Differential response to neoadjuvant hormonal therapy in prostate cancer: Predictive morphological parameters and molecular markers. Issue 7 (2nd March 2019)

• Record Type: Journal Article
• Authors: Wang, Xueli; Qi, Mei; Zhang, Jing; Sun, Xiubin; Guo, Hongwei; Pang, Yu; Zhang, Qian; Chen, Xinyi; Zhang, Ruifeng; Liu, Zhiyan; Liu, Long; Hao, Xiaomeng; Han, Bo
• Is Part Of: Prostate. Volume 79:Issue 7(2019)
• Page Start: 709
• Page End: 719
• Publication Date: 2019-03-02
• Subjects: drug‐resistant -- drug‐sensitive -- neoadjuvant hormonal therapy -- pathological variables -- prostate cancer; Prostate -- Diseases -- Periodicals; 616
• DOI: 10.1002/pros.23777
• Languages: English
• ISSNs: 0270-4137

Abstract

Abstract : Objectives: The predictive value of the histological parameters and molecular markers for neoadjuvant hormonal therapy (NHT) in prostate cancer (PCa) has not been well established. The aim of this study is to determine pathological variables that can predict differences in response to NHT in PCa. Methods: A total of 85 locally high risk PCa patients with matched preoperative needle biopsies and radical prostatectomy (RP) specimens were included. All patients were treated with NHT for at least 3 months. We quantified the response to NHT using a new proposed pathological grading system. The system classified tumors into five groups (grades 0‐4) according to the severity of histological response. We then categorized the PCa patients into drug‐sensitive (DS) group (Grades 2‐4) and drug‐resistant (DR) group (Grades 0‐1). Two pathologists assessed each pretreated tumors for presence or absence of nine morphological features. The expression of androgen receptor (AR), ERG, and PTEN were evaluated by immunohistochemistry (IHC) as well. Statistical analysis was performed to identify significant associations between differentially histological response to NHT and morphological features as well as molecular aberrations. We evaluated different prediction models using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) analysis. Results: 73% ( n = 62/85) of tumors in our cohort belonged to DS group, whereas 27% ( n = 23/85) of tumors were DR. Univariate logistic analysis suggested four pathological variables, cribriform growth pattern, macronucleoli, ductal adenocarcinoma differentiation, and PTEN loss in needle biopsies were significantly associated with DR effect, all with P ‐value < 0.05. Multivariate logistic regression analysis revealed that the three parameters as significant predictive factors for predicting DR effect. These were macronucleoli (RR = 4.008, P = 0.002), ductal adenocarcinoma differentiation (RR = 11.659, P = 0.009) and PTEN loss expression (RR = 7.275, P = 0.015). The AUC of three integrated indicators model was 0.781. Conclusions: Our study suggested that the presence of tumor cribriform growth pattern, macronucleoli, ductal adenocarcinoma differentiation, and PTEN loss in needle biopsies are of value in predicting tumor response to NHT regimen. Multivariate logistic regression analysis revealed the performance of combined pathological indicators in predicting DR response was better than that of model based on individual factor alone.