Relationship between androgen receptor and androgen receptor-related protein expression in breast cancers focusing on morphologically identified carcinoma with apocrine differentiation
In women, the incidence of breast cancer (BC) is the highest among all cancers in several countries (159/185 countries); furthermore, BC accounts for the highest cancer-related mortality in 110 of 185 countries . BC treatment is dictated by histological type and biomarker status . It is pathologically classified into four molecular subtypes, namely, luminal A, luminal B, human epidermal growth factor receptor 2 (HER2)-enriched, or basal-like (triple-negative), based on the expression of biomarkers, such as estrogen receptor α (ER), progesterone receptor (PgR), HER2, and Ki67, as determined using immunohistochemistry (IHC) . Although this classification is widely used in clinical settings for hormone therapy and chemotherapy, some BCs exhibit apocrine differentiation, and carcinoma with apocrine differentiation (CAD) (formerly apocrine carcinoma; AC) is defined based on the morphology as per the World Health Organization (WHO) classification of breast tumors (5th edition) . CAD is histologically distinguished by large cells with abundant eosinophilic granular cytoplasm and enlarged nuclei with prominent nucleoli, with some cases showing apocrine decapitation . The expression of androgen receptor (AR) or gross cystic disease fluid protein-15 (GCDFP15) is also used to diagnose CAD supportively – . Although the expression of ARs has been studied in different molecular subtypes, these molecules have not been comprehensively examined in BC . Therefore, in the present study, we aimed to identify the characteristic biomarkers of morphologically identified CAD through comparison with other subtypes of BC and propose a new method for the diagnosis of CAD, which could in turn guide treatment.   AR and AR-related protein expression in BC The IHC results for AR or AR-related protein expression in CAD and non-CAD are presented in Table . Sensitivity indicates the proportion of cases that were IHC-positive among the CAD cases, whereas specificity indicates the proportion of cases that were IHC-negative among the non-CAD cases. In the CAD group, high sensitivity was observed for AR (100%, 27/27 cases) and forkhead box protein A1 (FOXA1) (92.6%, 25/27 cases); GCDFP15 (74.1%, 20/27 cases), sex-determining regions in Y-related high-mobility group box 9 (SOX9) (55.6%, 15/27 cases), transcriptional repressor GATA-binding 1 (TRPS1) (59.3%, 16/27 cases), and cytoplasmic phosphorylated-mammalian target of rapamycin (p-mTOR) (74.1%, 20/27 cases) also exhibited relatively high sensitivity (Table ). The sensitivity for AR was high (100%), and its specificity was relatively high (53.0%). Notably, while the sensitivity for FOXA1 was high (92.6%), its specificity was low (18.4%). In triple-negative breast cancer (TNBC), the expression of SOX9 and TRPS1 was high (SOX9: 100% [14/14 cases] and TRPS1: 92.9% [13/14 cases]). In contrast, the expression of the other AR-related proteins was low (Table ). The positivity of nuclear p-mTOR was significantly associated with CAD compared with that in some non-CAD groups ( p < 0.01). Although the specificity of nuclear p-mTOR was high (97.7%), its sensitivity was low (33.3%). Expression of AR-related proteins in AR-positive cases Table presents a comparison of the expression patterns of AR-related proteins between AR + CAD and AR + non-CAD. In the 27 AR + CAD cases, GCDFP15, SOX9, FOXA1, TRPS1, and cytoplasmic p-mTOR positivity was observed in 20 (74.1%), 15 (55.6%), 25 (92.6%), 16 (59.3%), and 20 (74.0%) cases, respectively. In contrast, 41 patients in the non-CAD group were AR+. Among those 41 AR + non-CAD cases, 34 (82.9%), 28 (68.3%), 34 (82.9%), 40 (97.6%), 37 (90.2%), and 33 (80.5%) cases were positive for GATA-binding protein 3 (GATA3), GCDFP15, SOX9, FOXA1, TRPS1, and cytoplasmic p-mTOR, respectively. Furthermore, 4 (9.76%) and 10 (24.4%) cases were negative for ER and PgR, respectively. Additionally, among the 41 AR + non-CAD cases, 3 (7.32%) cases were GCDFP15 + and ER−, and 4 (9.76%) were FOXA1 + and ER−. Correlation between AR and AR-related proteins in the CAD and non-CAD groups We examined the relationship between AR and AR-related proteins in the CAD and non-CAD groups based on correlation coefficients and found that the correlations between the proteins were different in each of the groups. In the CAD group, the AR-α-methylacyl-CoA racemase (AMACR) ( r = 0.59, Fig. a), GATA3-TRPS1 ( r = 0.65, Fig. b), GCDFP15-AMACR ( r = 0.53, Fig. c), SOX9-TRPS1 ( r = 0.44, Fig. d), and AMACR-nuclear p-mTOR ( r = 0.41, Fig. e) levels correlated strongly ( r > 0.4). The correlations between AR and AMACR ( r = 0.29), GATA3 and TRPS1 ( r = 0.29), and GCDFP15 and AMACR ( r = 0.28) were weak; however, the correlations between SOX9 and TRPS1 ( r = 0.13), and AMACR and nuclear p-mTOR ( r = −0.13) were not confirmed in the non-CAD group (Fig. ).   The IHC results for AR or AR-related protein expression in CAD and non-CAD are presented in Table . Sensitivity indicates the proportion of cases that were IHC-positive among the CAD cases, whereas specificity indicates the proportion of cases that were IHC-negative among the non-CAD cases. In the CAD group, high sensitivity was observed for AR (100%, 27/27 cases) and forkhead box protein A1 (FOXA1) (92.6%, 25/27 cases); GCDFP15 (74.1%, 20/27 cases), sex-determining regions in Y-related high-mobility group box 9 (SOX9) (55.6%, 15/27 cases), transcriptional repressor GATA-binding 1 (TRPS1) (59.3%, 16/27 cases), and cytoplasmic phosphorylated-mammalian target of rapamycin (p-mTOR) (74.1%, 20/27 cases) also exhibited relatively high sensitivity (Table ). The sensitivity for AR was high (100%), and its specificity was relatively high (53.0%). Notably, while the sensitivity for FOXA1 was high (92.6%), its specificity was low (18.4%). In triple-negative breast cancer (TNBC), the expression of SOX9 and TRPS1 was high (SOX9: 100% [14/14 cases] and TRPS1: 92.9% [13/14 cases]). In contrast, the expression of the other AR-related proteins was low (Table ). The positivity of nuclear p-mTOR was significantly associated with CAD compared with that in some non-CAD groups ( p < 0.01). Although the specificity of nuclear p-mTOR was high (97.7%), its sensitivity was low (33.3%).   Table presents a comparison of the expression patterns of AR-related proteins between AR + CAD and AR + non-CAD. In the 27 AR + CAD cases, GCDFP15, SOX9, FOXA1, TRPS1, and cytoplasmic p-mTOR positivity was observed in 20 (74.1%), 15 (55.6%), 25 (92.6%), 16 (59.3%), and 20 (74.0%) cases, respectively. In contrast, 41 patients in the non-CAD group were AR+. Among those 41 AR + non-CAD cases, 34 (82.9%), 28 (68.3%), 34 (82.9%), 40 (97.6%), 37 (90.2%), and 33 (80.5%) cases were positive for GATA-binding protein 3 (GATA3), GCDFP15, SOX9, FOXA1, TRPS1, and cytoplasmic p-mTOR, respectively. Furthermore, 4 (9.76%) and 10 (24.4%) cases were negative for ER and PgR, respectively. Additionally, among the 41 AR + non-CAD cases, 3 (7.32%) cases were GCDFP15 + and ER−, and 4 (9.76%) were FOXA1 + and ER−.   We examined the relationship between AR and AR-related proteins in the CAD and non-CAD groups based on correlation coefficients and found that the correlations between the proteins were different in each of the groups. In the CAD group, the AR-α-methylacyl-CoA racemase (AMACR) ( r = 0.59, Fig. a), GATA3-TRPS1 ( r = 0.65, Fig. b), GCDFP15-AMACR ( r = 0.53, Fig. c), SOX9-TRPS1 ( r = 0.44, Fig. d), and AMACR-nuclear p-mTOR ( r = 0.41, Fig. e) levels correlated strongly ( r > 0.4). The correlations between AR and AMACR ( r = 0.29), GATA3 and TRPS1 ( r = 0.29), and GCDFP15 and AMACR ( r = 0.28) were weak; however, the correlations between SOX9 and TRPS1 ( r = 0.13), and AMACR and nuclear p-mTOR ( r = −0.13) were not confirmed in the non-CAD group (Fig. ).   Currently, the diagnosis of CAD is based on the presence of apocrine morphology and clinical or treatment-related classifications, and pathologists support the use of IHC (AR+, ER−, GCDFP15+, and GATA3+) . Apocrine morphology and the expression of the mentioned biomarkers are not always consistent; however, triple-negative AC, defined by ER−, PgR−, and HER2−, exhibits a better prognosis than other subtypes of TNBCs – . Similarly, another study reported a good prognosis for triple-negative CAD . In our study, CAD, which was morphologically defined, exhibited higher expression of FOXA1 (92.6%), GCDFP15 (74.1%), TRPS1 (59.3%), and cytoplasmic p-mTOR (74.1%). While the sensitivity of both AR and GCDFP15 positivity was 74.1%, that of AR and GATA3 positivity was only 40.7% in the CAD group. However, our results showed that the combination of AR and FOXA1 (92.6%) was highly sensitive (Table ). Therefore, in terms of a combination of two biomarkers, the combination of AR and FOXA1 might be more helpful than that of AR and GCDFP15 for the diagnosis of morphological CAD. In addition, when the ER − status was added to CAD diagnosis, the use of AR+/GCDFP15+/ER− (74.1%) or AR+/FOXA1+/ER− (92.6%) was more definitive because the false positivity rate of these combinations was only 7.32% and 9.76%, respectively, in the non-CAD group. Lastly, although the number of CAD cases positive for nuclear p-mTOR was relatively small, nuclear p-mTOR expression was very specific for CAD and might aid in the diagnosis of CAD in some cases. Nuclear mTOR expression was specifically higher in the CAD group than in some non-CAD groups. The development of luminal AR, a subtype of TNBC classified by the expression of AR and other genes, is related to the phosphoinositide 3-kinase (PI3K)/mTOR pathway – . Moreover, AR-driven p-mTOR translocates to the nucleus and acts as a transcriptional integrator of AR . Therefore, our findings suggest that mTOR may also be involved in CAD. If AR/PI3K/mTOR pathway involvement is distinctive for CAD, mTOR inhibitors or other drugs targeting the PI3K/mTOR pathway might be helpful in CAD treatment. Furthermore, based on our findings, we hypothesize that cases with nuclear mTOR expression are more sensitive to mTOR inhibitors than cases with cytoplasmic mTOR expression, although a detailed study is required to confirm the same. Our research revealed positive correlations between AR and seven AR-related proteins in CAD: AR-AMACR, GATA3-TRPS1, GCDFP15-AMACR, SOX9-TRPS1, and AMACAR-nuclear p-mTOR. The correlation between AR and GCDFP-15, AR and AMACR, and GCDFP15 and AMACR could be explained by the fact that GCDFP15 and AMACR are downstream targets of AR (Figs. and ) , . Additionally, a positive correlation among TRPS1, GATA3, and SOX9 expression in CAD was observed in this study. This finding was notable, given that these three biomarkers play a role in epithelial-mesenchymal transition (EMT) in BC tissue – . Since EMT mediates the resistance of tumors to therapies, these proteins may be related to the resistance of CAD to chemotherapies; however, further research is needed to clarify this relationship , . A correlation between GATA3 and TRPS1 expression was also observed in CAD, which can be explained by the fact that TRPS1 is a transcriptional product of GATA3 . However, although the correlations between AR and AMACR, GATA3 and TRPS1, and GCDFP15 and AMACR were weak, those between SOX9 and TRPS1, and AMACR and nuclear p-mTOR were not confirmed in the non-CAD group. The reason for this is not clear, but there is a possibility that some AR-related proteins may be more or less involved in androgen synthesis and CAD morphology. We examined the expression of AR and AR-related proteins in CAD and non-CAD tissues. The expression of FOXA1 and GCDFP15 in the CAD group was higher than that of AR, and nuclear p-mTOR and signal transducer and activator of transcription 5 (STAT5) were preferentially expressed in the CAD group. This finding might be useful for the diagnosis and the choice of CAD treatment. We classified CAD morphologically, and based on that, we performed IHC. The results of the present study indicate that morphological classification is the most important factor. In addition to apocrine morphology, the use of concurrent positivity for FOXA1 and GCDFP15, TRPS1, or nuclear p-mTOR could help diagnose CAD more accurately than the use of AR alone. Other characteristics of CAD include correlations between AR and AMACR, GATA3 and TRPS1, GCDFP15 and AMACR, SOX9 and TRPS1, and AMACAR and nuclear p-mTOR. In particular, the correlations between AR and AMACR, GATA3 and TRPS1, and GCDFP15 and AMACR were characteristic of the CAD group in this study. Our findings support AR and AR-related molecular targeted therapy research, which can guide the selection of optimal treatment methods based on morphology and IHC results. Based on the current drug selection guidelines, patients with HER2 + CAD are treated the same as patients with HER2 + BC; however, these targeting drugs may be useful in second-line treatment or in case of recurrence. Some clinical trials are using androgen-related drugs, such as androgen antagonists (enzalutamide) and selective androgen receptor modulators (enobosarm), for ER+/AR + breast cancer and AR + TNBC, including CAD. These trials are ongoing, and our results may be helpful for patient selection , , – . In this study, we selected some AR-related proteins, the molecules of which are related to AR and have been identified in BC and prostate cancer. For example, SOX9 and GATA3 reportedly influence AR expression , . SOX9, NKX3.1, FOXA1, STAT5, and p-mTOR interact with AR and are involved in binding AR to androgen response elements , , – . GCDFP15, AMACR, and NKX3.1 are direct targets of AR – . TRPS1 is a GATA3 transcription factor, and its expression is regulated by AR , . Based on the findings of previous studies, we hypothesized the relationship between AR and AR-related molecules and the underlying signaling pathways (Fig. ). Our results support this pathway. This study has some limitations. We conducted IHC on BCs; hence, we could only suggest the predicted relationships between AR or AR-related proteins from previous reports. To clarify these relationships in BCs, further detailed research is required; for example, research to clarify the roles of the AR/PI3K/mTOR pathway and confirm drug efficacy through cell and animal experiments. Additionally, we analyzed a small number of BC cases and used commercially available antibodies. We were unable to collect sufficient AR + TNBC cases, so a comparison between CAD and AR + TNBC could not be made. To corroborate our findings, more samples and the usage of diverse antibodies are required. In conclusion, here, we examined the expression of AR and several AR-related proteins in various subtypes of BC, focusing on CAD. We found that the combination of AR+, GCDFP15+, and ER − or AR+, FOXA1+, and ER − is potentially useful for the diagnosis and treatment of AR + CAD. Furthermore, our findings can aid androgen-related molecular targeted therapy research and guide treatment decisions for CAD to improve patient outcomes.   Ethical approval The need for informed consent to participate was waived, and this study was approved by the Institutional Ethics Committee and Review Board of Oita University (approval number: 2392). It was performed in accordance with the principles stated in the Declaration of Helsinki (2013). Case selection We collected 114 BC samples that were resected at Oita University Hospital between April 2009 and March 2022. Individuals who received adjuvant chemotherapy were excluded from the study. We selected 20 cases each of the following for histopathological diagnosis: ER+, PgR+, and HER2+; ER+, PgR+, and HER2−; ER−, PgR−, and HER2+; ER−, PgR−, and HER2−; lobular carcinoma (LC); and CAD. Formalin-fixed paraffin-embedded tissue blocks from histologically representative regions of each tumor were selected for each case and were cut to a thickness of 3 μm for hematoxylin and eosin staining and IHC. We recategorized the BC cases based on histopathological findings according to the WHO classification (5th edition) and IHC results and divided them into six groups: hormone receptor-positive carcinomas (HR; ER+, PgR+, and HER2−) ( n = 32); HER2 + carcinomas (HER2) ( n = 12); luminal-HER2 carcinomas (LH; ER+, PgR+, and HER2+) ( n = 8); TNBCs (ER−, PgR−, and HER2−) ( n = 14); LCs ( n = 21); and CAD ( n = 27) (Fig. ) . Of these, 66 were invasive ductal carcinomas and were further subclassified by receptor status. Apocrine differentiation was classified based on morphology, according to the WHO classification (5th edition), as follows: carcinoma cells with oval nuclei with prominent nucleoli and eosinophilic granular cytoplasm (Fig. a) . In the present study, we defined CAD as apocrine morphology > 90%. As we focused on high-frequency tumors, we did not include rare subtypes in our analysis. Immunohistochemistry IHC was performed both manually and automatically. Briefly, for manual staining, sections were deparaffinized in xylene and rehydrated in graded alcohol. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide for 20 min at 25 °C. Antigens were retrieved by autoclaving the sections in citrate buffer. Immunoreactivity was visualized using a streptavidin-labeled biotin-peroxidase complex system (Nichirei, Tokyo, Japan). The antibodies and antigen retrieval methods used in the present study are listed in Table . Automated staining for ER (clone SP-1; ERα), PgR, and HER2 was performed using the BenchMark ULTRA IHC/ISH System (Roche-Ventana Medical Systems, Tucson, AZ, USA). Evaluation of IHC staining We manually measured the immunostaining results in the largest representative surface of the tumor. Both the staining intensity and population of IHC-stained cells in the carcinoma tissues were evaluated, and the immunoreactivity score (intensity × population) was calculated. Reactivity was scored using a four-grade scale for colorimetric intensity and population of positive cells. The staining intensity was scored as follows: 0 = no staining, 1 = weak staining, 2 = moderate staining, and 3 = strong staining (Fig. b and c). The cell population exhibiting positive staining was scored as follows: 0 = none, 1 = < 1%, 2 = 1–10%, 3 = 10–50%, and 4 = 50–100% of the cells stained (Fig. d and e). For AR, even weak expression was considered positive if diffuse staining was observed. Positive immunoreactivity was defined as scores ≥ 6 (6, 8, 9, and 12). ER and PgR were evaluated according to the criteria of the J-score (0: none, 1: <1%, 2: 1–10%, 3a = 3: 10–50%, and 3b = 4: 50–100% of the cells stained), and the evaluation was repeated as mentioned above (scored as 0–4) – . HER2 was evaluated according to the ASCO/CAP guidelines 2018 , . For ER, PgR, and HER2, positive immunoreactivity was defined as scores ≥ 2 (2, 3, or 4) , . We defined HER2 positive as HER2 3 + and HER2 2 + with confirmed gene amplification by FISH. Statistical analyses The hypothesis test for difference in two population proportions and Bonferroni’s multiple comparison tests were used to assess statistical significance and identify specific biomarkers between CAD and HR, HER2, LH, TNBCs, and LCs. Results with P values < 0.01 (compensation value) were considered statistically significant. The hypothesis test for difference in two population proportions was used to assess statistical significance and identify specific biomarkers between CAD and non-CAD cases. Results with P values < 0.05 were considered statistically significant. Spearman’s rank correlation coefficient was used to examine the association among biomarkers. In all cases, a P value of < 0.05 indicated a statistically significant difference.   The need for informed consent to participate was waived, and this study was approved by the Institutional Ethics Committee and Review Board of Oita University (approval number: 2392). It was performed in accordance with the principles stated in the Declaration of Helsinki (2013).   We collected 114 BC samples that were resected at Oita University Hospital between April 2009 and March 2022. Individuals who received adjuvant chemotherapy were excluded from the study. We selected 20 cases each of the following for histopathological diagnosis: ER+, PgR+, and HER2+; ER+, PgR+, and HER2−; ER−, PgR−, and HER2+; ER−, PgR−, and HER2−; lobular carcinoma (LC); and CAD. Formalin-fixed paraffin-embedded tissue blocks from histologically representative regions of each tumor were selected for each case and were cut to a thickness of 3 μm for hematoxylin and eosin staining and IHC. We recategorized the BC cases based on histopathological findings according to the WHO classification (5th edition) and IHC results and divided them into six groups: hormone receptor-positive carcinomas (HR; ER+, PgR+, and HER2−) ( n = 32); HER2 + carcinomas (HER2) ( n = 12); luminal-HER2 carcinomas (LH; ER+, PgR+, and HER2+) ( n = 8); TNBCs (ER−, PgR−, and HER2−) ( n = 14); LCs ( n = 21); and CAD ( n = 27) (Fig. ) . Of these, 66 were invasive ductal carcinomas and were further subclassified by receptor status. Apocrine differentiation was classified based on morphology, according to the WHO classification (5th edition), as follows: carcinoma cells with oval nuclei with prominent nucleoli and eosinophilic granular cytoplasm (Fig. a) . In the present study, we defined CAD as apocrine morphology > 90%. As we focused on high-frequency tumors, we did not include rare subtypes in our analysis.   IHC was performed both manually and automatically. Briefly, for manual staining, sections were deparaffinized in xylene and rehydrated in graded alcohol. Endogenous peroxidase activity was blocked by incubation with 3% hydrogen peroxide for 20 min at 25 °C. Antigens were retrieved by autoclaving the sections in citrate buffer. Immunoreactivity was visualized using a streptavidin-labeled biotin-peroxidase complex system (Nichirei, Tokyo, Japan). The antibodies and antigen retrieval methods used in the present study are listed in Table . Automated staining for ER (clone SP-1; ERα), PgR, and HER2 was performed using the BenchMark ULTRA IHC/ISH System (Roche-Ventana Medical Systems, Tucson, AZ, USA).   We manually measured the immunostaining results in the largest representative surface of the tumor. Both the staining intensity and population of IHC-stained cells in the carcinoma tissues were evaluated, and the immunoreactivity score (intensity × population) was calculated. Reactivity was scored using a four-grade scale for colorimetric intensity and population of positive cells. The staining intensity was scored as follows: 0 = no staining, 1 = weak staining, 2 = moderate staining, and 3 = strong staining (Fig. b and c). The cell population exhibiting positive staining was scored as follows: 0 = none, 1 = < 1%, 2 = 1–10%, 3 = 10–50%, and 4 = 50–100% of the cells stained (Fig. d and e). For AR, even weak expression was considered positive if diffuse staining was observed. Positive immunoreactivity was defined as scores ≥ 6 (6, 8, 9, and 12). ER and PgR were evaluated according to the criteria of the J-score (0: none, 1: <1%, 2: 1–10%, 3a = 3: 10–50%, and 3b = 4: 50–100% of the cells stained), and the evaluation was repeated as mentioned above (scored as 0–4) – . HER2 was evaluated according to the ASCO/CAP guidelines 2018 , . For ER, PgR, and HER2, positive immunoreactivity was defined as scores ≥ 2 (2, 3, or 4) , . We defined HER2 positive as HER2 3 + and HER2 2 + with confirmed gene amplification by FISH.   The hypothesis test for difference in two population proportions and Bonferroni’s multiple comparison tests were used to assess statistical significance and identify specific biomarkers between CAD and HR, HER2, LH, TNBCs, and LCs. Results with P values < 0.01 (compensation value) were considered statistically significant. The hypothesis test for difference in two population proportions was used to assess statistical significance and identify specific biomarkers between CAD and non-CAD cases. Results with P values < 0.05 were considered statistically significant. Spearman’s rank correlation coefficient was used to examine the association among biomarkers. In all cases, a P value of < 0.05 indicated a statistically significant difference.   