Seminal plasma as a source of prostate cancer peptide biomarker candidates for detection of indolent and advanced disease

Background:Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease. <br>Methodology/Principal Findings: In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >>7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase​,prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.</br> <br>Conclusions/Significance: Seminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.</br&gt

Urinary CE-MS peptide marker pattern for detection of solid tumors

Urinary profiling datasets, previously acquired by capillary electrophoresis coupled to mass-spectrometry were investigated to identify a general urinary marker pattern for detection of solid tumors by targeting common systemic events associated with tumor-related inflammation. A total of 2,055 urinary profiles were analyzed, derived from a) a cancer group of patients (n = 969) with bladder, prostate, and pancreatic cancers, renal cell carcinoma, and cholangiocarcinoma and b) a control group of patients with benign diseases (n = 556), inflammatory diseases (n = 199) and healthy individuals (n = 331). Statistical analysis was conducted in a discovery set of 676 cancer cases and 744 controls. 193 peptides differing at statistically significant levels between cases and controls were selected and combined to a multi-dimensional marker pattern using support vector machine algorithms. Independent validation in a set of 635 patients (293 cancer cases and 342 controls) showed an AUC of 0.82. Inclusion of age as independent variable, significantly increased the AUC value to 0.85. Among the identified peptides were mucins, fibrinogen and collagen fragments. Further studies are planned to assess the pattern value to monitor patients for tumor recurrence. In this proof-of-concept study, a general tumor marker pattern was developed to detect cancer based on shared biomarkers, likely indicative of cancer-related features

Limitations in SELDI-TOF MS whole serum proteomic profiling with IMAC surface to specifically detect colorectal cancer

<p>Abstract</p> <p>Background</p> <p>Surface enhanced laser desorption and ionization time-of-flight mass spectrometry (SELDI-TOF-MS) analysis on serum samples was reported to be able to detect colorectal cancer (CRC) from normal or control patients. We carried out a validation study of a SELDI-TOF MS approach with IMAC surface sample processing to identify CRC.</p> <p>Methods</p> <p>A retrospective cohort of 338 serum samples including 154 CRCs, 67 control cancers and 117 non-cancerous conditions was profiled using SELDI-TOF-MS.</p> <p>Results</p> <p>No CRC "specific" classifier was found. However, a classifier consisting of two protein peaks separates cancer from non-cancerous conditions with high accuracy.</p> <p>Conclusion</p> <p>In this study, the SELDI-TOF-MS-based protein expression profiling approach did not perform to identify CRC. However, this technique is promising in distinguishing patients with cancer from a non-cancerous population; it may be useful for monitoring recurrence of CRC after treatment.</p

Proteomic pattern of cervico-vaginal fluid (CVF) in an ovarian cancer diagnosis — pilot study

Objectives: High grade serous ovarian cancer (HGSC) is the most common type of ovarian cancer and is responsible for about 90% of ovarian cancer deaths. The diagnostic tests currently used do not increase the detection rates for ovarian cancer. There is a great necessity to develop new and non-invasive diagnostic tests for ovarian cancer (OC). Cervico-vaginal fluid (CVF) seems to be a potential and valuable source of biomarkers for genital tract diseases including ovarian cancer. The aim of our pilot study was to undertake a preliminary proteomic analysis of CVF derived from ovarian cancer patients and to compare these with results from a control group.Material and methods: We analysed and compared samples from a group of ovarian cancer patients and a control group of healthy patients. The study used MALDI-TOF coupled with nanoLC and ClinProTools software for MS, MS/MS spectra collection and proteomic analysis.Results: We identified 404 different proteins in the OC group and 417 proteins in the control group. 239 of the proteins were found to be common to both study groups, 165 proteins were unique to the OC subjects, and 178 proteins were unique to the control subjects. We selected three proteins as the OC markers with the greatest potential: cysteine-rich secretory protein 3, fibronectin and Ly6/PLAUR domain-containing protein 3.Conclusions: The proteins we selected seem to possess great potential as markers for the screening and early detection of OC, especially in non-invasive and low-cost diagnostic tests. However, our findings require more advanced and validated proteomic analysis to confirm the suitability of the selected proteins in everyday medical diagnoses

Oncoproteomics: Opportunities, Challenges & Advanced Technologies

Oncoproteomics is nothing but the analysis of proteins and their interactions in a cancer cell through proteomic technologies. Oncoproteomics is playing a progressively significant part in diagnosis and the management of cancer. It also helps in the advancement of personalized therapy of cancer. Oncoproteomics holds great potential not only for opening the complicated molecular episodes of tumorigenesis but also for those that regulate clinically essential tumor habits, like metastasis, invasion, and resistance to treatment. Protein molecules show a significant impact on the evolution of cancer as it mainly develops due to abnormal signaling pathways. Detection and comprehension of these alterations is the major concept of oncoproteomics. Novel proteomic technologies related to cancer are defined in short, which are assisting not only in the comprehension of the mechanism of drug-resistant in cancer but also bestow some guides in management. For the diagnostic and prognostic categorization of the disease condition, and in measuring the drug efficiency and toxicity acclimatization of proteomic technologies in clinical laboratories is the fundamental objective of oncoproteomics. A considerable influence on the management of cancer patients and on a spectacular revolution in cancer research might notice by data obtained through such novel technologies. For the cancer therapy, the identification of novel targets, as well as an understanding of tumor development, might permit by the research of tumor-specific proteomic profiles. A wide perspective on drug-resistant and anticancer drug discovery, proteomic biomarkers and its function in the diagnosis of cancer, current innovation in proteomic technologies have tried to give in this review

Serum Proteomic Profiling of Lung Cancer in High-Risk Groups and Determination of Clinical Outcomes

HypothesisLung cancer remains the leading cause of cancer-related mortality worldwide. Currently known serum markers do not efficiently diagnose lung cancer at early stage.MethodsIn the present study, we developed a serum proteomic fingerprinting approach coupled with a three-step classification method to address two important clinical questions: (i) to determine whether or not proteomic profiling differs between lung cancer and benign lung diseases in a population of smokers and (ii) to assess the prognostic impact of this profiling in lung cancer. Proteomic spectra were obtained from 170 pathologically confirmed lung cancer or smoking patients with benign chronic lung disease serum samples.ResultsAmong the 228 protein peaks differentially expressed in the whole population, 88 differed significantly between lung cancer patients and benign lung disease, with area under the curve diagnostic values ranging from 0.63 to 0.84. Multiprotein classifiers based on differentially expressed peaks allowed the classification of lung cancer and benign disease with an area under the curve ranging from 0.991 to 0.994. Using a cross-validation methodology, diagnostic accuracy was 93.1% (sensitivity 94.3%, specificity 85.9%), and more than 90% of the stage I/II lung cancers were correctly classified. Finally, in the prognosis part of the study, a 4628 Da protein was found to be significantly and independently associated with prognosis in advanced stage non-small cell lung cancer patients (p = 0.0005).ConclusionsThe potential markers that we identified through proteomic fingerprinting could accurately classify lung cancers in a high-risk population and predict survival in a non-small cell lung cancer population

Discovery and identification of potential biomarkers of pediatric Acute Lymphoblastic Leukemia

<p>Abstract</p> <p>Background</p> <p>Acute lymphoblastic leukemia (ALL) is a common form of cancer in children. Currently, bone marrow biopsy is used for diagnosis. Noninvasive biomarkers for the early diagnosis of pediatric ALL are urgently needed. The aim of this study was to discover potential protein biomarkers for pediatric ALL.</p> <p>Methods</p> <p>Ninety-four pediatric ALL patients and 84 controls were randomly divided into a "training" set (45 ALL patients, 34 healthy controls) and a test set (49 ALL patients, 30 healthy controls and 30 pediatric acute myeloid leukemia (AML) patients). Serum proteomic profiles were measured using surface-enhanced laser desorption/ionization-time-of-flight mass spectroscopy (SELDI-TOF-MS). A classification model was established by Biomarker Pattern Software (BPS). Candidate protein biomarkers were purified by HPLC, identified by LC-MS/MS and validated using ProteinChip immunoassays.</p> <p>Results</p> <p>A total of 7 protein peaks (9290 m/z, 7769 m/z, 15110 m/z, 7564 m/z, 4469 m/z, 8937 m/z, 8137 m/z) were found with differential expression levels in the sera of pediatric ALL patients and controls using SELDI-TOF-MS and then analyzed by BPS to construct a classification model in the "training" set. The sensitivity and specificity of the model were found to be 91.8%, and 90.0%, respectively, in the test set. Two candidate protein peaks (7769 and 9290 m/z) were found to be down-regulated in ALL patients, where these were identified as platelet factor 4 (PF4) and pro-platelet basic protein precursor (PBP). Two other candidate protein peaks (8137 and 8937 m/z) were found up-regulated in the sera of ALL patients, and these were identified as fragments of the complement component 3a (C3a).</p> <p>Conclusion</p> <p>Platelet factor (PF4), connective tissue activating peptide III (CTAP-III) and two fragments of C3a may be potential protein biomarkers of pediatric ALL and used to distinguish pediatric ALL patients from healthy controls and pediatric AML patients. Further studies with additional populations or using pre-diagnostic sera are needed to confirm the importance of these findings as diagnostic markers of pediatric ALL.</p