Immunohistochemical Expression of Androgen Receptors (AR) in Various Breast Cancer Subtypes

An androgen receptor regulated gene score is associated with epithelial to mesenchymal transition features in triple negative breast cancers

BACKGROUND

Androgen receptor (AR) is considered a marker of better prognosis in hormone receptor positive breast cancers (BC), however, its role in triple negative breast cancer (TNBC) is controversial. This may be attributed to intrinsic molecular differences or scoring methods for AR positivity. We derived AR regulated gene score and examined its utility in BC subtypes.

METHODS

AR regulated genes were derived by applying a bioinformatic pipeline on publicly available microarray data sets of AR+ BC cell lines and gene score was calculated as average expression of six AR regulated genes. Tumors were divided into AR high and low based on gene score and associations with clinical parameters, circulating androgens, survival and epithelial to mesenchymal transition (EMT) markers were examined, further evaluated in invitro models and public datasets.

RESULTS

53% (133/249) tumors were classified as AR gene score high and were associated with significantly better clinical parameters, disease-free survival (86.13 vs 72.69 months, log rank p = 0.032) when compared to AR low tumors. 36% of TNBC (N = 66) were AR gene score high with higher expression of EMT markers (p = 0.024) and had high intratumoral levels of 5α-reductase, enzyme involved in intracrine androgen metabolism. In MDA-MB-453 treated with dihydrotestosterone, SLUG expression increased, E-cadherin decreased with increase in migration and these changes were reversed with bicalutamide. Similar results were obtained in public datasets.

CONCLUSION

Deciphering the role of AR in BC is difficult based on AR protein levels alone. Our results support the context dependent function of AR in driving better prognosis in ER positive tumors and EMT features in TNBC tumors.

Convolutional neural network for biomarker discovery for triple negative breast cancer with RNA sequencing data

Triple negative breast cancers (TNBCs) are tumors with a poor treatment response and prognosis. In this study, we propose a new approach, candidate extraction from convolutional neural network (CNN) elements (CECE), for discovery of biomarkers for TNBCs. We used the GSE96058 and GSE81538 datasets to build a CNN model to classify TNBCs and non-TNBCs and used the model to make TNBC predictions for two additional datasets, the cancer genome atlas (TCGA) breast cancer RNA sequencing data and the data from Fudan University Shanghai Cancer Center (FUSCC). Using correctly predicted TNBCs from the GSE96058 and TCGA datasets, we calculated saliency maps for these subjects and extracted the genes that the CNN model used to separate TNBCs from non-TNBCs. Among the TNBC signature patterns that the CNN models learned from the training data, we found a set of 21 genes that can classify TNBCs into two major classes, or CECE subtypes, with distinct overall survival rates (P = 0.0074). We replicated this subtype classification in the FUSCC dataset using the same 21 genes, and the two subtypes had similar differential overall survival rates (P = 0.0490). When all TNBCs were combined from the 3 datasets, the CECE II subtype had a hazard ratio of 1.94 (95% CI, 1.25-3.01; P = 0.0032). The results demonstrate that the spatial patterns learned by the CNN models can be utilized to discover interacting biomarkers otherwise unlikely to be identified by traditional approaches.

Proteomics-Based Identification of Dysregulated Proteins in Breast Cancer

Immunohistochemistry (IHC) is still widely used as a morphology-based assay for in situ analysis of target proteins as specific tumor antigens. However, as a very heterogeneous collection of neoplastic diseases, breast cancer (BC) requires an accurate identification and characterization of larger panels of candidate biomarkers, beyond ER, PR, and HER2 proteins, for diagnosis and personalized treatment, without the limited availability of antibodies that are required to identify specific proteins. Top-down, middle-down, and bottom-up mass spectrometry (MS)-based proteomics approaches complement traditional histopathological tissue analysis to examine expression, modification, and interaction of hundreds to thousands of proteins simultaneously. In this review, we discuss the proteomics-based identification of dysregulated proteins in BC that are essential for the following issues: discovery and validation of new biomarkers by analysis of solid and liquid/non-invasive biopsies, cell lines, organoids and xenograft models; identification of panels of biomarkers for early detection and accurate discrimination between cancer, benign and normal tissues; identification of subtype-specific and stage-specific protein expression profiles in BC grading and measurement of disease progression; characterization of new subtypes of BC; characterization and quantitation of post-translational modifications (PTMs) and aberrant protein-protein interactions (PPI) involved in tumor development; characterization of the global remodeling of BC tissue homeostasis, diagnosis and prognostic information; and deciphering of molecular functions, biological processes and mechanisms through which the dysregulated proteins cause tumor initiation, invasion, and treatment resistance.

Androgen Receptor: A New Marker to Predict Pathological Complete Response in HER2-Positive Breast Cancer Patients Treated with Trastuzumab Plus Pertuzumab Neoadjuvant Therapy

(1) Background: Neoadjuvant therapy is the main therapeutic strategy for human epidermal growth factor receptor 2 (HER2)-positive breast cancer patients, and the combination of trastuzumab and pertuzumab (HP) has become a routine treatment. How to predict and screen patients who are less likely to respond to neoadjuvant therapy is the focus of research. The androgen receptor (AR) is a biomarker that is widely expressed in all breast cancer subtypes and is probably related to treatment response and prognosis. In this study, we investigated the relationship between AR expression and treatment response in HER2-positive breast cancer patients treated with HP neoadjuvant therapy. (2) Methods: We evaluated early breast cancer patients treated with HP neoadjuvant therapy from Jan. 2019 to Oct. 2020 at Peking University First Hospital Breast Cancer Center. The inclusion criteria were as follows: early HER2-positive breast cancer patients diagnosed by core needle biopsy who underwent both HP neoadjuvant therapy and surgery. We compared the clinical and pathological features between pathological complete response (pCR) and non-pCR patients. (3) Results: We included 44 patients. A total of 90.9% of patients received neoadjuvant therapy of taxanes, carboplatin, trastuzumab and pertuzumab (TCHP), and the total pCR rate was 50%. pCR was negatively related to estrogen receptor (ER) positivity (OR 0.075 [95% confidence interval (CI) 0.008–0.678], p = 0.021) and positively related to high expression levels of AR (OR 33.145 [95% CI 2.803–391.900], p = 0.005). We drew a receiver operating characteristic (ROC) curve to assess the predictive value of AR expression for pCR, and the area under the curve was 0.737 (95% CI 0.585–0.889, p = 0.007). The optimal cutoff of AR for predicting pCR was 85%. (4) Conclusion: AR is a potential marker for the prediction of pCR in HER2-positive breast cancer patients treated with HP neoadjuvant therapy.

Pre-Menopausal Women With Breast Cancers Having High AR/ER Ratios in the Context of Higher Circulating Testosterone Tend to Have Poorer Outcomes

Purpose

Women with breast tumors with higher expression of AR are in general known to have better survival outcomes while a high AR/ER ratio is associated with poor outcomes in hormone receptor positive breast cancers mostly in post menopausal women. We have evaluated the AR/ER ratio in the context of circulating androgens specifically in patients younger than 50 years most of whom are pre-menopausal and hence have a high estrogenic hormonal milieu.

Methods

Tumor samples from patients 50 years or younger at first diagnosis were chosen from a larger cohort of 270 patients with median follow-up of 72 months. Expression levels of ER and AR proteins were detected by immunohistochemistry (IHC) and the transcript levels by quantitative PCR. Ciculating levels of total testosterone were estimated from serum samples. A ratio of AR/ER was derived using the transcript levels, and tumors were dichotomized into high and low ratio groups based on the third quartile value. Survival and the prognostic significance of the ratio was compared between the low and high ratio groups in all tumors and also within ER positive tumors. Results were further validated in external datasets (TCGA and METABRIC).

Results

Eighty-eight (32%) patients were ≤50 years, with 22 having high AR/ER ratio calculated using the transcript levels. Circulating levels of total testosterone were higher in women whose tumors had a high AR/ER ratio (p = 0.02). Tumors with high AR/ER ratio had significantly poorer disease-free survival than those with low AR/ER ratio [HR-2.6 (95% CI-1.02-6.59) p = 0.04]. Evaluation of tumors with high AR/ER ratio within ER positive tumors alone reconfirmed the prognostic relevance of the high AR/ER ratio with a significant hazard ratio of 4.6 (95% CI-1.35-15.37, p = 0.01). Similar trends were observed in the TCGA and METABRIC dataset.

Conclusion

Our data in pre-menopausal women with breast cancer suggest that it is not merely the presence or absence of AR expression but the relative activity of ER, as well as the hormonal milieu of the patient that determine clinical outcomes, indicating that both context and interactions ultimately influence tumor behavior.

Serial [18F]-FDHT-PET to predict bicalutamide efficacy in patients with androgen receptor positive metastatic breast cancer

BACKGROUND

The androgen receptor (AR) is a potential target in metastatic breast cancer (MBC), and 16β-[¹⁸F]-fluoro-5α-dihydrotestosterone positron emission tomography ([¹⁸F]-FDHT-PET) can be used for noninvasive visualisation of AR. [¹⁸F]-FDHT uptake reduction during AR-targeting therapy reflects AR occupancy and might be predictive for treatment response. We assessed the feasibility of [¹⁸F]-FDHT-PET to detect changes in AR availability during bicalutamide treatment and correlated these changes with treatment response.

PATIENTS AND METHODS

Patients with AR + MBC, regardless of oestrogen receptor status, received an [¹⁸F]-FDHT-PET at baseline and after 4-6 weeks bicalutamide treatment. Baseline [¹⁸F]-FDHT uptake was expressed as maximum standardised uptake value. Percentage change in tracer uptake, corrected for background activity (SUV_cor), between baseline and follow-up PET scan (% reduction), was assessed per-patient and lesion. Clinical benefit was determined in accordance with Response Evaluation Criteria in Solid Tumours (RECIST) 1.1 or clinical evaluation (absence of disease progression for ≥24 weeks).

RESULTS

Baseline [¹⁸F]-FDHT-PET in 21 patients detected 341 of 515 lesions found with standard imaging and 21 new lesions. Follow-up [¹⁸F]-FDHT-PET was evaluable in 17 patients with 349 lesions, showing a decrease in median SUV_cor from 1.3 to 0.7 per-patient and lesion (P < 0.001). Median % reduction per-patient was -45% and per-lesion -39%. In patients with progressive disease (n = 11), median % reduction was -30% versus -53% for patients who showed clinical benefit (in accordance with RECIST (n = 3) or clinical evaluation (n = 3); P = 0.338).

CONCLUSION

In this feasibility study, a bicalutamide-induced reduction in [¹⁸F]-FDHT uptake could be detected by follow-up [¹⁸F]-FDHT-PET in patients with AR + MBC. However, this change could not predict bicalutamide response.

CLINICAL TRIAL INFORMATION

NCT02697032.