Programmed cell death 6 interacting protein (PDCD6IP) and Rabenosyn-5 (ZFYVE20) are potential urinary biomarkers for upper gastrointestinal cancer

PURPOSE: Cancer of the upper digestive tract (uGI) is a major contributor to cancer-related death worldwide. Due to a rise in occurrence, together with poor survival rates and a lack of diagnostic or prognostic clinical assays, there is a clear need to establish molecular biomarkers. EXPERIMENTAL DESIGN: Initial assessment was performed on urine samples from 60 control and 60 uGI cancer patients using MS to establish a peak pattern or fingerprint model, which was validated by a further set of 59 samples. RESULTS: We detected 86 cluster peaks by MS above frequency and detection thresholds. Statistical testing and model building resulted in a peak profiling model of five relevant peaks with 88% overall sensitivity and 91% specificity, and overall correctness of 90%. High-resolution MS of 40 samples in the 2-10 kDa range resulted in 646 identified proteins, and pattern matching identified four of the five model peaks within significant parameters, namely programmed cell death 6 interacting protein (PDCD6IP/Alix/AIP1), Rabenosyn-5 (ZFYVE20), protein S100A8, and protein S100A9, of which the first two were validated by Western blotting. CONCLUSIONS AND CLINICAL RELEVANCE: We demonstrate that MS analysis of human urine can identify lead biomarker candidates in uGI cancers, which makes this technique potentially useful in defining and consolidating biomarker patterns for uGI cancer screening

Novel Approaches to Visualization and Data Mining Reveals Diagnostic Information in the Low Amplitude Region of Serum Mass Spectra from Ovarian Cancer Patients

The ability to identify patterns of diagnostic signatures in proteomic data generated by high throughput mass spectrometry (MS) based serum analysis has recently generated much excitement and interest from the scientific community. These data sets can be very large, with high-resolution MS instrumentation producing 1-2 million data points per sample. Approaches to analyze mass spectral data using unsupervised and supervised data mining operations would greatly benefit from tools that effectively allow for data reduction without losing important diagnostic information. In the past, investigators have proposed approaches where data reduction is performed by a priori peak picking and alignment/warping/smoothing components using rule-based signal-to-noise measurements. Unfortunately, while this type of system has been employed for gene microarray analysis, it is unclear whether it will be effective in the analysis of mass spectral data, which unlike microarray data, is comprised of continuous measurement operations. Moreover, it is unclear where true signal begins and noise ends. Therefore, we have developed an approach to MS data analysis using new types of data visualization and mining operations in which data reduction is accomplished by culling via the intensity of the peaks themselves instead of by location. Applying this new analysis method on a large study set of high resolution mass spectra from healthy and ovarian cancer patients, shows that all of the diagnostic information is contained within the very lowest amplitude regions of the mass spectra. This region can then be selected and studied to identify the exact location and amplitude of the diagnostic biomarkers

Diagnosis of Ovarian Cancer Using Decision Tree Classification of Mass Spectral Data

Recent reports from our laboratory and others support the SELDI ProteinChip technology as a potential clinical diagnostic tool when combined with n-dimensional analyses algorithms. The objective of this study was to determine if the commercially available classification algorithm biomarker patterns software (BPS), which is based on a classification and regression tree (CART), would be effective in discriminating ovarian cancer from benign diseases and healthy controls. Serum protein mass spectrum profiles from 139 patients with either ovarian cancer, benign pelvic diseases, or healthy women were analyzed using the BPS software. A decision tree, using five protein peaks, resulted in an accuracy of 81.5% in the cross-validation analysis and 80% in a blinded set of samples in differentiating the ovarian cancer from the control groups. The potential, advantages, and drawbacks of the BPS system as a bioinformatic tool for the analysis of the SELDI high-dimensional proteomic data are discussed

Applications of Mass Spectrometry in Proteomics and Pharmacokinetics

Tremendous technology improvements of the last decades has given mass spectrometry a more and more expanding role in the study of a wide range of molecules: from the identification and quantification of small molecular weight molecules to the structural determination of biomacromolecules. Many are the fields of application for this technique and the various versions of it. In the present study three different applications have been explored. The first application is a pharmacokinetics study of anticancer drug Gemcitabine and its principal metabolite, where the role of the LC-MS/MS is essential both for the selectivity of the detection of the small analytes and the sensitivity enhanced by multi-reaction monitoring experiments. The design of the study involved the collection of several blood samples at selected times and from patients that would have met certain eligibility criteria. The ESI demonstrated to be the most suitable approach and it provided the necessary data to conclude that toxicity of Gemcitabine did not increase when administered at FDR (Fixed Dose Rate) infusion in patients with impaired hepatic function. The second application describes an example of how MS represents a powerful tool in cancer research, from serum profiling study with high resolution MALDITOF and bioinformatic analysis, to the identification of potential biomarker through peak identification. Almost 400 serum sample – homogeneously distributed between biopsy confirmed ovarian cancer and high risk serum samples – were analyzed on a high resolution MALDI-TOF instrument after automated reverse phase magnetic beads separation. The high throughput data have undergone sophisticated bioinformatic procedures that lead to a list of upand down-regulated peaks, although identification studies were possible only for those peaks that showed a good reproducibility. One down-regolated peak has been identified using the LC-MS/MS technique. The identified peak confirmed a basic role of fibrinogen in the ovarian cancer; the other four peaks that have been identified as down-regulated showed an absolutely not satisfactory ionization in electro-spray, therefore further analysis will be performed on these analytes in order to determinate their amino acidic sequence. The most suitable technique seems to be MALDI-TOF/TOF mass spectrometry, since the peptides already showed a good degree of ionization in MALDI. The third and last study belongs to a quite new field, which is the combination of immuno precipitation assays with MALDI-TOF (Immuno Precipitation Mass Spectrometry, IPMS) experiments in order to evaluate the specificity of a series of monoclonal antibodies to specific antigen. The automated assay that has been developed provides structural information about the antigen that binds the monoclonal antibody to be tested and previously conjugated to the surface of magnetic beads, ideal support for robotic automation. IPMS showed its potential as a complementary tool of crucial importance in the selection of the monoclonal antibody for the development of ELISA based assay to be applied in the screening of a consistent number of human specimens for the clinical validation of proteins indicated in literature as potential biomarkers. Mass spectrometry in association with fractionation techniques, such as liquid or magnetic beads chromatography, is a very flexible tool in the cancer research field. Further improvement in the instrumentation and in the technology will bring always more and more results to be confident in

Potential biomarkers for diagnosis of sarcoidosis using proteomics in serum

SummaryBackgroundSarcoidosis is a multi-systemic inflammatory disorder, which affects the lungs in 90% of the cases. The main pathologic feature is chronic inflammation resulting in non-caseating granuloma formation. Until now there is no satisfying biomarker for diagnosis or prognosis of sarcoidosis. This study is focused on the detection of potential biomarkers in serum for the diagnosis of sarcoidosis using surface-enhanced laser desorption ionization-time of flight-mass spectrometry (SELDI-TOF-MS).MethodsFor detection of potential biomarkers, protein profiles of anion exchange fractionated serum of 35 sarcoidosis patients and 35 healthy controls were compared using SELDI-TOF-MS. Sensitivities and specificities of the potential biomarkers obtained with SELDI-TOF-MS, generated with decision tree algorithm, were compared to the conventional markers angiotensin converting enzyme (ACE) and soluble interleukin-2 receptor (sIL-2R).ResultsOptimal classification was achieved with metal affinity binding arrays. A single marker with a mass-to-charge (m/z) value of 11,955 resulted in a sensitivity and specificity of 86% and 63%, respectively. A multimarker approach of two peaks, m/z values of 11,734 and 17,377, resulted in a sensitivity and specificity of 74% and 71%, respectively. These sensitivities and specificities were higher compared to measurements of ACE and sIL-2R. Identification of the peak at m/z 17,377 resulted in the α-2chain of haptoglobin.ConclusionsThis study acts as a proof-of-principle for the use of SELDI-TOF-MS in the detection of new biomarkers for sarcoidosis. The peak of the multimarker at m/z 17,377 was identified as the α-2chain of haptoglobin

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

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

Explainable Artificial Intelligence based Ensemble Machine Learning for Ovarian Cancer Stratification using Electronic Health Records

The purpose of this study is to show how ensemble learning-driven machine learning algorithms outperform individual machine learning algorithms at predicting ovarian cancer on a biomarker dataset. Additionally, this study provides model explanations using explainable Artificial Intelligence methods, The method involved gathering and combining 49 risk factors from 349 patients. We hypothesize that ensemble machine learning systems are superior to individual Machine Learning systems in predicting ovarian cancer. The Machine Learning system consists of five individual Machine Learning and five ensemble Machine Learning systems were trained using K-10 cross validation protocols. These training models were then used to predict the development of benign ovarian tumors and ovarian cancer tumors patients. The AUC and Accuracy metrics for ensemble machine learning increased by 19% and 16%. The MCC and Kappa scores for ensemble Machine Learning also increased over individual machine learning by 29% and 33%, respectively. As a result, we draw the conclusion that ensembled-based algorithms outperform individual machine learning in terms of ovarian carcinoma prediction