Standardization of diagnostic biomarker concentrations in urine; the hematuria caveat

Sensitive and specific urinary biomarkers can improve patient outcomes in many diseases through informing early diagnosis. Unfortunately, to date, the accuracy and translation of diagnostic urinary biomarkers into clinical practice has been disappointing. We believe this may be due to inappropriate standardization of diagnostic urinary biomarkers. Our objective was therefore to characterize the effects of standardizing urinary levels of IL-6, IL-8, and VEGF using the commonly applied standards namely urinary creatinine, osmolarity and protein. First, we report results based on the biomarker levels measured in 120 hematuric patients, 80 with pathologically confirmed bladder cancer, 27 with confounding pathologies and 13 in whom no underlying cause for their hematuria was identified, designated “no diagnosis”. Protein levels were related to final diagnostic categories (p = 0.022, ANOVA). Osmolarity (mean = 529 mOsm; median = 528 mOsm) was normally distributed, while creatinine (mean = 10163 µmol/l, median = 9350 µmol/l) and protein (0.3297, 0.1155 mg/ml) distributions were not. When we compared AUROCs for IL-6, IL-8 and VEGF levels, we found that protein standardized levels consistently resulted in the lowest AUROCs. The latter suggests that protein standardization attenuates the “true” differences in biomarker levels across controls and bladder cancer samples. Second, in 72 hematuric patients; 48 bladder cancer and 24 controls, in whom urine samples had been collected on recruitment and at follow-up (median = 11 (1 to 20 months)), we demonstrate that protein levels were approximately 24% lower at follow-up (Bland Altman plots). There was an association between differences in individual biomarkers and differences in protein levels over time, particularly in control patients. Collectively, our findings identify caveats intrinsic to the common practice of protein standardization in biomarker discovery studies conducted on urine, particularly in patients with hematuria

Collectives of diagnostic biomarkers identify high-risk subpopulations of hematuria patients: exploiting heterogeneity in large-scale biomarker data

<p>Abstract</p> <p>Background</p> <p>Ineffective risk stratification can delay diagnosis of serious disease in patients with hematuria. We applied a systems biology approach to analyze clinical, demographic and biomarker measurements (n = 29) collected from 157 hematuric patients: 80 urothelial cancer (UC) and 77 controls with confounding pathologies.</p> <p>Methods</p> <p>On the basis of biomarkers, we conducted agglomerative hierarchical clustering to identify patient and biomarker clusters. We then explored the relationship between the patient clusters and clinical characteristics using Chi-square analyses. We determined classification errors and areas under the receiver operating curve of Random Forest Classifiers (RFC) for patient subpopulations using the biomarker clusters to reduce the dimensionality of the data.</p> <p>Results</p> <p>Agglomerative clustering identified five patient clusters and seven biomarker clusters. Final diagnoses categories were non-randomly distributed across the five patient clusters. In addition, two of the patient clusters were enriched with patients with 'low cancer-risk' characteristics. The biomarkers which contributed to the diagnostic classifiers for these two patient clusters were similar. In contrast, three of the patient clusters were significantly enriched with patients harboring 'high cancer-risk" characteristics including proteinuria, aggressive pathological stage and grade, and malignant cytology. Patients in these three clusters included controls, that is, patients with other serious disease and patients with cancers other than UC. Biomarkers which contributed to the diagnostic classifiers for the largest 'high cancer- risk' cluster were different than those contributing to the classifiers for the 'low cancer-risk' clusters. Biomarkers which contributed to subpopulations that were split according to smoking status, gender and medication were different.</p> <p>Conclusions</p> <p>The systems biology approach applied in this study allowed the hematuric patients to cluster naturally on the basis of the heterogeneity within their biomarker data, into five distinct risk subpopulations. Our findings highlight an approach with the promise to unlock the potential of biomarkers. This will be especially valuable in the field of diagnostic bladder cancer where biomarkers are urgently required. Clinicians could interpret risk classification scores in the context of clinical parameters at the time of triage. This could reduce cystoscopies and enable priority diagnosis of aggressive diseases, leading to improved patient outcomes at reduced costs.</p

SDS PAGE on urine samples.

<p>SDS PAGE was carried out on urine from each patient. A dense band was frequently observed at approximately 64–66 kDa. This band represents albumin. Eight representative samples demonstrate the diverse relationship between this albumin band on the SDS PAGE and corresponding IL-8 levels measured in urine from the same patient sample. Corresponding IL-8 levels are illustrated in the 95% confidence limit error bar chart directly below each lane. The density of the albumin band was not always indicative of the IL-8 levels. Four patients had non-muscle invasive bladder cancer (NMI), one patient had muscle invasive bladder cancer (MI), two patients had no diagnosis (ND), and one patient had benign prostate enlargement.</p

Regression analyses to determine the relationship between differences in standards and biomarkers over time.

<p>Scatter plots, based on data from 72 hematuric patients, plotting the differences between biomarker levels on recruitment and follow-up against the differences between protein levels on recruitment and follow-up for (A) IL-6, (B) IL-8 and (C) VEGF. The regression line and 95% confidence interval show significant associations (p<0.0001 for all biomarkers). Differences in biomarker levels across time were associated with differences in protein levels.</p

Comparison between measured protein levels and protein dipstick analyses.

<p>Total protein levels (mg/ml) in urine were determined by Bradford assay A_{595 nm} (Hitachi U2800 spectrophotometer) using Bovine Serum Albumin as standard. Dipstick analyses were undertaken using Aution Sticks 10EA. Analyses were interpreted using PocketChem (Arkray factory, Inc. Japan). Protein levels were plotted against dipstick results with the Y –axis reference line indicating the usual lower limit of sensitivity for urine dipstick testing (0.25 mg/ml).</p

Relationship between osmolarity and creatinine.

<p>Triplicate levels of osmolarity and creatinine were measured in urine from 119 hematuric patients. There was a modest relationship between osmolarity and creatinine (R Square = 0.519).</p

Comparison of protein levels across final diagnostic categories.

<p>Urinary protein levels measured in 120 patients with hematuria were related to final diagnostic categories in (ANOVA; p = 0.022). Subsequently, we carried out a one way ANOVA with post-hoc Dunnett T3 analyses using log₁₀ transformed protein data. Higher protein levels were measured in urine from patients diagnosed with bladder cancer in comparison to those with no diagnosis (p = 0.073). There were no significant differences between the protein levels measured in patients with confounding pathologies and levels measured in the urines from bladder cancer patients (p = 0.621) or between patients with no diagnosis and patients with confounding pathologies (p = 0.316).</p

Paired t-test comparing standard levels measured on recruitment and at follow-up.

<p>Urine samples were obtained on two visits; one on recruitment and a second at follow-up (median = 11 (1 to 20 months)) from 72 patients who had presented with hematuria. The mean difference between log10 protein levels decreased over time (p = 0.097).</p

AUROC for IL-6, IL-8 and VEGF.

<p>The lowest area under receiver operating characteristic (AUROC) were determined after protein normalization as represented by the solid black curve which was always closest to the diagonal reference line i.e., IL-6 = 0.634 (0.523 to 0.745); IL-8 = 0.677 (0.570 to 0.784); and VEGF = 0.609 (0.501 to 0.716). The AUROCs for uncorrected biomarker levels (thick grey curve), and those standardized using osmolarity (dashed black curve) or creatinine (dashed grey curve) were very similar for individual biomarkers : (A) IL-6 = 0.693 (0.592 to 0.794), 0.683 (0.582 to 0.784) and 0.678 (0.578 to 0.779), respectively; (B) IL-8 = 0.706 (0.608 to 0.804), 0.701 (0.603 to 0.799) and 0.694 (0.592 to 0.795), respectively; and (C) VEGF = 0.705 (0.610 to 0.799), 0.687 (0.591 to 0.783) and 0.680 (0.583 to 0.777), respectively. Figures in brackets are 95% Confidence Intervals.</p