Improving the detection of aggressive prostate cancer using immunohistochemical staining of protein marker panels

Abstract

Prostate cancer (PCa) is a heterogeneous group of tumors, including non-aggressive (NAG) and aggressive (AG) cancer, with variable clinical outcomes. Clinically, in order to assess the aggressiveness of a PCa, a core needle biopsy of a tumor is usually obtained to evaluate the Gleason pattern and score of the tumor. However, it may be difficult to assign on a small biopsy sample using histology. Therefore, additional tool is needed to aid in the assessment. We studied the diagnostic utility of 12 protein markers to identify AG tumors using immunohistochemistry (IHC) and tumor tissue microarray (TMA), including 215 cores of PCa and 111 cores of tumor-matched normal adjacent tissue (NAT). Protein markers were evaluated for their potential utility as single or combined panels for identification of AG. Of 12 proteins, PSMA, phospho-EGFR, AR and P16 were over-expressed in AG. Galectin-3, DPP4 and MAN1B1 revealed stronger staining patterns in NAG. The sensitivity and specificity of individual marker varied widely. Based on AUC values of individual marker, we constructed two- and three-marker panels. In two-marker panels, especially in the panel of DPP4 and PSMA, the AUC value reached 0.83 (ranging from 0.76 to 0.83). In three-marker panels, containing both DPP4 and PSMA with either Galectin-3 or phospho-EGFR, the AUC value reached 0.86 (ranging from 0.83 to 0.86). The specificities at 95% sensitivity of three-marker panels were also significantly improved. In addition to Gleason score, our IHC panels provide a practical tool to assess the aggressiveness of PCa.

Introduction

Prostate cancer (PCa) is a heterogeneous group of tumors with variable clinical outcomes [1-3]. Most PCa presents as a localized disease with low or no risk for the tumor progression, and patients can be managed by the active surveillance program rather than aggressive/surgical interventions. Only a subset of PCa has an aggressive behavior leading to tumor progression, metastasis and caner-related death [4-7]. Overdiagnosis and overtreatment of indolent PCa are still clinical issues in the management of PCa patients. Several recent studies, including the US Preventive Services Task Force (USPSTF), have shown that the incidence of localized disease continues to decline, whereas, the incidence of advanced-stage disease continues to arise in men over 50-year old [4-7]. Multiple risk stratification systems have been developed to separate the high-risk aggressive PCa (AG) from low-risk non-aggressive indolent tumors (NAG), including the combination of both clinical and pathological parameters (such as serum PSA levels, Gleason score, ISUP grade of the tumor, as well as clinical and pathological staging). However, these tools are still insufficient for the prediction of disease progression or separation of AG from NAG [4,6-8]. Therefore, there is an urgent clinical need to identify AG in order to optimize the treatment strategy for patients.

Current genomic studies have identified a spectrum of molecular abnormalities associated with PCa [9-13]. These studies demonstrated distinct molecular abnormalities in subtypes of PCa. In the Cancer Genome Atlas (TCGA) study, the comprehensive analysis of a large cohort revealed that 74% of 333 primary PCas fell into one of seven subtypes defined by specific gene fusions (ERG, ETV1/4, FLI1) or mutations (SPOP, FOXA1, IDH1) [9]. Among numerous genomic aberrations, SPOP and FOXA1 mutants had the highest levels of AR-induced transcripts [9]. The study of whole-genomes and tumor methylomes revealed several unique tumor-specific RNA and methylation patterns in AG tumors [10]. In addition, many other epigenetic alterations have been also identified in PCa, involving in EGFR, PI3K, MAPK signaling pathways and DNA repair genes [11,12], loss of PTEN and alterations in TMPRSS2-ERG fusions [12-14]. These studies reveal not only genomic heterogeneity among primary PCa, but also identify potentially actionable therapeutic targets.

Furthermore, the dysregulation of genomics in PCa leads to aberrant transcription and expression of cellular proteins. Based upon multi-omic studies of proteogenomics and comprehensive proteomic analysis of tumor tissue, many potential protein biomarkers and molecular mechanisms associated with AG tumors have also been suggested [15-23]. Sinha A, et al. studied the proteomic signatures of 76 localized, intermediate-risk PCa tumor tissues, and identified four distinct protein clusters correlated with five clinical groups as well as genomic subtypes of PCa [15]. Interestingly, they also found that changes of mRNA abundance could not reflex the protein abundance variability, indicating the importance of proteomic study of tumors. In a comparative study of PCa cell lines with PCa tumor tissues, 12 mutant peptides were identified to be differentially expressed in PCa [16].

Our previous proteomic studies identified certain protein markers were differentially expressed, including 14 high-expression proteins in AG tumors and 14 high-expression proteins in NAG tumors [17]. Similarly, several other studies were also demonstrated that proteins were differentially expressed in subtypes of PCa [18-23]. In the study of 28 primary PCa with Gleason scores ranged from 6 to 9, authors found an increased expression of pro-NPY which was associated with a poor prognosis [18]. Recently, we identified a decreased proteinase activity in AG tumors [19]. All these findings demonstrate an extensive involvement of intracellular proteins and signaling pathways in PCa. Therefore, further evaluation of the role of potential protein markers as independent predictors of pathological AG tumor is necessary [20-23], in order to optimize the clinical management.

In PCa, the Gleason score of a tumor has been considered to be an indicator of the aggressiveness of a tumor, and is assigned based upon the morphology of the dominant tumor pattern plus secondary tumor pattern [24,25]. Although the Gleason score is the most useful tool, it has certain limitations. For example, Gleason 7 tumors can have either Gleason score of 3+4 or 4+3, in which Gleason score 4+3 tumors have more aggressive behavior than that of 3+4 tumors. Recently, based upon the Gleason score of tumors, a grading system has been further developed by the International Society of Urological Pathology (ISUP) to predict the clinical behavior of the tumor [24]. In the ISUP grading system, the grade group 1 (Gleason score 6 or less) has a low risk for disease progression, the grade group 2 (Gleason score 3+4=7) and grade group 3 (Gleason score 4+3=7) have an intermediate risk for progression, the grade group 4 (Gleason score 8) has a high risk for progression, and the grade group 5 (Gleason score 9 or 10) has the highest risk for tumor progression [24]. The ISUP grade separates Gleason score 7 tumors into group 2 and 3. Studies have shown that grade group 2 has a favorable prognosis than that of grade group 3 [25,26]. Thus, the current clinical guideline suggests that the active surveillance program should be selected for grade group 2 and surgical interventions should be selected for grade group 3 or higher PCa [27,28]. These studies and guidelines are indicated the importance of accurately evaluation of Gleason scores.

In PCa patients, a small amount of tumor tissue often obtained for the initial evaluation of tumor Gleason scores. However, the procedure can be difficult and may not represent the true nature of the tumor. The Gleason score, particularly the secondary pattern of an aggressive tumor may be difficult to identify and/or assigned in certain cases due to scant tumor cells and/or lack of the representative morphology [24,29]. In rare tumors, the subtle glandular differentiation may be difficult to appreciate on H&E slides in a biopsy specimen. Studies have also shown that patients with biopsy proven ISUP grade group 1 and grade group 2 tumor could upgrade to higher grade groups after evaluation of the surgical resected whole tumor tissue [29]. Therefore, the current assessment criteria still need to be further improved.

Based upon recent advancements in proteogenomic studies, an integrative evaluation of the tumor morphology (the Gleason score/ISUP grade) in combination with the expression of protein markers may be necessary to improve the identification of AG from NAG tumors. To access the utility of potential protein markers for the identification of AG and to understand molecular features of tumor progression in PCa, we evaluated expressional patterns of 12 protein markers identified by proteomic and genomic studies. The immunochemistry (IHC) was performed by using PCa tumor microarrays (TMA), including both indolent NAG and AG subtypes and tumor-matched normal/benign adjacent tissue (NAT). The optimal goals of our study were to evaluate the diagnostic utility of PCa-associated proteins, to optimize the performance of individual marker by combining them into panels, and to assess the protein marker panels, which may have a potential for the development of clinical assay to separate AG from NAG on small biopsy samples.

Methods

Case selection

PCa cases were collected from radical prostatectomy (except one case was collected from transurethral resection of the prostate (TURP)) with informed consents and in a manner to protect patients’ identity. A total of 57 cases were included in the study, including Gleason pattern 3 (i.e. tumors of 3+3), 4 (i.e. tumors of 3+4, 4+3, or 4+4) and 5 (i.e. tumors of 5+4 or 4+5). Among them, 54 cases were collected between January 2002 and December 2009, and additional 3 cases were collected in 2012. Electronic medical records were reviewed and the clinical and pathological data, including age, TNM T-stage, N-stage, M-stage, were obtained. The pathological stages of PCa were determined according to the eighth edition of the AJCC guideline [30]. AG and NAG tumor were defined using the criteria of the International Society of Urological Pathology (ISUP) [24].

The study was approval by the Institutional Review Board of Johns Hopkins Medical Institutions. In addition, all methods performed in the study were in accordance with the relevant guidelines and regulations.

Construction of tissue microarray

The PCa tissue microarray (TMA) was constructed using above surgical resected tumors, including 215 cores of PCa and 111 cores of NAT. All tumor tissue blocks were fixed in 10% formalin and embedded in paraffin. In addition to the original pathology reports, the hematoxylin and eosin (H&E) stained tumor sections were re-reviewed by the American Board of Pathology certified pathologists prior to TMA construction to ensure the representation of tumor area and tumor-matched normal/benign tissue (NAT). Cores (in diameter of 0.6 mm) were obtained from paraffin blocks and transplanted into the recipient TMA block. After the construction of the TMA, we re-reviewed the tumor morphology of each core on a H&E stained slide to confirm the Gleason score of individual core.

Resources of primary antibodies

We evaluated 12 proteins in the study. Details of primary antibodies are summarized in Table 1.

Table 1.

Summary of primary antibodies

#	Antibodies	Resource	Clone	Clonality	Dilution	Catalog #
1	DPP4/CD26	Cell Signaling	D6D8K	Rabbit Monoclonal	1:200	67138T
2	Total EGFR	Cell Signaling	D38B1	Rabbit monoclonal	1:50	4267
3	Phospho-EGFR (phospho Y1068)	Cell Signaling	D7A5	Rabbit Monoclonal	1:1,000	3777
4	MAN1B1	Sigma-Alderich	1D6	Mouse Monoclonal	1:100	MABS1222
5	PSMA	Dako	3E6	Mouse Monoclonal	1:100	M3620
6	P16	Ventana	INK4a	Mouse Monoclonal	Prediluted by manufacturer	705-4793
7	Galectin-3	Ventana	9C4	Mouse Monoclonal	Prediluted by manufacturer	790-4256
8	PD-1	Cell Marque	NAT105	Mouse Monoclonal	1:100	315M-96
9	PD-L1 (SP142)	Spring Bioscience	SP142	Rabbit Monoclonal	1:100	M4422
10	PD-L1 (22C3)	Dako	22C3	Mouse Monoclonal	1:100	SK006
11	AR	Cell Marque	SP107	Rabbit Monoclonal	Prediluted by manufacturer	200R-18
12	PTEN	Biocare Medical	6H2.1	Mouse Monoclonal	1:100	PM278AA

PSMA: prostate specific membrane antigen. AR: androgen receptor.

Immunohistochemical staining of PCa TMA

Immunohistochemical (IHC) study of protein markers was performed on the TMA using a Ventana Discovery Ultra autostainer (Roche Diagnostics). The TMA was cut into 4-μm sections prior to IHC stains. Briefly, following dewaxing and rehydration on board, epitope retrieval was performed using Ventana Ultra CC1 buffer (catalog # 6414575001, Roche Diagnostics) at 96°C for 48 minutes. Primary antibodies were applied at 36°C for 60 minutes. Primary antibodies were detected using an anti-mouse and/or anti-rabbit HQ detection system (catalog # 7017936001 and 7017812001, Roche Diagnostics) followed by Chromomap DAB IHC detection kit (catalog # 5266645001, Roche Diagnostics), counterstaining with Mayer’s hematoxylin, dehydration and mounting.

Distinctive membranous, cytoplasmic or nuclear staining was considered in each protein IHC staining. The intensity of IHC staining pattern on each protein was semi-quantitatively scored by three researchers QKL (the American Board of Pathology certified pathologist) YH and NH, using a 4-tier system as: score 0 (0%, no staining), score 1 (<10%, weak and focally staining), score 2 (10-50%, medium and focally staining), or score 3 (>50%, strong and diffusely staining) in tumor cells. Each core was considered as individual data point. The consent scores were used for the analysis. All IHC stains were scanned using Concentriq (Proscia Inc, Philadelphia, PA https://proscia.com) and stored as digital files. Depending on the TMA section, some of cores could not be evaluated due to the loss of tissue cores during the process (please see individual staining pattern in the result).

Statistical analysis

The discriminatory power of each protein marker and panels (composed of ≥1 candidate marker) was evaluated using logistic regression via receiver operating characteristic (ROC) curve analysis. To ensure statistical stability of results, we used bootstrap resampling (n=500) of the data to construct and evaluate the predictive models of protein marker panels. Bootstrap resampling with label permutation was also carried out to generate random models for examining the reliability of panels. The mean ROC curves were depicted based on bootstrap resampling results and an area under the curve (AUC) was computed for each mean ROC curve. All the analyses were carried out in R (version 3.5). The predictive models were built using caret (version 6.0-85) and ROC curves were generated using pROC (version 1.13).

Results

Clinical information

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.

Table 2.

Clinical features of prostate cancer patients

Characteristics	Prostate adenocarcinoma
Age (years)
Median	61
Range	40-73
Tumor Location (n=57)
Left side	20 (35.09%)
Right side	36 (63.16%)
N/A	1 (1.75%)
Pathological stage (n=57)
pT2	30 (52.63)
pT2X	1 (1.75%)
pT3A	13 (22.81%)
pT3B	11 (19.30%)
pT4	2 (3.51%)

N/A: not applicable.

Overall, our TMA contains a total of 215 tumor cores, including 90 cores (41.9%) of Gleason pattern 3, 62 cores (28.8%) of Gleason pattern 4, and 63 cores (29.3%) of Gleason pattern 5; and 111 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. IHC scores of 0, 1, 2 and 3 were used to assess the positive or negative staining patterns in tumor cells. The majority of protein markers revealed membrane and cytoplasmic staining patterns, except P16, which revealed nuclear staining pattern. Among 12 proteins, 7 proteins presented variably staining patterns (Figure 1A). The staining patterns of each protein in AG, NAG and NAT were analyzed using a semi-quantitative scoring system (Figure 1B).

Figure 1.

Heatmap of IHC staining patterns of individual protein in PCa TMA. A. Overall staining patterns. B. Individual marker in the separation of AG from NAG tumors using different cut-off Gleason patterns. The intensity of IHC stains were scored using s semi-quantitative 4-tire scoring system: score 0 (0%, no staining), score 1 (<10%, weak and focally staining), score 2 (10-50%, medium and focally staining), and score 3 (>50%, strong and diffusely staining) in tumor cells. AR: androgen receptor. pEGFR: phospho-EGFR. PSMA: prostate specific membrane antigen.

The IHC staining scores of 7 proteins are summarized in Table 3. Four proteins, including PSMA, phospho-EGFR, androgen receptor (AR), and P16, were over-expressed in AG tumors (Gleason pattern ≥4). Whereas, three proteins, including Galectin-3, DPP4, and MAN1B1, revealed stronger staining patterns in NAG tumors (Gleason pattern 3), but weak staining patterns in Gleason pattern 4 and Gleason pattern 5 tumors. Both PSMA and phospho-EGFR had a positive correlation with Gleason patterns of the tumor, whereas Galectin-3 and DPP4 had negative correlation with Gleason patterns of the tumor.

Table 3.

IHC staining scores of protein markers

Markers	IHC Staining Scores	NAT	Gleason pattern 3	Gleason pattern 4	Gleason pattern 5
AR	Mean ± SD	2.21±0.83	2.53±0.69	2.61±0.71	2.75±0.71
DPP4	Mean ± SD	2.51±0.75	2.66±0.62	1.92±0.94	1.40±0.91
Galectin-3	Mean ± SD	2.16±0.84	1.43±0.80	1.16±0.93	0.85±0.88
MAN1B1	Mean ± SD	1.90±0.97	2.40±0.66	2.30±0.78	2.00±0.92
P16	Mean ± SD	0.33±0.54	1.10±0.89	1.34±0.83	1.08±0.82
Phospho-EGFR	Mean ± SD	N/A	1.58±0.63	1.83±0.79	1.98±0.87
PSMA	Mean ± SD	0.96±0.87	1.54±0.79	2.40±0.91	2.48±0.95

N/A: not applicable, due to basal staining pattern in NAT and cytoplasmic/nuclear staining pattern in tumor cells. PSMA: prostate specific membrane antigen. AR: androgen receptor.

The IHC scores of PSMA, phospho-EGFR, Galectin-3 and DPP4; and correlations with Gleason patterns of tumors are also shown in Figure 2. However, we did not found such correlations of IHC scores with Gleason patterns in protein AR, P16 and MAN1B. In addition, we also found a basal staining pattern of phospho-EGFR in NAT and cytoplasmic/nuclear staining pattern in tumor cells. Thus, we only analyzed the staining pattern of tumor cells for the marker phospho-EGFR.

Figure 2.

IHC staining patterns of individual protein markers. A. Overall staining patterns of PSMA and phosphor-EGFR (pEGFR). B. Overall staining patterns of Galectin-3 and PDD4. The intensity of IHC stains were scored using s semi-quantitative 4-tire scoring system: score 0 (0%, no staining), score 1 (<10%, weak and focally staining), score 2 (10-50%, medium and focally staining), and score 3 (>50%, strong and diffusely staining) in tumor cells.

We did not detect IHC immune-activities 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 the TMA.

The sensitivity and specificity of individual proteins

Based upon the individual staining pattern, the sensitivity and specificity of phospho-EGFR, Galectin-3, DPP4 and PSMA for distinguishing AG from NAG are summarized in Figure 3. The receiver operating characteristic (ROC) analysis was performed. The value of an area under the curve (AUC) of individual marker was compared.

Figure 3.

Receiver operating characteristic (ROC) analysis of individual protein marker. A. Comparison of individual AUC curve. B. Specificities and sensitivities of individual protein marker. C. Comparison of values between real and random (label permutation analysis) of individual protein marker.

Among individual marker, values of AUC ranged from 0.48 to 0.7. DPP4 (AUC=0.68) and PSMA (AUC=0.70) demonstrated better performances than that of phospho-EGFR (AUC=0.48) and Galectin-3 (AUC=0.54) in the separation of AG tumors in PCa (Figure 3A). 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 of 68.1%, 36.3%, 79.6%, and 68.1%, respectively (Figure 3B). Among individual marker, Galectin-3 and PSMA had the best specificity of 89%; and DPP4 had the best sensitivity of 79.6%. To further compare the performance of individual marker, we also selected a fixed sensitivity at 95% and then compared the specificity. At 95% sensitivity, the specificity of four protein markers was ranged from 0% to 8.79% (Figure 3B).

To further evaluate statistical stability of the performance of markers for the detection of AG tumors, we used both label permutation and bootstrap methods (Figure 3C). Our data demonstrated that both DPP4 and PSMA had higher stability than that of phospho-EGFR and Galectin-3. Taken together, the elevated expression of PSMA and the reduced expression of DPP4 could be used as signatures of the aggressiveness of a 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 marker into two- and three-marker panels, and evaluated their discriminatory powers 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 these panels, compared to using individual maker. All AUC values of these panels were >0.70 (Figure 4A). In these 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 stable performances of two-marker panels (Figure 4C). Additionally, the panel of DPP4 and PSMA showed a better specificity (38.46%) when using the fixed sensitivity at 95% compared to individual markers and other two-marker panels (Figure 4B).

Figure 4.

Receiver operating characteristic (ROC) analysis of two-marker panels. A. Comparison of AUC curves of different panels, including Galectin-3 plus PSMA, phospho-EGFR plus PSMA, DPP4 plus Galectin-3 and DPP4 plus PSMA. B. Specificities and sensitivities of two-marker panels. C. Comparison of values between real and random (label permutation analysis) of different panels.

To further improve the specificity and discriminatory power of these markers, 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 AUC values were further improved to >80% (Figure 5A and 5B). 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 demonstrated well-separated patterns, indicating that the performances of three-marker panels were reliable (Figure 5C). The specificity at the fixed sensitivity of 95% of these three-marker panels was improved, especially in panels composed of both DPP4 and PSMA. Specificities at the fixed 95% sensitivity reached 46.15% and 48.35%, respectively (Figure 5B).

Figure 5.

Receiver operating characteristic (ROC) analysis of three-marker panels. A. Comparison of AUC curves of different panels, including DPP4 plus Galectin-3 plus phospho-EGFR, DPP4 plus Galectin-3 plus PSMA, and DPP4 plus phospho-EGFR plus PSMA. B. Specificities and sensitivities of three-marker panels. C. Comparison of values between real and random (label permutation analysis) of different panels.

Taken together, our data demonstrated that three-marker panel containing DPP4 and PSMA had better performances than that of individual makers as well as two-marker panels. They can significantly improve the separation of AG from NAG.

Discussion

Prostate cancer (PCa) is the most common cancer and the second leading cause of cancer death in men in the United States; with estimated new cases and cancer-related deaths in 2020 were 192,000 and 33,000, respectively [1-4]. The majority of PCa presents as an indolent tumor, in which the patient can be observed in an active surveillance program [4,24-28]. Only about 10% of PCa presents as an aggressive disease with high risk of tumor progression [4,26-28]. Therefore, in order to accurately predict the high-risk tumor and to limit overtreatment of indolent tumor, it is crucial to distinguish AG PCa from NAG tumors. Although both Gleason score and newly developed ISUP grade group system have been used to assess the potential clinical behavior of the tumor, these systems still have certain limitations to guide the therapeutic decision for patients [24].

To better characterize AG tumors, great efforts have been established to profile the proteogenomic landscape of PCa [9-23]. Our previous large-scale quantitative proteomic studies of PCa tumor tissue identified a spectrum of cellular proteins to be up- or down-regulated in subtypes of PCa [8,17,19]. These differentially expressed tissue proteins identified by proteomic approach are particularly interesting as biomarker candidates because of the high likelihood of their detectability in tumor tissue [17,19]. Using an immunochemical approach, we assessed the potential detectability of selected candidate protein markers in tumor tissue, and their utility as individual markers and/or as protein panels in the separation of AG tumors.

In this study, expressional patterns of four proteins, including PSMA, phospho-EGFR, AR, and P16, demonstrated higher levels in AG tumors in comparison to NAG. In contrast, three protein markers, including Galectin-3, DPP4 and MAN1B1, revealed stronger staining patterns in Gleason pattern 3 tumors, but weak staining patterns in Gleason pattern 4 and Gleason pattern 5 tumors. The AUCs of phospho-EGFR, Galectin-3, DPP4 and PSMA were 0.48, 0.54, 0.68, and 0.7, respectively. Among the individual marker, PSMA showed the best discrimination power with the specificity of 89%, where DPP4 showed a sensitivity of 79.6%. These two markers demonstrated a better performance than phospho-EGFR and Galectin-3 in the detection of AG tumors.

PSMA is a type II transmembrane glycoprotein, containing 750-amino acid. It has a long C-terminal extracellular domain and a short N-terminal intracellular domain [31]. Its extracellular domain has enzymatic activity functioning as folate hydrolase I or glutamate carboxypeptidase II [32]. In benign prostate tissue, the expression of PSMA is low. This low level of expressional pattern is also identified in the kidney, small intestine, and brain tissue. However, the expression of PSMA is significantly increased in PCa; and the overexpression of PSMA is also correlated with the disease progression and the tumor metastasis in PCa [33,34]. Similarly, DPP4 is also a type II transmembrane glycoprotein, but it has the serine exopeptidase activity. DPP4 plays a critical role in regulating cellular proliferation and migration [35]. The aberrant oncogenic and tumor suppressor activity of DPP4 have been identified in cancers [36,37]. A reduced serum DPP4 level is also found in PCa patients, especially in patients with metastatic disease [38]. In the study of primary and metastatic PCa, we recently identified that the decreased DPP4 expression and activity is associated with PCa aggressiveness [17,19]. The findings of decreased DPP4 levels in aggressive and metastatic PCa suggest its critical role in AG tumors.

Galectin-3 is a member of the lectin superfamily and plays critical roles in regulating cellular signaling pathways and cancer progression [39]. In prostate cancer, its expression elevated in the early stages of tumors, but this expression gradually decreased over disease progression and was completely lost in advanced stage tumors [39-41]. In the metastatic PCa, Galectin-3 regulates tumor cells to form aggregates and adhere to the microvascular endothelium [42]. Based upon its expression and biological roles, Galectin-3 has been suggested to be a predictive marker for the biochemical recurrence of PCa [40,41].

EGFR is a transmembrane glycoprotein and activated by the dimerization upon a ligand binding [43]. The phosphorylation of EGFR leads to several downstream intracellular phosphorylations, which plays critical roles in cell survival, proliferation, migration and differentiation [43-46]. In PCa, EGFR activation plays a key role in tumor cell proliferation, progression and metastasis [45,46]. In an earlier study by Schlomm T et al, they performed IHC stains of the EGFR in 1849 primary PCa tumor tissues; and found that the majority of tumors were negative (1484 cases) and weakly positive (340 cases) for the EGFR expression. Of 1849 cases, 25 tumors had a strong expression of EGFR [45]. The overexpression of EGFR was associated with poor prognosis in PCa. In the same study, Schlomm T et al also found that only 3.3% of tumor cases showed an increased EGFR copy number detected by fluorescence in situ hybridization analysis. Additionally, they observed different IHC staining patterns in normal prostate tissue (basal staining pattern) and in tumor cells (cytoplasmic and nuclear staining patterns). In a recent study by Nastaly P et al, they found that overexpression of EGFR by IHC staining was detected in 14% of 1039 primary PCa tumors, and associated with poor prognosis [46]. They also found that overexpression of EGFR could be detected in 13% of circulating tumor cells. In our study, we did not detect EGFR expression in tumor cores. We speculated that this could be due to the difference of primary anti-EGFR antibody used in the study. We used anti-EGFR antibody clone D38B1, whereas clone of E30 [46] and anti-EGFR antibody [45] were used in other studies. Interestingly, we detected an increased expression of phospho-EGFR in tumors, which was associated with higher Gleason grades of PCa. Similar to previous studies, we also found a basal staining pattern in NAT and cytoplasmic/nuclear staining pattern in tumor cells of phospho-EGFR.

The reduced expression of DPP4 and elevated expression of PSMA could be used together as signatures of aggressiveness of PCa. However, these two markers had limitations when we considered the specificity at 95% sensitivity. To further improve the discriminatory power, we combined individual marker into several panels and evaluated their performances in the separation of AG from NAG tumors. The two-marker panels were constructed by using combinations of PSMA or DPP4 with either Galectin-3 or phospho-EGFR. Higher AUCs were achieved in two-marker panels, compared to individual maker. Two-marker panels markedly improved the identification of AG tumors. All specificities of two-marker panels were over 68% (85.7%, 86.8%, 68.1% and 76.9%); and all the sensitivities were ≥69% (71.7%, 69%, 87.6% and 85%). Furthermore, the specificity at 95% sensitivity was also improved in general by using two-marker panels. Among the combinations, two-marker panel consisted of DPP4 and PSMA demonstrated a better specificity (38.46%) at the fixed sensitivity of 95%, indicating the improvement of the performance.

To investigate whether a higher discrimination power could be further achieved, three-marker panels were also constructed by using combinations of both DPP4 and PSMA with either Galectin-3 or phospho-EGFR. In three-marker panels, all AUCs were improved to ≥0.83, indication a further improvement in the separation of AG from NAG. All specificities of three-marker panels were >75% (75.8%, 83.5%, and 81.3%); and all sensitivities were >76% (83.2%, 76.1%, and 79.6%). In three-marker panels containing both DPP4 and PSMA, specificities at the fixed sensitivity of 95% reached 46.15% and 48.35%, respectively. These three-marker panels demonstrated the best performance, compared to individual marker and other two- and three-marker panels.

The unique feature of our study is the integrative analysis of protein expressional patterns with a spectrum of tumor subtypes, including Gleason score 6 NAG (Gleason score 3+3), Gleason score ≥7 (Gleason score 3+4, 4+3, 4+4, 4+5, 5+4 and 5+5) and NAT controls. This integrative study with the previously defined aggressive PCa subtypes demonstrated that three-marker panels can be used as a clinical tool for the separation of aggressive PCa from indolent tumors. Furthermore, our findings also demonstrated that loss of Galectin-3 expression and DPP4 activity may promote prostate cancer aggressiveness. The consequence of the decrease expression of these two proteins and subsequent increase in bio-active of phospho-EGFR and PSMA may promote tumor cell proliferation and disease progression. It also helps us to gain further knowledge into the proteomic heterogeneity of aggressive PCa and to investigate the molecular taxonomy of the tumor for future diagnostic, prognostic, and therapeutic stratification.

In summary, we assessed the diagnostic value of selected protein markers in the identification of AG tumors using TMA and IHC. The higher expressions of four protein markers, including PSMA, phospho-EGFR, AR, and P16, were identified in AG tumors of Gleason pattern ≥4. In contrast, three protein markers, including Galectin-3, DPP4 and MAN1B1, revealed stronger staining patterns in NAG tumors of Gleason pattern 3. The sensitivity and specificity of individual marker for distinguishing AG were variable. The combination of two protein markers could provide better performance for the separation of AG from NAG tumors, especially the panel composed of DPP4 and PSMA. We observed further improvement when combining DPP4 and PSMA with either Galectin-3 or phospho-EGFR. More importantly, higher specificities were also achieved by using the fixed sensitivity of 95%. These panels can be used to assess the aggressiveness of PCa and to improve the separation of AG from NAG tumor using small biopsy samples. The diagnostic utility of these panels provides an additional tool to address the urgent clinical need and to optimize the treatment strategy in PCa patients.

Disclosure of conflict of interest

None.

Abbreviations

PCa

prostate cancer

AG

aggressive prostate cancer

NAG

non-aggressive prostate cancer

C

control

USPSTF

US preventive services task force

ISUP

international society of urological pathology

MS

mass spectrometry

TMA

tumor microarray

TCGA

the Cancer Genome Atlas

H&E

hematoxylin and eosin

ACN

Acetonitrile

CV

coefficient of variations

SD

standard deviation

HPA

Human Proteome Atlas

QC

quality control

FDA

Food and Drug Administration

IHC

immunochemistry

ROC

receiver operating characteristic curve

AUC

area under the curve

AR

androgen receptor

pEGFR

phospho-EGFR

PSMA

prostate specific membrane antigen