PTEN Loss Is Associated with Adverse Outcomes in the Setting of Salvage Radiation Therapy.

BACKGROUND: Salvage radiation therapy (SRT) is a mainstay of treatment for biochemical relapse following radical prostatectomy; however, few studies have examined genomic biomarkers in this context. OBJECTIVE: We characterized the prognostic impact of previously identified deleterious molecular phenotypes-loss of PTEN, ERG expression, and TP53 mutation-for patients undergoing SRT. DESIGN, SETTING, AND PARTICIPANTS: We leveraged an institutional database of 320 SRT patients with available tissue and follow-up. Tissue microarrays were used for genetically validated immunohistochemistry assays. INTERVENTION: All men underwent SRT with or without androgen deprivation therapy OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Univariable and multivariable Cox-proportional hazard models assessed the association of molecular phenotypes with biochemical recurrence-free (bRFS) and metastasis-free (MFS) survival after SRT. RESULTS AND LIMITATIONS: Loss of PTEN (n = 123, 43%) and ERG expression (n = 118, 39%) were common in this cohort, while p53 overexpression (signifying TP53 missense mutation) was infrequent (n = 21, 7%). In univariable analyses, any loss of PTEN portended worse bRFS (hazard ratio [HR] 1.86; 95% confidence interval 1.36-2.57) and MFS (HR 1.89; 1.21-2.94), with homogeneous PTEN loss being associated with the highest risk of MFS (HR 2.47; 1.54-3.95). Similarly, p53 overexpression predicted worse bRFS (HR 1.95; 1.14-3.32) and MFS (HR 2.79; 1.50-5.19). ERG expression was associated with worse MFS only (HR 1.6; 1.03-2.48). On the multivariable analysis adjusting for known prognostic features, homogeneous PTEN loss remained predictive of adverse bRFS (HR 1.82; 1.12-2.96) and MFS (HR 2.08; 1.06-4.86). The study is limited by its retrospective and single-institution design. CONCLUSIONS: PTEN loss by immunohistochemistry is an independent adverse prognostic factor for bRFS and MFS in prostate cancer patients treated with SRT. Future trials will determine the optimal approach to treating SRT patients with adverse molecular prognostic features. PATIENT SUMMARY: Loss of the PTEN tumor suppressor protein is associated with worse outcomes after salvage radiotherapy, independent of other clinical or pathologic patient characteristics.

Predicting Prostate Cancer Molecular Subtype With Deep Learning on Histopathologic Images.

Microscopic examination of prostate cancer has failed to reveal a reproducible association between molecular and morphologic features. However, deep-learning algorithms trained on hematoxylin and eosin (H&E)-stained whole slide images (WSI) may outperform the human eye and help to screen for clinically-relevant genomic alterations. We created deep-learning algorithms to identify prostate tumors with underlying ETS-related gene (ERG) fusions or PTEN deletions using the following 4 stages: (1) automated tumor identification, (2) feature representation learning, (3) classification, and (4) explainability map generation. A novel transformer-based hierarchical architecture was trained on a single representative WSI of the dominant tumor nodule from a radical prostatectomy (RP) cohort with known ERG/PTEN status (n = 224 and n = 205, respectively). Two distinct vision transformer-based networks were used for feature extraction, and a distinct transformer-based model was used for classification. The ERG algorithm performance was validated across 3 RP cohorts, including 64 WSI from the pretraining cohort (AUC, 0.91) and 248 and 375 WSI from 2 independent RP cohorts (AUC, 0.86 and 0.89, respectively). In addition, we tested the ERG algorithm performance in 2 needle biopsy cohorts comprised of 179 and 148 WSI (AUC, 0.78 and 0.80, respectively). Focusing on cases with homogeneous (clonal) PTEN status, PTEN algorithm performance was assessed using 50 WSI reserved from the pretraining cohort (AUC, 0.81), 201 and 337 WSI from 2 independent RP cohorts (AUC, 0.72 and 0.80, respectively), and 151 WSI from a needle biopsy cohort (AUC, 0.75). For explainability, the PTEN algorithm was also applied to 19 WSI with heterogeneous (subclonal) PTEN loss, where the percentage tumor area with predicted PTEN loss correlated with that based on immunohistochemistry (r = 0.58, P = .0097). These deep-learning algorithms to predict ERG/PTEN status prove that H&E images can be used to screen for underlying genomic alterations in prostate cancer.