Discovery of putative breast cancer antigens using an integrative platform of genomics-driven immunoproteomics.

Recent advances in cancer immuno-therapeutics such as checkpoint inhibitors, chimeric antigen-receptor T cells, and tumor infiltrating T cells (TIL) are now significantly impacting cancer patients in a positive manner. Although very promising, reports indicate no more than 25% of cases result in complete remission. One of the limitations of these treatments is the identity of putative cancer antigens in each patient, as it is technically challenging to identify cancer antigens in a rapid fashion. Thus, identification of cancer antigens followed by targeted treatment will increase the efficacy of cancer immunotherapies. To achieve this goal, a combined technologies platform of deep genomic sequencing and personalized immune assessment was devised, termed Genomics Driven Immunoproteomics (GDI). Using this technological platform, we report the discovery of 149 tumor antigens from human breast cancer patients. Significant number of these putative cancer antigens arise from single nucleotide variants (SNVs), as well as insertions and deletions that results into frame-shift mutations. We propose a general model of anti-cancer immunity and suggest that the GDI platform may help identify patient-specific tumor antigens in a timely fashion for precision immunotherapies.

Large-scale proteomic characterization of melanoma expressed proteins reveals nestin and vimentin as biomarkers that can potentially distinguish melanoma subtypes.

Melanoma is an aggressive type of skin cancer, which accounts for only 4% of skin cancer cases but causes around 75% of skin cancer deaths. Currently, there is a limited set of protein biomarkers that can distinguish melanoma subtypes and provide an accurate prognosis of melanoma. Thus, we have selected and profiled the proteomes of five different melanoma cell lines from different stages of progression in comparison with a normal melanocytes using tandem mass spectrometry. We also profiled the proteome of a solid metastatic melanoma tumor. This resulted in the identification of 4758 unique proteins, among which ∼200-300 differentially expressed proteins from each set were found by quantitative proteomics. Correlating protein expression with aggressiveness of each melanoma cell line and literature mining resulted in the final selection of six proteins: vimentin, nestin, fibronectin, annexin A1, dipeptidyl peptidase IV, and histone H2A1B. Validation of nestin and vimentin using 40 melanoma samples revealed pattern of protein expression can help predict melanoma aggressiveness in different subgroups of melanoma. These results, together with the combined list of 4758 expressed proteins, provide a valuable resource for selecting melanoma biomarkers in the future for the clinical and research community.

Proteomic analysis of active multiple sclerosis lesions reveals therapeutic targets.

Understanding the neuropathology of multiple sclerosis (MS) is essential for improved therapies. Therefore, identification of targets specific to pathological types of MS may have therapeutic benefits. Here we identify, by laser-capture microdissection and proteomics, proteins unique to three major types of MS lesions: acute plaque, chronic active plaque and chronic plaque. Comparative proteomic profiles identified tissue factor and protein C inhibitor within chronic active plaque samples, suggesting dysregulation of molecules associated with coagulation. In vivo administration of hirudin or recombinant activated protein C reduced disease severity in experimental autoimmune encephalomyelitis and suppressed Th1 and Th17 cytokines in astrocytes and immune cells. Administration of mutant forms of recombinant activated protein C showed that both its anticoagulant and its signalling functions were essential for optimal amelioration of experimental autoimmune encephalomyelitis. A proteomic approach illuminated potential therapeutic targets selective for specific pathological stages of MS and implicated participation of the coagulation cascade.

Subcellular tissue proteomics of hepatocellular carcinoma for molecular signature discovery.

Hepatocellular carcinoma (HCC) is one of the leading causes of mortality from solid organ malignancy worldwide. Because of the complexity of proteins within liver cells and tissues, the discovery of therapeutic targets of HCC has been difficult. To investigate strategies for decreasing the complexity of tissue samples for detecting meaningful protein mediators of HCC, we employed subcellular fractionation combined with 1D-gel electrophoresis and liquid chromatography-tandem mass spectrometry analysis. Moreover, we utilized a statistical method, namely, the Power Law Global Error Model (PLGEM), to distinguish differentially expressed proteins in a duplicate proteomic data set. Mass spectrometric analysis identified 3045 proteins in nontumor and HCC from cytosolic, membrane, nuclear, and cytoskeletal fractions. The final lists of highly differentiated proteins from the targeted fractions were searched for potentially translocated proteins in HCC from soluble compartments to the nuclear or cytoskeletal compartments. This analysis refined our targets of interest to include 21 potential targets of HCC from these fractions. Furthermore, we validated the potential molecular targets of HCC, MATR3, LETM1, ILF2, and IQGAP2 by Western blotting, immunohistochemisty, and immunofluorescent microscopy. Here we demonstrate an efficient strategy of subcellular tissue proteomics toward molecular target discovery of one of the most complicated human disease, HCC.

Role of spectral counting in quantitative proteomics.

Spectral count, defined as the total number of spectra identified for a protein, has gained acceptance as a practical, label-free, semiquantitative measure of protein abundance in proteomic studies. In this review, we discuss issues affecting the performance of spectral counting relative to other label-free methods, as well as its limitations. Possible consequences of modifications, which are commonly applied to raw spectral counts to improve abundance estimations, are considered. The use of spectral counting for different types of quantitation studies is explored and critiqued. Different statistical methods and underlying frameworks that have been applied to spectral count analysis are described and compared, and problem areas that undermine confident statistical analysis are considered. Finally, the issue of accurate estimation of false-discovery rates is addressed and identified as a major current challenge in quantitative proteomics.

Janus-like opposing roles of CD47 in autoimmune brain inflammation in humans and mice.

Comparison of transcriptomic and proteomic data from pathologically similar multiple sclerosis (MS) lesions reveals down-regulation of CD47 at the messenger RNA level and low abundance at the protein level. Immunohistochemical studies demonstrate that CD47 is expressed in normal myelin and in foamy macrophages and reactive astrocytes within active MS lesions. We demonstrate that CD47(-/-) mice are refractory to experimental autoimmune encephalomyelitis (EAE), primarily as the result of failure of immune cell activation after immunization with myelin antigen. In contrast, blocking with a monoclonal antibody against CD47 in mice at the peak of paralysis worsens EAE severity and enhances immune activation in the peripheral immune system. In vitro assays demonstrate that blocking CD47 also promotes phagocytosis of myelin and that this effect is dependent on signal regulatory protein α (SIRP-α). Immune regulation and phagocytosis are mechanisms for CD47 signaling in autoimmune neuroinflammation. Depending on the cell type, location, and disease stage, CD47 has Janus-like roles, with opposing effects on EAE pathogenesis.

Discovery of putative pancreatic cancer biomarkers using subcellular proteomics.

Pancreatic cancer (PC) is a highly aggressive disease that frequently remains undetected until it has progressed to an advanced, systemic stage. Successful treatment of PC is hindered by the lack of early detection. The application of proteomic analysis to PC combined with subcellular fractionation has introduced new possibilities in the field of biomarker discovery. We utilized matched pairs of pancreas tumor and non-tumor pancreas from patients undergoing tumor resection. The tissues were treated to obtain cellular protein fractions corresponding to cytosol, membrane, nucleus and cytoskeleton. The fractions were then separated by molecular weight and digested with trypsin, followed by liquid chromatography and tandem mass spectrometry. The spectra obtained were searched using Sequest engine and combined into a single analysis file to obtain a semi-quantitative number, spectral count, using Scaffold software. We identified 2393 unique proteins in non-tumor and cancer pancreas. Utilizing PLGEM statistical analysis we determined 104 proteins were significantly changed in cancer. From these, we further validated four secreted proteins that are up-regulated in cancer and have potential for development as minimally-invasive diagnostic markers. We conclude that subcellular fractionation followed by gel electrophoresis and tandem mass spectrometry is a powerful strategy for identification of differentially expressed proteins in pancreatic cancer.

Differential protein expression profiles in estrogen receptor-positive and -negative breast cancer tissues using label-free quantitative proteomics.

Identification of the proteins that are associated with estrogen receptor (ER) status is a first step towards better understanding of the hormone-dependent nature of breast carcinogenesis. Although a number of gene expression analyses have been conducted, protein complement has not been systematically investigated to date. Because proteins are primary targets of therapeutic drugs, in this study, we have attempted to identify proteomic signatures that demarcate ER-positive and -negative breast cancers. Using highly enriched breast tumor cells, replicate analyses from 3 ERα+ and 3 ERα- human breast tumors resulted in the identification of 2,995 unique proteins with ≥2 peptides. Among these, a number of receptor tyrosine kinases and intracellular kinases that are abundantly expressed in ERα+ and ERα- breast cancer tissues were identified. Further, label-free quantitative proteome analysis revealed that 236 proteins were differentially expressed in ERα+ and ERα- breast tumors. Among these, 141 proteins were selectively up-regulated in ERα+, and 95 proteins were selectively up-regulated in ERα- breast tumors. Comparison of differentially expressed proteins with a breast cancer database revealed 98 among these have been previously reported to be involved in breast cancer. By Gene Ontology molecular function, dehydrogenase, reductase, cytoskeletal proteins, extracellular matrix, hydrolase, and lyase categories were significantly enriched in ERα+, whereas selected calcium-binding protein, membrane traffic protein, and cytoskeletal protein were enriched in ERα- breast tumors. Biological process and pathway analysis revealed that up-regulated proteins of ERα+ were overrepresented by proteins involved in amino acid metabolism, proteasome, and fatty acid metabolism, while up-regulated proteins of ERα- were overrepresented by proteins involved in glycolysis pathway. The presence and relative abundance of 4 selected differentially abundant proteins (liprin-α1, fascin, DAP5, and ß-arrestin-1) were quantified and validated by immunohistochemistry. In conclusion, unlike in vitro cell culture models, the in vivo signaling proteins and pathways that we have identified directly from human breast cancer tissues may serve as relevant therapeutic targets for the pharmacological intervention of breast cancer.

Protein identification using Sorcerer 2 and SEQUEST.

Sage-N's Sorcerer 2 provides an integrated data analysis system for comprehensive protein identification and characterization. It runs on a proprietary version of SEQUEST(R), the most widely used search engine for identifying proteins in complex mixtures. The protocol presented here describes the basic steps performed to process mass spectrometric data with Sorcerer 2 and how to analyze results using TPP and Scaffold. The unit also provides an overview of the SEQUEST(R) algorithm, along with Sorcerer-SEQUEST(R) enhancements, and a discussion of data filtering methods, important considerations in data interpretation, and additional resources that can be of assistance to users running Sorcerer and interpreting SEQUEST(R) results.

Quantitative phosphoproteomic analysis of T cell receptor signaling reveals system-wide modulation of protein-protein interactions.

Protein phosphorylation events during T cell receptor (TCR) signaling control the formation of complexes among proteins proximal to the TCR, the activation of kinase cascades, and the activation of transcription factors; however, the mode and extent of the influence of phosphorylation in coordinating the diverse phenomena associated with T cell activation are unclear. Therefore, we used the human Jurkat T cell leukemia cell line as a model system and performed large-scale quantitative phosphoproteomic analyses of TCR signaling. We identified 10,665 unique phosphorylation sites, of which 696 showed TCR-responsive changes. In addition, we analyzed broad trends in phosphorylation data sets to uncover underlying mechanisms associated with T cell activation. We found that, upon stimulation of the TCR, phosphorylation events extensively targeted protein modules involved in all of the salient phenomena associated with T cell activation: patterning of surface proteins, endocytosis of the TCR, formation of the F-actin cup, inside-out activation of integrins, polarization of microtubules, production of cytokines, and alternative splicing of messenger RNA. Further, case-by-case analysis of TCR-responsive phosphorylation sites on proteins belonging to relevant functional modules together with network analysis allowed us to deduce that serine-threonine (S-T) phosphorylation modulated protein-protein interactions (PPIs) in a system-wide fashion. We also provide experimental support for this inference by showing that phosphorylation of tubulin on six distinct serine residues abrogated PPIs during the assembly of microtubules. We propose that modulation of PPIs by stimulus-dependent changes in S-T phosphorylation state is a widespread phenomenon applicable to many other signaling systems.

Proteomics analysis of human coronary atherosclerotic plaque: a feasibility study of direct tissue proteomics by liquid chromatography and tandem mass spectrometry.

Cardiovascular disease presents significant variations in human populations with respect to the atherosclerotic plaque progression, inflammation, thrombosis, and rupture. To gain a more comprehensive picture of the pathogenic mechanism of atherosclerosis and the variations seen in patients, efficient methods to identify proteins from the normal and diseased arteries need to be developed. To accomplish this goal, we tested the feasibility and efficiency of protein identification by a recently developed method, termed direct tissue proteomics (DTP). We analyzed frozen and paraformaldehyde-fixed archival coronary arteries with the DTP method. We also validated the distinct expression of four proteins by immunohistochemistry. In addition, we demonstrated the compatibility of the DTP method with laser capture microdissection and the possibility of monitoring specific cytokines and growth factors by the absolute quantification of abundance method. Major findings from this feasibility study are that 1) DTP can be used to efficiently identify proteins from paraformaldehyde-fixed, paraffin-embedded, and frozen coronary arteries; 2) approximately twice the number of proteins were identified from the frozen sections when compared with the paraformaldehyde-fixed sections; 3) laser capture microdissection is compatible with DTP; and 4) detection of low abundance cytokines and growth factors in the coronary arteries required selective reaction monitoring experiments coupled to absolute quantification of abundance. The analysis of 35 human coronary atherosclerotic samples allowed identification of a total of 806 proteins. The present study provides the first large scale proteomics map of human coronary atherosclerotic plaques.

Global survey of human T leukemic cells by integrating proteomics and transcriptomics profiling.

A global protein survey is needed to gain systems-level insights into mammalian cell signaling and information flow. Human Jurkat T leukemic cells are one of the most important model systems for T cell signaling study, but no comprehensive proteomics survey has been carried out in this cell type. In the present study we combined subcellular fractionation, multiple protein enrichment methods, and replicate tandem mass spectrometry analyses to determine the protein expression pattern in a single Jurkat cell type. The proteome dataset was evaluated by comparison with the genome-wide mRNA expression pattern in the same cell type. A total of 5381 proteins were identified by mass spectrometry with high confidence. Rigorous comparison of RNA and protein expression afforded removal of the false positive identifications and redundant entries but rescued the proteins identified by a single high scoring peptide, resulting in the final identification of 6471 unique gene products among which 98% of the corresponding transcripts were detected with high probability. Using hierarchical clustering of the protein expression patterns in five subcellular fractions (cytosol, light membrane, heavy membrane, mitochondria, and nuclei), the primary subcellular localization of 2241 proteins was assigned with high confidence including 792 previously uncharacterized proteins. This proteome landscape can serve as a useful platform for systems-level understanding of organelle composition and cellular functions in human T cells.

Systematic characterization of nuclear proteome during apoptosis: a quantitative proteomic study by differential extraction and stable isotope labeling.

Identification and characterization of the nuclear proteome is important for detailed understanding of multiple signaling events in eukaryotic cells. Toward this goal, we extensively characterized the nuclear proteome of human T leukemia cells by sequential extraction of nuclear proteins with different physicochemical properties using three buffer conditions. This large scale proteomic study also tested the feasibility and technical challenges associated with stable isotope labeling by amino acids in cell culture (SILAC) to uncover quantitative changes during apoptosis. Analyzing proteins from three nuclear fractions extracted from naive and apoptotic cells generated 780,530 MS/MS spectra that were used for database searching using the SEQUEST algorithm. This analysis resulted in the identification and quantification of 1,174 putative nuclear proteins. A number of known nuclear proteins involved in apoptosis as well as novel proteins not known to be part of the nuclear apoptotic machinery were identified and quantified. Consistent with SILAC-based quantifications, immunofluorescence staining of nucleus, mitochondria, and some associated proteins from both organelles revealed a dynamic recruitment of mitochondria into nuclear invaginations during apoptosis.

Protein identification using TurboSEQUEST.

SEQUEST is the most widely used software tool for identifying proteins in complex mixtures. It is a mature, robust program that identifies peptides directly from uninterpreted tandem mass spectra, thus making large-scale proteomic studies possible. Thermo Electron's TurboSEQUEST provides a Windows-based graphical user interface for running SEQUEST and interpreting results. The protocol in this unit describes the basic steps involved in processing mass spectrometric data and analyzing results using TurboSEQUEST. It also provides an overview of the SEQUEST algorithm and a discussion of data filtering methods, critical issues in data interpretation, and available resources that can facilitate proper interpretation of SEQUEST results.

PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery.

Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.