Proteomics analysis of human breast milk to assess breast cancer risk

Detection of breast cancer (BC) in young women is challenging because mammography, the most common tool for detecting BC, is not effective on the dense breast tissue characteristic of young women. In addition to the limited means for detecting their BC, young women face a transient increased risk of pregnancy‐associated BC. As a consequence, reproductively active women could benefit significantly from a tool that provides them with accurate risk assessment and early detection of BC. One potential method for detection of BC is biochemical monitoring of proteins and other molecules in bodily fluids such as serum, nipple aspirate, ductal lavage, tear, urine, saliva and breast milk. Of all these fluids, only breast milk provides access to a large volume of breast tissue, in the form of exfoliated epithelial cells, and to the local breast environment, in the form of molecules in the milk. Thus, analysis of breast milk is a non‐invasive method with significant potential for assessing BC risk. Here we analyzed human breast milk by mass spectrometry (MS)‐based proteomics to build a biomarker signature for early detection of BC. Ten milk samples from eight women provided five paired‐groups (cancer versus control) for analysis of dysregulatedproteins: two within woman comparisons (milk from a diseased breast versus a healthy breast of the same woman) and three across women comparisons (milk from a woman with cancer versus a woman without cancer). Despite a wide range in the time between milk donation and cancer diagnosis (cancer diagnosis occurred from 1 month before to 24 months after milk donation), the levels of some proteins differed significantly between cancer and control in several of the five comparison groups. These pilot data are supportive of the idea that molecular analysis of breast milk will identify proteins informative for early detection and accurate assessment of BC risk, and warrant further research. Data are available via ProteomeXchange with identifier PXD007066.     

Comparative two‐dimensional polyacrylamide gel electrophoresis (2D‐PAGE) of human milk to identify dysregulated proteins in breast cancer

Breast cancer (BC) remains a major cause of mortality, and early detection is considered important for reducing BC‐associated deaths. Early detection of BC is challenging in young women, due to the limitations of mammography on the dense breast tissue of young women. We recently reported results of a pilot proteomics study, using one‐dimensional polyacrylamide gel electrophoresis (1D‐PAGE) and mass spectrometry (MS) to investigate differences in milk proteins from women with and without BC. Here, we applied two‐dimensional polyacrylamide gel electrophoresis (2D‐PAGE) and MS to compare the protein pattern in milk from the breasts of a single woman who was diagnosed with BC in one breast 24 months after donating her milk. Statistically different gel spots were picked for protein digestion followed by nanoliquid chromatography tandem MS (nanoLC‐MS/MS) analysis. The upregulated proteins in BC versus control are alpha‐amylase, gelsolin isoform a precursor, alpha‐2‐glycoprotein 1 zinc isoform CRA_b partial, apoptosis‐inducing factor 2 and vitronectin. Several proteins were downregulated in the milk of the breast later diagnosed with cancer as compared to the milk from the healthy breast, including different isoforms of albumin, cholesterol esterase, different isoforms of lactoferrin, different proteins from the casein family and different isoforms of lysozyme. Results warrant further studies to determine the usefulness of these milk proteins for assessing risk and detecting occult disease. MS data is available via ProteomeXchange with identifier PXD009860.     

Comparative profiling of plasma proteome from breast cancer patients reveals thrombospondin-1 and BRWD3 as serological biomarkers

Breast cancer is the most common cancer in women worldwide. It is necessary to identify biomarkers for early detection, to make accurate prognoses, and to monitor for any recurrence of the cancer. In order to identify potential breast cancer biomarkers, we analyzed the plasma samples of women diagnosed with breast cancer and age-matched normal healthy women by mTRAQ-based stable isotope-labeling mass spectrometry. We identified and quantified 204 proteins including thrombospondin-1 (THBS1) and bromodomain and WD repeat-containing protein 3 (BRWD3) which were increased by more than 5-fold in breast cancer plasma. The plasma levels of the two proteins were evaluated by Western blot assay to confirm for their diagnostic value as serum markers. A 1.8-fold increase in BRWD3 was observed while comparing the plasma levels of breast cancer patients (n = 54) with age-matched normal healthy controls (n = 30), and the area under the receiver operating characteristic curve (AUC) was 0.917. THBS1 was detected in pooled breast cancer plasma at the ratio similar to mTRAQ ratio (> 5-fold). The AUC value for THBS1 was 0.875. The increase of THBS1 was more prominent in estrogen receptor negative and progesterone receptor negative patients than receptor-positive patients. Our results are evidence of the diagnostic value of THBS1 in detecting breast cancer. Based on our findings, we suggest a proteomic method for protein identification and quantification lead to effective biomarker discovery.     

用于相对和绝对定量的等量异位标签-二维液相色谱-串联质谱法分析雌二醇和血清剥夺对乳腺癌细胞内蛋白质含量的影响

雌激素和血清中的激素及各种生长因子在乳腺癌的发生、发展过程中发挥着重要的作用。研究雌激素和血清对乳腺癌细胞中蛋白质组成和含量的影响对于阐明雌激素和血清对乳腺癌细胞影响的分子机理具有重要的意义。利用四重用于相对和绝对定量的等量异位标签(isobaric tags for relative and absolute quantification, iTRAQ)标记结合二维液相色谱-串联质谱(two-dimensional liquid chromatography-tandem mass spectrometry, 2D-LC-MS/MS)对雌二醇(17β-oestradiol, E2)和血清各自以及共同作用对MCF7乳腺癌细胞内蛋白质表达的影响进行了比较,共鉴定到置信度在95%以上的蛋白质576种,其中各组相对于正常培养组的细胞共找到26种差异在1倍以上的蛋白质,其中10种上调,16种下调。研究发现E2和血清可显著影响细胞内与蛋白质合成相关的蛋白质的水平。实验结果表明: iTRAQ-LC-MS/MS是进行多个样品差异蛋白组比较的一种有效方法。     

Murine fecal proteomics: A model system for the detection of potential biomarkers for colorectal cancer

Tumor related products shed into the feces offer a potential source of biomarkers for the detection of colorectal cancer (CRC). Using SDS-PAGE followed by nanoflow reversed-phased LC–MS/MS to analyse fecal samples from Apc^Min/+ mice (that develop spontaneous multiple intestinal neoplasia with age) we have identified 336 proteins (115 proteins of murine origin, 201 from fecal bacteria, 18 associated with food intake and 2 of apparent parasitic origin). 75% of the murine proteins identified in this study are predicted to be extracellular or associated with the cell plasma membrane. Of these proteins, a number of the murine homologues of colorectal cancer associated proteins (CCAP) such as hemoglobin, haptoglobin, hemopexin, alpha-2-macroglobulin and cadherin-17 have been identified, demonstrating the potential of fecal proteomics for detecting potential biomarkers and paving the way for subsequent MS/MS based biomarker studies on similar human samples.     

Identifying potential serum biomarkers of breast cancer through targeted free fatty acid profiles screening based on a GC–MS platform

Recent advances suggest that abnormal fatty acid metabolism highly correlates with breast cancer, which provide clues to discover potential biomarkers of breast cancer. This study aims to identify serum free fatty acid (FFA) metabolic profiles and screen potential biomarkers for breast cancer diagnosis. Gas chromatography–mass spectrometry and our in-house fatty acid methyl ester standard substances library were combined to accurately identify FFA profiles in serum samples of breast cancer patients and breast adenosis patients (as controls). Potential biomarkers were screened by applying statistical analysis. A total of 18 FFAs were accurately identified in serum sample. Two groups of patients were correctly discriminated by the orthogonal partial least squares–discriminant analysis model based on FFA profiles. Seven FFA levels were significantly higher in serum from breast cancer patients than that in controls, and exhibited positive correlation with malignant degrees of disease. Furthermore, five candidates (palmitic acid, oleic acid, cis-8,11,14-eicosatrienoic acid, docosanoic acid and the ratio of oleic acid to stearic acid) were selected as potential serum biomarkers for differential diagnosis of breast cancer. Our study will help to reveal the metabolic signature of FFAs in breast cancer patients, and provides valuable information for facilitating clinical noninvasive diagnosis.     

Automated Cancer Marker Characterization in Human Plasma Using SUrface PLASMON Resonance in Array combined with Mass Spectrometry (SUPRA-MS)

The combination of Surface Plasmon Resonance technology with Mass Spectrometry becomes a key method for the characterization of targeted proteins in the fields of diagnosis and functional proteomics. We demonstrated in this work the ability of our SPRi-chip to capture targeted protein in biological fluids and in situ analyze by MS and MS/MS modes through automated procedure to go beyond classical immunoassays. Here, we established a proof of concept of SUPRA-MS for the detection, the identification and the characterization of a potential breast cancer marker.     

Liquid chromatographic separation of proteins derivatized with a fluorogenic reagent at cysteinyl residues on a non-porous column for differential proteomics analysis

A wide-pore (30 nm) reversed-phase column (Intrada WP-RP, particle size 3 μm) was recently utilized for protein separation in differential proteomics analysis with fluorogenic derivatization-liquid chromatography-tandem mass spectrometry (FD-LC-MS/MS), and exerted a tremendous effect on finding biomarkers (e.g., for breast cancer). Further high-performance separation is required for highly complex protein mixtures. A recently prepared non-porous small-particle reversed-phase column (Presto FF-C18, particle size: 2 μm) was expected to more effectively separate derivatized protein mixtures than the wide-pore column. A preliminary experiment demonstrated that the peak capacity of the former was threefold greater than that of the latter in gradient elution of a fluorogenic derivatized model peptide, calcitonin. The FD-LC-MS/MS method with a non-porous column was then optimized and applied to separate liver mitochondrial proteins that were not efficiently separated with the wide-pore column. As a result, high-performance separation of mitochondrial proteins was accomplished, and differential proteomics analysis of liver mitochondrial proteins in a hepatitis-infected mouse model was achieved using the FD-LC-MS/MS method with the non-porous column. This result suggests the non-porous small-particle column as a replacement for the wide-pore column for differential proteomics analysis in the FD-LC-MS/MS method.     

Mass spectrometry in biodefense

Potential agents for biological attacks include both microorganisms and toxins. In mass spectrometry (MS), rapid identification of potential bioagents is achieved by detecting the masses of unique biomarkers, correlated to each agent. Currently, proteins are the most reliable biomarkers for detection and characterization of both microorganisms and toxins, and MS-based proteomics is particularly well suited for biodefense applications. Confident identification of an organism can be achieved by top-down proteomics following identification of individual protein biomarkers from their tandem mass spectra. In bottom-up proteomics, rapid digestion of intact protein biomarkers is again followed by MS/MS to provide unambiguous bioagent identification and characterization. Bioinformatics obviates the need for culturing and rigorous control of experimental variables to create and use MS fingerprint libraries for various classes of bioweapons. For specific applications, MS methods, instruments and algorithms have also been developed for identification based on biomarkers other than proteins and peptides.     

A proteomic approach to investigate potential biomarkers directed against membrane-associated breast cancer proteins

The identification of specific protein markers for breast cancer would provide the basis for early diagnosis. Particularly, membrane and membrane-associated proteins are rich in targets for antibodies that may constitute suitable biomarkers of carcinogenesis. However, membrane proteins separation using 2-DE remains difficult. In this work, the breast cancer cell line MCF7 was used as source of proteins for the screening of potential cell membrane-associated antigens recognized by autoantibodies in patients with breast cancer and healthy volunteers. The protein extract obtained using trifluoroethanol (TFE) as cosolvent was compared to a total cell lysate protein extract prepared by a current technique. After 2-DE separation of the two extracts, their protein patterns clearly differed. About 63% of the proteins identified in the TFE-extract were predicted to possess at least one transmembrane domain. 2-D blots probed with sera from cancer patients or from healthy volunteers showed that, as expected, additional antigens were provided in the TFE-extract. Thus, the method described here appeared well suited for proteomic investigation of potential biomarkers undetected by current techniques.     

The development of an integrated platform to identify breast cancer glycoproteome changes in human serum

Protein glycosylation represents one of the major post-translational modifications and can have significant effects on protein function. Moreover, changes in the carbohydrate structure are increasingly being recognized as an important modification associated with cancer etiology. In this report, we describe the development of a proteomics approach to identify breast cancer related changes in either concentration and/or the carbohydrate structures of glycoprotein(s) present in blood samples. Diseased and healthy serum samples were processed by an optimized sample preparation protocol using multiple lectin affinity chromatography (M-LAC) that partitions serum proteins based on glycan characteristics. Subsequently, three separate procedures, 1D SDS-PAGE, isoelectric focusing and an antibody microarray, were applied to identify potential candidate markers for future study. The combination of these three platforms is illustrated in this report with the analysis of control and cancer glycoproteomic fractions. Firstly, a molecular weight based separation of glycoproteins by 1D SDS-PAGE was performed, followed by protein, glycoprotein staining, lectin blotting and LC–MS analysis. To refine or confirm the list of interesting glycoproteins, isoelectric focusing (targeting sialic acid changes) and an antibody microarray (used to detect neutral glycan shifts) were selected as the orthogonal methods. As a result, several glycoproteins including alpha-1B-glycoprotein, complement C3, alpha-1-antitrypsin and transferrin were identified as potential candidates for further study.     

From Chemistry to Translational Medicine: The Application of Proteomics to Cancer Biomarker Discovery and Diagnosis

The study of complex protein mixtures and their interactions in cells and tissues has been difficult due to the tedious process involved in their characterization and analysis. The recent emergence of fast‐evolving and state‐of‐the‐art proteomics methodologies has provided a rapid and scalable platform for understanding the comprehensive proteome profiles from complex whole tissues or cells of various biological sources. Therefore, proteomics has been increasingly valuable to examine real‐time changes in protein expression of various tissues or body fluids from patients with various diseases, especially cancer, resulting in the identification of clinically useful biomarkers for diagnosis, prognosis and disease staging. In this review, we focus on potential biomarkers for (1) Helicobacter pylori‐associated gastric cancer, (2) hepatocellular carcinoma (HCC), and (3) renal cell carcinoma (RCC). In addition to the conventional gel‐based proteomics (1‐D or 2‐D gels), we have utilized a more advanced proteomic approach by incorporating stable isotope dimethyl labelling and shotgun proteomics strategy in combination with nanoliquid chromatography and tandem mass spectrometry (nanoLC‐MS/MS) to better characterize the biomarkers in several cancer tissues. By establishing a high‐throughput proteomics platform based on multiple reaction monitoring (MRM), we have successfully detected and analyzed potential protein markers at low concentrations in various normal and tumor tissues. This platform not only highlights the utility of proteomics for biomarker discovery but also can be uniquely applied to disease‐oriented translational medicine for diagnosis of diverse types of cancers and other diseases.     

QSAR model for alignment-free prediction of human breast cancer biomarkers based on electrostatic potentials of protein pseudofolding HP-lattice networks

Network theory allows relationships to be established between numerical parameters that describe the molecular structure of genes and proteins and their biological properties. These models can be considered as quantitative structure-activity relationships (QSAR) for biopolymers. The work described here concerns the first QSAR model for 122 proteins that are associated with human breast cancer (HBC), as identified experimentally by Sj?blom et al. (Science 2006, 314, 268) from over 10,000 human proteins. In this study, the 122 proteins related to HBC (HBCp) and a control group of 200 proteins that are not related to HBC (non-HBCp) were forced to fold in an HP lattice network. From these networks a series of electrostatic potential parameters (xi(k)) was calculated to describe each protein numerically. The use of xi(k) as an entry point to linear discriminant analysis led to a QSAR model to discriminate between HBCp and non-HBCp, and this model could help to predict the involvement of a certain gene and/or protein in HBC. In addition, validation procedures were carried out on the model and these included an external prediction series and evaluation of an additional series of 1000 non-HBCp. In all cases good levels of classification were obtained with values above 80%. This study represents the first example of a QSAR model for the computational chemistry inspired search of potential HBC protein biomarkers.     

Serological proteome analysis of dogs with breast cancer unveils common serum biomarkers with human counterparts

Canine mammary tumor is being touted as a model for investigating the human breast cancer. Breast cancer of the both species has similar biological behavior, histopathologic characteristics, and metastatic pattern. In this study, we used the serological proteome analysis to detect autoantigens that elicit a humoral response in dogs with mammary tumor in order to identify serum biomarkers with potential usefulness as diagnostic markers and to better understand molecular mechanisms underlying canine breast cancer development. Protein extract from a cell line was subject to 2DE followed by Western blotting using sera from 15 dogs with mammary tumor and sera from 15 healthy control dogs. Immunoreactive autoantigens were subsequently identified by the MALDI‐TOF MS. Four autoantigens, including manganese‐superoxide dismutase, triose phosphate isomerase, alpha‐enolase, and phosphoglycerate mutase1, with significantly higher immunoreactivity in the tumor samples than in the normal samples were identified as biomarker candidates. Immunohistochemistry and Western blotting revealed higher expression of these biomarkers in the malignant tumors than in the normal or benign tumors. The autoantigens found in this study have been reported to elicit autoantibody response in the human breast cancer, indicating the similarity of breast cancer proteome profile in dogs with that in human beings.     

膀胱癌血清及尿液代谢组学研究

应用代谢组学研究方法,对与膀胱癌(Bladder cancer,BC)发病相关的生物标志物进行筛选,采用液相色谱-电喷雾质谱(LC-ESI/MS)联用技术对20名膀胱癌患者与24名正常人的血清和尿液进行研究.多变量统计分析结果表明,膀胱癌患者和正常人聚类明显,血清和尿液中分别发现13个潜在标志物.其中,(2E,6E,8E)-二十二碳三烯-1-醇、7-((1S,2S)-2-(庚胺)环己基)庚酸和(11E,14E,17E)-三烯-二十碳-1-醇首次在血清中发现,有潜力成为膀胱癌诊断标志物.液相色谱-质谱联用结合多变量分析的代谢组学研究技术在膀胱癌诊断中展现出巨大潜力.     

Aberrant expression of acute-phase reactant proteins in sera and breast lesions of patients with malignant and benign breast tumors

We have analyzed unfractionated sera of newly diagnosed patients (n=10) with breast carcinoma (BC), prior to treatment, and patients (n=5) with fibrocystic disease of the breast (FDB) by two-dimensional gel electrophoresis (2-DE) and silver staining. The patients' 2-DE serum protein profiles obtained were then subjected to image analysis and compared to similar data generated from sera of normal healthy female controls (n=10) of the same range of age. The relative expression of alpha1-antichymotrypsin (ACT), clusterin, and complement factor B was significantly higher in all BC patients as compared to normal controls. However, the expression of alpha1-antitrypsin (AAT) in BC patients was apparently lower than that of the controls. Similar differential expression of ACT was detected in the FDB patients. The aberrant expression of the serum acute-phase proteins of patients with BC and FDB was confirmed by competitive enzyme-linked immunosorbent assay (ELISA). Similar altered proteins expression was also observed from immunohistochemical studies of malignant (n=5) and benign (n=5) breast lesions of the respective patients performed using antisera to the aberrantly expressed proteins. However, the malignant breast lesions were instead positively stained for AAT. The differential expression of the serum proteins was apparently abrogated when a six-month follow-up study was performed on nine of the BC patients subsequent to treatment.     

Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in‐gel electrophoresis

Ovarian cancer is the fifth leading cause of cancer death for women in the US, yet survival rates are over 90% when it is diagnosed at an early stage, highlighting the need for biomarkers for early detection. To enhance the discovery of tumor‐specific proteins that could represent novel serum biomarkers for ovarian cancer, we depleted serum of highly abundant proteins which can mask the detection of proteins present in serum at low concentrations. Three commercial immunoaffinity columns were used in parallel to deplete the highly abundant proteins in serum from 60 patients with serous ovarian carcinoma and 60 non‐cancer controls. Medium and low abundance serum proteins from each serum pool were then evaluated by the quantitative proteomic technique of differential in‐gel electrophoresis. The number of protein spots that were elevated in ovarian cancer sera by at least twofold ranged from 36 to 248, depending upon the depletion and separation methods. From the 33 spots picked for MS analysis, nine different proteins were identified, including the novel candidate ovarian cancer biomarkers leucine‐rich α2 glycoprotein‐1 and ficolin 3. Western blotting validated the relative increases in serum protein levels for three of the proteins identified, demonstrating the utility of this approach for the identification of novel serum biomarkers for ovarian cancer.     

Down-regulation of Vinculin in Human Colorectal Carcinoma Identified by Proteomics Analysis

In the present study, we aimed to globally profile the proteins involved in colorectal carcinoma(CRC), in order to find clues to the pathological process of CRC. Pairs of malignant tissues and their adjacent healthy tissues from patients with colorectal cancer were subject to differential proteomics analysis. Two dimensional electrophoresis coupled with mass spectrometry(2-DE/MS) was used to identify differentially expressed proteins between pairs of tissue samples. A list of proteins relevant to the progression of colorectal tumor was identified by two dimensional gel electrophoresis(2-DE)-based proteomics approach. Among the identified proteins, vinculin was found to be remarkably down-regulated in colorectal carcinoma tissues. In addition, three phosphorylation modifications within the isolated vinculin were identified by in-depth liquid chromatography-tandem mass spectrometry(LC-MS/MS) analysis. Our results provide a basis for further understanding the pathological significance of vinculin in the regulation of carcinogenesis, invasion and metastasis of colorectal tumors.     

Applications of Tandem Mass Spectrometry (MS/MS) in Protein Analysis for Biomedical Research

Mass Spectrometry (MS) allows the analysis of proteins and peptides through a variety of methods, such as Electrospray Ionization-Mass Spectrometry (ESI-MS) or Matrix-Assisted Laser Desorption Ionization-Mass Spectrometry (MALDI-MS). These methods allow identification of the mass of a protein or a peptide as intact molecules or the identification of a protein through peptide-mass fingerprinting generated upon enzymatic digestion. Tandem mass spectrometry (MS/MS) allows the fragmentation of proteins and peptides to determine the amino acid sequence of proteins (top-down and middle-down proteomics) and peptides (bottom-up proteomics). Furthermore, tandem mass spectrometry also allows the identification of post-translational modifications (PTMs) of proteins and peptides. Here, we discuss the application of MS/MS in biomedical research, indicating specific examples for the identification of proteins or peptides and their PTMs as relevant biomarkers for diagnostic and therapy.     

Toward high sequence coverage of proteins in human breast cancer cells using on-line monolith-based HPLC-ESI-TOF MS compared to CE MS

A method is developed toward high sequence coverage of proteins isolated from human breast cancer MCF10 cell lines using a 2-D liquid separations. Monolithic-capillary columns prepared by copolymerizing styrene with divinylbenzene are used to achieve high-resolution separation of peptides from protein digests. This separation is performed with minimal sample preparation directly from the 2-D liquid fractionation of the cell lysate. The monolithic column separation is directly interfaced to ESI-TOF MS to obtain a peptide map. The protein digests were also analyzed by MALDI-TOF MS and an accurate M(r) of the intact protein was obtained using an HPLC-ESI-TOF MS. The result is that these techniques provide complementary information where nearly complete sequence coverage of the protein is obtained and can be compared to the experimental M(r) value. The high sequence coverage provides information on isoforms and other post-translational modifications that would not be available from methods that result in low sequence coverage. The results from the use of monolithic columns are compared to that obtained by CE-MS. The monolithic column separations provide a rugged and highly reproducible method for separating protein digests prior to MS analysis and is suited to confidently identify biomarkers associated with cancer progression.