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. 2008 Aug;8(15):3051-60.
doi: 10.1002/pmic.200700951.

Validation of reverse phase protein array for practical screening of potential biomarkers in serum and plasma: accurate detection of CA19-9 levels in pancreatic cancer

Tobias Grote  1 , Doris R Siwak, Herbert A Fritsche, Corwin Joy, Gordon B Mills, Diane Simeone, David C Whitcomb, Craig D Logsdon
Affiliations

Validation of reverse phase protein array for practical screening of potential biomarkers in serum and plasma: accurate detection of CA19-9 levels in pancreatic cancer

Tobias Grote et al. Proteomics. 2008 Aug.

Abstract

The current study analyzed reverse phase protein arrays (RPPA) as a means to experimentally validate biomarkers in blood samples. One microliter samples of sera (n = 71), and plasma (n = 78) were serially diluted and printed on NC-coated slides. CA19-9 levels from RPPA results were compared with identical patient samples as measured by ELISA. There was a strong correlation between RPPA and ELISA (r = 0.87) as determined by scatter plots. Sample reproducibility of CA19-9 levels was excellent (interslide correlation r = 0.88; intraslide correlation r = 0.83). The ability of RPPA to accurately distinguish CA19-9 levels between cancer and noncancer samples were determined using receiver operating characteristic curves and compared with ELISA. The AUC for RPPA and ELISA was comparable (0.87 and 0.86, respectively). When the mean CA19-9 levels of normal samples was used as a cutoff for RPPA and compared with the standard clinical ELISA cutoff, comparable specificities (71% for both) were observed. Notably, RPPA samples normalized to albumin showed increased sensitivity compared to ELISA (90% vs. 75%). As RPPA is a high-throughput method that shows results comparable to that of ELISA, we propose that RPPA is a viable technique for rapid experimental screening and validation of candidate biomarkers in blood samples.

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Conflict of interest statement

The authors declare no financial or commercial conflicts of interest.

Figures

Figure 1
Figure 1
RPPA slides stained with CA19-9 (A) and albumin (B). Serum or plasma samples were initially diluted 1:10 then serially diluted 2-fold for a total of 6 dilutions. The 4×6 grid configuration programmed for arraying placed each sample and its dilutions in rows in each grid. The 192 sample positions on the slide include 149 unique serum and plasma samples, 30 duplicates, and 6 pancreatic cell lines. The remainder contains negative control samples consisting of lysis buffer only. (A) CA19-9 slide demonstrating sample dilution and range of signal levels between samples. Areas 1, 2, and 3 show magnified images of representative samples on corresponding locations on the slide. (B) Albumin slide demonstrating dilution and similar range of signal levels between samples. The albumin slide was printed from the same master plate as the CA19-9 slide; samples for both slides are in identical locations.
Figure 1
Figure 1
RPPA slides stained with CA19-9 (A) and albumin (B). Serum or plasma samples were initially diluted 1:10 then serially diluted 2-fold for a total of 6 dilutions. The 4×6 grid configuration programmed for arraying placed each sample and its dilutions in rows in each grid. The 192 sample positions on the slide include 149 unique serum and plasma samples, 30 duplicates, and 6 pancreatic cell lines. The remainder contains negative control samples consisting of lysis buffer only. (A) CA19-9 slide demonstrating sample dilution and range of signal levels between samples. Areas 1, 2, and 3 show magnified images of representative samples on corresponding locations on the slide. (B) Albumin slide demonstrating dilution and similar range of signal levels between samples. The albumin slide was printed from the same master plate as the CA19-9 slide; samples for both slides are in identical locations.
Figure 2
Figure 2
Supercurve graph (top) and residuals R2 map (bottom) of a representative slide stained for CA19-9. (Top) Supercurve graph shows the antibody response curve produced upon utilizing mean net intensities of all spots on the slide. The slide response curve and each sample concentration are initially estimated and an improved response curve is fit for the slide based on these sample concentrations; hence the x-axis (representing concentration) contains no units. The estimations for slide response curve and sample concentrations are continually iterated until there is convergence. Upper and lower lines represent cutoff levels for upper and lower limits of signal (reflected as minimum/maximum valid concentration); trimmed mean R2 represents goodness of spot fit on supercurve at the point beyond the indicated concentration (−5). (Bottom) Residuals R2 map represents goodness of fit of individual spots on respective positions on the slide to supercurve; green represents best fit; yellow, red, and colorless (blank) progressively worse fit. Large blank areas represent portions of the slide containing negative control samples that were excluded from supercurve analysis.
Figure 3
Figure 3
Comparison of CA19-9 intensities between two different positions on the same RPPA slide. The intraslide reproducibility was analyzed using Spearman correlation and ranking CA19-9 levels from lowest to highest. Thirty sample pairs representing six samples from each group (cancer, pancreatitis, and normal serum and plasma) were printed as duplicates in different areas of the slide and were included in this analysis. The intraslide correlation was r=0.83 (95% CI 0.66–0.92, p<0.001), indicating high reproducibility.
Figure 4
Figure 4
Comparison of CA19-9 intensities between different RPPA slides. (A) The interslide reproducibility due to the printing process was determined by comparing CA19-9 levels for samples from two slides printed on different days, and using Spearman correlation and ranking CA19-9 levels from lowest to highest. A significant interslide correlation between samples was observed with r=0.88, p<0.0001, 95% CI 0.84–0.91. (B) The interslide reproducibility due to the staining process was determined by comparing two slides printed together but stained on different days, and using Spearman correlation. A significant interslide correlation between samples was observed for different staining days with r=0.93, p<0.001, 95% CI 0.90–0.95.
Figure 5
Figure 5
CA19-9 and albumin levels determined from RPPA samples by supercurve and represented as log2 mean net intensities and compared between pancreatic cancer, pancreatitis, and normal serum and plasma samples. (A) Individual and mean CA19-9 intensities in blood samples. Mean CA19-9 values show significant differences between cancer and normal (*), as well as cancer and pancreatitis samples (#) as analyzed by Mann-Whitney two-tailed test, p<0.0001. (B) Individual and mean albumin levels of blood sample groups. No significant difference in albumin levels was detected between sample groups as analyzed by the Mann-Whitney two-tailed test.
Figure 6
Figure 6
Correlation of CA19-9 levels of samples assayed by both ELISA and RPPA. CA19-9 levels for ELISA were converted to log2 values and matched samples were plotted against RPPA samples. (A) Scatter plot comparing CA19-9 levels from ELISA with non-normalized RPPA values, r=0.87. (B) Scatter plot comparing ELISA with RPPA CA19-9 values after normalizing with albumin for sample loading differences, r=0.89.
Figure 7
Figure 7
ROC curves of CA19-9 levels determined from RPPA and ELISA assays. (A) ROC curves for CA19-9 levels determined from RPPA serum samples. Open squares show albumin normalized and closed squares non-normalized CA19-9 data. The AUCs are 0.87±0.05 and 0.80±0.06, respectively; p<0.0001. A cutoff value of ≥ log2(x)=−7.6 results in a sensitivity of 90% and a specificity of 71% for the normalized RPPA data. (B) ROC curve for the CA19-9 ELISA. The AUC is 0.86±0.06, p<0.0001. With a cutoff value of ≥ 37 Units/ml, the sensitivity is 75% and the specificity 71%. (C) ROC curves for the plasma RPPA results. Open squares show albumin normalized and closed squares non-normalized CA19-9 data. The AUCs are 0.87±0.05 and 0.81±0.05 respectively; p<0.0001. A cutoff value of ≥ log2(x)=−7.6 results in a sensitivity of 90% and a specificity of 71% for the normalized RPPA data.
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