In our cohort, the median age of patients was 61 years, ranging from 40 to 73 years. Based upon the grading criteria of International Society of Urological Pathology (ISUP) and the morphological feature of the dominant nodule, the ISUP grade of our cohort were: 20 cases of Grade 1, 18 cases of Grade 3, 5 cases of Grade 4, and 14 cases of Grade 5. In addition, 7 cases of Grade 2 were found in tertiary nodules. The pathological stages were 30 cases of pT2, 1 case of pT2x, 13 cases of pT3A, 11 cases of pT3B and 2 cases of pT4 (Table 2, Supplementary Table 1). Taken together, 20 indolent NAGs with the Gleason score of 6, and 37 AGs with the Gleason score≥7 were included in the TMA.
Overall, our TMA contains a total of 218 tumor cores, including 92 cores (42.2%) of Gleason score 3, 63 cores (28.9%) of Gleason score 4, and 63 cores (28.9%) of Gleason score 5; and 113 cores of NAT.
IHC Staining Pattern of Individual Protein in PCa TMA
IHC stains of 12 proteins were performed on the TMA. The information of primary antibodies is summarized in Table 1. Among them, antibodies for 7 proteins presented variably staining patterns (Figure 1A). The majority of protein markers revealed membrane and cytoplasmic staining patterns, except P16, which revealed nuclear staining pattern. The staining patterns of each protein in tumors and NAT were analyzed using a semi-quantitative scoring system.
The IHC staining patterns of antibodies to 7 proteins are summarized in Table 3. The higher expressions of four proteins, including PSMA, phospho-EGFR, androgen receptor (AR), and P16, were identified in AG tumors. In contrast, three antibodies, including anti-Galectin-3, anti-DPP4, and anti-MAN1B1, revealed stronger staining patterns in Gleason score 3 tumors, but weak staining patterns in Gleason score 4 and Gleason 5 tumors. IHC score 1, 2 and 3 were used as cut-off score for positive or negative stain in tumors, respectively. Both PSMA and phospho-EGFR had a positive correlation with Gleason scores of the tumor, whereas Galectin-3 and DPP4 had negative correlation with Gleason scores of the tumor (Figure 1B). The correlations of IHC scores of PSMA, phospho-EGFR, Galectin-3 and DPP4 with Gleason scores of tumors are shown in Figure 2.
We did not detect the expression of total EGFR with antibody D38B1, PD-1 with antibody NAT105, PD-L1 with antibodies 22C3 and SP142, and PTEN with antibody 6H2.1 in PCa.
The Sensitivity and Specificity of Individual Proteins
Based upon the individual staining pattern, the receiver operating characteristic (ROC) analysis was performed. The value of an area under the curve (AUC) of individual marker was compared. The sensitivity and specificity of phospho-EGFR, Galectin-3, DPP4 and PSMA for distinguishing AG from NAG are summarized in Figure 3.
Among individual marker, values of AUC ranged from 0.48 to 0.7, with best performance of DPP4 and PSMA. Expressions of DPP4 and PSMA were significantly altered in tumors with the Gleason ≥4 (p < 0.05) and demonstrated a better performance than phospho-EGFR and galectin-3 in the separation of AG tumors from NAG tumors (Figure 3A). To directly compare the performance of individual marker, we fixed the sensitivity at 95% and then compared the specificity. At 95% sensitivity, the specificity was ranged from 0% to 8.79% (Figure 3B). By examining the individual marker at its best cutoff point on ROC curves (the maximal summed sensitivity and specificity), we found that phospho-EGFR, Galectin-3, DPP4 and PSMA, had specificities of 49.5%, 89%, 73.6% and 89%; and the corresponding sensitivities were 68.1%, 36.3%, 79.6%, and 68.1%, respectively (Figure 3B). Among these markers, Galectin-3 and PSMA had the best specificity of 89%; and DPP4 had the best sensitivity of 79.6%. To evaluate statistical stability of the performance of markers for AG tumor detection, we used both label permutation and bootstrap methods (Figure 3C). Again, both DPP4 and PSMA demonstrated higher stability than that of phospho-EGFR and Galectin-3. Taken together, the reduced expression of DPP4 and elevated expression of PSMA could be used as a signature of aggressiveness of PCa.
Further Construction and Evaluation of Protein Panels in Separation of AG Tumors
Based upon the performance of individual protein marker, we combined individual protein biomarker into two- and three-marker panels, and evaluated their performances in the separation of AG from NAG tumors.
The two-marker panels were constructed by using combinations of Galectin-3 plus PSMA, phospho-EGFR plus PSMA, DPP4 plus Galectin-3, and DPP4 plus PSMA (Figure 4). The overall performance for distinguishing AG and NAG tumors was improved when using panels, compared to using individual maker. All AUCs of these panels were > 0.70 (Figure 4A). In panels, specificities were > 68% (85.7%, 86.8%, 68.1% and 76.9%); and sensitivities were > 69% (71.7%, 69%, 87.6% and 85%) (Figure 4B). The label permutation and bootstrap analyses demonstrated the stable performance of two-marker panels (Figure 4C). Additionally, the panel of DPP4 and PSMA showed a better specificity (38.46%) when fixed sensitivity at 95% compared to individual markers and other two-marker panels (Figure 4B).
To further improve the specificity of these markers using a 95% sensitivity as cutoff value, we also constructed three-marker panels and evaluated their performance. These panels included combinations of DPP4 plus Galectin-3 plus phospho-EGFR, DPP4 plus Galectin-3 plus PSMA, and DPP4 plus phospho-EGFR plus PSMA (Figure 5). All AUCs were further improved to > 80% (Figure 5A and Figure B). Specificities and sensitivities of three-marker panels were as follows: 75.8% and 83.2% in the panel of DPP4 plus Galectin-3 plus phospho-EGFR, 83.5% and 76.1% in the panel of DPP4 plus Galectin-3 plus PSMA, 81.3% and 79.6% in the panel of DPP4 plus phospho-EGFR plus PSMA (Figure 5B). All specificities of three-marker panels were > 75% (75.8%, 83.5%%, and 81.3%); and sensitivities were > 76% (83.2%, 76.1%, 79.6%). The random models (label permutation analysis) and the real data were well-separated, indicating that the performances of three-marker panels were reliable (Figure 5C). We observed that three-marker panels had much better performance than that of individual makers as well as two-marker panels, and the specificity at 95% sensitivity was improved, especially in panels composed of both DPP4 and PSMA. Specificities at 95% sensitivity of these panels reached 48.35% and 46.15%, respectively (Figure 5B).
Taken together, our data demonstrated that three-marker panel containing DPP4 and PSMA can significantly improve the separation of AG from NAG, in the comparison with individual marker or two-marker panels.