Optimized spatial filters as a new method for mass spectrometry-based cancer diagnosis

In the past two decades, mass spectrometry-based identification of serum proteomic patterns has emerged as a new diagnostic tool for the early detection of various types of cancers. However, due to its high dimensionality, the analysis of mass spectrometry data poses considerable challenges. Existing methods proposed for the analysis of mass spectrometry data usually consist of a number of steps. In this study, a comparatively simple but efficient method, namely, an optimal spatial filter (OSF) method, is proposed for the classification of mass spectrometry data. The newly proposed method is based on the theory of common spatial patterns (CSPs), which are widely used to classify motor imagery EEG signals in brain-computer interface (BCI) applications. The CSP method aims to find spatial filters to project the data into a new space in which optimal discrimination between classes is achieved. Although it has been shown that the CSP method performs quite well in classifying motor imagery EEG signals, it has a major drawback. In the CSP method, the between-class variance is maximized, but the minimization of within-class variance is ignored. As a result, the projected data may have large within-class variances. To overcome this problem, in this study, optimal filters are found by using the differential evolution (DE) algorithm. For the fitness function of the differential evolution algorithm, a divergence analysis is used. In the divergence analysis, both the between-class and within-class distributions of the projected data are considered. The experimental results obtained using publicly available mass spectrometry datasets showed that, when compared to existing methods, the proposed OSF method is quite simple and achieves the minimum classification error for each dataset. Furthermore, the proposed OSF method highlights the importance of certain parts of the spectra, which is highly valuable for the identification of biomarkers that lie outside the pathological pathway of the disease.     

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Automatic classification of high-resolution mass spectrometry data has increasing potential to support physicians in diagnosis of diseases like cancer. The proteomic data exhibit variations among different disease states. A precise and reliable classification of mass spectra is essential for a successful diagnosis and treatment. The underlying process to obtain such reliable classification results is a crucial point. In this paper such a method is explained and a corresponding semi automatic parameterization procedure is derived. Thereby a simple straightforward classification procedure to assign mass spectra to a particular disease state is derived. The method is based on an initial preprocessing stage of the whole set of spectra followed by the bi-orthogonal discrete wavelet transform (DWT) for feature extraction. The approximation coefficients calculated from the scaling function exhibit a high peak pattern matching property and feature a denoising of the spectrum. The discriminating coefficients, selected by the Kolmogorov–Smirnov test are finally used as features for training and testing a support vector machine with both a linear and a radial basis kernel. For comparison the peak areas obtained with the it ClinProt-System ¹ [33] were analyzed using the same support vector machines. The introduced approach was evaluated on clinical MALDI-MS data sets with two classes each originating from cancer studies. The cross validated error rates using the wavelet coefficients where better than those obtained from the peak areas².     

Biomarkers for the central nervous system complications of sickle cell disease: are we there yet?

Sickle cell disease (SCD, OMIM #603903), an autosomal recessively inherited β-hemoglobinopathy, was the first human disorder delineated at a molecular level. The putative single nucleotide mutation in the HBB gene generates an abnormal hemoglobin species, which polymerizes in deoxygenated conditions causing irreversible changes in erythrocyte shape and function. Sickling erythrocytes are in turn responsible for microvascular vaso-occlusion, hemolysis and a systemic vasculopathy in patients. SCD has represented an attractive field for proteomic investigation since its methodological infancy. Clinically actionable biomarkers, especially for the prevention of cerebrovascular complications in children with the condition, are urgently needed and their discovery remains a major challenge. In this issue, Lance and colleagues report of their unbiased proteomic studies on samples from the participants of the landmark prospective, randomized, single-blind SIT trial (NEJM 2014). Their results reveal numerous brain-enriched plasma proteins specific for SCD, and for silent cerebral infarcts in this disorder, and further analyses highlight novel cellular mechanisms behind the brain damage in SCD. Although the goal of identifying reliable biomarker candidates for cerebrovascular complications could not be met, the dataset produced by the authors constitutes a significant contribution to the field and opens new horizons for further clinical and laboratory investigation.     

Discovery and validation of urinary biomarkers for prostate cancer

Only 30% of patients with elevated serum prostate specific antigen (PSA) levels who undergo prostate biopsy are diagnosed with prostate cancer (PCa). Novel methods are needed to reduce the number of unnecessary biopsies. We report on the identification and validation of a panel of 12 novel biomarkers for prostate cancer (PCaP), using CE coupled MS. The biomarkers could be defined by comparing first void urine of 51 men with PCa and 35 with negative prostate biopsy. In contrast, midstream urine samples did not allow the identification of discriminatory molecules, suggesting that prostatic fluids may be the source of the defined biomarkers. Consequently, first void urine samples were tested for sufficient amounts of prostatic fluid, using a prostatic fluid indicative panel (“informative” polypeptide panel; IPP). A combination of IPP and PCaP to predict positive prostate biopsy was evaluated in a blinded prospective study. Two hundred thirteen of 264 samples matched the IPP criterion. PCa was detected with 89% sensitivity, 51% specificity. Including age and percent free PSA to the proteomic signatures resulted in 91% sensitivity, 69% specificity.     

Formalin-fixed paraffin-embedded tissue: The holy grail of clinical proteomics?

Tissue is the most relevant biological material to gather insight in disease mechanisms by means of omics technologies. However, fresh frozen tissue, which is generally regarded as the best imaginable source for such studies, is often not available. In case it is available, the different ways of storage (e.g. −20°C, −80°C, liquid nitrogen, etc.) hamper the conduction of reproducible multicenter studies because of different protein degradation rates. Formalin-fixed paraffin-embedded (FFPE) tissue on the contrary is considered as a valuable alternative for fresh frozen tissue, because only a few standard operation procedures are applied worldwide for the preparation of these tissues and because they are all stored in the same way. However, a study on the impact of the different preparation protocols for FFPE tissue was still lacking. Therefore, Bronsert et al. in this issue [Bronsert, P., Weißer, J., Biniossek, M. L., Kuehs, M. et al., Proteomics Clin. Appl. 2014, 8 786–804] conducted such a study that provides proof that there is no significant effect between these sample preparations procedures, and thereby they further open the gate for FFPE tissues to enter the field of clinical proteomics.     

Grand rounds in proteomics at the FDA white oak, silver spring, MD, USA, april 3, 2007

The application of proteomics in drug development could be a major source of novel biomarkers to improve the efficacy and safety of new drugs. Training of US Food and Drug Administration (FDA) reviewers on current applications of proteomics is important for the future review of proteomic data. A Grand Rounds in Proteomics was held on April 3, 2007 at the FDA in White Oak, Silver Spring, MD, USA. The goal of this activity was to contribute to reviewer training as well as to generate discussions regarding the readiness of proteomic platforms in drug development, similar in scope to applications in genomics and metabolomics. Several speakers from industry and academia presented data on proteomic applications in drug development (meeting agenda available in the Supporting Information). An additional goal of this meeting was to encourage proteomic data submissions within the Voluntary eXploratory Data Submissions (VXDS) at the FDA. VXDS meetings represent key venues for exchange between the FDA and sponsors of scientific and clinical data on exploratory biomarkers. The FDA has received a limited number of VXDS submissions containing proteomic data. This meeting was an opportunity to identify possible areas in proteomics where future VXDS submissions may be received. Voluntary submissions have been transformed into regulatory submissions in genomics, and a similar path may also be followed by proteomic data in the future. Proteomic biomarkers may also be suitable for submission to the Pilot Process for Biomarker Qualification at the FDA.     

Is label-free LC-MS/MS ready for biomarker discovery?

Label-free LC-MS methods are attractive for high-throughput quantitative proteomics, as the sample processing is straightforward and can be scaled to a large number of samples. Label-free methods therefore facilitate biomarker discovery in studies involving dozens of clinical samples. However, despite the increased popularity of label-free workflows, there is a hesitance in the research community to use it in clinical proteomics studies. Therefore, we here discuss pros and cons of label-free LC-MS/MS for biomarker discovery, and delineate the main prerequisites for its successful employment. Furthermore, we cite studies where label-free LC-MS/MS was successfully used to identify novel biomarkers, and foresee an increased acceptance of label-free techniques by the proteomics community in the near future.