Prediagnostic distributions of serum biomarker levels.

<p>Levels of 67 biomarkers were evaluated in sera obtained from 135 subjects enrolled in the PLCO cancer screening trial who were subsequently diagnosed with pancreatic cancer and 540 matched controls. Circulating levels of biomarkers demonstrating significant differences between cases and healthy controls are presented. Level of significance: * - p<0.03, ** - p<0.01, *** - p<0.001, **** - p<0.0001.</p

Individual Performance of Significantly Altered Serum Biomarkers.

<p>Cut-point – minimum (maximum for prolactin) value (pg/ml) for diagnosis as case at 95% specificity.</p><p>SN – sensitivity at 95% specificity.</p><p>AUC – area under ROC curve.</p

Biomarker levels in relation to time to diagnosis.

<p>Biomarker levels were plotted against the elapsed time interval between blood draw and cancer diagnosis and plots were evaluated by linear regression. Biomarkers demonstrating slopes differing significantly from zero are presented.</p

Reproducibility of measurements of representative biomarkers in duplicate PLCO samples.

<p>Reproducibility of measurements of representative biomarkers in duplicate PLCO samples.</p

Complete List of Evaluated Biomarkers.

#<p>- our unpublished observations</p

Demographic and Clinical Characteristics of PLCO-selected Study Population.

<p>Demographic and Clinical Characteristics of PLCO-selected Study Population.</p

Performance of Multimarker Combinations in PLCO Training and Validation Sets.

<p>*Case/Control set described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094928#pone.0094928-Brand1" target="_blank">[8]</a>.</p>#<p>Statistical significance of differences in SN in comparison with CA 19-9 alone, method descrived in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094928#pone.0094928-Hawass1" target="_blank">[18]</a>.</p><p>SN/SP/AUC – sensitivity/specificity/area under ROC curve.</p><p>MTD 1–12 – months to diagnosis 1–12, samples collected <12 months prior to diagnosis.</p><p>MTD 12–35 – months to diagnosis 12–35, samples collected 12 to 35 months prior to diagnosis.</p

Biomarker panel performance in the complete PLCO cohort.

<p>A Metropolis algorithm with Monte-Carlo simulation was utilized to identify the top performing biomarker combinations in the discrimination of PDAC cases from matched controls within the PLCO cancer screening trial. ROC curves reflecting the performance of CA 19-9, the top two biomarker panel (CA 19-9/CEA), and the top three biomarker panel (CA 19-9/CEA/Cyfra 21-1) are shown. AUCs for the three models did not differ significantly according to the method of Hanley and McNeil <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094928#pone.0094928-Hanley1" target="_blank">[19]</a>.</p

An Extensive Targeted Proteomic Analysis of Disease-Related Protein Biomarkers in Urine from Healthy Donors

<div><p>The analysis of protein biomarkers in urine is expected to lead to advances in a variety of clinical settings. Several characteristics of urine including a low-protein matrix, ease of testing and a demonstrated proteomic stability offer distinct advantages over current widely used biofluids, serum and plasma. Improvements in our understanding of the urine proteome and in methods used in its evaluation will facilitate the clinical development of urinary protein biomarkers. Multiplexed bead-based immunoassays were utilized to evaluate 211 proteins in urines from 103 healthy donors. An additional 25 healthy donors provided serial urine samples over the course of two days in order to assess temporal variation in selected biomarkers. Nearly one-third of the evaluated biomarkers were detected in urine at levels greater than 1ng/ml, representing a diverse panel of proteins with respect to structure, function and biological role. The presence of several biomarkers in urine was confirmed by western blot. Several methods of data normalization were employed to assess impact on biomarker variability. A complex pattern of correlations with urine creatinine, albumin and beta-2-microglobulin was observed indicating the presence of highly specific mechanisms of renal filtration. Further investigation of the urinary protein biomarkers identified in this preliminary study along with a consideration of the underlying proteomic trends suggested by these findings should lead to an improved capability to identify candidate biomarkers for clinical development.</p></div

Effect of several normalization methods on the population variability of urine proteins.

<p>Urines obtained from 103 healthy donors were evaluated for levels of 211 proteins using multiplexed bead-based immunoassays. Coefficients of variation (CV) were determined for each of the 211 urine proteins based on absolute and normalized values. Correlation between the two sets of values was evaluated using Pearson's test of correlation. Normalized values were calculated by dividing absolute biomarker concentration by the level of several urine parameters: <b>A</b>. urine creatinine (UCr); <b>B</b>. urine albumin; <b>C</b>. urine total protein; <b>D</b>. ratio of urine albumin to urine creatinin (ACR); <b>E</b>. β-2-microglobulin.</p