Formalin-fixed paraffin-embedded (FFPE) tissues are a real treasure for retrospective analysis considering the amount of samples present in hospital archives, combined with pathological, clinical, and outcome information available for every sample. Although unlocking the proteome of these tissues is still a challenge, new approaches are being developed. In this review, we summarize the different mass spectrometry platforms that are used in human clinical studies to unravel the FFPE proteome. The different ways of extracting crosslinked proteins and the analytical strategies are pointed out. Also, the pitfalls and challenges concerning the quality of FFPE proteomic approaches are depicted. We also evaluated the potential of these analytical methods for future clinical FFPE proteomics applications.

Naturally occurring peptides, including growth factors, hormones, and neurotransmitters, represent an important class of biomolecules and have crucial roles in human physiology. The study of these peptides in clinical samples is therefore as relevant as ever. Compared to more routine proteomics applications in clinical research, peptidomics research questions are more challenging and have special requirements with regard to sample handling, experimental design, and bioinformatics. In this review, we describe the issues that confront peptidomics in a clinical context. After these hurdles are (partially) overcome, peptidomics will be ready for a successful translation into medical practice.

In quantitative proteomics applications, the use of isobaric labels is a very popular concept as they allow for multiplexing, such that peptides from multiple biological samples are quantified simultaneously in one mass spectrometry experiment. Although this multiplexing allows that peptide intensities are affected by the same amount of instrument variability, systematic effects during sample preparation can also introduce a bias in the quantitation measurements. Therefore, normalization methods are required to remove this systematic error. At present, a few dedicated normalization methods for isobaric labeled data are at hand. Most of these normalization methods include a framework for statistical data analysis and rely on ANOVA or linear mixed models. However, for swift quality control of the samples or data visualization a simple normalization technique is sufficient. To this aim, we present a new and easy-to-use data-driven normalization method, named CONSTANd. The CONSTANd method employs constrained optimization and prior information about the labeling strategy to normalize the peptide intensities. Further, it allows maintaining the connection to any biological effect while reducing the systematic and technical errors. As a result, peptides can not only be compared directly within a multiplexed experiment, but are also comparable between other isobaric labeled datasets from multiple experimental designs that are normalized by the CONSTANd method, without the need to include a reference sample in every experimental setup. The latter property is especially useful when more than six, eight or ten (TMT/iTRAQ) biological samples are required to detect differential peptides with sufficient statistical power and to optimally make use of the multiplexing capacity of isobaric labels. Molecular & Cellular Proteomics 15:

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

Bio-active peptides are involved in the regulation of most physiological processes in the body. Classical bio-active peptides (CBAPs) are cleaved from a larger precursor protein and stored in secretion vesicles from which they are released in the extracellular space. Recently, another non-classical type of bio-active peptides (NCBAPs) has gained interest. These typically are not secreted but instead appear to be translated from short open reading frames (sORF) and released directly into the cytoplasm. In contrast to CBAPs, these peptides are involved in the regulation of intra-cellular processes such as transcriptional control, calcium handling and DNA repair. However, bio-chemical evidence for the translation of sORFs remains elusive. Comprehensive analysis of sORF-encoded polypeptides (SEPs) is hampered by a number of methodological and biological challenges: the low molecular mass (many 4-10 kDa), the low abundance, transient expression and complications in data analysis. We developed a strategy to address a number of these issues. Our strategy is to exclude false positive identifications. In total sample, we identified 926 peptides originated from 37 known (neuro)peptide precursors in mouse striatum. In addition, four SEPs were identified including NoBody, a SEP that was previously discovered in humans and three novel SEPS from 5' untranslated transcript regions (UTRs).

(1) Background: Therapeutic blocking of the interaction between programmed death-1 (PD-1) with its ligand PD-L1, an immune checkpoint, is a promising approach to restore the antitumor immune response. Improved clinical outcomes have been shown in different human cancers, including non-small cell lung cancer (NSCLC). Unfortunately, still a high number of NSCLC patients are treated with immunotherapy without obtaining any clinical benefit, due to the limitations of PD-L1 protein expression as the currently sole predictive biomarker for clinical use; (2) Methods: In this study, we applied mass spectrometry imaging (MSI) to discover new protein biomarkers, and to assess the possible correlation between candidate biomarkers and a positive immunotherapy response by matrix-assisted laser desorption/ionization (MALDI) MSI in 25 formalin-fixed paraffin-embedded (FFPE) pretreatment tumor biopsies (Biobank@UZA); (3) Results: Using MALDI MSI, we revealed that the addition of neutrophil defensin 1, 2 and 3 as pretreatment biomarkers may more accurately predict the outcome of immunotherapy treatment in NSCLC. These results were verified and confirmed with immunohistochemical analyses. In addition, we provide in-vitro evidence of the immune stimulatory effect of neutrophil defensins towards cancer cells; and (4) Conclusions: With proteomic approaches, we have discovered neutrophil defensins as additional prospective biomarkers for an anti-PD-(L)1 immunotherapy response. Thereby, we also demonstrated that the neutrophil defensins contribute in the activation of the immune response towards cancer cells, which could provide a new lead towards an anticancer therapy.

-Omics data have become indispensable to systems biology, which aims to describe the full complexity of functional cells, tissues, organs and organisms. Generating vast amounts of data via such methods, researchers have invested in ways of handling and interpreting these. From the large volumes of -omics data that have been gathered over the years, it is clear that the information derived from one -ome is usually far from complete. Now, individual techniques and methods for integration are maturing to the point that researchers can focus on network-based integration rather than simply interpreting single -ome studies. This review evaluates the application of integrated -omics approaches with a focus on Caenorhabditis elegans studies, intending to direct researchers in this field to useful databases and inspiring examples.

Formalin-fixed paraffin-embedded (FFPE) tissue specimens represent a potential valuable source of samples for clinical research. Since these specimens are banked in hospital archives, large cohorts of samples can be collected in short periods of time which can all be linked with a patients' clinical history. Therefore, the use of FFPE tissue in protein biomarker discovery studies gains interest. However, despite the growing number of FFPE proteome studies in the literature, there is a lack of a FFPE proteomics standard operating procedure (SOP). One of the challenging steps in the development of such a SOP is the ability to obtain an efficient and repeatable extraction of full length FFPE proteins. In this study, the protein extraction efficiency of eight protein extraction buffers is critically compared with GeLC-MS/MS (1D gel electrophoresis followed by in-gel digestion and LC-MS/MS). The data variation caused by using these extraction buffers was investigated since the variation is a very important aspect when using FFPE tissue as a source for biomarker detection. In addition, a qualitative comparison was made between the protein extraction efficiency and repeatability for FFPE tissue and fresh frozen tissue.