The impact of methodological choices when developing predictive models using urinary metabolite data.

The continuous evolution of metabolomics over the past two decades has stimulated the search for metabolic biomarkers of many diseases. Metabolomic data measured from urinary samples can provide rich information of the biological events triggered by organ rejection in pediatric kidney transplant recipients. With additional validation, metabolic markers can be used to build clinically useful diagnostic tools. However, there are many methodological steps ranging from data processing to modeling that can influence the performance of the resulting metabolomic classifiers. In this study we focus on the comparison of various classification methods that can handle the complex structure of metabolomic data, including regularized classifiers, partial least squares discriminant analysis, and nonlinear classification models. We also examine the effectiveness of a physiological normalization technique widely used in the clinical and biochemical literature but not extensively analyzed and compared in urine metabolomic studies. While the main objective of this work is to interrogate metabolomic data of pediatric kidney transplant recipients to improve the diagnosis of T cell-mediated rejection (TCMR), we also analyze three independent datasets from other disease conditions to investigate the generalizability of our findings.

Novel Blood-based Transcriptional Biomarker Panels Predict the Late-Phase Asthmatic Response.

RATIONALE: The allergen inhalation challenge is used in clinical trials to test the efficacy of new treatments in attenuating the late-phase asthmatic response (LAR) and associated airway inflammation in subjects with allergic asthma. However, not all subjects with allergic asthma develop the LAR after allergen inhalation. Blood-based transcriptional biomarkers that can identify such individuals may help in subject recruitment for clinical trials as well as provide novel molecular insights. OBJECTIVES: To identify blood-based transcriptional biomarker panels that can predict an individual's response to allergen inhalation challenge. METHODS: We applied RNA sequencing to total RNA from whole blood (n = 36) collected before and after allergen challenge and generated both genome-guided and de novo datasets: genes, gene-isoforms (University of California, Santa Cruz, UCSC Genome Browser), Ensembl, and Trinity. Candidate biomarker panels were validated using the NanoString platform in an independent cohort of 33 subjects. MEASUREMENTS AND MAIN RESULTS: The Trinity biomarker panel consisting of known and novel biomarker transcripts had an area under the receiver operating characteristic curve of greater than 0.70 in both the discovery and validation cohorts. The Trinity biomarker panel was useful in predicting the response of subjects that elicited different responses (accuracy between 0.65 and 0.71) and subjects that elicit a dual response (accuracy between 0.70 and 0.75) upon repeated allergen inhalation challenges. CONCLUSIONS: Interestingly, the biomarker panel containing novel transcripts successfully validated compared with panels with known, well-characterized genes. These biomarker-blood tests may be used to identify subjects with asthma who develop the LAR, and may also represent members of novel molecular mechanisms that can be targeted for therapy.

Computational biomarker pipeline from discovery to clinical implementation: plasma proteomic biomarkers for cardiac transplantation.

Recent technical advances in the field of quantitative proteomics have stimulated a large number of biomarker discovery studies of various diseases, providing avenues for new treatments and diagnostics. However, inherent challenges have limited the successful translation of candidate biomarkers into clinical use, thus highlighting the need for a robust analytical methodology to transition from biomarker discovery to clinical implementation. We have developed an end-to-end computational proteomic pipeline for biomarkers studies. At the discovery stage, the pipeline emphasizes different aspects of experimental design, appropriate statistical methodologies, and quality assessment of results. At the validation stage, the pipeline focuses on the migration of the results to a platform appropriate for external validation, and the development of a classifier score based on corroborated protein biomarkers. At the last stage towards clinical implementation, the main aims are to develop and validate an assay suitable for clinical deployment, and to calibrate the biomarker classifier using the developed assay. The proposed pipeline was applied to a biomarker study in cardiac transplantation aimed at developing a minimally invasive clinical test to monitor acute rejection. Starting with an untargeted screening of the human plasma proteome, five candidate biomarker proteins were identified. Rejection-regulated proteins reflect cellular and humoral immune responses, acute phase inflammatory pathways, and lipid metabolism biological processes. A multiplex multiple reaction monitoring mass-spectrometry (MRM-MS) assay was developed for the five candidate biomarkers and validated by enzyme-linked immune-sorbent (ELISA) and immunonephelometric assays (INA). A classifier score based on corroborated proteins demonstrated that the developed MRM-MS assay provides an appropriate methodology for an external validation, which is still in progress. Plasma proteomic biomarkers of acute cardiac rejection may offer a relevant post-transplant monitoring tool to effectively guide clinical care. The proposed computational pipeline is highly applicable to a wide range of biomarker proteomic studies.

Robust Prediction and Protein Selection with Adaptive PENSE.

Adaptive PENSE is a method that can be used to build models for predicting clinical outcomes from a small subset of a potentially large number of candidate proteins. Adaptive PENSE is designed to give reliable results under two common challenges often encountered in these kinds of studies: (1) the number of samples with known clinical outcome and proteomic data is small, while the number of candidate proteins is large and/or (2) proteomic data and the clinical outcome measurements suffer from data quality issues in a small fraction of samples. Even in the presence of these challenges, adaptive PENSE reliably identifies proteins relevant for prediction and estimates accurate predictive models. Adaptive PENSE is designed to be resilient to data quality issues in up to 50% of samples. Almost half of the samples could have aberrant values in the measured protein levels and clinical outcome values without causing severe detrimental effects to the estimated predictive model. The method is implemented as an R package and supports the user in the model selection process by automating most steps and providing diagnostic visualizations to guide the user. Users can choose among several predictive models to select the model with high prediction accuracy and an appropriate number of selected proteins.

Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures.

BACKGROUND: Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use. METHODS: Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms. FINDINGS: Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77-80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89-97%) accurately predicted endotype status in both ER and ICU validation cohorts. INTERPRETATION: The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

Molecular signatures of end-stage heart failure.

BACKGROUND: To date, gene expression studies related to chronic heart failure (CHF) have mainly involved microarray analysis of myocardial tissues. The potential utility of blood to infer the etiology, pathogenesis, and course of CHF remains unclear. Further, the use of proteomic and metabolomic platforms for molecular profiling of CHF is relatively unexplored. METHODS: Microarray genomic, iTRAQ proteomic, and nuclear magnetic resonance metabolomic analyses were carried out on blood samples from 29 end-stage CHF patients (16 ischemic heart disease [IHD], 13 nonischemic cardiomyopathy [NICM]), and 20 normal cardiac function (NCF) controls. Robust statistical tests and bioinformatical tools were applied to identify and compare the molecular signatures among these subject groups. RESULTS: No genes or proteins, and only two metabolites, were differentially expressed between IHD and NICM patients at end stage. However, CHF versus NCF comparison revealed differential expression of 7,426 probe sets, 71 proteins, and 8 metabolites. Functional enrichment analyses of the CHF versus NCF results revealed several in-common biological themes and potential mechanisms underlying advanced heart failure. CONCLUSION: Multiple "-omic" analyses support the convergence of dramatic changes in molecular processes underlying IHD and NICM at end stage.

TMEM30A loss-of-function mutations drive lymphomagenesis and confer therapeutically exploitable vulnerability in B-cell lymphoma.

Transmembrane protein 30A (TMEM30A) maintains the asymmetric distribution of phosphatidylserine, an integral component of the cell membrane and 'eat-me' signal recognized by macrophages. Integrative genomic and transcriptomic analysis of diffuse large B-cell lymphoma (DLBCL) from the British Columbia population-based registry uncovered recurrent biallelic TMEM30A loss-of-function mutations, which were associated with a favorable outcome and uniquely observed in DLBCL. Using TMEM30A-knockout systems, increased accumulation of chemotherapy drugs was observed in TMEM30A-knockout cell lines and TMEM30A-mutated primary cells, explaining the improved treatment outcome. Furthermore, we found increased tumor-associated macrophages and an enhanced effect of anti-CD47 blockade limiting tumor growth in TMEM30A-knockout models. By contrast, we show that TMEM30A loss-of-function increases B-cell signaling following antigen stimulation-a mechanism conferring selective advantage during B-cell lymphoma development. Our data highlight a multifaceted role for TMEM30A in B-cell lymphomagenesis, and characterize intrinsic and extrinsic vulnerabilities of cancer cells that can be therapeutically exploited.

Multiplexed LC-ESI-MRM-MS-based Assay for Identification of Coronary Artery Disease Biomarkers in Human Plasma.

PURPOSE: A highly-multiplexed LC-ESI-multiple reaction monitoring-MS-based assay is developed for the identification of coronary artery disease (CAD) biomarkers in human plasma. EXPERIMENTAL DESIGN: The assay is used to measure 107 stable isotope labeled peptide standards and native peptides from 64 putative biomarkers of cardiovascular diseases in tryptic digests of plasma from subjects with (n = 70) and without (n = 45) angiographic evidence of CAD and no subsequent cardiovascular mortality during follow-up. RESULTS: Extensive computational and statistical analysis reveals six plasma proteins associated with CAD, namely apolipoprotein CII, C reactive protein, CD5 antigen-like, fibronectin, inter alpha trypsin inhibitor heavy chain H1, and protein S. The identified proteins are combined into a LASSO-logistic score with high classification performance (cross-validated area under the curve = 0.74). When combined with a separate score computed from markers currently used in the clinic with similar performance, the area under the receiver operating curve increases to 0.84. Similar results are observed in an independent set of subjects (n = 87). CONCLUSIONS AND CLINICAL RELEVANCE: If externally validated, the assay and identified biomarkers can improve CAD risk stratification.

MDQC: a new quality assessment method for microarrays based on quality control reports.

MOTIVATION: The process of producing microarray data involves multiple steps, some of which may suffer from technical problems and seriously damage the quality of the data. Thus, it is essential to identify those arrays with low quality. This article addresses two questions: (1) how to assess the quality of a microarray dataset using the measures provided in quality control (QC) reports; (2) how to identify possible sources of the quality problems. RESULTS: We propose a novel multivariate approach to evaluate the quality of an array that examines the 'Mahalanobis distance' of its quality attributes from those of other arrays. Thus, we call it Mahalanobis Distance Quality Control (MDQC) and examine different approaches of this method. MDQC flags problematic arrays based on the idea of outlier detection, i.e. it flags those arrays whose quality attributes jointly depart from those of the bulk of the data. Using two case studies, we show that a multivariate analysis gives substantially richer information than analyzing each parameter of the QC report in isolation. Moreover, once the QC report is produced, our quality assessment method is computationally inexpensive and the results can be easily visualized and interpreted. Finally, we show that computing these distances on subsets of the quality measures in the report may increase the method's ability to detect unusual arrays and helps to identify possible reasons of the quality problems. AVAILABILITY: The library to implement MDQC will soon be available from Bioconductor.

Genomic and proteomic expression analysis of Leishmania promastigote and amastigote life stages: the Leishmania genome is constitutively expressed.

Leishmania are protozoan parasites that cause a wide spectrum of clinical diseases in humans and are a major public health risk in several countries. Leishmania life cycle consists of an extracellular flagellated promastigote stage within the midgut of a sandfly vector, and a morphological distinct intracellular amastigote stage within macrophages of a mammalian host. This study reports the use of DNA oligonucleotide genome microarrays representing 8160 genes to analyze the mRNA expression profiles of L. major promastigotes and lesion derived amastigotes. Over 94% of the genes were expressed in both life stages. Advanced statistical analysis identified a surprisingly low degree of differential mRNA expression: 1.4% of the total genes in amastigotes and 1.5% in promastigotes. These microarray results demonstrate that the L. major genome is essentially constitutively expressed in both life stages and suggest that Leishmania is constitutively adapted for survival and replication in either the sandfly vector or macrophage host utilizing an appropriate set of genes for each vastly different environment. Quantitative proteomics, using the isotope coded affinity tag (ICAT) technology and mass spectrometry, was used to identify L. infantum promastigote and axenic amastigote differentially expressed proteins. Of the 91 distinct proteins identified, 8% were differentially expressed in the amastigote stage, 20% were differentially expressed in the promastigote stage, and the remaining 72% were considered constitutively expressed. The differential expression was validated by the identification of previously reported stage specific proteins and identified several amastigote and promastigote novel stage specific proteins.