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Lack of the established noninvasive diagnostic biomarkers causes delay in diagnosis of lung cancer (LC). The aim of this study was to explore the association between inflammatory and cancer‐associated plasma proteins and LC and thereby discover potential biomarkers. Patients referred for suspected LC and later diagnosed with primary LC, other cancers, or no cancer (NC) were included in this study. Demographic information and plasma samples were collected, and diagnostic information was later retrieved from medical records. Relative quantification of 92 plasma proteins was carried out using the Olink Immuno‐Onc‐I panel. Association between expression levels of panel of proteins with different diagnoses was assessed using generalized linear model (GLM) with the binomial family and a logit‐link function, considering confounder effects of age, gender, smoking, and pulmonary diseases. The analysis showed that the combination of five plasma proteins (CD83, GZMA, GZMB, CD8A, and MMP12) has higher diagnostic performance for primary LC in both early and advanced stages compared with NC. This panel demonstrated lower diagnostic performance for other cancer types. Moreover, inclusion of four proteins (GAL9, PDCD1, CD4, and HO1) to the aforementioned panel significantly increased the diagnostic performance for primary LC in advanced stage as well as for other cancers. Consequently, the collective expression profiles of select plasma proteins, especially when analyzed in conjunction, might have the potential to distinguish individuals with LC from NC. This suggests their utility as predictive biomarkers for identification of LC patients. The synergistic application of these proteins as biomarkers could pave the way for the development of diagnostic tools for early‐stage LC detection. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) cancer stem cells (GSCs) contribute to GBM's origin, recurrence, and resistance to treatment. However, the understanding of how mRNA expression patterns of GBM subtypes are reflected at global proteome level in GSCs is limited. To characterize protein expression in GSCs, we performed in‐depth proteogenomic analysis of patient‐derived GSCs by RNA‐sequencing and mass‐spectrometry. We quantified > 10 000 proteins in two independent GSC panels and propose a GSC‐associated proteomic signature characterizing two distinct phenotypic conditions; one defined by proteins upregulated in proneural and classical GSCs (GPC‐like), and another by proteins upregulated in mesenchymal GSCs (GM‐like). The GM‐like protein set in GBM tissue was associated with necrosis, recurrence, and worse overall survival. Through proteogenomics, we discovered 252 non‐canonical peptides in the GSCs, i.e., protein sequences that are variant or derive from genome regions previously considered non‐protein‐coding, including variants of the heterogeneous ribonucleoproteins implicated in RNA splicing. In summary, GSCs express two protein sets that have an inverse association with clinical outcomes in GBM. The discovery of non‐canonical protein sequences questions existing gene models and pinpoints new protein targets for research in GBM. [ABSTRACT FROM AUTHOR]

The mechanism by which pancreatic beta cells are destroyed in type 1 diabetes (T1D) remains to be fully understood. Recent observations indicate that the disease may arise because of different pathobiological mechanisms (endotypes). The discovery of one or several protein biomarkers measurable in readily available liquid biopsies (e.g. blood plasma) during the pre-diabetic period may enable personalized disease interventions. Recent studies have shown that extracellular vesicles (EVs) are a source of tissue proteins in liquid biopsies. Using plasma samples collected from pre-diabetic non-obese diabetic (NOD) mice (an experimental model of T1D) we addressed if combined analysis of whole plasma samples and plasma-derived EV fractions increases the number of unique proteins identified by mass spectrometry (MS) compared to the analysis of whole plasma samples alone. LC-MS/MS analysis of plasma samples depleted of abundant proteins and subjected to peptide fractionation identified more than 2300 proteins, while the analysis of EVenriched plasma samples identified more than 600 proteins. Of the proteins detected in EV-enriched samples, more than a third were not identified in whole plasma samples and many were classified as either tissue-enriched or of tissue-specific origin. In conclusion, parallel profiling of EV-enriched plasma fractions and whole plasma samples increases the overall proteome depth and facilitates the discovery of tissue-enriched proteins in plasma. If applied to plasma samples collected longitudinally from the NOD mouse or from models with other pathobiological mechanisms, the integrated proteome profiling scheme described herein may be useful for the discovery of new and potentially endotype specific biomarkers in T1D. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) are increasingly tested as therapeutic vehicles and biomarkers, but still EV subtypes are not fully characterised. To isolate EVs with few co‐isolated entities, a combination of methods is needed. However, this is time‐consuming and requires large sample volumes, often not feasible in most clinical studies or in studies where small sample volumes are available. Therefore, we compared EVs rendered by five commonly used methods based on different principles from conditioned cell medium and 250 μl or 3 ml plasma, that is, precipitation (ExoQuick ULTRA), membrane affinity (exoEasy Maxi Kit), size‐exclusion chromatography (qEVoriginal), iodixanol gradient (OptiPrep), and phosphatidylserine affinity (MagCapture). EVs were characterised by electron microscopy, Nanoparticle Tracking Analysis, Bioanalyzer, flow cytometry, and LC‐MS/MS. The different methods yielded samples of different morphology, particle size, and proteomic profile. For the conditioned medium, Izon 35 isolated the highest number of EV proteins followed by exoEasy, which also isolated fewer non‐EV proteins. For the plasma samples, exoEasy isolated a high number of EV proteins and few non‐EV proteins, while Izon 70 isolated the most EV proteins. We conclude that no method is perfect for all studies, rather, different methods are suited depending on sample type and interest in EV subtype, in addition to sample volume and budget. [ABSTRACT FROM AUTHOR]

Abstract Background Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. Results We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. Conclusions The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

Abstract Background In-depth proteomics analyses of tumors are frequently biased by the presence of blood components and stromal contamination, which leads to large experimental variation and decreases the proteome coverage. We have established a reproducible method to prepare freshly collected lung tumors for proteomics analysis, aiming at tumor cell enrichment and reduction of plasma protein contamination. We obtained enriched tumor-cell suspensions (ETS) from six lung cancer cases (two adenocarcinomas, two squamous-cell carcinomas, two large-cell carcinomas) and from two normal lung samples. The cell content of resulting ETS was evaluated with immunocytological stainings and compared with the histologic pattern of the original specimens. By means of a quantitative mass spectrometry-based method we evaluated the reproducibility of the sample preparation protocol and we assessed the proteome coverage by comparing lysates from ETS samples with the direct lysate of corresponding fresh-frozen samples. Results Cytological analyses on cytospin specimens showed that the percentage of tumoral cells in the ETS samples ranged from 20% to 70%. In the normal lung samples the percentage of epithelial cells was less then 10%. The reproducibility of the sample preparation protocol was very good, with coefficient of variation at the peptide level and at the protein level of 13% and 7%, respectively. Proteomics analysis led to the identification of a significantly higher number of proteins in the ETS samples than in the FF samples (244 vs 109, respectively). Albumin and hemoglobin were among the top 5 most abundant proteins identified in the FF samples, showing a high contamination with blood and plasma proteins, whereas ubiquitin and the mitochondrial ATP synthase 5A1 where among the top 5 most abundant proteins in the ETS samples. Conclusion The method is feasible and reproducible. We could obtain a fair enrichment of cells but the major benefit of the method was an effective removal of contaminants from red blood cells and plasma proteins resulting in larger proteome coverage compared to the direct lysis of frozen samples. This sample preparation method may be successfully implemented for the discovery of lung cancer biomarkers on tissue samples using mass spectrometry-based proteomics.

Despite significant advancements in breast cancer (BC) research, clinicians lack robust serological protein markers for accurate diagnostics and tumor stratification. Tumor interstitial fluid (TIF) accumulates aberrantly externalized proteins within the local tumor space, which can potentially gain access to the circulatory system. As such, TIF may represent a valuable starting point for identifying relevant tumor‐specific serological biomarkers. The aim of the study was to perform comprehensive proteomic profiling of TIF to identify proteins associated with BC tumor status and subtype. A liquid chromatography tandem mass spectrometry (LC‐MS/MS) analysis of 35 TIFs of three main subtypes: luminal (19), Her2 (4), and triple‐negative (TNBC) (12) resulted in the identification of > 8800 proteins. Unsupervised hierarchical clustering segregated the TIF proteome into two major clusters, luminal and TNBC/Her2 subgroups. High‐grade tumors enriched with tumor infiltrating lymphocytes (TILs) were also stratified from low‐grade tumors. A consensus analysis approach, including differential abundance analysis, selection operator regression, and random forest returned a minimal set of 24 proteins associated with BC subtypes, receptor status, and TIL scoring. Among them, a panel of 10 proteins, AGR3, BCAM, CELSR1, MIEN1, NAT1, PIP4K2B, SEC23B, THTPA, TMEM51, and ULBP2, was found to stratify the tumor subtype‐specific TIFs. In particular, upregulation of BCAM and CELSR1 differentiates luminal subtypes, while upregulation of MIEN1 differentiates Her2 subtypes. Immunohistochemistry analysis showed a direct correlation between protein abundance in TIFs and intratumor expression levels for all 10 proteins. Sensitivity and specificity were estimated for this protein panel by using an independent, comprehensive breast tumor proteome dataset. The results of this analysis strongly support our data, with eight of the proteins potentially representing biomarkers for stratification of BC subtypes. Five of the most representative proteomics databases currently available were also used to estimate the potential for these selected proteins to serve as putative serological markers. [ABSTRACT FROM AUTHOR]

Dysregulation of the kynurenine pathway has been regarded as a mechanism of tumor immune escape by the enzymatic activity of indoleamine 2, 3 dioxygenase and kynurenine production. However, the immune-modulatory properties of other kynurenine metabolites such as kynurenic acid, 3-hydroxykynurenine, and anthranilic acid are poorly understood. In this study, plasma from patients diagnosed with metastatic cutaneous malignant melanoma (CMM) was obtained before (PRE) and during treatment (TRM) with inhibitors of mitogen-activated protein kinase pathway (MAPKIs). Immuno-oncology related protein profile and kynurenine metabolites were analyzed by proximity extension assay (PEA) and LC/MS-MS, respectively. Correlation network analyses of the data derived from PEA and LC/MS-MS identified a set of proteins that modulate the differentiation of Th1 cells, which is linked to 3-hydroxykynurenine levels. Moreover, MAPKIs treatments are associated with alteration of 3-hydroxykynurenine and 3hydroxyanthranilic acid (3HAA) concentrations and led to higher "CXCL11," and "KLRD1" expression that are involved in T and NK cells activation. These findings imply that the kynurenine pathway is pathologically relevant in patients with CMM. [ABSTRACT FROM AUTHOR]

Superparamagnetic iron oxide nanoparticles (SPIONs) have emerged as promising contrast agents for magnetic resonance imaging. The influence of different surface coatings on the biocompatibility of SPIONs has been addressed, but the potential impact of the so-called corona of adsorbed proteins on the surface of SPIONs on their biological behavior is less well studied. Here, we determined the composition of the plasma protein corona on silica-coated versus dextran-coated SPIONs using mass spectrometry-based proteomics approaches. Notably, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed distinct protein corona compositions for the two different SPIONs. Relaxivity of silica-coated SPIONs was modulated by the presence of a protein corona. Moreover, the viability of primary human monocyte-derived macrophages was influenced by the protein corona on silica-coated, but not dextran-coated SPIONs, and the protein corona promoted cellular uptake of silica-coated SPIONs, but did not affect internalization of dextran-coated SPIONs. [ABSTRACT FROM AUTHOR]

Biological heterogeneity is common in many diseases and it is often the reason for therapeutic failures. Thus, there is great interest in classifying a disease into subtypes that have clinical significance in terms of prognosis or therapy response. One of the most popular methods to uncover unrecognized subtypes is cluster analysis. However, classical clustering methods such as k-means clustering or hierarchical clustering are not guaranteed to produce clinically interesting subtypes. This could be because the main statistical variability—the basis of cluster generation—is dominated by genes not associated with the clinical phenotype of interest. Furthermore, a strong prognostic factor might be relevant for a certain subgroup but not for the whole population; thus an analysis of the whole sample may not reveal this prognostic factor. To address these problems we investigate methods to identify and assess clinically interesting subgroups in a heterogeneous population. The identification step uses a clustering algorithm and to assess significance we use a false discovery rate- (FDR-) based measure. Under the heterogeneity condition the standard FDR estimate is shown to overestimate the true FDR value, but this is remedied by an improved FDR estimation procedure. As illustrations, two real data examples from gene expression studies of lung cancer are provided. [ABSTRACT FROM AUTHOR]

Cystic papillary thyroid carcinoma (cPTC) is a subgroup of PTC presenting a diagnostic challenge at fine needle aspiration biopsy (FNAB). To further investigate this entity we aimed to characterize protein profiles of cyst fluids from cPTC and benign thyroid cystic lesions. In total, 20 cPTCs and 56 benign thyroid cystic lesions were studied. Profiling by liquid chromatography tandem mass spectrometry (LC-MS/MS) was performed on cyst fluids from a subset of cases after depletion, and selected proteins were further analyzed by Western blot (WB), immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA). A total of 1,581 proteins were detected in cyst fluids, of which 841 were quantified in all samples using LC-MS/MS. Proteins with different expression levels between cPTCs and benign lesions were identified by univariate analysis (41 proteins) and multivariate analysis (59 proteins in an orthogonal partial least squares model). WB analyses of cyst fluid and IHC on corresponding tissue samples confirmed a significant up-regulation of cytokeratin 19 (CK-19/CYFRA 21-1) and S100A13 in cPTC vs. benign lesions. These findings were further confirmed by ELISA in an extended material of non-depleted cyst fluids from cPTCs (n = 17) and benign lesions (n = 55) (p<0.05). Applying a cut-off at >55 ng/ml for CK-19 resulted in 82% specificity and sensitivity. For S100A13 a cut-off at >230 pg/ml revealed a 94% sensitivity, but only 35% specificity. This is the first comprehensive catalogue of the protein content in fluid from thyroid cysts. The up-regulations of CK-19 and S100A13 suggest their possible use in FNAB based preoperative diagnostics of cystic thyroid lesions. [ABSTRACT FROM AUTHOR]

Lung cancer is the leading cause of cancer-related death worldwide with a 5-year survival rate of less than 15%, despite significant advances in both diagnostic and therapeutic approaches. Combined genomic and transcriptomic sequencing studies have identified numerous genetic driver mutations that are responsible for the development of lung cancer. In addition, molecular profiling studies identify gene products and their mutations which predict tumour responses to targeted therapies such as protein tyrosine kinase inhibitors and also can offer explanation for drug resistance mechanisms. The profiling of circulating micro-RNAs has also provided an ability to discriminate patients in terms of prognosis/diagnosis and high-throughput DNA sequencing strategies are beginning to elucidate cell signalling pathway mutations associated with oncogenesis, including potential stem cell associated pathways, offering the promise that future therapies may target this sub-population, preventing disease relapse post treatment and improving patient survival. This review provides an assessment of molecular profiling within lung cancer concerning molecular mechanisms, treatment options and disease-progression. Current areas of development within lung cancer profiling are discussed (i.e. profiling of circulating tumour cells) and future challenges for lung cancer treatment addressed such as detection of micro-metastases and cancer stem cells. [ABSTRACT FROM AUTHOR]

Abstract: Non-small cell lung cancer (NSCLC) accounts for >80% of lung cancer cases and currently has an overall five-year survival rate of only 15%. Patients presenting with advanced stage NSCLC die within 18-months of diagnosis. Metastatic spread accounts for >70% of these deaths. Thus elucidation of the mechanistic basis of NSCLC-metastasis has potential to impact on patient quality of life and survival. Research on NSCLC metastasis has recently expanded to include non-cancer cell components of tumors-the stromal cellular compartment and extra-cellular matrix components comprising the tumor-microenvironment. Metastasis (from initial primary tumor growth through angiogenesis, intravasation, survival in the bloodstream, extravasation and metastatic growth) is an inefficient process and few released cancer cells complete the entire process. Micro-environmental interactions assist each of these steps and discovery of the mechanisms by which tumor cells co-operate with the micro-environment are uncovering key molecules providing either biomarkers or potential drug targets. The major sites of NSCLC metastasis are brain, bone, adrenal gland and the liver. The mechanistic basis of this tissue-tropism is beginning to be elucidated offering the potential to target stromal components of these tissues thus targeting therapy to the tissues affected. This review covers the principal steps involved in tumor metastasis. The role of cell–cell interactions, ECM remodeling and autocrine/paracrine signaling interactions between tumor cells and the surrounding stroma is discussed. The mechanistic basis of lung cancer metastasis to specific organs is also described. The signaling mechanisms outlined have potential to act as future drug targets minimizing lung cancer metastatic spread and morbidity. [Copyright &y& Elsevier]

Background: In-depth proteomics analyses of tumors are frequently biased by the presence of blood components and stromal contamination, which leads to large experimental variation and decreases the proteome coverage. We have established a reproducible method to prepare freshly collected lung tumors for proteomics analysis, aiming at tumor cell enrichment and reduction of plasma protein contamination. We obtained enriched tumor-cell suspensions (ETS) from six lung cancer cases (two adenocarcinomas, two squamous-cell carcinomas, two large-cell carcinomas) and from two normal lung samples. The cell content of resulting ETS was evaluated with immunocytological stainings and compared with the histologic pattern of the original specimens. By means of a quantitative mass spectrometry-based method we evaluated the reproducibility of the sample preparation protocol and we assessed the proteome coverage by comparing lysates from ETS samples with the direct lysate of corresponding fresh-frozen samples. Results: Cytological analyses on cytospin specimens showed that the percentage of tumoral cells in the ETS samples ranged from 20% to 70%. In the normal lung samples the percentage of epithelial cells was less then 10%. The reproducibility of the sample preparation protocol was very good, with coefficient of variation at the peptide level and at the protein level of 13% and 7%, respectively. Proteomics analysis led to the identification of a significantly higher number of proteins in the ETS samples than in the FF samples (244 vs 109, respectively). Albumin and hemoglobin were among the top 5 most abundant proteins identified in the FF samples, showing a high contamination with blood and plasma proteins, whereas ubiquitin and the mitochondrial ATP synthase 5A1 where among the top 5 most abundant proteins in the ETS samples. Conclusion: The method is feasible and reproducible. We could obtain a fair enrichment of cells but the major benefit of the method was an effective removal of contaminants from red blood cells and plasma proteins resulting in larger proteome coverage compared to the direct lysis of frozen samples. This sample preparation method may be successfully implemented for the discovery of lung cancer biomarkers on tissue samples using mass spectrometry-based proteomics. [ABSTRACT FROM AUTHOR]

Recent improvements in proteomics technologies have fundamentally altered our capacities to characterize human biology. There is an ever-growing interest in using these novel methods for studying the circulating proteome, as blood offers an accessible window into human health. However, every methodological innovation and analytical progress calls for reassessing our existing approaches and routines to ensure that the new data will add value to the greater biomedical research community and avoid previous errors. As representatives of HUPO's Human Plasma Proteome Project (HPPP), we present our 2024 survey of the current progress in our community, including the latest build of the Human Plasma Proteome PeptideAtlas that now comprises 4608 proteins detected in 113 data sets. We then discuss the updates of established proteomics methods, emerging technologies, and investigations of proteoforms, protein networks, extracellualr vesicles, circulating antibodies and microsamples. Finally, we provide a prospective view of using the current and emerging proteomics tools in studies of circulating proteins.

Plasma-derived extracellular vesicles (pEVs) are a potential source of diseased biomarker proteins. However, characterizing the pEV proteome is challenging due to its relatively low abundance and difficulties in enrichment. This study presents a streamlined workflow to identify EV proteins from cancer patient plasma using minimal sample input. Starting with 400 μL of plasma, we generated a comprehensive pEV proteome using size exclusion chromatography (SEC) combined with HiRIEF prefractionation-based mass spectrometry (MS). First, we compared the performance of HiRIEF and long gradient MS workflows using control pEVs, quantifying 2076 proteins with HiRIEF. In a proof-of-concept study, we applied SEC-HiRIEF-MS to a small cohort (12) of metastatic lung adenocarcinoma (LUAD) and malignant melanoma (MM) patients. We also analyzed plasma samples from the same patients to study the relationship between plasma and pEV proteomes. We identified and quantified 1583 proteins in cancer pEVs and 1468 proteins in plasma across all samples. While there was substantial overlap, the pEV proteome included several unique EV markers and cancer-related proteins. Differential analysis revealed 30 DEPs in LUAD vs the MM group, highlighting the potential of pEVs as biomarkers. This work demonstrates the utility of a prefractionation-based MS for comprehensive pEV proteomics and EV biomarker discovery. Data are available via ProteomeXchange with the identifiers PXD039338 and PXD038528.

High abundant protein depletion is a common strategy applied to increase analytical depth in global plasma proteomics experiment setups. The standard strategies for depletion of the highest abundant proteins currently rely on multiple-use HPLC columns or multiple-use spin columns. Here we evaluate the performance of single-use spin columns for plasma depletion and show that the single-use spin reduces handling time by allowing parallelization and is easily adapted to a nonspecialized lab environment without reducing the high plasma proteome coverage and reproducibility. In addition, we evaluate the effect of viral heat inactivation on the plasma proteome, an additional step in the plasma preparation workflow that allows the sample preparation of SARS-Cov2-infected samples to be performed in a BSL3 laboratory, and report the advantage of performing the heat inactivation postdepletion. We further show the possibility of expanding the use of the depletion column cross-species to macaque plasma samples. In conclusion, we report that single-use spin columns for high abundant protein depletion meet the requirements for reproducibly in in-depth plasma proteomics and can be applied on a common animal model while also reducing the sample handling time.

Purpose: Liquid biopsy has great potential to improve the management of brain tumor patients at high risk of surgery-associated complications. Here, the aim was to explore plasma extracellular vesicle (plEV) immunoprofiling as a tool for noninvasive diagnosis of glioma., Experimental Design: PlEV isolation and analysis were optimized using advanced mass spectrometry, nanoparticle tracking analysis, and electron microscopy. We then established a new procedure that combines size exclusion chromatography isolation and proximity extension assay-based ultrasensitive immunoprofiling of plEV proteins that was applied on a well-defined glioma study cohort ( n = 82)., Results: Among potential candidates, we for the first time identify syndecan-1 (SDC1) as a plEV constituent that can discriminate between high-grade glioblastoma multiforme (GBM, WHO grade IV) and low-grade glioma [LGG, WHO grade II; area under the ROC curve (AUC): 0.81; sensitivity: 71%; specificity: 91%]. These findings were independently validated by ELISA. Tumor SDC1 mRNA expression similarly discriminated between GBM and LGG in an independent glioma patient population from The Cancer Genome Atlas cohort (AUC: 0.91; sensitivity: 79%; specificity: 91%). In experimental studies with GBM cells, we show that SDC1 is efficiently sorted to secreted EVs. Importantly, we found strong support of plEV SDC1 originating from GBM tumors, as plEV SDC1 correlated with SDC1 protein expression in matched patient tumors, and plEV SDC1 was decreased postoperatively depending on the extent of surgery., Conclusions: Our studies support the concept of circulating plEVs as a tool for noninvasive diagnosis and monitoring of gliomas and should move this field closer to the goal of improving the management of cancer patients., (©2019 American Association for Cancer Research.)

Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy., Competing Interests: MP, AS, YZ, JB, DT, JS, AB, MW, HÅ, CÖ, MW, JL No competing interests declared, (© 2019, Pernemalm et al.)

A majority of patients with BRAF-mutated metastatic melanoma respond to therapy with BRAF inhibitors (BRAFi), but relapses are common owing to acquired resistance. To unravel BRAFi resistance mechanisms we have performed gene expression and mass spectrometry based proteome profiling of the sensitive parental A375 BRAF V600E-mutated human melanoma cell line and of daughter cell lines with induced BRAFi resistance. Increased expression of two novel resistance candidates, aminopeptidase-N (CD13/ANPEP) and ETS transcription factor FLI1 was observed in the BRAFi-resistant daughter cell lines. In addition, increased levels of the previously reported resistance mediators, receptor tyrosine kinase ephrine receptor A2 (EPHA2) and the hepatocyte growth factor receptor MET were also identified. The expression of these proteins was assessed in matched tumor samples from melanoma patients obtained before BRAFi and after disease progression. MET was overexpressed in all progression samples while the expression of the other candidates varied between the individual patients. Targeting CD13/ANPEP by a blocking antibody induced apoptosis in both parental A375- and BRAFi-resistant daughter cells as well as in melanoma cells with intrinsic BRAFi resistance and led to dephosphorylation of EPHA2 on S897, previously demonstrated to cause inhibition of the migratory capacity. AKT and RSK, both reported to induce EPHA2 S897 phosphorylation, were also dephosphorylated after inhibition of CD13/ANPEP. FLI1 silencing also caused decreases in EPHA2 S897 phosphorylation and in total MET protein expression. In addition, silencing of FLI1 sensitized the resistant cells to BRAFi. Furthermore, we show that BRAFi in combination with the multi kinase inhibitor dasatinib can abrogate BRAFi resistance and decrease both EPHA2 S897 phosphorylation and total FLI1 protein expression. This is the first report presenting CD13/ANPEP and FLI1 as important mediators of resistance to BRAF inhibition with potential as drug targets in BRAFi refractory melanoma.

Lung cancer is the most frequent cause of cancer-related death world-wide. Radiotherapy alone or in conjunction with chemotherapy is the standard treatment for locally advanced non-small cell lung cancer (NSCLC). Currently there is no predictive marker with clinical utility to guide treatment decisions in NSCLC patients undergoing radiotherapy. Identification of such markers would allow treatment options to be considered for more effective therapy. To enable the identification of appropriate protein biomarkers, plasma samples were collected from patients with non-small cell lung cancer before and during radiotherapy for longitudinal comparison following a protocol that carries sufficient power for effective discovery proteomics. Plasma samples from patients pre- and during radiotherapy who had survived > 18 mo were compared to the same time points from patients who survived < 14 mo using an 8 channel isobaric tagging tandem mass spectrometry discovery proteomics platform. Over 650 proteins were detected and relatively quantified. Proteins which showed a change during radiotherapy were selected for validation using an orthogonal antibody-based approach. Two of these proteins were verified in a separate patient cohort: values of CRP and LRG1 combined gave a highly significant indication of extended survival post one week of radiotherapy treatment.

High sample complexity is one of the major challenges in mass spectrometry-based proteomics today. Despite massive improvement in instrumentation, sample prefractionation is still needed to reduce sample complexity and improve proteome coverage. Isoelectric focusing (IEF) has been traditionally used as a first-dimension protein separation technique in two-dimensional gel electrophoresis-based proteomics. Recently, peptide IEF has emerged as appealing alternative for anion exchange chromatography in multidimensional LC-MS/MS workflows. The rationale behind using narrow-range peptide isoelectric focusing as a prefractionation method prior to ms/ms is to reduce the complexity induced by tryptic digestion. This is done by selectively analyzing a sub-fraction of peptides with an acidic pI. The pI range is chosen as it has previously been shown that 96 % of human proteins have at least one tryptic peptide between pH 3.4 and 4.9. This ensures high proteome coverage while reducing the number of peptides with 2/3. In addition the focusing precision is optimal in this range. Therefore, by analyzing this sub-fraction of peptides the complexity of the sample can be reduced without significant loss of proteome coverage. As the theoretical pI of peptides can be calculated, the pI of the identified peptides can be used to validate the peptide sequence (identified peptides with pI outside the pH range 3.4-4.9 are more likely to be false positives). In addition, this approach is compatible with iTRAQ labelling as the different iTRAQ labels migrate similarly in IEF.

We present a tool to improve quantitative accuracy and precision in mass spectrometry based on shotgun proteomics: protein quantification by peptide quality control, PQPQ. The method is based on the assumption that the quantitative pattern of peptides derived from one protein will correlate over several samples. Dissonant patterns arise either from outlier peptides or because of the presence of different protein species. By correlation analysis, protein quantification by peptide quality control identifies and excludes outliers and detects the existence of different protein species. Alternative protein species are then quantified separately. By validating the algorithm on seven data sets related to different cancer studies we show that data processing by protein quantification by peptide quality control improves the information output from shotgun proteomics. Data from two labeling procedures and three different instrumental platforms was included in the evaluation. With this unique method using both peptide sequence data and quantitative data we can improve the quantitative accuracy and precision on the protein level and detect different protein species.

The aim of this study was to evaluate three principally different top-down protein prefractionation methods for plasma: high-abundance protein depletion, size fractionation and peptide ligand affinity beads, focusing in particular on compatibility with downstream analysis, reproducibility and analytical depth. Our data clearly demonstrates the benefit of high-abundance protein depletion. However, MS/MS analysis of the proteins eluted from the high-abundance protein depletion column show that more proteins than aimed for are removed and, in addition, that the depletion efficacy varies between the different high-abundance proteins. Although a smaller number of proteins were identified per fraction using the peptide ligand affinity beads, this technique showed to be both robust and versatile. Size fractionation, as performed in this study, focusing on the low molecular weight proteome using a combination of gel filtration chromatography and molecular weight cutoff filters, showed limitations in the molecular weight cutoff precision leading detection of high molecular weight proteins and, in the case of the cutoff filters, high variability. GeLC-MS/MS analysis of the fractionation methods in combination with pathway analysis demonstrates that increased fractionation primarily leads to high proteome coverage of pathways related to biological functions of plasma, such as acute phase reaction, complement cascade and coagulation. Further, the prefractionation methods in this study induces limited effect on the proportion of tissue proteins detected, thereby highlighting the importance of extensive or targeted downstream fractionation.

Our goal in this paper is to show an analytical workflow for selecting protein biomarker candidates from SELDI-MS data. The clinical question at issue is to enable prediction of the complete remission (CR) duration for acute myeloid leukemia (AML) patients. This would facilitate disease prognosis and make individual therapy possible. SELDI-mass spectrometry proteomics analyses were performed on blast cell samples collected from AML patients pre-chemotherapy. Although the biobank available included approximately 200 samples, only 58 were available for analysis. The presented workflow includes sample selection, experimental optimization, repeatability estimation, data preprocessing, data fusion, and feature selection. Specific difficulties have been the small number of samples and the skew distribution of the CR duration among the patients. Further, we had to deal with both noisy SELDI-MS data and a diverse patient cohort. This has been handled by sample selection and several methods for data preprocessing and feature detection in the analysis workflow. Four conceptually different methods for peak detection and alignment were considered, as well as two diverse methods for feature selection. The peak detection and alignment methods included the recently developed annotated regions of significance (ARS) method, the SELDI-MS software Ciphergen Express which was regarded as the standard method, segment-wise spectral alignment by a genetic algorithm (PAGA) followed by binning, and, finally, binning of raw data. In the feature selection, the "standard" Mann-Whitney t test was compared with a hierarchical orthogonal partial least-squares (O-PLS) analysis approach. The combined information from all these analyses gave a collection of 21 protein peaks. These were regarded as the most potential and robust biomarker candidates since they were picked out as significant features in several of the models. The chosen peaks will now be our first choice for the continuing work on protein identification and biological validation. The identification will be performed by chromatographic purification and MALDI MS/MS. Thus, we have shown that the use of several data handling methods can improve a protein profiling workflow from experimental optimization to a predictive model. The framework of this methodology should be seen as general and could be used with other one-dimensional spectral omics data than SELDI MS including an adequate number of samples.

The cellular response to genotoxic stress is a complex cascade of events including altered protein expression, interactions, modifications, and relocalization, leading to cell cycle arrest and DNA repair or to apoptosis. p53 protein has a central role in this process, and p53 status is an important factor in the response of a tumor to genotoxic anticancer therapy. We studied p53-related changes postexposure to ionizing radiation using top-down mass spectrometry. Initially two cell lines were compared, HCT116 p53 wild type (wt) and p53(-/-), in a time course study postirradiation. In the p53 wt cell line a striking increase of a 10.2-kDa protein was detected, and this protein was identified with MS/MS analysis as S100A6. Further MS profiling led to detection of two post-translationally modified variants of S100A6, namely glutathionylated and cysteinylated forms. In p53 wt cells, a specific shift from glutathionylated to cysteinylated S100A6 occurred postirradiation. The p53 dependence of this specific change in protein level and modification pattern of S100A6 postirradiation was confirmed in a panel of four lung cancer cell lines (H23, U1810, H69, and A549) with different p53 status and using small interfering RNA against p53. Interestingly the closely related S100 family protein S100A4 showed the same changes in modification pattern post-ionizing radiation in the p53 wt lung cancer cell line, and S100A4 also showed p53-dependent expression. Using confocal microscopy, relocalization of S100A6 from nucleus to cytosol and a colocalization with tropomyosin in stress fibers was detected in A549 cells postirradiation. This relocalization coincided with the change in S100A6 modification pattern. Based on these results, we suggest that S100A6 and S100A4 are regulated via redox modifications in vivo and that these proteins are involved in the cellular response to genotoxic stress.