Disrupting prostate cancer research: Challenge accepted; report from the 2023 Coffey-Holden Prostate Cancer Academy Meeting.

INTRODUCTION: The 2023 Coffey-Holden Prostate Cancer Academy (CHPCA) Meeting, themed "Disrupting Prostate Cancer Research: Challenge Accepted," was convened at the University of California, Los Angeles, Luskin Conference Center, in Los Angeles, CA, from June 22 to 25, 2023. METHODS: The 2023 marked the 10th Annual CHPCA Meeting, a discussion-oriented scientific think-tank conference convened annually by the Prostate Cancer Foundation, which centers on innovative and emerging research topics deemed pivotal for advancing critical unmet needs in prostate cancer research and clinical care. The 2023 CHPCA Meeting was attended by 81 academic investigators and included 40 talks across 8 sessions. RESULTS: The central topic areas covered at the meeting included: targeting transcription factor neo-enhancesomes in cancer, AR as a pro-differentiation and oncogenic transcription factor, why few are cured with androgen deprivation therapy and how to change dogma to cure metastatic prostate cancer without castration, reducing prostate cancer morbidity and mortality with genetics, opportunities for radiation to enhance therapeutic benefit in oligometastatic prostate cancer, novel immunotherapeutic approaches, and the new era of artificial intelligence-driven precision medicine. DISCUSSION: This article provides an overview of the scientific presentations delivered at the 2023 CHPCA Meeting, such that this knowledge can help in facilitating the advancement of prostate cancer research worldwide.

Artificial Intelligence-Based PTEN Loss Assessment as an Early Predictor of Prostate Cancer Metastasis After Surgery: A Multicenter Retrospective Study.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and can be measured via immunohistochemistry. The purpose of the study was to establish the clinical application of an in-house developed artificial intelligence (AI) image analysis workflow for automated detection of PTEN loss on digital images for identifying patients at risk of early recurrence and metastasis. Postsurgical tissue microarray sections from the Canary Foundation (n = 1264) stained with anti-PTEN antibody were evaluated independently by pathologist conventional visual scoring (cPTEN) and an automated AI-based image analysis pipeline (AI-PTEN). The relationship of PTEN evaluation methods with cancer recurrence and metastasis was analyzed using multivariable Cox proportional hazard and decision curve models. Both cPTEN scoring by the pathologist and quantification of PTEN loss by AI (high-risk AI-qPTEN) were significantly associated with shorter metastasis-free survival (MFS) in univariable analysis (cPTEN hazard ratio [HR], 1.54; CI, 1.07-2.21; P = .019; AI-qPTEN HR, 2.55; CI, 1.83-3.56; P < .001). In multivariable analyses, AI-qPTEN showed a statistically significant association with shorter MFS (HR, 2.17; CI, 1.49-3.17; P < .001) and recurrence-free survival (HR, 1.36; CI, 1.06-1.75; P = .016) when adjusting for relevant postsurgical clinical nomogram (Cancer of the Prostate Risk Assessment [CAPRA] postsurgical score [CAPRA-S]), whereas cPTEN does not show a statistically significant association (HR, 1.33; CI, 0.89-2; P = .2 and HR, 1.26; CI, 0.99-1.62; P = .063, respectively) when adjusting for CAPRA-S risk stratification. More importantly, AI-qPTEN was associated with shorter MFS in patients with favorable pathological stage and negative surgical margins (HR, 2.72; CI, 1.46-5.06; P = .002). Workflow also demonstrated enhanced clinical utility in decision curve analysis, more accurately identifying men who might benefit from adjuvant therapy postsurgery. This study demonstrates the clinical value of an affordable and fully automated AI-powered PTEN assessment for evaluating the risk of developing metastasis or disease recurrence after radical prostatectomy. Adding the AI-qPTEN assessment workflow to clinical variables may affect postoperative surveillance or management options, particularly in low-risk patients.

A DNA copy number alteration classifier as a prognostic tool for prostate cancer patients.

BACKGROUND: Distinguishing between true indolent and potentially life-threatening prostate cancer is challenging in tumours displaying clinicopathologic features associated with low or intermediate risk of relapse. Several somatic DNA copy number alterations (CNAs) have been identified as potential prognostic biomarkers, but the standard cytogenetic method to assess them has a limited multiplexing capability. METHODS: Multiplex ligation-dependent probe amplification (MLPA) targeting 14 genes was optimised to survey 448 tumours of patients with low or intermediate risk (Grade Group 1-3, Gleason score ≤7) who underwent radical prostatectomy. A 6-gene CNA classifier was developed using random survival forest and Cox proportional hazard modelling to predict biochemical recurrence. RESULTS: The classifier score was significantly associated with biochemical recurrence after adjusting for standard clinicopathologic variables and the known prognostic index CAPRA-S score with a hazard ratio of 2.17 and 1.80, respectively (n = 406, P < 0.01). The prognostic value of this classifier was externally validated in published CNA data from three radical prostatectomy cohorts and one radiation therapy pre-treatment biopsy cohort. CONCLUSION: The 6-gene CNA classifier generated by a single MLPA assay compatible with the small quantities of DNA extracted from formalin-fixed paraffin-embedded (FFPE) tissue specimens has the potential to improve the clinical management of patients with low or intermediate risk disease.

A Computational Approach to Identification of Candidate Biomarkers in High-Dimensional Molecular Data.

Complex high-dimensional datasets that are challenging to analyze are frequently produced through '-omics' profiling. Typically, these datasets contain more genomic features than samples, limiting the use of multivariable statistical and machine learning-based approaches to analysis. Therefore, effective alternative approaches are urgently needed to identify features-of-interest in '-omics' data. In this study, we present the molecular feature selection tool, a novel, ensemble-based, feature selection application for identifying candidate biomarkers in '-omics' data. As proof-of-principle, we applied the molecular feature selection tool to identify a small set of immune-related genes as potential biomarkers of three prostate adenocarcinoma subtypes. Furthermore, we tested the selected genes in a model to classify the three subtypes and compared the results to models built using all genes and all differentially expressed genes. Genes identified with the molecular feature selection tool performed better than the other models in this study in all comparison metrics: accuracy, precision, recall, and F1-score using a significantly smaller set of genes. In addition, we developed a simple graphical user interface for the molecular feature selection tool, which is available for free download. This user-friendly interface is a valuable tool for the identification of potential biomarkers in gene expression datasets and is an asset for biomarker discovery studies.

Analysis of Prostate Cancer Tumor Microenvironment Identifies Reduced Stromal CD4 Effector T-cell Infiltration in Tumors with Pelvic Nodal Metastasis.

BACKGROUND: Pelvic nodal metastasis in prostate cancer impacts patient outcome negatively. OBJECTIVE: To explore tumor-infiltrating immune cells as a potential predictive tool for regional lymph node (LN) metastasis. DESIGN SETTING AND PARTICIPANTS: We applied multiplex immunofluorescence and targeted transcriptomic analysis on 94 radical prostatectomy specimens in patients with (LN+) or without (LN-) pelvic nodal metastases. Both intraepithelial and stromal infiltrations of immune cells and differentially expressed genes (mRNA and protein levels) were correlated with the nodal status. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The identified CD4 effector cell signature of nodal metastasis was validated in a comparable independent patient cohort of 184 informative cases. Patient outcome analysis and decision curve analysis were performed with the CD4 effector cell density-based signature. RESULTS AND LIMITATIONS: In the discovery cohort, both tumor epithelium and stroma from patients with nodal metastasis had significantly lower infiltration of multiple immune cell types, with stromal CD4 effector cells highlighted as the top candidate marker. Targeted gene expression analysis and confirmatory protein analysis revealed key alteration of extracellular matrix components in tumors with nodal metastasis. Of note, stromal CD4 immune cell density was a significant independent predictor of LN metastasis (odds ratio [OR] = 0.15, p = 0.004), and was further validated as a significant predictor of nodal metastasis in the validation cohort (OR = 0.26, p < 0.001). CONCLUSIONS: Decreased T-cell infiltrates in the primary tumor (particularly CD4 effector cells) are associated with a higher risk of LN metastasis. Future evaluation of CD4-based assays on prostate cancer diagnostic biopsy materials may improve selection of at-risk patients for the treatment of LN metastasis. PATIENT SUMMARY: In this report, we found that cancer showing evidence of cancer metastasis to the lymph nodes tends to have less immune cells present within the tumor. We conclude that the extent of immune cells present within a prostate tumor can help doctors determine the most appropriate treatment plan for individual patients.

High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker. The objective of this work was to develop an artificial intelligence (AI) system for automated detection and localization of PTEN loss on immunohistochemically (IHC) stained sections. PTEN loss was assessed using IHC in two prostate tissue microarrays (TMA) (internal cohort, n = 272 and external cohort, n = 129 patients). TMA cores were visually scored for PTEN loss by pathologists and, if present, spatially annotated. Cores from each patient within the internal TMA cohort were split into 90% cross-validation (N = 2048) and 10% hold-out testing (N = 224) sets. ResNet-101 architecture was used to train core-based classification using a multi-resolution ensemble approach (×5, ×10, and ×20). For spatial annotations, single resolution pixel-based classification was trained from patches extracted at ×20 resolution, interpolated to ×40 resolution, and applied in a sliding-window fashion. A final AI-based prediction model was created from combining multi-resolution and pixel-based models. Performance was evaluated in 428 cores of external cohort. From both cohorts, a total of 2700 cores were studied, with a frequency of PTEN loss of 14.5% in internal (180/1239) and external 13.5% (43/319) cancer cores. The final AI-based prediction of PTEN status demonstrated 98.1% accuracy (95.0% sensitivity, 98.4% specificity; median dice score = 0.811) in internal cohort cross-validation set and 99.1% accuracy (100% sensitivity, 99.0% specificity; median dice score = 0.804) in internal cohort test set. Overall core-based classification in the external cohort was significantly improved in the external cohort (area under the curve = 0.964, 90.6% sensitivity, 95.7% specificity) when further trained (fine-tuned) using 15% of cohort data (19/124 patients). These results demonstrate a robust and fully automated method for detection and localization of PTEN loss in prostate cancer tissue samples. AI-based algorithms have potential to streamline sample assessment in research and clinical laboratories.

Design and Development of a Fully Synthetic Multiplex Ligation-Dependent Probe Amplification-Based Probe Mix for Detection of Copy Number Alterations in Prostate Cancer Formalin-Fixed, Paraffin-Embedded Tissue Samples.

DNA copy number alterations (CNAs) are promising biomarkers to predict prostate cancer (PCa) outcome. However, fluorescence in situ hybridization (FISH) cannot assess complex CNA signatures because of low multiplexing capabilities. Multiplex ligation-dependent probe amplification (MLPA) can detect multiple CNAs in a single PCR assay, but PCa-specific probe mixes available commercially are lacking. Synthetic MLPA probes were designed to target 10 CNAs relevant to PCa: 5q15-21.1 (CHD1), 6q15 (MAP3K7), 8p21.2 (NKX3-1), 8q24.21 (MYC), 10q23.31 (PTEN), 12p13.1 (CDKN1B), 13q14.2 (RB1), 16p13.3 (PDPK1), 16q23.1 (GABARAPL2), and 17p13.1 (TP53), with 9 control probes. In cell lines, CNAs were detected when the cancer genome was as low as 30%. Compared with FISH in radical prostatectomy formalin-fixed, paraffin-embedded samples (n = 18: 15 cancers and 3 matched benign), the MLPA assay showed median sensitivity and specificity of 80% and 93%, respectively, across all CNAs assessed. In the validation set (n = 40: 20 tumors sampled in two areas), the respective sensitivity and specificity of MLPA compared advantageously with FISH and TaqMan droplet digital PCR (ddPCR) when assessing PTEN deletion (FISH: 85% and 100%; ddPCR: 100% and 83%) and PDPK1 gain (FISH: 100% and 92%; ddPCR: 93% and 100%). This new PCa probe mix accurately identifies CNAs by MLPA across multiple genes using low quality and quantities (50 ng) of DNA extracted from clinical formalin-fixed, paraffin-embedded samples.

Risk Stratification of Prostate Cancer Through Quantitative Assessment of PTEN Loss (qPTEN).

BACKGROUND: Phosphatase and tensin homolog (PTEN) loss has long been associated with adverse findings in early prostate cancer. Studies to date have yet to employ quantitative methods (qPTEN) for measuring of prognostically relevant amounts of PTEN loss in postsurgical settings and demonstrate its clinical application. METHODS: PTEN protein levels were measured by immunohistochemistry in radical prostatectomy samples from training (n = 410) and validation (n = 272) cohorts. PTEN loss was quantified per cancer cell and per tissue microarray core. Thresholds for identifying clinically relevant PTEN loss were determined using log-rank statistics in the training cohort. Univariate (Kaplan-Meier) and multivariate (Cox proportional hazards) analyses on various subpopulations were performed to assess biochemical recurrence-free survival (BRFS) and were independently validated. All statistical tests were two-sided. RESULTS: PTEN loss in more than 65% cancer cells was most clinically relevant and had statistically significant association with reduced BRFS in training (hazard ratio [HR] = 2.48, 95% confidence interval [CI] = 1.59 to 3.87; P < .001) and validation cohorts (HR = 4.22, 95% CI = 2.01 to 8.83; P < .001). The qPTEN scoring method identified patients who recurred within 5.4 years after surgery (P < .001). In men with favorable risk of biochemical recurrence (Cancer of the Prostate Risk Assessment - Postsurgical scores <5 and no adverse pathological features), qPTEN identified a subset of patients with shorter BRFS (HR = 5.52, 95% CI = 2.36 to 12.90; P < .001) who may be considered for intensified monitoring and/or adjuvant therapy. CONCLUSIONS: Compared with previous qualitative approaches, qPTEN improves risk stratification of postradical prostatectomy patients and may be considered as a complementary tool to guide disease management after surgery.

Abi1 loss drives prostate tumorigenesis through activation of EMT and non-canonical WNT signaling.

BACKGROUND: Prostate cancer development involves various mechanisms, which are poorly understood but pointing to epithelial mesenchymal transition (EMT) as the key mechanism in progression to metastatic disease. ABI1, a member of WAVE complex and actin cytoskeleton regulator and adaptor protein, acts as tumor suppressor in prostate cancer but the role of ABI1 in EMT is not clear. METHODS: To investigate the molecular mechanism by which loss of ABI1 contributes to tumor progression, we disrupted the ABI1 gene in the benign prostate epithelial RWPE-1 cell line and determined its phenotype. Levels of ABI1 expression in prostate organoid tumor cell lines was evaluated by Western blotting and RNA sequencing. ABI1 expression and its association with prostate tumor grade was evaluated in a TMA cohort of 505 patients and metastatic cell lines. RESULTS: Low ABI1 expression is associated with biochemical recurrence, metastasis and death (p = 0.038). Moreover, ABI1 expression was significantly decreased in Gleason pattern 5 vs. pattern 4 (p = 0.0025) and 3 (p = 0.0012), indicating an association between low ABI1 expression and highly invasive prostate tumors. Disruption of ABI1 gene in RWPE-1 cell line resulted in gain of an invasive phenotype, which was characterized by a loss of cell-cell adhesion markers and increased migratory ability of RWPE-1 spheroids. Through RNA sequencing and protein expression analysis, we discovered that ABI1 loss leads to activation of non-canonical WNT signaling and EMT pathways, which are rescued by re-expression of ABI1. Furthermore, an increase in STAT3 phosphorylation upon ABI1 inactivation and the evidence of a high-affinity interaction between the FYN SH2 domain and ABI1 pY421 support a model in which ABI1 acts as a gatekeeper of non-canonical WNT-EMT pathway activation downstream of the FZD2 receptor. CONCLUSIONS: ABI1 controls prostate tumor progression and epithelial plasticity through regulation of EMT-WNT pathway. Here we discovered that ABI1 inhibits EMT through suppressing FYN-STAT3 activation downstream from non-canonical WNT signaling thus providing a novel mechanism of prostate tumor suppression.

A three-gene DNA methylation biomarker accurately classifies early stage prostate cancer.

BACKGROUND: We identify and validate accurate diagnostic biomarkers for prostate cancer through a systematic evaluation of DNA methylation alterations. MATERIALS AND METHODS: We assembled three early prostate cancer cohorts (total patients = 699) from which we collected and processed over 1300 prostatectomy tissue samples for DNA extraction. Using real-time methylation-specific PCR, we measured normalized methylation levels at 15 frequently methylated loci. After partitioning sample sets into independent training and validation cohorts, classifiers were developed using logistic regression, analyzed, and validated. RESULTS: In the training dataset, DNA methylation levels at 7 of 15 genomic loci (glutathione S-transferase Pi 1 [GSTP1], CCDC181, hyaluronan, and proteoglycan link protein 3 [HAPLN3], GSTM2, growth arrest-specific 6 [GAS6], RASSF1, and APC) showed large differences between cancer and benign samples. The best binary classifier was the GAS6/GSTP1/HAPLN3 logistic regression model, with an area under these curves of 0.97, which showed a sensitivity of 94%, and a specificity of 93% after external validation. CONCLUSION: We created and validated a multigene model for the classification of benign and malignant prostate tissue. With false positive and negative rates below 7%, this three-gene biomarker represents a promising basis for more accurate prostate cancer diagnosis.

Reliable identification of prostate cancer using mass spectrometry metabolomic imaging in needle core biopsies.

Metabolomic profiling can aid in understanding crucial biological processes in cancer development and progression and can also yield diagnostic biomarkers. Desorption electrospray ionization coupled to mass spectrometry imaging (DESI-MSI) has been proposed as a potential adjunct to diagnostic surgical pathology, particularly for prostate cancer. However, due to low resolution sampling, small numbers of mass spectra, and little validation, published studies have yet to test whether this method is sufficiently robust to merit clinical translation. We used over 900 spatially resolved DESI-MSI spectra to establish an accurate, high-resolution metabolic profile of prostate cancer. We identified 25 differentially abundant metabolites, with cancer tissue showing increased fatty acids (FAs) and phospholipids, along with utilization of the Krebs cycle, and benign tissue showing increased levels of lyso-phosphatidylethanolamine (PE). Additionally, we identified, for the first time, two lyso-PEs with abundance that decreased with cancer grade and two phosphatidylcholines (PChs) with increased abundance with increasing cancer grade. Importantly, we developed and internally validated a multivariate metabolomic classifier for prostate cancer using 534 spatial regions of interest (ROIs) in the training cohort and 430 ROIs in the test cohort. With excellent statistical power, the training cohort achieved a balanced accuracy of 97% and validation on testing data set demonstrated 85% balanced accuracy. Given the validated accuracy of this classifier and the correlation of differentially abundant metabolites with established patterns of prostate cancer cell metabolism, we conclude that DESI-MSI is an effective tool for characterizing prostate cancer metabolism with the potential for clinical translation.

PTEN-deficient prostate cancer is associated with an immunosuppressive tumor microenvironment mediated by increased expression of IDO1 and infiltrating FoxP3+ T regulatory cells.

BACKGROUND: Accumulating evidence shows that tumor cell-specific genomic changes can influence the cross talk between cancer cells and the surrounding tumor microenvironment (TME). Loss of the PTEN tumor suppressor gene is observed in 20% to 30% of prostate cancers (PCa) when first detected and the rate increases with PCa progression and advanced disease. Recent findings implicate a role for PTEN in cellular type I interferon response and immunosuppression in PCa. However, the way that PTEN inactivation alters antitumor immune response in PCa is poorly understood. MATERIALS AND METHODS: To investigate the changes associated with PTEN loss and an immunosuppressive TME in PCa, we used CIBERSORT to estimate the relative abundance of 22 immune-cell types from 741 primary and 96 metastatic tumors. Our in silico findings were then validated by immunohistochemical analysis of immune cells and IDO1 and PDL1 checkpoint proteins in a cohort of 94 radical prostatectomy specimens. RESULTS: FoxP3+ T regulatory cells (Tregs) were significantly increased in PTEN-deficient PCa in all three public domain cohorts. Loss of PTEN in bone metastases was associated with lower CD8+ T-cell abundance, but in liver metastasis, FoxP3+ Tregs were present at higher levels. PTEN-deficient lymph node metastasis had a distinct profile, with high levels of CD8+ T cells. Moreover, we found that metastatic PCa presents higher abundance of FoxP3+ Treg when compared to primary lesions. Since PTEN-deficient tumors are likely to be immunosuppressed as a consequence of increased FoxP3+ Tregs, we then evaluated the localization and expression of IDO1, PDL1 immune checkpoints, and the corresponding density of FoxP3+ Treg and CD8+ T cells using our validation cohort (n = 94). We found that IDO1 protein expression and FoxP3+ Treg density were higher in neoplastic glands compared with benign adjacent tissue. Moreover, higher densities of FoxP3+ Treg cells in both stromal (P = 0.04) and tumor (P = 0.006) compartments were observed in PTEN-deficient tumors compared to tumors that retained PTEN activity. Similarly, IDO1 protein expression was significantly increased in the tumor glands of PTEN-deficient PCa (P < 0.0001). Spearman correlation analysis showed that IDO1 expression was significantly associated with FoxP3+ Treg and CD8+ T-cell density (P < 0.01). CONCLUSIONS: Our findings imply that PTEN deficiency is linked to an immunosuppressive state in PCa with distinct changes in the frequency of immune cell types in tumors from different metastatic sites. Our data suggest that determining PTEN status may also help guide the selection of patients for future immunotherapy trials in localized and metastatic PCa.

Prognostic pathological factors in radical cystectomy after neoadjuvant chemotherapy.

AIMS: We undertook a systematic evaluation of the prognostic value of numerous histological factors in 165 radical cystectomies (RCs) of patients with invasive urothelial carcinoma (UC) who underwent surgery after neoadjuvant chemotherapy (NAC). METHODS AND RESULTS: Tumour regression grade (TRG) and therapy-related stromal and epithelial changes were also recorded. Locally advanced disease (≥pT2 and/or pN+) was present in 64% of patients, 22% had no evidence of residual carcinoma (pT0 + pN0), and 28% had no evidence of residual muscle-invasive carcinoma (≤pT1 + N0). TRG1, TRG2 and TRG3 were found in 32%, 15% and 50% of patients, respectively. Histological variants of UC were reported in 25% of cases. The most common therapy-related stromal change was fibroblastic reaction (78%), and the most common epithelial change in residual UC was smudgy and poorly preserved chromatin (28%). Prominent stromal and epithelial changes were noted in 41% and 5% of RCs, respectively. Progression was found in 45% of patients, and cancer-related deaths occurred in 30%. Multivariate analysis showed that the only independent prognostic parameters for progression were T stage, N stage, lymphovascular invasion, and margin status. Similarly, only T stage, N stage and margin status correlated with cancer-related deaths. Neither TRG nor any of the stromal-related or epithelial-related variables correlated with outcome. CONCLUSIONS: We confirm that the traditional and routinely reported histological parameters in RC post-NAC remain the most powerful prognosticators of disease course. The significance of TRG in the bladder remains unconfirmed.

Clinical implications of PTEN loss in prostate cancer.

Genomic aberrations of the PTEN tumour suppressor gene are among the most common in prostate cancer. Inactivation of PTEN by deletion or mutation is identified in ∼20% of primary prostate tumour samples at radical prostatectomy and in as many as 50% of castration-resistant tumours. Loss of phosphatase and tensin homologue (PTEN) function leads to activation of the PI3K-AKT (phosphoinositide 3-kinase-RAC-alpha serine/threonine-protein kinase) pathway and is strongly associated with adverse oncological outcomes, making PTEN a potentially useful genomic marker to distinguish indolent from aggressive disease in patients with clinically localized tumours. At the other end of the disease spectrum, therapeutic compounds targeting nodes in the PI3K-AKT-mTOR (mechanistic target of rapamycin) signalling pathway are being tested in clinical trials for patients with metastatic castration-resistant prostate cancer. Knowledge of PTEN status might be helpful to identify patients who are more likely to benefit from these therapies. To enable the use of PTEN status as a prognostic and predictive biomarker, analytically validated assays have been developed for reliable and reproducible detection of PTEN loss in tumour tissue and in blood liquid biopsies. The use of clinical-grade assays in tumour tissue has shown a robust correlation between loss of PTEN and its protein as well as a strong association between PTEN loss and adverse pathological features and oncological outcomes. In advanced disease, assessing PTEN status in liquid biopsies shows promise in predicting response to targeted therapy. Finally, studies have shown that PTEN might have additional functions that are independent of the PI3K-AKT pathway, including those affecting tumour growth through modulation of the immune response and tumour microenvironment.

Analytic validation of a clinical-grade PTEN immunohistochemistry assay in prostate cancer by comparison with PTEN FISH.

PTEN loss is a promising prognostic and predictive biomarker in prostate cancer. Because it occurs most commonly via PTEN gene deletion, we developed a clinical-grade, automated, and inexpensive immunohistochemical assay to detect PTEN loss. We studied the sensitivity and specificity of PTEN immunohistochemistry relative to four-color fluorescence in situ hybridization (FISH) for detection of PTEN gene deletion in a multi-institutional cohort of 731 primary prostate tumors. Intact PTEN immunostaining was 91% specific for the absence of PTEN gene deletion (549/602 tumors with two copies of the PTEN gene by FISH showed intact expression of PTEN by immunohistochemistry) and 97% sensitive for the presence of homozygous PTEN gene deletion (absent PTEN protein expression by immunohistochemistry in 65/67 tumors with homozygous deletion). PTEN immunohistochemistry was 65% sensitive for the presence of hemizygous PTEN gene deletion, with protein loss in 40/62 hemizygous tumors. We reviewed the 53 cases where immunohistochemistry showed PTEN protein loss and FISH showed two intact copies of the PTEN gene. On re-review, there was ambiguous immunohistochemistry loss in 6% (3/53) and failure to analyze the same tumor area by both methods in 34% (18/53). Of the remaining discordant cases, 41% (13/32) revealed hemizygous (n=8) or homozygous (n=5) PTEN gene deletion that was focal in most cases (11/13). The remaining 19 cases had two copies of the PTEN gene detected by FISH, representing truly discordant cases. Our automated PTEN immunohistochemistry assay is a sensitive method for detection of homozygous PTEN gene deletions. Immunohistochemistry screening is particularly useful to identify cases with heterogeneous PTEN gene deletion in a subset of tumor glands. Mutations, small insertions, or deletions and/or epigenetic or microRNA-mediated mechanisms may lead to PTEN protein loss in tumors with normal or hemizygous PTEN gene copy number.

A multicenter study shows PTEN deletion is strongly associated with seminal vesicle involvement and extracapsular extension in localized prostate cancer.

BACKGROUND: Loss of the phosphatase and tensin homolog (PTEN) tumor suppressor gene is a promising marker of aggressive prostate cancer. Active surveillance and watchful waiting are increasingly recommended to patients with small tumors felt to be low risk, highlighting the difficulties of Gleason scoring in this setting. There is an urgent need for predictive biomarkers that can be rapidly deployed to aid in clinical decision-making. Our objectives were to assess the incidence and ability of PTEN alterations to predict aggressive disease in a multicenter study. METHODS: We used recently developed probes optimized for sensitivity and specificity in a four-color FISH deletion assay to study the Canary Retrospective multicenter Prostate Cancer Tissue Microarray (TMA). This TMA was constructed specifically for biomarker validation from radical prostatectomy specimens, and is accompanied by detailed clinical information with long-term follow-up. RESULTS: In 612 prostate cancers, the overall rate of PTEN deletion was 112 (18.3%). Hemizygous PTEN losses were present in 55/612 (9.0%) of cancers, whereas homozygous PTEN deletion was observed in 57/612 (9.3%) of tumors. Significant associations were found between PTEN status and pathologic stage (P < 0.0001), seminal vesicle invasion (P = 0.0008), extracapsular extension (P < 0.0001), and Gleason score (P = 0.0002). In logistic regression analysis of clinical and pathological variables, PTEN deletion was significantly associated with extracapsular extension, seminal vesicle involvement, and higher Gleason score. In the 406 patients in which clinical information was available, PTEN homozygous (P = 0.009) deletion was associated with worse post-operative recurrence-free survival (number of events = 189), pre-operative prostate specific antigen (PSA) (P < 0.001), and pathologic stage (P = 0.03). CONCLUSION: PTEN status assessed by FISH is an independent predictor for recurrence-free survival in multivariate models, as were seminal vesicle invasion, extracapsular extension, and Gleason score, and preoperative PSA. Furthermore, these data demonstrate that the assay can be readily introduced at first diagnosis in a cost effective manner analogous to the use of FISH for analysis of HER2/neu status in breast cancer. Combined with published research beginning 17 years ago, both the data and tools now exist to implement a PTEN assay in the clinic.