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

Clinical applications of plasma proteomics and peptidomics: Towards precision medicine

In the context of precision medicine, disease treatment requires individualized strategies based on the underlying molecular characteristics to overcome therapeutic challenges posed by heterogeneity. For this purpose, it is essential to develop new biomarkers to diagnose, stratify, or possibly prevent diseases. Plasma is an available source of biomarkers that greatly reflects the physiological and pathological conditions of the body. An increasing number of studies are focusing on proteins and peptides, including many involving the Human Proteome Project (HPP) of the Human Proteome Organization (HUPO), and proteomics and peptidomics techniques are emerging as critical tools for developing novel precision medicine preventative measures. Excitingly, the emerging plasma proteomics and peptidomics toolbox exhibits a huge potential for studying pathogenesis of diseases (e.g., COVID-19 and cancer), identifying valuable biomarkers and improving clinical management. However, the enormous complexity and wide dynamic range of plasma proteins makes plasma proteome profiling challenging. Herein, we summarize the recent advances in plasma proteomics and peptidomics with a focus on their emerging roles in COVID-19 and cancer research, aiming to emphasize the significance of plasma proteomics and peptidomics in clinical applications and precision medicine.

Convolutional neural network in proteomics and metabolomics for determination of comorbidity between cancer and schizophrenia

The association between cancer risk and schizophrenia is widely debated. Despite many epidemiological studies, there is still no strong evidence regarding the molecular basis for the comorbidity between these two pathological conditions. The vast majority of assays have been performed using clinical records of schizophrenic patients or those undergoing cancer treatment and monitored for sufficient time to find shared features between the considered conditions. We performed mass spectrometry-based proteomic and metabolomic investigations of patients with different cancer phenotypes (breast, ovarian, renal, and prostate) and patients with schizophrenia. The resulting vast quantity of proteomic and metabolomic data were then processed using systems biology and one-dimensional (1D) convolutional neural network (1DCNN) machine learning approaches. Traditional systematic approaches permit the segregation of schizophrenia and cancer phenotypes on the level of biological processes, while 1DCNN recognized "signatures" that could segregate distinct cancer phenotypes and schizophrenia at the comorbidity level. The designed network efficiently discriminated unrelated pathologies with a model accuracy of 0.90 and different subtypes of oncophenotypes with an accuracy of 0.94. The proposed strategy integrates systematic analysis of identified compounds and application of 1DCNN model for unidentified ones to reveal the similarity between distinct phenotypes.

The liver matrisome - looking beyond collagens

The extracellular matrix (ECM) is a diverse microenvironment that maintains bidirectional communication with surrounding cells to regulate cell and tissue homeostasis. The classical definition of the ECM has more recently been extended to include non-fibrillar proteins that either interact or are structurally affiliated with the ECM, termed the 'matrisome.' In addition to providing the structure and architectural support for cells and tissue, the matrisome serves as a reservoir for growth factors and cytokines, as well as a signaling hub via which cells can communicate with their environment and vice-versa. The matrisome is a master regulator of tissue homeostasis and organ function, which can dynamically and appropriately respond to any stress or injury. Failure to properly regulate these responses can lead to changes in the matrisome that are maladaptive. Hepatic fibrosis is a canonical example of ECM dyshomeostasis, leading to accumulation of predominantly collagenous ECM; indeed, hepatic fibrosis is considered almost synonymous with collagen accumulation. However, the qualitative and quantitative alterations of the hepatic matrisome during fibrosis are much more diverse than simple accumulation of collagens and occur long before fibrosis is histologically detected. A deeper understanding of the hepatic matrisome and its response to injury could yield new mechanistic insights into disease progression and regression, as well as potentially identify new biomarkers for both. In this review, we discuss the role of the ECM in liver diseases and look at new "omic" approaches to study this compartment.

Single Cell Proteomics for Molecular Targets in Lung Cancer: High-Dimensional Data Acquisition and Analysis

In the proteomic and genomic era, lung cancer researchers are increasingly under challenge with traditional protein analyzing tools. High output, multiplexed analytical procedures are in demand for disclosing the post-translational modification, molecular interactions and signaling pathways of proteins precisely, specifically, dynamically and systematically, as well as for identifying novel proteins and their functions. This could be better realized by single-cell proteomic methods than conventional proteomic methods. Using single-cell proteomic tools including flow cytometry, mass cytometry, microfluidics and chip technologies, chemical cytometry, single-cell western blotting, the quantity and functions of proteins are analyzed simultaneously. Aside from deciphering disease mechanisms, single-cell proteomic techniques facilitate the identification and screening of biomarkers, molecular targets and promising compounds as well. This review summarized single-cell proteomic tools and their use in lung cancer.

Proteomics-based insights into mitogen-activated protein kinase inhibitor resistance of cerebral melanoma metastases

Background

MAP kinase inhibitor (MAPKi) therapy for BRAF mutated melanoma is characterized by high response rates but development of drug resistance within a median progression-free survival (PFS) of 9-12 months. Understanding mechanisms of resistance and identifying effective therapeutic alternatives is one of the most important scientific challenges in melanoma. Using proteomics, we want to specifically gain insight into the pathophysiological process of cerebral metastases.

Methods

Cerebral metastases from melanoma patients were initially analyzed by a LC-MS shotgun approach performed on a QExactive HF hybrid quadrupole-orbitrap mass spectrometer. For further validation steps after bioinformatics analysis, a targeted LC-QQQ-MS approach, as well as Western blot, immunohistochemistry and immunocytochemistry was performed.

Results

In this pilot study, we were able to identify 5977 proteins by LC-MS analysis (data are available via ProteomeXchange with identifier PXD007592). Based on PFS, samples were classified into good responders (PFS ≥ 6 months) and poor responders (PFS [Formula: see text] 3 months). By evaluating these proteomic profiles according to gene ontology (GO) terms, KEGG pathways and gene set enrichment analysis (GSEA), we could characterize differences between the two distinct groups. We detected an EMT feature (up-regulation of N-cadherin) as classifier between the two groups, V-type proton ATPases, cell adhesion proteins and several transporter and exchanger proteins to be significantly up-regulated in poor responding patients, whereas good responders showed an immune activation, among other features. We identified class-discriminating proteins based on nearest shrunken centroids, validated and quantified this signature by a targeted approach and could correlate parts of this signature with resistance using the CPL/MUW proteome database and survival of patients by TCGA analysis. We further validated an EMT-like signature as a major discriminator between good and poor responders on primary melanoma cells derived from cerebral metastases. Higher immune activity is demonstrated in patients with good response to MAPKi by immunohistochemical staining of biopsy samples of cerebral melanoma metastases.

Conclusions

Employing proteomic analysis, we confirmed known extra-cerebral resistance mechanisms in the cerebral metastases and further discovered possible brain specific mechanisms of drug efflux, which might serve as treatment targets or as predictive markers for these kinds of metastasis.

Origins, Technological Development, and Applications of Peptidomics

Peptidomics is the comprehensive characterization of peptides from biological sources mainly by HPLC and mass spectrometry. Mass spectrometry allows the detection of a multitude of single peptides in complex mixtures. The term first appeared in full papers in the year 2001, after over 100 years of peptide research with a main focus on one or a few specific peptides. Within the last 15 years, this new field has grown to over 1200 publications. Mass spectrometry techniques, in combination with other analytical methods, were developed for the fast and comprehensive analysis of peptides in proteomics and specifically adjusted to implement peptidomics technologies. Although peptidomics is closely linked to proteomics, there are fundamental differences with conventional bottom-up proteomics. The development of peptidomics is described, including the most important implementations for its technological basis. Different strategies are covered which are applied to several important applications, such as neuropeptidomics and discovery of bioactive peptides or biomarkers. This overview includes links to all other chapters in the book as well as recent developments of separation, mass spectrometric, and data processing technologies. Additionally, some new applications in food and plant peptidomics as well as immunopeptidomics are introduced.

Characterization of the Low-Molecular-Weight Human Plasma Peptidome

The human plasma proteome represents an important secreted sub-proteome. Proteomic analysis of blood plasma with mass spectrometry is a challenging task. The high complexity and wide dynamic range of proteins as well as the presence of several proteins at very high concentrations complicate the profiling of the human plasma proteome. The peptidome (or low-molecular-weight fraction, LMF) of the human plasma proteome is an invaluable source of biological information, especially in the context of identifying plasma-based markers of disease. Peptides are generated by active synthesis and proteolytic processing, often yielding proteolytic fragments that mediate a variety of physiological and pathological functions. As such, degradomic studies, investigating cleavage products via peptidomics and top-down proteomics in particular, have warranted significant research interest. However, due to their molecular weight, abundance, and solubility, issues with identifying specific cleavage sites and coverage of peptide fragments remain challenging. Peptidomics is currently focused toward comprehensively studying peptides cleaved from precursor proteins by endogenous proteases. This protocol outlines a standardized rapid and reproducible procedure for peptidomic profiling of human plasma using centrifugal ultrafiltration and mass spectrometry. Ultrafiltration is a convective process that uses anisotropic semipermeable membranes to separate macromolecular species on the basis of size. We have optimized centrifugal ultrafiltration (cellulose triacetate membrane) for plasma fractionation with respect to buffer and solvent composition, centrifugal force, duration, and temperature to facilitate recovery >95% and enrichment of the human plasma peptidome. This method serves as a comprehensive and facile process to enrich and identify a key, underrepresented sub-proteome of human blood plasma.

Evaluation of a tyrosine kinase peptide microarray for tyrosine kinase inhibitor therapy selection in cancer

Personalized cancer medicine aims to accurately predict the response of individual patients to targeted therapies, including tyrosine kinase inhibitors (TKIs). Clinical implementation of this concept requires a robust selection tool. Here, using both cancer cell lines and tumor tissue from patients, we evaluated a high-throughput tyrosine kinase peptide substrate array to determine its readiness as a selection tool for TKI therapy. We found linearly increasing phosphorylation signal intensities of peptides representing kinase activity along the kinetic curve of the assay with 7.5–10 μg of lysate protein and up to 400 μM adenosine triphosphate (ATP). Basal kinase activity profiles were reproducible with intra- and inter-experiment coefficients of variation of <15% and <20%, respectively. Evaluation of 14 tumor cell lines and tissues showed similar consistently high phosphorylated peptides in their basal profiles. Incubation of four patient-derived tumor lysates with the TKIs dasatinib, sunitinib, sorafenib and erlotinib primarily caused inhibition of substrates that were highly phosphorylated in the basal profile analyses. Using recombinant Src and Axl kinase, relative substrate specificity was demonstrated for a subset of peptides, as their phosphorylation was reverted by co-incubation with a specific inhibitor. In conclusion, we demonstrated robust technical specifications of this high-throughput tyrosine kinase peptide microarray. These features required as little as 5–7 μg of protein per sample, facilitating clinical implementation as a TKI selection tool. However, currently available peptide substrates can benefit from an enhancement of the differential potential for complex samples such as tumor lysates. We propose that mass spectrometry-based phosphoproteomics may provide such an enhancement by identifying more discriminative peptides.

Screening the low molecular weight fraction of human serum using ATR-IR spectroscopy

Vibrational spectroscopic techniques can detect small variations in molecular content, linked with disease, showing promise for screening and early diagnosis. Biological fluids, particularly blood serum, are potentially valuable for diagnosis purposes. The so-called Low Molecular Weight Fraction (LMWF) contains the associated peptidome and metabolome and has been identified as potentially the most relevant molecular population for disease-associated biomarker research. Although vibrational spectroscopy can deliver a specific chemical fingerprint of the samples, the High Molecular Weight Fraction (HMWF), composed of the most abundant serum proteins, strongly dominates the response and ultimately makes the detection of minor spectral variations a challenging task. Spectroscopic detection of potential serum biomarkers present at relatively low concentrations can be improved using pre-analytical depletion of the HMWF. In the present study, human serum fractionation by centrifugal filtration was used prior to analysis by Attenuated Total Reflection infrared spectroscopy. Using a model sample based on glycine spiked serum, it is demonstrated that the screening of the LMWF can be applied to quantify blinded concentrations up to 50 times lower. Moreover, the approach is easily transferable to different bodily fluids which would support the development of more efficient and suitable clinical protocols exploring vibrational spectroscopy based ex-vivo diagnostic tools. Revealing serum LMWF for spectral serological diagnostic applications.

Evaluation of potential circulating biomarkers for prediction of response to chemoradiation in patients with glioblastoma

Surgery followed by chemoradiation and adjuvant chemotherapy is standard of care for patients with a glioblastoma (GBM). Due to its limited benefit, an upfront method to predict dismal outcome would prevent unnecessary toxic treatment. We searched for a predictive blood derived biomarker in a cohort of 55 patients with GBM. Increasing age (HR 1.03, 95 % CI 1.01–1.06), and postoperative tumor residue (HR 1.07, 95 % CI 1.02–1.15) were independently associated with unfavourable progression free survival (PFS) in these patients. Corticosteroid use before start of chemoradiaton was strongly predictive for outcome (HR 3.26, 95 % CI 1.67–6.39) with a mean PFS and OS in patients using corticosteroids of 7.3 and 14.6 months, versus 16.1 and 21.6 months in patients not using corticosteroids (p = 0.0005, p < 0.0067 respectively). Despite earlier reports, blood concentrations of YKL-40, Fetuin-a and haptoglobin were not predictive for response. In addition, serum peptide profiles, determined by MALDI-TOF mass spectroscopy, were not predictive as well. In conclusion, further biomarker discovery studies are needed to predict treatment outcome for patients with GBM in the near future.

Peptidome profiling of umbilical cord plasma associated with gestational diabetes-induced fetal macrosomia

Fetal macrosomia, defined as a birth weight ≥4000g, may affect 15-45% of newborns of women with gestational diabetes mellitus (GDM). The associations between endogenous peptides and gestational diabetes-induced macrosomia have not been investigated extensively by peptidome analysis. Here, we analyzed the umbilical cord plasma by combining ultrafiltration using molecular weight cut-off filters and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to investigate potential associations of GDM with macrosomia. As macrosomic babies have increased susceptibility to obesity, diabetes and cardiovascular diseases in later life, we also aimed to identify specific biomarkers to detect these future diseases. Thirty pairs of GDM mothers and controls were randomly divided into three subgroups. We identified 235 peptides of around 1000-3000Da, originating from 115 proteins. Analyzing the cleavage sites revealed that these peptides were cleaved in regulation, which may reflect the protease activity and distribution in umbilical cord plasma. Four identified peptides, of 2471.7, 1077.2, 1446.5 and 2372.7Da, were significantly differentially expressed in the GDM macrosomia groups compared with controls, whose precursors may play a critical role in developing GDM macrosomia. We provide for the first time a validated GDM macrosomia peptidome profile and identify potential biomarkers linking the effects of macrosomia to later-life diseases.

BIOLOGICAL SIGNIFICANCE

Fetal macrosomia is the predominant adverse outcome of gestational diabetes mellitus (GDM), which is a frequent medical condition during pregnancy. Till now, the detailed molecular mechanisms underlying gestational diabetes-induced macrosomia are still not elucidated. With high detection sensitivity and high throughput of peptidome technology, it is now possible to systemically identify peptides possibly involved in the umbilical cord plasma of GDM induced macrosomia cases. With LC-MS/MS based quantification, totally, we identified 235 peptides originated from 115 precursor proteins. And four peptides of 2471.7, 1077.2, 1446.5 and 2372.7Da differentially expressed between GDM cases and compared controls. A precursor protein of 1077.2Da was fibrinogen alpha chain (FGA), which was also identified in the Ai et al. [29] study with a downregulated manner in the serum samples of GDM cases. And further analysis the cleavage pattern of the identified peptides revealed that the enzymes in tissues cleaved the protein according to their rules. Thus, this quantitative peptidome approach can identify related peptides that may play a role in the gestational diabetes-induced macrosomia, and give candidate biomarkers contributing to the development of later-life diseases in macrosomic babies.

Serum peptidome profiling analysis for the identification of potential biomarkers in cervical intraepithelial neoplasia patients

Cervical intraepithelial neoplasia (CIN) is a precancerous disease of cervical squamous cell carcinoma. We Used Mass Spectrometry based peptidome profile study to predict the transformation of CIN1, which is the primary stage of this lesion. . Serum samples of 34 Cervical squamous cell carcinoma patients, 31 healthy controls, and 29 CIN1 samples were analyzed. Peptides were purified by WCX magnetic beads (Bioyong), and analyzed by MALDI TOF (Bruker). Raw data were analyzed by BioExplorer software (Bioyong). The results showed 14 mass peaks with significant differences. The diagnosis model is established by analyzing peptide profiles of 15 SCC patients and 20 healthy women serum, with a sensitivity of 100% and specificity of 100.00%. In validation set, the SCC diagnosis model also had good performance with a sensitivity of 80%, a specificity of 100%. In addition, this model could predict 29 CIN1 patients with accuracy of 55.17%. These results would provide a new method to predict the trend of CIN1 and take effective measures for high risk group timely.

Is isolation of comprehensive human plasma peptidomes an achievable quest?

The low molecular weight (LMW; <10kDa)* plasma peptidome has been considered a source of useful diagnostic biomarkers and potentially therapeutic molecules, as it contains many cytokines, peptide hormones, endogenous peptide products and potentially bioactive fragments derived from the parent proteome. The small size of the peptides allows them almost unrestricted vascular and interstitial access, and hence distribution across blood-brain barriers, tumour and other vascular permeability barriers. Therefore, the peptidome may carry specific signatures or fingerprints of an individual's health, wellbeing or disease status. This occurs primarily because of the advantage the peptidome has in being readily accessible in human blood and/or other biofluids. However, the co-expression of highly abundant proteins (>10kDa) and other factors present inherently in human plasma make direct analysis of the blood peptidome one of the most challenging tasks faced in contemporary analytical biochemistry. A comprehensive compendium of extraction and fractionation tools has been collected concerning the isolation and micromanipulation of peptides. However, the search for a reliable, accurate and reproducible single or combinatorial separation process for capturing and analysing the plasma peptidome remains a challenge. This review outlines current techniques used for the separation and detection of plasma peptides and suggests potential avenues for future investigation. This article is part of a Special Issue entitled: HUPO 2014.