Just like the androgen receptor (AR), the estrogen receptor α (ERα) is expressed in the prostate and is thought to influence prostate cancer (PCa) biology. Yet the incomplete understanding of ERα functions in PCa hinders our ability to fully comprehend its clinical relevance and restricts the repurposing of estrogen-targeted therapies for the treatment of this disease. Using 2 human PCa tissue microarray cohorts, we first demonstrate that nuclear ERα expression was heterogeneous among patients, being detected in only half of the tumors. Positive nuclear ERα levels were correlated with disease recurrence, progression to metastatic PCa, and patient survival. Using in vitro and in vivo models of the normal prostate and PCa, bulk and single-cell RNA-Seq analyses revealed that estrogens partially mimicked the androgen transcriptional response and activated specific biological pathways linked to proliferation and metabolism. Bioenergetic flux assays and metabolomics confirmed the regulation of cancer metabolism by estrogens, supporting proliferation. Using cancer cell lines and patient-derived organoids, selective estrogen receptor modulators, a pure anti-estrogen, and genetic approaches impaired cancer cell proliferation and growth in an ERα-dependent manner. Overall, our study revealed that, when expressed, ERα functionally reprogrammed PCa metabolism, was associated with disease progression, and could be targeted for therapeutic purposes.

Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarkers to characterize PC, particularly to confirm the presence of intraductal carcinoma of the prostate (IDC-P), an aggressive histopathological variant for which therapeutic options are now available. Our aim was to identify IDC-P with Raman micro-spectroscopy (RμS) and machine learning technology following a protocol suitable for routine clinical histopathology laboratories. We used RμS to differentiate IDC-P from PC, as well as PC and IDC-P from benign tissue on formalin-fixed paraffin-embedded first-line radical prostatectomy specimens (embedded in tissue microarrays [TMAs]) from 483 patients treated in 3 Canadian institutions between 1993 and 2013. The main measures were the presence or absence of IDC-P and of PC, regardless of the clinical outcomes. The median age at radical prostatectomy was 62 years. Most of the specimens from the first cohort (Centre hospitalier de l’Université de Montréal) were of Gleason score 3 + 3 = 6 (51%) while most of the specimens from the 2 other cohorts (University Health Network and Centre hospitalier universitaire de Québec–Université Laval) were of Gleason score 3 + 4 = 7 (51% and 52%, respectively). Most of the 483 patients were pT2 stage (44%–69%), and pT3a (22%–49%) was more frequent than pT3b (9%–12%). To investigate the prostate tissue of each patient, 2 consecutive sections of each TMA block were cut. The first section was transferred onto a glass slide to perform immunohistochemistry with H&E counterstaining for cell identification. The second section was placed on an aluminum slide, dewaxed, and then used to acquire an average of 7 Raman spectra per specimen (between 4 and 24 Raman spectra, 4 acquisitions/TMA core). Raman spectra of each cell type were then analyzed to retrieve tissue-specific molecular information and to generate classification models using machine learning technology. Models were trained and cross-validated using data from 1 institution. Accuracy, sensitivity, and specificity were 87% ± 5%, 86% ± 6%, and 89% ± 8%, respectively, to differentiate PC from benign tissue, and 95% ± 2%, 96% ± 4%, and 94% ± 2%, respectively, to differentiate IDC-P from PC. The trained models were then tested on Raman spectra from 2 independent institutions, reaching accuracies, sensitivities, and specificities of 84% and 86%, 84% and 87%, and 81% and 82%, respectively, to diagnose PC, and of 85% and 91%, 85% and 88%, and 86% and 93%, respectively, for the identification of IDC-P. IDC-P could further be differentiated from high-grade prostatic intraepithelial neoplasia (HGPIN), a pre-malignant intraductal proliferation that can be mistaken as IDC-P, with accuracies, sensitivities, and specificities > 95% in both training and testing cohorts. As we used stringent criteria to diagnose IDC-P, the main limitation of our study is the exclusion of borderline, difficult-to-classify lesions from our datasets. In this study, we developed classification models for the analysis of RμS data to differentiate IDC-P, PC, and benign tissue, including HGPIN. RμS could be a next-generation histopathological technique used to reinforce the identification of high-risk PC patients and lead to more precise diagnosis of IDC-P.

Intraductal carcinoma of the prostate (IDC-P) is an independent biomarker of recurrence and survival with particular treatment response, yet no study has tested its response to radiotherapy. The aim of our project was to test the impact of adjuvant radiotherapy (ART) in patients with localized to locally advanced prostate cancer (PC) and IDC-P. We performed a retrospective study of men with pT2-T3 PC treated by radical prostatectomy (RP) with or without ART, from two centres (1993–2015). Exclusion criteria were the use of another type of treatment prior to biochemical recurrence (BCR), and detectable prostate- specific antigen (PSA) following RP or ART. Primary outcome was BCR (2 consecutive PSA ≥ 0.2 ng/ml). Patients were grouped by treatment (RPonly/RP + ART), IDC-P status, and presence of high-risk features (HRF: Grade Groups 4–5, positive margins, pT3 stage). We reviewed 293 RP specimens (median follow-up 99 months, 69 BCR). Forty-eight patients (16.4%) were treated by RP + ART. Multivariate Cox regression for BCR indicated that IDC-P had the strongest impact (hazard ratio [HR] = 2.39, 95% confidence interval [CI]:1.44–3.97), while ART reduced the risk of BCR (HR = 0.38, 95%CI: 0.17–0.85). Other HRF were all significant except for pT3b stage. IDC-P[+] patients who did not receive ART had the worst BCR-free survival (log-rank P = 0.023). Furthermore, IDC-P had the same impact on BCR-free survival as ≥1 HRF (log-rank P = 0.955). Men with IDC-P who did not receive ART had the highest BCR rates, and IDC-P had the same impact as ≥1 HRF, which are often used as ART indications. Once validated, ART should be considered in patients with IDC-P.

The tumor proliferative index marker Ki-67 was shown to be associated with clinically significant outcomes in prostate cancer, but its clinical application has limitations due to lack of uniformity and consistency in quantification. Our objective was to compare the measurements obtained with digital image analysis (DIA) versus virtual microscopy (visual scoring (VS)). To do so, we compared the measurement distributions of each technique and their ability to predict clinically useful endpoints. A tissue microarray series from a cohort of 225 men who underwent radical prostatectomy was immunostained for Ki-67. The percentage of Ki-67 positive nuclei in malignant cells was assessed both by VS and DIA, and a H–score was calculated. The distribution and predictive ability of these scoring methods to predict biochemical recurrence (BCR) and death from prostate cancer (DPCa) were compared using Mann–Whitney test and C-index. The measurements obtained with VS were similar to the DIA measurements (p = 0.73) but dissimilar to the H-score (p < 0.001). Cox regression models showed that Ki-67 was associated with BCR (HR 1.46, 95 % CI 1.10-1.94) and DPCa (HR 1.26, 95 % CI 1.06-1.50). C-indexes revealed that Ki-67 was a better predictor of DPCa (0.803, 0.8059 and 0.789; VS, DIA and H-score, respectively) than of BCR (0.625, 0.632 and 0.604; VS, DIA and H-score, respectively). The measurement distributions and the predictive abilities of VS and DIA were similar and presented the same predictive behaviour in our cohort, supporting the role of Ki-67 proliferative index as an important prognostic factor of BCR and DPCa in prostate cancer post RP. The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/6656878501536663

Intraductal carcinoma of the prostate (IDC-P) is an aggressive variant of prostate cancer (PC) recognized as a distinct entity in 2016 by the World Health Organization classification.¹ Current biomarkers used by pathologists for IDC-P identification, phosphatase and tensin homolog loss, and ETS-related gene overexpression, have low sensitivity limiting their use. However, by comparing PC and IDC-P Raman spectra, high sensitivity ($\ge 85\%$) biomarkers were recently identified in our previous work and led to the first machine learning model for the diagnosis of IDC-P using Raman micro-spectroscopy ($\mathrm{R}\mu \mathrm{S}$).² In this past study, Raman spectra from three institutes were collected independently to distinguish various types of prostate cancer on completely independent testing sets. Employing Raman spectroscopy for the identification of various pathologies is currently well-established.³