Identification of Novel Hemostatic Biomarkers of Adverse Clinical Events in Patients Implanted With a Continuous-Flow Left Ventricular Assist Device

Heart failure affects over 5 million people in the United States. Its rising prevalence and the limited supply of donor hearts is increasing the use of mechanical cardiac support with the implantation of continuous-flow ventricular assist devices (CF-VAD). Patients with CF-VAD implants are at risk of complications, specifically adverse hemostatic events such as nonsurgical bleeding and thrombosis. Development of a pump thrombus requires clinical intervention and/or surgical replacement significantly increasing the risk of patient morbidity and mortality. Identification of biomarkers for these events could improve current risk assessment models, subsequent treatment, and quality of life prognoses for VAD-implanted patients. The standard means for identifying thrombus in VAD patients is currently limited to monitoring levels of lactate dehydrogenase (>2× upper limit of normal), which is incapable of predicting a future event, but describes the risk of a present thrombus. Surface-enhanced laser desorption ionization time-of-flight mass spectrometry is a technique used to identify biomarkers. In this study, 3 groups of unique peaks were identified in plasma from patients with left ventricular assist devices: 8.1-kDa, 11.7-kDa, and a 15.2-/16.1-kDa pair. Unique correlations were found for each peak, respectively, with microparticles (MPs) and MP procoagulant activity, C-reactive protein, and MP-tissue factor. Furthermore, the use of 8.1-kDa peaks may be able to differentiate thrombotic events from other hemostatic events.

Biomarkers of inflammation and innate immunity in atrophic nonunion fracture

BACKGROUND

Nonunion is a failure of healing following a bone fracture. Its physiopathology remains partially unclear and the discovery of new mediators could promote the understanding of bone healing.

METHODS

Thirty-three atrophic nonunion (NU) patients that failed to demonstrate any radiographic improvement for 6 consecutive months were recruited for providing serum samples. Thirty-five healthy volunteers (HV) served as the control group. Proteomics studies were performed using SELDI-TOF-MS and 2D-DIGE approaches, associated or not with Proteominer® preprocessing, to highlight biomarkers specific to atrophic nonunion pathology. Peak intensities were analyzed by two statistical approaches, a nonparametric Mann-Whitney U tests (univariate approach) and a machine-learning algorithm called extra-trees (multivariate approach). Validation of highlighted biomarkers was performed by alternative approaches such as microfluidic LC-MS/MS, nephelometry, western blotting or ELISA assays.

RESULTS

From the 35 HV and 33 NU crude serum samples and Proteominer® eluates, 136 spectra were collected by SELDI-TOF-MS using CM10 and IMAC-Cu(2+) ProteinChip arrays, and 665 peaks were integrated for extra-trees multivariate analysis. Accordingly, seven biomarkers and several variants were identified as potential NU biomarkers. Their levels of expression were found to be down- or up-regulated in serum of HV vs NU. These biomarkers are inter-α-trypsin inhibitor H4, hepcidin, S100A8, S100A9, glycated hemoglobin β subunit, PACAP related peptide, complement C3 α-chain. 2D-DIGE experiment allowed to detect 14 biomarkers as being down- or up-regulated in serum of HV vs NU including a cleaved fragment of apolipoprotein A-IV, apolipoprotein E, complement C3 and C6. Several biomarkers such as hepcidin, complement C6, S100A9, apolipoprotein E, complement C3 and C4 were confirmed by an alternative approach as being up-regulated in serum of NU patients compared to HV controls.

CONCLUSION

Two proteomics approaches were used to identify new biomarkers up- or down-regulated in the nonunion pathology, which are involved in bone turn-over, inflammation, innate immunity, glycation and lipid metabolisms. High expression of hepcidin or S100A8/S100A9 by myeloid cells and the presence of advanced glycation end products and complement factors could be the result of a longstanding inflammatory process. Blocking macrophage activation and/or TLR4 receptor could accelerate healing of fractured bone in at-risk patients.

Mass spectrometry-based serum and plasma peptidome profiling for prediction of treatment outcome in patients with solid malignancies

INTRODUCTION

Treatment selection tools are needed to enhance the efficacy of targeted treatment in patients with solid malignancies. Providing a readout of aberrant signaling pathways and proteolytic events, mass spectrometry-based (MS-based) peptidomics enables identification of predictive biomarkers, whereas the serum or plasma peptidome may provide easily accessible signatures associated with response to treatment. In this systematic review, we evaluate MS-based peptide profiling in blood for prompt clinical implementation.

METHODS

PubMed and Embase were searched for studies using a syntax based on the following hierarchy: (a) blood-based matrix-assisted or surface-enhanced laser desorption/ionization time-of-flight MS peptide profiling (b) in patients with solid malignancies (c) prior to initiation of any treatment modality, (d) with availability of outcome data.

RESULTS

Thirty-eight studies were eligible for review; the majority were performed in patients with non-small cell lung cancer (NSCLC). Median classification prediction accuracy was 80% (range: 66%-93%) in 11 models from 14 studies reporting an MS-based classification model. A pooled analysis of 9 NSCLC studies revealed clinically significant median progression-free survival in patients classified as "poor outcome" and "good outcome" of 2.0 ± 1.06 months and 4.6 ± 1.60 months, respectively; median overall survival was also clinically significant at 4.01 ± 1.60 months and 10.52 ± 3.49 months, respectively.

CONCLUSION

Pretreatment MS-based serum and plasma peptidomics have shown promising results for prediction of treatment outcome in patients with solid tumors. Limited sample sizes and absence of signature validation in many studies have prohibited clinical implementation thus far. Our pooled analysis and recent results from the PROSE study indicate that this profiling approach enables treatment selection, but additional prospective studies are warranted.

Laser ionization mass spectrometry in oral squamous cell carcinoma

Biomarker research in oral squamous cell carcinoma (OSCC) aims for screening/early diagnosis and in predicting its recurrence, metastasis and overall prognosis. This article reviews the current molecular perspectives and diagnosis of oral cancer with proteomics using matrix-assisted laser desorption ionization (MALDI) and surface-enhanced laser desorption ionization (SELDI) mass spectrometry (MS). This method shows higher sensitivity, accuracy, reproducibility and ability to handle complex tissues and biological fluid samples. However, the data interpretation tools of contemporary mass spectrometry still warrant further improvement. Based on the data available with laser-based mass spectrometry, biomarkers of OSCC are classified as (i) diagnosis and prognosis, (ii) secretory, (iii) recurrence and metastasis, and (iv) drug targets. Majority of these biomarkers are involved in cell homeostasis and are either physiologic responders or enzymes. Therefore, proteins directly related to tumorigenesis have more diagnostic value. Salivary secretory markers are another group that offers a favourable and easy strategy for non-invasive screening and early diagnosis in oral cancer. Key molecular inter-related pathways in oral carcinogenesis are also intensely researched with software analysis to facilitate targeted drug therapeutics. The review suggested the need for incorporating 'multiple MS or tandem approaches' and focusing on a 'group of biomarkers' instead of single protein entities, for making early diagnosis and treatment for oral cancer a reality.

Feed forward artificial neural network: tool for early detection of ovarian cancer

Pathological changes in an organ or tissue may be reflected in proteomic patterns in serum. The early detection of cancer is crucial for successful treatment. Some cancers affect the concentration of certain molecules in the blood, which allows early diagnosis by analyzing the blood mass spectrum. It is possible that exclusive serum proteomic patterns could be used to differentiate cancer samples from non-cancer ones. Several techniques have been developed for the analysis of mass-spectrum curve, and use them for the detection of prostate, ovarian, breast, bladder, pancreatic, kidney, liver, and colon cancers. In present study, we applied data mining to the diagnosis of ovarian cancer and identified the most informative points of the mass-spectrum curve, then used student t-test and neural networks to determine the differences between the curves of cancer patients and healthy people. Two serum SELDI MS data sets were used in this research to identify serum proteomic patterns that distinguish the serum of ovarian cancer cases from non-cancer controls. Statistical testing and genetic algorithm-based methods are used for feature selection respectively. The results showed that (1) data mining techniques can be successfully applied to ovarian cancer detection with a reasonably high performance; (2) the discriminatory features (proteomic patterns) can be very different from one selection method to another.

Identification of Anoectochilus based on rDNA ITS sequences alignment and SELDI-TOF-MS

The internal transcribed spacer (ITS) sequences alignment and proteomic difference of Anoectochilus interspecies have been studied by means of ITS molecular identification and surface enhanced laser desorption ionization time of flight mass spectrography. Results showed that variety certification on Anoectochilus by ITS sequences can not determine species, and there is proteomic difference among Anoectochilus interspecies. Moreover, proteomic finger printings of five Anoectochilus species have been established for identifying species, and genetic relationships of five species within Anoectochilus have been deduced according to proteomic differences among five species.

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Recent advancement in mass spectrometry leads us to a new era of proteomic analysis. Human saliva can be easily collected; however, the complexity of the salivary proteome in the past prevented the use of saliva for proteomic analysis. Here we review the development of proteomic analyses for human saliva and focus on the use of a new mass spectrometric technology known as surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF). SELDI-TOF, a modification of matrix-assisted laser desorption/ionization mass spectrometry (MALDI-TOF), combines the precision of mass spectrometry and the high-through-put nature of protein arrays known as Protein Chips. This technology shows a promising future for salivary proteomic analysis in monitoring treatments and diseases, as well as novel biomarker discovery.

Comparison of computational algorithms for the classification of liver cancer using SELDI mass spectrometry: a case study

INTRODUCTION

As an alternative to DNA microarrays, mass spectrometry based analysis of proteomic patterns has shown great potential in cancer diagnosis. The ultimate application of this technique in clinical settings relies on the advancement of the technology itself and the maturity of the computational tools used to analyze the data. A number of computational algorithms constructed on different principles are available for the classification of disease status based on proteomic patterns. Nevertheless, few studies have addressed the difference in the performance of these approaches. In this report, we describe a comparative case study on the classification accuracy of hepatocellular carcinoma based on the serum proteomic pattern generated from a Surface Enhanced Laser Desorption/Ionization (SELDI) mass spectrometer.

METHODS

Nine supervised classification algorithms are implemented in R software and compared for the classification accuracy.

RESULTS

We found that the support vector machine with radial function is preferable as a tool for classification of hepatocellular carcinoma using features in SELDI mass spectra. Among the rest of the methods, random forest and prediction analysis of microarrays have better performance. A permutation-based technique reveals that the support vector machine with a radial function seems intrinsically superior in learning from the training data since it has a lower prediction error than others when there is essentially no differential signal. On the other hand, the performance of the random forest and prediction analysis of microarrays rely on their capability of capturing the signals with substantial differentiation between groups.

CONCLUSIONS

Our finding is similar to a previous study, where classification methods based on the Matrix Assisted Laser Desorption/Ionization (MALDI) mass spectrometry are compared for the prediction accuracy of ovarian cancer. The support vector machine, random forest and prediction analysis of microarrays provide better prediction accuracy for hepatocellular carcinoma using SELDI proteomic data than six other approaches.

Plasma protein profiles differ between women diagnosed with cervical intraepithelial neoplasia (cin) 1 and 3

Early detection of precancerous cells in the cervix and their clinical management is the main purpose of cervical cancer prevention and treatment programs. Cytological findings or testing for high risk (HR)-human papillomavirus (HPV) are inadequately sensitive for use in triage of women at high risk for cervical cancer. The current study is an exploratory study to identify candidate surface-enhanced laser desorption/ionization (SELDI) time of flight (TOF) mass spectrometry (MS) protein profiles in plasma that may distinguish cervical intraepithelial neoplasia (CIN 3) from CIN 1 among women infected with HR-HPV. We evaluated the SELDI-TOF-MS plasma protein profiles of HR-HPV positive 32 women with CIN 3 (cases) and 28 women with CIN1 (controls). Case-control status was kept blinded and triplicates of each sample and quality control plasma samples were randomized and after robotic sample preparations were run on WCX2 chips. After alignment of mass/charge (m-z values), an iterative method was used to develop a classifier on a training data set that had 28 cases and 22 controls. The classifier developed was used to classify the subjects in a test data set that has six cases and six controls. The classifier separated the cases from controls in the test set with 100% sensitivity and 100% specificity suggesting the possibility of using plasma SELDI protein profiles to identify women who are likely to have CIN 3 lesions.

Novel approaches to smoothing and comparing SELDI TOF spectra

BACKGROUND

Most published literature using SELDI-TOF has used traditional techniques in Spectral Analysis such as Fourier transforms and wavelets for denoising. Most of these publications also compare spectra using their most prominent feature, i.e, peaks or local maximums.

METHODS

The maximum intensity value within each window of differentiable m/z values was used to represent the intensity level in that window. We also calculated the 'Area under the Curve' (AUC) spanned by each window.

RESULTS

Keeping everything else constant, such as pre-processing of the data and the classifier used, the AUC performed much better as a metric of comparison than the peaks in two out of three data sets. In the third data set both metrics performed equivalently.

CONCLUSIONS

This study shows that the feature used to compare spectra can have an impact on the results of a study attempting to identify biomarkers using SELDI TOF data.

Understanding the characteristics of mass spectrometry data through the use of simulation

BACKGROUND

Mass spectrometry is actively being used to discover disease-related proteomic patterns in complex mixtures of proteins derived from tissue samples or from easily obtained biological fluids. The potential importance of these clinical applications has made the development of better methods for processing and analyzing the data an active area of research. It is, however, difficult to determine which methods are better without knowing the true biochemical composition of the samples used in the experiments.

METHODS

We developed a mathematical model based on the physics of a simple MALDI-TOF mass spectrometer with time-lag focusing. Using this model, we implemented a statistical simulation of mass spectra. We used the simulation to explore some of the basicoperating characteristics of MALDI or SELDI instruments.

RESULTS

The simulation reproduced several characteristics of actual instruments. We found that the relative mass error is affected by the time discretization of the detector (about 0.01%) and the spread of initial velocities (about 0.1%). The accuracy of calibration based on external standards decays rapidly outside the range spanned by the calibrants. Natural isotope distributions play a major role inbroadening peaks associated with individual proteins. The area of a peak is a more accurate measure of its size than the height.

CONCLUSIONS

The model described here is capable of simulating realistic mass spectra. The simulation should become a useful tool forgenerating spectra where the true inputs are known, allowing researchers to evaluate the performance of new methods for processing and analyzing mass spectra.

AVAILABILITY

http://bioinformatics.mdanderson.org/cromwell.html.