Relative Quantitation of Proteins in Expressed Prostatic Secretion with a Stable Isotope Labeled Secretome Standard

Expressed prostatic secretion (EPS) is a proximal fluid directly derived from the prostate and, in the case of prostate cancer (PCa), is hypothesized to contain a repertoire of cancer-relevant proteins. Quantitative analysis of the EPS proteome may enable identification of proteins with utility for PCa diagnosis and prognosis. The present investigation demonstrates selective quantitation of proteins in EPS samples from PCa patients using a stable isotope labeled proteome standard (SILAP) generated through the selective harvest of the “secretome” from the PC3 prostate cancer cell line grown in stable isotope labeled cell culture medium. This stable isotope labeled secretome was digested with trypsin and equivalently added to each EPS digest, after which the resultant mixtures were analyzed by liquid chromatography–tandem mass spectrometry for peptide identification and quantification. Relative quantification of endogenous EPS peptides was accomplished by comparison of reconstructed mass chromatograms to those of the chemically identical SILAP peptides. A total of 86 proteins were quantified from 263 peptides in all of the EPS samples, 38 of which were found to be relevant to PCa. This work demonstrates the feasibility of using a SILAP secretome standard to simultaneously quantify many PCa-relevant proteins in EPS samples

ROC curve performance of the 12-biomarker naïve Bayes NSCLC classifier by study site.

<p>ROC curve performance of the 12-biomarker naïve Bayes NSCLC classifier by study site.</p

Performance of classifier in demographic subsets.

<p>Performance of classifier in demographic subsets.</p

Classifier specificity by level of airflow obstruction.

§<p>Spirometric classification of airflow obstruction based on GOLD staging <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0015003#pone.0015003-GOLD1" target="_blank">[60]</a>.</p

Performance of Bayesian Classifier to distinguish NSCLC cases from controls.

<p>Performance of Bayesian Classifier to distinguish NSCLC cases from controls.</p

Heat map shows the magnitude of difference for each protein measured (columns) between subject populations for the comparison of NSCLC to controls (top row) and comparisons of cases or controls between study sites (bottom row).

<p>Top row: KS distances for NSCLC versus control distributions. Bottom row: mean KS distances for all 12 pair-wise comparisons, between the four sites, of case and control samples analyzed separately. Proteins were ordered by subtracting the NSCLC KS distance from the mean site KS distance. This revealed groups of NSCLC biomarkers (top right) contrasting with preanalytical markers (bottom left).</p

Potential NSCLC biomarkers^§.

§<p>Measure of the relative importance of potential biomarkers selected with KS distance (KS), KS FDR-corrected q-value (q-value), frequency for naïve Bayes (NB Freq),</p

Twelve biomarker classifier proteins^§.

§<p>Up or down regulation in NSCLC cases relative to controls.</p

Clinical characteristics of NSCLC case and control sets for training and verification.

§<p>For continuous data the differences were tested using t-tests. For categorical data significant differences were tested using the Pearson Chi-Squared Test for independence.</p>‡<p>Pack-years: product of the self reported number of packs of cigarettes smoked per day and the number of years of smoking.</p

Clinical characteristics of NSCLC cases in the training and verification sets.

§<p>Clinical staging for 17 Stage I, 5 Stage II and 29 Stage III cases, NOS not otherwise specified.</p