Search

IntroductionWhile Immune checkpoint inhibition (ICI) therapy shows significant efficacy in metastatic melanoma, only about 50% respond, lacking reliable predictive methods. We introduce a panel of six proteins aimed at predicting response to ICI therapy.MethodsEvaluating previously reported proteins in two untreated melanoma cohorts, we used a published predictive model (EaSIeR score) to identify potential proteins distinguishing responders and non-responders.ResultsSix proteins initially identified in the ICI cohort correlated with predicted response in the untreated cohort. Additionally, three proteins correlated with patient survival, both at the protein, and at the transcript levels, in an independent immunotherapy treated cohort.DiscussionOur study identifies predictive biomarkers across three melanoma cohorts, suggesting their use in therapeutic decision-making.

Abstract Testosterone is a hormone that plays a key role in carbohydrate, fat, and protein metabolism. Testosterone deficiency is associated with multiple comorbidities, e.g., metabolic syndrome and type 2 diabetes. Despite its importance in many metabolic pathways, the mechanisms by which it controls metabolism are not fully understood. The present study investigated the short-term metabolic changes of pharmacologically induced castration and, subsequently, testosterone supplementation in healthy young males. Thirty subjects were submitted to testosterone depletion (TD) followed by testosterone supplementation (TS). Plasma samples were collected three times corresponding to basal, low, and restored testosterone levels. An untargeted metabolomics study was performed by liquid chromatography–high resolution mass spectrometry (UHPLC–HRMS) to monitor the metabolic changes induced by the altered hormone levels. Our results demonstrated that TD was associated with major metabolic changes partially restored by TS. Carnitine and amino acid metabolism were the metabolic pathways most impacted by variations in testosterone. Furthermore, our results also indicated that LH and FSH might strongly alter the plasma levels of indoles and lipids, especially glycerophospholipids and sphingolipids. Our results demonstrated major metabolic changes induced by low testosterone that may be important for understanding the mechanisms behind the association of testosterone deficiency and its comorbidities.

Abstract Background Small‐cell lung cancer (SCLC) molecular subtypes have been primarily characterized based on the expression pattern of the following key transcription regulators: ASCL1 (SCLC‐A), NEUROD1 (SCLC‐N), POU2F3 (SCLC‐P) and YAP1 (SCLC‐Y). Here, we investigated the proteomic landscape of these molecular subsets with the aim to identify novel subtype‐specific proteins of diagnostic and therapeutic relevance. Methods Pellets and cell media of 26 human SCLC cell lines were subjected to label‐free shotgun proteomics for large‐scale protein identification and quantitation, followed by in‐depth bioinformatic analyses. Proteomic data were correlated with the cell lines’ phenotypic characteristics and with public transcriptomic data of SCLC cell lines and tissues. Results Our quantitative proteomic data highlighted that four molecular subtypes are clearly distinguishable at the protein level. The cell lines exhibited diverse neuroendocrine and epithelial–mesenchymal characteristics that varied by subtype. A total of 367 proteins were identified in the cell pellet and 34 in the culture media that showed significant up‐ or downregulation in one subtype, including known druggable proteins and potential blood‐based markers. Pathway enrichment analysis and parallel investigation of transcriptomics from SCLC cell lines outlined unique signatures for each subtype, such as upregulated oxidative phosphorylation in SCLC‐A, DNA replication in SCLC‐N, neurotrophin signalling in SCLC‐P and epithelial–mesenchymal transition in SCLC‐Y. Importantly, we identified the YAP1‐driven subtype as the most distinct SCLC subgroup. Using sparse partial least squares discriminant analysis, we identified proteins that clearly distinguish four SCLC subtypes based on their expression pattern, including potential diagnostic markers for SCLC‐Y (e.g. GPX8, PKD2 and UFO). Conclusions We report for the first time, the protein expression differences among SCLC subtypes. By shedding light on potential subtype‐specific therapeutic vulnerabilities and diagnostic biomarkers, our results may contribute to a better understanding of SCLC biology and the development of novel therapies.

Abstract The tryptophan to kynurenine ratio (Trp/Kyn) has been proposed as a cancer biomarker. Non-invasive topical sampling of Trp/Kyn can therefore serve as a promising concept for skin cancer diagnostics. By performing in vitro pig skin permeability studies, we conclude that non-invasive topical sampling of Trp and Kyn is feasible. We explore the influence of different experimental conditions, which are relevant for the clinical in vivo setting, such as pH variations, sampling time, and microbial degradation of Trp and Kyn. The permeabilities of Trp and Kyn are overall similar. However, the permeated Trp/Kyn ratio is generally higher than unity due to endogenous Trp, which should be taken into account to obtain a non-biased Trp/Kyn ratio accurately reflecting systemic concentrations. Additionally, prolonged sampling time is associated with bacterial Trp and Kyn degradation and should be considered in a clinical setting. Finally, the experimental results are supported by the four permeation pathways model, predicting that the hydrophilic Trp and Kyn molecules mainly permeate through lipid defects (i.e., the porous pathway). However, the hydrophobic indole ring of Trp is suggested to result in a small but noticeable relative increase of Trp diffusion via pathways across the SC lipid lamellae, while the shunt pathway is proposed to slightly favor permeation of Kyn relative to Trp.

Abstract Background Distal cholangiocarcinoma is an aggressive malignancy with a dismal prognosis. Diagnostic and prognostic biomarkers for distal cholangiocarcinoma are lacking. The aim of the present study was to identify differentially expressed proteins between distal cholangiocarcinoma and normal bile duct samples. Methods A workflow utilizing discovery mass spectrometry and verification by parallel reaction monitoring was used to analyze surgically resected formalin-fixed, paraffin-embedded samples from distal cholangiocarcinoma patients and normal bile duct samples. Bioinformatic analysis was used for functional annotation and pathway analysis. Immunohistochemistry was performed to validate the expression of thrombospondin-2 and investigate its association with survival. Results In the discovery study, a total of 3057 proteins were identified. Eighty-seven proteins were found to be differentially expressed (q

Abstract Background Pancreatic cancer is a major cause of cancer-related mortality. The identification of effective biomarkers is essential in order to improve management of the disease. Yes-associated protein 1 (YAP1) is a downstream effector of the Hippo pathway, a signal transduction system implicated in tissue repair and regeneration, as well as tumorigenesis. Here we evaluate the biomarker potential of YAP1 in pancreatic cancer tissue. Methods YAP1 was selected as a possible biomarker for pancreatic cancer from global protein sequencing of fresh frozen pancreatic cancer tissue samples and normal pancreas controls. The prognostic utility of YAP1 was evaluated using mRNA expression data from 176 pancreatic cancer patients in The Cancer Genome Atlas (TCGA), as well as protein expression data from immunohistochemistry analysis of a local tissue microarray (TMA) cohort comprising 140 pancreatic cancer patients. Ingenuity Pathway Analysis was applied to outline the interaction network for YAP1 in connection to the pancreatic tumor microenvironment. The expression of YAP1 target gene products was evaluated after treatment of the pancreatic cancer cell line Panc-1 with three substances interrupting YAP–TEAD interaction, including Super-TDU, Verteporfin and CA3. Results Mass spectrometry based proteomics showed that YAP1 is the top upregulated protein in pancreatic cancer tissue when compared to normal controls (log2 fold change 6.4; p = 5E−06). Prognostic analysis of YAP1 demonstrated a significant correlation between mRNA expression level data and reduced overall survival (p = 0.001). In addition, TMA and immunohistochemistry analysis suggested that YAP1 protein expression is an independent predictor of poor overall survival [hazard ratio (HR) 1.870, 95% confidence interval (CI) 1.224–2.855, p = 0.004], as well as reduced disease-free survival (HR 1.950, 95% CI 1.299–2.927, p = 0.001). Bioinformatic analyses coupled with in vitro assays indicated that YAP1 is involved in the transcriptional control of target genes, associated with extracellular matrix remodeling, which could be modified by selected substances disrupting the YAP1-TEAD interaction. Conclusions Our findings indicate that YAP1 is an important prognostic biomarker for pancreatic cancer and may play a regulatory role in the remodeling of the extracellular matrix.

Background: Reliable biomarkers of androgen activity in humans are lacking. The aim of this study was, therefore, to identify new protein markers of biological androgen activity and test their predictive value in relation to low vs normal testosterone values and some androgen deficiency linked pathologies. Methods: Blood samples from 30 healthy GnRH antagonist treated males were collected at three time points: (1) before GnRH antagonist administration; (2) 3 weeks later, just before testosterone undecanoate injection, and (3) after additional 2 weeks. Subsequently, they were analyzed by mass spectrometry to identify potential protein biomarkers of testosterone activity. Levels of proteins most significantly associated with testosterone fluctuations were further tested in a cohort of 75 hypo- and eugonadal males suffering from infertility. Associations between levels of those markers and cardiometabolic parameters, bone mineral density as well as androgen receptor (AR) CAG repeat lengths, were explored. Results: Using receiver operating characteristic analysis, 4-hydroxyphenylpyruvate dioxygenase (4HPPD), insulin-like growth factor-binding protein 6 (IGFBP6), and fructose-bisphosphate aldolase (ALDOB), as well as a Multi Marker Algorithm, based on levels of 4HPPD and IGFBP6, were shown to be best predictors of low (12 nmol/l) testosterone. They were also more strongly associated with metabolic syndrome and diabetes than testosterone levels. Levels of ALDOB and 4HPPD also showed association with AR CAG repeat lengths. Conclusions: We identified potential new protein biomarkers of testosterone action. Further investigations to elucidate their clinical potential are warranted. Funding: The work was supported by ReproUnion2.0 (grant no. 20201846), which is funded by the Interreg V EU program.

Alzheimer’s disease (AD) is a neurodegenerative disorder and the most common cause of dementia worldwide. In AD, neurodegeneration spreads throughout different areas of the central nervous system (CNS) in a gradual and predictable pattern, causing progressive memory decline and cognitive impairment. Deposition of neurofibrillary tangles (NFTs) in specific CNS regions correlates with the severity of AD and constitutes the basis for disease classification into different Braak stages (I-VI). Early clinical symptoms are typically associated with stages III-IV (i.e., limbic stages) when the involvement of the hippocampus begins. Histopathological changes in AD have been linked to brain proteome alterations, including aberrant posttranslational modifications (PTMs) such as the hyperphosphorylation of Tau. Most proteomic studies to date have focused on AD progression across different stages of the disease, by targeting one specific brain area at a time. However, in AD vulnerable regions, stage-specific proteomic alterations, including changes in PTM status occur in parallel and remain poorly characterized. Here, we conducted proteomic, phosphoproteomic, and acetylomic analyses of human postmortem tissue samples from AD (Braak stage III-IV, n=11) and control brains (n=12), covering all anatomical areas affected during the limbic stage of the disease (total hippocampus, CA1, entorhinal and perirhinal cortices). Overall, ~6000 proteins, ~9000 unique phosphopeptides and 221 acetylated peptides were accurately quantified across all tissues. Our results reveal significant proteome changes in AD brains compared to controls. Among others, we have observed the dysregulation of pathways related to the adaptive and innate immune responses, including several altered antimicrobial peptides (AMPs). Notably, some of these changes were restricted to specific anatomical areas, while others altered according to disease progression across the regions studied. Our data highlights the molecular heterogeneity of AD and the relevance of neuroinflammation as a major player in AD pathology. Data are available via ProteomeXchange with identifier PXD027173.

Abstract The MM500 study is an initiative to map the protein levels in malignant melanoma tumor samples, focused on in‐depth histopathology coupled to proteome characterization. The protein levels and localization were determined for a broad spectrum of diverse, surgically isolated melanoma tumors originating from multiple body locations. More than 15,500 proteoforms were identified by mass spectrometry, from which chromosomal and subcellular localization was annotated within both primary and metastatic melanoma. The data generated by global proteomic experiments covered 72% of the proteins identified in the recently reported high stringency blueprint of the human proteome. This study contributes to the NIH Cancer Moonshot initiative combining detailed histopathological presentation with the molecular characterization for 505 melanoma tumor samples, localized in 26 organs from 232 patients.

Abstract The MM500 meta‐study aims to establish a knowledge basis of the tumor proteome to serve as a complement to genome and transcriptome studies. Somatic mutations and their effect on the transcriptome have been extensively characterized in melanoma. However, the effects of these genetic changes on the proteomic landscape and the impact on cellular processes in melanoma remain poorly understood. In this study, the quantitative mass‐spectrometry‐based proteomic analysis is interfaced with pathological tumor characterization, and associated with clinical data. The melanoma proteome landscape, obtained by the analysis of 505 well‐annotated melanoma tumor samples, is defined based on almost 16 000 proteins, including mutated proteoforms of driver genes. More than 50 million MS/MS spectra were analyzed, resulting in approximately 13,6 million peptide spectrum matches (PSMs). Altogether 13 176 protein‐coding genes, represented by 366 172 peptides, in addition to 52 000 phosphorylation sites, and 4 400 acetylation sites were successfully annotated. This data covers 65% and 74% of the predicted and identified human proteome, respectively. A high degree of correlation (Pearson, up to 0.54) with the melanoma transcriptome of the TCGA repository, with an overlap of 12 751 gene products, was found. Mapping of the expressed proteins with quantitation, spatiotemporal localization, mutations, splice isoforms, and PTM variants was proven not to be predicted by genome sequencing alone. The melanoma tumor molecular map was complemented by analysis of blood protein expression, including data on proteins regulated after immunotherapy. By adding these key proteomic pillars, the MM500 study expands the knowledge on melanoma disease.

Background: Pancreatic cancer is a heterogenous disease with a poor prognosis. This study aimed to discover and validate prognostic tissue biomarkers in pancreatic cancer using a mass spectrometry (MS) based proteomics approach. Methods: Global protein sequencing of fresh frozen pancreatic cancer and healthy pancreas tissue samples was conducted by MS to discover potential protein biomarkers. Selected candidate proteins were further verified by targeted proteomics using parallel reaction monitoring (PRM). The expression of biomarker candidates was validated by immunohistochemistry in a large tissue microarray (TMA) cohort of 141 patients with resectable pancreatic cancer. Kaplan-Meier and Cox proportional hazard modelling was used to investigate the prognostic utility of candidate protein markers. Findings: In the initial MS-discovery phase, 165 proteins were identified as potential biomarkers. In the subsequent MS-verification phase, a panel of 45 candidate proteins was verified by the development of a PRM assay. Brain acid soluble protein 1 (BASP1) was identified as a new biomarker candidate for pancreatic cancer possessing largely unknown biological and clinical functions and was selected for further analysis. Importantly, bioinformatic analysis indicated that BASP1 interacts with Wilms tumour protein (WT1) in pancreatic cancer. TMA-based immunohistochemistry analysis showed that BASP1 was an independent predictor of prolonged survival (HR 0.468, 95% CI 0.257–0.852, p = .013) and predicted favourable response to adjuvant chemotherapy, whereas WT1 indicated a worsened survival (HR 1.636, 95% CI 1.083–2.473, p = .019) and resistance to chemotherapy. Interaction analysis showed that patients with negative BASP1 and high WT1 expression had the poorest outcome (HR 3.536, 95% CI 1.336–9.362, p = .011). Interpretation: We here describe an MS-based proteomics platform for developing biomarkers for pancreatic cancer. Bioinformatic analysis and clinical data from our study suggest that BASP1 and its putative interaction partner WT1 can be used as biomarkers for predicting outcomes in pancreatic cancer patients. Keywords: Pancreatic cancer, Mass spectrometry, Biomarkers, BASP1, WT1, Prognosis, Chemotherapy response

BackgroundSex-differences in the pathobiology of myocardial infarction are well established but incompletely understood. Improved knowledge on this topic may help clinicians to improve management of men and women with myocardial infarction.MethodsIn this registry-based cohort study (SWEDEHEART), we analyzed 175 circulating biomarkers reflecting various pathobiological axes in 856 men and 243 women admitted to Swedish coronary care units because of myocardial infarction. Two multimarker panels were applied (Proximity Extension Assay [Olink Bioscience], Multiple Reaction Monitoring mass spectrometry). Lasso analysis (penalized logistic regression), multiple testing-corrected Mann-Whitney tests and Cox regressions were used to assess sex-differences in the concentrations of these biomarkers and their implications on all-cause mortality and major adverse events (median follow-up up to 6.6 years).ResultsBiomarkers provided a very high discrimination between both sexes, when considered simultaneously (c-statistics 0.972). Compared to women, men had higher concentrations of six biomarkers with the most pronounced differences seen for those reflecting atherogenesis, myocardial necrosis and metabolism. Women had higher concentrations of 14 biomarkers with the most pronounced differences seen for those reflecting activation of the renin-angiotensin-aldosterone axis, inflammation and for adipokines. There were no major variations between sexes in the associations of these biomarkers with outcome.ConclusionsSeverable sex-differences exist in the expression of biomarkers in patients with myocardial infarction. While these differences had no impact on outcome, our data suggest the presence of various sex-related pathways involved in the development of coronary atherosclerosis, the progression to plaque rupture and acute myocardial damage, with a greater heterogeneity in women.

Typing of amyloidosis by mass spectrometry (MS) based proteomic analysis contribute to the diagnosis of amyloidosis. For MS analysis, laser microdissection (LMD) is used for amyloid specific sampling. This study aimed to establish a method for selectively extracting amyloids from formalin-fixed, paraffin-embedded (FFPE) specimens by organic solvent instead of LMD. The extracts using dimethyl sulfoxide (DMSO), dimethylformamide (DMF), methanol, trifluoroethanol (TFE) or hexafluoro-2-propanol from FFPE brain of alzheimer’s disease mouse model generated protein bands on SDS-PAGE, and Aβ was identified in the extract of DMF using mass spectrometry. The extract using DMSO from the kidney of a AA amyloidosis patient produced a protein band in SDS-PAGE. This protein band was identified to be serum amyloid A (SAA) by Western blotting and mass spectrometry. Circular dichroism spectrometry revealed that the secondary structures of Aβ and transthyretin were converted to α-helices from β-sheets in TFE. Our results suggest that organic solvents can extract amyloids from FFPE specimens by converting their secondary structure. This method could eliminate the LMD step and simplified amyloid typing by MS analysis. • DMSO, DMF, methanol, TFE and HFIP can extract Aβ specifically from the FFPE brain of a Alzheimer’ disease mouse model. • DMSO can extract SAA specifically from a FFPE section of AA amyloidosis. • Secondary structures of Aβ and transthyretin converted from β-sheet to α-helix in TFE.

Polycystic ovaries (PCO) contain antral follicles that arrest growing around 3–11 mm in diameter, perturbing the dominant follicle’s selection and the subsequent ovulatory process. Proteomic alterations of PCO follicular fluid (FF) (i.e., microenvironment in which the oocyte develops until ovulation) have been studied from large follicles in connection with oocyte pickup during ovarian stimulation. The present study aimed to detect proteomic alterations in FF from unstimulated human small antral follicles (hSAF) obtained from PCO. After performing deep-sequencing label-free proteomics on 10 PCO and 10 non-PCO FF samples from unstimulated hSAF (4.6–9.8 mm), 1436 proteins were identified, of which 115 were dysregulated in PCO FF samples. Pathways and processes related to the immune system, inflammation, and oxidative stress appeared to be upregulated in PCO, while extracellular matrix receptors interactions, the collagens-containing extracellular matrix, and the regulation of signaling were downregulated. The secreted proteins SFRP1, THBS4, and C1QC significantly decreased their expression in PCO FF, and this downregulation was suggested to affect future oocyte competence. In conclusion, our study revealed, for the first time, evidence of proteomic alterations occurring in the FF of PCO hSAF that may be related to the dysfunction of follicular growth and subsequent oocyte competence.

Abstract We present the Cancer Moonshot clinical project located at the European center in Lund. Here, tissue and blood samples have been collected and stored in a large-scale biobank. Multiple clinical centers around the world are participating and tissue and blood samples are sent to the European Cancer Moonshot Lund Center that acts as the clinical hub. Our center has been developed to generate and build large-scale biostorage archives of patient melanoma samples, which is then combined with a histopathological capability to characterize the patient tumours. Such a large-scale clinical sample processing initiative has begun with the aim of creating high-end histopathology indexing with database computational power and including proteogenomic analysis. The biobank at Lund has become an important resource in clinical research worldwide. Following suite, several national health programs are being initiated with the aim of also building large-scale biobank storages with a wealth of high-quality patient samples. In our Cancer Moonshot R&D activities, samples in the biobanks and the data derived from these samples are being used to build an understanding of disease presentation and using this information to move towards ‘Big Data’ proteogenomic and mass spectrometry imaging studies. Additionally, we report here a sample processing workflow that has been adapted to a fully-automated biobank processing strategy for large-scale studies.

Abstract Background Currently, only a limited number of molecular biomarkers for malignant melanoma exist. This is the case for both diagnosing the disease, staging, and efficiently measuring the response to therapy by tracing the progression of disease development and drug impact. There is a great need to identify novel landmarks of disease progression and alterations. Methods Matrix-assisted laser desorption ionisation mass spectrometry imaging (MALDI-MSI) has been developed within our group to study drug localisation within micro-environmental tissue compartments. Here, we expand further on this technology development and introduce for the first time melanoma tumour tissues to map metabolite localisation utilising high resolution mass spectrometry. MALDI-MSI can measure and localise the distribution pattern of a number of small molecule metabolites within tissue compartments of tumours isolated from melanoma patients. Data on direct measurements of metabolite identities attained at the local sites in tissue compartments has not been readily available as a measure of a clinical index for most cancer diseases. The current development on the mapping of endogenous molecular expression melanoma tumours by mass spectrometry imaging focuses on the establishment of a cancer tissue preparation process whereby a matrix crystal formation is homogenously built on the tissue surface, providing uniform molecular mapping. We apply this micro-preparation technology to disease presentation by mapping the molecular signatures from patient tumour sections. Results We have automated the process with a micro-technological dispensing platform. This provides the basis for thin film generation of the cancer patient tissues prior to imaging screening. Compartmentalisation of the tumour regions are displayed within the image analysis interfaced with histopathological grading and characterisation. Conclusions This enables site localisation within the tumour with image mapping to disease target areas such as melanoma cells, macrophages, and lymphocytes.

Long term effect of testosterone (T) deficiency impairs metabolism and is associated with muscle degradation and metabolic disease. The association seems to have a bidirectional nature and is not well understood. The present study aims to investigate the early and unidirectional metabolic effect of induced T changes by measuring fasting amino acid (AA) levels in a human model, in which short-term T alterations were induced. We designed a human model of 30 healthy young males with pharmacologically induced T changes, which resulted in three time points for blood collection: (A) baseline, (B) low T (3 weeks post administration of gonadotropin releasing hormone antagonist) and (C) restored T (2 weeks after injection of T undecanoate). The influence of T on AAs was analyzed by spectrophotometry on plasma samples. Levels of 9 out of 23 AAs, of which 7 were essential AAs, were significantly increased at low T and are restored upon T supplementation. Levels of tyrosine and phenylalanine were most strongly associated to T changes. Short-term effect of T changes suggests an increased protein breakdown that is restored upon T supplementation. Fasting AA levels are able to monitor the early metabolic changes induced by the T fluctuations.

Abstract Background Epigenetic alterations have been recognized as important contributors to the pathogenesis of PDAC. However, the role of histone variants in pancreatic tumor progression is still not completely understood. The aim of this study was to explore the expression and prognostic significance of histone protein variants in PDAC patients. Methods Liquid chromatography-tandem mass spectrometry (LC-MS/MS) was employed for qualitative analysis of histone variants and histone related post-translational modifications (PTMs) in PDAC and normal pancreatic tissues. Survival analysis was conducted using the Kaplan-Meier method and Cox proportional hazards regression. Results Histone variant H1.3 was found to be differentially expressed (p = 0.005) and was selected as a PDAC specific histone variant candidate. The prognostic role of H1.3 was evaluated in an external cohort of patients with resected PDAC using immunohistochemistry. Intratumor expression of H1.3 was found to be an important risk factor for overall survival in PDAC, with an adjusted HR value of 2.6 (95% CI 1.1–6.1), p = 0.029. Conclusion We suggest that the intratumor histone H1.3 expression as reported herein, may serve as a new epigenetic biomarker for PDAC.

Abstract Background Data from biological samples and medical evaluations plays an essential part in clinical decision making. This data is equally important in clinical studies and it is critical to have an infrastructure that ensures that its quality is preserved throughout its entire lifetime. We are running a 5-year longitudinal clinical study, KOL-Örestad, with the objective to identify new COPD (Chronic Obstructive Pulmonary Disease) biomarkers in blood. In the study, clinical data and blood samples are collected from both private and public health-care institutions and stored at our research center in databases and biobanks, respectively. The blood is analyzed by Mass Spectrometry and the results from this analysis then linked to the clinical data. Method We built an infrastructure that allows us to efficiently collect and analyze the data. We chose to use REDCap as the EDC (Electronic Data Capture) tool for the study due to its short setup-time, ease of use, and flexibility. REDCap allows users to easily design data collection modules based on existing templates. In addition, it provides two functions that allow users to import batches of data; through a web API (Application Programming Interface) as well as by uploading CSV-files (Comma Separated Values). Results We created a software, DART (Data Rapid Translation), that translates our biomarker data into a format that fits REDCap's CSV-templates. In addition, DART is configurable to work with many other data formats as well. We use DART to import our clinical chemistry data to the REDCap database. Conclusion We have shown that a powerful and internationally adopted EDC tool such as REDCap can be extended so that it can be used efficiently in proteomic studies. In our study, we accomplish this by using DART to translate our clinical chemistry data to a format that fits the templates of REDCap.

Background: Alzheimer's disease (AD) is the most common neurodegenerative disorder. Depositions of amyloid β peptide (Aβ) and tau protein are among the major pathological hallmarks of AD. Aβ and tau burden follows predictable spatial patterns during the progression of AD. Nevertheless, it remains obscure why certain brain regions are more vulnerable than others; to investigate this and dysregulated pathways during AD progression, a mass spectrometry-based proteomics study was performed. Methods: In total 103 tissue samples from regions early (entorhinal and parahippocampal cortices - medial temporal lobe (MTL)) and late affected (temporal and frontal cortices - neocortex) by tau pathology were subjected to label-free quantitative proteomics analysis. Results: Considering dysregulated proteins during AD progression, the majority (625 out of 737 proteins) was region specific, while some proteins were shared between regions (101 proteins altered in two areas and 11 proteins altered in three areas). Analogously, many dysregulated pathways during disease progression were exclusive to certain regions, but a few pathways altered in two or more areas. Changes in protein expression indicate that synapse loss occurred in all analyzed regions, while translation dysregulation was preponderant in entorhinal, parahippocampal and frontal cortices. Oxidative phosphorylation impairment was prominent in MTL. Differential proteomic analysis of brain areas in health state (controls) showed higher metabolism and increased expression of AD-related proteins in the MTL compared to the neocortex. In addition, several proteins that differentiate brain regions in control tissue were dysregulated in AD. Conclusions: This work provides the comparison of proteomic changes in brain regions affected by tau pathology at different stages of AD. Although we identified commonly regulated proteins and pathways during disease advancement, we found that the dysregulated processes are predominantly region specific. In addition, a distinct proteomic signature was found between MTL and neocortex in healthy subjects that might be related to AD vulnerability. These findings highlight the need for investigating AD's cascade of events throughout the whole brain and studies spanning more brain areas are required to better understand AD etiology and region vulnerability to disease.

Although natural products are an important source of drugs and drug leads, identification and validation of their target proteins have proven difficult. Here, we report the development of a systematic strategy for target identification and validation employing drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) without modifying or labeling natural compounds. Through a validation step using curcumin, which targets aminopeptidase N (APN), we successfully standardized the systematic strategy. Using label-free voacangine, an antiangiogenic alkaloid molecule as the model natural compound, DARTS analysis revealed vascular endothelial growth factor receptor 2 (VEGFR2) as a target protein. Voacangine inhibits VEGFR2 kinase activity and its downstream signaling by binding to the kinase domain of VEGFR2, as was revealed by docking simulation. Through cell culture assays, voacangine was found to inhibit the growth of glioblastoma cells expressing high levels of VEGFR2. Specific localization of voacangine to tumor compartments in a glioblastoma xenograft mouse was revealed by MSI analysis. The overlap of histological images with the MSI signals for voacangine was intense in the tumor regions and showed colocalization of voacangine and VEGFR2 in the tumor tissues by immunofluorescence analysis of VEGFR2. The strategy employing DARTS and MSI to identify and validate the targets of a natural compound as demonstrated for voacangine in this study is expected to streamline the general approach of drug discovery and validation using other biomolecules including natural products.

A targeted mass spectrometric assay was developed for identification and quantification of apoE isoforms (apoE2, E3 and E4), and it was utilized for screening of samples from AD patients (n = 39) and patients with other neurodegenerative disorders (n = 38). The assay showed good linearity with LOQ corresponds to total apoE concentration of 0.8 and 40 ng/mL in CSF and plasma/serum, respectively. We identified apoE phenotypes with 100% accuracy in clinical samples. We found strong association between genotypes of the individuals and their apoE levels in blood; ϵ4 allele carriers had significantly lower apoE levels in blood than non-carriers.

The aim of the present study was to create an optimal workflow for analysing a large cohort of malignant melanoma tissue samples. Samples were lysed with urea and enzymatically digested with trypsin or trypsin/Lys C. Buffer exchange or dilution was used to reduce urea concentration prior to digestion. The tissue digests were analysed directly or following strong cation exchange (SCX) fractionation by nano LC–MS/MS. The approach which resulted in the largest number of protein IDs involved a buffer exchange step before enzymatic digestion with trypsin and chromatographic separation in 120 min gradient followed by SCX–RP separation of peptides.

Selected reaction monitoring mass spectrometry (SRM-MS) -based semi-quantitation was performed to assess the validity of 46 selected candidate proteins for specifically diagnosing large-cell neuroendocrine lung carcinoma (LCNEC) and differentiating it from other lung cancer subtypes. The scaling method was applied in this study using specific SRM peak areas (AUCs) derived from the endogenous reference protein that normalizes all SRM AUCs obtained for the candidate proteins. In a screening verification study, we found that seven out of the 46 candidate proteins were statistically significant for the LCNEC phenotype, including 4F2hc cell surface antigen heavy chain (4F2hc/CD98) (p-ANOVA ≤ 0.0012), retinal dehydrogenase 1 (p-ANOVA ≤ 0.0029), apolipoprotein A-I (p-ANOVA ≤ 0.0004), β-enolase (p-ANOVA ≤ 0.0043), creatine kinase B-type (p-ANOVA ≤ 0.0070), and galectin-3-binding protein (p-ANOVA = 0.0080), and phosphatidylethanolamine-binding protein 1 (p-ANOVA ≤ 0.0012). In addition, we also identified candidate proteins specific to the small-cell lung carcinoma (SCLC) subtype. These candidates include brain acid soluble protein 1 (p-ANOVA < 0.0001) and γ-enolase (p-ANOVA ≤ 0.0013). This new relative quantitation-based approach utilizing the scaling method can be applied to assess hundreds of protein candidates obtained from discovery proteomic studies as a first step of the verification phase in biomarker development processes.

Metastatic melanoma is still one of the most prevalent skin cancers, which upon progression has neither a prognostic marker nor a specific and lasting treatment. Proteomic analysis is a versatile approach with high throughput data and results that can be used for characterizing tissue samples. However, such analysis is hampered by the complexity of the disease, heterogeneity of patients, tumors, and samples themselves. With the long term aim of quest for better diagnostics biomarkers, as well as predictive and prognostic markers, we focused on relating high resolution proteomics data to careful histopathological evaluation of the tumor samples and patient survival information.Regional lymph node metastases obtained from ten patients with metastatic melanoma (stage III) were analyzed by histopathology and proteomics using mass spectrometry. Out of the ten patients, six had clinical follow-up data. The protein deep mining mass spectrometry data was related to the histopathology tumor tissue sections adjacent to the area used for deep-mining. Clinical follow-up data provided information on disease progression which could be linked to protein expression aiming to identify tissue-based specific protein markers for metastatic melanoma and prognostic factors for prediction of progression of stage III disease.In this feasibility study, several proteins were identified that positively correlated to tumor tissue content including IF6, ARF4, MUC18, UBC12, CSPG4, PCNA, PMEL and MAGD2. The study also identified MYC, HNF4A and TGFB1 as top upstream regulators correlating to tumor tissue content. Other proteins were inversely correlated to tumor tissue content, the most significant being; TENX, EHD2, ZA2G, AOC3, FETUA and THRB. A number of proteins were significantly related to clinical outcome, among these, HEXB, PKM and GPNMB stood out, as hallmarks of processes involved in progression from stage III to stage IV disease and poor survival.In this feasibility study, promising results show the feasibility of relating proteomics to histopathology and clinical outcome, and insight thus can be gained into the molecular processes driving the disease. The combined analysis of histological features including the sample cellular composition with protein expression of each metastasis enabled the identification of novel, differentially expressed proteins. Further studies are necessary to determine whether these putative biomarkers can be utilized in diagnostics and prognostic prediction of metastatic melanoma.

The development of an MRM assay for the measurements of six inflammatory markers is presented. We report a robust and sensitive quantitative assay with a relative standard deviation of

A high frequency of IgA1-positive tumour cells was found in tissue micro-arrays of oesophagus, colon, testis, lung, breast, bladder and ovarian cancer. IgA1 was observed in the cytoplasm and the plasma membrane. A correlation was found between intra-tumour IgA1 and poor overall survival in a large cohort of bladder cancer patients (n = 99, p = 0.011, log-rank test). The number of IgA1-positive tumour cells was also found to be higher in female than male bladder cancer patients. The presence of IgA1 was confirmed in formalin-fixed paraffin-embedded ovarian carcinoma samples using LC-MS/MS analysis. Uptake of IgA1 was also observed in breast cancer and melanoma cell lines when cultivated in the presence of serum from healthy individuals, indicating a possible origin of the IgA1 antibodies in cancer cells.

Abstract BackgroundIn the new pathologic classification of lung adenocarcinoma proposed by IASLC/ATS/ERS in 2011, lepidic type adenocarcinomas are constituted by three subtypes; adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and lepidic predominant invasive adenocarcinoma (LPIA). Although these subtypes are speculated to show sequential progression from preinvasive lesion to invasive lung cancer, changes of protein expressions during these processes have not been fully studied yet. This study aims to glimpse a proteomic view of the early lepidic type lung adenocarcinomas. MethodsA total of nine formalin‐fixed and paraffin‐embedded (FFPE) lepidic type lung adenocarcinoma tissues were selected from our archives, three tissues each in AIS, MIA and LPIA. The tumor and peripheral non‐tumor cells in these FFPE tissues were collected with laser microdissection (LMD). Using liquid chromatography‐tandem mass spectrometry (MS/MS), protein compositions were compared with respect to the peptide separation profiles among tumors collected from three types of tissues, AIS, MIA and LPIA. Proteins identified were semi‐quantified by spectral counting‐based or identification‐based approach, and statistical evaluation was performed by pairwise G‐tests. ResultsA total of 840 proteins were identified. Spectral counting‐based semi‐quantitative comparisons of all identified proteins through AIS to LPIA have revealed that the protein expression profile of LPIA was significantly differentiated from other subtypes. 70 proteins including HPX, CTTN, CDH1, EGFR, MUC1 were found as LPIA‐type marker candidates, 15 protein candidates for MIA‐type marker included CRABP2, LMO7, and RNPEP, and 26 protein candidates for AIS‐type marker included LTA4H and SOD2. The STRING gene set enrichment resulted from the protein‐protein interaction (PPI) network analysis suggested that AIS was rather associated with pathways of focal adhesion, adherens junction, tight junction, that MIA had a strong association predominantly with pathways of proteoglycans in cancer and with PI3K‐Akt. In contrast, LPIA was associated broadly with numerous tumor‐progression pathways including ErbB, Ras, Rap1 and HIF‐1 signalings. ConclusionsThe proteomic profiles obtained in this study demonstrated the technical feasibility to elucidate protein candidates differentially expressed in FFPE tissues of LPIA. Our results may provide candidates of disease‐oriented proteins which may be related to mechanisms of the early‐stage progression of lung adenocarcinoma.

Abstract BackgroundIn the postgenomic era, it has become evident that analysis of genetic and protein expression changes alone is not sufficient to understand most disease processes in e.g. cardiovascular and cancer disease. Biobanking has been identified as an important area for development and discovery of better diagnostic tools and new treatment modalities. Biobanks are developed in order to integrate the collection of clinical samples from both healthy individuals and patients and provide valuable information that will make possible improved patient care. Modern healthcare developments are intimately linked to information based on studies of patient samples from biobank archives in large scale studies. Today biobanks form important national, as well as international, networks that share and combine global resources. MethodsWe have developed and validated a novel biobanking workflow process that utilizes 384‐tube systems with a high speed sample array robot with unique processing principles. ResultsThe 384‐tube format and robotic processing is incorporated into a cancer and cardiovascular diagnostic/prognostic research program with therapeutic interventions. Our biobank practice has gained acceptance within many hospitals and research units and is based on high‐density sample storage with small aliquot sample volumes. The previous standard of 5–10 mL sample volume tubes is being replaced by smaller volumes of 50–70 μL blood fractions that typically result in hundreds of thousands of aliquot fractions in 384‐tube systems. ConclusionsOur novel biobanking workflow process is robust and well suited for clinical studies.

Malignant melanoma has the highest increase of incidence of malignancies in the western world. In early stages, front line therapy is surgical excision of the primary tumor. Metastatic disease has very limited possibilities for cure. Recently, several protein kinase inhibitors and immune modifiers have shown promising clinical results but drug resistance in metastasized melanoma remains a major problem. The need for routine clinical biomarkers to follow disease progression and treatment efficacy is high. The aim of the present study was to build a protein sequence database in metastatic melanoma, searching for novel, relevant biomarkers. Ten lymph node metastases (South-Swedish Malignant Melanoma Biobank) were subjected to global protein expression analysis using two proteomics approaches (with/without orthogonal fractionation). Fractionation produced higher numbers of protein identifications (4284). Combining both methods, 5326 unique proteins were identified (2641 proteins overlapping). Deep mining proteomics may contribute to the discovery of novel biomarkers for metastatic melanoma, for example dividing the samples into two metastatic melanoma "genomic subtypes", ("pigmentation" and "high immune") revealed several proteins showing differential levels of expression. In conclusion, the present study provides an initial version of a metastatic melanoma protein sequence database producing a total of more than 5000 unique protein identifications. The raw data have been deposited to the ProteomeXchange with identifiers PXD001724 and PXD001725.

Abstract The Tokyo Medical University Hospital in Japan and the Lund University hospital in Sweden have recently initiated a research program with the objective to impact on patient treatment by clinical disease stage characterization (phenotyping), utilizing proteomics sequencing platforms. By sharing clinical experiences, patient treatment principles, and biobank strategies, our respective clinical teams in Japan and Sweden will aid in the development of predictive and drug related protein biomarkers. Data from joint lung cancer studies are presented where protein expression from Neuro‐ Endocrine lung cancer (LCNEC) phenotype patients can be separated from Small cell‐ (SCLC) and Large Cell lung cancer (LCC) patients by deep sequencing and spectral counting analysis. LCNEC, a subtype of large cell carcinoma (LCC), is characterized by neuroendocrine differentiation that small cell lung carcinoma (SCLC) shares. Pre‐therapeutic histological distinction between LCNEC and SCLC has so far been problematic, leading to adverse clinical outcome. An establishment of protein targets characteristic of LCNEC is quite helpful for decision of optimal therapeutic strategy by diagnosing individual patients. Proteoform annotation and clinical biobanking is part of the HUPO initiative (http://www.hupo.org) within chromosome 10 and chromosome 19 consortia.

Globally, malignant melanoma shows a steady increase in the incidence among cancer diseases. Malignant melanoma represents a cancer type where currently no biomarker or diagnostics is available to identify disease stage, progression of disease or personalized medicine treatment. The aim of this study was to assess the tissue expression of alpha-synuclein, a protein implicated in several disease processes, in metastatic tissues from malignant melanoma patients. A targeted Selected Reaction Monitoring (SRM) assay was developed and utilized together with stable isotope labeling for the relative quantification of two target peptides of alpha-synuclein. Analysis of alpha-synuclein protein was then performed in ten metastatic tissue samples from the Lund Melanoma Biobank. The calibration curve using peak area ratio (heavy/light) versus concentration ratios showed linear regression over three orders of magnitude, for both of the selected target peptide sequences. In support of the measurements of specific protein expression levels, we also observed significant correlation between the protein and mRNA levels of alpha-synuclein in these tissues. Investigating levels of tissue alpha-synuclein may add novel aspect to biomarker development in melanoma, help to understand disease mechanisms and ultimately contribute to discriminate melanoma patients with different prognosis.

Abstract The impact of mapping the human proteome: Globally, the health care organizations are under resource and cost constrains due to the increasing number of patients that are due to a fast increase of the 65+ age group requiring extensive medical hospitalization and treatments. Hospitals worldwide strive to seek the best cure for patients, suffering from various diseases. A consequence of these global changes, of healthy populations in relation to patients forms the basis for the build of large and centralized biobank facilities, with strategies where the search for an understanding of diseases at a molecular level is at heart. The efforts made lies within large governmental resource allocations where patient centers are collecting samples from clinical study participants in order to try to discover universal expression patterns and molecular signatures of disease and disease stages. Most developments in this area are aimed towards the discovery, and understanding diagnosis implementations. By providing the right treatment alternatives for patients care, at the right time point i.e., at a given disease stage development becomes a major goal where pharmaceutical industry, academia and the health care sector joins forces in large clinical epidemiological, population‐, and disease based studies. This becomes a clear strategic link to the enhancement and prospects for personalized medicines and target directed diagnosis developments (Companion Diagnostics), which require coordinated efforts across a wide range of disciplines. Currently, companion diagnostics is at the core of the personalized medicine paradigm shift. It will identify patients who are most likely to benefit from a particular therapeutic product, as well as identify patients likely to be at increased risk for serious adverse reactions as a result of treatment with a particular therapeutic agent. It is predicted that more than half of all new drugs will require a companion diagnostic, which opens up for an endeavor for Proteomics research implementations.

Abstract Background The objectives and goals of the Southern Swedish Malignant Melanoma (SSMM)are to develop, build and utilize cutting edge biobanks and OMICS platformsto better understand disease pathology and drug mechanisms. The SSMMresearch team is a truly cross‐functional group with members from oncology, surgery, bioinformatics, proteomics, and genomics initiatives. Within theresearch team there are members who daily diagnose patients with suspectmelanomas, do follow‐ups on malignant melanoma patients and remove primaryor metastatic lesions by surgery. This inter‐disciplinary clinical patientcare ensures a competence build as well as a best practice procedure wherethe patient benefits. Methods Clinical materials from patients before, during and after treatments withclinical end points are being collected. Tissue samples as well as bio‐fluidsamples such as blood fractions, plasma, serum and whole blood will bearchived in 384‐high density sample tube formats. Standardized approachesfor patient selections, patient sampling, sample‐processing and analysisplatforms with dedicated protein assays and genomics platforms that willhold value for the research community are used. The patient biobank archivesare fully automated with novel ultralow temperature biobank storage unitsand used as clinical resources. Results An IT‐infrastructure using a laboratory information management system (LIMS)has been established, that is the key interface for the research teams inorder to share and explore data generated within the project. The cross‐sitedata repository in Lund forms the basis for sample processing, together withbiological samples in southern Sweden, including blood fractions and tumortissues. Clinical registries are associated with the biobank materials, including pathology reports on disease diagnosis on the malignant melanoma(MM) patients. Conclusions We provide data on the developments of protein profiling and targeted proteinassays on isolated melanoma tumors, as well as reference blood standardsthat is used by the team members in the respective laboratories. These pilotdata show biobank access and feasibility of performing quantitativeproteomics in MM biobank repositories collected in southern Sweden. Thescientific outcomes further strengthen the build of healthcare benefit inthe complex challenges of malignant melanoma pathophysiology that isaddressed by the novel personalized medicines entering the market.

Abstract Background For many common global diseases, such as cancer, diabetes, neurodegenerative and cardiovascular diseases there is an unmet need for diagnosing early indications of disease that could enable medical intervention and early treatment. The treatment of these diseases will require detailed knowledge of targeted pathways involved in disease pathogenesis but also the mode of drug actions at the biological location on these targets. Translational medicine is a new area of research where expert from different disciplines involved in basic science and clinical disciplines meet and join forces. Mode‐of‐drug‐action mechanisms elucidation is key in the characterization of drugs that can relate to both efficacy and safety. Methods Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI‐MSI) was used providing evidence into the fate (destinations and distributions) of administered drugs within tumor regions of lung compartments. Results We hereby present a pulmonary study in which we have isolated lung tissue after inhaled drug administration and then localized the drug within airway wall compartments. The histology also provides evidence of drug binding to smooth muscle cell microenvironments. We also identified lung tissue regions with tumor cell invasion in these COPD patients. Conclusions The ultimate goal is to identify bridging comprehension that forms a knowledge base that can be used by society to develop a better treatment and medicine for patients. Our results demonstrated that robust imaging data could be generated confirming drug localization in pulmonary regions of COPD patients with tumor pathology. Trial registration Tallinn Medical Research Ethical Committee decision #1724, 18.06.2009

Interstitial lung disease (ILD) events have been reported in Japanese non-small-cell lung cancer (NSCLC) patients receiving EGFR tyrosine kinase inhibitors. We investigated proteomic biomarkers for mechanistic insights and improved prediction of ILD. Blood plasma was collected from 43 gefitinib-treated NSCLC patients developing acute ILD (confirmed by blinded diagnostic review) and 123 randomly selected controls in a nested case-control study within a pharmacoepidemiological cohort study in Japan. We generated ∼7 million tandem mass spectrometry (MS/MS) measurements with extensive quality control and validation, producing one of the largest proteomic lung cancer datasets to date, incorporating rigorous study design, phenotype definition, and evaluation of sample processing. After alignment, scaling, and measurement batch adjustment, we identified 41 peptide peaks representing 29 proteins best predicting ILD. Multivariate peptide, protein, and pathway modeling achieved ILD prediction comparable to previously identified clinical variables; combining the two provided some improvement. The acute phase response pathway was strongly represented (17 of 29 proteins, p = 1.0×10(-25)), suggesting a key role with potential utility as a marker for increased risk of acute ILD events. Validation by Western blotting showed correlation for identified proteins, confirming that robust results can be generated from an MS/MS platform implementing strict quality control.

Readouts that define the physiological distributions of drugs in tissues are an unmet challenge and at best imprecise, but are needed in order to understand both the pharmacokinetic and pharmacodynamic properties associated with efficacy. Here we demonstrate that it is feasible to follow the in vivo transport of unlabeled drugs within specific organ and tissue compartments on a platform that applies MALDI imaging mass spectrometry to tissue sections characterized with high definition histology. We have tracked and quantified the distribution of an inhaled reference compound, tiotropium, within the lungs of dosed rats, using systematic point by point MS and MS/MS sampling at 200 microm intervals. By comparing drug ion distribution patterns in adjacent tissue sections, we observed that within 15 min following exposure, tiotropium parent MS ions (mass-to-charge; m/z 392.1) and fragmented daughter MS/MS ions (m/z 170.1 and 152.1) were dispersed in a concentration gradient (80 fmol-5 pmol) away from the central airways into the lung parenchyma and pleura. These drug levels agreed well with amounts detected in lung compartments by chemical extraction. Moreover, the simultaneous global definition of molecular ion signatures localized within 2-D tissue space provides accurate assignment of ion identities within histological landmarks, providing context to dynamic biological processes occurring at sites of drug presence. Our results highlight an important emerging technology allowing specific high resolution identification of unlabeled drugs at sites of in vivo uptake and retention.

Abstract Malignant melanoma is a difficult-to-treat skin cancer with increasing incidence worldwide. Although type-I interferon (IFN) is no longer part of guidelines, several melanoma patients are treated with type-I interferon (IFN) at some point of the disease, potentially affecting its genetic progression. We run genome-wide copy number variation (CNV) analysis on previously type-I IFN-treated (n = 17) and control (n = 11) visceral metastases of melanoma patients. Results were completed with data from the TCGA and MM500 databases. We identified metastasis- and brain metastasis-specific gene signatures mostly affected by CN gains. Some cases were genetically resistant to IFN showing characteristic gene alterations (e.g. ABCA4 or ZEB2 gain and alterations of DNA repair genes). Analysis of a previously identified type-I IFN resistance gene set indicates that only a proportion of these genes was exclusive for the IFN-treated metastases reflecting a possible selective genomic pressure of endogenous IFNs during progression. Our data suggest that previous type-I IFN treatment and/or endogenous IFN production by immune response affect genomic progression of melanoma which may have clinical relevance, potentially influence immune checkpoint regulation in the tumor microenvironment.