Ali Tiss

Mass spectrometry in clinical proteomics - from the present to the future.

MS is an important analytical tool in clinical proteomics, primarily in the disease‐specific discovery, identification and characterisation of proteomic biomarkers and patterns. MS‐based proteomics is increasingly used in clinical validation and diagnostic method development. The latter departs from the typical application of MS‐based proteomics by exchanging some of the high performance of analysis for the throughput, robustness and simplicity required for clinical diagnostics. Although conventional MS‐based proteomics has become an important field in clinical applications, some of the most recent MS technologies have not yet been extensively applied in clinical proteomics. In this review, we will describe the current state of MS in clinical proteomics and look to the future of this field.

A well‐characterised peak identification list of MALDI MS profile peaks for human blood serum

MALDI MS profiling, using easily available body fluids such as blood serum, has attracted considerable interest for its potential in clinical applications. Despite the numerous reports on MALDI MS profiling of human serum, there is only scarce information on the identity of the species making up these profiles, particularly in the mass range of larger peptides. Here, we provide a list of more than 90 entries of MALDI MS profile peak identities up to 10u2009kDa obtained from human blood serum. Various modifications such as phosphorylation were detected among the peptide identifications. The overlap with the few other MALDI MS peak lists published so far was found to be limited and hence our list significantly extends the number of identified peaks commonly found in MALDI MS profiling of human blood serum.

Peptides Generated Ex Vivo from Serum Proteins by Tumor-Specific Exopeptidases Are Not Useful Biomarkers in Ovarian Cancer

BACKGROUNDnThe serum peptidome may be a valuable source of diagnostic cancer biomarkers. Previous mass spectrometry (MS) studies have suggested that groups of related peptides discriminatory for different cancer types are generated ex vivo from abundant serum proteins by tumor-specific exopeptidases. We tested 2 complementary serum profiling strategies to see if similar peptides could be found that discriminate ovarian cancer from benign cases and healthy controls.nnnMETHODSnWe subjected identically collected and processed serum samples from healthy volunteers and patients to automated polypeptide extraction on octadecylsilane-coated magnetic beads and separately on ZipTips before MALDI-TOF MS profiling at 2 centers. The 2 platforms were compared and case control profiling data analyzed to find altered MS peak intensities. We tested models built from training datasets for both methods for their ability to classify a blinded test set.nnnRESULTSnBoth profiling platforms had CVs of approximately 15% and could be applied for high-throughput analysis of clinical samples. The 2 methods generated overlapping peptide profiles, with some differences in peak intensity in different mass regions. In cross-validation, models from training data gave diagnostic accuracies up to 87% for discriminating malignant ovarian cancer from healthy controls and up to 81% for discriminating malignant from benign samples. Diagnostic accuracies up to 71% (malignant vs healthy) and up to 65% (malignant vs benign) were obtained when the models were validated on the blinded test set.nnnCONCLUSIONSnFor ovarian cancer, altered MALDI-TOF MS peptide profiles alone cannot be used for accurate diagnoses.

Conformal predictors in early diagnostics of ovarian and breast cancers

The work describes an application of a recently developed machine-learning technique called Mondrian predictors to risk assessment of ovarian and breast cancers. The analysis is based on mass spectrometry profiling of human serum samples that were collected in the United Kingdom Collaborative Trial of Ovarian Cancer Screening. The work describes the technique and presents the results of classification (diagnosis) and the corresponding measures of confidence of the diagnostics. The main advantage of this approach is a proven validity of prediction. The work also describes an approach to improve early diagnosis of ovarian and breast cancers since the data in the United Kingdom Collaborative Trial of Ovarian Cancer Screening were collected over a period of 7xa0years and do allow to make observations of changes in human serum over that period of time. Significance of improvement is confirmed statistically (for up to 11xa0months for ovarian cancer and 9xa0months for breast cancer). In addition, the methodology allowed us to pinpoint the same mass spectrometry peaks as previously detected as carrying statistically significant information for discrimination between healthy and diseased patients. The results are discussed.

Highly accurate detection of ovarian cancer using CA125 but limited improvement with serum matrix-assisted laser desorption/ionization time-of-flight mass spectrometry profiling.

Objectives: Our objective was to test the performance of CA125 in classifying serum samples from a cohort of malignant and benign ovarian cancers and age-matched healthy controls and to assess whether combining information from matrix-assisted laser desorption/ionization (MALDI) time-of-flight profiling could improve diagnostic performance. Materials and Methods: Serum samples from women with ovarian neoplasms and healthy volunteers were subjected to CA125 assay and MALDI time-of-flight mass spectrometry (MS) profiling. Models were built from training data sets using discriminatory MALDI MS peaks in combination with CA125 values and tested their ability to classify blinded test samples. These were compared with models using CA125 threshold levels from 193 patients with ovarian cancer, 290 with benign neoplasm, and 2236 postmenopausal healthy controls. Results: Using a CA125 cutoff of 30 U/mL, an overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls, whereas a cutoff of 65 U/mL provided a sensitivity of 83.9% (99.6% specificity). High classification accuracies were obtained for early-stage cancers (93.5% sensitivity). Reasons for high accuracies include recruitment bias, restriction to postmenopausal women, and inclusion of only primary invasive epithelial ovarian cancer cases. The combination of MS profiling information with CA125 did not significantly improve the specificity/accuracy compared with classifications on the basis of CA125 alone. Conclusions: We report unexpectedly good performance of serum CA125 using threshold classification in discriminating healthy controls and women with benign masses from those with invasive ovarian cancer. This highlights the dependence of diagnostic tests on the characteristics of the study population and the crucial need for authors to provide sufficient relevant details to allow comparison. Our study also shows that MS profiling information adds little to diagnostic accuracy. This finding is in contrast with other reports and shows the limitations of serum MS profiling for biomarker discovery and as a diagnostic tool.

Multiprobabilistic Venn Predictors with Logistic Regression

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.

Multiprobabilistic prediction in early medical diagnoses

This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. Is allows to output a valid probability interval. The methodology is designed for mass spectrometry data. For demonstrative purposes, we applied this methodology to MALDI-TOF data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer and breast cancer. The experiments showed that probability intervals are narrow, that is, the output of the multiprobability predictor is similar to a single probability distribution. In addition, probability intervals produced for heart disease and ovarian cancer data were more accurate than the output of corresponding probability predictor. When Venn machines were forced to make point predictions, the accuracy of such predictions is for the most data better than the accuracy of the underlying algorithm that outputs single probability distribution of a label. Application of this methodology to MALDI-TOF data sets empirically demonstrates the validity. The accuracy of the proposed method on ovarian cancer data rises from 66.7xa0% 11xa0months in advance of the moment of diagnosis to up to 90.2xa0% at the moment of diagnosis. The same approach has been applied to heart disease data without time dependency, although the achieved accuracy was not as high (up to 69.9xa0%). The methodology allowed us to confirm mass spectrometry peaks previously identified as carrying statistically significant information for discrimination between controls and cases.

Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Dataset

BackgroundDiabetes like many diseases and biological processes is not mono-causal. On the one hand multi-factorial 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.ResultsWe present a comprehensive work-flow tailored for analyzing complex data including data from multi-factorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and diet. Exploitation of redundancy enables the visualization of peptide correlation and provides a natural way of feature selection for classification and prediction. Classification based on the features selected using our approach performs similar to classifications based on more complex feature selection methods.ConclusionsThe combination of ANOVA and redundancy exploitation allows for identification of biomarker candidates in multi-dimensional MALDI-TOF MS profiling studies with complex experimental design. With respect to feature selection our method provides a fast and intuitive alternative to global optimization strategies with comparable performance. The method is implemented in R and the scripts are available by contacting the corresponding author.

Proteomics approaches towards early detection and diagnosis of ovarian cancer

Early stage detection of cancer is the key to provide a better outcome for therapeutic intervention. Proteomic technologies hold recently great promise in the search of new clinical biomarkers for the early detection and diagnosis of cancer or for the development of new vaccines. n nIn this perspective, we will present our recent work in improving early diagnosis of ovarian cancer (OC) by combining MS analysis of serum peptidome with data collected over a period of 7 years from the United Kingdom Collaborative Trial of Ovarian Cancer Screening. Using 295 patients with OC, 290 with benign neoplasm, and 2236 postmenopausal healthy controls, our results showed that OC could be accurately predicted up to 15 months before its clinical diagnosis, based a combination of two MS peaks with CA125 clinical test. An overall sensitivity of 94.8% (96.6% specificity) was obtained when comparing malignancies versus healthy postmenopausal controls. High classification accuracies were also obtained for early-stage cancers (93.5% sensitivity). MS discriminatory peaks were identified as connective tissue-activating peptide III (CTAPIII) and platelet factor 4 (PF4), platelet-derived chemokines, suggesting a link between platelet function and tumour development. Those markers might be promising for clinical use in cancer early detection and treatment.

GLP-1 Analogue, Exendin-4, Modulates MAPKs Activity but not the Heat Shock Response in Human HepG2 Cells

Glucagon‐like peptide‐1 (GLP‐1) analogues reduce ER stress and inflammation in key metabolic organs, including the liver. However, their effects on heat shock response (HSR) and mitogen‐activated protein kinases (MAPKs) have not yet been elucidated. In the present study, we investigate whether the GLP‐1 analogue, exendin‐4, triggers the expression of HSR and increases MAPK activity under metabolic stress.