Use of Multiple Machine Learning Approaches for Selecting Urothelial Cancer-Specific DNA Methylation Biomarkers in Urine.

Diagnosing urothelial cancer (UCa) via invasive cystoscopy is painful, specifically in men, and can cause infection and bleeding. Because the UCa risk is higher for male patients, urinary non-invasive UCa biomarkers are highly desired to stratify men for invasive cystoscopy. We previously identified multiple DNA methylation sites in urine samples that detect UCa with a high sensitivity and specificity in men. Here, we identified the most relevant markers by employing multiple statistical approaches and machine learning (random forest, boosted trees, LASSO) using a dataset of 251 male UCa patients and 111 controls. Three CpG sites located in ALOX5, TRPS1 and an intergenic region on chromosome 16 have been concordantly selected by all approaches, and their combination in a single decision matrix for clinical use was tested based on their respective thresholds of the individual CpGs. The combination of ALOX5 and TRPS1 yielded the best overall sensitivity (61%) at a pre-set specificity of 95%. This combination exceeded both the diagnostic performance of the most sensitive bioinformatic approach and that of the best single CpG. In summary, we showed that overlap analysis of multiple statistical approaches identifies the most reliable biomarkers for UCa in a male collective. The results may assist in stratifying men for cystoscopy.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

Systematic Comparison of Label-Free, SILAC, and TMT Techniques to Study Early Adaption toward Inhibition of EGFR Signaling in the Colorectal Cancer Cell Line DiFi.

We evaluated the quantification strategies label-free (LF), stable isotope labeling by amino acids in cell culture (SILAC), and tandem mass tags (TMT) and their performance in quantification of proteins and phosphosites (p-sites) to identify the most powerful approach for monitoring cellular signaling. We analyzed the epidermal growth factor receptor (EGFR) signaling network, which plays an essential role in colorectal cancer, and studied its dynamics within 24 h upon treatment with the EGFR-blocking antibody cetuximab, representing the first cellular adaption toward therapy. LF achieved superior coverage but was outperformed by label-based approaches regarding technical variability, especially for quantification of p-sites. TMT showed the lowest coverage and most missing values. We found that its performance considerably decreases when experimental replicates are distributed over several TMT plexes. SILAC showed the highest precision and outstanding performance for quantification of p-sites, rendering it the method of choice for analyzing cellular signaling in cell culture models. On the protein level, we observed only little regulation upon cetuximab treatment, whereas a great fraction of p-sites was significantly regulated. These dynamics represented an initial downregulation of the MAPK pathway, which was partially rescued as early as 24 h after treatment. We identified upregulation and signaling via ERBB3 as well as calcium and cAMP signaling as possible mechanisms bypassing the blockage of EGFR.

Monocytes-neutrophils-ratio as predictive marker for failure of first induction therapy in AML.

INTRODUCTION: Acute myeloid leukemia (AML) is, if untreated, a fatal hematologic neoplasia. Failure of the first induction chemotherapy is a hallmark for a poor prognosis. Early recognition of therapy failure is crucial for planning further therapies. Therefore, international guidelines recommend a bone marrow biopsy around day 14 after the beginning of induction therapy. Hypocellular bone marrow on day 14 is still gold standard for therapy assessment and further therapy strategy. Despite this, non-invasive ways for the evaluation of induction therapy were looked for in the past years. METHODS: We collected peripheral blood cell counts and routine laboratory values of patients treated with "7 + 3" induction therapy. Ratios of absolute cell counts of monocytes and neutrophils (MNR) were calculated daily, and the values were compared in patients with failure of the first induction therapy and patients with therapy response. RESULTS: 54 patients were included, 12 of which had failure of first induction therapy. The MNR following therapy was highly correlated with the bone marrow results. With the right cut-off, the MNR provides a valid and reliable tool for identification of patients with failure of first induction therapy with a sensitivity of 83.3% and a specificity of 87.8% on day 18. CONCLUSIONS: We propose a novel and non-invasive method for detection of failure of first induction therapy in patients with de novo AML and "7 + 3" induction therapy. The MNR is free of cost since the required cell counts are performed routinely for each patient undergoing intensive chemotherapy.

MicroRNA-30c as a novel diagnostic biomarker for primary and secondary B-cell lymphoma of the CNS.

Primary lymphomas of the central nervous system (PCNSL) are highly aggressive tumors affecting exclusively the CNS, meninges, and eyes. PCNSL must be separated from secondary spread of systemic lymphoma to the CNS (SCNSL), which may occur at diagnosis or relapse of systemic lymphomas. At present, there are no valid methods to distinguish PCNSL from SCNSL based on tumor biopsy because of similar histological presentation. However, SCNSL and PCNSL are different in terms of prognosis and adequate therapy protocols. MicroRNA expression profiles of CSF samples collected from SCNSL and PCNSL patients were compared using microRNA arrays. MiR-30c revealed the largest differential expression and was selected for validation by RT-PCR on 61 CSF samples from patients with PCNSL and 14 samples from SCNSL. MiR-30c was significantly increased in patients with SCNSL compared to PCNSL (p < 0.001). MiR-30c levels in CSF enabled the differentiation of patients with PCNSL from SCNSL with an area under the curve (AUC) of 0.86, with a sensitivity of 90.9% and a specificity of 85.5%. Our data suggest that miR-30c detected in the CSF can serve as biomarker for distinction between PCNSL and SCNSL. The validation in a larger cohort is needed. With respect to its function, miR-30c may facilitate lymphoma cells to engraft into CNS by interaction with CELSR3 gene that controls the function of ependymal cilia and, thus, affects the circulation of CSF.

Quantitative proteome analysis reveals the correlation between endocytosis-associated proteins and hepatocellular carcinoma dedifferentiation.

The majority of poorly differentiated hepatocellular carcinomas (HCCs) develop from well-differentiated tumors. Endocytosis is a cellular function which is likely to take part in this development due to its important role in regulating the abundances of vital signaling receptors. Here, we aimed to investigate the abundance of endocytosis-associated proteins in HCCs with various differentiation grades. Therefore, we analyzed 36 tissue specimens from HCC patients via LC-MS/MS-based label-free quantitative proteomics including 19 HCC tissue samples with different degrees of histological grades and corresponding non-tumorous tissue controls. As a result, 277 proteins were differentially regulated between well-differentiated tumors and controls. In moderately and poorly differentiated tumors, 278 and 1181 proteins, respectively, were significantly differentially regulated compared to non-tumorous tissue. We explored the regulated proteins based on their functions and identified thirty endocytosis-associated proteins, mostly overexpressed in poorly differentiated tumors. These included proteins that have been shown to be up-regulated in HCC like clathrin heavy chain-1 (CLTC) as well as unknown proteins, such as secretory carrier-associated membrane protein 3 (SCAMP3). The abundances of SCAMP3 and CLTC were immunohistochemically examined in tissue sections of 84 HCC patients. We demonstrate the novel association of several endocytosis-associated proteins, in particular, SCAMP3 with HCC progression.

PAA: an R/bioconductor package for biomarker discovery with protein microarrays.

UNLABELLED: The R/Bioconductor package Protein Array Analyzer (PAA) facilitates a flexible analysis of protein microarrays for biomarker discovery (esp., ProtoArrays). It provides a complete data analysis workflow including preprocessing and quality control, uni- and multivariate feature selection as well as several different plots and results tables to outline and evaluate the analysis results. As a main feature, PAA's multivariate feature selection methods are based on recursive feature elimination (e.g. SVM-recursive feature elimination, SVM-RFE) with stability ensuring strategies such as ensemble feature selection. This enables PAA to detect stable and reliable biomarker candidate panels. AVAILABILITY AND IMPLEMENTATION: PAA is freely available (BSD 3-clause license) from http://www.bioconductor.org/packages/PAA/ CONTACT: michael.turewicz@rub.de or martin.eisenacher@rub.de.

Quantitative Tissue Proteomics Analysis Reveals Versican as Potential Biomarker for Early-Stage Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most aggressive tumors, and the treatment outcome of this disease is improved when the cancer is diagnosed at an early stage. This requires biomarkers allowing an accurate and early tumor diagnosis. To identify potential markers for such applications, we analyzed a patient cohort consisting of 50 patients (50 HCC and 50 adjacent nontumorous tissue samples as controls) using two independent proteomics approaches. We performed label-free discovery analysis on 19 HCC and corresponding tissue samples. The data were analyzed considering events known to take place in early events of HCC development, such as abnormal regulation of Wnt/b-catenin and activation of receptor tyrosine kinases (RTKs). 31 proteins were selected for verification experiments. For this analysis, the second set of the patient cohort (31 HCC and corresponding tissue samples) was analyzed using selected (multiple) reaction monitoring (SRM/MRM). We present the overexpression of ATP-dependent RNA helicase (DDX39), Fibulin-5 (FBLN5), myristoylated alanine-rich C-kinase substrate (MARCKS), and Serpin H1 (SERPINH1) in HCC for the first time. We demonstrate Versican core protein (VCAN) to be significantly associated with well differentiated and low-stage HCC. We revealed for the first time the evidence of VCAN as a potential biomarker for early-HCC diagnosis.

Autoimmune profiling with protein microarrays in clinical applications.

In recent years, knowledge about immune-related disorders has substantially increased, especially in the field of central nervous system (CNS) disorders. Recent innovations in protein-related microarray technology have enabled the analysis of interactions between numerous samples and up to 20,000 targets. Antibodies directed against ion channels, receptors and other synaptic proteins have been identified, and their causative roles in different disorders have been identified. Knowledge about immunological disorders is likely to expand further as more antibody targets are discovered. Therefore, protein microarrays may become an established tool for routine diagnostic procedures in the future. The identification of relevant target proteins requires the development of new strategies to handle and process vast quantities of data so that these data can be evaluated and correlated with relevant clinical issues, such as disease progression, clinical manifestations and prognostic factors. This review will mainly focus on new protein array technologies, which allow the processing of a large number of samples, and their various applications with a deeper insight into their potential use as diagnostic tools in neurodegenerative diseases and other diseases. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Improving the default data analysis workflow for large autoimmune biomarker discovery studies with ProtoArrays.

Contemporary protein microarrays such as the ProtoArray® are used for autoimmune antibody screening studies to discover biomarker panels. For ProtoArray data analysis, the software Prospector and a default workflow are suggested by the manufacturer. While analyzing a large data set of a discovery study for diagnostic biomarkers of the Parkinson's disease (ParkCHIP), we have revealed the need for distinct improvements of the suggested workflow concerning raw data acquisition, normalization and preselection method availability, batch effects, feature selection, and feature validation. In this work, appropriate improvements of the default workflow are proposed. It is shown that completely automatic data acquisition as a batch, a re-implementation of Prospector's pre-selection method, multivariate or hybrid feature selection, and validation of the selected protein panel using an independent test set define in combination an improved workflow for large studies.

Characterization of peptide-protein relationships in protein ambiguity groups via bipartite graphs.

In bottom-up proteomics, proteins are enzymatically digested into peptides before measurement with mass spectrometry. The relationship between proteins and their corresponding peptides can be represented by bipartite graphs. We conduct a comprehensive analysis of bipartite graphs using quantified peptides from measured data sets as well as theoretical peptides from an in silico digestion of the corresponding complete taxonomic protein sequence databases. The aim of this study is to characterize and structure the different types of graphs that occur and to compare them between data sets. We observed a large influence of the accepted minimum peptide length during in silico digestion. When changing from theoretical peptides to measured ones, the graph structures are subject to two opposite effects. On the one hand, the graphs based on measured peptides are on average smaller and less complex compared to graphs using theoretical peptides. On the other hand, the proportion of protein nodes without unique peptides, which are a complicated case for protein inference and quantification, is considerably larger for measured data. Additionally, the proportion of graphs containing at least one protein node without unique peptides rises when going from database to quantitative level. The fraction of shared peptides and proteins without unique peptides as well as the complexity and size of the graphs highly depends on the data set and organism. Large differences between the structures of bipartite peptide-protein graphs have been observed between database and quantitative level as well as between analyzed species. In the analyzed measured data sets, the proportion of protein nodes without unique peptides ranged from 6.4% to 55.0%. This highlights the need for novel methods that can quantify proteins without unique peptides. The knowledge about the structure of the bipartite peptide-protein graphs gained in this study will be useful for the development of such algorithms.

A Current Encyclopedia of Bioinformatics Tools, Data Formats and Resources for Mass Spectrometry Lipidomics.

Mass spectrometry is a widely used technology to identify and quantify biomolecules such as lipids, metabolites and proteins necessary for biomedical research. In this study, we catalogued freely available software tools, libraries, databases, repositories and resources that support lipidomics data analysis and determined the scope of currently used analytical technologies. Because of the tremendous importance of data interoperability, we assessed the support of standardized data formats in mass spectrometric (MS)-based lipidomics workflows. We included tools in our comparison that support targeted as well as untargeted analysis using direct infusion/shotgun (DI-MS), liquid chromatography-mass spectrometry, ion mobility or MS imaging approaches on MS1 and potentially higher MS levels. As a result, we determined that the Human Proteome Organization-Proteomics Standards Initiative standard data formats, mzML and mzTab-M, are already supported by a substantial number of recent software tools. We further discuss how mzTab-M can serve as a bridge between data acquisition and lipid bioinformatics tools for interpretation, capturing their output and transmitting rich annotated data for downstream processing. However, we identified several challenges of currently available tools and standards. Potential areas for improvement were: adaptation of common nomenclature and standardized reporting to enable high throughput lipidomics and improve its data handling. Finally, we suggest specific areas where tools and repositories need to improve to become FAIRer.

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data.

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

BIONDA: a free database for a fast information on published biomarkers.

Summary: Because of the steadily increasing and already manually unmanageable total number of biomarker-related articles in biomedical research, there is a need for intelligent systems that extract all relevant information from biomedical texts and provide it as structured information to researchers in a user-friendly way. To address this, BIONDA was implemented as a free text mining-based online database for molecular biomarkers including genes, proteins and miRNAs and for all kinds of diseases. The contained structured information on published biomarkers is extracted automatically from Europe PMC publication abstracts and high-quality sources like UniProt and Disease Ontology. This allows frequent content updates. Availability and implementation: BIONDA is freely accessible via a user-friendly web application at http://bionda.mpc.ruhr-uni-bochum.de. The current BIONDA code is available at GitHub via https://github.com/mpc-bioinformatics/bionda. Supplementary information: Supplementary data are available at Bioinformatics Advances online.

Computertomography-Based Prediction of Complete Response Following Neoadjuvant Chemoradiotherapy of Locally Advanced Rectal Cancer.

Therapeutic strategies for patients with locally advanced rectal cancer (LARC) who are achieving a pathological complete response (pCR) after neoadjuvant radio-chemotherapy (neoCRT) are being increasingly investigated. Recent trials challenge the current standard therapy of total mesorectal excision (TME). For some patients, the treatment strategy of "watch-and-wait" seems a preferable procedure. The key factor in determining individual treatment strategies following neoCRT is the precise evaluation of the tumor response. Contrast-enhanced computer tomography (ceCT) has proven its ability to discriminate benign and malign lesions in multiple cancers. In this study, we retrospectively analyzed the ceCT based density of LARC in 30 patients, undergoing neoCRT followed by TME. We compared the tumors´ pre- and post-neoCRT density and correlated the results to the amount of residual vital tumor cells in the resected tissue. Overall, the density decreased after neoCRT, with the highest decrease in patients achieving pCR. Densitometry demonstrated a specificity of 88% and sensitivity of 68% in predicting pCR. Thus, we claim that ceCT based densitometry is a useful tool in identifying patients with LARC who may benefit from a "watch-and-wait" strategy and suggest further prospective studies.

Advanced Fiber Type-Specific Protein Profiles Derived from Adult Murine Skeletal Muscle.

Skeletal muscle is a heterogeneous tissue consisting of blood vessels, connective tissue, and muscle fibers. The last are highly adaptive and can change their molecular composition depending on external and internal factors, such as exercise, age, and disease. Thus, examination of the skeletal muscles at the fiber type level is essential to detect potential alterations. Therefore, we established a protocol in which myosin heavy chain isoform immunolabeled muscle fibers were laser microdissected and separately investigated by mass spectrometry to develop advanced proteomic profiles of all murine skeletal muscle fiber types. All data are available via ProteomeXchange with the identifier PXD025359. Our in-depth mass spectrometric analysis revealed unique fiber type protein profiles, confirming fiber type-specific metabolic properties and revealing a more versatile function of type IIx fibers. Furthermore, we found that multiple myopathy-associated proteins were enriched in type I and IIa fibers. To further optimize the assignment of fiber types based on the protein profile, we developed a hypothesis-free machine-learning approach, identified a discriminative peptide panel, and confirmed our panel using a public data set.

Small RNAs as biomarkers to differentiate benign and malign prostate diseases: An alternative for transrectal punch biopsy of the prostate?

Prostate cancer (PCa) is the most common cancer and the third most frequent cause of male cancer death in Germany. MicroRNAs (miRNA) appear to be involved in the development and progression of PCa. A diagnostic differentiation from benign prostate hyperplasia (BPH) is often only possible through transrectal punch biopsy. This procedure is described as painful and carries risks. It was investigated whether urinary miRNAs can be used as biomarkers to differentiate the prostate diseases above. Therefore urine samples from urological patients with BPH (25) or PCa (28) were analysed using Next-Generation Sequencing to detect the expression profile of total and exosomal miRNA/piRNA. 79 miRNAs and 5 piwi-interacting RNAs (piRNAs) were significantly differentially expressed (adjusted p-value < 0.05 and log2-Fc > 1 or < -1). Of these, 6 miRNAs and 2 piRNAs could be statistically validated (AUC on test cohort > = 0.7). In addition, machine-learning algorithms were used to identify a panel of 22 additional miRNAs, whose interaction makes it possible to differentiate the groups as well. There are promising individual candidates for potential use as biomarkers in prostate cancer. The innovative approach of applying machine learning methods to this kind of data could lead to further small RNAs coming into scientific focus, which have so far been neglected.

A proteomics sample metadata representation for multiomics integration and big data analysis.

The amount of public proteomics data is rapidly increasing but there is no standardized format to describe the sample metadata and their relationship with the dataset files in a way that fully supports their understanding or reanalysis. Here we propose to develop the transcriptomics data format MAGE-TAB into a standard representation for proteomics sample metadata. We implement MAGE-TAB-Proteomics in a crowdsourcing project to manually curate over 200 public datasets. We also describe tools and libraries to validate and submit sample metadata-related information to the PRIDE repository. We expect that these developments will improve the reproducibility and facilitate the reanalysis and integration of public proteomics datasets.

Using Laboratory Information Management Systems as central part of a proteomics data workflow.

The organization and storage of proteomics data are challenging issues today and even more for the rising amount of information in the future. This review article describes the advantages of using Laboratory Information Management Systems (LIMS) in proteomics laboratories. Seven typical LIMS are explored in detail to describe their role in an even bigger interrelation. They are a central part of the proteomics data workflow, starting with data generation and ending with the publication in journals and repositories. Therefore, they enable community-wide data utilization and further Systems Biology discoveries.

Peek a peak: a glance at statistics for quantitative label-free proteomics.

Today, label-free mass spectrometry methods are frequently used for quantification of proteins and peptides. There have been several proposals of measurable parameters that best reflect quantities, such as peak areas as well as spectral counts. This review provides a systematic overview of the proposed methods. Owing to the shotgun proteomics approach generally used today for label-free mass spectrometry, any quantitative measure in the first place is a measure of peptide quantity. There has been no systematic research on how to best infer protein quantity from its measured peptides' quantities. The way peptide identifications are assembled to protein lists may especially lead to significantly different results in protein quantification. A further focus of this review will thus be the assembly of measured peptide quantities to a protein quantity.