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Abstract Aims Studies have reported a strongly varying co‐prevalence of aortic stenosis (AS) and cardiac amyloidosis (CA). We sought to histologically determine the co‐prevalence of AS and CA in patients undergoing transcatheter aortic valve replacement (TAVR). Consequently, we aimed to derive an algorithm to identify cases in which to suspect the co‐prevalence of AS and CA. Methods and results In this prospective, monocentric study, endomyocardial biopsies of 162 patients undergoing TAVR between January 2017 and March 2021 at the University Medical Centre Göttingen were analysed by one pathologist blinded to clinical data using haematoxylin–eosin staining, Elastica van Gieson staining, and Congo red staining of endomyocardial biopsies. CA was identified in only eight patients (4.9%). CA patients had significantly higher N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) levels (4356.20 vs. 1938.00 ng/L, P = 0.034), a lower voltage‐to‐mass ratio (0.73 vs. 1.46 × 10−2 mVm2/g, P = 0.022), and lower transaortic gradients (Pmean 17.5 vs. 38.0 mmHg, P = 0.004) than AS patients. Concomitant CA was associated with a higher prevalence of post‐procedural acute kidney injury (50.0% vs. 13.1%, P = 0.018) and sudden cardiac death [SCD; P (log‐rank test) = 0.017]. Following propensity score matching, 184 proteins were analysed to identify serum biomarkers of concomitant CA. CA patients expressed lower levels of chymotrypsin (P = 0.018) and carboxypeptidase 1 (P = 0.027). We propose an algorithm using commonly documented parameters—stroke volume index, ejection fraction, NT‐proBNP levels, posterior wall thickness, and QRS voltage‐to‐mass ratio—to screen for CA in AS patients, reaching a sensitivity of 66.6% with a specificity of 98.1%. Conclusions The co‐prevalence of AS and CA was lower than expected, at 4.9%. Despite excellent 1 year mortality, AS + CA patients died significantly more often from SCD. We propose a multimodal algorithm to facilitate more effective screening for CA containing parameters commonly documented during clinical routine. Proteomic biomarkers may yield additional information in the future.

Abstract Background Osteoclasts are the tissue-specific macrophage population of the bone and unique in their bone-resorbing activity. Hence, they are fundamental for bone physiology in health and disease. However, efficient protocols for the isolation and study of primary human osteoclasts are scarce. In this study, we aimed to establish a protocol, which enables the efficient differentiation of functional human osteoclasts from monocytes. Results Human monocytes were isolated through a double-density gradient from donor blood. Compared to standard differentiation schemes in polystyrene cell culture dishes, the yield of multinuclear osteoclasts was significantly increased upon initial differentiation of monocytes to macrophages in fluorinated ethylene propylene (FEP) Teflon bags. This initial differentiation phase was then followed by the development of terminal osteoclasts by addition of Receptor Activator of NF-κB Ligand (RANKL). High concentrations of RANKL and Macrophage colony-stimulating factor (M-CSF) as well as an intermediate cell density further supported efficient cell differentiation. The generated cells were highly positive for CD45, CD14 as well as the osteoclast markers CD51/ITGAV and Cathepsin K/CTSK, thus identifying them as osteoclasts. The bone resorption of the osteoclasts was significantly increased when the cells were differentiated from macrophages derived from Teflon bags compared to macrophages derived from conventional cell culture plates. Conclusion Our study has established a novel protocol for the isolation of primary human osteoclasts that improves osteoclastogenesis in comparison to the conventionally used cultivation approach.

Abstract Background Colorectal cancer liver metastases (CRCLM) are associated with a poor prognosis, reflected by a five-year survival rate of 14%. Anti-angiogenic therapy through anti-VEGF antibody administration is one of the limited therapies available. However, only a subgroup of metastases uses sprouting angiogenesis to secure their nutrients and oxygen supply, while others rely on vessel co-option (VCO). The distinct mode of vascularization is reflected by specific histopathological growth patterns (HGPs), which have proven prognostic and predictive significance. Nevertheless, their molecular mechanisms are poorly understood. Methods We evaluated CRCLM from 225 patients regarding their HGP and clinical data. Moreover, we performed spatial (21,804 spots) and single-cell (22,419 cells) RNA sequencing analyses to explore molecular differences in detail, further validated in vitro through immunohistochemical analysis and patient-derived organoid cultures. Results We detected specific metabolic alterations and a signature of WNT signalling activation in metastatic cancer cells related to the VCO phenotype. Importantly, in the corresponding healthy liver of CRCLM displaying sprouting angiogenesis, we identified a predominantly expressed capillary subtype of endothelial cells, which could be further explored as a possible predictor for HGP relying on sprouting angiogenesis. Conclusion These findings may prove to be novel therapeutic targets to the treatment of CRCLM, in special the ones relying on VCO.

Abstract Traumatic brain injury (TBI) is frequently associated with neuropsychiatric impairments such as symptoms of post-traumatic stress disorder (PTSD), which can be screened using self-report instruments such as the Post-Traumatic Stress Disorder Checklist for DSM-5 (PCL-5). The current study aims to inspect the factorial validity and cross-linguistic equivalence of the PCL-5 in individuals after TBI with differential severity. Data for six language groups (n ≥ 200; Dutch, English, Finnish, Italian, Norwegian, Spanish) were extracted from the CENTER-TBI study database. Factorial validity of PTSD was evaluated using confirmatory factor analyses (CFA), and compared between four concurrent structural models. A multi-group CFA approach was utilized to investigate the measurement invariance (MI) of the PCL-5 across languages. All structural models showed satisfactory goodness-of-fit with small between-model variation. The original DSM-5 model for PTSD provided solid evidence of MI across the language groups. The current study underlines the validity of the clinical DSM-5 conceptualization of PTSD and demonstrates the comparability of PCL-5 symptom scores between language versions in individuals after TBI. Future studies should apply MI methods to other sociodemographic (e.g., age, gender) and injury-related (e.g., TBI severity) characteristics to improve the monitoring and clinical care of individuals suffering from PTSD symptoms after TBI.

The oncological impact of portal vein resection (PVR) in pancreatic cancer surgery remains contradictory. Different variables might have an impact on the outcome. The aim of the present study is the retrospective assessment of the frequency of PVR, histological confirmation of tumor infiltration, and comparison of oncological outcomes in PVR patients. We retrieved n = 90 patients from a prospectively collected data bank who underwent pancreas surgery between 2012 and 2019 at the University Medical Centre Göttingen (Germany) and showed a histologically confirmed pancreatic ductal adenocarcinoma (PDAC). While 50 patients (55.6%) underwent pancreatic resection combined with PVR, 40 patients (44.4%) received standard pancreatic surgery. Patients with distal pancreatectomy or a tumor other than PDAC were excluded. PVR was performed either as local excision or circular resection of the portal vein. Clinical/patient data and follow-ups were retrieved. The median follow-up period was 20.5 months. Regarding the oncological outcome, a statistically poorer CSS (p = 0.04) was observed in PVR patients. There was no difference (p = 0.18) in patients’ outcomes between tangential and complete PVR, while n = 21 (42% of PVR patients) showed portal vein infiltration. The correlation between performed PVR and resection status was statistically significant: 48.6% of PVR patients achieved R0 resections compared to 75% in non-PVR patients (p = 0.03). Patients who underwent PDAC surgery with PVR show a significantly poorer outcome regardless of PVR type. Tumor size and R-status remain two important variables significantly associated with outcome. Since there is a lack of standardization for the indication of PVR, it remains unknown if the need for resection of vein structures during pancreatic resection represents the biological aggressiveness of the tumor or is biased by the experience of the surgeon.

Abstract Background Triple negative breast cancer (TNBC) is the most aggressive subtype of breast cancer (BC). Treatment options for TNBC patients are limited and further insights into disease aetiology are needed to develop better therapeutic approaches. microRNAs’ ability to regulate multiple targets could hold a promising discovery approach to pathways relevant for TNBC aggressiveness. Thus, we address the role of miRNAs in controlling three signalling pathways relevant to the biology of TNBC, and their downstream phenotypes. Methods To identify miRNAs regulating WNT/β-catenin, c-Met, and integrin signalling pathways, we performed a high-throughput targeted proteomic approach, investigating the effect of 800 miRNAs on the expression of 62 proteins in the MDA-MB-231 TNBC cell line. We then developed a novel network analysis, Pathway Coregulatory (PC) score, to detect miRNAs regulating these three pathways. Using in vitro assays for cell growth, migration, apoptosis, and stem-cell content, we validated the function of candidate miRNAs. Bioinformatic analyses using BC patients’ datasets were employed to assess expression of miRNAs as well as their pathological relevance in TNBC patients. Results We identified six candidate miRNAs coordinately regulating the three signalling pathways. Quantifying cell growth of three TNBC cell lines upon miRNA gain-of-function experiments, we characterised miR-193b as a strong and consistent repressor of proliferation. Importantly, the effects of miR-193b were stronger than chemical inhibition of the individual pathways. We further demonstrated that miR-193b induced apoptosis, repressed migration, and regulated stem-cell markers in MDA-MB-231 cells. Furthermore, miR-193b expression was the lowest in patients classified as TNBC or Basal compared to other subtypes. Gene Set Enrichment Analysis showed that miR-193b expression was significantly associated with reduced activity of WNT/β-catenin and c-Met signalling pathways in TNBC patients. Conclusions Integrating miRNA-mediated effects and protein functions on networks, we show that miRNAs predominantly act in a coordinated fashion to activate or repress connected signalling pathways responsible for metastatic traits in TNBC. We further demonstrate that our top candidate, miR-193b, regulates these phenotypes to an extent stronger than individual pathway inhibition, thus emphasizing that its effect on TNBC aggressiveness is mediated by the coordinated repression of these functionally interconnected pathways.

Abstract Background Breast cancer has been associated with activation of the WNT signaling pathway, although no driver mutations in WNT genes have been found yet. Instead, a high expression of the alternative WNT receptor ROR2 was observed, in particular in breast cancer brain metastases. However, its respective ligand and downstream signaling in this context remained unknown. Methods We modulated the expression of ROR2 in human breast cancer cells and characterized their gene and protein expression by RNA-Seq, qRT-PCR, immunoblots and reverse phase protein array (RPPA) combined with network analyses to understand the molecular basis of ROR2 signaling in breast cancer. Using co-immunoprecipitations, we verified the interaction of ROR2 with the identified ligand, WNT11. The functional consequences of WNT11/ROR2 signaling for tumor cell aggressiveness were assessed by microscopy, impedance sensing as well as viability and invasion assays. To evaluate the translational significance of our findings, we performed gene set enrichment, expression and survival analyses on human breast cancer brain metastases. Results We found ROR2 to be highly expressed in aggressive breast tumors and associated with worse metastasis-free survival. ROR2 overexpression induced a BRCAness-like phenotype in a cell-context specific manner and rendered cells resistant to PARP inhibition. High levels of ROR2 were furthermore associated with defects in cell morphology and cell-cell-contacts leading to increased tumor invasiveness. On a molecular level, ROR2 overexpression upregulated several non-canonical WNT ligands, in particular WNT11. Co-immunoprecipitation confirmed that WNT11 indeed interacts with the cysteine-rich domain of ROR2 and triggers its invasion-promoting signaling via RHO/ROCK. Knockdown of WNT11 reversed the pro-invasive phenotype and the cellular changes in ROR2-overexpressing cells. Conclusions Taken together, our study revealed a novel auto-stimulatory loop in which ROR2 triggers the expression of its own ligand, WNT11, resulting in enhanced tumor invasion associated with breast cancer metastasis.

Abstract Machine reading (MR) is essential for unlocking valuable knowledge contained in millions of existing biomedical documents. Over the last two decades1,2, the most dramatic advances in MR have followed in the wake of critical corpus development3. Large, well-annotated corpora have been associated with punctuated advances in MR methodology and automated knowledge extraction systems in the same way that ImageNet4 was fundamental for developing machine vision techniques. This study contributes six components to an advanced, named entity analysis tool for biomedicine: (a) a new, Named Entity Recognition Ontology (NERO) developed specifically for describing textual entities in biomedical texts, which accounts for diverse levels of ambiguity, bridging the scientific sublanguages of molecular biology, genetics, biochemistry, and medicine; (b) detailed guidelines for human experts annotating hundreds of named entity classes; (c) pictographs for all named entities, to simplify the burden of annotation for curators; (d) an original, annotated corpus comprising 35,865 sentences, which encapsulate 190,679 named entities and 43,438 events connecting two or more entities; (e) validated, off-the-shelf, named entity recognition (NER) automated extraction, and; (f) embedding models that demonstrate the promise of biomedical associations embedded within this corpus.

Psychopathological symptoms are common sequelae after traumatic brain injury (TBI), leading to increased personal and societal burden. Previous studies on factors influencing Post-traumatic Stress Disorder (PTSD), Generalized Anxiety Disorder (GAD), and Major Depressive Disorder (MDD) after TBI have produced inconclusive results, partly due to methodological limitations. The current study investigated the influence of commonly proposed factors on the clinical impairment, occurrence, frequency, and intensity of symptoms of PTSD, GAD, and MDD after TBI. The study sample comprised 2069 individuals (65% males). Associations between psychopathological outcomes and sociodemographic, premorbid, and injury-related factors were analyzed using logistic regression, standard, and zero-inflated negative binomial models. Overall, individuals experienced moderate levels of PTSD, GAD, and MDD. Outcomes correlated with early psychiatric assessments across domains. The clinical impairment, occurrence, frequency, and intensity of all outcomes were associated with the educational level, premorbid psychiatric history, injury cause, and functional recovery. Distinct associations were found for injury severity, LOC, and clinical care pathways with PTSD; age and LOC:sex with GAD; and living situation with MDD, respectively. The use of suitable statistical models supported the identification of factors associated with the multifactorial etiology of psychopathology after TBI. Future research may apply these models to reduce personal and societal burden.

Abstract Background The question whether lymphocyte radiosensitivity is representative of patients’ response to radiotherapy (RT) remains unsolved. We analyzed lymphocyte cytogenetic damage in patients who were homogeneously treated with preoperative radiochemotherapy (RCT) for rectal cancer within clinical trials. We tested for interindividual variation and consistent radiosensitivity after in-vivo and in-vitro irradiation, analyzed the effect of patients’ and RCT characteristics on cytogenetic damage, and tested for correlations with patients’ outcome in terms of tumor response, survival and treatment-related toxicity. Methods The cytokinesis-block micronucleus cytome (CBMNcyt) assay was performed on the peripheral blood lymphocytes (PBLCs) of 134 patients obtained before, during, at the end of RCT, and during the 2-year follow-up. A subset of PBLCs obtained before RCT was irradiated in-vitro with 3 Gy. RCT included 50.4 Gy of pelvic RT with 5-fluorouracil (5-FU) alone (n = 78) or 5-FU plus oxaliplatin (n = 56). The analyzed variables included patients’ age, gender, RT characteristics (planning target volume size [PTV size], RT technique), and chemotherapy characteristics (5-FU plasma levels, addition of oxaliplatin). Outcome was analyzed as tumor regression, patient survival, and acute and late toxicity. Results Cytogenetic damage increased significantly with the radiation dose and varied substantially between individuals. Women were more sensitive than men; no significant age-dependent differences were observed. There was a significant correlation between the cytogenetic damage after in-vitro irradiation and in-vivo RCT. We found a significant effect of the PTV size on the yields of cytogenetic damage after RCT, while the RT technique had no effect. Neither the addition of oxaliplatin nor the 5-FU levels influenced cytogenetic damage. We found no correlation between patient outcome and the cytogenetic damage. Conclusions We found consistent cytogenetic damage in lymphocytes after in-vivo RCT and in-vitro irradiation. Gender was confirmed as a well-known, and the PTV size was identified as a less well-known influencing variable on lymphocyte cytogenetic damage after partial-body irradiation. A consistent level of cytogenetic damage after in-vivo and in-vitro irradiation may indicate the importance of genetic factors for individual radiosensitivity. However, we found no evidence that in-vivo or in-vitro irradiation-induced cytogenetic damage is an adequate biomarker for the response to RCT in rectal cancer patients.

Abstract Background Contemporary deep learning approaches show cutting-edge performance in a variety of complex prediction tasks. Nonetheless, the application of deep learning in healthcare remains limited since deep learning methods are often considered as non-interpretable black-box models. However, the machine learning community made recent elaborations on interpretability methods explaining data point-specific decisions of deep learning techniques. We believe that such explanations can assist the need in personalized precision medicine decisions via explaining patient-specific predictions. Methods Layer-wise Relevance Propagation (LRP) is a technique to explain decisions of deep learning methods. It is widely used to interpret Convolutional Neural Networks (CNNs) applied on image data. Recently, CNNs started to extend towards non-Euclidean domains like graphs. Molecular networks are commonly represented as graphs detailing interactions between molecules. Gene expression data can be assigned to the vertices of these graphs. In other words, gene expression data can be structured by utilizing molecular network information as prior knowledge. Graph-CNNs can be applied to structured gene expression data, for example, to predict metastatic events in breast cancer. Therefore, there is a need for explanations showing which part of a molecular network is relevant for predicting an event, e.g., distant metastasis in cancer, for each individual patient. Results We extended the procedure of LRP to make it available for Graph-CNN and tested its applicability on a large breast cancer dataset. We present Graph Layer-wise Relevance Propagation (GLRP) as a new method to explain the decisions made by Graph-CNNs. We demonstrate a sanity check of the developed GLRP on a hand-written digits dataset and then apply the method on gene expression data. We show that GLRP provides patient-specific molecular subnetworks that largely agree with clinical knowledge and identify common as well as novel, and potentially druggable, drivers of tumor progression. Conclusions The developed method could be potentially highly useful on interpreting classification results in the context of different omics data and prior knowledge molecular networks on the individual patient level, as for example in precision medicine approaches or a molecular tumor board.

Abstract Background Brain metastasis represents a major complication with a significantly shorter overall survival of many oncological diseases, in particular of lung cancer, breast cancer and malignant melanoma patients. However, despite the poor prognosis, sometimes clinical decision-making, between on the one hand not to harm the patient and on the other hand not withholding a potential therapeutic option, is very challenging. Thus the aim of this retrospective study was to compare various scores, including scores for activities of daily living (ADL) before resection of brain metastases and to analyse their impact on survival. Methods Our single institution retrospective patient cohort (N = 100) with a median age of 63.6 years, which had all undergone resection of one or more brain metastases, was categorized using the original patient files. The cohort includes 52 patients with lung cancer, 27 patients with breast cancer, 8 patients with colorectal carcinoma and 13 patients with kidney cancer. To categorize, we used different score systems which were capable to evaluate the patient in relation to self-sufficiency, activity and self-determination as part of ADL. The retrospective analysis includes the ECOG-Status, Karnofsky-Index, Barthel-Index, ASA-Classification and Katz-Index. Pre-processing and the analysis of the data was implemented using KNIME, where we used the R-plugin nodes to perform the final statistical tests with R. Results Our analysis reveals that most of the ADL scores we tested are able to give a reliable prediction on overall survival after brain metastasis surgery. The survival rates decrease significantly with a lower score in all tested score systems, with the exception of the ASA-Risk score. In particular, the Katz Index

Abstract Background CREB-binding protein (CBP) and p300 represent histone acetyltransferases (HATs) and transcriptional coactivators that play essential roles in tumour initiation and progression. Both proteins are generally thought to function as tumour suppressors, although their distinct roles in colorectal cancer (CRC) remain inconsistent and ambiguous. Thus, we analysed the expression of these two HATs in human tissue samples from patients with locally advanced rectal cancer via immunohistochemistry and evaluated their potential impacts on future CRC diagnosis and treatment. Methods In our analysis, we included ninety-three (n = 93) patients diagnosed with adenocarcinoma in the upper third of the rectum. None of the patients received preoperative chemoradiotherapy, but the patients did undergo primary resection of the tumour within the phase II GAST-05 trial. By using H-scores, the expression of both proteins was visualised via immunohistochemistry in resected specimens from the patients. CBP and p300 expression were correlated with clinical and follow-up data. Results Our analysis showed that high expression of CBP was significantly associated with prolonged cancer-specific survival (CSS; p = 0.002). In univariate analysis, CBP was an independent prognostic parameter for CSS (p = 0.042). High nuclear CBP expression was observed in two-thirds of patients. In contrast, we could not find any significant correlation between the expression of p300 and cancer-specific survival in this cohort of patients (p = 0.09). We did not observe any cooperation between CBP and p300 in our analysis. Conclusions High expression of CBP was significantly associated with improved oncological outcomes. This finding could help to stratify patients in the future for CRC treatment. Histone deacetylase (HDAC) inhibitors are increasingly playing a role in oncological treatment and could additionally become therapeutic options in CRC. Our findings need to be further evaluated and verified in future clinical analyses.

ABSTRACT The advent of high-throughput sequencing techniques has recently provided an astonishing insight into the composition and function of the human microbiome. Next-generation sequencing (NGS) has become the gold standard for advanced microbiome analysis; however, 3rd generation real-time sequencing, such as Oxford Nanopore Technologies (ONT), enables rapid sequencing from several kilobases to >2 Mb with high resolution. Despite the wide availability and the enormous potential for clinical and translational applications, ONT is poorly standardized in terms of sampling and storage conditions, DNA extraction, library creation, and bioinformatic classification. Here, we present a comprehensive analysis pipeline with sampling, storage, DNA extraction, library preparation, and bioinformatic evaluation for complex microbiomes sequenced with ONT. Our findings from buccal and rectal swabs and DNA extraction experiments indicate that methods that were approved for NGS microbiome analysis cannot be simply adapted to ONT. We recommend using swabs and DNA extractions protocols with extended washing steps. Both 16S rRNA and metagenomic sequencing achieved reliable and reproducible results. Our benchmarking experiments reveal thresholds for analysis parameters that achieved excellent precision, recall, and area under the precision recall values and is superior to existing classifiers (Kraken2, Kaiju, and MetaMaps). Hence, our workflow provides an experimental and bioinformatic pipeline to perform a highly accurate analysis of complex microbial structures from buccal and rectal swabs. IMPORTANCE Advanced microbiome analysis relies on sequencing of short DNA fragments from microorganisms like bacteria, fungi, and viruses. More recently, long fragment DNA sequencing of 3rd generation sequencing has gained increasing importance and can be rapidly conducted within a few hours due to its potential real-time sequencing. However, the analysis and correct identification of the microbiome relies on a multitude of factors, such as the method of sampling, DNA extraction, sequencing, and bioinformatic analysis. Scientists have used different protocols in the past that do not allow us to compare results across different studies and research fields. Here, we provide a comprehensive workflow from DNA extraction, sequencing, and bioinformatic workflow that allows rapid and accurate analysis of human buccal and rectal swabs with reproducible protocols. This workflow can be readily applied by many scientists from various research fields that aim to use long-fragment microbiome sequencing.

Only 2% of glioblastoma multiforme (GBM) patients respond to standard therapy and survive beyond 36 months (long-term survivors, LTS), while the majority survive less than 12 months (short-term survivors, STS). To understand the mechanism leading to poor survival, we analyzed publicly available datasets of 113 STS and 58 LTS. This analysis revealed 198 differentially expressed genes (DEGs) that characterize aggressive tumor growth and may be responsible for the poor prognosis. These genes belong largely to the Gene Ontology (GO) categories “epithelial-to-mesenchymal transition” and “response to hypoxia.” In this article, we applied an upstream analysis approach that involves state-of-the-art promoter analysis and network analysis of the dysregulated genes potentially responsible for short survival in GBM. Binding sites for transcription factors (TFs) associated with GBM pathology like NANOG, NF-κB, REST, FRA-1, PPARG, and seven others were found enriched in the promoters of the dysregulated genes. We reconstructed the gene regulatory network with several positive feedback loops controlled by five master regulators [insulin-like growth factor binding protein 2 (IGFBP2), vascular endothelial growth factor A (VEGFA), VEGF165, platelet-derived growth factor A (PDGFA), adipocyte enhancer-binding protein (AEBP1), and oncostatin M (OSMR)], which can be proposed as biomarkers and as therapeutic targets for enhancing GBM prognosis. A critical analysis of this gene regulatory network gives insights into the mechanism of gene regulation by IGFBP2 via several TFs including the key molecule of GBM tumor invasiveness and progression, FRA-1. All the observations were validated in independent cohorts, and their impact on overall survival has been investigated.

Abstract Background Abrogation of growth factor-dependent signaling represents an effective therapeutic strategy for patients with colorectal cancer (CRC). Here we evaluated the effectiveness of targeting the epidermal growth factor (EGF) receptors HER-2 and HER-3 in the three cell lines LS513, LS1034 and SW837. Methods Treatment with HER-2-specific antibodies trastuzumab and pertuzumab resulted in a mild reduction of cellular viability. In contrast, the antibody-drug conjugate T-DM1 mediated a strong and dose-dependent decrease of viability and Akt phosphorylation. Results The most striking effects were observed with the dual tyrosine kinase inhibitor lapatinib, and the Pan-ErbB inhibitor afatinib. Selectively, the effect of EGF receptor inhibition was augmented by a combination with 5-fluorouracil and oxaliplatin. Finally, high expression of HER-3 was detected in 121 of 172 locally advanced rectal cancers (70.3%). In conclusion, inhibition of EGF receptors effectively blocks downstream signaling and significantly impairs viability of CRC cells. However, the effectiveness of receptor inhibition highly depends on the inhibitors’ mode of action, as targeting HER-2 alone is not sufficient. Conclusion Since HER-2 and HER-3 are expressed in a relevant number of patients, targeting both receptors may represent a promising therapeutic strategy for CRC.

Seventy percent of patients with colorectal cancer develop liver metastases (CRLM), which are a decisive factor in cancer progression. Therapy outcome is largely influenced by tumor heterogeneity, but the intra- and inter-patient heterogeneity of CRLM has been poorly studied. In particular, the contribution of the WNT and EGFR pathways, which are both frequently deregulated in colorectal cancer, has not yet been addressed in this context. To this end, we comprehensively characterized normal liver tissue and eight CRLM from two patients by standardized histopathological, molecular, and proteomic subtyping. Suitable fresh-frozen tissue samples were profiled by transcriptome sequencing (RNA-Seq) and proteomic profiling with reverse phase protein arrays (RPPA) combined with bioinformatic analyses to assess tumor heterogeneity and identify WNT- and EGFR-related master regulators and metastatic effectors. A standardized data analysis pipeline for integrating RNA-Seq with clinical, proteomic, and genetic data was established. Dimensionality reduction of the transcriptome data revealed a distinct signature for CRLM differing from normal liver tissue and indicated a high degree of tumor heterogeneity. WNT and EGFR signaling were highly active in CRLM and the genes of both pathways were heterogeneously expressed between the two patients as well as between the synchronous metastases of a single patient. An analysis of the master regulators and metastatic effectors implicated in the regulation of these genes revealed a set of four genes (SFN, IGF2BP1, STAT1, PIK3CG) that were differentially expressed in CRLM and were associated with clinical outcome in a large cohort of colorectal cancer patients as well as CRLM samples. In conclusion, high-throughput profiling enabled us to define a CRLM-specific signature and revealed the genes of the WNT and EGFR pathways associated with inter- and intra-patient heterogeneity, which were validated as prognostic biomarkers in CRC primary tumors as well as liver metastases.

Abstract Objective The mortality associated with sepsis remains unacceptably high, despite modern high-quality intensive care. Based on the results from previous studies, anaemia and its management in patients with sepsis appear to impact outcomes; however, the transfusion policy is still being debated, and the ideal approach may be extremely specific to the individual. This study aimed to investigate the long-term impact of anaemia requiring red blood cell (RBC) transfusion on mortality and disease severity in patients with sepsis. We studied a general surgical intensive care unit (ICU) population, excluding cardiac surgery patients. 435 patients were enrolled in this observational study between 2012 and 2016. Results Patients who received RBC transfusion between 28 days before and 28 days after the development of sepsis (n = 302) exhibited a significantly higher 90-day mortality rate (34.1% vs 19.6%; P = 0.004, Kaplan–Meier analysis). This association remained significant after adjusting for confounders in the multivariate Cox regression analysis (hazard ratio 1.68; 95% confidence interval 1.03–2.73; P = 0.035). Patients who received transfusions also showed significantly higher morbidity scores, such as SOFA scores, and ICU lengths of stay compared to patients without transfusions (n = 133). Our results indicate that anaemia and RBC transfusion are associated with unfavourable outcomes in patients with sepsis.

Biomedical and life science literature is an essential way to publish experimental results. With the rapid growth of the number of new publications, the amount of scientific knowledge represented in free text is increasing remarkably. There has been much interest in developing techniques that can extract this knowledge and make it accessible to aid scientists in discovering new relationships between biological entities and answering biological questions. Making use of the word2vec approach, we generated word vector representations based on a corpus consisting of over 16 million PubMed abstracts. We developed a text mining pipeline to produce word2vec embeddings with different properties and performed validation experiments to assess their utility for biomedical analysis. An important pre-processing step consisted in the substitution of synonymous terms by their preferred terms in biomedical databases. Furthermore, we extracted gene-gene networks from two embedding versions and used them as prior knowledge to train Graph-Convolutional Neural Networks (CNNs) on large breast cancer gene expression data and on other cancer datasets. Performances of resulting models were compared to Graph-CNNs trained with protein-protein interaction (PPI) networks or with networks derived using other word embedding algorithms. We also assessed the effect of corpus size on the variability of word representations. Finally, we created a web service with a graphical and a RESTful interface to extract and explore relations between biomedical terms using annotated embeddings. Comparisons to biological databases showed that relations between entities such as known PPIs, signaling pathways and cellular functions, or narrower disease ontology groups correlated with higher cosine similarity. Graph-CNNs trained with word2vec-embedding-derived networks performed sufficiently good for the metastatic event prediction tasks compared to other networks. Such performance was good enough to validate the utility of our generated word embeddings in constructing biological networks. Word representations as produced by text mining algorithms like word2vec, therefore are able to capture biologically meaningful relations between entities. Our generated embeddings are publicly available at https://github.com/genexplain/Word2vec-based-Networks/blob/main/README.md.

Cell differentiation is a complex process orchestrated by sets of regulators precisely appearing at certain time points, resulting in regulatory cascades that affect the expression of broader sets of genes, ending up in the formation of different tissues and organ parts. The identification of stage-specific master regulators and the mechanism by which they activate each other is a key to understanding and controlling differentiation, particularly in the fields of tissue regeneration and organoid engineering. Here we present a workflow that combines a comprehensive general regulatory network based on binding site predictions with user-provided temporal gene expression data, to generate a a temporally connected series of stage-specific regulatory networks, which we call a temporal regulatory cascade (TRC). A TRC identifies those regulators that are unique for each time point, resulting in a cascade that shows the emergence of these regulators and regulatory interactions across time. The model was implemented in the form of a user-friendly, visual web-tool, that requires no expert knowledge in programming or statistics, making it directly usable for life scientists. In addition to generating TRCs the tool links multiple interactive visual workflows, in which a user can track and investigate further different regulators, target genes, and interactions, directing the tool along the way into biologically sensible results based on the given dataset. We applied the TRC model on two different expression datasets, one based on experiments conducted on human induced pluripotent stem cells (hiPSCs) undergoing differentiation into mature cardiomyocytes and the other based on the differentiation of H1-derived human neuronal precursor cells. The model was successful in identifying previously known and new potential key regulators, in addition to the particular time points with which these regulators are associated, in cardiac and neural development.

Breast cancer is the most common cancer in women worldwide. The tumor microenvironment contributes to tumor progression by inducing cell dissemination from the primary tumor and metastasis. TGFβ signaling is involved in breast cancer progression and is specifically elevated during metastatic transformation in aggressive breast cancer. In this study, we performed genomewide correlation analysis of TGFBR2 expression in a panel of 51 breast cancer cell lines and identified that MET is coregulated with TGFBR2. This correlation was confirmed at the protein level in breast cancer cell lines and human tumor tissues. Flow cytometric analysis of luminal and basal‐like breast cancer cell lines and examination of 801 tumor specimens from a prospective cohort of breast cancer patients using reverse phase protein arrays revealed that expression of TGFBR2 and MET is increased in basal‐like breast cancer cell lines, as well as in triple‐negative breast cancer tumor tissues, compared to other subtypes. Using real‐time cell analysis technology, we demonstrated that TGFβ1 triggered hepatocyte growth factor (HGF)‐induced and MET‐dependent migration in vitro. Bioinformatic analysis predicted that TGFβ1 induces expression of C‐ets‐1 as a candidate transcription factor regulating MET expression. Indeed, TGFβ1‐induced expression of ETS1 and breast cancer cell migration was blocked by knockdown of ETS1. Further, we identified that MET is a direct target of miR‐128‐3p and that this miRNA is negatively regulated by TGFβ1. Overexpression of miR‐128‐3p reduced MET expression and abrogated HGF‐induced cell migration of invasive breast cancer cells. In conclusion, we have identified that TGFβ1 regulates HGF‐induced and MET‐mediated cell migration, through positive regulation of C‐ets‐1 and negative regulation of miR‐128‐3p expression in basal‐like breast cancer cell lines and in triple‐negative breast cancer tissue.

Abstract Background A comprehensive understanding of cancer has been furthered with technological improvements and decreasing costs of next-generation sequencing (NGS). However, the complexity of interpreting genomic data is hindering the implementation of high-throughput technologies in the clinical context: increasing evidence on gene–drug interactions complicates the task of assigning clinical significance to genomic variants. Methods Here we present a method that automatically matches patient-specific genomic alterations to treatment options. The method relies entirely on public knowledge of somatic variants with predictive evidence on drug response. The output report is aimed at supporting clinicians in the task of finding the clinical meaning of genomic variants. We applied the method to 1) The Cancer Genome Atlas (TCGA) and Genomics Evidence Neoplasia Information Exchange (GENIE) cohorts and 2) 11 patients from the NCT MASTER trial whose treatment discussions included information on their genomic profiles. Results Our reporting strategy showed a substantial number of patients with actionable variants in the analyses of TCGA and GENIE samples. Notably, it was able to reproduce experts’ treatment suggestions in a retrospective study of 11 patients from the NCT MASTER trial. Our results establish a proof of concept for comprehensive, evidence-based reports as a supporting tool for discussing treatment options in tumor boards. Conclusions We believe that a standardized method to report actionable somatic variants will smooth the incorporation of NGS in the clinical context. We anticipate that tools like the one we present here will become essential in summarizing for clinicians the growing evidence in the field of precision medicine. The R code of the presented method is provided in Additional file 6 and available at https://github.com/jperera-bel/MTB-Report.

(1) Background: Sepsis is a leading cause of death and a global public health problem. Accordingly, deciphering the underlying molecular mechanisms of this disease and the determinants of its morbidity and mortality is pivotal. This study examined the effect of the rs951818 SNP of the negative costimulatory lymphocyte-activation gene 3 (LAG-3) on sepsis mortality and disease severity. (2) Methods: 707 consecutive patients with sepsis were prospectively enrolled into the present study from three surgical ICUs at University Medical Center Goettingen. Both 28- and 90-day mortality were analyzed as the primary outcome, while parameters of disease severity served as secondary endpoints. (3) Results: In the Kaplan–Meier analysis LAG-3 rs951818 AA-homozygote patients showed a significantly lower 28-day mortality (17.3%) compared to carriers of the C-allele (23.7%, p = 0.0476). In addition, these patients more often received invasive mechanical ventilation (96%) during the course of disease than C-allele carriers (92%, p = 0.0466). (4) Conclusions: Genetic profiling of LAG-3 genetic variants alone or in combination with other genetic biomarkers may represent a promising approach for risk stratification of patients with sepsis. Patient-individual therapeutic targeting of immune checkpoints, such as LAG-3, may be a future component of sepsis therapy. Further detailed investigations in clinically relevant sepsis models are necessary.

Abstract 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.

Abstract Background Colorectal cancer (CRC) is the second leading cause of cancer-related death in men and women. Systemic disease with metastatic spread to distant sites such as the liver reduces the survival rate considerably. The aim of this study was to investigate the changes in gene expression that occur on invasion and expansion of CRC cells when forming metastases in the liver. Methods The livers of syngeneic C57BL/6NCrl mice were inoculated with 1 million CRC cells (CMT-93) via the portal vein, leading to the stable formation of metastases within 4 weeks. RNA sequencing performed on the Illumina platform was employed to evaluate the expression profiles of more than 14,000 genes, utilizing the RNA of the cell line cells and liver metastases as well as from corresponding tumour-free liver. Results A total of 3329 differentially expressed genes (DEGs) were identified when cultured CMT-93 cells propagated as metastases in the liver. Hierarchical clustering on heat maps demonstrated the clear changes in gene expression of CMT-93 cells on propagation in the liver. Gene ontology analysis determined inflammation, angiogenesis, and signal transduction as the top three relevant biological processes involved. Using a selection list, matrix metallopeptidases 2, 7, and 9, wnt inhibitory factor, and chemokine receptor 4 were the top five significantly dysregulated genes. Conclusion Bioinformatics assists in elucidating the factors and processes involved in CRC liver metastasis. Our results support the notion of an invasion-metastasis cascade involving CRC cells forming metastases on successful invasion and expansion within the liver. Furthermore, we identified a gene expression signature correlating strongly with invasiveness and migration. Our findings may guide future research on novel therapeutic targets in the treatment of CRC liver metastasis.

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

Background: Despite substantial progress made in the last decades in colorectal cancer (CRC) research, new treatment approaches are still needed to improve patients’ long-term survival. To date, the promising strategy to target tumor angiogenesis metabolically together with a sensitization of CRC to chemo- and/or radiotherapy by PFKFB3 (6-phosphofructo-2-kinase/fructose-2,6-biphosphatase-3) inhibition has never been tested. Therefore, initial evaluation and validation of newly developed compounds such as KAN0438757 and their effects on CRC cells are crucial steps preceding to in vivo preclinical studies, which in turn may consolidate new therapeutic targets. Materials and Methods: The efficiency of KAN0438757 to block PFKFB3 expression and translation in human CRC cells was evaluated by immunoblotting and real-time PCR. Functional in vitro assays assessed the effects of KAN0438757 on cell viability, proliferation, survival, adhesion, migration and invasion. Additionally, we evaluated the effects of KAN0438757 on matched patient-derived normal and tumor organoids and its systemic toxicity in vivo in C57BL6/N mice. Results: High PFKFB3 expression is correlated with a worse survival in CRC patients. KAN0438757 reduces PFKFB3 protein expression without affecting its transcriptional regulation. Additionally, a concentration-dependent anti-proliferative effect was observed. The migration and invasion capacity of cancer cells were significantly reduced, independent of the anti-proliferative effect. When treating colonic patient-derived organoids with KAN0438757 an impressive effect on tumor organoids growth was apparent, surprisingly sparing normal colonic organoids. No high-grade toxicity was observed in vivo. Conclusion: The PFKFB3 inhibitor KAN0438757 significantly reduced CRC cell migration, invasion and survival. Moreover, on patient-derived cancer organoids KAN0438757 showed significant effects on growth, without being overly toxic in normal colon organoids and healthy mice. Our findings strongly encourage further translational studies to evaluate KAN0438757 in CRC therapy.

Resistance of tumor cells to chemoradiotherapy represents a fundamental problem in clinical oncology. The underlying mechanisms are actively debated. Here we show that blocking inflammatory cytokine receptor signaling via STAT3 re-sensitized treatment-refractory cancer cells and abolished tumor growth in a xenograft mouse model when applied together with chemoradiotherapy. STAT3 executed treatment resistance by triggering the expression of RBPJ, the key transcriptional regulator of the NOTCH pathway. The mandatory RBPJ interaction partner, NOTCH intracellular domain, was provided by tumor cell-intrinsic expression of NOTCH ligands that caused tonic NOTCH proteolysis. In fact, NOTCH inhibition phenocopied the effect of blocking STAT3 signaling. Moreover, genetic profiling of rectal cancer patients revealed the importance of the STAT3/NOTCH axis as NOTCH expression correlated with clinical outcome. Our data uncovered an unprecedented signal alliance between inflammation and cellular development that orchestrated resistance to chemoradiotherapy. Clinically, our findings allow for biomarker-driven patient stratification and offer novel treatment options.

Background: Previous studies have reported the fundamental role of immunoregulatory proteins in the clinical phenotype and outcome of sepsis. This study investigated two functional single nucleotide polymorphisms (SNPs) of T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), which has a negative stimulatory function in the T cell immune response. Methods: Patients with sepsis (n = 712) were prospectively enrolled from three intensive care units (ICUs) at the University Medical Center Goettingen since 2012. All patients were genotyped for the TIM-3 SNPs rs1036199 and rs10515746. The primary outcome was 28-day mortality. Disease severity and microbiological findings were secondary endpoints. Results: Kaplan–Meier survival analysis demonstrated a significantly lower 28-day mortality for TIM-3 rs1036199 AA homozygous patients compared to C-allele carriers (18% vs. 27%, p = 0.0099) and TIM-3 rs10515746 CC homozygous patients compared to A-allele carriers (18% vs. 26%, p = 0.0202). The TIM-3 rs1036199 AA genotype and rs10515746 CC genotype remained significant predictors for 28-day mortality in the multivariate Cox regression analysis after adjustment for relevant confounders (adjusted hazard ratios: 0.67 and 0.70). Additionally, patients carrying the rs1036199 AA genotype presented more Gram-positive and Staphylococcus epidermidis infections, and rs10515746 CC homozygotes presented more Staphylococcus epidermidis infections. Conclusion: The studied TIM-3 genetic variants are associated with altered 28-day mortality and susceptibility to Gram-positive infections in sepsis.

Abstract Patients with head‐and‐neck cancer can develop both lung metastasis and primary lung cancer during the course of their disease. Despite the clinical importance of discrimination, reliable diagnostic biomarkers are still lacking. Here, we have characterised a cohort of squamous cell lung (SQCLC) and head‐and‐neck (HNSCC) carcinomas by quantitative proteomics. In a training cohort, we quantified 4,957 proteins in 44 SQCLC and 30 HNSCC tumours. A total of 518 proteins were found to be differentially expressed between SQCLC and HNSCC, and some of these were identified as genetic dependencies in either of the two tumour types. Using supervised machine learning, we inferred a proteomic signature for the classification of squamous cell carcinomas as either SQCLC or HNSCC, with diagnostic accuracies of 90.5% and 86.8% in cross‐ and independent validations, respectively. Furthermore, application of this signature to a cohort of pulmonary squamous cell carcinomas of unknown origin leads to a significant prognostic separation. This study not only provides a diagnostic proteomic signature for classification of secondary lung tumours in HNSCC patients, but also represents a proteomic resource for HNSCC and SQCLC.

Septic shock is a frequent life-threatening condition and a leading cause of mortality in intensive care units (ICUs). Previous investigations have reported a potentially protective effect of obesity in septic shock patients. However, prior results have been inconsistent, focused on short-term in-hospital mortality and inadequately adjusted for confounders, and they have rarely applied the currently valid Sepsis-3 definition criteria for septic shock. This investigation examined the effect of obesity on 90-day mortality in patients with septic shock selected from a prospectively enrolled cohort of septic patients. A total of 352 patients who met the Sepsis-3 criteria for septic shock were enrolled in this study. Body-mass index (BMI) was used to divide the cohort into 24% obese (BMI ≥ 30 kg/m2) and 76% non-obese (BMI < 30 kg/m2) patients. Kaplan-Meier survival analysis revealed a significantly lower 90-day mortality (31% vs. 43%; p = 0.0436) in obese patients compared to non-obese patients. Additional analyses of baseline characteristics, disease severity, and microbiological findings outlined further statistically significant differences among the groups. Multivariate Cox regression analysis estimated a significant protective effect of obesity on 90-day mortality after adjustment for confounders. An understanding of the underlying physiologic mechanisms may improve therapeutic strategies and patient prognosis.

Mass spectrometry-based proteomics methods are finding increasing use in structural biology research. Beyond simple interaction networks, information about stable protein-protein complexes or spatially proximal proteins helps to elucidate the biological functions of proteins in a wider cellular context. To shed light on new developments in this field, the Göttingen Proteomics Forum organized a one-day symposium focused on complexome profiling and proximity labeling, two emerging technologies that are gaining significant attention in biomolecular research. The symposium was held in Göttingen, Germany on 23 May, 2019, as part of a series of regular symposia organized by the Göttingen Proteomics Forum.

BACKGROUND: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. METHODS: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. RESULTS: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. CONCLUSION: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.

Breast cancer is a heterogeneous disease and has been classified into five molecular subtypes based on gene expression profiles. Signaling processes linked to different breast cancer molecular subtypes and different clinical outcomes are still poorly understood. Aberrant regulation of Wnt signaling has been implicated in breast cancer progression. In particular Ror1/2 receptors and several other members of the non-canonical Wnt signaling pathway were associated with aggressive breast cancer behavior. However, Wnt signals are mediated via multiple complex pathways, and it is clinically important to determine which particular Wnt cascades, including their domains and targets, are deregulated in poor prognosis breast cancer. To investigate activation and outcome of the Ror2-dependent non-canonical Wnt signaling pathway, we overexpressed the Ror2 receptor in MCF-7 and MDA-MB231 breast cancer cells, stimulated the cells with its ligand Wnt5a, and we knocked-down Ror1 in MDA-MB231 cells. We measured the invasive capacity of perturbed cells to assess phenotypic changes, and mRNA was profiled to quantify gene expression changes. Differentially expressed genes were integrated into a literature-based non-canonical Wnt signaling network. The results were further used in the analysis of an independent dataset of breast cancer patients with metastasis-free survival annotation. Overexpression of the Ror2 receptor, stimulation with Wnt5a, as well as the combination of both perturbations enhanced invasiveness of MCF-7 cells. The expression–responsive targets of Ror2 overexpression in MCF-7 induced a Ror2/Wnt module of the non-canonical Wnt signaling pathway. These targets alter regulation of other pathways involved in cell remodeling processing and cell metabolism. Furthermore, the genes of the Ror2/Wnt module were assessed as a gene signature in patient gene expression data and showed an association with clinical outcome. In summary, results of this study indicate a role of a newly defined Ror2/Wnt module in breast cancer progression and present a link between Ror2 expression and increased cell invasiveness.

Analysis of large-scale proteomic data sets requires specialized software tools, tailored toward the requirements of individual approaches. Here we introduce an extension of an open-source software solution for analyzing reverse phase protein array (RPPA) data. The R package RPPanalyzer was designed for data preprocessing followed by basic statistical analyses and proteomic data visualization. In this update, we merged relevant data preprocessing steps into a single user-friendly function and included a new method for background noise correction as well as new methods for noise estimation and averaging of replicates to transform data in such a way that they can be used as input for a new time course plotting function. We demonstrate the robustness of our enhanced RPPanalyzer platform by analyzing longitudinal RPPA data of MET receptor signaling upon stimulation with different hepatocyte growth factor concentrations.

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.

Sepsis is a life-threatening condition and a significant challenge for those working in intensive care, where it remains one of the leading causes of mortality. According to the sepsis-3 definition, sepsis is characterized by dysregulation of the host response to infection. The TREM-1 gene codes for the triggering receptor expressed on myeloid cells 1, which is part of the pro-inflammatory response of the immune system. This study aimed to determine whether the functional TREM-1 rs2234237 single nucleotide polymorphism was associated with mortality in a cohort of 649 Caucasian patients with sepsis. The 90-day mortality rate was the primary outcome, and disease severity and microbiological findings were analyzed as secondary endpoints. TREM-1 rs2234237 TT homozygous patients were compared to A-allele carriers for this purpose. Kaplan–Meier survival analysis revealed no association between the clinically relevant TREM-1 rs2234237 single nucleotide polymorphism and the 90-day or 28-day survival rate in this group of septic patients. In addition, the performed analyses of disease severity and the microbiological findings did not show significant differences between the TREM-1 rs2234237 genotypes. The TREM-1 rs2234237 genotype was not significantly associated with sepsis mortality and sepsis disease severity. Therefore, it was not a valuable prognostic marker for the survival of septic patients in the studied cohort.

Intrinsic and acquired resistance to the monoclonal antibody drug trastuzumab is a major problem in the treatment of HER2-positive breast cancer. A deeper understanding of the underlying mechanisms could help to develop new agents. Our intention was to detect genes and single nucleotide polymorphisms (SNPs) affecting trastuzumab efficiency in cell culture. Three HER2-positive breast cancer cell lines with different resistance phenotypes were analyzed. We chose BT474 as model of trastuzumab sensitivity, HCC1954 as model of intrinsic resistance, and BTR50, derived from BT474, as model of acquired resistance. Based on RNA-Seq data, we performed differential expression analyses on these cell lines with and without trastuzumab treatment. Differentially expressed genes between the resistant cell lines and BT474 are expected to contribute to resistance. Differentially expressed genes between untreated and trastuzumab treated BT474 are expected to contribute to drug efficacy. To exclude false positives from the candidate gene set, we removed genes that were also differentially expressed between untreated and trastuzumab treated BTR50. We further searched for SNPs in the untreated cell lines which could contribute to trastuzumab resistance. The analysis resulted in 54 differentially expressed candidate genes that might be connected to trastuzumab efficiency. 90% of 40 selected candidates were validated by RT-qPCR. ALPP, CALCOCO1, CAV1, CYP1A2 and IGFBP3 were significantly higher expressed in the trastuzumab treated than in the untreated BT474 cell line. GDF15, IL8, LCN2, PTGS2 and 20 other genes were significantly higher expressed in HCC1954 than in BT474, while NCAM2, COLEC12, AFF3, TFF3, NRCAM, GREB1 and TFF1 were significantly lower expressed. Additionally, we inferred SNPs in HCC1954 for CAV1, PTGS2, IL8 and IGFBP3. The latter also had a variation in BTR50. 20% of the validated subset have already been mentioned in literature. For half of them we called and analyzed SNPs. These results contribute to a better understanding of trastuzumab action and resistance mechanisms.

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.

According to previous investigations, CD14 is suggested to play a pivotal role in initiating and perpetuating the pro-inflammatory response during sepsis. A functional polymorphism within the CD14 gene, rs2569190, has been shown to impact the pro-inflammatory response upon stimulation with lipopolysaccharide, a central mediator of inflammation in sepsis. In this study, we hypothesized that the strong pro-inflammatory response induced by the TT genotype of CD14 rs2569190 may have a beneficial effect on survival (30-day) in patients with sepsis. A total of 417 adult patients with sepsis (and of western European descent) were enrolled into this observational study. Blood samples were collected for rs2569190 genotyping. Patients were followed over the course of their stay in the ICU, and the 30-day mortality risk was recorded as the primary outcome parameter. Sepsis-related organ failure assessment (SOFA) scores were quantified at sepsis onset and throughout the observational period to monitor organ failure as a secondary variable. Moreover, organ support-free days were evaluated as a secondary outcome parameter. TT-homozygous patients were compared to C-allele carriers. Kaplan-Meier survival analysis revealed a higher 30-day mortality risk among C-allele carriers compared with T homozygotes (p = 0.0261). To exclude the effect of potential confounders (age, gender, BMI and type of infection) and covariates that varied at baseline with a p-value < 0.2 (e.g., comorbidities), we performed multivariate Cox regression analysis to examine the survival time. The CD14 rs2569190 C allele remained a significant covariate for the 30-day mortality risk in the multivariate analysis (hazard ratio, 2.11; 95% CI, 1.08-4.12; p = 0.0282). The 30-day mortality rate among C allele carriers was 23%, whereas the T homozygotes had a mortality rate of 13%. Additionally, an analysis of organ-specific SOFA scores revealed a significantly higher SOFA-Central nervous system score among patients carrying the C allele compared with T-homozygous patients (1.9±1.1 and 1.6±1.0, respectively; p = 0.0311). In conclusion, CD14 rs2569190 may act as a prognostic variable for the short-term outcome (30-day survival) in patients with sepsis.

Genetic variants within the endothelin-1 gene (EDN1) have been associated with several cardiovascular diseases and may act as genetic prognostic markers. Here, we explored the overall relevance of EDN1 polymorphisms for long-term survival in patients undergoing on-pump cardiac surgery. A prospectively collected cohort of 455 Caucasian patients who underwent cardiac surgery with cardiopulmonary bypass was followed up for 5 years. The obtained genotypes and inferred haplotypes were analyzed for their associations with the five-year mortality rate (primary endpoint). The EDN1 T-1370G and K198N genotype distributions did not deviate from Hardy-Weinberg equilibrium and the major allele frequencies were 83% and 77%, respectively. The cardiovascular risk factors were equally distributed in terms of the different genotypes and haplotypes associated with the two polymorphisms. The five-year mortality rate did not differ among the different EDN1 T-1370G and K198N genotypes and haplotypes. Haplotype analysis revealed that carriers of the G-T (compound EDN1 T-1370G G/K198N T) haplotype had a higher cardiac index than did non-carriers (p = 0.0008); however, this difference did not reach significance after adjusting for multiple testing. The results indicate that common variations in EDN1 do not act as prognostic markers for long-term survival in patients undergoing on-pump cardiac surgery.

Background: Patients with locally advanced rectal cancer are treated with preoperative chemoradiotherapy followed by surgical resection. Despite similar clinical parameters (uT2-3, uN+) and standard therapy, patients’ prognoses differ widely. A possible prediction of prognosis through microRNAs as biomarkers out of treatment-naïve biopsies would allow individualized therapy options. Methods: Microarray analysis of 45 microdissected preoperative biopsies from patients with rectal cancer was performed to identify potential microRNAs to predict overall survival, disease-free survival, cancer-specific survival, distant-metastasis-free survival, tumor regression grade, or nodal stage. Quantitative real-time polymerase chain reaction (qPCR) was performed on an independent set of 147 rectal cancer patients to validate relevant miRNAs. Results: In the microarray screen, 14 microRNAs were significantly correlated to overall survival. Five microRNAs were included from previous work. Finally, 19 miRNAs were evaluated by qPCR. miR-515-5p, miR-573, miR-579 and miR-802 demonstrated significant correlation with overall survival and cancer-specific survival (p < 0.05). miR-573 was also significantly correlated with the tumor regression grade after preoperative chemoradiotherapy. miR-133b showed a significant correlation with distant-metastasis-free survival. miR-146b expression levels showed a significant correlation with nodal stage. Conclusion: Specific microRNAs can be used as biomarkers to predict prognosis of patients with rectal cancer and possibly stratify patients’ therapy if validated in a prospective study.

Expression levels of mRNAs are among other factors regulated by microRNAs. A particular microRNA can bind specifically to several target mRNAs and lead to their degradation. Expression levels of both, mRNAs and microRNAs, can be obtained by microarray experiments. In order to increase the power of detecting microRNAs that are differentially expressed between two different groups of samples, we incorporate expression levels of their related target gene sets. Group effects are determined individually for each microRNA, and by enrichment tests and global tests for target gene sets. The resulting lists of p-values from individual and set-wise testing are combined by means of meta analysis. We propose a new approach to connect microRNA-wise and gene set-wise information by means of p-value combination as often used in meta-analysis. In this context, we evaluate the usefulness of different approaches of gene set tests. In a simulation study we reveal that our combination approach is more powerful than microRNA-wise testing alone. Furthermore, we show that combining microRNA-wise results with 'competitive' gene set tests maintains a pre-specified false discovery rate. In contrast, a combination with 'self-contained' gene set tests can harm the false discovery rate, particularly when gene sets are not disjunct.