Advances in Proximal Fluid Proteomics for Disease Biomarker

Advances in Proximal Fluid Proteomics for Disease Biomarker Discovery Pang-ning Teng,† Nicholas W. Bateman,† Brian L. Hood,† and Thomas P. Conrads*,‡ Department of Pharmacology and Chemical Biology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States, and the Women’s Health Integrated Research Center at Inova Health System, Annandale, Virginia, United States Received September 3, 2010

Although serum/plasma has been the preferred source for identification of disease biomarkers, these efforts have been met with little success, in large part due the relatively small number of highly abundant proteins that render the reliable detection of low abundant disease-related proteins challenging due to the expansive dynamic range of concentration of proteins in this sample. Proximal fluid, the fluid derived from the extracellular milieu of tissues, contains a large repertoire of shed and secreted proteins that are likely to be present at higher concentrations relative to plasma/serum. It is hypothesized that many, if not all, proximal fluid proteins exchange with peripheral circulation, which has provided significant motivation for utilizing proximal fluids as a primary sample source for protein biomarker discovery. The present review highlights recent advances in proximal fluid proteomics, including the various protocols utilized to harvest proximal fluids along with detailing the results from mass spectrometryand antibody-based analyses. Keywords: proximal fluid • proteomics • mass spectrometry • biomarker • tissue interstitial fluid

Introduction Biomarkers are envisaged to not only provide opportunities for disease early detection and screening, but also for molecular classification, differential diagnosis, prediction of disease progression, patient selection and stratification, prediction of therapeutic response and response monitoring. For example, the breast cancer biomarker human epidermal growth factor receptor 2 (HER2) has been used for the detection of a highly metastatic type of breast cancer, and targeted therapies such as Herceptin and Tykerb have been developed to effectively treat HER2 positive patients.1 The present review discusses recent disease biomarker discoveries from proteomic characterization of proximal fluids as well as novel sampling techniques, validation methods, and biomarkers that have been successfully validated in proximal fluids and/or serum. Proximal fluid, the fluid that is located close in proximity to the tissue of interest, could be secreted naturally or extracted by various techniques. Proximal fluids have been hypothesized to provide a rich source for biomarker discovery that can be then validated in proximal fluid and/or serum in a targeted manner. This hypothesis is supported by the notion that the fluids closest to the site of a malignancy, for example, are likely to have a high local concentration of soluble proteins and protein fragments that are produced by active secretion and * To whom correspondence should be addressed. Thomas P. Conrads, Ph.D., 3289 Woodburn Road, Woodburn II, Suite 370 Annandale, VA, 22003. Tel: 412-641-7556. Fax: 412-641-2356. E-mail: [email protected]. † University of Pittsburgh School of Medicine. ‡ Women’s Health Integrated Research Center at Inova Health System. 10.1021/pr100904q

 2010 American Chemical Society

shedding from the tissue microenvironment. Discovery of biomarkers may be facilitated in proximal fluids due to the high loco-regional concentration of proteins that otherwise are highly diluted in peripheral circulation. This concentration gradient may span greater than 3 orders of magnitude, as evidenced by the report of Sedlaczek et al. on the average concentration of CA-125, the serum tumor marker most closely associated with epithelial ovarian cancer (EOC), in serum, ascites and cyst fluid of 67 patients with EOC.2 The median level of CA-125 from ascites and ovarian cyst fluid was 18,563 and 44,850 U/mL, respectively, compared to a median serum level of only 696 U/mL. Similarly, tissue polypeptide specific antigen and soluble interleukin 2 receptor R both have higher loco-regional concentrations proximal to the tumor than in the serum.2 In addition to the potential dilution of biomarkers in serum/plasma, the large dynamic range of proteins is also a factor that renders biomarker identification a challenge. For instance, serum levels of hemopexin range from 0.5 to 1.15 mg/ mL whereas by comparison tumor necrosis factor R is present at less than 10 pg/mL.3 In addition to the potentially enriched concentration of biomarkers relative to serum, collection of proximal fluids, such as amniotic fluid, cervico-vaginal fluid, nipple aspirate, prostatic secretion, saliva, and tears are clinically viable for collection and therefore could be a convenient source for routine screening. This review discusses the use of proximal fluids, including tissue interstitial fluid, for discovery of novel protein-based disease biomarkers by proteomics and the strategies utilized for their analytical verification (Figure 1). Journal of Proteome Research 2010, 9, 6091–6100 6091 Published on Web 10/28/2010

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Figure 1. Summary of workflow from harvesting proximal fluids for discovery of disease biomarkers by proteomics to validation strategies.

Biomarker Discovery from Proximal Fluids Cerebrospinal Fluid. Cerebrospinal fluid (CSF) is a clear fluid circulating the ventricular system and spinal cord of the central nervous system (CNS) and comprises secretions derived from choroid plexus cells of the brain as well as contributions from blood plasma and interstitial fluids of the CNS.4,5 CSF is commonly collected by lumbar puncture (LP) into the subarachnoid cavity of the spinal cord as well as by cisternal puncture (CP) for sampling of more ventricular sites, a strategy typically yielding a CSF sample with a comparatively lower protein concentration.6 The protein complement of human CSF has been reported as being 100 to 400-fold lower than human blood serum and to be similarly impacted by factors such as interpatient variability, age, disease status and sample handling methods, such as the duration from sample collection to storage and repeated freeze-thaw cycles.6-9 Further, proteomic analyses of CSF is similarly confounded by protein dynamic range biases due to the presence of high abundant proteins, such as serum albumin and immunoglobulins,6,10 and significant increases in protein identifications have been achieved by liquid chromatography-mass spectrometry (LC-MS) analyses of CSF samples depleted of these abundant protein species.10,11 A recent report by Shores et al.10 comparing LC-MS/MS analyses of tryptic peptides derived from human CSF pools processed utilizing two commercial immunodepletion strategies or from CSF filtered through a 50 kDa sizeexclusion column revealed an average of 72% more unique protein identifications from immunodepleted versus CSF pools which were filtered alone. A recent global proteomic analyses of the human CSF proteome revealed this proximal fluid contains as many as 1500 unique proteins.12 Interestingly, a recent peptidomics analysis of human CSF resulted in the identification of 563 peptide products, which were shown to be derived from 91 independent precursor proteins.13 Proteomic investigations of post-translationally modified peptides from human CSF have further proven fruitful,13-15 as Nakaumura et al.14 recently reported identification of 123 phosphopeptides from human CSF by TiO2 enrichment and Hwang et al.15 reported identification of 216 glycoproteins from human CSF by hydrazide chemistry and lectin-affinity purification. As CSF encounters a vast array of CNS sites, this proximal fluid has been an attractive resource for the discovery of biomarkers specific for various neurological diseases, such as amyotrophic lateral sclerosis (ALS),16,17 multiple sclerosis (MS),18-22 Guillian-Barr syndrome (GBS),23,24 Creutzfeldt-Jakob disease (CJD),25 Alzheimer’s and Parkinson’s disease,26,27 as well as temporal lobe epilepsy.28 In a recent analysis of CSF biomarkers for GBS,23 a proteomic analysis which utilized a 6092

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Teng et al. combined 2DE and matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS approach resulted in the identification of increased levels of cystatin C in the CSF of GBS patients relative to nondiseased groups, which was further confirmed by enzyme-linked immunosorbent assay (ELISA) analysis of CSF derived from a larger cohort of GBS patients. CSF has further been investigated for biomarkers of neuroinflammatory processes, such as those associated with traumatic brain injury29 and with acute infection, such as bacterial meningitis or HIV,30-32 as well as for neoplasms of the CNS, such as astrocytoma33,34 and CNS lymphoma.35 In a recent analysis of CSF biomarkers predictive of disease grade in patients with astrocytoma,33 a proteomic analysis which utilized a combined 2DE and MALDI-TOF MS approach resulted in the identification of decreased levels of gelsolin in the CSF of patients with higher grade disease. Ohnishi et al. further confirmed this observation by immunohistochemistry (IHC) analysis in tissues derived from astrocytoma patients, further showing that loss of gelsolin expression occurs with increasing disease grade and that low expression of gelsolin correlate with a decreased rate of survival.33 Bronchoalveolar Lavage Fluid. Proteins present in bronchoalveolar lavage fluid (BALF) are hypothesized reflect the physiological condition of the lung and proteomics has been utilized to study numerous lung diseases including cystic fibrosis.36-38 459 proteins identified from 12 BALF samples from patients with cystic fibrosis and compared to healthy matched control individuals by spectral counting and unsupervised hierarchical cluster analysis (Figure 2).36 Significant protein levels have been shown in BALF from smokers suffering chronic obstructive pulmonary disease and nonsmokers.39 Protein profiling of pulmonary edema fluid and plasma from acute lung injury was compared to BALF and plasma from normal subjects and qualitative changes were observed.40 Elevated eosinophil-associated proteins and matrix metallopeptidase 9 (MMP-9) has been observed in BALF of asthmatics induced by segmental antigen challenge.41 BALF proteins of acute respiratory distress syndrome have also been characterized and computational network analysis of proteins identified clustered around several proteins including TNF-R, IL-1β, LPS-binding protein, p38 MAPK, β-estradiol, retinoic acid, and S100 proteins.42 An alternative to collecting BALF for lung disease biomarker analysis is to directly collect undiluted epithelial lining fluid using a bronchoscopic microsampling probe.43 Cervicovaginal Fluid. Cervicovaginal fluid (CVF) is the fluid that is secreted by the cervix, endometrium, oviduct, and vagina that is routinely collected during colposcopy. The proteomic analysis of CVF may enabled identification of biomarkers for diseases of the female reproductive organs, along with pathologies related to high risk obstetrics and maternal-fetal medicine.44 CVF has been analyzed by 1D-SDS-PAGE and SCX, followed by LC-MS/MS45 as well as by MALDI-TOF MS.46 A total of 150 unique proteins were identified by 2D LC-MS/ MS, of which 56 and 17 have previously been identified from serum and amniotic fluid respectively.47 Differential proteins were observed in CVF of individuals with high numbers of polymorphonuclear leukocytes when compared to healthy individuals.48 Using a stable isotope labeled proteome (SILAP) approach, heavy labeled human columnar epithelia endocervical-1 and vaginal cell secretomes were spiked in CVF samples where desmoplakin isoform 1, stratifin, and thrombospondin

Proximal Fluid Proteomics

Figure 2. Hierarchial clustering of differential proteins identified from cystic fibrosis and normal control broanchoalveolar lavage fluid. Relative protein abundance in BALF between 8 cystic fibrosis and 4 control were as determined by a spectral count approach. Figure adapted from Gharib et al. J. Proteome Res. 2009, 8 (6), 3020-9.

1 were identified as potential biomarkers that may indicate a woman’s elevated risk of preterm birth.49 Cyst Fluid. Cysts are fluid filled sacs containing a plethora of cytokines and cells. Fine needle aspiration is a noninvasive technique to sample cells and cyst fluid, and has been applied to several tissues of interest. Cyst fluid of the mammary apocrine cyst has been characterized using proteomic and IHC approaches.50 In an immunoassay of renal cyst fluid obtained from 28 patients by fine-needle aspiration, elevated carbonic anhydrase 9 levels were found in all 16 malignant tumors and not in 11 out of 12 benign cystic tumors.51 Glutathione S-transferase P1-1 levels have been shown to be higher in ovarian cyst fluid from malignant tumors as compared with benign tumors by ELISA, from a population of 165 patients.52 O-linked glycans have been isolated from ovarian cyst glycoproteins and characterized by MS analysis to identify blood group active glycotopes.53 Proteomic characterization has also been performed in pancreatic cyst fluid by MALDI-TOF MS/ MS and GeLC-MS/MS using less than 40 µL of cyst fluid, which makes analyzing cysts smaller than 1 cm in diameter possible.54 Protein profiling of the pancreatic cyst fluid has been per-

reviews formed to compare serous cystadenoma and intraductal papillary mucinous neoplasm (IPMN) where numerous proteins with significant differential abundances were observed.55 Carcinogenic embryonic antigen levels have been found to be higher in cyst fluid of malignant pancreatic cysts when compared to benign cysts.56,57 In addition, proteomic analysis resulted in the identification of a 120 kDa protein complex in neurocysticercosis cyst fluid had lead to the development of serodiagnostic assays.58 Ascites Fluid. Ascites fluid is the fluid that is accumulated in the peritoneal cavity of patients with epithelial ovarian cancer, and contains shed and secreted proteins from the ovarian tumor cells. Over 2500 proteins have been identified from ovarian cancer ascites using GeLC-MS/MS and multidimensional protein identification technology (MudPIT).59 Proteomic analysis of ascites fluid obtained from ovarian cancer patients has also been performed using strong cation exchange (SCX) followed by LC-MS/MS and resulted in the identification of 25 known ovarian cancer markers as well as 52 novel potential markers.60 Kallikrein-related peptidase KLK10 has been shown to be active in ovarian cancer ascites.61 Signaling proteins such as AKT, cAMP-responsive element binding protein and JNK have also been identified from malignant ascites fluid by protein microarray and immunostaining.62 Although ascites are relatively easy to obtain and likely contain high concentrations of potential biomarkers, it is only present in 80% of advanced stage ovarian cancer patients, therefore potentially limiting the usefulness of this sample in a diagnostic setting.63 Validation of putative markers identified from ascites fluid in serum, plasma, and/or urine will therefore be necessary for clinical implementation. Nipple Aspirate Fluid. Nipple aspirate fluid, or breast ductal fluid, is produced by the alveolar-ductal system of the breast and has been an attractive resource for breast cancer biomarker discovery as it is a breast-specific proximal fluid that is easily attainable by noninvasive means.64-66 NAF is commonly acquired by breast massage and nipple cleansing to remove ambient keratins, followed by application of a 10-20 cc syringe affixed with a plastic cup to the nipple in which negative pressure is applied for up to 15 s which typically results in yields of 5-50 µL of NAF.66-68 Nipple fluid can also be obtained by spontaneous discharge as a product of a benign or inflammatory process or due to the presence of papillary lesions or breast cancer, and profiling of carcinoembryonic antigen (CEA) and the proto-oncogene HER2/neu levels in abnormal nipple discharge has been approved for the diagnosis of breast cancer in Japan.64,69,70 The predominant constituents of NAF include proteins, lactose, sterol lipids and steroid hormones as well as an array of cell types, such as ductal epithelial cells, foam cells as well as several subtypes of leukocytes and can be impacted by patient age, menopausal as well as disease status.64,66,71,72 Total protein concentrations of NAF have been reported to be 171 mg/mL ((115 mg/mL) and to contain high abundances of immunoglobulins, such as IgA and IgG H and L subclasses, as well as lysozyme.66 Several proteomic analyses of NAF have been reported that have focused on optimizing strategies for analysis of the NAF proteome,73-75 characterizing interpatient variability of NAF proteome characteristics76 and establishing proteomic profiles and identifying biomarkers that are specific for breast cancer patients.77-84 A recent LC-MS analysis of the human NAF proteome endeavored to compare samples derived from normal and breast cancer patients revealed this proximal fluid to Journal of Proteome Research • Vol. 9, No. 12, 2010 6093

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Figure 3. Venn diagram showing numbers of proteins identified in the nipple aspirate fluid from breast cancer and healthy individuals. (A) Overlap between the 3 normal and 3 cancer samples. (B) Overlap between normal and cancer patients. Figure adapted from Pavlou et al. Clin. Chem. 2010, 56 (5), 848-55.

contain as many as 896 proteins with an average of 37% identified being localized to the extracellular matrix and 27% to the plasma membrane with 41% further overlapping with proteins which have previously been observed in blood plasma.78 Pavlou et al. further show that the NAF proteome of breast cancer patients exhibits significant heterogeneity relative to NAF samples derived from healthy individuals (Figure 3) and reported the identification of several proteins that have been proposed as biomarkers for breast cancer, i.e. urokinase-type plasminogen activator (uPA),85 cathepsin-D,86 cancer antigen (CA) 15.387 and tissue plasminogen activator (tPA).88 An analysis of NAF samples derived from normal and breast cancer patients utilizing a 2DE MALDI-TOF MS approach revealed differential abundance of gross cystic disease fluid protein (GCDFP)-15 and R-1-acid glycoprotein (AAG) in NAF from breast cancer versus nondiseased patients that was dependent on patient menopausal status, a finding that was further confirmed in a larger cohort of patients by ELISA.77 In a separate proteomic analysis comparing NAF derived from normal and breast cancer patients utilizing isotope-coded affinity tags (ICAT) revealed increased abundance of vitamin D-binding protein in NAF derived from breast cancer patients, which was further verified in NAF pooled from a cohort of patients by Western blot analysis.81 Expressed Prostatic Secretion and Seminal Plasma. Expressed prostatic secretion (EPS) and seminal plasma are proximal fluids of the prostate that represent a novel biospecimen for biomarker discovery of prostatic diseases, including prostate cancer. Expressed prostatic secretion can be collected by prostate massage-induced ejection of prostatic fluids into the urethra. Alternatively, EPS can also be collected prior to prostatectomy while the patient is under anesthesia.89 Seminal plasma contains fluids from prostate, testes, seminal vesicles and other male accessory organs and is essential for the survival of spermatozoa. Proteomic profiling of EPS from prostate 6094


Teng et al. cancer patients has been performed where 916 unique proteins were identified and compared to the secretomes of three prostate cancer-derived cell lines along with other body fluids including seminal plasma and urine.89 Seminal plasma proteomic analysis revealed 923 proteins of which many were extracellular proteins and proteases.90 Seminal plasma proteomic analysis of Asthenozoospermia patients revealed a lower abundance of oncogene DJ1 when compared to normal controls.91 Tumor proliferation-related proteins including prostate specific antigen, prostatic acid phosphatase, zinc R2-glycoprotein, and progastricsin which were identified by 2-DE MALDITOF/MS as increased in seminal plasma obtained from prostate cancer patients.92 Amniotic Fluid. Amniotic fluid provides a barrier and support for the fetus and contains both fetal and maternal proteins along with shed cells that can be collected by amniocentesis, an ultrasound-guided needle extraction. Prenatal testing for Down syndrome and other fetal chromosome abnormalities has been performed by testing the amnion fluid. Amnion fluid (8-10 mL) from 16 patients were collected during the second trimester of normal pregnancy and subjected to IgG depletion followed by LC-MS/MS where 842 nonunique proteins were identified with the majority having been extracellular (42%) or membrane (26%) in origin.93 Proteomic analysis of amnion fluid from normal and Down syndrome pregnancies was performed where 542 proteins were identified, with 60 proteins having a 2-fold or greater change in relative abundance in the Down syndrome amnion fluid by spectral counting. Of those proteins, amyloid precursor protein and tenascin-C were confirmed by ELISA.94 Amniotic fluid obtained from pregnancies with Turner syndrome has also been analyzed by 2DE MALDI-TOF MS where serotransferrin, lumican, plasma retinol-binding protein, and apolipoprotein A-I were increased in abundance, the results of which were confirmed by Western blot.95 Amniotic fluid also contains proteins secreted by amnion cells, which may reflect the physiological and pathological changes of the uterus and amniotic fluid proteomics have been studied to identify biomarkers for highrisk pregnancies.96 Intra-amnionic infections have been associated with preterm labor and poor neonatal outcome.97 Isobaric tag for relative and absolute quantitation (iTRAQ) labeling technologies have been used to quantify differential proteins in amniotic fluid in preterm labor where different protein profiles for preterm labor with and without intraamniotic infection/inflammation were found and results were confirmed by ELISA.98 Proteins were identified from term amniontic fluid by 2-DE MALDI-TOF MS analysis in which calgranulin A and B were observed in the Ureaplasma urealyticum-infected patients but not in healthy patients.96 In addition, human neutrophil protein 1-3 and calgranulin A and B were overexpressed in amniotic fluid of preterm labor patients with intra-amniotic inflammation as compared to those with preterm premature rupture of membranes.99 Calgranulin C has also been found to be correlated with histologic chorioamnionitis by surface-enhanced laser desorption ionization (SELDI) (TOF) analysis of amniotic fluid.97 A panel of candidates including IL-6, calgranulin A, C, and nutrophil defensin 1, 2 were screened by ELISA and Western blot in amniotic fluid from 86 patients with improved prediction of preterm labor with intact membranes.100 Site specific phosphorylation of insulin-like growth factor binding protein-1 has been examined from less than 1 mL of amniotic fluid of fetus with fetal growth restriction.101


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Figure 4. Blister fluids containing biomarkers can be induced by suction pressure and harvested by needle aspiration. (A) Suction chamber during the development of blisters. (B) Puncture of blisters and collection of suction blister fluid. Figure adapted from Kool et al. Proteomics 2007, 7 (20), 3638-50.

Pancreatic Juice. Pancreatic juice, a proximal fluid secreted directly from the pancreatic duct, is a potential rich source for disease biomarker identification and has been analyzed by various proteomic approaches.102,103 Pancreatic juice from pancreatic cancer, benign pancreatic diseases and cholelithiasis were collected and analyzed by 2-DE and MALDI-TOF-MS.104 Overexpression of MMP-1, DJ-1, and R-1B-glycoprotein precursor (A1BG) in pancreatic juice from pancreatic ductal adenocarcinoma was identified by difference gel electrophoresis (DIGE) and MS/MS, and confirmed by Western blot and IHC in pancreatic juice and the serum level of MMP-9 was verified by ELISA.105 Anterior gradient-2 was found to be elevated in pancreatic juice from premalignant pancreatic neoplasia patients by quantitative proteomics where results were confirmed by ELISA assay.106 Bile. Bile is produced by the liver and contains proteins that may serve as disease biomarkers for proximal organs such as the liver or pancreas. Bile must be collected through invasive procedures, such as surgery or endoscopy. Bile proteomic analysis is made challenging due to the bile acids and other compounds present in the fluid that interfere with protein and peptide separation and identification by MS.107 However, advances in sample preparation have enabled a limited number of proteomic analyses, including that which demonstrated the successful identification of 87 unique proteins and 33 glycosylation sites.108 Proteomics of bile from malignant billary stenosis induced by pancreatic cancer was characterized by SDS-PAGE and LC-MS/MS, revealing 127 proteins of which carcinoembryonic antigen-related cell adhesion molecule 6 (CEACAM6) and mucin-1 (MUC1) were elevated in abundance the bile from pancreatic cancer and cholangiocarcinoma patients.109 Proteomic composition of the bile from cholangiocarcinoma and cholelithiasis patients has also been conducted.110 Tear. Tears are secreted by the lacrimal glands to maintain eye moisture and can be collected by glass microcapillary tubes111 or Schirmer’s strips, from which proteins can be extracted by soaking the strips in PBS.112 The tear proteome has been studied using antibody protein arrays113 and MS.114 Proteomic characterization of the tear has been utilized for identifying candidate biomarkers for autoimmune thyroid eye diease,115 pterygium,116 mycotic keratitis,117 Sjogren Syndrome,118 and dry eye.117,119,120 Defensins and S100 calcumbinding proteins A8 and A9 were found to be increased in abundance in tear fluid of Pterygium patients.116 Glutaredoxinrelated proteins were found to be present only in the tears of mycotic keratitis patients.117 Lipocalin-1 and lipophilins A and C were decreased in abundance in tear from dry eye patients when compared to control individuals and the results were confirmed by Western blot. Quantitative analysis of the tear global proteome from patients suffering dry eye syndrome has

been conducted using iTRAQ, where the candidates R-enolase and S100A4 were verified by ELISA.112 N-linked glygoproteins have also been characterized in the tear fluid of climatic droplet keratopathy patients using glycopeptide capture and iTRAQ.121 Saliva. Saliva, which is produced and secreted by salivary glands contains amylase and lipase that help to digest starch and fat, is easy to collect for biomarker discovery and diagnosis of various diseases. Abundant proteins such as amylases and immunoglobulins can be depleted prior to proteomic analysis to improve identification of lower abundant proteins.122 Saliva has been analyzed by 2DE MALDI-TOF MS and LC-MS/MS.123 Reports indicate there are at least 1929 proteins from human saliva compared to a reported 3020 in the plasma proteome, of which 597 can be found in common between these two sample types.124 Preanalytic processing of saliva has been shown to positively impact the identification of proteins by MS where an in-gel digestion followed by LC-MS/MS provided the most saliva proteins.125 The saliva proteome has been characterized from patients with diseases including oral cancer,126-128 head and neck cancer,129 salivary gland disease,130 primary Sjo¨gren’s syndrome,131 and periodontitis,132 in which alterations of proteins were found when compared to normal saliva. Higher levels of leptin was found to be expressed in the saliva of patients with salivary gland tumors as measured by ELISA.130 Salivary proteomic changes in primary Sjo¨gren’s syndrome, a chronic autoimmune disorder characterized with dry eye and dry mouth symptoms, have been identified by 2DE MALDI-TOF MS where 25 proteins were found to be increased and 16 proteins decreased in abundance when compared to normal controls.131 Quantitative proteomic analysis of saliva has also shown myosin and actin as potential biomarkers of premalignant and malignant oral cancer.133 Blister Fluid. Blister fluid is the interstitial fluid of the skin that can be collected by mechanical suction and has been proposed as a potential rich source from which biomarker discovery can be conducted (Figure 4).134 Proteomic analysis of suction blister fluids from normal and plaque psoriasis human skin has been performed using 2DE MALDI-TOF MS, which resulted in the identification of 670 proteins.135 The global proteome of suction blister fluid has been analyzed and compared to that of serum where a 45% overlap was identified along with the identification of 34 known biomarkers.134 Active matrix-metalloproteinases 2, 8, and 9 have been shown to be increased in abundance in skin blister fluid and serum with from patients with severe sepsis.136 Tissue Interstitial Fluid. Tissue interstitial fluid (TIF) is the fluid that bathes cells in tissues that can be passively harvested by incubating the tissue of interest in a neutral buffer system (Figure 5). The advantage of using TIF as sample source for biomarker discovery is that in most cases matched normal Journal of Proteome Research • Vol. 9, No. 12, 2010 6095

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Figure 5. Tissue interstitial fluid containing high loco-regional concentration of disease biomarkers can be extracted by incubating tumor tissues obtained from a mastectomy. (A) Tumor. (B) Small tumor pieces used to collect the TIF. Figure adapted from Celis et al. Mol. Cell. Proteomics 2004, 3 (4), 327-44.

tissues can be harvested to collect normal interstitial fluid (NIF) from the same individual. TIF is harvested using surgically derived tissues and can be collected from tissues representing different disease states, along with specific regions of a tissue to find specific soluble molecular signatures. The first global proteomics characterization of TIF was performed in breast tissue.137 S100A4, a protein involved in metastasis, was found to be increased abundance in breast tumor TIF when compared to normal TIF, a finding that was verified in 68 tumor biopsies of high-risk breast cancer patients by IHC.138 TIF and NIF were collected from 69 breast cancer patients for proteomics analysis utilizing 2DE PAGE MS and IHC where 46 proteins were found to be increased in abundance in TIF when compared to NIF.139 In addition to breast cancer tumor tissues, TIF has been also extracted from mammary adipose tissue distant from tumor from high risk patients breast cancer and characterized by MS and antibody arrays.140 The technique and concept of harvesting TIF has gained recent interest and TIF of several tissues including the liver,141 kidney142 and ovary143 tissues been analyzed. Stress-induced phosphoprotein 1 (SIPP1) has been found to be increased in ovarian tumor TIF, and found to be elevated in serum from more than 40 ovarian cancer patients with age-matched normal individuals.143 The collection of TIF is highly novel in that it enables direct investigation of shed and secreted proteins from diseased tissues and organ sites that do not directly secrete proximal fluids.

Summary and Future Directions Proximal fluids contain a plethora of shed and secreted proteins from the diseased region and provide an excellent source for proteomic-based biomarker discovery. Recent efforts in proximal fluid proteomics utilizing MS and other methods have shown promising results. Although many studies to date have been exploratory, several reports have demonstrated verification of markers in proximal fluids or in serum, adding support to the notion that proximal fluids have utility for discovery-based proteomics.22,33,34,77,81,94,95,98,100,105,106,112,130,138,143 Many newly developed sample preparative and analytic technologies have enabled the identification of thousands of proteins from proximal fluids, which combined with bioinformatics efforts, will likely lead to advancements in biomarker discovery, further understanding of disease mechanisms, and the development of targeted therapies. Sys-BodyFluid, a web-based body fluid proteome database, has compiled 11 body fluid proteomes from 50 citations where a table with numbers of proteins identified from each fluid is provided (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2686600/ table/T1/).144 Protein families such as keratins, annexins and peroxiredoxins along with proteins such as heat shock protein 27 (HSP27), enolase 1, and triosephosphate isomerase among others have been identified as differentially abundant in multiple 6096


human biofluids and diseases. This phenomenon suggests that in most cases a single biomarker will not possess sufficient sensitivity and specificity in an in vitro diagnostic test, particularly in highly heterogeneous diseases such as cancer, a setting in which it is clear that panels of biomarkers will be needed to be established to achieve utility in the clinical setting. In addition to protein identification, functional protein analysis such as post-translational modification and mapping of signaling pathways should be catalogued and combined with transcriptomics, genomics, metabolomics, as well as the secreted protein database. Most importantly, standardization of sample collection, storage, analytical and validation protocols for various proximal fluids should be established to minimize variability and provide reproducible data sets. Therefore, similarly to what has been realized for serum and plasma, distinct pipelines are likely to be necessary to assemble for standardization of sample collection through to biomarker assay for proximal fluids if they are to be useful in the development and implementation of in vitro diagnostic assays. Abbreviations: HER2, human epithelial growth factor receptor 2; CA125, carbohydrate antigen 125; CSF, cerebrospinal fluid; CNS, central nervous sytem; LP, lumbar puncture; CP, cisternal puncture; LC-MS, liquid chromatography-mass spectrometry; LC-MS/MS, liquid chromatography-tandem mass spectrometry; ALS, amyotrphic lateral sclerosis; GBS, Guillian-Barr syndrome; CJD, Creutzfeldt-Jakob disease; 2-DE, two- dimensional gel electrophoresis; MALDI-TOF MS, matrixassisted laser desorption/ionization time-of-flight mass spectrometry; ELISA, enzyme-linked immunosorbent assay; IHC, immunohistochemistry; BALF, bronchoalveolar lavage fluid; MMP-9, matrix metallopeptidase 9; CVF, cervical-vaginal fluid; SILAP, stable isotope labeled proteome; CA9, carbonic anhydrase 9; IPMN, intraductal papillary mucinous neoplasm; MudPIT, multidimensional protein identification technology; SCX, strong cation exchange; NAF, nipple aspirate fluid; CEA, carcinoembryonic antigen, uPA, urokinase-type plasminogen activator; CA, cancer antigen; tPA, tissue plasminogen activator; GCDFP, gross cystic disease fluid protein; AAG, R-1-acid glycoprotein; ICAT, isotope-coded affinity tag; EPS, expressed prostatic secretion; DJ-1, ongogene DJ1; iTRAQ, isobaric tag for relative and absolute quantitation; SELDI-TOF MS, surfaceenhanced laser desorption ionization time-of-flight mass spectrometry; A1BG, R-1B-glycoprotein precursor; DIGE, difference gel electrophoresis; CEACAM6, carcinoembryonic antigenrelated cell adhesion molecule 6; MUC1, mucin-1; TIF, tissue interstitial fluid or tumor interstitial fluid; NIF, normal interstitial fluid; STIP1, stress-induced phosphoprotein 1; HSP27, heat shock protein 27.

References (1) Murphy, C. G.; Modi, S. HER2 breast cancer therapies: a review. Biologics 2009, 3, 289–301. (2) Sedlaczek, P.; Frydecka, I.; Gabrys, M.; Van Dalen, A.; Einarsson, R.; Harlozinska, A. Comparative analysis of CA125, tissue polypeptide specific antigen, and soluble interleukin-2 receptor alpha levels in sera, cyst, and ascitic fluids from patients with ovarian carcinoma. Cancer 2002, 95 (9), 1886–93. (3) Anderson, N. L.; Anderson, N. G.; Haines, L. R.; Hardie, D. B.; Olafson, R. W.; Pearson, T. W. Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J. Proteome Res. 2004, 3 (2), 235–44. (4) Wolburg, H.; Paulus, W. Choroid plexus: biology and pathology. Acta Neuropathol. 2010, 119 (1), 75–88. (5) Segal, M. B. The choroid plexuses and the barriers between the blood and the cerebrospinal fluid. Cell Mol. Neurobiol. 2000, 20 (2), 183–96.

Proximal Fluid Proteomics (6) Maurer, M. H. Proteomics of brain extracellular fluid (ECF) and cerebrospinal fluid (CSF). Mass Spectrom. Rev. 2010, 29 (1), 17– 28. (7) Roche, S.; Gabelle, A.; Lehmann, S. Clinical proteomics of the cerebrospinal fluid: Towards the discovery off now biomarkers. Proteomics Clin. Appl. 2008, 2 (3), 428–436. (8) Rosenling, T.; Slim, C. L.; Christin, C.; Coulier, L.; Shi, S.; Stoop, M. P.; Bosman, J.; Suits, F.; Horvatovich, P. L.; StockhofeZurwieden, N.; Vreeken, R.; Hankemeier, T.; van Gool, A. J.; Luider, T. M.; Bischoff, R. The effect of preanalytical factors on stability of the proteome and selected metabolites in cerebrospinal fluid (CSF). J. Proteome Res. 2009, 8 (12), 5511–22. (9) Stoop, M. P.; Coulier, L.; Rosenling, T.; Shi, S.; Smolinska, A. M.; Buydens, L.; Ampt, K.; Stingl, C.; Dane, A.; Muilwijk, B.; Luitwieler, R. L.; Sillevis Smitt, P. A.; Hintzen, R. Q.; Bischoff, R.; Wijmenga, S. S.; Hankemeier, T.; van Gool, A. J.; Luider, T. M. Quantitative proteomics and metabolomics analysis of normal human cerebrospinal fluid samples. Mol. Cell. Proteomics 2010, 9 (9), 2063– 75. (10) Shores, K. S.; Knapp, D. R. Assessment approach for evaluating high abundance protein depletion methods for cerebrospinal fluid (CSF) proteomic analysis. J. Proteome Res. 2007, 6 (9), 3739– 51. (11) Zuberovic, A.; Hanrieder, J.; Hellman, U.; Bergquist, J.; Wetterhall, M. Proteome profiling of human cerebrospinal fluid: exploring the potential of capillary electrophoresis with surface modified capillaries for analysis of complex biological samples. Eur. J. Mass Spectrom. 2008, 14 (4), 249–60. (12) Waybright, T.; Avellino, A. M.; Ellenbogen, R. G.; Hollinger, B. J.; Veenstra, T. D.; Morrison, R. S. Characterization of the human ventricular cerebrospinal fluid proteome obtained from hydrocephalic patients. J. Proteomics 2010, 73 (6), 1156–62. (13) Zougman, A.; Pilch, B.; Podtelejnikov, A.; Kiehntopf, M.; Schnabel, C.; Kumar, C.; Mann, M. Integrated analysis of the cerebrospinal fluid peptidome and proteome. J. Proteome Res. 2008, 7 (1), 386– 99. (14) Nakamura, T.; Myint, K. T.; Oda, Y. Ethylenediaminetetraacetic acid increases identification rate of phosphoproteomics in real biological samples. J. Proteome Res. 2010, 9 (3), 1385–91. (15) Hwang, H. J.; Quinn, T.; Zhang, J. Identification of glycoproteins in human cerebrospinal fluid. Methods Mol. Biol. 2009, 566, 263– 76. (16) Ranganathan, S.; Nicholl, G. C.; Henry, S.; Lutka, F.; Sathanoori, R.; Lacomis, D.; Bowser, R. Comparative proteomic profiling of cerebrospinal fluid between living and post mortem ALS and control subjects. Amyotroph. Lateral Scler. 2007, 8 (6), 373–9. (17) Ryberg, H.; An, J.; Darko, S.; Lustgarten, J. L.; Jaffa, M.; Gopalakrishnan, V.; Lacomis, D.; Cudkowicz, M.; Bowser, R. Discovery and verification of amyotrophic lateral sclerosis biomarkers by proteomics. Muscle Nerve 2010, 42 (1), 104–11. (18) Ottervald, J.; Franzen, B.; Nilsson, K.; Andersson, L. I.; Khademi, M.; Eriksson, B.; Kjellstrom, S.; Marko-Varga, G.; Vegvari, A.; Harris, R. A.; Laurell, T.; Miliotis, T.; Matusevicius, D.; Salter, H.; Ferm, M.; Olsson, T. Multiple sclerosis: Identification and clinical evaluation of novel CSF biomarkers. J. Proteomics 2010, 73 (6), 1117–32. (19) Comabella, M.; Fernandez, M.; Martin, R.; Rivera-Vallve, S.; Borras, E.; Chiva, C.; Julia, E.; Rovira, A.; Canto, E.; AlvarezCermeno, J. C.; Villar, L. M.; Tintore, M.; Montalban, X. Cerebrospinal fluid Chitinase 3-like 1 levels are associated with conversion to multiple sclerosis. Brain 2010, 133 (Pt 4), 1082– 93. (20) Liu, S.; Bai, S.; Qin, Z.; Yang, Y.; Cui, Y.; Qin, Y. Quantitative proteomic analysis of the cerebrospinal fluid of patients with multiple sclerosis. J. Cell. Mol. Med. 2009, 13 (8A), 1586–603. (21) Stoop, M. P.; Dekker, L. J.; Titulaer, M. K.; Lamers, R. J.; Burgers, P. C.; Sillevis Smitt, P. A.; van Gool, A. J.; Luider, T. M.; Hintzen, R. Q. Quantitative matrix-assisted laser desorption ionizationfourier transform ion cyclotron resonance (MALDI-FT-ICR) peptide profiling and identification of multiple-sclerosis-related proteins. J. Proteome Res. 2009, 8 (3), 1404–14. (22) Tumani, H.; Lehmensiek, V.; Rau, D.; Guttmann, I.; Tauscher, G.; Mogel, H.; Palm, C.; Hirt, V.; Suessmuth, S. D.; Sapunova-Meier, I.; Ludolph, A. C.; Brettschneider, J. CSF proteome analysis in clinically isolated syndrome (CIS): candidate markers for conversion to definite multiple sclerosis. Neurosci. Lett. 2009, 452 (2), 214–7. (23) Yang, Y. R.; Liu, S. L.; Qin, Z. Y.; Liu, F. J.; Qin, Y. J.; Bai, S. M.; Chen, Z. Y. Comparative proteomics analysis of cerebrospinal fluid of patients with Guillain-Barre syndrome. Cell Mol. Neurobiol. 2008, 28 (5), 737–44.

reviews (24) Yang, Y.; Liu, S.; Qin, Z.; Cui, Y.; Qin, Y.; Bai, S. Alteration of cystatin C levels in cerebrospinal fluid of patients with GuillainBarre Syndrome by a proteomical approach. Mol. Biol. Rep. 2009, 36 (4), 677–82. (25) Steinacker, P.; Rist, W.; Swiatek-de-Lange, M.; Lehnert, S.; Jesse, S.; Pabst, A.; Tumani, H.; von Arnim, C. A.; Mitrova, E.; Kretzschmar, H. A.; Lenter, M.; Wiltfang, J.; Otto, M. Ubiquitin as potential cerebrospinal fluid marker of Creutzfeldt-Jakob disease. Proteomics 2010, 10 (1), 81–9. (26) Hanisch, K.; Soininen, H.; Alafuzoff, I.; Hoffmann, R. Analysis of human tau in cerebrospinal fluid. J. Proteome Res. 2010, 9 (3), 1476–82. (27) Pan, S.; Rush, J.; Peskind, E. R.; Galasko, D.; Chung, K.; Quinn, J.; Jankovic, J.; Leverenz, J. B.; Zabetian, C.; Pan, C.; Wang, Y.; Oh, J. H.; Gao, J.; Zhang, J.; Montine, T. Application of targeted quantitative proteomics analysis in human cerebrospinal fluid using a liquid chromatography matrix-assisted laser desorption/ ionization time-of-flight tandem mass spectrometer (LC MALDI TOF/TOF) platform. J. Proteome Res. 2008, 7 (2), 720–30. (28) Xiao, F.; Chen, D.; Lu, Y.; Xiao, Z.; Guan, L. F.; Yuan, J.; Wang, L.; Xi, Z. Q.; Wang, X. F. Proteomic analysis of cerebrospinal fluid from patients with idiopathic temporal lobe epilepsy. Brain Res. 2009, 1255, 180–9. (29) Sjodin, M. O.; Bergquist, J.; Wetterhall, M. Mining ventricular cerebrospinal fluid from patients with traumatic brain injury using hexapeptide ligand libraries to search for trauma biomarkers. J. Chromatogr., B: Analyt. Technol. Biomed. Life Sci. 2010, 878 (22), 2003–12. (30) Peng, J.; Yin, F.; Zhang, H. Y.; Duan, Y. D.; Gan, N.; Wu, L. W. A pilot study on the proteome of cerebrospinal fluid of Staphylococcus epidermidis meningitis in children. Zhongguo Dang Dai Er Ke Za Zhi 2008, 10 (3), 280–4. (31) Laspiur, J. P.; Anderson, E. R.; Ciborowski, P.; Wojna, V.; Rozek, W.; Duan, F.; Mayo, R.; Rodriguez, E.; Plaud-Valentin, M.; Rodriguez-Orengo, J.; Gendelman, H. E.; Melendez, L. M. CSF proteomic fingerprints for HIV-associated cognitive impairment. J. Neuroimmunol. 2007, 192 (1-2), 157–70. (32) Rozek, W.; Ricardo-Dukelow, M.; Holloway, S.; Gendelman, H. E.; Wojna, V.; Melendez, L. M.; Ciborowski, P. Cerebrospinal fluid proteomic profiling of HIV-1-infected patients with cognitive impairment. J. Proteome Res. 2007, 6 (11), 4189–99. (33) Ohnishi, M.; Matsumoto, T.; Nagashio, R.; Kageyama, T.; Utsuki, S.; Oka, H.; Okayasu, I.; Sato, Y. Proteomics of tumor-specific proteins in cerebrospinal fluid of patients with astrocytoma: usefulness of gelsolin protein. Pathol. Int. 2009, 59 (11), 797– 803. (34) Schuhmann, M. U.; Zucht, H. D.; Nassimi, R.; Heine, G.; Schneekloth, C. G.; Stuerenburg, H. J.; Selle, H. Peptide screening of cerebrospinal fluid in patients with glioblastoma multiforme. Eur. J. Surg. Oncol. 2010, 36 (2), 201–7. (35) Roy, S.; Josephson, S. A.; Fridlyand, J.; Karch, J.; Kadoch, C.; Karrim, J.; Damon, L.; Treseler, P.; Kunwar, S.; Shuman, M. A.; Jones, T.; Becker, C. H.; Schulman, H.; Rubenstein, J. L. Protein biomarker identification in the CSF of patients with CNS lymphoma. J. Clin. Oncol. 2008, 26 (1), 96–105. (36) Gharib, S. A.; Vaisar, T.; Aitken, M. L.; Park, D. R.; Heinecke, J. W.; Fu, X. Mapping the lung proteome in cystic fibrosis. J. Proteome Res. 2009, 8 (6), 3020–8. (37) Hirsch, J.; Hansen, K. C.; Burlingame, A. L.; Matthay, M. A. Proteomics: current techniques and potential applications to lung disease. Am. J. Physiol. Lung Cell Mol. Physiol. 2004, 287 (1), L1– 23. (38) Rottoli, P.; Bargagli, E.; Landi, C.; Magi, B. Proteomic analysis in interstitial lung diseases: a review. Curr. Opin. Pulm. Med. 2009, 15 (5), 470–8. (39) Merkel, D.; Rist, W.; Seither, P.; Weith, A.; Lenter, M. C. Proteomic study of human bronchoalveolar lavage fluids from smokers with chronic obstructive pulmonary disease by combining surfaceenhanced laser desorption/ionization-mass spectrometry profiling with mass spectrometric protein identification. Proteomics 2005, 5 (11), 2972–80. (40) Bowler, R. P.; Duda, B.; Chan, E. D.; Enghild, J. J.; Ware, L. B.; Matthay, M. A.; Duncan, M. W. Proteomic analysis of pulmonary edema fluid and plasma in patients with acute lung injury. Am. J. Physiol. Lung Cell Mol. Physiol. 2004, 286 (6), L1095–104. (41) Wu, J.; Kobayashi, M.; Sousa, E. A.; Liu, W.; Cai, J.; Goldman, S. J.; Dorner, A. J.; Projan, S. J.; Kavuru, M. S.; Qiu, Y.; Thomassen, M. J. Differential proteomic analysis of bronchoalveolar lavage fluid in asthmatics following segmental antigen challenge. Mol. Cell. Proteomics 2005, 4 (9), 1251–64.

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reviews (42) Chang, D. W.; Hayashi, S.; Gharib, S. A.; Vaisar, T.; King, S. T.; Tsuchiya, M.; Ruzinski, J. T.; Park, D. R.; Matute-Bello, G.; Wurfel, M. M.; Bumgarner, R.; Heinecke, J. W.; Martin, T. R. Proteomic and computational analysis of bronchoalveolar proteins during the course of the acute respiratory distress syndrome. Am. J. Respir. Crit. Care Med. 2008, 178 (7), 701–9. (43) Kipnis, E.; Hansen, K.; Sawa, T.; Moriyama, K.; Zurawel, A.; Ishizaka, A.; Wiener-Kronish, J. Proteomic analysis of undiluted lung epithelial lining fluid. Chest 2008, 134 (2), 338–45. (44) Klein, L. L.; Jonscher, K. R.; Heerwagen, M. J.; Gibbs, R. S.; McManaman, J. L. Shotgun proteomic analysis of vaginal fluid from women in late pregnancy. Reprod. Sci. 2008, 15 (3), 263– 73. (45) Shaw, J. L.; Smith, C. R.; Diamandis, E. P. Proteomic analysis of human cervico-vaginal fluid. J. Proteome Res. 2007, 6 (7), 2859– 65. (46) Zegels, G.; Van Raemdonck, G. A.; Coen, E. P.; Tjalma, W. A.; Van Ostade, X. W. Comprehensive proteomic analysis of human cervical-vaginal fluid using colposcopy samples. Proteome Sci. 2009, 7, 17. (47) Dasari, S.; Pereira, L.; Reddy, A. P.; Michaels, J. E.; Lu, X.; Jacob, T.; Thomas, A.; Rodland, M.; Roberts, C. T., Jr.; Gravett, M. G.; Nagalla, S. R. Comprehensive proteomic analysis of human cervical-vaginal fluid. J. Proteome Res. 2007, 6 (4), 1258–68. (48) Tang, L. J.; De Seta, F.; Odreman, F.; Venge, P.; Piva, C.; Guaschino, S.; Garcia, R. C. Proteomic analysis of human cervicalvaginal fluids. J. Proteome Res. 2007, 6 (7), 2874–83. (49) Shah, S. J.; Yu, K. H.; Sangar, V.; Parry, S. I.; Blair, I. A. Identification and quantification of preterm birth biomarkers in human cervicovaginal fluid by liquid chromatography/tandem mass spectrometry. J. Proteome Res. 2009, 8 (5), 2407–17. (50) Celis, J. E.; Gromov, P.; Moreira, J. M.; Cabezon, T.; Friis, E.; Vejborg, I. M.; Proess, G.; Rank, F.; Gromova, I. Apocrine cysts of the breast: biomarkers, origin, enlargement, and relation with cancer phenotype. Mol. Cell. Proteomics 2006, 5 (3), 462–83. (51) Li, G.; Feng, G.; Cuilleron, M.; Zhao, A.; Gentil-Perret, A.; Cottier, M.; Genin, C.; Tostain, J. CA9 level in renal cyst fluid: a possible molecular diagnosis of malignant tumours. Histopathology 2009, 54 (7), 880–4. (52) Kolwijck, E.; Zusterzeel, P. L.; Roelofs, H. M.; Hendriks, J. C.; Peters, W. H.; Massuger, L. F. GSTP1-1 in ovarian cyst fluid and disease outcome of patients with ovarian cancer. Cancer Epidemiol. Biomarkers Prev. 2009, 18 (8), 2176–81. (53) Yu, S. Y.; Yang, Z.; Khoo, K. H.; Wu, A. M. Identification of blood group A/A-Leb/y and B/B-Leb/y active glycotopes co-expressed on the O-glycans isolated from two distinct human ovarian cyst fluids. Proteomics 2009, 9 (13), 3445–62. (54) Ke, E.; Patel, B. B.; Liu, T.; Li, X. M.; Haluszka, O.; Hoffman, J. P.; Ehya, H.; Young, N. A.; Watson, J. C.; Weinberg, D. S.; Nguyen, M. T.; Cohen, S. J.; Meropol, N. J.; Litwin, S.; Tokar, J. L.; Yeung, A. T. Proteomic analyses of pancreatic cyst fluids. Pancreas 2009, 38 (2), e33-42. (55) Allen, P. J.; Qin, L. X.; Tang, L.; Klimstra, D.; Brennan, M. F.; Lokshin, A. Pancreatic cyst fluid protein expression profiling for discriminating between serous cystadenoma and intraductal papillary mucinous neoplasm. Ann. Surg. 2009, 250 (5), 754–60. (56) Sreenarasimhaiah, J.; Lara, L. F.; Jazrawi, S. F.; Barnett, C. C.; Tang, S. J. A comparative analysis of pancreas cyst fluid CEA and histology with DNA mutational analysis in the detection of mucin producing or malignant cysts. J. Pancreas 2009, 10 (2), 163–8. (57) Snozek, C. L.; Mascarenhas, R. C.; O’Kane, D. J.; Use of cyst fluid, C. E. A. CA19-9, and amylase for evaluation of pancreatic lesions. Clin. Biochem. 2009, 42 (15), 1585–8. (58) Lee, E. G.; Bae, Y. A.; Jeong, Y. T.; Chung, J. Y.; Je, E. Y.; Kim, S. H.; Na, B. K.; Ju, J. W.; Kim, T. S.; Ma, L.; Cho, S. Y.; Kong, Y. Proteomic analysis of a 120 kDa protein complex in cyst fluid of Taenia solium metacestode and preliminary evaluation of its value for the serodiagnosis of neurocysticercosis. Parasitology 2005, 131 (Pt 6), 867–79. (59) Gortzak-Uzan, L.; Ignatchenko, A.; Evangelou, A. I.; Agochiya, M.; Brown, K. A.; St Onge, P.; Kireeva, I.; Schmitt-Ulms, G.; Brown, T. J.; Murphy, J.; Rosen, B.; Shaw, P.; Jurisica, I.; Kislinger, T. A proteome resource of ovarian cancer ascites: integrated proteomic and bioinformatic analyses to identify putative biomarkers. J. Proteome Res. 2008, 7 (1), 339–51. (60) Kuk, C.; Kulasingam, V.; Gunawardana, C. G.; Smith, C. R.; Batruch, I.; Diamandis, E. P. Mining the ovarian cancer ascites proteome for potential ovarian cancer biomarkers. Mol. Cell. Proteomics 2009, 8 (4), 661–9. (61) Oikonomopoulou, K.; Batruch, I.; Smith, C. R.; Soosaipillai, A.; Diamandis, E. P.; Hollenberg, M. D. Functional proteomics of

6098


Teng et al.

(62)

(63) (64) (65) (66) (67) (68) (69)

(70) (71) (72) (73)

(74)

(75)

(76)

(77)

(78)

(79)

(80)

(81)

kallikrein-related peptidases in ovarian cancer ascites fluid. Biol. Chem. 2010, 391 (4), 381–90. Davidson, B.; Espina, V.; Steinberg, S. M.; Florenes, V. A.; Liotta, L. A.; Kristensen, G. B.; Trope, C. G.; Berner, A.; Kohn, E. C. Proteomic analysis of malignant ovarian cancer effusions as a tool for biologic and prognostic profiling. Clin. Cancer Res. 2006, 12 (3 Pt 1), 791–9. Cadron, I.; Van Gorp, T.; Timmerman, D.; Amant, F.; Waelkens, E.; Vergote, I. Application of proteomics in ovarian cancer: which sample should be used. Gynecol. Oncol. 2009, 115 (3), 497–503. Klein, P. M.; Lawrence, J. A. Lavage and nipple aspiration of breast ductal fluids: a source of biomarkers for environmental mutagenesis. Environ. Mol. Mutagen 2002, 39 (2-3), 127–33. Laronga, C.; Drake, R. R. Proteomic approach to breast cancer. Cancer Control 2007, 14 (4), 360–8. Petrakis, N. L. Physiologic, biochemical, and cytologic aspects of nipple aspirate fluid. Breast Cancer Res. Treat. 1986, 8 (1), 7– 19. Khan, S. A. The role of ductal lavage in the management of women at high risk for breast carcinoma. Curr. Treat. Options Oncol. 2004, 5 (2), 145–51. Sartorius, O. W.; Smith, H. S.; Morris, P.; Benedict, D.; Friesen, L. Cytologic evaluation of breast fluid in the detection of breast disease. J. Natl. Cancer Inst. 1977, 59 (4), 1073–80. Das, D. K.; Al-Ayadhy, B.; Ajrawi, M. T.; Shaheen, A. A.; Sheikh, Z. A.; Malik, M.; Pathan, S. K.; Ebrahim, B.; Francis, I. M.; Satar, S. A.; Abdulla, M. A.; Luthra, U. K.; Junaid, T. A. Cytodiagnosis of nipple discharge: a study of 602 samples from 484 cases. Diagn. Cytopathol. 2001, 25 (1), 25–37. Kurebayashi, J. Biomarkers in breast cancer. Gan To Kagaku Ryoho 2004, 31 (7), 1021–6. King, E. B.; Barrett, D.; King, M. C.; Petrakis, N. L. Cellular composition of the nipple aspirate specimen of breast fluid. I. The benign cells. Am. J. Clin. Pathol. 1975, 64 (6), 728–38. King, E. B.; Barrett, D.; Petrakis, N. L. Cellular composition of the nipple aspirate specimen of breast fluid. II. Abnormal findings. Am. J. Clin. Pathol. 1975, 64 (6), 739–48. Coombes, K. R.; Fritsche, H. A., Jr.; Clarke, C.; Chen, J. N.; Baggerly, K. A.; Morris, J. S.; Xiao, L. C.; Hung, M. C.; Kuerer, H. M. Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clin. Chem. 2003, 49 (10), 1615–23. Coombes, K. R.; Tsavachidis, S.; Morris, J. S.; Baggerly, K. A.; Hung, M. C.; Kuerer, H. M. Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 2005, 5 (16), 4107– 17. Varnum, S. M.; Covington, C. C.; Woodbury, R. L.; Petritis, K.; Kangas, L. J.; Abdullah, M. S.; Pounds, J. G.; Smith, R. D.; Zangar, R. C. Proteomic characterization of nipple aspirate fluid: identification of potential biomarkers of breast cancer. Breast Cancer Res. Treat. 2003, 80 (1), 87–97. Noble, J.; Dua, R. S.; Locke, I.; Eeles, R.; Gui, G. P.; Isacke, C. M. Proteomic analysis of nipple aspirate fluid throughout the menstrual cycle in healthy pre-menopausal women. Breast Cancer Res. Treat. 2007, 104 (2), 191–6. Alexander, H.; Stegner, A. L.; Wagner-Mann, C.; Du Bois, G. C.; Alexander, S.; Sauter, E. R. Proteomic analysis to identify breast cancer biomarkers in nipple aspirate fluid. Clin. Cancer Res. 2004, 10 (22), 7500–10. Pavlou, M. P.; Kulasingam, V.; Sauter, E. R.; Kliethermes, B.; Diamandis, E. P. Nipple aspirate fluid proteome of healthy females and patients with breast cancer. Clin. Chem. 2010, 56 (5), 848–55. Paweletz, C. P.; Trock, B.; Pennanen, M.; Tsangaris, T.; Magnant, C.; Liotta, L. A.; Petricoin, E. F. 3rd, Proteomic patterns of nipple aspirate fluids obtained by SELDI-TOF: potential for new biomarkers to aid in the diagnosis of breast cancer. Dis. Markers 2001, 17 (4), 301–7. Pawlik, T. M.; Fritsche, H.; Coombes, K. R.; Xiao, L.; Krishnamurthy, S.; Hunt, K. K.; Pusztai, L.; Chen, J. N.; Clarke, C. H.; Arun, B.; Hung, M. C.; Kuerer, H. M. Significant differences in nipple aspirate fluid protein expression between healthy women and those with breast cancer demonstrated by time-of-flight mass spectrometry. Breast Cancer Res. Treat. 2005, 89 (2), 149–57. Pawlik, T. M.; Hawke, D. H.; Liu, Y.; Krishnamurthy, S.; Fritsche, H.; Hunt, K. K.; Kuerer, H. M. Proteomic analysis of nipple aspirate fluid from women with early-stage breast cancer using isotope-coded affinity tags and tandem mass spectrometry

reviews


(82)

(83) (84)

(85) (86)

(87) (88) (89)

(90) (91)

(92)

(93) (94) (95)

(96)

(97)

(98)

(99)

(100)

(101)

reveals differential expression of vitamin D binding protein. BMC Cancer 2006, 6, 68. Sauter, E. R.; Davis, W.; Qin, W.; Scanlon, S.; Mooney, B.; Bromert, K.; Folk, W. R. Identification of a beta-casein-like peptide in breast nipple aspirate fluid that is associated with breast cancer. Biomark. Med. 2009, 3 (5), 577–88. Sauter, E. R.; Shan, S.; Hewett, J. E.; Speckman, P.; Du Bois, G. C. Proteomic analysis of nipple aspirate fluid using SELDI-TOF-MS. Int. J. Cancer 2005, 114 (5), 791–6. Sauter, E. R.; Zhu, W.; Fan, X. J.; Wassell, R. P.; Chervoneva, I.; Du Bois, G. C. Proteomic analysis of nipple aspirate fluid to detect biologic markers of breast cancer. Br. J. Cancer 2002, 86 (9), 1440– 3. Duffy, M. J. Urokinase plasminogen activator and its inhibitor, PAI-1, as prognostic markers in breast cancer: from pilot to level 1 evidence studies. Clin. Chem. 2002, 48 (8), 1194–7. Foekens, J. A.; Look, M. P.; Bolt-de Vries, J.; Meijer-van Gelder, M. E.; van Putten, W. L.; Klijn, J. G. Cathepsin-D in primary breast cancer: prognostic evaluation involving 2810 patients. Br. J. Cancer 1999, 79 (2), 300–7. Duffy, M. J. Serum tumor markers in breast cancer: are they of clinical value. Clin. Chem. 2006, 52 (3), 345–51. Nicolini, A.; Anselmi, L.; Michelassi, C.; Carpi, A. Prolonged survival by ‘early’ salvage treatment of breast cancer patients: a retrospective 6-year study. Br. J. Cancer 1997, 76 (8), 1106–11. Drake, R.; Elschenbroich, S.; Lopez-Perez, O.; Kim, Y.; Ignatchenko, V.; Ignatchenko, A.; Nyalwidhe, J. O.; Basu, G.; Wilkins, C.; Gjurich, B.; Lance, R. S.; Semmes, J.; Medin, J.; Kislinger, T. Indepth Proteomic Analyses of Direct Expressed Prostatic Secretions. J. Proteome Res. 2010, 9 (5), 2109–16. Pilch, B.; Mann, M. Large-scale and high-confidence proteomic analysis of human seminal plasma. Genome Biol. 2006, 7 (5), R40. Wang, J.; Zhang, H. R.; Shi, H. J.; Ma, D.; Zhao, H. X.; Lin, B.; Li, R. S. Proteomic analysis of seminal plasma from asthenozoospermia patients reveals proteins that affect oxidative stress responses and semen quality. Asian J. Androl. 2009, 11 (4), 484–91. Hassan, M. I.; Kumar, V.; Kashav, T.; Alam, N.; Singh, T. P.; Yadav, S. Proteomic approach for purification of seminal plasma proteins involved in tumor proliferation. J. Sep. Sci. 2007, 30 (12), 1979– 88. Cho, C. K.; Shan, S. J.; Winsor, E. J.; Diamandis, E. P. Proteomics analysis of human amniotic fluid. Mol. Cell. Proteomics 2007, 6 (8), 1406–15. Cho, C. K.; Smith, C. R.; Diamandis, E. P. Amniotic Fluid Proteome Analysis from Down Syndrome Pregnancies for Biomarker Discovery. J. Proteome Res. 2010, 9 (7), 3574–82. Mavrou, A.; Anagnostopoulos, A. K.; Kolialexi, A.; Vougas, K.; Papantoniou, N.; Antsaklis, A.; Fountoulakis, M.; Tsangaris, G. T. Proteomic analysis of amniotic fluid in pregnancies with Turner syndrome fetuses. J. Proteome Res. 2008, 7 (5), 1862–6. Park, S. J.; Yoon, W. G.; Song, J. S.; Jung, H. S.; Kim, C. J.; Oh, S. Y.; Yoon, B. H.; Jung, G.; Kim, H. J.; Nirasawa, T. Proteome analysis of human amnion and amniotic fluid by two-dimensional electrophoresis and matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry. Proteomics 2006, 6 (1), 349–63. Buhimschi, I. A.; Zambrano, E.; Pettker, C. M.; Bahtiyar, M. O.; Paidas, M.; Rosenberg, V. A.; Thung, S.; Salafia, C. M.; Buhimschi, C. S. Using proteomic analysis of the human amniotic fluid to identify histologic chorioamnionitis. Obstet. Gynecol. 2008, 111 (2 Pt 1), 403–12. Romero, R.; Kusanovic, J. P.; Gotsch, F.; Erez, O.; Vaisbuch, E.; Mazaki-Tovi, S.; Moser, A.; Tam, S.; Leszyk, J.; Master, S. R.; Juhasz, P.; Pacora, P.; Ogge, G.; Gomez, R.; Yoon, B. H.; Yeo, L.; Hassan, S. S.; Rogers, W. T. Isobaric labeling and tandem mass spectrometry: a novel approach for profiling and quantifying proteins differentially expressed in amniotic fluid in preterm labor with and without intra-amniotic infection/inflammation. J. Matern. Fetal Neonatal Med. 2010, 23 (4), 261–80. Ruetschi, U.; Rosen, A.; Karlsson, G.; Zetterberg, H.; Rymo, L.; Hagberg, H.; Jacobsson, B. Proteomic analysis using protein chips to detect biomarkers in cervical and amniotic fluid in women with intra-amniotic inflammation. J. Proteome Res. 2005, 4 (6), 2236–42. Cobo, T.; Palacio, M.; Navarro-Sastre, A.; Ribes, A.; Bosch, J.; Filella, X.; Gratacos, E. Predictive value of combined amniotic fluid proteomic biomarkers and interleukin-6 in preterm labor with intact membranes. Am. J. Obstet. Gynecol. 2009, 200 (5), 499 e1-6. Abu Shehab, M.; Inoue, S.; Han, V. K.; Gupta, M. B. Site specific phosphorylation of insulin-like growth factor binding protein-1

(102) (103) (104) (105)

(106)

(107) (108)

(109)

(110)

(111)

(112)

(113) (114)

(115) (116)

(117) (118) (119) (120) (121)

(122) (123)

(IGFBP-1) for evaluating clinical relevancy in fetal growth restriction. J. Proteome Res. 2009, 8 (11), 5325–35. Tonack, S.; Aspinall-O’Dea, M.; Neoptolemos, J. P.; Costello, E. Pancreatic cancer: proteomic approaches to a challenging disease. Pancreatology 2009, 9 (5), 567–76. Chen, R.; Pan, S.; Aebersold, R.; Brentnall, T. A. Proteomics studies of pancreatic cancer. Proteomics Clin. Appl. 2007, 1 (12), 1582– 1591. Zhou, L.; Lu, Z.; Yang, A.; Deng, R.; Mai, C.; Sang, X.; Faber, K. N.; Lu, X. Comparative proteomic analysis of human pancreatic juice: methodological study. Proteomics 2007, 7 (8), 1345–55. Tian, M.; Cui, Y. Z.; Song, G. H.; Zong, M. J.; Zhou, X. Y.; Chen, Y.; Han, J. X. Proteomic analysis identifies MMP-9, DJ-1 and A1BG as overexpressed proteins in pancreatic juice from pancreatic ductal adenocarcinoma patients. BMC Cancer 2008, 8, 241. Chen, R.; Pan, S.; Duan, X.; Nelson, B. H.; Sahota, R. A.; de Rham, S.; Kozarek, R. A.; McIntosh, M.; Brentnall, T. A. Elevated level of anterior gradient-2 in pancreatic juice from patients with premalignant pancreatic neoplasia. Mol. Cancer 2010, 9, 149. Farina, A.; Dumonceau, J. M.; Lescuyer, P. Proteomic analysis of human bile and potential applications for cancer diagnosis. Expert Rev. Proteomics 2009, 6 (3), 285–301. Kristiansen, T. Z.; Bunkenborg, J.; Gronborg, M.; Molina, H.; Thuluvath, P. J.; Argani, P.; Goggins, M. G.; Maitra, A.; Pandey, A. A proteomic analysis of human bile. Mol. Cell. Proteomics 2004, 3 (7), 715–28. Farina, A.; Dumonceau, J. M.; Frossard, J. L.; Hadengue, A.; Hochstrasser, D. F.; Lescuyer, P. Proteomic analysis of human bile from malignant biliary stenosis induced by pancreatic cancer. J. Proteome Res. 2009, 8 (1), 159–69. Chen, B.; Dong, J. Q.; Chen, Y. J.; Wang, J. M.; Tian, J.; Wang, C. B.; Zou, S. Q. Two-dimensional electrophoresis for comparative proteomic analysis of human bile. Hepatobiliary Pancreat. Dis. Int. 2007, 6 (4), 402–6. Zhou, L.; Beuerman, R. W.; Foo, Y.; Liu, S.; Ang, L. P.; Tan, D. T. Characterisation of human tear proteins using high-resolution mass spectrometry. Ann. Acad. Med. Singapore 2006, 35 (6), 400– 7. Zhou, L.; Beuerman, R. W.; Chan, C. M.; Zhao, S. Z.; Li, X. R.; Yang, H.; Tong, L.; Liu, S.; Stern, M. E.; Tan, D. Identification of tear fluid biomarkers in dry eye syndrome using iTRAQ quantitative proteomics. J. Proteome Res. 2009, 8 (11), 4889–905. Li, S.; Sack, R.; Vijmasi, T.; Sathe, S.; Beaton, A.; Quigley, D.; Gallup, M.; McNamara, N. A. Antibody protein array analysis of the tear film cytokines. Optom. Vis. Sci. 2008, 85 (8), 653–60. Nguyen-Khuong, T.; Fitzgerald, A.; Zhao, Z.; Willcox, M.; Walsh, B. J. Improvements for the visualization of low-molecular weight protein and peptides of human tears using MALDI. Proteomics 2008, 8 (17), 3424–32. Okrojek, R.; Grus, F. H.; Matheis, N.; Kahaly, G. J. Proteomics in autoimmune thyroid eye disease. Horm. Metab. Res. 2009, 41 (6), 465–70. Zhou, L.; Beuerman, R. W.; Ang, L. P.; Chan, C. M.; Li, S. F.; Chew, F. T.; Tan, D. T. Elevation of human alpha-defensins and S100 calcium-binding proteins A8 and A9 in tear fluid of patients with pterygium. Invest. Ophthalmol. Vis. Sci. 2009, 50 (5), 2077–86. Ananthi, S.; Chitra, T.; Bini, R.; Prajna, N. V.; Lalitha, P.; Dharmalingam, K. Comparative analysis of the tear protein profile in mycotic keratitis patients. Mol. Vis. 2008, 14, 500–7. Nguyen, C. Q.; Peck, A. B. Unraveling the pathophysiology of Sjogren syndrome-associated dry eye disease. Ocul. Surf. 2009, 7 (1), 11–27. Versura, P.; Nanni, P.; Bavelloni, A.; Blalock, W. L.; Piazzi, M.; Roda, A.; Campos, E. C. Tear proteomics in evaporative dry eye disease. Eye (London) 2010, 24 (8), 1396–402. Nichols, J. J.; Green-Church, K. B. Mass spectrometry-based proteomic analyses in contact lens-related dry eye. Cornea 2009, 28 (10), 1109–17. Zhou, L.; Beuerman, R.; Chew, A. P.; Koh, S. K.; Cafaro, T.; UrretsZavalia, E.; Urrets-Zavalia, J.; Li, S.; Serra, H. Quantitative analysis of N-linked glycoproteins in tear fluid of climatic droplet keratopathy by glycopeptide capture and iTRAQ. J. Proteome Res. 2009, 8 (4), 1992–2003. Hu, S.; Loo, J. A.; Wong, D. T. Human body fluid proteome analysis. Proteomics 2006, 6 (23), 6326–53. Hu, S.; Xie, Y.; Ramachandran, P.; Ogorzalek Loo, R. R.; Li, Y.; Loo, J. A.; Wong, D. T. Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry. Proteomics 2005, 5 (6), 1714–28.

Journal of Proteome Research • Vol. 9, No. 12, 2010 6099

reviews (124) Yan, W.; Apweiler, R.; Balgley, B. M.; Boontheung, P.; Bundy, J. L.; Cargile, B. J.; Cole, S.; Fang, X.; Gonzalez-Begne, M.; Griffin, T. J.; Hagen, F.; Hu, S.; Wolinsky, L. E.; Lee, C. S.; Malamud, D.; Melvin, J. E.; Menon, R.; Mueller, M.; Qiao, R.; Rhodus, N. L.; Sevinsky, J. R.; States, D.; Stephenson, J. L.; Than, S.; Yates, J. R.; Yu, W.; Xie, H.; Xie, Y.; Omenn, G. S.; Loo, J. A.; Wong, D. T. Systematic comparison of the human saliva and plasma proteomes. Proteomics Clin. Appl. 2009, 3 (1), 116–134. (125) Ohshiro, K.; Rosenthal, D. I.; Koomen, J. M.; Streckfus, C. F.; Chambers, M.; Kobayashi, R.; El-Naggar, A. K. Pre-analytic saliva processing affect proteomic results and biomarker screening of head and neck squamous carcinoma. Int. J. Oncol. 2007, 30 (3), 743–9. (126) Hu, S.; Arellano, M.; Boontheung, P.; Wang, J.; Zhou, H.; Jiang, J.; Elashoff, D.; Wei, R.; Loo, J. A.; Wong, D. T. Salivary proteomics for oral cancer biomarker discovery. Clin. Cancer Res. 2008, 14 (19), 6246–52. (127) Hu, S.; Yu, T.; Xie, Y.; Yang, Y.; Li, Y.; Zhou, X.; Tsung, S.; Loo, R. R.; Loo, J. R.; Wong, D. T. Discovery of oral fluid biomarkers for human oral cancer by mass spectrometry. Cancer Genomics Proteomics 2007, 4 (2), 55–64. (128) Xie, H.; Onsongo, G.; Popko, J.; de Jong, E. P.; Cao, J.; Carlis, J. V.; Griffin, R. J.; Rhodus, N. L.; Griffin, T. J. Proteomics analysis of cells in whole saliva from oral cancer patients via value-added three-dimensional peptide fractionation and tandem mass spectrometry. Mol. Cell. Proteomics 2008, 7 (3), 486–98. (129) Dowling, P.; Wormald, R.; Meleady, P.; Henry, M.; Curran, A.; Clynes, M. Analysis of the saliva proteome from patients with head and neck squamous cell carcinoma reveals differences in abundance levels of proteins associated with tumour progression and metastasis. J. Proteomics 2008, 71 (2), 168–75. (130) Schapher, M.; Wendler, O.; Groschl, M.; Schafer, R.; Iro, H.; Zenk, J. Salivary leptin as a candidate diagnostic marker in salivary gland tumors. Clin. Chem. 2009, 55 (5), 914–22. (131) Hu, S.; Wang, J.; Meijer, J.; Ieong, S.; Xie, Y.; Yu, T.; Zhou, H.; Henry, S.; Vissink, A.; Pijpe, J.; Kallenberg, C.; Elashoff, D.; Loo, J. A.; Wong, D. T. Salivary proteomic and genomic biomarkers for primary Sjogren’s syndrome. Arthritis Rheum. 2007, 56 (11), 3588–600. (132) Haigh, B. J.; Stewart, K. W.; Whelan, J. R.; Barnett, M. P.; Smolenski, G. A.; Wheeler, T. T. Alterations in the salivary proteome associated with periodontitis. J. Clin. Periodontol. 2010, 37 (3), 241–7. (133) de Jong, E. P.; Xie, H.; Onsongo, G.; Stone, M. D.; Chen, X. B.; Kooren, J. A.; Refsland, E. W.; Griffin, R. J.; Ondrey, F. G.; Wu, B.; Le, C. T.; Rhodus, N. L.; Carlis, J. V.; Griffin, T. J. Quantitative proteomics reveals myosin and actin as promising saliva biomarkers for distinguishing pre-malignant and malignant oral lesions. PLoS One 2010, 5 (6), e11148. (134) Kool, J.; Reubsaet, L.; Wesseldijk, F.; Maravilha, R. T.; Pinkse, M. W.; D’Santos, C. S.; van Hilten, J. J.; Zijlstra, F. J.; Heck, A. J. Suction blister fluid as potential body fluid for biomarker proteins. Proteomics 2007, 7 (20), 3638–50. (135) Macdonald, N.; Cumberbatch, M.; Singh, M.; Moggs, J. G.; Orphanides, G.; Dearman, R. J.; Griffiths, C. E.; Kimber, I.

6100


Teng et al.

(136)

(137)

(138)

(139)

(140)

(141)

(142)

(143)

(144)

(145)

Proteomic analysis of suction blister fluid isolated from human skin. Clin. Exp. Dermatol. 2006, 31 (3), 445–8. Gaddnas, F. P.; Sutinen, M. M.; Koskela, M.; Tervahartiala, T.; Sorsa, T.; Salo, T. A.; Laurila, J. J.; Koivukangas, V.; Ala-Kokko, T. I.; Oikarinen, A. Matrix-metalloproteinase-2,-8 and-9 in serum and skin blister fluid in patients with severe sepsis. Crit. Care 2010, 14 (2), R49. Celis, J. E.; Gromov, P.; Cabezon, T.; Moreira, J. M.; Ambartsumian, N.; Sandelin, K.; Rank, F.; Gromova, I. Proteomic characterization of the interstitial fluid perfusing the breast tumor microenvironment: a novel resource for biomarker and therapeutic target discovery. Mol. Cell. Proteomics 2004, 3 (4), 327–44. Cabezon, T.; Celis, J. E.; Skibshoj, I.; Klingelhofer, J.; Grigorian, M.; Gromov, P.; Rank, F.; Myklebust, J. H.; Maelandsmo, G. M.; Lukanidin, E.; Ambartsumian, N. Expression of S100A4 by a variety of cell types present in the tumor microenvironment of human breast cancer. Int. J. Cancer 2007, 121 (7), 1433–44. Gromov, P.; Gromova, I.; Bunkenborg, J.; Cabezon, T.; Moreira, J. M.; Timmermans-Wielenga, V.; Roepstorff, P.; Rank, F.; Celis, J. E. Up-regulated proteins in the fluid bathing the tumour cell microenvironment as potential serological markers for early detection of cancer of the breast. Mol. Oncol. 2010, 4 (1), 65–89. Celis, J. E.; Moreira, J. M.; Cabezon, T.; Gromov, P.; Friis, E.; Rank, F.; Gromova, I. Identification of extracellular and intracellular signaling components of the mammary adipose tissue and its interstitial fluid in high risk breast cancer patients: toward dissecting the molecular circuitry of epithelial-adipocyte stromal cell interactions. Mol. Cell. Proteomics 2005, 4 (4), 492–522. Sun, W.; Ma, J.; Wu, S.; Yang, D.; Yan, Y.; Liu, K.; Wang, J.; Sun, L.; Chen, N.; Wei, H.; Zhu, Y.; Xing, B.; Zhao, X.; Qian, X.; Jiang, Y.; He, F. Characterization of the liver tissue interstitial fluid (TIF) proteome indicates potential for application in liver disease biomarker discovery. J. Proteome Res. 2010, 9 (2), 1020–31. Teng, P. N.; Rungruang, B.; Hood, B. L.; Sun, M.; Flint, M. S.; Bateman, N. W.; Dhir, R.; Bhargava, R.; Richard, S. D.; Edwards, R. P.; Conrads, T. Assessment of Buffer Systems for Harvesting Proteins from Tissue Interstitial Fluid for Proteomic Analysis. J. Proteome Res. 2010, 9 (8), 4161–9. Wang, T. H.; Chao, A.; Tsai, C. L.; Chang, C. L.; Chen, S. H.; Lee, Y. S.; Chen, J. K.; Lin, Y. J.; Chang, P. Y.; Wang, C. J.; Chao, A. S.; Chang, S. D.; Chang, T. C.; Lai, C. H.; Wang, H. S. Stress-induced phosphoprotein 1 as a secreted biomarker for human ovarian cancer promotes cancer cell proliferation. Mol. Cell. Proteomics 2010, 9 (9), 1873–84. Li, S. J.; Peng, M.; Li, H.; Liu, B. S.; Wang, C.; Wu, J. R.; Li, Y. X.; Zeng, R. Sys-BodyFluid: a systematical database for human body fluid proteome research. Nucleic Acids Res. 2009, 37 (Database issue), D907–12. Petrak, J.; Ivanek, R.; Toman, O.; Cmejla, R.; Cmejlova, J.; Vyoral, D.; Zivny, J.; Vulpe, C. D. Deja vu in proteomics. A hit parade of repeatedly identified differentially expressed proteins. Proteomics 2008, 8 (9), 1744–9.

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