Insights into the Relationship between Pentraxin-3 and Cancer

Although cancer can be cured if detected early and treated effectively, it is still a leading cause of death worldwide. Tumor development can be limited by an appropiate immune response, but it can be promoted by chronic extensive inflammation through metabolic dysregulation and angiogenesis. In the past decade, numerous efforts have been made in order to identify novel candidates with predictive values in cancer diagnostics. In line with this, researchers have investigated the involvement of pentraxin-3 (PTX-3) in cellular proliferation and immune escape in various types of cancers, although it has not been clearly elucidated. PTX-3 is a member of the long pentraxin subfamily which plays an important role in regulating inflammation, innate immunity response, angiogenesis, and tissue remodeling. Increased synthesis of inflammatory biomarkers and activation of different cellular mechanisms can induce PTX-3 expression in various types of cells (neutrophils, monocytes, lymphocytes, myeloid dendritic cells, fibroblasts, and epithelial cells). PTX-3 has both pro- and anti-tumor functions, thus dual functions in oncogenesis. This review elucidates the potential usefulness of PTX-3 as a serum biomarker in cancer. While future investigations are needed, PTX-3 is emerging as a promising tool for cancer's diagnosis and prognosis, and also treatment monitoring.

A Targeted Bioinformatics Assessment of Adrenocortical Carcinoma Reveals Prognostic Implications of GABA System Gene Expression

Adrenocortical carcinoma (ACC) is a rare but deadly cancer for which few treatments exist. Here, we have undertaken a targeted bioinformatics study of The Cancer Genome Atlas (TCGA) ACC dataset focusing on the 30 genes encoding the γ-aminobutyric acid (GABA) system—an under-studied, evolutionarily-conserved system that is an emerging potential player in cancer progression. Our analysis identified a subset of ACC patients whose tumors expressed a distinct GABA system transcriptome. Transcript levels of ABAT (encoding a key GABA shunt enzyme), were upregulated in over 40% of tumors, and this correlated with several favorable clinical outcomes including patient survival; while enrichment and ontology analysis implicated two cancer-related biological pathways involved in metastasis and immune response. The phenotype associated with ABAT upregulation revealed a potential metabolic heterogeneity among ACC tumors associated with enhanced mitochondrial metabolism. Furthermore, many GABA_A receptor subunit-encoding transcripts were expressed, including two (GABRB2 and GABRD) prognostic for patient survival. Transcripts encoding GABA_B receptor subunits and GABA transporters were also ubiquitously expressed. The GABA system transcriptome of ACC tumors is largely mirrored in the ACC NCI-H295R cell line, suggesting that this cell line may be appropriate for future functional studies investigating the role of the GABA system in ACC cell growth phenotypes and metabolism.

Breast Cancer Classification Based on Proteotypes Obtained by SWATH Mass Spectrometry

Accurate classification of breast tumors is vital for patient management decisions and enables more precise cancer treatment. Here, we present a quantitative proteotyping approach based on sequential windowed acquisition of all theoretical fragment ion spectra (SWATH) mass spectrometry and establish key proteins for breast tumor classification. The study is based on 96 tissue samples representing five conventional breast cancer subtypes. SWATH proteotype patterns largely recapitulate these subtypes; however, they also reveal varying heterogeneity within the conventional subtypes, with triple negative tumors being the most heterogeneous. Proteins that contribute most strongly to the proteotype-based classification include INPP4B, CDK1, and ERBB2 and are associated with estrogen receptor (ER) status, tumor grade status, and HER2 status. Although these three key proteins exhibit high levels of correlation with transcript levels (R > 0.67), general correlation did not exceed R = 0.29, indicating the value of protein-level measurements of disease-regulated genes. Overall, this study highlights how cancer tissue proteotyping can lead to more accurate patient stratification.

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Proteotyping of 96 breast tumors by SWATH mass spectrometry

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Three key proteins for breast tumor classification

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Varying degrees of heterogeneity within conventional breast cancer subtypes

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Generally modest correlation between protein and transcript levels in tumor tissue

Gender proteomics II. Which proteins in sexual organs

In continuity with the review dealing with differences by gender in non-sexual organs [1], this review collects data on the proteomes of the sexual organs as involved in human reproduction, under both physiological and pathological conditions. It also collects data on the tissue structures and biological fluids typical of pregnancy, such as placenta and amniotic fluid, as well as what may be tested on preimplantation embryos during medically assisted reproduction. The review includes as well mention to all fluids and secretions connected with sex organs and/or reproduction, including sperm and milk, to exemplify two distinctive items in male and female physiology.

SIGNIFICANCE

The causes of infertility are only incompletely understood; the same holds for the causes, and even the early markers, of the most frequent complications of pregnancy. To these established medical challenges, present day practice adds new issues connected with medically assisted reproduction. Omics approaches, including proteomics, are building the database for basic knowledge to possibly translate into clinical testing and eventually into medical routine in this critical branch of health care.

Variant peptide detection utilizing mass spectrometry: laying the foundations for proteogenomic identification and validation

BACKGROUND

Proteogenomics is an emerging field at the intersection of genomics and proteomics. Many variant peptides corresponding to single nucleotide variations (SNVs) are associated with specific diseases. The aim of this study was to demonstrate the feasibility of proteogenomic-based variant peptide detection in disease models and clinical specimens.

METHODS

We sought to detect p53 single amino acid variant (SAAV) peptides in breast cancer tumor samples that have been previously subjected to sequencing analysis. Initially, two cancer cell lines having a cellular tumor antigen p53 (TP53) mutation and one wild type for TP53 were analyzed by selected reaction monitoring (SRM) assays as controls. One pool of wild type and one pool of mutated for TP53 cytosolic extracts were assayed with a shotgun proteogenomic workflow. Furthermore, 18 individual samples having a mutation in TP53 were assayed by SRM.

RESULTS

Two mutant p53 peptides were successfully detected in two cancer cell lines as expected from their DNA sequence. Wild type p53 peptides were detected in both cytosolic pools, however, none of the mutant p53 peptides were identified. Mutations at the protein level were detected in two cytosolic extracts and whole tumor lysates from the same patients by SRM analysis. Six thousand and six hundred and twenty eight non-redundant proteins were identified in the two cytosolic pools, thus greatly improving a previously reported cytosolic proteome.

CONCLUSIONS

In the current study we show the great potential of using proteogenomics for the direct identification of cancer-associated mutations in clinical samples and we discuss current limitations and future perspectives.

Covariation of Peptide Abundances Accurately Reflects Protein Concentration Differences

Most implementations of mass spectrometry-based proteomics involve enzymatic digestion of proteins, expanding the analysis to multiple proteolytic peptides for each protein. Currently, there is no consensus of how to summarize peptides' abundances to protein concentrations, and such efforts are complicated by the fact that error control normally is applied to the identification process, and do not directly control errors linking peptide abundance measures to protein concentration. Peptides resulting from suboptimal digestion or being partially modified are not representative of the protein concentration. Without a mechanism to remove such unrepresentative peptides, their abundance adversely impacts the estimation of their protein's concentration. Here, we present a relative quantification approach, Diffacto, that applies factor analysis to extract the covariation of peptides' abundances. The method enables a weighted geometrical average summarization and automatic elimination of incoherent peptides. We demonstrate, based on a set of controlled label-free experiments using standard mixtures of proteins, that the covariation structure extracted by the factor analysis accurately reflects protein concentrations. In the 1% peptide-spectrum match-level FDR data set, as many as 11% of the peptides have abundance differences incoherent with the other peptides attributed to the same protein. If not controlled, such contradicting peptide abundance have a severe impact on protein quantifications. When adding the quantities of each protein's three most abundant peptides, we note as many as 14% of the proteins being estimated as having a negative correlation with their actual concentration differences between samples. Diffacto reduced the amount of such obviously incorrectly quantified proteins to 1.6%. Furthermore, by analyzing clinical data sets from two breast cancer studies, our method revealed the persistent proteomic signatures linked to three subtypes of breast cancer. We conclude that Diffacto can facilitate the interpretation and enhance the utility of most types of proteomics data.

Differential proteomic and tissue expression analyses identify valuable diagnostic biomarkers of hepatocellular differentiation and hepatoid adenocarcinomas

The exact discrimination of lesions with true hepatocellular differentiation from secondary tumours and neoplasms with hepatocellular histomorphology like hepatoid adenocarcinomas (HAC) is crucial. Therefore, we aimed to identify ancillary protein biomarkers by using complementary proteomic techniques (2D-DIGE, label-free MS). The identified candidates were immunohistochemically validated in 14 paired samples of hepatocellular carcinoma (HCC) and non-tumourous liver tissue (NT). The candidates and HepPar1/Arginase1 were afterwards tested for consistency in a large cohort of hepatocellular lesions and NT (n = 290), non-hepatocellular malignancies (n = 383) and HAC (n = 13). Eight non-redundant, differentially expressed proteins were suitable for further immunohistochemical validation and four (ABAT, BHMT, FABP1, HAOX1) for further evaluation. Sensitivity and specificity rates for HCC/HAC were as follows: HepPar1 80.2%, 94.3% / 80.2%, 46.2%; Arginase1 82%, 99.4% / 82%, 69.2%; BHMT 61.4%, 93.8% / 61.4%, 100%; ABAT 84.4%, 33.7% / 84.4%, 30.8%; FABP1 87.2%, 95% / 87.2%, 69.2%; HAOX1 95.5%, 36.3% / 95.5%, 46.2%. The best 2×/3× biomarker panels for the diagnosis of HCC consisted of Arginase1/HAOX1 and BHMT/Arginase1/HAOX1 and for HAC consisted of Arginase1/FABP1 and BHMT/Arginase1/FABP1. In summary, we successfully identified, validated and benchmarked protein biomarker candidates of hepatocellular differentiation. BHMT in particular exhibited superior diagnostic characteristics in hepatocellular lesions and specifically in HAC. BHMT is therefore a promising (panel based) biomarker candidate in the differential diagnostic process of lesions with hepatocellular aspect.

Transcriptomic and Proteomic Investigation of HSP90A as a Potential Biomarker for HCC

Background

Hepatocellular carcinoma (HCC) is the third most frequent cause of cancer-related death in adults. Despite recent advances in the clinical technologies, the screening and diagnostic efficacy for HCC remains poor. Discovering novel and reliable HCC biomarkers is urgently needed.

Material/Methods

We performed a transcriptome-proteome integrated assay to track the possible HCC biomarkers from the process of HCC-derived gene expression in malignant cells to its protein product released into serum.

Results

Our screening results demonstrated that heat shock protein 90A (HSP90A), which participates in the PI3K-Akt signaling pathway and many other cancer-related pathways, warrants further investigation. The expression of HSP90A was increased in the HCC cells, serum, and tissues. Immunohistochemistry analysis on 76 clinical tissue samples also suggested the relevance between HSP90A expression and HCC metastatic behavior.

Conclusions

These findings suggest a role for HSP90A in HCC pathogenesis and the potential use of HSP90A for the screening and diagnosis of this malignancy.

Elevated Pentraxin 3 in bone metastatic breast cancer is correlated with osteolytic function

Pentraxin 3 (PTX3), a modulator of tumor-associated inflammation, is known to be positively correlated with tumor grade and severity of malignancies, but its exact role remains unclear. This study found that PTX3 expression was up-regulated in distant bone metastases of breast cancer compared to lung, liver, and brain metastases in 64 human breast cancer patients. Elevated expression of PTX3 was correlated with poor survival in patients with breast cancer. PTX3 expression was also up-regulated in a bone metastatic breast cancer cell line and further enhanced by pro-inflammatory cytokine TNFα. Administration of PTX3 promoted the migratory potential of breast cancer cells and the mobilization of macrophages, a precursor of osteoclasts (OCs), toward breast cancer cells. In addition, elevated expression of PTX3 by TNFα led to enhanced OC formation, implying the distinct role of PTX3 in osteolytic bone metastasis of breast cancer cells. Furthermore, PTX3 silencing using siRNA-specific siRNA prevented breast cancer cell migration, macrophage Chemotaxis, and subsequent OC formation. These findings provide an important insight into the key role of PTX3 in inflammation-associated osteolytic complications of breast cancer.

Comprehensive N-glycome profiling of cultured human epithelial breast cells identifies unique secretome N-glycosylation signatures enabling tumorigenic subtype classification

The secreted cellular sub-proteome (secretome) is a rich source of biologically active glycoproteins. N-Glycan profiling of secretomes of cultured cancer cells provides an opportunity to investigate the link between protein N-glycosylation and tumorigenesis. Utilizing carbon-LC-ESI-CID-MS/MS of protein released native N-glycans, we accurately profiled the secretome N-glycosylation of six human epithelial breast cells including normal mammary epithelial cells (HMEC) and breast cancer cells belonging to luminal A subtype (MCF7), HER2-overexpressing subtype (SKBR3), and basal B subtype (MDA-MB157, MDA-MB231, HS578T). On the basis of intact molecular mass, LC retention time, and MS/MS fragmentation, a total of 74 N-glycans were confidently identified and quantified. The secretomes comprised significant levels of highly sialylated and fucosylated complex type N-glycans, which were elevated in all cancer cells relative to HMEC (57.7-87.2% vs 24.9%, p < 0.0001 and 57.1-78.0% vs 38.4%, p < 0.0001-0.001, respectively). Similarly, other glycan features were found to be altered in breast cancer secretomes including paucimannose and complex type N-glycans containing bisecting β1,4-GlcNAc and LacdiNAc determinants. Subtype-specific glycosylation were observed, including the preferential expression of α2,3-sialylation in the basal B breast cancer cells. Pathway analysis indicated that the regulated N-glycans were biosynthetically related. Tight clustering of the breast cancer subtypes based on N-glycome signatures supported the involvement of N-glycosylation in cancer. In conclusion, we are the first to report on the secretome N-glycosylation of a panel of breast epithelial cell lines representing different subtypes. Complementing proteome and lipid profiling, N-glycome mapping yields important pieces of structural information to help understand the biomolecular deregulation in breast cancer development and progression, knowledge that may facilitate the discovery of candidate cancer markers and potential drug targets.

Inferring alterations in cell-to-cell communication in HER2+ breast cancer using secretome profiling of three cell models

Challenges in demonstrating durable clinical responses to molecular-targeted therapies have sparked a re-emergence in viewing cancer as an evolutionary process. In somatic evolution, cellular variants are introduced through a random process of somatic mutation and are selected for improved fitness through a competition for survival. In contrast to Darwinian evolution, cellular variants that are retained may directly alter the fitness competition. If cell-to-cell communication is important for selection, the biochemical cues secreted by malignant cells that emerge should be altered to bias this fitness competition. To test this hypothesis, we compared the proteins secreted in vitro by two human HER2+ breast cancer cell lines (BT474 and SKBR3) relative to a normal human mammary epithelial cell line (184A1) using a proteomics workflow that leveraged two-dimensional gel electrophoresis (2DE) and MALDI-TOF mass spectrometry. Supported by the 2DE secretome maps and identified proteins, the two breast cancer cell lines exhibited secretome profiles that were similar to each other and, yet, were distinct from the 184A1 secretome. Using protein-protein interaction and pathway inference tools for functional annotation, the results suggest that all three cell lines secrete exosomes, as confirmed by scanning electron microscopy. Interestingly, the HER2+ breast cancer cell line exosomes are enriched in proteins involved in antigen-processing and presentation and glycolytic metabolism. These pathways are associated with two of the emerging hallmarks of cancer: evasion of tumor immunosurveillance and deregulating cellular energetics.

Secretome profiling of primary cells reveals that THBS2 is a salivary biomarker of oral cavity squamous cell carcinoma

Oral cavity squamous cell carcinoma (OSCC), which is a leading cause of cancer-related death worldwide, is frequently associated with poor prognosis and mortality. The discovery of body fluid-accessible biomarkers may help improve the detection of OSCC. In the present work, we established primary cell cultures derived from OSCC and adjacent noncancerous epithelium and performed comparative profiling of their secretomes. Using spectral counting-based label-free quantification, we found that 64 proteins were significantly higher in primary OSCC cells compared with primary adjacent noncancerous cells. We then retrieved the mRNA expression levels of these 64 proteins in oral cavity tumor and noncancerous tissues from public domain array-based transcriptome data sets and used this information to prioritize the biomarker candidates. We identified 19 candidates; among them, the protein levels of THBS2, UFD1L, and DNAJB11 were found to be elevated in OSCC tissues compared with adjacent noncancerous epithelia. Importantly, higher levels of THBS2 in OSCC tissues were associated with a higher overall pathological stage, positive perineural invasion, and a poorer prognosis. Moreover, the salivary levels of THBS2 in OSCC patients were elevated compared to those of noncancer controls. Our results collectively indicate that analysis of the primary cell secretome is a feasible strategy for biomarker identification, and that THBS2 is a potentially useful salivary marker for the detection of OSCC.

Systematic nucleo-cytoplasmic trafficking of proteins following exposure of MCF7 breast cancer cells to estradiol

We have used a proteomics subcellular spatial razor approach to look at changes in total protein abundance and in protein distribution between the nucleus and cytoplasm following exposure of MCF7 breast cancer cells to estradiol. The dominant response of MCF7 cells to estrogen stimulation involves dynamic changes in protein subcellular spatial distribution rather than changes in total protein abundance. Of the 3604 quantitatively monitored proteins, only about 2% show substantial changes in total abundance (>2-fold), whereas about 20% of the proteins show substantial changes in local abundance and/or redistribution of their subcellular location, with up to 16-fold changes in their local concentration in the nucleus or the cytoplasm. We propose that dynamic redistribution of the subcellular location of multiple proteins in response to stimuli is a fundamental characteristic of cells and suggest that perturbation of cellular spatial control may be an important feature of cancer.

Breast cancer classification by proteomic technologies: current state of knowledge

Breast cancer is traditionally considered as a heterogeneous disease. Molecular profiling of breast cancer by gene expression studies has provided us an important tool to discriminate a number of subtypes. These breast cancer subtypes have been shown to be associated with clinical outcome and treatment response. In order to elucidate the functional consequences of altered gene expressions related to each breast cancer subtype, proteomic technologies can provide further insight by identifying quantitative differences at the protein level. In recent years, proteomic technologies have matured to an extent that they can provide proteome-wide expressions in different clinical materials. This technology can be applied for the identification of proteins or protein profiles to further refine breast cancer subtypes or for discovery of novel protein biomarkers pointing towards metastatic potential or therapy resistance in a specific subtype. In this review, we summarize the current state of knowledge of proteomic research on molecular breast cancer classification and discuss important aspects of the potential usefulness of proteomics for discovery of breast cancer-associated protein biomarkers in the clinic.