Serial analysis of 38 proteins during the progression of human breast tumor in mice using an antibody colocalization microarray

Abstract

Proteins in serum or plasma hold great potential for use in disease diagnosis and monitoring. However, the correlation between tumor burden and protein biomarker concentration has not been established. Here, using an antibody colocalization microarray, the protein concentration in serum was measured and compared with the size of mammary xenograft tumors in 11 individual mice from the time of injection; seven blood samples were collected from each tumor-bearing mouse as well as control mice on a weekly basis. The profiles of 38 proteins detected in sera from these animals were analyzed by clustering, and we identified 10 proteins with the greatest relative increase in serum concentration that correlated with growth of the primary mammary tumor. To evaluate the diagnosis of cancer based on these proteins using either an absolute threshold (i.e. a concentration cutoff) or self-referenced differential threshold based on the increase in concentration before cell injection, receiver operating characteristic curves were produced for 10 proteins with increased concentration, and the area under curve was calculated for each time point based on a single protein or on a panel of proteins, in each case showing a rapid increase of the area under curve. Next, the sensitivity and specificity of individual and optimal protein panels were calculated, showing high accuracy as early as week 2. These results provide a foundation for studies of tumor growth through measuring serial changes of protein concentration in animal models.

Fig. 1.

Schematic outlining the process flow for preparing the slides and performing an antibody colocalization microarray in a snap chip format. a, dAbs are spotted onto an aminosilane-coated slide that is stored in a freezer. cAbs are spotted onto another aminosilane-coated slide with the same spotting layout and transferred to a nitrocellulose-coated assay slide followed by blocking and storage in a freezer. b, both slides are removed from the freezer prior to use. The assay slide is incubated with sample solutions, and then the dAbs are transferred to the assay slide by snapping followed by incubation with streptavidin-Cy 5. Next, the assay results are imaged with a fluorescence microarray scanner, and the data are analyzed.

Fig. 2.

Hierarchical cluster analysis of the 38 human proteins detected in mouse sera. The average concentration of each protein of the 11 tumor-bearing mice subtracted by the average concentration of three control mice is shown. Row Z-scores were used for color rendering.

Fig. 3.

Tumor volumes and protein concentrations during the time course of tumor growth. Top left panel, tumor volume of the 14 mice (comprising three controls) calculated for weeks after the injection of cancer cells and fitted with an exponential growth curve. The remaining panels show the time course of the 10 proteins (G-CSF, IL-8, TNF-RI, uPA, uPAR, GM-CSF, CEA, MMP-3, FAS, and EGFR) that increased during the growth of the human breast cancer xenografts in mice. For each protein, curves on the left show average (Avg) protein levels for the tumor-bearing mice and controls during the growth of tumor, and curves on the right show protein levels during the time course for each of the 11 individual tumor-bearing mice and three controls. Error bars on the average curves are the standard deviations of protein concentrations among mice. M3451–M3465 represent the identity of each mouse.

Fig. 4.

Comparison between tumor volume and protein concentration of the six proteins G-CSF, IL-8, TNF-RI, uPA, uPAR, and GM-CSF in serum for each of the 11 mice along with linear regression curves. Despite the genetic homogeneity of the mice, important variations are seen among mice. A high or low excretion rate for one protein is often mirrored by the excretion rate for other proteins, suggesting that metabolic differences between tumors underlie this variation.

Fig. 5.

ROC curves of the 10-protein panel and six individual human proteins at each time point before and after injection of cancer cells using the absolute threshold method. For overlapping curves for AUC = 1, the color from the earliest week at this value is shown.

Fig. 6.

ROC curves of the 10-protein panel and six individual proteins at each time point after cancer cell injection using the self-referenced differential method. Some curves with AUC = 1 are invisible due to overlap with other curves. Unlike for the absolute threshold, no ROC curves were plotted for weeks −1 and 0 because they are not meaningful.

Fig. 7.

Time course of sensitivity and specificity calculated with the absolute threshold-based method and differential measurement for the six proteins whose AUC = 1 at week 3 as well as the panels with sensitivity = 1 and specificity = 1.