Molecular Hallmarks of Prostate-specific Membrane Antigen in Treatment-naïve Prostate Cancer.

BACKGROUND AND OBJECTIVE: We characterized tumor prostate-specific membrane antigen (PSMA) levels as a reflection of cancer biology and treatment sensitivities for treatment-naïve prostate cancer. METHODS: We first correlated PSMA positron emission tomography (PET) maximum standardized uptake values (SUVmax) in primary prostate cancer with tumor FOLH1 (PSMA RNA abundance) to establish RNA as a proxy (n = 55). We then discovered and validated molecular pathways associated with PSMA RNA levels in two large primary tumor cohorts. We validated those associations in independent cohorts (18 total; 5684 tumor samples) to characterize the pathways and treatment responses associated with PSMA. KEY FINDINGS AND LIMITATIONS: PSMA RNA abundance correlates moderately with SUVmax (ρ = 0.41). In independent cohorts, androgen receptor signaling is more active in tumors with high PSMA. Accordingly, patients with high PSMA tumors experienced longer cancer-specific survival when managed with androgen deprivation therapy for biochemical recurrence (adjusted hazard ratio [AHR] 0.54 [0.34-0.87]; n = 174). PSMA low tumors possess molecular markers of resistance to radiotherapy. Consistent with this, patients with high PSMA tumors experience longer time to recurrence following primary radiotherapy (AHR 0.50 [0.28-0.90]; n = 248). In the SAKK09/10 trial (n = 224), patients with high PSMA tumors who were managed with salvage radiotherapy experienced longer time to progression in the 64-Gy arm (restricted mean survival time [RMST] +7.60 [0.05-15.16]), but this effect was mitigated in the 70-Gy arm (RMST 3.52 [-3.30 to 10.33]). Limitations include using PSMA RNA as a surrogate for PET SUVmax. CONCLUSIONS AND CLINICAL IMPLICATIONS: PSMA levels in treatment-naïve prostate cancer differentiate tumor biology and treatment susceptibilities. These results warrant validation using PET metrics to substantiate management decisions based on imaging.

Association Between the Decipher Genomic Classifier and Prostate Cancer Outcome in the Real-world Setting.

BACKGROUND AND OBJECTIVE: Although the prognostic significance of the Decipher prostate cancer genomic classifier (GC) has been established largely from analyses of archival tissue, less is known about the associations between the results of Decipher testing and oncologic outcomes among patients receiving contemporaneous testing and treatment in the real-world practice setting. Our objective was to assess the associations between the Decipher GC and risks of metastasis and biochemical recurrence (BCR) following prostate biopsy and radical prostatectomy (RP) among patients tested and treated in the real-world setting. METHODS: A retrospective cohort study was conducted using a novel longitudinal linkage of transcriptomic data from the Decipher GC and real-world clinical data (RWD) aggregated from insurance claims, pharmacy records, and electronic health record data across payors and sites of care. Kaplan-Meier and Cox proportional hazards regressions were used to examine the associations between the GC and study outcomes, adjusting for clinical and pathologic factors. KEY FINDINGS AND LIMITATIONS: Metastasis from prostate cancer and BCR after radical prostatectomy, Decipher GC continuous score, and risk categories were evaluated. We identified 58 935 participants who underwent Decipher testing, including 33 379 on a biopsy specimen and 25 556 on an RP specimen. The median age was 67 yr (interquartile range [IQR] 62-72) at biopsy testing and 65 yr (IQR 59-69) at RP. The median GC score was 0.43 (IQR 0.27-0.66) among biopsy-tested patients and 0.54 (0.32-0.79) among RP-tested patients. The GC was independently associated with the risk of metastasis among biopsy-tested (hazard ratio [HR] per 0.1 unit increase in GC 1.21 [95% confidence interval {CI} 1.16-1.27], p < 0.001) and RP-tested (HR 1.20 [95% CI 1.17-1.24], p < 0.001) patients after adjusting for baseline clinical and pathologic risk factors. In addition, the GC was associated with the risk of BCR among RP-tested patients (HR 1.12 [95% CI 1.10-1.14], p < 0.001) in models adjusted for age and Cancer of the Prostate Risk Assessment postsurgical score. CONCLUSIONS AND CLINICAL IMPLICATIONS: This real-world study of a novel transcriptomic linkage conducted at a national scale supports the external prognostic validity of the Decipher GC among patients managed in contemporary practice. PATIENT SUMMARY: This study looked at the use of the Decipher genomic classifier, a test used to help understand the aggressiveness of a patient's prostate cancer. Looking at the results of 58 935 participants who underwent testing, we found that the Decipher test helped estimate the risk of cancer recurrence and metastasis.

Olaparib Without Androgen Deprivation for High-Risk Biochemically Recurrent Prostate Cancer Following Prostatectomy: A Nonrandomized Controlled Trial.

Importance: Olaparib is a poly(adenosine diphosphate-ribose) polymerase inhibitor that provides benefit in combination with hormonal therapies in patients with metastatic prostate cancer who harbor homologous recombination repair (HRR) alterations. Its efficacy in the absence of androgen deprivation therapy has not been tested. Objective: To determine the activity of olaparib monotherapy among patients with high-risk biochemically recurrent (BCR) prostate cancer after radical prostatectomy. Design, Setting, and Participants: This phase 2, single-arm nonrandomized controlled trial enrolled genetically unselected patients across 4 sites in the US from May 2017 to November 2022. Eligible patients had BCR disease following radical prostatectomy, a prostate-specific antigen (PSA) doubling time of 6 months or shorter, an absolute PSA value of 1.0 ng/mL or higher, and a testosterone level of 150 ng/dL or higher. Intervention: Treatment was with olaparib, 300 mg, by mouth twice daily until doubling of the baseline PSA, clinical or radiographic progression, or unacceptable toxic effects. Main Outcome and Measure: The primary end point was a confirmed 50% or higher decline in PSA from baseline (PSA50). Key secondary end points were outcomes by HRR alteration status, as well as safety and tolerability. Results: Of the 51 male patients enrolled (mean [SD] age, 63.8 [6.8] years), 13 participants (26%) had a PSA50 response, all within the HRR-positive group (13 of 27 participants [48%]). All 11 participants with BRCA2 alterations experienced a PSA50 response. Common adverse events were fatigue in 32 participants (63%), nausea in 28 (55%), and leukopenia in 22 (43%), and were consistent with known adverse effects of olaparib. Conclusions and Relevance: In this nonrandomized controlled trial, olaparib monotherapy led to high and durable PSA50 response rates in patients with BRCA2 alterations. Olaparib warrants further study as a treatment strategy for some patients with BCR prostate cancer but does not have sufficient activity in those without HRR alterations and should not be considered for those patients. Trial Registration: ClinicalTrials.gov Identifier: NCT03047135.

Use of Decipher Prostate Biopsy Test in Patients with Favorable-risk Disease Undergoing Conservative Management or Radical Prostatectomy in the Surveillance, Epidemiology, and End Results Registry.

BACKGROUND AND OBJECTIVE: The extent of prostate cancer found on biopsy, as well as prostate cancer grade and genomic tests, can affect clinical decision-making. The impact of these factors on the initial management approach and subsequent patient outcomes for men with favorable-grade prostate cancer has not yet been determined on a population level. Our objective was to explore the association of Decipher 22-gene genomic classifier (GC) biopsy testing on the initial use of conservative management versus radical prostatectomy (RP) and to determine the independent effect of GC scores on RP pathologic outcomes. METHODS: A total of 87 140 patients diagnosed with grade group 1 and 2 prostate cancer between 2016 and 2018 from the Surveillance, Epidemiology, and End Results registry data were linked to GC testing results (2576 tested and 84 564 untested with a GC). The primary endpoints of interest were receipt of conservative management or RP, pathologic upgrading (pathologic grade group 3-5), upstaging (pathologic ≥T3b), and adverse pathologic features (pathologic upgrading, upstaging, or lymph node invasion). Multivariable logistic regressions quantified the association of variables with outcomes of interest. KEY FINDINGS AND LIMITATIONS: GC tested patients were more likely to have grade group 2 on biopsy (51% vs 46%, p < 0.001) and lower prostate-specific antigen (6.1 vs 6.3, p = 0.016). Conservative management increased from 37% to 39% and from 22% to 24% during 2016-2018 for the GC tested and untested populations, respectively. GC testing was significantly associated with increased odds of conservative management (odds ratio [OR] 2.1, 95% confidence interval [CI] 1.9-2.4, p < 0.001). The distribution of biopsy GC risk was as follows: 45% low risk, 30% intermediate risk, and 25% high risk. In adjusted analyses, higher GC (per 0.1 increment) scores (OR 1.24, 95% CI 1.17-1.31, p < 0.001) and percent positive cores (1.07, 95% CI 1.02-1.12, p = 0.009) were significantly associated with the receipt of RP. A higher GC score was significantly associated with all adverse outcomes (pathologic upgrading [OR 1.29, 95% CI 1.12-1.49, p < 0.001], upstaging [OR 1.31, 95% CI 1.05-1.62, p = 0.020], and adverse pathology [OR 1.27, 95% CI 1.12-1.45, p < 0.001]). Limitations include observational biases associated with the retrospective study design. CONCLUSIONS AND CLINICAL IMPLICATIONS: Men who underwent GC testing were more likely to undergo conservative management. GC testing at biopsy is prognostic of adverse pathologic outcomes in a large population-based registry. PATIENT SUMMARY: In this population analysis of men with favorable-risk prostate cancer, those who underwent genomic testing at biopsy were more likely to undergo conservative management. Of men who initially underwent radical prostatectomy, higher genomic risk but not tumor volume was associated with adverse pathologic outcomes. The use of genomic testing at prostate biopsy improves risk stratification and may better inform treatment decisions than the use of tumor volume alone.

Transcriptomic Profiling of Primary Prostate Cancers and Nonlocalized Disease on Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography: A Multicenter Retrospective Study.

PURPOSE: To characterize the relationship between Decipher genomic classifier scores and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT)-based metastatic spread. MATERIALS AND METHODS: We identified patients from four institutions who underwent PSMA PET/CT scans pretreatment for primary staging or postradical prostatectomy (RP) for suspected recurrence and had Decipher transcriptomic data available from biopsy or RP specimens. PSMA PET/CT-based patterns of spread were classified as localized (miT + N0M0) or nonlocalized (miN1M0 or miM1a-c). We calculated the association between Decipher scores and the risk of nonlocalized disease on PSMA PET/CT using multivariable logistic regression for pretreatment patients and multivariable Cox regression for post-RP patients. We also compared select transcriptomic signatures between patients with localized and nonlocalized diseases. RESULTS: Five hundred eighty-six patients were included (pretreatment: n = 329; post-RP: n = 257). Higher Decipher scores were associated with nonlocalized disease on PSMA PET/CT both pretreatment (odds ratio, 1.18 [95% CI, 1.03 to 1.36] per 0.1 increase in Decipher score, P = .02) and post-RP (hazard ratio, 1.15 [95% CI, 1.05 to 1.27] per 0.1 increase in Decipher score, P = .003). In the pretreatment setting, nonlocalized disease was associated with higher rates of TP53 mutations and lower rates of PAM50 luminal A subtype compared with localized disease. In the post-RP setting, overexpression of signatures related to metabolism, DNA repair, and androgen receptor signaling were associated with higher rates of nonlocalized disease. CONCLUSION: Higher Decipher scores were associated with nonlocalized disease identified on PSMA PET/CT both pretreatment and post-RP. There were several transcriptomic differences between localized and nonlocalized diseases in both settings.

Development of a Longitudinal Prostate Cancer Transcriptomic and Clinical Data Linkage.

Importance: Although tissue-based gene expression testing has become widely used for prostate cancer risk stratification, its prognostic performance in the setting of clinical care is not well understood. Objective: To develop a linkage between a prostate genomic classifier (GC) and clinical data across payers and sites of care in the US. Design, Setting, and Participants: In this cohort study, clinical and transcriptomic data from clinical use of a prostate GC between 2016 and 2022 were linked with data aggregated from insurance claims, pharmacy records, and electronic health record (EHR) data. Participants were anonymously linked between datasets by deterministic methods through a deidentification engine using encrypted tokens. Algorithms were developed and refined for identifying prostate cancer diagnoses, treatment timing, and clinical outcomes using diagnosis codes, Common Procedural Terminology codes, pharmacy codes, Systematized Medical Nomenclature for Medicine clinical terms, and unstructured text in the EHR. Data analysis was performed from January 2023 to January 2024. Exposure: Diagnosis of prostate cancer. Main Outcomes and Measures: The primary outcomes were biochemical recurrence and development of prostate cancer metastases after diagnosis or radical prostatectomy (RP). The sensitivity of the linkage and identification algorithms for clinical and administrative data were calculated relative to clinical and pathological information obtained during the GC testing process as the reference standard. Results: A total of 92 976 of 95 578 (97.2%) participants who underwent prostate GC testing were successfully linked to administrative and clinical data, including 53 871 who underwent biopsy testing and 39 105 who underwent RP testing. The median (IQR) age at GC testing was 66.4 (61.0-71.0) years. The sensitivity of the EHR linkage data for prostate cancer diagnoses was 85.0% (95% CI, 84.7%-85.2%), including 80.8% (95% CI, 80.4%-81.1%) for biopsy-tested participants and 90.8% (95% CI, 90.5%-91.0%) for RP-tested participants. Year of treatment was concordant in 97.9% (95% CI, 97.7%-98.1%) of those undergoing GC testing at RP, and 86.0% (95% CI, 85.6%-86.4%) among participants undergoing biopsy testing. The sensitivity of the linkage was 48.6% (95% CI, 48.1%-49.1%) for identifying RP and 50.1% (95% CI, 49.7%-50.5%) for identifying prostate biopsy. Conclusions and Relevance: This study established a national-scale linkage of transcriptomic and longitudinal clinical data yielding high accuracy for identifying key clinical junctures, including diagnosis, treatment, and early cancer outcome. This resource can be leveraged to enhance understandings of disease biology, patterns of care, and treatment effectiveness.

Transcriptome-Based Prognostic and Predictive Biomarker Analysis of ENACT: A Randomized Controlled Trial of Enzalutamide in Men Undergoing Active Surveillance.

PURPOSE: Few studies have explored the potential for pharmacological interventions to delay disease progression in patients undergoing active surveillance (AS). This preplanned transcriptomic analysis of patient samples from the ENACT trial aims to identify biomarkers in patients on AS who are at increased risk for disease progression or who may derive the greatest benefit from enzalutamide treatment. PATIENTS AND METHODS: In the phase II ENACT (ClinicalTrials.gov identifier: NCT02799745) trial, patients on AS were randomly assigned 1:1 to 160 mg orally once daily enzalutamide monotherapy or continued AS for 1 year. Transcriptional analyses were conducted on biopsies collected at trial screening, year 1, and year 2. Three gene expression signatures were evaluated in samples collected at screening and in available samples from patients on AS at any time during surveillance (expanded cohort): Decipher genomic classifier, androgen receptor activity (AR-A) score, and Prediction Analysis of Microarray 50 (PAM50) cell subtype signature. RESULTS: The Decipher genomic classifier score was prognostic; higher scores were associated with disease progression in the expanded cohort and AS arm of the expanded cohort. Patients with higher Decipher scores had greater positive treatment effect from enzalutamide as measured by time to secondary rise in prostate-specific antigen >25% above baseline. In patients treated with enzalutamide, higher AR-A scores and PAM50 luminal subtypes were associated with a greater likelihood of negative biopsy incidence at year 2. CONCLUSION: This analysis suggests that the Decipher genomic classifier may be prognostic for disease progression in AS patients with low- to intermediate-risk prostate cancer. Higher Decipher and AR-A scores, as well as PAM50 luminal subtypes, may also serve as biomarkers for treatment response.

Transcriptomic Signatures Associated With Outcomes in Recurrent Prostate Cancer Treated With Salvage Radiation, Androgen-Deprivation Therapy, and Enzalutamide: Correlative Analysis of the STREAM Trial.

PURPOSE: Men with rising prostate-specific antigen (PSA) after radical prostatectomy (RP) may progress despite radiation and androgen-deprivation therapy (ADT). Tissue-based transcriptomic signatures can identify who may benefit from a more aggressive systemic approach. METHODS: We performed a retrospective analysis of a prospective phase II multicenter trial of enzalutamide, ADT, and salvage radiotherapy in men with rising PSA after RP. Tumor tissue was analyzed using the Decipher platform for gene expression, including a novel prostate subtyping classifier, PTEN loss, homologous recombination deficiency (HRD), and ADT response. Cox models were used to associate signature scores with progression-free survival (PFS). RESULTS: Of the 38 men enrolled, 31 had tissue with sufficient-quality RNA for genomic analysis. Luminal differentiated (LD) subtype tumors had the longest 3-year PFS at 89% compared with 19% in the luminal proliferating subtype. Men with signatures of PTEN loss (hazard ratio [HR], 1.32; 95% CI, 1.07 to 1.64; P = .01) or HRD (HR, 1.21; 95% CI, 1.05 to 1.39; P = .009) had worse PFS, while those with higher ADT response signature scores (HR, 0.75; 95% CI, 0.61 to 0.94; P = .01) were associated with improved PFS. Analysis of these signatures in a large cohort (n = 5,330) of RP samples from patients with biochemical recurrence found that these signatures provide complementary information related to outcomes with salvage radiation. CONCLUSION: Despite aggressive systemic therapy with salvage radiation, nearly 50% of high-risk men relapse within 3 years. We show that LD and higher ADT sensitivity tumors had favorable outcomes. Those with a luminal proliferating subtype, PTEN loss, and/or HRD signatures had poor outcomes despite ADT/radiation and enzalutamide and may benefit from alternative approaches.

Use of the Decipher genomic classifier among men with prostate cancer in the United States.

BACKGROUND: Management of localized or recurrent prostate cancer since the 1990s has been based on risk stratification using clinicopathological variables, including Gleason score, T stage (based on digital rectal exam), and prostate-specific antigen (PSA). In this study a novel prognostic test, the Decipher Prostate Genomic Classifier (GC), was used to stratify risk of prostate cancer progression in a US national database of men with prostate cancer. METHODS: Records of prostate cancer cases from participating SEER (Surveillance, Epidemiology, and End Results) program registries, diagnosed during the period from 2010 through 2018, were linked to records of testing with the GC prognostic test. Multivariable analysis was used to quantify the association between GC scores or risk groups and use of definitive local therapy after diagnosis in the GC biopsy-tested cohort and postoperative radiotherapy in the GC-tested cohort as well as adverse pathological findings after prostatectomy. RESULTS: A total of 572 545 patients were included in the analysis, of whom 8927 patients underwent GC testing. GC biopsy-tested patients were more likely to undergo active active surveillance or watchful waiting than untested patients (odds ratio [OR] =2.21, 95% confidence interval [CI] = 2.04 to 2.38, P < .001). The highest use of active surveillance or watchful waiting was for patients with a low-risk GC classification (41%) compared with those with an intermediate- (27%) or high-risk (11%) GC classification (P < .001). Among National Comprehensive Cancer Network patients with low and favorable-intermediate risk, higher GC risk class was associated with greater use of local therapy (OR = 4.79, 95% CI = 3.51 to 6.55, P < .001). Within this subset of patients who were subsequently treated with prostatectomy, high GC risk was associated with harboring adverse pathological findings (OR = 2.94, 95% CI = 1.38 to 6.27, P = .005). Use of radiation after prostatectomy was statistically significantly associated with higher GC risk groups (OR = 2.69, 95% CI = 1.89 to 3.84). CONCLUSIONS: There is a strong association between use of the biopsy GC test and likelihood of conservative management. Higher genomic classifier scores are associated with higher rates of adverse pathology at time of surgery and greater use of postoperative radiotherapy.In this study the Decipher Prostate Genomic Classifier (GC) was used to analyze a US national database of men with prostate cancer. Use of the GC was associated with conservative management (ie, active surveillance). Among men who had high-risk GC scores and then had surgery, there was a 3-fold higher chance of having worrisome findings in surgical specimens.

A novel prostate cancer subtyping classifier based on luminal and basal phenotypes.

BACKGROUND: Prostate cancer (PCa) is a clinically heterogeneous disease. The creation of an expression-based subtyping model based on prostate-specific biological processes was sought. METHODS: Unsupervised machine learning of gene expression profiles from prospectively collected primary prostate tumors (training, n = 32,000; evaluation, n = 68,547) was used to create a prostate subtyping classifier (PSC) based on basal versus luminal cell expression patterns and other gene signatures relevant to PCa biology. Subtype molecular pathways and clinical characteristics were explored in five other clinical cohorts. RESULTS: Clustering derived four subtypes: luminal differentiated (LD), luminal proliferating (LP), basal immune (BI), and basal neuroendocrine (BN). LP and LD tumors both had higher androgen receptor activity. LP tumors also had a higher expression of cell proliferation genes, MYC activity, and characteristics of homologous recombination deficiency. BI tumors possessed significant interferon γactivity and immune infiltration on immunohistochemistry. BN tumors were characterized by lower androgen receptor activity expression, lower immune infiltration, and enrichment with neuroendocrine expression patterns. Patients with LD tumors had less aggressive tumor characteristics and the longest time to metastasis after surgery. Only patients with BI tumors derived benefit from radiotherapy after surgery in terms of time to metastasis (hazard ratio [HR], 0.09; 95% CI, 0.01-0.71; n = 855). In a phase 3 trial that randomized patients with metastatic PCa to androgen deprivation with or without docetaxel (n = 108), only patients with LP tumors derived survival benefit from docetaxel (HR, 0.21; 95% CI, 0.09-0.51). CONCLUSIONS: With the use of expression profiles from over 100,000 tumors, a PSC was developed that identified four subtypes with distinct biological and clinical features. PLAIN LANGUAGE SUMMARY: Prostate cancer can behave in an indolent or aggressive manner and vary in how it responds to certain treatments. To differentiate prostate cancer on the basis of biological features, we developed a novel RNA signature by using data from over 100,000 prostate tumors-the largest data set of its kind. This signature can inform patients and physicians on tumor aggressiveness and susceptibilities to treatments to help personalize cancer management.

Transcriptomic Heterogeneity in High-risk Prostate Cancer and Implications for Extraprostatic Disease at Presentation on Prostate-specific Membrane Antigen Positron Emission Tomography.

Prostate-specific membrane antigen (PSMA) positron emission tomography (PET) has greater specificity and sensitivity for detection of extraprostatic prostate cancer (PCa) at presentation than conventional imaging. Although the long-term clinical significance of acting on these findings is unknown, it has been shown that the risk of upstaging is prognostic for long-term outcomes in men with high-risk (HR) or very high-risk (VHR) PCa. We evaluated the association between the risk of upstaging on PSMA PET and the Decipher genomic classifier score, a known prognostic biomarker in localized PCa that is being evaluated for its predictive ability to direct systemic therapy intensification. In a cohort of 4625 patients with HR or VHR PCa, the risk of upstaging on PSMA PET was significantly correlated with the Decipher score (p < 0.001). These results should be seen as hypothesis-generating and warrant further studies on the causal pathways linking PSMA findings, Decipher scores, extraprostatic disease, and long-term clinical outcomes. PATIENT SUMMARY: We found significant correlation between the risk of having prostate cancer outside the prostate gland on a sensitive scan (based on prostate-specific membrane antigen [PSMA]) at initial staging and the Decipher genetic score. The results warrant further studies on the causal pathways between PSMA scan findings, Decipher scores, disease outside the prostate, and long-term outcomes.

Prostate Cancer Tumor Volume and Genomic Risk.

Background: Despite the historic association of higher prostate cancer volume with worse oncologic outcomes, little is known about the impact of tumor volume on cancer biology. Objective: To characterize the relationship between tumor volume (measured by percent positive cores [PPC]) and cancer biology (measured by Decipher genomic risk classifier [GC]) in men who underwent prostate biopsy. Design setting and participants: Prostate biopsies from 52 272 men profiled with Decipher captured in a population-based prospective tumor registry were collected from 2016 to 2021. Outcome measurements and statistical analysis: The degree of distribution and correlation of PPC with a GC score across grade group (GG) strata were examined using the Mann-Whitney U test, Pearson correlation coefficient, and multivariable linear regression controlled for clinicopathologic characteristics. Results and limitations: A total of 38 921 (74%) biopsies passed quality control (14 331 GG1, 16 159 GG2, 5661 GG3, 1775 GG4, and 995 GG5). Median PPC and GC increased with sequentially higher GG. There was an increasingly positive correlation (r) between PPC and GC in GG2-5 prostate cancer (r [95% confidence interval {CI}]: 0.07 [0.5, 0.8] in GG2, 0.15 [0.12, 0.17] in GG3, 0.20 [0.15, 0.24] in GG4, and 0.25 [0.19, 0.31] in GG5), with no correlation in GG1 disease (r = 0.01, 95% CI [-0.001, 0.03]). In multivariable linear regression, GC was significantly associated with higher PPC for GG2-5 (all p < 0.05) but was not significantly associated with PPC for GG1. Limitations include retrospective design and a lack of final pathology from radical prostatectomy specimens. Conclusions: Higher tumor volume was associated with worse GC for GG2-5 prostate cancer, whereas tumor volume was not associated with worse GC for GG1 disease. The finding that tumor volume is not associated with worse cancer biology in GG1 disease encourages active surveillance for most patients irrespective of tumor volume. Patient summary: We studied the relationship between prostate cancer tumor volume and cancer biology, as measured by the Decipher genomic risk score, in men who underwent prostate biopsy. We found that tumor volume was not associated with worse cancer biology for low-grade prostate cancer. Our findings reassuringly support recent guidelines to recommend active surveillance for grade group 1 prostate cancer in most patients, irrespective of tumor volume.

Macula structural and vascular differences in glaucoma eyes with and without high axial myopia.

AIMS: To identify clinically relevant parameters for identifying glaucoma in highly myopic eyes, an investigation was conducted of the relationship between the thickness of various retinal layers and the superficial vessel density (sVD) of the macula with axial length (AL) and visual field mean deviation (VFMD). METHODS: 270 glaucoma patients (438 eyes) participating in the Diagnostic Innovations in Glaucoma cross-sectional study representing three axial myopia groups (non-myopia: n=163 eyes; mild myopia: n=218 eyes; high myopia (AL>26 mm): n=57 eyes) who completed macular optical coherence tomography (OCT) and OCT-angiography imaging were included. Associations of AL and VFMD with the thickness of the ganglion cell inner plexiform layer (GCIPL), macular retinal nerve fibre layer (mRNFL), ganglion cell complex (GCC), macular choroidal thickness (mCT) and sVD were evaluated. RESULTS: Thinner Global GCIPL and GCC were significantly associated with worse VFMD (R2=34.5% and R2=32.9%; respectively p<0.001), but not with AL (all p>0.1). Thicker mRNFL showed a weak association with increasing AL (R2=2.4%; p=0.005) and a positive association with VFMD (global R2=19.2%; p<0.001). Lower sVD was weakly associated with increasing AL (R2=1.8%; p=0.028) and more strongly associated with more severe glaucoma VFMD (R2=29.6%; p<0.001). Thinner mCT was associated with increasing AL (R2=15.5% p<0.001) and not associated with VFMD (p=0.194). mRNFL was thickest while mCT was thinnest in all sectors of high myopic eyes. CONCLUSIONS: As thinner GCIPL and GCC were associated with increasing severity of glaucoma but were not significantly associated with AL, they may be useful for monitoring glaucoma in highly myopic eyes.

Relationship of macular ganglion cell complex thickness to choroidal microvasculature drop-out in primary open-angle glaucoma.

BACKGROUND/AIMS: To investigate the rate of ganglion cell complex (GCC) thinning in primary open-angle glaucoma (POAG) patients with and without deep-layer microvasculature drop-out (MvD). METHODS: POAG patients who had at least 1.5 years of follow-up and a minimum of three visits were included from the Diagnostic Innovations in Glaucoma Study. MvD was detected at baseline by optical coherence tomography angiography (OCT-A). Area and angular circumference of MvD were evaluated on en face choroidal vessel density images and horizontal B-scans. Rates of global and hemisphere GCC thinning were compared in MvD and non-MvD eyes using linear mixed-effects models. RESULTS: Thirty-six eyes with MvD and 37 eyes without MvD of 63 patients were followed for a mean of 3.3 years. In 30 out of 36 eyes, MvD was localised in the inferotemporal region. While mean baseline visual field mean deviation was similar between the two groups (p=0.128), global GCC thinning was significantly faster in eyes with MvD than in those without MvD (mean differences: -0.50 (95% CI -0.83 to -0.17) µm/year; p=0.003)). Presence of MvD, area and angular circumference of MvD were independently associated with a faster rate of thinning (p=0.002, p=0.031 and p=0.013, respectively). CONCLUSION: In POAG eyes, GCC thinning is faster in eyes with MvD. Detection of MvD in OCT-A images can assist clinicians to identify patients who are at higher risk for central macula thinning and glaucomatous progression and may require more intensive management.

Long-term reproducibility of optical coherence tomography angiography in healthy and stable glaucomatous eyes.

BACKGROUND/AIMS: To assess and compare long-term reproducibility of optic nerve head (ONH) and macula optical coherence tomography angiography (OCTA) vascular parameters and optical coherence tomography (OCT) thickness parameters in stable primary open-angle glaucoma (POAG), glaucoma suspect and healthy eyes. METHODS: Eighty-eight eyes (15 healthy, 38 glaucoma suspect and 35 non-progressing POAG) of 68 subjects who had at least three visits within 1-1.5 years with OCTA and OCT imaging (Angiovue; Optovue, Fremont, California, USA) on the same day were included. A series of vascular and thickness parameters were measured including macular parafoveal vessel density (pfVD), ONH circumpapillary capillary density (cpCD), macular parafoveal ganglion cell complex (pfGCC) and ONH circumpapillary retinal nerve fibre layer (cpRNFL). A random effects analysis of variance model was used to estimate intraclass correlation (ICC) coefficients and long-term variability estimates. RESULTS: ICC was lower for OCTA (pfVD 0.823 (95% CI 0.736 to 0.888) and cpCD 0.871 (0.818 to 0.912)) compared with OCT (pfGCC 0.995 (0.993 to 0.997) and cpRNFL 0.975 (0.964 to 0.984)). Within-subject test-retest SD was 1.17% and 1.22% for pfVD and cpCD, and 0.57 and 1.22 µm for pfGCC and cpRNFL. Older age and lower signal strength index were associated with decreasing long-term variability of vessel densities. CONCLUSIONS: OCTA-measured macula and ONH vascular parameters have good long-term reproducibility, supporting the use of this instrument for longitudinal analysis. OCTA long-term reproducibility is less than OCT-measured thickness reproducibility. This needs to be taken into consideration when serial OCTA images are evaluated for change. TRIAL REGISTRATION NUMBER: NCT00221897.

Correlation of ganglion cell complex thinning with baseline deep and superficial macular vessel density in glaucoma.

BACKGROUND/AIMS: To investigate the relationship between ganglion cell complex (GCC) thinning and baseline deep and superficial macular vessel density (VD) in glaucoma. METHODS: 97 eyes of 69 primary open-angle glaucoma (POAG) and glaucoma suspect patients from the Diagnostics Innovations in Glaucoma Study with a minimum of 4 visits and 2 years of follow-up after baseline optical coherence tomography angiography (OCTA) examination were included. OCTA 3×3 mm2 macular scans were acquired at each visit and used to calculate superficial and deep parafoveal VD (pfVD) and OCT-based parafoveal GCC (pfGCC) thickness. Association of baseline superficial and deep pfVD with pfGCC thinning rate was evaluated using linear mixed model. RESULTS: The included subjects had a baseline mean visual field mean deviation (95% CI) of -2.9 (-3.7 to -2.1) dB and a mean follow-up period of 3.6 years. In the univariable model, lower baseline superficial pfVD and higher mean intraocular pressure (IOP) during follow-up were significantly associated with a faster pfGCC thinning rate (p<0.05 for all), while deep pfVD was not (p=0.177). In the multivariable model, faster pfGCC thinning was correlated with higher mean IOP during follow-up (ß=-0.05, p=0.002) and lower baseline superficial pfVD (ß=-0.04, p=0.011). Eyes with a baseline superficial pfVD in the lowest tertile (≤46%) had significantly faster pfGCC loss compared with eyes with baseline superficial pfVD greater than 46% (p=0.015). CONCLUSION: Lower baseline superficial pfVD, but not deep pfVD, was associated with faster pfGCC thinning in glaucoma. Moreover, superficial macular VD may help predict central macula thinning in patients with glaucoma.

Effect of Testing Frequency on the Time to Detect Glaucoma Progression With Optical Coherence Tomography (OCT) and OCT Angiography.

PURPOSE: To determine how the frequency of testing affects the time required to detect statistically significant glaucoma progression for circumpapillary retinal nerve fiber layer (cpRNFL) with optical coherence tomography (OCT) and circumpapillary capillary density (cpCD) with OCT angiography (OCTA). DESIGN: Retrospective, observational cohort study. METHODS: In this longitudinal study, 156 eyes of 98 patients with glaucoma followed up over an average of 3.5 years were enrolled. Participants with 4 or more OCT and OCTA tests were included to measure the longitudinal rates of cpRNFL thickness and cpCD change over time using linear regression. Estimates of variability were then used to re-create real-world cpRNFL and cpCD data by computer simulation to evaluate the time required to detect progression for various loss rates and different testing frequencies. RESULTS: The time required to detect a statistically significant negative cpRNFL and cpCD slope decreased as the testing frequency increased, albeit not proportionally. cpCD detected progression slightly earlier than cpRNFL. Eighty percent of eyes with a cpCD loss of -1%/y were detected after 6.0, 4.2, and 4 years when testing was performed 1, 2, and 3 times per year, respectively. Progression in 80% of eyes with a cpRNFL loss of -1 µm/y was detected after 6.3, 5.0, and 4.2 years, respectively. CONCLUSIONS: cpRNFL and cpCD are comparable in detecting progression. As there were only small changes in the time to detect progression when testing increased from 2 to 3 times per year, testing twice per year may provide sufficient information for detecting progression with either OCT or OCTA in clinical settings.

A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design.

PURPOSE: To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness for use in glaucoma neuroprotection clinical trials. DESIGN: Cross-sectional study. PARTICIPANTS: Glaucoma patients with good quality macula and ONH scans enrolled in 2 longitudinal studies, the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovations in Glaucoma Study. METHODS: Spectralis macula posterior pole scans and ONH circle scans on 3327 pairs of GCIPL/RNFL scans from 1096 eyes (550 patients) were included. Participants were randomly distributed into a training and validation dataset (90%) and a test dataset (10%) by participant. Networks had access to GCIPL and RNFL data from one hemiretina of the probe eye and all data of the fellow eye. The models were then trained to predict the GCIPL or RNFL thickness of the remaining probe eye hemiretina. MAIN OUTCOME MEASURES: Mean absolute error (MAE) and squared Pearson correlation coefficient (r2) were used to evaluate model performance. RESULTS: The deep learning model was able to predict superior and inferior GCIPL thicknesses with a global r2 value of 0.90 and 0.86, r2 of mean of 0.90 and 0.86, and mean MAE of 3.72 µm and 4.2 µm, respectively. For superior and inferior RNFL thickness predictions, model performance was slightly lower, with a global r2 of 0.75 and 0.84, r2 of mean of 0.81 and 0.82, and MAE of 9.31 µm and 8.57 µm, respectively. There was only a modest decrease in model performance when predicting GCIPL and RNFL in more severe disease. Using individualized hemiretinal predictions to account for variability across patients, we estimate that a clinical trial can detect a difference equivalent to a 25% treatment effect over 24 months with an 11-fold reduction in the number of patients compared to a conventional trial. CONCLUSIONS: Our deep learning models were able to accurately estimate both macula GCIPL and ONH RNFL hemiretinal thickness. Using an internal control based on these model predictions may help reduce clinical trial sample size requirements and facilitate investigation of new glaucoma neuroprotection therapies. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found after the references.

Phase II Randomized Study of Salvage Radiation Therapy Plus Enzalutamide or Placebo for High-Risk Prostate-Specific Antigen Recurrent Prostate Cancer After Radical Prostatectomy: The SALV-ENZA Trial.

PURPOSE: We sought to investigate whether enzalutamide (ENZA), without concurrent androgen deprivation therapy, increases freedom from prostate-specific antigen (PSA) progression (FFPP) when combined with salvage radiation therapy (SRT) in men with recurrent prostate cancer after radical prostatectomy (RP). PATIENTS AND METHODS: Men with biochemically recurrent prostate cancer after RP were enrolled into a randomized, double-blind, phase II, placebo-controlled, multicenter study of SRT plus ENZA or placebo (ClinicalTrials.gov identifier: NCT02203695). Random assignment (1:1) was stratified by center, surgical margin status (R0 v R1), PSA before salvage treatment (PSA ≥ 0.5 v < 0.5 ng/mL), and pathologic Gleason sum (7 v 8-10). Patients were assigned to receive either ENZA 160 mg once daily or matching placebo for 6 months. After 2 months of study drug therapy, external-beam radiation (66.6-70.2 Gy) was administered to the prostate bed (no pelvic nodes). The primary end point was FFPP in the intention-to-treat population. Secondary end points were time to local recurrence within the radiation field, metastasis-free survival, and safety as determined by frequency and severity of adverse events. RESULTS: Eighty-six (86) patients were randomly assigned, with a median follow-up of 34 (range, 0-52) months. Trial arms were well balanced. The median pre-SRT PSA was 0.3 (range, 0.06-4.6) ng/mL, 56 of 86 patients (65%) had extraprostatic disease (pT3), 39 of 86 (45%) had a Gleason sum of 8-10, and 43 of 86 (50%) had positive surgical margins (R1). FFPP was significantly improved with ENZA versus placebo (hazard ratio [HR], 0.42; 95% CI, 0.19 to 0.92; P = .031), and 2-year FFPP was 84% versus 66%, respectively. Subgroup analyses demonstrated differential benefit of ENZA in men with pT3 (HR, 0.22; 95% CI, 0.07 to 0.69) versus pT2 disease (HR, 1.54; 95% CI, 0.43 to 5.47; Pinteraction = .019) and R1 (HR, 0.14; 95% CI, 0.03 to 0.64) versus R0 disease (HR, 1.00; 95% CI, 0.36 to 2.76; Pinteraction = .023). There were insufficient secondary end point events for analysis. The most common adverse events were grade 1-2 fatigue (65% ENZA v 53% placebo) and urinary frequency (40% ENZA v 49% placebo). CONCLUSION: SRT plus ENZA monotherapy for 6 months in men with PSA-recurrent high-risk prostate cancer after RP is safe and delays PSA progression relative to SRT alone. The impact of ENZA on distant metastasis or survival is unknown at this time.

Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions: Transformer for Improved Generalization.

Purpose: To compare the diagnostic accuracy and explainability of a Vision Transformer deep learning technique, Data-efficient image Transformer (DeiT), and ResNet-50, trained on fundus photographs from the Ocular Hypertension Treatment Study (OHTS) to detect primary open-angle glaucoma (POAG) and identify the salient areas of the photographs most important for each model's decision-making process. Design: Evaluation of a diagnostic technology. Subjects Participants and Controls: Overall 66 715 photographs from 1636 OHTS participants and an additional 5 external datasets of 16 137 photographs of healthy and glaucoma eyes. Methods: Data-efficient image Transformer models were trained to detect 5 ground-truth OHTS POAG classifications: OHTS end point committee POAG determinations because of disc changes (model 1), visual field (VF) changes (model 2), or either disc or VF changes (model 3) and Reading Center determinations based on disc (model 4) and VFs (model 5). The best-performing DeiT models were compared with ResNet-50 models on OHTS and 5 external datasets. Main Outcome Measures: Diagnostic performance was compared using areas under the receiver operating characteristic curve (AUROC) and sensitivities at fixed specificities. The explainability of the DeiT and ResNet-50 models was compared by evaluating the attention maps derived directly from DeiT to 3 gradient-weighted class activation map strategies. Results: Compared with our best-performing ResNet-50 models, the DeiT models demonstrated similar performance on the OHTS test sets for all 5 ground-truth POAG labels; AUROC ranged from 0.82 (model 5) to 0.91 (model 1). Data-efficient image Transformer AUROC was consistently higher than ResNet-50 on the 5 external datasets. For example, AUROC for the main OHTS end point (model 3) was between 0.08 and 0.20 higher in the DeiT than ResNet-50 models. The saliency maps from the DeiT highlight localized areas of the neuroretinal rim, suggesting important rim features for classification. The same maps in the ResNet-50 models show a more diffuse, generalized distribution around the optic disc. Conclusions: Vision Transformers have the potential to improve generalizability and explainability in deep learning models, detecting eye disease and possibly other medical conditions that rely on imaging for clinical diagnosis and management.