Targeted MS Assay Predicting Tamoxifen Resistance in Estrogen-Receptor-Positive Breast Cancer Tissues and Sera

Targeted protein quantitation in human body fluids by mass spectrometry

Human body fluids (biofluids) contain various proteins, some of which reflect individuals' physiological conditions or predict diseases. Therefore, the analysis of biofluids can provide substantial information on novel biomarkers for clinical diagnosis and prognosis. In the past decades, mass spectrometry (MS)-based technologies have been developed as proteomic strategies not only for the identification of protein biomarkers but also for biomarker verification/validation in body fluids for clinical applications. The main advantage of targeted MS-based methodologies is the accurate and specific simultaneous quantitation of multiple biomarkers with high sensitivity. Here, we review MS-based methodologies that are currently used for the targeted quantitation of protein components in human body fluids, especially in plasma, urine, cerebrospinal fluid, and saliva. In addition, the currently used MS-based methodologies are summarized with a specific focus on applicable clinical sample types, MS configurations, and acquisition modes.

Dissecting the Roles of PDCD4 in Breast Cancer

The human programmed cell death 4 (PDCD4) gene was mapped at chromosome 10q24 and encodes the PDCD4 protein comprised of 469 amino acids. PDCD4 inhibits protein translation PDCD4 inhibits protein translation to suppress tumor progression, and its expression is frequently decreased in breast cancer. PDCD4 blocks translation initiation complex by binding eIF4A via MA-3 domains or by directly binding 5’ mRNA internal ribosome entry sites with an RNA binding domain to suppress breast cancer progression and proliferation. Numerous regulators and biological processes including non-coding RNAs, proteasomes, estrogen, natural compounds and inflammation control PDCD4 expression in breast cancer. Loss of PDCD4 expression is also responsible for drug resistance in breast cancer. HER2 activation downregulates PDCD4 expression by activating MAPK, AKT, and miR-21 in aromatase inhibitor-resistant breast cancer cells. Moreover, modulating the microRNA/PDCD4 axis maybe an effective strategy for overcoming chemoresistance in breast cancer. Down-regulation of PDCD4 is significantly associated with short overall survival of patients, which suggests that PDCD4 may be an independent prognostic marker for breast cancer.

Affinity capture in bottom-up protein analysis - Overview of current status of proteolytic peptide capture using antibodies and molecularly imprinted polymers

Antibody-based affinity capture has become the gold standard in sample preparation for determination of low-abundance protein biomarkers in biological matrices prior to liquid chromatography-mass spectrometry (LC-MS) determination. This comprises both capture of intact proteins prior to the digestion step and capture of proteolytic peptides after digestion of the sample. The latter can be performed both using antibodies specifically developed to capture target proteolytic peptides, as well as by the less explored use of anti-protein antibodies to capture the proteolytic epitope peptide. Molecularly imprinted polymers (MIPs), also called plastic antibodies are another affinity-based approach emerging as sample preparation technique in LC-MS based protein biomarker analysis. The current review gives a critical and comprehensive overview of proteolytic peptide capture using antibodies and MIPs in LC-MS based protein biomarker determination during the last five years. The main emphasis is on capture of non-modified peptides, while a brief overview of affinity capture of peptides containing post-translational modifications (PTMs) is provided.

A novel lncRNA derived from an ultraconserved region: lnc-uc.147, a potential biomarker in luminal A breast cancer

The human genome contains 481 ultraconserved regions (UCRs), which are genomic stretches of over 200 base pairs conserved among human, rat, and mouse. The majority of these regions are transcriptionally active (T-UCRs), and several have been found to be differentially expressed in tumours. Some T-UCRs have been functionally characterized, but of those few have been associated to breast cancer (BC). Using TCGA data, we found 302 T-UCRs related to clinical features in BC: 43% were associated with molecular subtypes, 36% with oestrogen-receptor positivity, 17% with HER2 expression, 12% with stage, and 10% with overall survival. The expression levels of 12 T-UCRs were further analysed in a cohort of 82 Brazilian BC patients using RT-qPCR. We found that uc.147 is high expressed in luminal A and B patients. For luminal A, a subtype usually associated with better prognosis, high uc.147 expression was associated with a poor prognosis and suggested as an independent prognostic factor. The lncRNA from uc.147 (lnc-uc.147) is located in the nucleus. Northern blotting results show that uc.147 is a 2,8 kb monoexonic trancript, and its sequence was confirmed by RACE. The silencing of uc.147 increases apoptosis, arrests cell cycle, and reduces cell viability and colony formation in BC cell lines. Additionally, we identifed 19 proteins that interact with lnc-uc.147 through mass spectrometry and demonstrated a high correlation of lnc-uc.147 with the neighbour gene expression and miR-18 and miR-190b. This is the first study to analyse the expression of all T-UCRs in BC and to functionally assess the lnc-uc.147.

Proteogenomic Workflow Reveals Molecular Phenotypes Related to Breast Cancer Mammographic Appearance

Proteogenomic approaches have enabled the generat̲ion of novel information levels when compared to single omics studies although burdened by extensive experimental efforts. Here, we improved a data-independent acquisition mass spectrometry proteogenomic workflow to reveal distinct molecular features related to mammographic appearances in breast cancer. Our results reveal splicing processes detectable at the protein level and highlight quantitation and pathway complementarity between RNA and protein data. Furthermore, we confirm previously detected enrichments of molecular pathways associated with estrogen receptor-dependent activity and provide novel evidence of epithelial-to-mesenchymal activity in mammography-detected spiculated tumors. Several transcript-protein pairs displayed radically different abundances depending on the overall clinical properties of the tumor. These results demonstrate that there are differentially regulated protein networks in clinically relevant tumor subgroups, which in turn alter both cancer biology and the abundance of biomarker candidates and drug targets.

Analytical techniques for multiplex analysis of protein biomarkers

INTRODUCTION

The importance of biomarkers for pharmaceutical drug development and clinical diagnostics is more significant than ever in the current shift toward personalized medicine. Biomarkers have taken a central position either as companion markers to support drug development and patient selection, or as indicators aiming to detect the earliest perturbations indicative of disease, minimizing therapeutic intervention or even enabling disease reversal. Protein biomarkers are of particular interest given their central role in biochemical pathways. Hence, capabilities to analyze multiple protein biomarkers in one assay are highly interesting for biomedical research.

AREAS COVERED

We here review multiple methods that are suitable for robust, high throughput, standardized, and affordable analysis of protein biomarkers in a multiplex format. We describe innovative developments in immunoassays, the vanguard of methods in clinical laboratories, and mass spectrometry, increasingly implemented for protein biomarker analysis. Moreover, emerging techniques are discussed with potentially improved protein capture, separation, and detection that will further boost multiplex analyses.

EXPERT COMMENTARY

The development of clinically applied multiplex protein biomarker assays is essential as multi-protein signatures provide more comprehensive information about biological systems than single biomarkers, leading to improved insights in mechanisms of disease, diagnostics, and the effect of personalized medicine.

Single-cell expression and Mendelian randomization analyses identify blood genes associated with lifespan and chronic diseases

The human lifespan is a heritable trait, which is intricately linked to the development of disorders. Here, we show that genetic associations for the parental lifespan are enriched in open chromatin of blood cells. By using blood expression quantitative trait loci (eQTL) derived from 31,684 samples, we identified for the lifespan 125 cis- and 559 trans-regulated expressed genes (eGenes) enriched in adaptive and innate responses. Analysis of blood single-cell expression data showed that eGenes were enriched in dendritic cells (DCs) and the modelling of cell ligand-receptor interactions predicted crosstalk between DCs and a cluster of monocytes with a signature of cytotoxicity. In two-sample Mendelian randomization (MR), we identified 16 blood cis-eGenes causally associated with the lifespan. In MR, the majority of cis-eGene-disorder association pairs had concordant effects with the lifespan. The present work underlined that the lifespan is linked with the immune response and identifies eGenes associated with the lifespan and disorders.

Tamoxifen-induced hepatotoxicity via lipid accumulation and inflammation in zebrafish

Tamoxifen is a clinical drug for estrogen receptor (ER)-positive breast cancer. Recently, it has been detected in aquatic environment. The residual drugs will produce certain biological activity and create a risk to aquatic organism when they enter the water environment. Therefore, it has great significance to study the ecotoxicity of tamoxifen. In the study, we used zebrafish as a model of aquatic to investigate the ecotoxic mechanism of tamoxifen to aquatic. We found that tamoxifen induced liver lipid accumulation in zebrafish, which showed a significant hepatotoxicity with smaller liver area and bigger yolk area. Though biochemical and pathologic measurement, tamoxifen treated group showed higher transaminase and lipid content. The elevated liver lipid synthesis might due to the increase of lipid metabolism related gene Srebf1, Srebf2 and Fasn. Moreover, inflammatory cytokine Tnf-α, Il-1β And Il-6 were increased. This result confirmed the toxicity of tamoxifen to aquatic, suggested liver injury was the main characteristic of its ecotoxicity. This study indicated it is important to avoid tamoxifen discharging into the aquatic ecology and provided a theoretical basis of prevention tamoxifen-induced ecotoxicity to aquatic.

OCIAD1 Controls Electron Transport Chain Complex I Activity to Regulate Energy Metabolism in Human Pluripotent Stem Cells

Pluripotent stem cells (PSCs) derive energy predominantly from glycolysis and not the energy-efficient oxidative phosphorylation (OXPHOS). Differentiation is initiated with energy metabolic shift from glycolysis to OXPHOS. We investigated the role of mitochondrial energy metabolism in human PSCs using molecular, biochemical, genetic, and pharmacological approaches. We show that the carcinoma protein OCIAD1 interacts with and regulates mitochondrial complex I activity. Energy metabolic assays on live pluripotent cells showed that OCIAD1-depleted cells have increased OXPHOS and may be poised for differentiation. OCIAD1 maintains human embryonic stem cells, and its depletion by CRISPR/Cas9-mediated knockout leads to rapid and increased differentiation upon induction, whereas OCIAD1 overexpression has the opposite effect. Pharmacological alteration of complex I activity was able to rescue the defects of OCIAD1 modulation. Thus, hPSCs can exist in energy metabolic substates. OCIAD1 provides a target to screen for additional modulators of mitochondrial activity to promote transient multipotent precursor expansion or enhance differentiation.

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OCIAD1 regulates energy metabolism in human pluripotent stem cells

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OCIAD1 interacts with electron transport chain proteins and downregulates OXPHOS

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OCIAD1 regulates complex I activity and early mesodermal progenitor formation

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Pharmacological increase in complex I activity enhances stem cell differentiation

Current trends in quantitative proteomics - an update

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.

A Timely Shift from Shotgun to Targeted Proteomics and How It Can Be Groundbreaking for Cancer Research

The fact that cancer is a leading cause of death all around the world has naturally sparked major efforts in the pursuit of novel and more efficient biomarkers that could better serve as diagnostic tools, prognostic predictors, or therapeutical targets in the battle against this type of disease. Mass spectrometry-based proteomics has proven itself as a robust and logical alternative to the immuno-based methods that once dominated the field. Nevertheless, intrinsic limitations of classic proteomic approaches such as the natural gap between shotgun discovery-based methods and clinically applicable results have called for the implementation of more direct, hypothesis-based studies such as those made available through targeted approaches, that might be able to streamline biomarker discovery and validation as a means to increase survivability of affected patients. In fact, the paradigm shifting potential of modern targeted proteomics applied to cancer research can be demonstrated by the large number of advancements and increasing examples of new and more useful biomarkers found during the course of this review in different aspects of cancer research. Out of the many studies dedicated to cancer biomarker discovery, we were able to devise some clear trends, such as the fact that breast cancer is the most common type of tumor studied and that most of the research for any given type of cancer is focused on the discovery diagnostic biomarkers, with the exception of those that rely on samples other than plasma and serum, which are generally aimed toward prognostic markers. Interestingly, the most common type of targeted approach is based on stable isotope dilution-selected reaction monitoring protocols for quantification of the target molecules. Overall, this reinforces that notion that targeted proteomics has already started to fulfill its role as a groundbreaking strategy that may enable researchers to catapult the number of viable, effective, and validated biomarkers in cancer clinical practice.

Pathology-Driven Comprehensive Proteomic Profiling of the Prostate Cancer Tumor Microenvironment

Prostate cancer is the second most common cancer in men worldwide. Gleason grading is an important predictor of prostate cancer outcomes and is influential in determining patient treatment options. Clinical decisions based on a Gleason score of 7 are difficult as the prognosis for individuals diagnosed with Gleason 4+3 cancer is much worse than for those diagnosed with Gleason 3+4 cancer. Laser capture microdissection (LCM) is a highly precise method to isolate specific cell populations or discrete microregions from tissues. This report undertook a detailed molecular characterization of the tumor microenvironment in prostate cancer to define the proteome in the epithelial and stromal regions from tumor foci of Gleason grades 3 and 4. Tissue regions of interest were isolated from several Gleason 3+3 and Gleason 4+4 tumors using telepathology to leverage specialized pathology expertise to support LCM. Over 2,000 proteins were identified following liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of all regions of interest. Statistical analysis revealed significant differences in protein expression (>100 proteins) between Gleason 3 and Gleason 4 regions-in both stromal and epithelial compartments. A subset of these proteins has had prior strong association with prostate cancer, thereby providing evidence for the authenticity of the approach. Finally, validation of these proteins by immunohistochemistry has been obtained using an independent cohort of prostate cancer tumor specimens.Implications: This unbiased strategy provides a strong foundation for the development of biomarker protein panels with significant diagnostic and prognostic potential. Mol Cancer Res; 15(3); 281-93. ©2017 AACR.

A Strong Neutrophil Elastase Proteolytic Fingerprint Marks the Carcinoma Tumor Proteome

Proteolytic cascades are deeply involved in critical stages of cancer progression. During the course of peptide-wise analysis of shotgun proteomic data sets representative of colon adenocarcinoma (AC) and ulcerative colitis (UC), we detected a cancer-specific proteolytic fingerprint composed of a set of numerous protein fragments cleaved C-terminally to V, I, A, T, or C residues, significantly overrepresented in AC. A peptide set linked by a common VIATC cleavage consensus was the only prominent cancer-specific proteolytic fingerprint detected. This sequence consensus indicated neutrophil elastase as a source of the fingerprint. We also found that a large fraction of affected proteins are RNA processing proteins associated with the nuclear fraction and mostly cleaved within their functionally important RNA-binding domains. Thus, we detected a new class of cancer-specific peptides that are possible markers of tumor-infiltrating neutrophil activity, which often correlates with the clinical outcome. Data are available via ProteomeXchange with identifiers: PXD005274 (Data set 1) and PXD004249 (Data set 2). Our results indicate the value of peptide-wise analysis of large global proteomic analysis data sets as opposed to protein-wise analysis, in which outlier differential peptides are usually neglected.

The advantage of laser-capture microdissection over whole tissue analysis in proteomic profiling studies

Laser-capture microdissection (LCM) offers a reliable cell population enrichment tool and has been successfully coupled to MS analysis. Despite this, most proteomic studies employ whole tissue lysate (WTL) analysis in the discovery of disease biomarkers and in profiling analyses. Furthermore, the influence of tissue heterogeneity in WTL analysis, nor its impact in biomarker discovery studies have been completely elucidated. In order to address this, we compared previously obtained high resolution MS data from a cohort of 38 breast cancer tissues, of which both LCM enriched tumor epithelial cells and WTL samples were analyzed. Label-free quantification (LFQ) analysis through MaxQuant software showed a significantly higher number of identified and quantified proteins in LCM enriched samples (3404) compared to WTLs (2837). Furthermore, WTL samples displayed a higher amount of missing data compared to LCM both at peptide and protein levels (p-value < 0.001). 2D analysis on co-expressed proteins revealed discrepant expression of immune system and lipid metabolisms related proteins between LCM and WTL samples. We hereby show that LCM better dissected the biology of breast tumor epithelial cells, possibly due to lower interference from surrounding tissues and highly abundant proteins. All data have been deposited in the ProteomeXchange with the dataset identifier PXD002381 (http://proteomecentral.proteomexchange.org/dataset/PXD002381).