A novel method for sample preparation of fresh lung cancer tissue for proteomics analysis by tumor cell enrichment and removal of blood contaminants

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusion</p> <p>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.</p

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks

<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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.</p> <p>Conclusions</p> <p>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.</p

Glioblastoma stem cells express non-canonical proteins and exclusive mesenchymal-like or non-mesenchymal-like protein signatures

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 &gt; 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

The viral protein corona directs viral pathogenesis and amyloid aggregation

Artificial nanoparticles accumulate a protein corona layer in biological fluids, which significantly influences their bioactivity. As nanosized obligate intracellular parasites, viruses share many biophysical properties with artificial nanoparticles in extracellular environments and here we show that respiratory syncytial virus (RSV) and herpes simplex virus type 1 (HSV-1) accumulate a rich and distinctive protein corona in different biological fluids. Moreover, we show that corona pre-coating differentially affects viral infectivity and immune cell activation. In addition, we demonstrate that viruses bind amyloidogenic peptides in their corona and catalyze amyloid formation via surface-assisted heterogeneous nucleation. Importantly, we show that HSV-1 catalyzes the aggregation of the amyloid beta-peptide (A beta(42)), a major constituent of amyloid plaques in Alzheimer's disease, in vitro and in animal models. Our results highlight the viral protein corona as an acquired structural layer that is critical for viral-host interactions and illustrate a mechanistic convergence between viral and amyloid pathologies.Peer reviewe

Cancer proteomics : Method development for mass spectrometry based analysis of clinical materials

To improve cancer treatment, biomarkers for diagnostics and therapeutic guidance are desperately needed. Mass spectrometry (MS) based proteomics is one of the most promising methods for biomarker discovery. Clinical materials such as blood and tumor tissue provide an excellent starting material for biomarker discovery studies. However, at present, there are several analytical challenges related to biomarker discovery from clinical materials using mass spectrometry. In this thesis several methodological aspects in mass spectrometry based biomarker discovery workflows are optimized, including sample preparation, sample prefractionation and data management. In paper I an analytical workflow for SELDI-TOF MS of acute myeloid leukemia (AML) cells is presented including sample selection, experimental optimization, repeatability estimation, data preprocessing, data fusion, and feature selection. The study illustrates the benefit of combining the information from several data analysis methods when dealing with complex data from global proteomics analysis. Papers II, III and IV, deals with analytical challenges when performing biomarker discovery studies using plasma as a starting material. The studies highlight the benefit of prefractionation on the analytical depth and in addition show the importance of identifying a large number of proteins to reach low abundant tissue leakage proteins. Paper IV shows the added value of combining high abundant protein depletion and narrow range peptide isoelectric focusing for plasma biomarker discovery studies. In paper IV, pleural effusion, a proximal fluid in lung cancer, is collected and prepared according to the same protocol as plasma; an approach that previously has not been described. The potential of using pleural effusion as discovery material is also shown. Paper V describes a protocol for removal of blood contamination and enrichment of tumor cells from lung cancer tumor tissue. By removal of blood and stromal contaminants, twice as many proteins could be identified from lung cancer tissue, as compared with direct lysis of fresh frozen tissue. In general this thesis highlights the importance of experimental design and optimization prior to performing biomarker discovery experiments from clinical materials, especially as clinical materials usually are limited both in amounts and numbers and the sample sets contains a high inherent variability

A Novel Prefractionation Method Combining Protein and Peptide Isoelectric Focusing in Immobilized pH Gradient Strips

To increase sensitivity and analytical depth in shotgun proteomics, prefractionation of complex samples is often used. Here we describe a novel prefractionation method, Sandwich high resolution isoelectric focusing, which combines both protein and peptide isoelectric focusing. In the first step, intact proteins are separated on the basis of isoelectric point (p<i>I</i>) using traditional immobilized pH gradient (IPG) strips. Segments in the IPG-strip containing proteins of interest are subsequently cut out and applied to in-strip digestion, without subsequent peptide elution. In the second <i>peptide</i> isoelectric focusing step, the strip segments are used as loading bridges. The peptides are thereby directly applied to the peptide isoelectric focusing, without an intermediate elution step, and separated on narrow range IPG strips to reduce the complexity on the peptide level. In the final step, the peptides are eluted into 96-well plates and analyzed with mass spectrometry. In a proof of principle experiment, using this method to zoom in on p<i>I</i> regions of interest in human plasma, we identify over 800 proteins, with concentrations spanning over 6 orders of magnitude

A Novel Prefractionation Method Combining Protein and Peptide Isoelectric Focusing in Immobilized pH Gradient Strips

To increase sensitivity and analytical depth in shotgun proteomics, prefractionation of complex samples is often used. Here we describe a novel prefractionation method, Sandwich high resolution isoelectric focusing, which combines both protein and peptide isoelectric focusing. In the first step, intact proteins are separated on the basis of isoelectric point (p<i>I</i>) using traditional immobilized pH gradient (IPG) strips. Segments in the IPG-strip containing proteins of interest are subsequently cut out and applied to in-strip digestion, without subsequent peptide elution. In the second <i>peptide</i> isoelectric focusing step, the strip segments are used as loading bridges. The peptides are thereby directly applied to the peptide isoelectric focusing, without an intermediate elution step, and separated on narrow range IPG strips to reduce the complexity on the peptide level. In the final step, the peptides are eluted into 96-well plates and analyzed with mass spectrometry. In a proof of principle experiment, using this method to zoom in on p<i>I</i> regions of interest in human plasma, we identify over 800 proteins, with concentrations spanning over 6 orders of magnitude

In-depth plasma proteomics reveals increase in circulating PD-1 during anti-PD-1 immunotherapy in patients with metastatic cutaneous melanoma

Background Immune checkpoint inhibitors (ICIs) have significantly improved the outcome in metastatic cutaneous melanoma (CM). However, therapy response is limited to subgroups of patients and clinically useful predictive biomarkers are lacking.Methods To discover treatment-related systemic changes in plasma and potential biomarkers associated with treatment outcome, we analyzed serial plasma samples from 24 patients with metastatic CM, collected before and during ICI treatment, with mass-spectrometry-based global proteomics (high-resolution isoelectric focusing liquid chromatography–mass spectrometry (HiRIEF LC-MS/MS)) and targeted proteomics with proximity extension assays (PEAs). In addition, we analyzed plasma proteomes of 24 patients with metastatic CM treated with mitogen-activated protein kinase inhibitors (MAPKis), to pinpoint changes in protein plasma levels specific to the ICI treatment. To detect plasma proteins associated with treatment response, we performed stratified analyses in anti-programmed cell death protein 1 (anti-PD-1) responders and non-responders. In addition, we analyzed the association between protein plasma levels and progression-free survival (PFS) by Cox proportional hazards models.Results Unbiased HiRIEF LC-MS/MS-based proteomics showed plasma levels’ alterations related to anti-PD-1 treatment in 80 out of 1160 quantified proteins. Circulating PD-1 had the highest increase during anti-PD-1 treatment (log2-FC=2.03, p=0.0008) and in anti-PD-1 responders (log2-FC=2.09, p=0.005), but did not change in the MAPKis cohort. Targeted, antibody-based proteomics by PEA confirmed this observation. Anti-PD-1 responders had an increase in plasma proteins involved in T-cell response, neutrophil degranulation, inflammation, cell adhesion, and immune suppression. Furthermore, we discovered new associations between plasma proteins (eg, interleukin 6, interleukin 10, proline-rich acidic protein 1, desmocollin 3, C-C motif chemokine ligands 2, 3 and 4, vascular endothelial growth factor A) and PFS, which may serve as predictive biomarkers.Conclusions We detected an increase in circulating PD-1 during anti-PD-1 treatment, as well as diverse immune plasma proteomic signatures in anti-PD-1 responders. This study demonstrates the potential of plasma proteomics as a liquid biopsy method and in discovery of putative predictive biomarkers for anti-PD-1 treatment in metastatic CM