue responses that are believed to support and promote tumor progression. Identification of the molecular characteristics of the tumor microenvironment and elucidation of its crosstalk with tumor cells may therefore be crucial for improving our understanding of the processes implicated in cancer progression, identifying potential therapeutic targets, and uncovering stromal gene expression signatures that may predict clinical outcome. A key issue to resolve, therefore, is whether the stromal response to tumor growth is largely a generic phenomenon, irrespective of the tumor type or whether the response reflects tumor-specific properties. To address similarity or distinction of stromal gene expression changes during cancer progression, oligonucleotide-based Affymetrix microarray technology was used to compare the transcriptomes of laser-microdissected stromal cells derived from invasive human breast and prostate carcinoma. Invasive breast and prostate cancer-associated stroma was observed to display distinct transcriptomes, with a limited number of shared genes. Interestingly, both breast and prostate tumor-specific dysregulated stromal genes were observed to cluster breast and prostate cancer patients, respectively, into two distinct groups with statistically different clinical outcomes. By contrast, a gene signature that was common to the reactive stroma of both tumor types did not have survival predictive value. Univariate Cox analysis identified genes whose expression level was most strongly associated with patient survival. Taken together, these observations suggest that the tumor microenvironment displays distinct features according to the tumor type that provides survival-predictive value. [ABSTRACT FROM AUTHOR]

Background: The ability to accurately classify cancer patients into risk classes, i.e. to predict the outcome of the pathology on an individual basis, is a key ingredient in making therapeutic decisions. In recent years gene expression data have been successfully used to complement the clinical and histological criteria traditionally used in such prediction. Many "gene expression signatures" have been developed, i.e. sets of genes whose expression values in a tumor can be used to predict the outcome of the pathology. Here we investigate the use of several machine learning techniques to classify breast cancer patients using one of such signatures, the well established 70-gene signature. Results: We show that Genetic Programming performs significantly better than Support Vector Machines, Multilayered Perceptrons and Random Forests in classifying patients from the NKI breast cancer dataset, and comparably to the scoring-based method originally proposed by the authors of the 70-gene signature. Furthermore, Genetic Programming is able to perform an automatic feature selection. Conclusions: Since the performance of Genetic Programming is likely to be improvable compared to the out-of-the-box approach used here, and given the biological insight potentially provided by the Genetic Programming solutions, we conclude that Genetic Programming methods are worth further investigation as a tool for cancer patient classification based on gene expression data. [ABSTRACT FROM AUTHOR]

Simple Summary: Gene expression profiling of tumors is an essential approach for the selection of biomarkers and the investigation of the molecular mechanisms of cancer, but transcriptomic results are often difficult to reproduce due to technical biases, sample heterogeneity, or small sample sizes. Combining many datasets can help to reduce artefacts and improve statistical power. Therefore, we aimed at creating a comprehensive resource of transcriptomic datasets investigating breast cancers, focusing on microdissected tumors, which enable the distinguishing of the contribution of the tumor microenvironment from that of cancer cells. We define robust lists of differentially expressed genes and describe their relationships with clinical features in each cellular compartment, identifying clinically relevant markers that can only be retrieved by measuring their expression in the sole tumor microenvironment. Transcriptome data provide a valuable resource for the study of cancer molecular mechanisms, but technical biases, sample heterogeneity, and small sample sizes result in poorly reproducible lists of regulated genes. Additionally, the presence of multiple cellular components contributing to cancer development complicates the interpretation of bulk transcriptomic profiles. To address these issues, we collected 48 microarray datasets derived from laser capture microdissected stroma or epithelium in breast tumors and performed a meta-analysis identifying robust lists of differentially expressed genes. This was used to create a database with carefully harmonized metadata that we make freely available to the research community. As predicted, combining the results of multiple datasets improved statistical power. Moreover, the separate analysis of stroma and epithelium allowed the identification of genes with different contributions in each compartment, which would not be detected by bulk analysis due to their distinct regulation in the two compartments. Our method can be profitably used to help in the discovery of biomarkers and the identification of functionally relevant genes in both the stroma and the epithelium. This database was made to be readily accessible through a user-friendly web interface. [ABSTRACT FROM AUTHOR]

Breast cancer is a heterogeneous disease whose clinical management is very challenging. Although specific molecular features characterize breast cancer subtypes with different prognosis, the identification of specific markers predicting disease outcome within the single subtypes still lags behind. Both the non-canonical Wingless-type MMTV Integration site (WNT) and the Signal Transducer and Activator of Transcription (STAT)3 pathways are often constitutively activated in breast tumors, and both can induce the small GTPase Ras Homolog Family Member U RhoU. Here we show that RhoU transcription can be triggered by both canonical and non-canonical WNT ligands via the activation of c-JUN N-terminal kinase (JNK) and the recruitment of the Specificity Protein 1 (SP1) transcription factor to the RhoU promoter, identifying for the first time SP1 as a JNK-dependent mediator of WNT signaling. RhoU down-regulation by silencing or treatment with JNK, SP1 or STAT3 inhibitors leads to impaired migration and invasion in basal-like MDA-MB-231 and BT-549 cells, suggesting that STAT3 and SP1 can cooperate to induce high RhoU expression and enhance breast cancer cells migration. Moreover, in vivo concomitant binding of STAT3 and SP1 defines a subclass of genes belonging to the non-canonical WNT and the Interleukin (IL)-6/STAT3 pathways and contributing to breast cancer aggressiveness, suggesting the relevance of developing novel targeted therapies combining inhibitors of the STAT3 and WNT pathways or of their downstream mediators. [ABSTRACT FROM AUTHOR]

Metastasis depends on the ability of tumor cells to establish a relationship with the newly seeded tissue that is conducive to their survival and proliferation. However, the factors that render tissues permissive for metastatic tumor growth have yet to be fully elucidated. Breast tumors arising during pregnancy display early metastatic proclivity, raising the possibility that pregnancy may constitute a physiological condition of permissiveness for tumor dissemination. Here we have shown that during murine gestation, metastasis is enhanced regardless of tumor type, and that decreased NK cell activity is responsible for the observed increase in experimental metastasis. Gene expression changes in pregnant mouse lung and liver were shown to be similar to those detected in premetastatic sites and indicative of myeloid cell infiltration. Indeed, myeloid-derived suppressor cells (MDSCs) accumulated in pregnant mice and exerted an inhibitory effect on NK cell activity, providing a candidate mechanism for the enhanced metastatic tumor growth observed in gestant mice. Although the functions of MDSCs are not yet understood in the context of pregnancy, our observations suggest that they may represent a shared mechanism of immune suppression occurring during gestation and tumor growth. [ABSTRACT FROM AUTHOR]

Abstract: ErbB2 over-expression is detected in approximately 25% of invasive breast cancers and is strongly associated with poor patient survival. We have previously demonstrated that p130Cas adaptor is a crucial mediator of ErbB2 transformation. Here, we analysed the molecular mechanisms through which p130Cas controls ErbB2-dependent invasion in three-dimensional cultures of mammary epithelial cells. Concomitant p130Cas over-expression and ErbB2 activation enhance PI3K/Akt and Erk1/2 MAPK signalling pathways and promote invasion of mammary acini. By using pharmacological inhibitors, we demonstrate that both signalling cascades are required for the invasive behaviour of p130Cas over-expressing and ErbB2 activated acini. Erk1/2 MAPK and PI3K/Akt signalling triggers invasion through distinct downstream effectors involving mTOR/p70S6K and Rac1 activation, respectively. Moreover, in silico analyses indicate that p130Cas expression in ErbB2 positive human breast cancers significantly correlates with higher risk to develop distant metastasis, thus underlying the value of the p130Cas/ErbB2 synergism in regulating breast cancer invasion. In conclusion, high levels of p130Cas favour progression of ErbB2-transformed cells towards an invasive phenotype. [Copyright &y& Elsevier]