Mining whole sample mass spectrometry proteomics data for biomarkers: an overview

In this paper we aim to provide a concise overview of designing and conducting an MS proteomics experiment in such a way as to allow statistical analysis that may lead to the discovery of novel biomarkers. We provide a summary of the various stages that make up such an experiment, highlighting the need for experimental goals to be decided upon in advance. We discuss issues in experimental design at the sample collection stage, and good practise for standardising protocols within the proteomics laboratory. We then describe approaches to the data mining stage of the experiment, including the processing steps that transform a raw mass spectrum into a useable form. We propose a permutation-based procedure for determining the significance of reported error rates. Finally, because of its general advantages in speed and cost, we suggest that MS proteomics may be a good candidate for an early primary screening approach to disease diagnosis, identifying areas of risk and making referrals for more specific tests without necessarily making a diagnosis in its own right. Our discussion is illustrated with examples drawn from experiments on bovine blood serum conducted in the Centre for Proteomic Research (CPR) at Southampton University

Addressing the needs of traumatic brain injury with clinical proteomics.

BackgroundNeurotrauma or injuries to the central nervous system (CNS) are a serious public health problem worldwide. Approximately 75% of all traumatic brain injuries (TBIs) are concussions or other mild TBI (mTBI) forms. Evaluation of concussion injury today is limited to an assessment of behavioral symptoms, often with delay and subject to motivation. Hence, there is an urgent need for an accurate chemical measure in biofluids to serve as a diagnostic tool for invisible brain wounds, to monitor severe patient trajectories, and to predict survival chances. Although a number of neurotrauma marker candidates have been reported, the broad spectrum of TBI limits the significance of small cohort studies. Specificity and sensitivity issues compound the development of a conclusive diagnostic assay, especially for concussion patients. Thus, the neurotrauma field currently has no diagnostic biofluid test in clinical use.ContentWe discuss the challenges of discovering new and validating identified neurotrauma marker candidates using proteomics-based strategies, including targeting, selection strategies and the application of mass spectrometry (MS) technologies and their potential impact to the neurotrauma field.SummaryMany studies use TBI marker candidates based on literature reports, yet progress in genomics and proteomics have started to provide neurotrauma protein profiles. Choosing meaningful marker candidates from such 'long lists' is still pending, as only few can be taken through the process of preclinical verification and large scale translational validation. Quantitative mass spectrometry targeting specific molecules rather than random sampling of the whole proteome, e.g., multiple reaction monitoring (MRM), offers an efficient and effective means to multiplex the measurement of several candidates in patient samples, thereby omitting the need for antibodies prior to clinical assay design. Sample preparation challenges specific to TBI are addressed. A tailored selection strategy combined with a multiplex screening approach is helping to arrive at diagnostically suitable candidates for clinical assay development. A surrogate marker test will be instrumental for critical decisions of TBI patient care and protection of concussion victims from repeated exposures that could result in lasting neurological deficits

Proteomic-biostatistic integrated approach for finding the underlying molecular determinants of hypertension in human plasma

Despite advancements in lowering blood pressure, the best approach to lower it remains controversial because of the lack of information on the molecular basis of hypertension. We, therefore, performed plasma proteomics of plasma from patients with hypertension to identify molecular determinants detectable in these subjects but not in controls and vice versa. Plasma samples from hypertensive subjects (cases; n=118) and controls (n=85) from the InGenious HyperCare cohort were used for this study and performed mass spectrometric analysis. Using biostatistical methods, plasma peptides specific for hypertension were identified, and a model was developed using least absolute shrinkage and selection operator logistic regression. The underlying peptides were identified and sequenced off-line using matrix-assisted laser desorption ionization orbitrap mass spectrometry. By comparison of the molecular composition of the plasma samples, 27 molecular determinants were identified differently expressed in cases from controls. Seventy percent of the molecular determinants selected were found to occur less likely in hypertensive patients. In cross-validation, the overall R(2) was 0.434, and the area under the curve was 0.891 with 95% confidence interval 0.8482 to 0.9349, P<0.0001. The mean values of the cross-validated proteomic score of normotensive and hypertensive patients were found to be -2.007±0.3568 and 3.383±0.2643, respectively, P<0.0001. The molecular determinants were successfully identified, and the proteomic model developed shows an excellent discriminatory ability between hypertensives and normotensives. The identified molecular determinants may be the starting point for further studies to clarify the molecular causes of hypertension

Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset

Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics

Advances in mass spectrometry-based cancer research and analysis: from cancer proteomics to clinical diagnostics

Introduction: The last 20 years have seen significant improvements in the analytical capabilities of biological mass spectrometry. Studies using advanced mass spectrometry (MS) have resulted in new insights into cell biology and the aetiology of diseases as well as its use in clinical applications. Areas Covered: This review will discuss recent developments in MS-based technologies and their cancer-related applications with a focus on proteomics. It will also discuss the issues around translating the research findings to the clinic and provide an outline of where the field is moving. Expert Opinion: Proteomics has been problematic to adapt for the clinical setting. However, MS-based techniques continue to demonstrate potential in novel clinical uses beyond classical cancer proteomics

Proteome analyses of hepatocellular carcinoma

Proteomics has evolved into a powerful and widely used bioanalytical technique in the study of cancer, especially hepatocellular carcinoma (HCC). In this review, we provide an up to date overview of feasible proteome-analytical techniques for clinical questions. In addition, we present a broad summary of proteomic studies of HCC utilizing various technical approaches for the analysis of samples derived from diverse sources like HCC cell lines, animal models, human tissue and body fluids

Matrix-Assisted Laser Desorption/Ionization Imaging Mass Spectrometry

Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) is a powerful tool that enables the simultaneous detection and identification of biomolecules in analytes. MALDI-imaging mass spectrometry (MALDI-IMS) is a two-dimensional MALDI-mass spectrometric technique used to visualize the spatial distribution of biomolecules without extraction, purification, separation, or labeling of biological samples. MALDI-IMS has revealed the characteristic distribution of several biomolecules, including proteins, peptides, amino acids, lipids, carbohydrates, and nucleotides, in various tissues. The versatility of MALDI-IMS has opened a new frontier in several fields such as medicine, agriculture, biology, pharmacology, and pathology. MALDI-IMS has a great potential for discovery of unknown biomarkers. In this review, we describe the methodology and applications of MALDI-IMS for biological samples

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

This work was in part supported by the PRIME-XS project, grant agreement number 262067, funded by the European Union seventh Framework Programme; The Netherlands Proteomics Centre, embedded in The Netherlands Genomics Initiative; The Netherlands Bioinformatics Centre; and the Centre for Biomedical Genetics (to S.C., B.B. and A.J.R.H); by NIH grants NCRR RR001614 and RR019934 (to the UCSF Mass Spectrometry Facility, director: A.L. Burlingame, P.B.); and by grants from the MRC, CR-UK, BBSRC and Barts and the London Charity (to P.C.

The identification of biomarkers of chemotherapy resistance in breast cancer using comparative proteomics

Background:Chemotherapy resistance is a major obstacle in effective neoadjuvant treatment for locally advanced breast cancer. The ability to predict tumour response would allow chemotherapy administration to be directed towards only those patients who would benefit, thus maximising treatment efficiency. This project aimed to identify predictive protein biomarkers associated with chemotherapy resistance, using proteomic analysis of fresh breast cancer tissue samples.Materials and Methods:Chemotherapy-sensitive (CS) and chemotherapy-resistant (CR) tumour samples were collected from breast cancer patients who received neoadjuvant therapy consisting of epirubicin with cyclophosphamide followed by docetaxel. Comparative proteomic analysis was performed, to identify differentially expressed proteins (DEPs) between CS and CR invasive ductal carcinoma samples, using 2-dimensional polyacrylamide gel electrophoresis (2D-PAGE) with MALDI-TOF/TOF mass spectrometry and antibody microarray analysis. DEPs were submitted to Ingenuity Pathway Analysis (IPA) to identify any canonical pathway links, confirmed using western blotting and clinically validated in a pilot series of archival breast cancer samples, from patients treated with neoadjuvant chemotherapy.Results:Five datasets were generated by antibody microarray analysis, revealing 38 targets. Of these, 7 DEPs were identified in at least 2 datasets and these included 14-3-3 theta/tau, BID and Bcl-xL. Three datasets were generated using 2D-PAGE with MALDI-TOF/TOF MS, containing 132 unique DEPs. These included several isoforms of 14-3-3 proteins. The differential expression of 14-3-3, BID and Bcl-xL was confirmed by immunoblotting in samples used for the discovery phase. Clinical validation using immunohistochemical analysis of archival breast cancers revealed 14-3-3 theta/tau and tBID to be significantly associated with chemotherapy resistance.Discussion:The use of comparative proteomic techniques using fresh clinical tumour samples, for the search for putative biomarkers of chemotherapy resistance has been successful. Two DEPs; 14-3-3 theta/tau and tBID have passed through all stages of the biomarker discovery pipeline, and present themselves as putative predictive biomarkers of neoadjuvant chemotherapy resistance in breast cancer