Multicenter evaluation of blood-based biomarkers for the detection of endometriosis and adenomyosis: A prospective non-interventional study.

OBJECTIVE: To evaluate blood-based biomarkers to detect endometriosis and/or adenomyosis across nine European centers (June 2014-April 2018). METHODS: This prospective, non-interventional study assessed the diagnostic accuracy of 54 blood-based biomarker immunoassays in samples from 919 women (aged 18-45 years) with suspicion of endometriosis and/or adenomyosis versus symptomatic controls. Endometriosis was stratified by revised American Society for Reproductive Medicine stage. Symptomatic controls were "pathologic symptomatic controls" or "pathology-free symptomatic controls". The main outcome measure was receiver operating characteristic-area under the curve (ROC-AUC) and Wilcoxon P values corrected for multiple testing (q values). RESULTS: CA-125 performed best in "all endometriosis cases" versus "all symptomatic controls" (AUC 0.645, 95% confidence interval [CI] 0.600-0.690, q < 0.001) and increased (P < 0.001) with disease stage. In "all endometriosis cases" versus "pathology-free symptomatic controls", S100-A12 performed best (AUC 0.692, 95% CI 0.614-0.769, q = 0.001) followed by CA-125 (AUC 0.649, 95% CI 0.569-0.729, q = 0.021). In "adenomyosis only cases" versus "symptomatic controls" or "pathology-free symptomatic controls", respectively, the top-performing biomarkers were sFRP-4 (AUC 0.615, 95% CI 0.551-0.678, q = 0.045) and S100-A12 (AUC 0.701, 95% CI 0.611-0.792, q = 0.004). CONCLUSION: This study concluded that no biomarkers tested could diagnose or rule out endometriosis/adenomyosis with high certainty.

[Nutrition for diabetic patients (Update 2023)]. / Ernährungsempfehlungen für Menschen mit Diabetes (Update 2023).

All patients with diabetes require individual and personalized nutritional consultation with professionals. The patient's needs should be the primary focus of the dietary therapy, taking their lifestyle and the type of diabetes into consideration. With the recommendations to the patient's diet, there need to be specific metabolic goals to reduce the disease's progression and to avoid long term health effects. Therefore, practical guidelines such as portion size and meal planning tips should be the main focus.According to the latest national and international standards, patients suffering from diabetes should have access to nutrition consulting and nutritional training. During consultation they can be supported on- how to manage their health condition and choosing food and beverage to improve their health.These practical recommendations sum up the latest literature on nutritional aspects of diabetes treatment.

Comprehensive evaluation of microRNA as a biomarker for the diagnosis of hepatocellular carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. Current guidelines for HCC management recommend surveillance of high-risk patients every 6 mo using ultrasonography. Serum biomarkers, like alpha-fetoprotein (AFP), protein induced by vitamin K absence/antagonist-II (PIVKA-II) and lectin-reactive AFP, show suboptimal performance for detection of HCC, which is crucial for successful resection or treatment. Thus, there is a significant need for new biomarkers to aid early diagnosis of HCC. Studies have shown that the expression level of human microRNAs (miRNAs), a small, non-coding RNA species released into the blood, can serve as an early marker for various diseases, including HCC. AIM: To evaluate the diagnostic role of miRNAs in HCC as single markers, signatures or in combination with known protein biomarkers. METHODS: Our prospective, multicenter, case-control study recruited 660 participants (354 controls with chronic liver disease and 306 participants with HCC) and employed a strategy of initial screening by two independent methods, real-time quantitative PCR (n = 60) and next-generation sequencing (n = 100), to assess a large number of miRNAs. The results from the next-generation sequencing and real-time quantitative PCR screening approaches were then combined to select 26 miRNAs (including two putative novel miRNAs). Those miRNAs were analyzed for their diagnostic potential as single markers or in combination with other miRNAs or established protein biomarkers AFP and PIVKA-II via real-time quantitative PCR in training (n = 200) and validation cohorts (n = 300). RESULTS: We identified 26 miRNAs that differentiated chronic liver disease controls from (early) HCC via two independent discovery approaches. Three miRNAs, miR-21-5p (miR-21), miR-320a and miR-186-5p, were selected by both methods. In the training cohort, only miR-21, miR-320d and miR-423 could significantly distinguish (Q < 0.05) between the HCC and chronic liver disease control groups. In the multivariate setting, miR-21 with PIVKA-II was selected as the best combination, resulting in an area under the curve of 0.87 for diagnosis and area under the curve of 0.74 for early diagnosis of HCC. In the validation cohort, only miR-21 and miR-423 could be confirmed as potential HCC biomarkers. A combination of miRNAs did not perform better than any single miRNA. Improvement of PIVKA-II performance through combination with miRNAs could not be confirmed in the validation panel. Two putative miRs, put-miR-6 and put-miR-99, were tested in the training and validation panels, but their expression could only be detected in very few samples and at a low level (cycle threshold between 31.24 and 34.97). CONCLUSION: miRNAs alone or as a signature in combination with protein biomarkers AFP and PIVKA-II do not improve the diagnostic performance of the protein biomarkers.

Soluble Angiotensin-Converting Enzyme 2 as a Prognostic Biomarker for Disease Progression in Patients Infected with SARS-CoV-2.

Predicting disease severity in patients infected with SARS-CoV-2 is difficult. Soluble angiotensin-converting enzyme 2 (sACE2) arises from the shedding of membrane ACE2 (mACE2), which is a receptor for SARS-CoV-2 spike protein. We evaluated the predictive value of sACE2 compared with known biomarkers of inflammation and tissue damage (CRP, GDF-15, IL-6, and sFlt-1) in 850 patients with and without SARS-CoV-2 with different clinical outcomes. For univariate analyses, median differences between biomarker levels were calculated for the following patient groups (classified by clinical outcome): RT-PCR-confirmed SARS-CoV-2 positive (Groups 1−4); RT-PCR-confirmed SARS-CoV-2 negative following previous SARS-CoV-2 infection (Groups 5 and 6); and 'SARS-CoV-2 unexposed' patients (Group 7). Median levels of CRP, GDF-15, IL-6, and sFlt-1 were significantly higher in hospitalized patients with SARS-CoV-2 compared with discharged patients (all p < 0.001), whereas levels of sACE2 were significantly lower (p < 0.001). ROC curve analysis of sACE2 provided cut-offs for predicting hospital admission (≤0.05 ng/mL (positive predictive value: 89.1%) and ≥0.42 ng/mL (negative predictive value: 84.0%)). These findings support further investigation of sACE2, as a single biomarker or as part of a panel, to predict hospitalization risk and disease severity in patients with SARS-CoV-2 infection.

New Assay System Elecsys Anti-p53 to Detect Serum Anti-p53 Antibodies in Esophageal Cancer Patients and Colorectal Cancer Patients: Multi-institutional Study.

BACKGROUND: Several recent studies suggest that serum anti-p53 antibodies (s-p53-Abs) may be combined with other markers to detect esophageal and colorectal cancer. In this study, we assessed the sensitivity and specificity of s-p53-Abs detection of a new electrochemiluminescence immunoassay (ECLIA; Elecsys anti-p53). METHODS: Elecsys anti-p53 assay was used to analyze the level of s-p53-Abs in blood sera from patients with esophageal or colorectal cancer taken before treatment. Control blood sera from healthy volunteers, patients with benign diseases, and patients with autoimmune diseases served as a reference. In addition, squamous cell carcinoma antigen (SCC-Ag) and cytokeratin 19 fragments (CYFRA21-1) were assessed in patients with esophageal cancer, and carcinoembryonic antigen (CEA) and carbohydrate antigen (CA) 19-9 were assessed in patients with colorectal cancer. RESULTS: Samples from 281 patients with esophageal cancer, 232 patients with colorectal cancer, and 532 controls were included in the study. The median value of s-p53-Abs in control samples was < 0.02 µg/mL (range < 0.02-29.2 µg/mL). Assuming 98% specificity, the cut-off value was determined as 0.05 µg/mL. s-p53-Abs were detected in 20% (57/281) of patients with esophageal cancer and 18% (42/232) of patients with colorectal cancer. In combination with SCC-Ag and CEA, respectively, s-p53-Abs detected 51% (144/281) of patients with esophageal and 53% (124/232) of patients with colorectal cancer. CONCLUSIONS: The new s-p53-Abs assay Elecsys anti-p53 was useful in detecting esophageal and colorectal cancers with high specificity. Adding s-p53-Abs to conventional markers significantly improved the overall detection rates.

Translation and evaluation of a pre-clinical 5-protein response prediction signature in a breast cancer phase Ib clinical trial.

Human protein biomarker discovery relies heavily on pre-clinical models, in particular established cell lines and patient-derived xenografts, but confirmation studies in primary tissue are essential to demonstrate clinical relevance. We describe in this study the process that was followed to clinically translate a 5-protein response signature predictive for the activity of an anti-HER3 monoclonal antibody (lumretuzumab) originally measured in fresh frozen xenograft tissue. We detail the development, qualification, and validation of the multiplexed targeted mass spectrometry assay used to assess the signature performance in formalin-fixed, paraffin-embedded human clinical samples collected in a phase Ib trial designed to evaluate lumretuzumab in patients with metastatic breast cancer. We believe that the strategy delineated here provides a path forward to avoid the time- and cost-consuming step of having to develop immunological reagents against unproven targets. We expect that mass spectrometry-based platforms may become part of a rational process to rapidly test and qualify large number of candidate biomarkers to identify the few that stand a chance for further development and validation.

Quantitative Phosphoproteomics Analysis of ERBB3/ERBB4 Signaling.

The four members of the epidermal growth factor receptor (EGFR/ERBB) family form homo- and heterodimers which mediate ligand-specific regulation of many key cellular processes in normal and cancer tissues. While signaling through the EGFR has been extensively studied on the molecular level, signal transduction through ERBB3/ERBB4 heterodimers is less well understood. Here, we generated isogenic mouse Ba/F3 cells that express full-length and functional membrane-integrated ERBB3 and ERBB4 or ERBB4 alone, to serve as a defined cellular model for biological and phosphoproteomics analysis of ERBB3/ERBB4 signaling. ERBB3 co-expression significantly enhanced Ba/F3 cell proliferation upon neuregulin-1 (NRG1) treatment. For comprehensive signaling studies we performed quantitative mass spectrometry (MS) experiments to compare the basal ERBB3/ERBB4 cell phosphoproteome to NRG1 treatment of ERBB3/ERBB4 and ERBB4 cells. We employed a workflow comprising differential isotope labeling with mTRAQ reagents followed by chromatographic peptide separation and final phosphopeptide enrichment prior to MS analysis. Overall, we identified 9686 phosphorylation sites which could be confidently localized to specific residues. Statistical analysis of three replicate experiments revealed 492 phosphorylation sites which were significantly changed in NRG1-treated ERBB3/ERBB4 cells. Bioinformatics data analysis recapitulated regulation of mitogen-activated protein kinase and Akt pathways, but also indicated signaling links to cytoskeletal functions and nuclear biology. Comparative assessment of NRG1-stimulated ERBB4 Ba/F3 cells revealed that ERBB3 did not trigger defined signaling pathways but more broadly enhanced phosphoproteome regulation in cells expressing both receptors. In conclusion, our data provide the first global picture of ERBB3/ERBB4 signaling and provide numerous potential starting points for further mechanistic studies.

Comparative proteome analysis across non-small cell lung cancer cell lines.

Non-small cell lung cancer (NSCLC) cell lines are widely used model systems to study molecular aspects of lung cancer. Comparative and in-depth proteome expression data across many NSCLC cell lines has not been generated yet, but would be of utility for the investigation of candidate targets and markers in oncogenesis. We employed a SILAC reference approach to perform replicate proteome quantifications across 23 distinct NSCLC cell lines. On average, close to 4000 distinct proteins were identified and quantified per cell line. These included many known targets and diagnostic markers, indicating that our proteome expression data represents a useful resource for NSCLC pre-clinical research. To assess proteome diversity within the NSCLC cell line panel, we performed hierarchical clustering and principal component analysis of proteome expression data. Our results indicate that general proteome diversity among NSCLC cell lines supersedes potential effects common to K-Ras or epidermal growth factor receptor (EGFR) oncoprotein expression. However, we observed partial segregation of EGFR or KRAS mutant cell lines for certain principal components, which reflected biological differences according to gene ontology enrichment analyses. Moreover, statistical analysis revealed several proteins that were significantly overexpressed in KRAS or EGFR mutant cell lines.

Systematic evaluation of label-free and super-SILAC quantification for proteome expression analysis.

RATIONALE: Advanced implementations of mass spectrometry (MS)-based proteomics allow for comprehensive proteome expression profiling across many biological samples. The outcome of such studies critically depends on accurate and precise quantification, which has to be ensured for high-coverage proteome analysis possible on fast and sensitive mass spectrometers such as quadrupole orbitrap instruments. METHODS: We conducted ultra-high-performance liquid chromatography (UHPLC)/MS experiments on a Q Exactive to systematically compare label-free proteome quantification across six human cancer cell lines with quantification against a shared reference mix generated by stable isotope labeling with amino acids in cell culture (super-SILAC). RESULTS: Single-shot experiments identified on average about 5000 proteins in the label-free compared to about 3500 in super-SILAC experiments. Label-free quantification was slightly less precise than super-SILAC in replicate measurements, verifying previous results obtained for lower proteome coverage. Due to the higher number of quantified proteins, more significant differences were detected in label-free cell line comparisons, whereas a higher percentage of quantified proteins was identified as differentially expressed in super-SILAC experiments. Additional label-free replicate analyses effectively compensated for lower precision of quantification. Finally, peptide fractionation by high pH reversed-phase chromatography prior to LC/MS analysis further increased the robustness and precision of label-free quantification in conjunction with higher proteome coverage. CONCLUSIONS: Our results benchmark and highlight the utility of label-free proteome quantification for applications such as target and biomarker discovery on state-of-the-art UHPLC/MS workflows.

Pareto Optimization Identifies Diverse Set of Phosphorylation Signatures Predicting Response to Treatment with Dasatinib.

Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin ß4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.

Identification of significant features by the Global Mean Rank test.

With the introduction of omics-technologies such as transcriptomics and proteomics, numerous methods for the reliable identification of significantly regulated features (genes, proteins, etc.) have been developed. Experimental practice requires these tests to successfully deal with conditions such as small numbers of replicates, missing values, non-normally distributed expression levels, and non-identical distributions of features. With the MeanRank test we aimed at developing a test that performs robustly under these conditions, while favorably scaling with the number of replicates. The test proposed here is a global one-sample location test, which is based on the mean ranks across replicates, and internally estimates and controls the false discovery rate. Furthermore, missing data is accounted for without the need of imputation. In extensive simulations comparing MeanRank to other frequently used methods, we found that it performs well with small and large numbers of replicates, feature dependent variance between replicates, and variable regulation across features on simulation data and a recent two-color microarray spike-in dataset. The tests were then used to identify significant changes in the phosphoproteomes of cancer cells induced by the kinase inhibitors erlotinib and 3-MB-PP1 in two independently published mass spectrometry-based studies. MeanRank outperformed the other global rank-based methods applied in this study. Compared to the popular Significance Analysis of Microarrays and Linear Models for Microarray methods, MeanRank performed similar or better. Furthermore, MeanRank exhibits more consistent behavior regarding the degree of regulation and is robust against the choice of preprocessing methods. MeanRank does not require any imputation of missing values, is easy to understand, and yields results that are easy to interpret. The software implementing the algorithm is freely available for academic and commercial use.

Comparison of SILAC and mTRAQ quantification for phosphoproteomics on a quadrupole orbitrap mass spectrometer.

Advances in mass spectrometric methodology and instrumentation have promoted a continuous increase in analytical performance in the field of phosphoproteomics. Here, we employed the recently introduced quadrupole Orbitrap (Q Exactive) mass spectrometer for quantitative signaling analysis to a depth of more than 15 000 phosphorylation sites. In parallel to the commonly used SILAC approach, we evaluated the nonisobaric chemical labeling reagent mTRAQ as an alternative quantification technique. Both enabled high phosphoproteome coverage in H3122 lung cancer cells. Replicate quantifications by mTRAQ identified almost as many significant phosphorylation changes upon treatment with ALK kinase inhibitor crizotinib as found by SILAC quantification. Overall, mTRAQ was slightly less precise than SILAC as evident from a somewhat higher variance of replicate phosphosite ratios. Direct comparison of SILAC- and mTRAQ-quantified phosphosites revealed that the majority of changes were detected by either quantification techniques, but also highlighted the aspect of false negative identifications in quantitative proteomics applications. Further inspection of crizotinib-regulated phosphorylation changes unveiled interference with multiple antioncogenic mechanisms downstream of ALK fusion kinase in H3122 cells. In conclusion, our results demonstrate a strong analytical performance of the Q Exactive in global phosphoproteomics, and establish mTRAQ quantification as a useful alternative to metabolic isotope labeling.

Global quantitative phosphoproteome analysis of human tumor xenografts treated with a CD44 antagonist.

The cell surface glycoprotein CD44 plays an important role in the development and progression of various tumor types. RG7356 is a humanized antibody targeting the constant region of CD44 that shows antitumor efficacy in mice implanted with CD44-expressing tumors such as MDA-MB-231 breast cancer cells. CD44 receptor seems to function as the main receptor for hyaluronic acid and osteopontin, serving as coreceptor for growth factor pathways like cMet, EGFR, HER-2, and VEGFR and by cytoskeletal modulation via ERM and Rho kinase signaling. To assess the direct impact of RG7356 binding to the CD44 receptor, a global mass spectrometry-based phosphoproteomics approach was applied to freshly isolated MDA-MB-231 tumor xenografts. Results from a global phosphoproteomics screen were further corroborated by Western blot and ELISA analyses of tumor lysates from CD44-expressing tumors. Short-term treatment of tumor-bearing mice with RG7356 resulted in modifications of the MAPK pathway in the responsive model, although no effects on downstream phosphorylation were observed in a nonresponsive xenograft model. Taken together, our approach augments the value of other high throughput techniques to identify biomarkers for clinical development of targeted agents.

Phosphosignature predicts dasatinib response in non-small cell lung cancer.

Targeted drugs are less toxic than traditional chemotherapeutic therapies; however, the proportion of patients that benefit from these drugs is often smaller. A marker that confidently predicts patient response to a specific therapy would allow an individual therapy selection most likely to benefit the patient. Here, we used quantitative mass spectrometry to globally profile the basal phosphoproteome of a panel of non-small cell lung cancer cell lines. The effect of the kinase inhibitor dasatinib on cellular growth was tested against the same panel. From the phosphoproteome profiles, we identified 58 phosphorylation sites, which consistently differ between sensitive and resistant cell lines. Many of the corresponding proteins are involved in cell adhesion and cytoskeleton organization. We showed that a signature of only 12 phosphorylation sites is sufficient to accurately predict dasatinib sensitivity. Four of the phosphorylation sites belong to integrin ß4, a protein that mediates cell-matrix or cell-cell adhesion. The signature was validated in cross-validation and label switch experiments and in six independently profiled breast cancer cell lines. The study supports that the phosphorylation of integrin ß4, as well as eight further proteins comprising the signature, are candidate biomarkers for predicting response to dasatinib in solid tumors. Furthermore, our results show that identifying predictive phosphorylation signatures from global, quantitative phosphoproteomic data is possible and can open a new path to discovering molecular markers for response prediction.

Identifying differentially regulated subnetworks from phosphoproteomic data.

BACKGROUND: Various high throughput methods are available for detecting regulations at the level of transcription, translation or posttranslation (e.g. phosphorylation). Integrating these data with protein networks should make it possible to identify subnetworks that are significantly regulated. Furthermore, such integration can support identification of regulated entities from often noisy high throughput data. In particular, processing mass spectrometry-based phosphoproteomic data in this manner may expose signal transduction pathways and, in the case of experiments with drug-treated cells, reveal the drug's mode of action. RESULTS: Here, we introduce SubExtractor, an algorithm that combines phosphoproteomic data with protein network information from STRING to identify differentially regulated subnetworks and individual proteins. The method is based on a Bayesian probabilistic model combined with a genetic algorithm and rigorous significance testing. The Bayesian model accounts for information about both differential regulation and network topology. The method was tested with artificial data and subsequently applied to a comprehensive phosphoproteomics study investigating the mode of action of sorafenib, a small molecule kinase inhibitor. CONCLUSIONS: SubExtractor reliably identifies differentially regulated subnetworks from phosphoproteomic data by integrating protein networks. The method can also be applied to gene or protein expression data.

MetaTM - a consensus method for transmembrane protein topology prediction.

BACKGROUND: Transmembrane (TM) proteins are proteins that span a biological membrane one or more times. As their 3-D structures are hard to determine, experiments focus on identifying their topology (i. e. which parts of the amino acid sequence are buried in the membrane and which are located on either side of the membrane), but only a few topologies are known. Consequently, various computational TM topology predictors have been developed, but their accuracies are far from perfect. The prediction quality can be improved by applying a consensus approach, which combines results of several predictors to yield a more reliable result. RESULTS: A novel TM consensus method, named MetaTM, is proposed in this work. MetaTM is based on support vector machine models and combines the results of six TM topology predictors and two signal peptide predictors. On a large data set comprising 1460 sequences of TM proteins with known topologies and 2362 globular protein sequences it correctly predicts 86.7% of all topologies. CONCLUSION: Combining several TM predictors in a consensus prediction framework improves overall accuracy compared to any of the individual methods. Our proposed SVM-based system also has higher accuracy than a previous consensus predictor. MetaTM is made available both as downloadable source code and as DAS server at http://MetaTM.sbc.su.se.

jSquid: a Java applet for graphical on-line network exploration.

UNLABELLED: jSquid is a graph visualization tool for exploring graphs from protein-protein interaction or functional coupling networks. The tool was designed for the FunCoup web site, but can be used for any similar network exploring purpose. The program offers various visualization and graph manipulation techniques to increase the utility for the user. AVAILABILITY: jSquid is available for direct usage and download at http://jSquid.sbc.su.se including source code under the GPLv3 license, and input examples. It requires Java version 5 or higher to run properly. CONTACT: erik.sonnhammer@sbc.su.se SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.