Androgen receptor expression is associated with prostate cancer-specific survival in castrate patients with metastatic disease.

OBJECTIVE: To investigate whether baseline (before treatment) clinical variables and tumour specimen characteristics (including the androgen receptor, AR) from patients with castrate-resistant metastatic prostate cancer can be used to predict the time to prostate cancer-specific mortality and overall survival, as AR levels in prostate cancer have been associated with disease progression, including prostate-specific antigen (PSA) recurrence and systemic metastasis. PATIENTS AND METHODS: Haematoxylin and eosin (H&E) slides/blocks and outcome data from a 104 castrate patients with metastatic disease (43 prostatectomy and 61 prostate needle biopsy samples), were independently reviewed; H&E morphometry and quantitative immunofluorescence were used to assess the samples. Sections were analysed with a multiplex quantitative immunofluorescence (IF) assay for cytokeratin-18 (epithelial cells), 4',6-diamidino-2-phenylindole (nuclei), p63/high molecular weight keratin (basal cells), AR and alpha-methyl CoA-racemase. Images were acquired with spectral imaging software and processed for quantification with IF algorithms. RESULTS: The median follow-up was 12 years from diagnosis; 49 men (47%) baseline PSA levels of > or = 20 ng/mL, 55 (53%) had a Gleason sum of 8, 63 (60%) died from the disease and 40% were alive (censored). In all, 66 patients had evaluable IF features, and the association with outcome was evaluated by univariate Cox modelling and support-vector regression. PSA was the only clinical variable associated with outcome (concordance index, CoI, 0.41; P < 0.05, log-rank test). The amount of AR present within tumour nuclei (regardless of tissue provenance and primary treatment) significantly correlated with a greater risk of a shorter time to prostate cancer-specific mortality (CoI 0.36; P < 0.05 log-rank test). There were no H&E features that correlated with mortality. CONCLUSION: By univariate analysis, increased nuclear AR expression in either the diagnostic biopsy and/or radical prostatectomy specimen, from patients with advanced disease, was associated with a reduced time to prostate cancer-specific mortality.

Personalized prediction of tumor response and cancer progression on prostate needle biopsy.

PURPOSE: To our knowledge in patients with prostate cancer there are no available tests except clinical variables to determine the likelihood of disease progression. We developed a patient specific, biology driven tool to predict outcome at diagnosis. We also investigated whether biopsy androgen receptor levels predict a durable response to therapy after secondary treatment. MATERIALS AND METHODS: We evaluated paraffin embedded prostate needle biopsy tissue from 1,027 patients with cT1c-T3 prostate cancer treated with surgery and followed a median of 8 years. Machine learning was done to integrate clinical data with biopsy quantitative biometric features. Multivariate models were constructed to predict disease progression with the C index to estimate performance. RESULTS: In a training set of 686 patients (total of 87 progression events) 3 clinical and 3 biopsy tissue characteristics were identified to predict clinical progression within 8 years after prostatectomy with 78% sensitivity, 69% specificity, a C index of 0.74 and a HR of 5.12. Validation in an independent cohort of 341 patients (total of 44 progression events) yielded 76% sensitivity, 64% specificity, a C index of 0.73 and a HR of 3.47. Increased androgen receptor in tumor cells in the biopsy highly significantly predicted resistance to therapy, ie androgen ablation with or without salvage radiotherapy, and clinical failure (p <0.0001). CONCLUSIONS: Morphometry reliably classifies Gleason pattern 3 tumors. When combined with biomarker data, it adds to the hematoxylin and eosin analysis, and prostate specific antigen values currently used to assess outcome at diagnosis. Biopsy androgen receptor levels predict the likelihood of a response to therapy after recurrence and may guide future treatment decisions.

Improved prediction of prostate cancer recurrence through systems pathology.

We have developed an integrated, multidisciplinary methodology, termed systems pathology, to generate highly accurate predictive tools for complex diseases, using prostate cancer for the prototype. To predict the recurrence of prostate cancer following radical prostatectomy, defined by rising serum prostate-specific antigen (PSA), we used machine learning to develop a model based on clinicopathologic variables, histologic tumor characteristics, and cell type-specific quantification of biomarkers. The initial study was based on a cohort of 323 patients and identified that high levels of the androgen receptor, as detected by immunohistochemistry, were associated with a reduced time to PSA recurrence. The model predicted recurrence with high accuracy, as indicated by a concordance index in the validation set of 0.82, sensitivity of 96%, and specificity of 72%. We extended this approach, employing quantitative multiplex immunofluorescence, on an expanded cohort of 682 patients. The model again predicted PSA recurrence with high accuracy, concordance index being 0.77, sensitivity of 77% and specificity of 72%. The androgen receptor was selected, along with 5 clinicopathologic features (seminal vesicle invasion, biopsy Gleason score, extracapsular extension, preoperative PSA, and dominant prostatectomy Gleason grade) as well as 2 histologic features (texture of epithelial nuclei and cytoplasm in tumor only regions). This robust platform has broad applications in patient diagnosis, treatment management, and prognostication.