Identification of accessible human cancer biomarkers using ex vivo chemical proteomic strategies

One promising avenue towards the development of more selective, better anticancer drugs lies in the targeted delivery of bioactive compounds to the tumor environment by means of binding molecules specific for tumor-associated biomarkers. Eligibility of such markers for therapeutic ideally use three criteria: accessibility from the bloodstream; expression at sufficient level, and no (or much lower) expression in normal tissues. Most current discovery strategies (such as biomarker searching into body fluids) provide no clue as to whether proteins of interest are accessible, in human tissues, to suitable high-affinity ligands, such as systemically delivered monoclonal antibodies. To address this limitation, our group recently developed two methodologies based on chemical proteomic modifications, enabling the discovery of proteins accessible from the bloodstream and the extracellular space in human pathological tissues. In this review, we will discuss the potential benefits of these methodologies for the fast discovery of therapeutically valuable biomarkers.     

Identification of stromal proteins overexpressed in nodular sclerosis Hodgkin lymphoma

Hodgkin lymphoma (HL) represents a category of lymphoid neoplasms with unique features, notably the usual scarcity of tumour cells in involved tissues. The most common subtype of classical HL, nodular sclerosis HL, characteristically comprises abundant fibrous tissue stroma. Little information is available about the protein composition of the stromal environment from HL. Moreover, the identification of valid protein targets, specifically and abundantly expressed in HL, would be of utmost importance for targeted therapies and imaging, yet the biomarkers must necessarily be accessible from the bloodstream. To characterize HL stroma and to identify potentially accessible proteins, we used a chemical proteomic approach, consisting in the labelling of accessible proteins and their subsequent purification and identification by mass spectrometry. We performed an analysis of potentially accessible proteins in lymph node biopsies from HL and reactive lymphoid tissues, and in total, more than 1400 proteins were identified in 7 samples. We have identified several extracellular matrix proteins overexpressed in HL, such as versican, fibulin-1, periostin, and other proteins such as S100-A8. These proteins were validated by immunohistochemistry on a larger series of biopsy samples, and bear the potential to become targets for antibody-based anti-cancer therapies.     

Identification of specific reachable molecular targets in human breast cancer using a versatile ex vivo proteomic method

Targeting of tumoral tissues is one of the most promising approaches to improve both the efficacy and safety of anticancer treatments. The identification of valid targets, including proteins specifically and abundantly expressed in cancer lesions, is of utmost importance. Despite state-of-the-art technologies, the discovery of cancer-associated target proteins still faces the limitation, in human tissues, of antigen accessibility to suitable high-affinity ligands such as human mAb bound to bioactive molecules. Terminal perfusion of tumor-bearing mice or ex vivo perfusion of human cancer-bearing organs with a reactive biotin ester solution has successfully led to the identification of novel accessible biomarkers. This methodology is however restricted to perfusable organs, and excludes most of the tissues of interest to targeted therapies, e.g. primary breast cancer and metastases. Herein, we report on the development of a new chemical proteomic method that bypasses the perfusion step and thus offers the potential to identify accessible molecular targets in virtually all types of animal and human tissues. We have validated our new procedure by identifying biomarkers selectively expressed in human breast carcinoma. Overall, this powerful technology may lay the ground not only for custom-made therapies in cancer, but also for the development of therapies that need to be selectively delivered in a specific tissue.     

Computational prediction of human proteins that can be secreted into the bloodstream

We present a novel computational method for predicting which proteins from highly and abnormally expressed genes in diseased human tissues, such as cancers, can be secreted into the bloodstream, suggesting possible marker proteins for follow-up serum proteomic studies. A main challenging issue in tackling this problem is that our understanding about the downstream localization after proteins are secreted outside the cells is very limited and not sufficient to provide useful hints about secretion to the bloodstream. To bypass this difficulty, we have taken a data mining approach by first collecting, through extensive literature searches, human proteins that are known to be secreted into the bloodstream due to various pathological conditions as detected by previous proteomic studies, and then asking the question: 'what do these secreted proteins have in common in terms of their physical and chemical properties, amino acid sequence and structural features that can be used to predict them?' We have identified a list of features, such as signal peptides, transmembrane domains, glycosylation sites, disordered regions, secondary structural content, hydrophobicity and polarity measures that show relevance to protein secretion. Using these features, we have trained a support vector machine-based classifier to predict protein secretion to the bloodstream. On a large test set containing 98 secretory proteins and 6601 non-secretory proteins of human, our classifier achieved approximately 90% prediction sensitivity and approximately 98% prediction specificity. Several additional datasets are used to further assess the performance of our classifier. On a set of 122 proteins that were found to be of abnormally high abundance in human blood due to various cancers, our program predicted 62 as blood-secreted proteins. By applying our program to abnormally highly expressed genes in gastric cancer and lung cancer tissues detected through microarray gene expression studies, we predicted 13 and 31 as blood secreted, respectively, suggesting that they could serve as potential biomarkers for these two cancers, respectively. Our study demonstrated that our method can provide highly useful information to link genomic and proteomic studies for disease biomarker discovery. Our software can be accessed at http://csbl1.bmb.uga.edu/cgi-bin/Secretion/secretion.cgi.     

In-depth analysis of the human tear proteome

The tears, a critical body fluid of the surface of the eye, contain an unknown number of molecules including proteins/peptides, lipids, small molecule metabolites, and electrolytes. There have been continued efforts for exploring the human tear proteome to develop biomarkers of disease. In this study, we used the high speed TripleTOF 5600 system as the platform to analyze the human tear proteome from healthy subjects (3 females and 1 male, average age: 36±14). We have identified 1543 proteins in the tears with less than 1% false discovery rate, which represents the largest number of human tear proteins reported to date. The data set was analyzed for gene ontology (GO) and compared with the human plasma proteome, NEIBank lacrimal gland gene dataset and NEIBank cornea gene dataset. This comprehensive tear protein list may serve as a reference list of human tear proteome for biomarker research of ocular diseases or establishment of MRM (Multiple Reaction Monitoring) assays for targeted analysis. Tear fluid is a useful and an accessible source not only for evaluating ocular surface tissues (cornea and conjunctiva), inflammation, lacrimal gland function and a number of disease conditions, such as dry eye as well as response to treatment.     

In vivo protein biotinylation for identification of organ-specific antigens accessible from the vasculature

We describe a new methodology, based on terminal perfusion of rodents with a reactive ester derivative of biotin that enables the covalent modification of proteins readily accessible from the bloodstream. Biotinylated proteins from total organ extracts can be purified on streptavidin resin in the presence of strong detergents, digested on the resin and subjected to liquid chromatography-tandem mass spectrometry for identification. In the present study, in vivo biotinylation procedure led to the identification of hundreds of proteins in different mouse organs, including some showing a restricted pattern of expression in certain body tissues. Furthermore, biotinylation of mice with F9 subcutaneous tumors or orthotopic kidney tumors revealed both quantitative and qualitative differences in the recovery of biotinylated proteins, as compared to normal tissues. This technology is applicable to proteomic investigations of the differential expression of accessible proteins in physiological and pathological processes in animal models, and to human surgical specimens using ex vivo perfusion procedures.     

A chemical proteomics approach for the identification of accessible antigens expressed in human kidney cancer

A promising avenue toward the development of more selective anticancer drugs consists in the targeted delivery of bioactive molecules to the tumor environment by means of binding molecules specific to tumor-associated markers. We have used a chemical proteomics approach based on the ex vivo perfusion and biotinylation of accessible structures within surgically resected human kidneys with tumor to gain information about accessible and abundant antigens that are overexpressed in human cancer. This procedure led to the selective labeling with biotin of vascular structures. Biotinylated proteins were purified on streptavidin resin and identified using mass spectrometric methodologies, revealing 637 proteins, 184 of which were only found in tumor specimens and 223 of which were only found in portions of normal kidneys. Immunohistochemical and PCR analysis confirmed that several of the putative cancer antigens identified in this study are indeed preferentially expressed in tumors. In conclusion, we have developed a methodology that allows the identification of accessible biomarkers in human tissues. The tumor-associated antigens identified in this study may be suitable targets for antibody-based anticancer therapies. The experimental approach described here should be applicable to other surgical specimens and to other pathologies as well as to the study of basic physiological and immunological processes.     

Novel comprehensive approach for accessible biomarker identification and absolute quantification from precious human tissues

The identification of specific biomarkers obtained directly from human pathological lesions remains a major challenge, because the amount of tissue available is often very limited. We have developed a novel, comprehensive, and efficient method permitting the identification and absolute quantification of potentially accessible proteins in such precious samples. This protein subclass comprises cell membrane associated and extracellular proteins, which are reachable by systemically deliverable substances and hence especially suitable for diagnosis and targeted therapy applications. To isolate such proteins, we exploited the ability of chemically modified biotin to label ex vivo accessible proteins and the fact that most of these proteins are glycosylated. This approach consists of three successive steps involving first the linkage of potentially accessible proteins to biotin molecules followed by their purification. The remaining proteins are then subjected to glycopeptide isolation. Finally, the analysis of the nonglycosylated peptides and their involvement in an in silico method increased the confident identification of glycoproteins. The value of the technique was demonstrated on human breast cancer tissue samples originating from 5 individuals. Altogether, the method delivered quantitative data on more than 400 potentially accessible proteins (per sample and replicate). In comparison to biotinylation or glycoprotein analysis alone, the sequential method significantly increased the number (≥30% and ≥50% respectively) of potentially therapeutically and diagnostically valuable proteins. The sequential method led to the identification of 93 differentially modulated proteins, among which several were not reported to be associated with the breast cancer. One of these novel potential biomarkers was CD276, a cell membrane-associated glycoprotein. The immunohistochemistry analysis showed that CD276 is significantly differentially expressed in a series of breast cancer lesions. Due to the fact that our technology is applicable to any type of tissue biopsy, it bears the ability to accelerate the discovery of new relevant biomarkers in a broad spectrum of pathologies.     

Utilizing human blood plasma for proteomic biomarker discovery

Candidate proteomic biomarker discovery from human plasma holds both incredible clinical potential as well as significant challenges. The dynamic range of proteins within plasma is known to exceed 10(10), and many potential biomarkers are likely present at lower protein abundances. At present, proteomic based MS analyses provide a dynamic range typically not exceeding approximately 10(3) in a single spectrum, and approximately 10(4)-10(6) when combined with on-line separations (e.g., reversed-phase gradient liquid chromatography), and thus are generally insufficient for low level biomarker detection directly from human plasma. This limitation is providing an impetus for the development of experimental methodologies and strategies to increase the possible number of detections within this biofluid. Discussed is the diversity of available approaches currently used by our laboratory and others to utilize human plasma as a viable medium for biomarker discovery. Various separation, depletion, enrichment, and quantitative efforts as well as recent improvements in MS capabilities have resulted in measurable improvements in the detection and identification of lower abundance proteins (by approximately 10-10(2)). Despite these improvements, further advances are needed to provide a basis for discovery of candidate biomarkers at very low levels. Continued development of depletion and enrichment techniques, coupled with improved pre-MS separations (both at the protein and peptide level) holds promise in extending the dynamic range of proteomic analysis.     

Relative Quantification of Proteins in Formalin-Fixed Paraffin-Embedded Breast Cancer Tissue Using Multiplexed Mass Spectrometry Assays

The identification of clinically relevant biomarkers represents an important challenge in oncology. This problem can be addressed with biomarker discovery and verification studies performed directly in tumor samples using formalin-fixed paraffin-embedded (FFPE) tissues. However, reliably measuring proteins in FFPE samples remains challenging. Here, we demonstrate the use of liquid chromatography coupled to multiple reaction monitoring mass spectrometry (LC-MRM/MS) as an effective technique for such applications. An LC-MRM/MS method was developed to simultaneously quantify hundreds of peptides extracted from FFPE samples and was applied to the targeted measurement of 200 proteins in 48 triple-negative, 19 HER2-overexpressing, and 20 luminal A breast tumors. Quantitative information was obtained for 185 proteins, including known markers of breast cancer such as HER2, hormone receptors, Ki-67, or inflammation-related proteins. LC-MRM/MS results for these proteins matched immunohistochemistry or chromogenic in situ hybridization data. In addition, comparison of our results with data from the literature showed that several proteins representing potential biomarkers were identified as differentially expressed in triple-negative breast cancer samples. These results indicate that LC-MRM/MS assays can reliably measure large sets of proteins using the analysis of surrogate peptides extracted from FFPE samples. This approach allows to simultaneously quantify the expression of target proteins from various pathways in tumor samples. LC-MRM/MS is thus a powerful tool for the relative quantification of proteins in FFPE tissues and for biomarker discovery.     

A mouse model repository for cancer biomarker discovery

Early detection of cancer using biomarkers obtained from blood or other easily accessible tissues would have a significant impact on reducing cancer mortality. However, identifying new blood-based biomarkers has been hindered by the dynamic complexity of the human plasma proteome, confounded by genetic and environmental variability, and the scarcity of high quality controlled samples. In this report, we discuss a new paradigm for biomarker discovery through the use of mouse models. Inbred mouse models of cancer recapitulate many critical features of human cancer, while eliminating sources of environmental and genetic variability. The ability to collect samples from highly matched cases and controls under identical conditions further reduces variability which is critical for successful biomarker discovery. We describe the establishment of a repository containing tumor, plasma, urine, and other tissues from 10 different mouse models of human cancer, including two breast, two lung, two prostate, two gastrointestinal, one ovarian, and one skin tumor model. We present the overall design of this resource and its potential use by the research community for biomarker discovery.     

Identification of novel accessible proteins bearing diagnostic and therapeutic potential in human pancreatic ductal adenocarcinoma

Pancreas ductal adenocarcinoma (PDAC) remains a deadly malignancy with poor early diagnostic and no effective therapy. Although several proteomic studies have performed comparative analysis between normal and malignant tissues, there is a lack of clear characterization of proteins that could be of clinical value. Systemically reachable ("potentially accessible") proteins, suitable for imaging technologies and targeted therapies represent a major group of interest. The current study explores potentially accessible proteins overexpressed in PDAC, employing innovative proteomics technologies. In the discovery phase, potentially accessible proteins from fresh human normal and PDAC tissues were ex vivo biotinylated, isolated and identified using 2D-nano-HPLC-MS/MS method. The analysis revealed 422 up-regulated proteins in the tumor, of which 83 (including protein isoforms) were evaluated as potentially accessible. Eleven selected candidates were further confirmed as up-regulated using Western blot and multiple reaction monitoring protein quantification. Of these, transforming growth factor beta-induced (TGFBI), latent transforming growth factor beta binding 2 (LTBP2), and asporin (ASPN) were further investigated by employing large scale immunohistochemistry-based validations. They were found to be significantly expressed in a large group of clinical PDAC samples compared to corresponding normal and inflammatory tissues. In conclusion, TGFBI, LTBP2, and ASPN are novel, overexpressed, and potentially accessible proteins in human PDAC. They bear the potential to be of clinical value for diagnostic and therapeutic applications and merit further studies using in vivo models.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein-protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

Discovery of regulatory molecular events and biomarkers using 2D capillary chromatography and mass spectrometry

An important component of proteomic research is the high-throughput discovery of novel proteins and protein–protein interactions that control molecular events that contribute to critical cellular functions and human disease. The interactions of proteins are essential for cellular functions. Identifying perturbation of normal cellular protein interactions is vital for understanding the disease process and intervening to control the disease. A second area of proteomics research is the discovery of proteins that will serve as biomarkers for the early detection, diagnosis and drug treatment response for specific diseases. These studies have been referred to as clinical proteomics. To discover biomarkers, proteomics research employs the quantitative comparison of peptide and protein expression in body fluids and tissues from diseased individuals (case) versus normal individuals (control). Methods that couple 2D capillary liquid chromatography (LC) and tandem mass spectrometry (MS/MS) analysis have greatly facilitated this discovery science. Coupling 2D-LC/MS/MS analysis with automated genome-assisted spectra interpretation allows a direct, high-throughput and high-sensitivity identification of thousands of individual proteins from complex biological samples. The systematic comparison of experimental conditions and controls allows protein function or disease states to be modeled. This review discusses the different purification and quantification strategies that have been developed and used in combination with 2D-LC/MS/MS and computational analysis to examine regulatory protein networks and clinical samples.     

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.     

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers.     

Glycoblotting method allows for rapid and efficient glycome profiling of human Alzheimer's disease brain,serum and cerebrospinal fluid towards potential biomarker discovery

BackgroundUnderstanding of the significance of posttranslational glycosylation in Alzheimer's disease (AD) is of growing importance for the investigation of the pathogenesis of AD as well as discovery research of the disease-specific serum biomarkers.MethodsWe designed a standard protocol for the glycoblotting combined with MALDI-TOFMS to perform rapid and quantitative profiling of the glycan parts of glycoproteins (N-glycans) and glycosphingolipids (GSLs) using human AD's post-mortem samples such as brain tissues (dissected cerebral cortices such as frontal, parietal, occipital, and temporal domains), serum and cerebrospinal fluid (CSF).ResultsThe structural profiles of the major N-glycans released from glycoproteins and the total expression levels of the glycans were found to be mostly similar between the brain tissues of the AD patients and those of the normal control group. In contrast, the expression levels of the serum and CSF protein N-glycans such as bisect-type and multiply branched glycoforms were increased significantly in AD patient group. In addition, the levels of some gangliosides such as GM1, GM2 and GM3 appeared to alter in the AD patient brain and serum samples when compared with the normal control groups.ConclusionAlteration of the expression levels of major N- and GSL-glycans in human brain tissues, serum and CSF of AD patients can be monitored quantitatively by means of the glycoblotting-based standard protocols.General significanceThe changes in the expression levels of the glycans derived from the human post-mortem samples uncovered by the standardized glycoblotting method provides potential serum biomarkers in central nervous system disorders and can contribute to the insight into the molecular mechanisms in the pathogenesis of neurodegenerative diseases and future drug discovery. Most importantly, the present preliminary trials using human post-mortem samples of AD patients suggest that large-scale serum glycomics cohort by means of various-types of human AD patients as well as the normal control sera can facilitate the discovery research of highly sensitive and reliable serum biomarkers for an early diagnosis of AD. This article is part of a Special Issue entitled "Glycans in personalised medicine" Guest Editor: Professor Gordan Lauc.     

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Integrative analysis of N-linked human glycoproteomic data sets reveals PTPRF ectodomain as a novel plasma biomarker candidate for prostate cancer

In an attempt to identify prostate cancer biomarkers with greater diagnostic and prognostic capabilities, we have developed an integrative proteomic discovery workflow focused on N-linked glycoproteins that refines the target selection process. In this work, hydrazide-based chemistry was used to identify N-linked glycopeptides from 22Rv1 prostate cancer cells cultured in vitro, which were compared with glycopeptides identified from explanted 22Rv1 murine tumor xenografts. One hundred and four human glycoproteins were identified in the former analysis and 75 in the latter, with 40 proteins overlapping between data sets. Of the 40 overlapping proteins, 80% have multiple literature references to the neoplastic process and ～40% to prostatic neoplasms. These include a number of well-known prostate cancer-associated biomarkers, such as prostate-specific membrane antigen (PSMA). By integrating gene expression data and available literature, we identified members of the overlap data set that deserve consideration as potential prostate cancer biomarkers. Specifically, the identification of the extracellular domain of protein tyrosine phosphatase receptor type F (PTPRF) was of particular interest due to the direct involvement of PTPRF in the control of β-catenin signaling, as well as dramatically elevated gene expression levels in the prostate compared to other tissues. In this investigation, we demonstrate that the PTPRF E-subunit is more abundant in human prostate tumor tissue compared to normal control and also detectable in murine plasma by immunoblot and ELISA. Specifically, PTPRF distinguishes between animals xenografted with the 22Rv1 cells and control animals as early as 14 days after implantation. This result suggests that the ectodomain of PTPRF has the potential to function as a novel plasma or tissue-based biomarker for prostate cancer. The workflow described adds to the literature of potential biomarker candidates for prostate cancer and demonstrates a pathway to developing new diagnostic assays.