• SERPINA6
• biomarker failures
• biomarker validation
• biomarkers
• false discovery
• glioma

• Galleri test
• Grail
• cancer screening
• circulating tumor DNA
• massive parallel sequencing
• multi-cancer detection

• Humans
• Early Detection of Cancer/methods
• Neoplasms/diagnosis
• Neoplasms/blood
• Sensitivity and Specificity

• biomarkers
• cancer
• colorectal
• diagnosis
• polyps

• Humans
• Colorectal Neoplasms/diagnosis
• Colorectal Neoplasms/blood
• Biomarkers, Tumor/blood
• Adenoma/diagnosis
• Adenoma/blood
• Proteomics/methods

• Breast cancer subtypes
• EXplainable AI
• Gene expression
• PAM50

• Humans
• Female
• Breast Neoplasms/genetics
• Breast Neoplasms/diagnosis
• Biomarkers, Tumor/genetics
• Proteomics
• Gene Expression Profiling/methods
• Genetic Techniques

• Biomarkers
• Glioblastoma
• Liquid biopsy
• Methodology
• O6-methylguanine-DNA methyltransferase (MGMT) methylation
• Research
• Selection bias
• Translational research

• Humans
• Aged
• Glioblastoma/drug therapy
• Temozolomide/therapeutic use
• Dacarbazine
• Survivorship
• DNA Methylation
• Brain Neoplasms/diagnosis
• Brain Neoplasms/therapy
• Brain Neoplasms/genetics
• Prognosis
• Biomarkers, Tumor/metabolism
• DNA Repair Enzymes/genetics
• DNA Repair Enzymes/metabolism
• DNA Repair Enzymes/therapeutic use

• 23969 Cancer Research UK

• biomarker discovery
• data-independent acquisition
• dynamic biological systems
• gene annotation
• precision medicine
• proteomics

• Humans
• Proteomics/methods
• Systems Biology
• Proteins/analysis
• Mass Spectrometry/methods
• Software

• 200829/Z/16/Z Wellcome Trust
• MR/T03050X/1 Medical Research Council
• 200829/Z/16 Wellcome Trust

• confounders
• metabolomics
• multicenter
• plasma NMR
• preanalytical conditions

• 633983 European Union

We present a new R package PRECISION.array for assessing the performance of data normalization methods in connection with methods for sample classification. It includes two microRNA microarray datasets for the same set of tumor samples: a re-sampling-based algorithm for simulating additional paired datasets under various designs of sample-to-array assignment and levels of signal-to-noise ratios and a collection of numerical and graphical tools for method performance assessment. The package allows users to specify their own methods for normalization and classification, in addition to implementing three methods for training data normalization, seven methods for test data normalization, seven methods for classifier training, and two methods for classifier validation. It enables an objective and systemic evaluation of the operating characteristics of normalization and classification methods in microRNA microarrays. To our knowledge, this is the first such tool available. The R package can be downloaded freely at https://github.com/LXQin/PRECISION.array.

• P30 CA008748 NCI NIH HHS
• R21 HG012124 NHGRI NIH HHS
• National Institutes of Health

• Consensus
• Delphi Technique
• Humans
• Peptic Ulcer Perforation/surgery
• Prognosis
• Research Design
• Risk Factors
• Surveys and Questionnaires
• Treatment Outcome

• Automation
• Biofluid
• Biomarker
• Cohort
• Human
• MS
• Mass Spectrometry
• Plasma
• Proteomics

Deep sequencing has become one of the most popular tools for transcriptome profiling in biomedical studies. While an abundance of computational methods exists for ‘normalizing’ sequencing data to remove unwanted between-sample variations due to experimental handling, there is no consensus on which normalization is the most suitable for a given data set. To address this problem, we developed ‘DANA’—an approach for assessing the performance of normalization methods for microRNA sequencing data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and chromosomal clustering to simultaneously assess (i) how effectively normalization removes handling artifacts and (ii) how aptly normalization preserves biological signals. With DANA, we confirm that the performance of eight commonly used normalization methods vary widely across different data sets and provide guidance for selecting a suitable method for the data at hand. Hence, it should be adopted as a routine preprocessing step (preceding normalization) for microRNA sequencing data analysis. DANA is implemented in R and publicly available at https://github.com/LXQin/DANA.

• Biology
• Cluster Analysis
• Computational Biology/methods
• Gene Expression Profiling/methods
• MicroRNAs/genetics
• Sequence Analysis, RNA/methods

• P30 CA008748 NCI NIH HHS
• R21 HG012124 NHGRI NIH HHS
• National Institutes of Health
• Deutsche Forschungsgemeinschaft

• Bias
• Biomarker discovery
• COVID-19
• Confounding factor
• Machine learning
• Multiplex assay
• SARS-CoV-2

• COVID-19/diagnosis
• Humans
• Machine Learning
• Pandemics
• SARS-CoV-2

• Biomarker
• chromosomal instability
• intra-tumor heterogeneity
• pan-cancer

Accurate assessment of a molecular classifier that guides patient care is of paramount importance in precision oncology. Recent years have seen an increasing use of external validation for such assessment. However, little is known about how it is affected by ubiquitous unwanted variations in test data because of disparate experimental handling and by the use of data normalization for alleviating such variations.

In this paper, we studied these issues using two microarray data sets for the same set of tumor samples and additional data simulated by resampling under various levels of signal-to-noise ratio and different designs for array-to-sample allocation.

We showed that (1) unwanted variations can lead to biased classifier assessment and (2) data normalization mitigates the bias to varying extents depending on the specific method used. In particular, frozen normalization methods for test data outperform their conventional forms in terms of both reducing the bias in accuracy estimation and increasing robustness to handling effects. We make available our benchmarking tool as an R package on GitHub for performing such evaluation on additional methods for normalization and classification.

Our findings thus highlight the importance of proper test-data normalization for valid assessment by external validation and call for caution on the choice of normalization method for molecular classifier development.

• Deep Learning
• Humans
• Neoplasms/diagnosis

• Biomarker
• Clinical trial
• Confirmation analysis
• Exploratory analysis

• pancreatic cancer
• biomarkers
• early detection research network
• edrn
• surveillance
• early detection

• Aged
• Biomarkers, Tumor/metabolism
• Early Detection of Cancer/methods
• Female
• Humans
• Male
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms

• U01 CA200468 NCI NIH HHS
• U01 CA200466 NCI NIH HHS
• P50 CA221707 NCI NIH HHS
• U01 CA152653 NCI NIH HHS
• R01 CA218004 NCI NIH HHS
• U01 CA226158 NCI NIH HHS
• U01 CA196403 NCI NIH HHS
• U01 CA210240 NCI NIH HHS
• U24 CA086368 NCI NIH HHS
• U01 CA086368 NCI NIH HHS

The traditional approach in identifying cancer related protein biomarkers has focused on evaluation of a single peptide/protein in tissue or circulation. At best, this approach has had limited success for clinical applications, since multiple pathological tumor pathways may be involved during initiation or progression of cancer which diminishes the significance of a single candidate protein/peptide. Emerging sensitive proteomic based technologies like liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics can provide a platform for evaluating serial serum or plasma samples to interrogate secreted products of tumor–host interactions, thereby revealing a more “complete” repertoire of biological variables encompassing heterogeneous tumor biology. However, several challenges need to be met for successful application of serum/plasma based proteomics. These include uniform pre-analyte processing of specimens, sensitive and specific proteomic analytical platforms and adequate attention to study design during discovery phase followed by validation of discovery-level signatures for prognostic, predictive, and diagnostic cancer biomarker applications.

Blood is a readily accessible biofluid containing a plethora of important proteins, nucleic acids, and metabolites that can be used as clinical diagnostic tools in diseases, including cancer. Like the on-going efforts for cancer biomarker discovery using the liquid biopsy detection of circulating cell-free and cell-based tumor nucleic acids, the circulatory proteome has been underexplored for clinical cancer biomarker applications. A comprehensive proteome analysis of human serum/plasma with high-quality data and compelling interpretation can potentially provide opportunities for understanding disease mechanisms, although several challenges will have to be met. Serum/plasma proteome biomarkers are present in very low abundance, and there is high complexity involved due to the heterogeneity of cancers, for which there is a compelling need to develop sensitive and specific proteomic technologies and analytical platforms. To date, liquid chromatography mass spectrometry (LC-MS)-based quantitative proteomics has been a dominant analytical workflow to discover new potential cancer biomarkers in serum/plasma. This review will summarize the opportunities of serum proteomics for clinical applications; the challenges in the discovery of novel biomarkers in serum/plasma; and current proteomic strategies in cancer research for the application of serum/plasma proteomics for clinical prognostic, predictive, and diagnostic applications, as well as for monitoring minimal residual disease after treatments. We will highlight some of the recent advances in MS-based proteomics technologies with appropriate sample collection, processing uniformity, study design, and data analysis, focusing on how these integrated workflows can identify novel potential cancer biomarkers for clinical applications.

• biomarkers
• mass spectrometry
• proteomics
• serum

• bias
• case-cohort study
• discovery study
• nested case-control study
• sample size
• specimen repository
• two-phase designs

• Batch effects
• Experimental design
• Quantitative proteomics
• Statistical analysis

• Artifacts
• Data Interpretation, Statistical
• Genomics
• Humans
• Pathology, Molecular
• Proteomics
• Research Design

• Cancer
• Conceptual framework
• Diagnosis
• Diagnostic strategies
• Early detection
• Primary care

• Early Detection of Cancer/methods
• Humans
• Models, Biological
• Neoplasms/diagnosis
• Triage

• 17940 Cancer Research UK
• 18180 Cancer Research UK
• C8640/A23385 Cancer Research UK

• Algorithms
• Breast Neoplasms/genetics
• Computational Biology/methods
• Female
• Forecasting/methods
• Gene Expression Profiling/methods
• Gene Expression Regulation, Neoplastic/genetics
• Gene Regulatory Networks
• Humans
• Prognosis

• Biomarker
• Liquid chromatography
• Mass spectrometry
• Post-analytical variability
• Pre-analytical variability
• Proteomics
• Regulated bioanalysis
• Sample preparation
• Stability

Lower urinary tract symptoms (LUTS) and prostate specific antigen-based parameters seem to have only a limited utility for the differential diagnosis of prostate cancer (PCa). MALDI-TOF/MS peptidomic profiling could be a useful diagnostic tool for biomarker discovery, although reproducibility issues have limited its applicability until now. The current study aimed to evaluate a new MALDI-TOF/MS candidate biomarker.

Within- and between-subject variability of MALDI-TOF/MS-based peptidomic urine and serum analyses were evaluated in 20 and 15 healthy donors, respectively. Normalizations and approaches for accounting below limit of detection (LOD) values were utilized to enhance reproducibility, while Monte Carlo experiments were performed to verify whether measurement error can be dealt with LOD data. Post-prostatic massage urine and serum samples from 148 LUTS patients were analysed using MALDI-TOF/MS. Regression-calibration and simulation and extrapolation methods were used to derive the unbiased association between peptidomic features and PCa.

Although the median normalized peptidomic variability was 24.9%, the within- and between-subject variability showed that median normalization, LOD adjustment, and log₂ data transformation were the best combination in terms of reliability; in measurement error conditions, intraclass correlation coefficient was a reliable estimate when the LOD/2 was substituted for below LOD values. In the patients studied, 43 peptides were shared by the urine and serum, and several features were found to be associated with PCa. Only few serum features, however, show statistical significance after the multiple testing procedures were completed. Two serum fragmentation patterns corresponded to the complement C4-A.

MALDI-TOF/MS serum peptidome profiling was more efficacious with respect to post-prostatic massage urine analysis in discriminating PCa.

The online version of this article (10.1186/s12014-018-9199-8) contains supplementary material, which is available to authorized users.

• Analytical variability
• Biomarkers
• Data normalization
• MALDI-TOF/MS
• Measurement error
• Peptidomic profiling
• Prostate cancer
• Regression calibration
• SIMEX
• Serum
• Urine

• Classification
• Microarray
• Normalization
• Validation

• Empirical null
• Primary 62J15
• batch effect
• robust regression
• secondary 62H25
• surrogate variable analysis
• unwanted variation

• P50 DA039838 NIDA NIH HHS
• R01 EB001988 NIBIB NIH HHS

Traumatic brain injury (TBI) is a complex disease with a multifaceted pathophysiology. Impairment of energy metabolism is a key component of secondary insults. This phenomenon is a consequence of multiple potential mechanisms including diffusion hypoxia, mitochondrial failure, and increased energy needs due to systemic trauma responses, seizures, or spreading depolarization. The degree of disturbance in brain metabolism is affected by treatment interventions and reflected in clinical patient outcome. Hence, monitoring of these secondary events in peripheral blood will provide a window into the pathophysiological course of severe TBI. New methods for assessing perturbation of brain metabolism are needed in order to monitor on-going pathophysiological processes and thus facilitate targeted interventions and predict outcome. Circulating metabolites in peripheral blood may serve as sensitive markers of pathological processes in TBI. The levels of these small molecules in blood are less dependent on the integrity of the blood–brain barrier as compared to protein biomarkers. We have recently characterized a specific metabolic profile in serum that is associated with both initial severity and patient outcome of TBI. We found that two medium-chain fatty acids, octanoic and decanoic acids, as well as several sugar derivatives are significantly associated with the severity of TBI. The top ranking peripheral blood metabolites were also highly correlated with their levels in cerebral microdialyzates. Based on the metabolite profile upon admission, we have been able to develop a model that accurately predicts patient outcome. Moreover, metabolomics profiling improved the performance of the well-established clinical prognostication model. In this review, we discuss metabolomics profiling in patients with severe TBI. We present arguments in support of the need for further development and validation of circulating biomarkers of cerebral metabolism and for their use in assessing patients with severe TBI.

• biomarker
• mass spectrometry
• metabolomics
• neuromonitoring
• outcome
• traumatic brain injury

• animal models
• bias
• qualification
• quality
• reproducibility
• rigor
• statistics
• transparency

• Animals
• Biomedical Research
• Disease Models, Animal
• Information Storage and Retrieval/standards
• Reproducibility of Results

• Epigenesis, Genetic
• Gene Expression Regulation, Neoplastic
• Humans
• Mutation
• Neoplasms/genetics
• Neoplasms/metabolism
• Neoplasms/pathology
• Neoplasms/therapy
• Precision Medicine/methods