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Background Breast cancer tumors are known to be highly heterogeneous and differences in their metabolic phenotypes, especially at protein level, are less well-understood. Profiling of metabolism-related proteins harbors the potential to establish new patient stratification regimes and biomarkers promoting individualized therapy. In our study, we aimed to examine the relationship between metabolism-associated protein expression profiles and clinicopathological characteristics in a large cohort of breast cancer patients. Methods Breast cancer specimens from 801 consecutive patients, diagnosed between 2009 and 2011, were investigated using reverse phase protein arrays (RPPA). Patients were treated in accordance with national guidelines in five certified German breast centers. To obtain quantitative expression data, 37 antibodies detecting proteins relevant to cancer metabolism, were applied. Hierarchical cluster analysis and individual target characterization were performed. Clustering results and individual protein expression patterns were associated with clinical data. The Kaplan-Meier method was used to estimate survival functions. Univariate and multivariate Cox regression models were applied to assess the impact of protein expression and other clinicopathological features on survival. Results We identified three metabolic clusters of breast cancer, which do not reflect the receptor-defined subtypes, but are significantly correlated with overall survival (OS, p  ≤ 0.03) and recurrence-free survival (RFS, p  ≤ 0.01). Furthermore, univariate and multivariate analysis of individual protein expression profiles demonstrated the central role of serine hydroxymethyltransferase 2 (SHMT2) and amino acid transporter ASCT2 (SLC1A5) as independent prognostic factors in breast cancer patients. High SHMT2 protein expression was significantly correlated with poor OS (hazard ratio (HR) = 1.53, 95% confidence interval (CI) = 1.10–2.12, p  ≤ 0.01) and RFS (HR = 1.54, 95% CI = 1.16–2.04, p  ≤ 0.01). High protein expression of ASCT2 was significantly correlated with poor RFS (HR = 1.31, 95% CI = 1.01–1.71, p  ≤ 0.05). Conclusions Our data confirm the heterogeneity of breast tumors at a functional proteomic level and dissects the relationship between metabolism-related proteins, pathological features and patient survival. These observations highlight the importance of SHMT2 and ASCT2 as valuable individual prognostic markers and potential targets for personalized breast cancer therapy. Trial registration ClinicalTrials.gov, NCT01592825 . Registered on 3 May 2012.

The transcriptomic classification of primary colorectal cancer (CRC) into distinct consensus molecular subtypes (CMSs) is a well‐described strategy for patient stratification. However, the molecular nature of CRC metastases remains poorly investigated. To this end, this study aimed to identify and compare organotropic CMS frequencies in CRC liver and brain metastases. Compared to reported CMS frequencies in primary CRC, liver metastases from CRC patients were CMS4‐enriched and CMS3‐depleted, whereas brain metastases mainly clustered as CMS3 and rarely as CMS4. Regarding overall survival rates, CMS4 was the most favorable subtype for patients with hepatic lesions, followed by CMS1 and CMS2. The survival of patients with brain metastases did not correlate with CMS. However, we identified a CMS3‐related metabolic gene signature, specifically upregulated in central nervous system (CNS)‐infiltrating CRC, as a negative prognostic marker and potential tumor progressor. In summary, subtyping of CRC metastases revealed an organotropic CMS distribution in liver and brain with impact on patient survival. CNS‐infiltrating CRC samples were enriched for CMS3 and predictive metabolic biomarkers, suggesting metabolic dysregulation of CRC cells as a prerequisite for metastatic colonization of the brain. Colorectal cancer (CRC) metastases in liver and brain exhibited organ‐specific frequencies of consensus molecular subtypes (CMS). In terms of overall survival, CMS4 was the most beneficial subtype for patients with CRC metastases in the liver. Brain metastases were enriched for the metabolic subtype (CMS3) and certain metabolic markers that correlated with a poor overall survival of CRC patients.

Background Enrichment analysis is a popular approach to identify pathways or sets of genes which are significantly enriched in the context of differentially expressed genes. The traditional gene set enrichment approach considers a pathway as a simple gene list disregarding any knowledge of gene or protein interactions. In contrast, the new group of so called pathway topology-based methods integrates the topological structure of a pathway into the analysis. Methods We comparatively investigated gene set and pathway topology-based enrichment approaches, considering three gene set and four topological methods. These methods were compared in two extensive simulation studies and on a benchmark of 36 real datasets, providing the same pathway input data for all methods. Results In the benchmark data analysis both types of methods showed a comparable ability to detect enriched pathways. The first simulation study was conducted with KEGG pathways, which showed considerable gene overlaps between each other. In this study with original KEGG pathways, none of the topology-based methods outperformed the gene set approach. Therefore, a second simulation study was performed on non-overlapping pathways created by unique gene IDs. Here, methods accounting for pathway topology reached higher accuracy than the gene set methods, however their sensitivity was lower. Conclusions We conducted one of the first comprehensive comparative works on evaluating gene set against pathway topology-based enrichment methods. The topological methods showed better performance in the simulation scenarios with non-overlapping pathways, however, they were not conclusively better in the other scenarios. This suggests that simple gene set approach might be sufficient to detect an enriched pathway under realistic circumstances. Nevertheless, more extensive studies and further benchmark data are needed to systematically evaluate these methods and to assess what gain and cost pathway topology information introduces into enrichment analysis. Both types of methods for enrichment analysis require further improvements in order to deal with the problem of pathway overlaps.

Pipeline comparisons for gene expression data are highly valuable for applied real data analyses, as they enable the selection of suitable analysis strategies for the dataset at hand. Such pipelines for RNA-Seq data should include mapping of reads, counting and differential gene expression analysis or preprocessing, normalization and differential gene expression in case of microarray analysis, in order to give a global insight into pipeline performances. Four commonly used RNA-Seq pipelines (STAR/HTSeq-Count/edgeR, STAR/RSEM/edgeR, Sailfish/edgeR, TopHat2/Cufflinks/CuffDiff)) were investigated on multiple levels (alignment and counting) and cross-compared with the microarray counterpart on the level of gene expression and gene ontology enrichment. For these comparisons we generated two matched microarray and RNA-Seq datasets: Burkitt Lymphoma cell line data and rectal cancer patient data. The overall mapping rate of STAR was 98.98% for the cell line dataset and 98.49% for the patient dataset. Tophat's overall mapping rate was 97.02% and 96.73%, respectively, while Sailfish had only an overall mapping rate of 84.81% and 54.44%. The correlation of gene expression in microarray and RNA-Seq data was moderately worse for the patient dataset (ρ = 0.67-0.69) than for the cell line dataset (ρ = 0.87-0.88). An exception were the correlation results of Cufflinks, which were substantially lower (ρ = 0.21-0.29 and 0.34-0.53). For both datasets we identified very low numbers of differentially expressed genes using the microarray platform. For RNA-Seq we checked the agreement of differentially expressed genes identified in the different pipelines and of GO-term enrichment results. In conclusion the combination of STAR aligner with HTSeq-Count followed by STAR aligner with RSEM and Sailfish generated differentially expressed genes best suited for the dataset at hand and in agreement with most of the other transcriptomics pipelines.

WNT signaling is a complex process comprising multiple pathways: the canonical β-catenin-dependent pathway and several alternative non-canonical pathways that act in a β-catenin-independent manner. Representing these intricate signaling mechanisms through bioinformatic approaches is challenging. Nevertheless, a simplified but reliable bioinformatic WNT pathway model is needed, which can be further utilized to decipher specific WNT activation states within e.g. high-throughput data. In order to build such a model, we collected, parsed, and curated available WNT signaling knowledge from different pathway databases. The data were assembled to construct computationally suitable models of different WNT signaling cascades in the form of directed signaling graphs. This resulted in four networks representing canonical WNT signaling, non-canonical WNT signaling, the inhibition of canonical WNT signaling and the regulation of WNT signaling pathways, respectively. Furthermore, these networks were integrated with microarray and RNA sequencing data to gain deeper insight into the underlying biology of gene expression differences between MCF-7 and MDA-MB-231 breast cancer cell lines, representing weakly and highly invasive breast carcinomas, respectively. Differential genes up-regulated in the MDA-MB-231 compared to the MCF-7 cell line were found to display enrichment in the gene set originating from the non-canonical network. Moreover, we identified and validated differentially regulated modules representing canonical and non-canonical WNT pathway components specific for the aggressive basal-like breast cancer subtype. In conclusion, we demonstrated that these newly constructed WNT networks reliably reflect distinct WNT signaling processes. Using transcriptomic data, we shaped these networks into comprehensive modules of the genes implicated in the aggressive basal-like breast cancer subtype and demonstrated that non-canonical WNT signaling is important in this context. The topology of these networks can be further refined in the future by integration with complementary data such as protein-protein interactions, in order to gain greater insight into signaling processes.

[This corrects the article DOI: 10.1371/journal.pone.0197162.].[This corrects the article DOI: 10.1371/journal.pone.0197162.].

Background The growth factor heregulin (HRG) potently stimulates epithelial cell survival and proliferation through the binding of its cognate receptor ErbB3 (also known as HER3). ErbB3-dependent signal transmission relies on the dimerization partner ErbB2, a receptor tyrosine kinase that is frequently overexpressed and/or amplified in breast cancer cells. Substantial evidence suggests that deregulated ErbB3 expression also contributes to the transformed phenotype of breast cancer cells. Results By genome-wide screening, we identify 43 microRNAs (miRNAs) that specifically impact HRG-induced activation of the PI3K-Akt pathway. Bioinformatic analysis combined with experimental validation reveals a highly connected molecular miRNA-gene interaction network particularly for the negative screen hits. For selected miRNAs, namely miR-149, miR-148b, miR-326, and miR-520a-3p, we demonstrate the simultaneous downregulation of the ErbB3 receptor and multiple downstream signaling molecules, explaining their efficient dampening of HRG responses and ascribing to these miRNAs potential context-dependent tumor suppressive functions. Conclusions Given the contribution of HRG signaling and the PI3K-Akt pathway in particular to tumorigenesis, this study not only provides mechanistic insight into the function of miRNAs but also has implications for future clinical applications.

GRAPHICAL ABSTRACT

Background Idiopathic nephrotic syndrome (INS) is a heterogenous disease and current classification is based on observational responses to therapies or kidney histology. The National Unified Renal Translational Research Enterprise (NURTuRE)-INS cohort aims to facilitate novel ways of stratifying INS patients to improve disease understanding, therapeutics and design of clinical trials. Methods NURTuRE-INS is a prospective cohort study of children and adults with INS in a linked biorepository. All recruits had at least one sampling visit collecting serum, plasma, urine and blood for RNA and DNA extraction, frozen within 2 hours of collection. Clinical histology slides and biopsy tissue blocks were also collected. Results A total of 739 participants were recruited from 23 centres to NURTuRE-INS, half of whom were diagnosed in childhood [n = 365 (49%)]. The majority were white [n = 525 (71%)] and the median age at recruitment was 32 years (interquartile range 12-54). Steroid-sensitive nephrotic syndrome (SSNS) was the most common clinical diagnosis [n = 518 (70%)]. Of patients diagnosed in childhood who underwent a kidney biopsy, for SSNS (n =103), 76 demonstrated minimal change disease (MCD), whereas for steroid-resistant nephrotic syndrome (n =80), 21 had MCD. Almost all patients diagnosed in adulthood had a kidney biopsy [n = 352 (94%)]; 187 had MCD and 162 had focal segmental glomerulosclerosis. Conclusions NURTuRE-INS is a prospective cohort study with high-quality biosamples and longitudinal data that will assist research into the mechanistic stratification of INS. Samples and data will be available through a Strategic Access and Oversight Committee. Graphical Abstract Graphical Abstract

Putting new findings into the context of available literature knowledge is one approach to deal with the surge of high-throughput data results. Furthermore, prior knowledge can increase the performance and stability of bioinformatic algorithms, for example, methods for network reconstruction. In this review, we examine software packages for the statistical computing framework R, which enable the integration of pathway data for further bioinformatic analyses. Different approaches to integrate and visualize pathway data are identified and packages are stratified concerning their features according to a number of different aspects: data import strategies, the extent of available data, dependencies on external tools, integration with further analysis steps and visualization options are considered. A total of 12 packages integrating pathway data are reviewed in this manuscript. These are supplemented by five R-specific packages for visualization and six connector packages, which provide access to external tools.