Validation of the Decipher Test for predicting adverse pathology in candidates for prostate cancer active surveillance

Integration of Genomic Tests in Prostate Cancer Care: Implications for Clinical Practice and Patient Outcomes

Prostate cancer (PCa) is a common malignancy in men and is among the leading causes of cancer-related death worldwide. Genomic tests assess disease aggressiveness and guide treatment, particularly in low- and intermediate-risk PCa. We reviewed the literature on the use of four genomic tests (Prolaris®, Promark®, Oncotype DX®, and Decipher®) in assessing the prognosis of PCa and their use in treatment decision-making. Most of the studies showed that Prolaris® has a strong correlation with biochemical recurrence, metastasis risk, PCa-specific mortality (PCSM), and pathological features. Similarly, three studies on Promark® indicated a connection between results and pathological features in the subsequent prostatectomy, time to metastasis, and biochemical recurrence. Fourteen studies on Oncotype DX® showed a clear correlation between high scores, death, and PCSM. One study found that routine biopsy pathology reports, combined with serum PSA levels, provide a risk assessment comparable to Oncotype DX® testing. Results from 22 studies on Decipher® were controversial. The test was associated with conservative management, suggesting that patients with a high GC score are more likely to need radiation after surgery. Comparative studies indicated that Oncotype DX® is preferable for assessing PCSM, Decipher® for predicting metastasis, and Prolaris® for predicting recurrence. With the incidence rate of PCa dramatically increasing, genomic tests appear to be useful adjunctive precision medicine tools with significant potential in improving prognostic discrimination, facilitating better risk stratification, and guiding personalized treatment, especially in the intermediate-risk patient group. Large-scale, prospective, multi-sectional studies are required to validate the utility of these tests prior to their integration into clinical practice.

Risks of grade reclassification among patients with Gleason grade group 1 prostate cancer and PI-RADS 5 findings on prostate MRI

BACKGROUND AND OBJECTIVE

As most Prostate Imaging Reporting and Data System (PI-RADS) 5 lesions on MRI harbor Gleason grade (GG) group ≥2 disease on biopsy, optimal management of patients with imaging-biopsy discordance remains unclear. To estimate grade misclassification, we evaluated the incidence of Gleason upgrading among patients with GG1 disease in the setting of a PI-RADS 5 lesion.

METHODS

We conducted a single-institution retrospective analysis to identify patients with GG1 prostate cancer on fusion biopsy with MRI demonstrating ≥1 PI-RADS 5 lesion. Primary study outcome was identification of ≥GG2 disease on subsequent active surveillance (AS) biopsy or radical prostatectomy (RP). We used multivariable models to examine factors associated with reclassification.

RESULTS

We identified 110 patients with GG1 disease on initial biopsy and ≥1 PI-RADS 5 lesion. There were 104 patients (94.6%) initially managed with AS and 6 (5.5%) received treatment. Sixty-one patients (58.7%) on AS underwent additional biopsies. Of these, 43 (70.5%) patients had tumor upgrading, with 32 (74.4%) upgraded on first surveillance biopsy. Forty-four (40%) patients ultimately received treatment, including prostatectomy in 15 (13.6%) and radiation in 25 (22.7%). Two patients (1.8%) developed metastases. In multivariable models, genomic classifier score was associated with upgrading. Limitations include a lack of multi-institutional data and long-term outcomes data.

CONCLUSIONS

Most patients diagnosed with GG1 prostate cancer on MRI-Ultrasound fusion biopsy in the setting of a PI-RADS 5 lesion were found to have ≥GG2 disease on subsequent tissue sampling, suggesting substantial initial misclassification and reinforcing the need for confirmatory testing.

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.

Identifying the best candidate for focal therapy: a comprehensive review

BACKGROUND

Despite the evidence supporting the use of focal therapy (FT) in patients with localized prostate cancer (PCa), considerable variability exists in the patient selection criteria across current studies. This study aims to review the most recent evidence concerning the optimal approach to patient selection for FT in PCa.

METHODS

PubMed database was systematically queried for studies reporting patient selection criteria in FT for PCa before December 31, 2023. After excluding non-relevant articles and a quality assessment, data were extracted, and results were described qualitatively.

RESULTS

There is no level I evidence regarding the best patient selection approach for FT in patients with PCa. Current international multidisciplinary consensus statements recommend multiparametric magnetic resonance imaging (mpMRI) followed by MRI-targeted and systematic biopsy for all candidates. FT may be considered in clinically localized, intermediate risk (Gleason 3 + 4 and 4 + 3), and preferably unifocal disease. Patients should have an acceptable life expectancy. Those with prostate volume >50 ml and erectile dysfunction should not be excluded from FT. Prostate-specific antigen (PSA) level of < 20 (ideally < 10) ng/mL is recommended. However, the utility of other molecular and genomic biomarkers in patient selection for FT remains unknown.

CONCLUSIONS

FT may be considered in well-selected patients with localized PCa. This review provides a comprehensive insight regarding the optimal approach for patient selection in FT.

Active surveillance selection and 3-year durability in intermediate-risk prostate cancer following genomic testing

BACKGROUND

Genomic testing can add risk stratification information to clinicopathological features in prostate cancer, aiding in shared medical decision-making between the clinician and patient regarding whether active surveillance (AS) or definitive treatment (DT) is most appropriate. Here we examined initial AS selection and 3-year AS durability in patients diagnosed with localized intermediate-risk prostate cancer who underwent Prolaris testing before treatment decision-making.

METHODS

This retrospective observational cohort study included 3208 patients from 10 study sites who underwent Prolaris testing at diagnosis from September 2015 to December 2018. Prolaris utilizes a combined clinical cell cycle risk score calculated at diagnostic biopsy to stratify patients by the Prolaris AS threshold (below threshold, patient recommended to AS or above threshold, patient recommended to DT). AS selection rates and 3-year AS durability were compared in patients recommended to AS or DT by Prolaris testing. Univariable and multivariable logistic regression models and Cox proportional hazard models were used with molecular and clinical variables as predictors of initial treatment decision and AS durability, respectively.

RESULTS

AS selection was ~2 times higher in patients recommended to AS by Prolaris testing than in those recommended to DT (p < 0.0001). Three-year AS durability was ~1.5 times higher in patients recommended to AS by Prolaris testing than in those recommended to DT (p < 0.0001). Prolaris treatment recommendation remained a statistically significant predictor of initial AS selection and AS durability after accounting for CAPRA or Gleason scores.

CONCLUSIONS

Prolaris added significant information to clinical risk stratification to aid in treatment decision making. Intermediate-risk prostate cancer patients who were recommended to AS by Prolaris were more likely to initially pursue AS and were more likely to remain on AS at 3 years post-diagnosis than patients recommended to DT.

Reporting tumor genomic test results to SEER registries via linkages

BACKGROUND

Precision medicine has become a mainstay of cancer care in recent years. The National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program has been an authoritative source of cancer statistics and data since 1973. However, tumor genomic information has not been adequately captured in the cancer surveillance data, which impedes population-based research on molecular subtypes. To address this, the SEER Program has developed and implemented a centralized process to link SEER registries' tumor cases with genomic test results that are provided by molecular laboratories to the registries.

METHODS

Data linkages were carried out following operating procedures for centralized linkages established by the SEER Program. The linkages used Match*Pro, a probabilistic linkage software, and were facilitated by the registries' trusted third party (an honest broker). The SEER registries provide to NCI limited datasets that undergo preliminary evaluation prior to their release to the research community.

RESULTS

Recently conducted genomic linkages included OncotypeDX Breast Recurrence Score, OncotypeDX Breast Ductal Carcinoma in Situ, OncotypeDX Genomic Prostate Score, Decipher Prostate Genomic Classifier, DecisionDX Uveal Melanoma, DecisionDX Preferentially Expressed Antigen in Melanoma, DecisionDX Melanoma, and germline tests results in Georgia and California SEER registries.

CONCLUSIONS

The linkages of cancer cases from SEER registries with genomic test results obtained from molecular laboratories offer an effective approach for data collection in cancer surveillance. By providing de-identified data to the research community, the NCI's SEER Program enables scientists to investigate numerous research inquiries.

What is cancer? A focus on Grade Group 1 prostate cancer

Since the widespread adoption of prostate-specific antigen-based screening for prostate cancer, the prevalence of Grade Group 1 (GG1) prostate cancer has risen. Historically, these patients were subjected to overtreatment of this otherwise indolent disease process, leading to significant quality-of-life detriments. Active surveillance as a primary management strategy has allowed for a focus on early detection while minimising morbidity from unnecessary intervention. Here we provide a comprehensive overview of the characteristics of GG1 prostatic adenocarcinoma, including its histological features, genomic differentiators, clinical progression, and implications for treatment guidelines, all supporting the movement to reclassify GG1 disease as a non-cancerous entity.

A Pilot Study on Patient-specific Computational Forecasting of Prostate Cancer Growth during Active Surveillance Using an Imaging-informed Biomechanistic Model

Active surveillance (AS) is a suitable management option for newly diagnosed prostate cancer, which usually presents low to intermediate clinical risk. Patients enrolled in AS have their tumor monitored via longitudinal multiparametric MRI (mpMRI), PSA tests, and biopsies. Hence, treatment is prescribed when these tests identify progression to higher-risk prostate cancer. However, current AS protocols rely on detecting tumor progression through direct observation according to population-based monitoring strategies. This approach limits the design of patient-specific AS plans and may delay the detection of tumor progression. Here, we present a pilot study to address these issues by leveraging personalized computational predictions of prostate cancer growth. Our forecasts are obtained with a spatiotemporal biomechanistic model informed by patient-specific longitudinal mpMRI data (T2-weighted MRI and apparent diffusion coefficient maps from diffusion-weighted MRI). Our results show that our technology can represent and forecast the global tumor burden for individual patients, achieving concordance correlation coefficients from 0.93 to 0.99 across our cohort (n = 7). In addition, we identify a model-based biomarker of higher-risk prostate cancer: the mean proliferation activity of the tumor (P = 0.041). Using logistic regression, we construct a prostate cancer risk classifier based on this biomarker that achieves an area under the ROC curve of 0.83. We further show that coupling our tumor forecasts with this prostate cancer risk classifier enables the early identification of prostate cancer progression to higher-risk disease by more than 1 year. Thus, we posit that our predictive technology constitutes a promising clinical decision-making tool to design personalized AS plans for patients with prostate cancer.

SIGNIFICANCE

Personalization of a biomechanistic model of prostate cancer with mpMRI data enables the prediction of tumor progression, thereby showing promise to guide clinical decision-making during AS for each individual patient.

Artificial intelligence applications in prostate cancer

Artificial intelligence (AI) applications have enabled remarkable advancements in healthcare delivery. These AI tools are often aimed to improve accuracy and efficiency of histopathology assessment and diagnostic imaging interpretation, risk stratification (i.e., prognostication), and prediction of therapeutic benefit for personalized treatment recommendations. To date, multiple AI algorithms have been explored for prostate cancer to address automation of clinical workflow, integration of data from multiple domains in the decision-making process, and the generation of diagnostic, prognostic, and predictive biomarkers. While many studies remain within the pre-clinical space or lack validation, the last few years have witnessed the emergence of robust AI-based biomarkers validated on thousands of patients, and the prospective deployment of clinically-integrated workflows for automated radiation therapy design. To advance the field forward, multi-institutional and multi-disciplinary collaborations are needed in order to prospectively implement interoperable and accountable AI technology routinely in clinic.

The Impact of Positron Emission Tomography Imaging and Tumor Molecular Profiling on Risk Stratification, Treatment Choice, and Oncological Outcomes of Patients with Primary or Relapsed Prostate Cancer: An International Collaborative Review of the Existing Literature

CONTEXT

The clinical introduction of next-generation imaging methods and molecular biomarkers ("radiogenomics") has revolutionized the field of prostate cancer (PCa). While the clinical validity of these tests has thoroughly been vetted, their clinical utility remains a matter of investigation.

OBJECTIVE

To systematically review the evidence to date on the impact of positron emission tomography (PET) imaging and tissue-based prognostic biomarkers, including Decipher, Prolaris, and Oncotype Dx, on the risk stratification, treatment choice, and oncological outcomes of men with newly diagnosed PCa or those with biochemical failure (BCF).

EVIDENCE ACQUISITION

We performed a quantitative systematic review of the literature using the MEDLINE, EMBASE, and Web of Science databases (2010-2022) following the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement guidelines. The validated Quality Assessment of Diagnostic Accuracy Studies 2 scoring system was used to assess the risk of bias.

EVIDENCE SYNTHESIS

A total of 148 studies (130 on PET and 18 on biomarkers) were included. In the primary PCa setting, prostate-specific membrane antigen (PSMA) PET imaging was not useful in improving T staging, moderately useful in improving N staging, but consistently useful in improving M staging in patients with National Comprehensive Cancer Network (NCCN) unfavorable intermediate- to very-high-risk PCa. Its use led to a management change in 20-30% of patients. However, the effect of these treatment changes on survival outcomes was not clear. Similarly, biomarkers in the pretherapy primary PCa setting increased and decreased the risk, respectively, in 7-30% and 32-36% of NCCN low-risk and 31-65% and 4-15% of NCCN favorable intermediate-risk patients being considered for active surveillance. A change in management was noted in up to 65% of patients, with the change being in line with the molecular risk-based reclassification, but again, the impact of these changes on survival outcomes remained unclear. Notably, in the postsurgical primary PCa setting, biomarker-guided adjuvant radiation therapy (RT) was associated with improved oncological control: Δ↓ 2-yr BCF by 22% (level 2b). In the BCF setting, the data were more mature. PSMA PET was consistently useful in improving disease localization-Δ↑ detection for T, N, and M staging was 13-32%, 19-58%, and 9-29%, respectively. Between 29% and 73% of patients had a change in management. Most importantly, these management changes were associated with improved survival outcomes in three trials: Δ↑ 4-yr disease-free survival by 24.3%, Δ↑ 6-mo metastasis-free survival (MFS) by 46.7%, and Δ↑ androgen deprivation therapy-free survival by 8 mo in patients who received PET-concordant RT (level 1b-2b). Biomarker testing in these patients also appeared to be helpful in risk stratifying and guiding the use of early salvage RT (sRT) and concomitant hormonal therapy. Patients with high-genomic-risk scores benefitted from treatment intensification: Δ↑ 8-yr MFS by 20% with the use of early sRT and Δ↑ 12-yr MFS by 11.2% with the use of hormonal therapy alongside early sRT, while low-genomic-risk score patients did equally well with initial conservative management (level 3).

CONCLUSIONS

Both PSMA PET imaging and tumor molecular profiling provide actionable information in the management of men with primary PCa and those with BCF. Emerging data suggest that radiogenomics-guided treatments translate into direct survival benefits for patients, however, additional prospective data are awaited.

PATIENT SUMMARY

In this review, we evaluated the utility of prostate-specific membrane antigen positron emission tomography and tumor molecular profiling in guiding the care of men with prostate cancer (PCa). We found that these tests augmented risk stratification, altered management, and improved cancer control in men with a new diagnosis of PCa or for those experiencing a relapse.

How to Integrate Prostate Cancer Biomarkers in Urology Clinical Practice: An Update

Nowadays, the management of prostate cancer has become more and more challenging due to the increasing number of available treatment options, therapeutic agents, and our understanding of its carcinogenesis and disease progression. Moreover, currently available risk stratification systems used to facilitate clinical decision-making have limitations, particularly in providing a personalized and patient-centered management strategy. Although prognosis and prostate cancer-specific survival have improved in recent years, the heterogenous behavior of the disease among patients included in the same risk prognostic group negatively impacts not only our clinical decision-making but also oncological outcomes, irrespective of the treatment strategy. Several biomarkers, along with available tests, have been developed to help clinicians in difficult decision-making scenarios and guide management strategies. In this review article, we focus on the scientific evidence that supports the clinical use of several biomarkers considered by professional urological societies (and included in uro-oncological guidelines) in the diagnosis process and specific difficult management strategies for clinically localized or advanced prostate cancer.

Genomic Biomarker Discovery in Disease Progression and Therapy Response in Bladder Cancer Utilizing Machine Learning

Cancer in all its forms of expression is a major cause of death. To identify the genomic reason behind cancer, discovery of biomarkers is needed. In this paper, genomic data of bladder cancer are examined for the purpose of biomarker discovery. Genomic biomarkers are indicators stemming from the study of the genome, either at a very low level based on the genome sequence itself, or more abstractly such as measuring the level of gene expression for different disease groups. The latter method is pivotal for this work, since the available datasets consist of RNA sequencing data, transformed to gene expression levels, as well as data on a multitude of clinical indicators. Based on this, various methods are utilized such as statistical modeling via logistic regression and regularization techniques (elastic-net), clustering, survival analysis through Kaplan-Meier curves, and heatmaps for the experiments leading to biomarker discovery. The experiments have led to the discovery of two gene signatures capable of predicting therapy response and disease progression with considerable accuracy for bladder cancer patients which correlates well with clinical indicators such as Therapy Response and T-Stage at surgery with Disease Progression in a time-to-event manner.

Can We Predict Prostate Cancer Metastasis Based on Biomarkers? Where Are We Now?

The incidence of prostate cancer (PC) has risen annually. PC mortality is explained by the metastatic disease (mPC). There is an intermediate scenario in which patients have non-mPC but have initiated a metastatic cascade through epithelial-mesenchymal transition. There is indeed a need for more and better tools to predict which patients will progress in the future to non-localized clinical disease or already have micrometastatic disease and, therefore, will clinically progress after primary treatment. Biomarkers for the prediction of mPC are still under development; there are few studies and not much evidence of their usefulness. This review is focused on tissue-based genomic biomarkers (TBGB) for the prediction of metastatic disease. We develop four main research questions that we attempt to answer according to the current evidence. Why is it important to predict metastatic disease? Which tests are available to predict metastatic disease? What impact should there be on clinical guidelines and clinical practice in predicting metastatic disease? What are the current prostate cancer treatments? The importance of predicting metastasis is fundamental given that, once metastasis is diagnosed, quality of life (QoL) and survival drop dramatically. There is still a need and space for more cost-effective TBGB tests that predict mPC disease.

A harmonized resource of integrated prostate cancer clinical, -omic, and signature features

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or  not standardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

Histology Specific Molecular Biomarkers: Ushering in a New Era of Precision Radiation Oncology

Histopathology and clinical staging have historically formed the backbone for allocation of treatment decisions in oncology. Although this has provided an extremely practical and fruitful approach for decades, it has long been evident that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. As efficient and affordable DNA and RNA sequencing have become available, the ability to provide precision therapy has become within grasp. This has been realized with systemic oncologic therapy, as targeted therapies have demonstrated immense promise for subsets of patients with oncogene-driver mutations. Further, several studies have evaluated predictive biomarkers for response to systemic therapy within a variety of malignancies. Within radiation oncology, the use of genomics/transcriptomics to guide the use, dose, and fractionation of radiation therapy is rapidly evolving but still in its infancy. The genomic adjusted radiation dose/radiation sensitivity index is one such early and exciting effort to provide genomically guided radiation dosing with a pan-cancer approach. In addition to this broad method, a histology specific approach to precision radiation therapy is also underway. Herein we review select literature surrounding the use of histology specific, molecular biomarkers to allow for precision radiotherapy with the greatest emphasis on commercially available and prospectively validated biomarkers.

Outcomes of Grade Group 2 and 3 Prostate Cancer on Initial Versus Confirmatory Biopsy: Implications for Active Surveillance

BACKGROUND

Active surveillance (AS) is recommended as the preferred treatment for men with low-risk disease. In order to optimize risk stratification and exclude undiagnosed higher-grade disease, most AS protocols recommend a confirmatory biopsy.

OBJECTIVE

We aimed to compare outcomes among men with grade group (GG) 2/3 prostate cancer on initial biopsy with those among men whose disease was initially GG1 but was upgraded to GG2/3 on confirmatory biopsy.

DESIGN, SETTING, AND PARTICIPANTS

We reviewed patients undergoing radical prostatectomy (RP) in two cohorts: "immediate RP group," with GG2/3 cancer on diagnostic biopsy, and "AS group," with GG1 cancer on initial biopsy that was upgraded to GG2/3 on confirmatory biopsy.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Probabilities of biochemical recurrence (BCR) and salvage therapy were determined using multivariable Cox regression models with risk adjustment. Risks of adverse pathology at RP were also compared using logistic regression.

RESULTS AND LIMITATIONS

The immediate RP group comprised 4009 patients and the AS group comprised 321 patients. The AS group had lower adjusted rates of adverse pathology (27% vs 35%, p = 0.003). BCR rates were lower in the AS group, although this did not reach conventional significance (hazard ratio [HR] 0.73, 95% confidence interval [CI] 0.50-1.06, p = 0.10) compared with the immediate RP group. Risk-adjusted 1- and 5-yr BCR rates were 4.6% (95% CI 3.0-6.5%) and 10.4% (95% CI 6.9-14%), respectively, for the AS group compared with 6.3% (95% CI 5.6-7.0%) and 20% (95% CI 19-22%), respectively, in the immediate RP group. A nonsignificant association was observed for salvage treatment-free survival favoring the AS group (HR 0.67, 95% CI 0.42, 1.06, p = 0.087).

CONCLUSIONS

We found that men with GG1 cancer who were upgraded on confirmatory biopsy tend to have less aggressive disease than men with the same grade found at initial biopsy. These results must be confirmed in larger series before recommendations can be made regarding a more conservative approach in men with upgraded pathology on surveillance biopsy.

PATIENT SUMMARY

We studied men with low-risk prostate cancer who were initially eligible for active surveillance but presented with more aggressive cancer on confirmatory biopsy. We found that outcomes for these men were better than the outcomes for those diagnosed initially with more serious cancer.

Radiogenomic profiling of prostate tumors prior to external beam radiotherapy converges on a transcriptomic signature of TGF-β activity driving tumor recurrence

Background

Patients with localized prostate cancer have historically been assigned to clinical risk groups based on local disease extent, serum prostate specific antigen (PSA), and tumor grade. Clinical risk grouping is used to determine the intensity of treatment with external beam radiotherapy (EBRT) and androgen deprivation therapy (ADT), yet a substantial proportion of patients with intermediate and high risk localized prostate cancer will develop biochemical recurrence (BCR) and require salvage therapy. Prospective identification of patients destined to experience BCR would allow treatment intensification or selection of alternative therapeutic strategies.

Methods

Twenty-nine individuals with intermediate or high risk prostate cancer were prospectively recruited to a clinical trial designed to profile the molecular and imaging features of prostate cancer in patients undergoing EBRT and ADT. Whole transcriptome cDNA microarray and whole exome sequencing were performed on pretreatment targeted biopsy of prostate tumors (n=60). All patients underwent pretreatment and 6-month post EBRT multiparametric MRI (mpMRI), and were followed with serial PSA to assess presence or absence of BCR. Genes differentially expressed in the tumor of patients with and without BCR were investigated using pathways analysis tools and were similarly explored in alternative datasets. Differential gene expression and predicted pathway activation were evaluated in relation to tumor response on mpMRI and tumor genomic profile. A novel TGF-β gene signature was developed in the discovery dataset and applied to a validation dataset.

Findings

Baseline MRI lesion volume and PTEN/TP53 status in prostate tumor biopsies correlated with the activation state of TGF-β signaling measured using pathway analysis. All three measures correlated with the risk of BCR after definitive RT. A prostate cancer-specific TGF-β signature discriminated between patients that experienced BCR vs. those that did not. The signature retained prognostic utility in an independent cohort.

Interpretation

TGF-β activity is a dominant feature of intermediate-to-unfavorable risk prostate tumors prone to biochemical failure after EBRT with ADT. TGF-β activity may serve as a prognostic biomarker independent of existing risk factors and clinical decision-making criteria.

Funding

This research was supported by the Prostate Cancer Foundation, the Department of Defense Congressionally Directed Medical Research Program, National Cancer Institute, and the Intramural Research Program of the NIH, National Cancer Institute, Center for Cancer Research.

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Genomic and transcriptomic data have been generated across a wide range of prostate cancer (PCa) study cohorts. These data can be used to better characterize the molecular features associated with clinical outcomes and to test hypotheses across multiple, independent patient cohorts. In addition, derived features, such as estimates of cell composition, risk scores, and androgen receptor (AR) scores, can be used to develop novel hypotheses leveraging existing multi-omic datasets. The full potential of such data is yet to be realized as independent datasets exist in different repositories, have been processed using different pipelines, and derived and clinical features are often not provided or unstandardized. Here, we present the curatedPCaData R package, a harmonized data resource representing >2900 primary tumor, >200 normal tissue, and >500 metastatic PCa samples across 19 datasets processed using standardized pipelines with updated gene annotations. We show that meta-analysis across harmonized studies has great potential for robust and clinically meaningful insights. curatedPCaData is an open and accessible community resource with code made available for reproducibility.

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.

Molecular Genetics of Prostate Cancer and Role of Genomic Testing

Prostate cancer (PCa) is characterized by profound genomic heterogeneity. Recent advances in personalized treatment entail an increasing need of genomic profiling. For localized PCa, gene expression assays can support clinical decisions regarding active surveillance and adjuvant treatment. In metastatic PCa, homologous recombination deficiency, microsatellite instability-high (MSI-H), and CDK12 deficiency constitute main actionable alterations. Alterations in DNA repair genes confer variable sensitivities to poly(ADP-ribose)polymerase inhibitors, and the use of genomic instability assays as predictive biomarker is still incipient. MSI can be assessed by immunohistochemistry To date there is a lack of consensus as to testing standards.

Transcriptomic Features of Cribriform and Intraductal Carcinoma of the Prostate

BACKGROUND

Cribriform (CF) and/or intraductal carcinoma (IDC) are associated with more aggressive prostate cancer (CaP) and worse outcomes.

OBJECTIVE

The transcriptomic features that typify CF/IDC are not well described and the capacity for clinically utilized genomic classifiers to improve risk modeling for CF/IDC remains undefined.

DESIGN, SETTING, AND PARTICIPANTS

We performed a retrospective review of CaP patients who had Decipher testing at a single high-volume institution. Index lesions from radical prostatectomy specimens were identified by genitourinary pathologists who simultaneously reviewed prostatectomy specimens for the presence of CF and IDC features. Patients were grouped based on pathologic features, specifically the absence of CF/IDC (CF-/IDC-), CF positive only (CF+/IDC-), and CF/IDC positive (CF+/IDC+).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Clinical, pathologic, and genomic categorical variables were assessed using the Pearson chi-square test, while quantitative variables were assessed with the Kruskal-Wallis test. Multivariable logistic regression was used to identify the predictors of high-risk Decipher scores (>0.60). A gene set enrichment analysis was performed to identify genes and gene networks associated with CF/IDC status.

RESULTS AND LIMITATIONS

A total of 463 patients were included. Patients who were CF+/IDC+ had the highest Decipher risk scores (CF+/IDC+: 0.79 vs CF+/IDC-: 0.71 vs CF-/IDC-: 0.56, p < 0.001). On multivariate logistic regression, predictors of high-risk Decipher scores included the presence of CF, both alone (CF+/IDC-; odds ratio [OR]: 5.45, p < 0.001) or in combination with positive IDC status (CF+/IDC+; OR: 6.87, p < 0.001). On the gene set enrichment analysis, MYC pathway upregulation was significantly enriched in tumor samples from CF/IDC-positive patients (normalized enrichment score [NES]: 1.65, p = 0.046). Other enriched pathways included E2F targets (NES: 1.69, p = 0.031) and oxidative phosphorylation (NES: 1.68, =0 .033).

CONCLUSIONS

This is the largest series identifying an association between a clinically validated genomic classifier and the presence of CF and IDC at radical prostatectomy. Tumors with CF and intraductal features were associated with aggressive transcriptomic signatures.

PATIENT SUMMARY

Genomic-based tests are becoming readily available for the management of prostate cancer. We observed that Decipher, a commonly used genomic test in prostate cancer, correlates with unfavorable features in tissue specimens.

Active Surveillance in Intermediate-Risk Prostate Cancer: A Review of the Current Data

Simple Summary

AS is an option for the initial management of selected patients with intermediate-risk PC. The proper way to predict which men will have an aggressive clinical course or indolent PC who would benefit from AS has not been unveiled. Genetics and MRI can help in the decision-making, but it remains unclear which men would benefit from which tests. In addition, there are several differences between AS protocols in inclusion criteria, monitoring follow-up, and triggers for active treatment. Large series and a few RCTs are under investigation, and more research is needed to establish an optimal therapeutic strategy for patients with intermediate-risk PC. This study summarizes the current data on patients with intermediate-risk PC under AS, recent findings, and discusses future directions.

Abstract

Active surveillance (AS) is a monitoring strategy to avoid or defer curative treatment, minimizing the side effects of radiotherapy and prostatectomy without compromising survival. AS in intermediate-risk prostate cancer (PC) has increasingly become used. There is heterogeneity in intermediate-risk PC patients. Some of them have an aggressive clinical course and require active treatment, while others have indolent disease and may benefit from AS. However, intermediate-risk patients have an increased risk of metastasis, and the proper way to select the best candidates for AS is unknown. In addition, there are several differences between AS protocols in inclusion criteria, monitoring follow-up, and triggers for active treatment. A few large series and randomized trials are under investigation. Therefore, more research is needed to establish an optimal therapeutic strategy for patients with intermediate-risk disease. This study summarizes the current data on patients with intermediate-risk PC under AS, recent findings, and discusses future directions.

Genomic biomarkers to guide precision radiotherapy in prostate cancer

Our ability to prognosticate the clinical course of patients with cancer has historically been limited to clinical, histopathological, and radiographic features. It has long been clear however, that these data alone do not adequately capture the heterogeneity and breadth of disease trajectories experienced by patients. The advent of efficient genomic sequencing has led to a revolution in cancer care as we try to understand and personalize treatment specific to patient clinico-genomic phenotypes. Within prostate cancer, emerging evidence suggests that tumor genomics (e.g., DNA, RNA, and epigenetics) can be utilized to inform clinical decision making. In addition to providing discriminatory information about prognosis, it is likely tumor genomics also hold a key in predicting response to oncologic therapies which could be used to further tailor treatment recommendations. Herein we review select literature surrounding the use of tumor genomics within the management of prostate cancer, specifically leaning toward analytically validated and clinically tested genomic biomarkers utilized in radiotherapy and/or adjunctive therapies given with radiotherapy.

Prostate Surface Distension and Tumor Texture Descriptors From Pre-Treatment MRI Are Associated With Biochemical Recurrence Following Radical Prostatectomy: Preliminary Findings

Objective

To derive and evaluate the association of prostate shape distension descriptors from T2-weighted MRI (T2WI) with prostate cancer (PCa) biochemical recurrence (BCR) post-radical prostatectomy (RP) independently and in conjunction with texture radiomics of PCa.

Methods

This retrospective study comprised 133 PCa patients from two institutions who underwent 3T-MRI prior to RP and were followed up with PSA measurements for ≥3 years. A 3D shape atlas-based approach was adopted to derive prostate shape distension descriptors from T2WI, and these descriptors were used to train a random forest classifier (C_S) to predict BCR. Texture radiomics was derived within PCa regions of interest from T2WI and ADC maps, and another machine learning classifier (C_R) was trained for BCR. An integrated classifier C_S+R was then trained using predictions from C_S and C_R. These models were trained on D₁ (N = 71, 27 BCR+) and evaluated on independent hold-out set D₂ (N = 62, 12 BCR+). C_S+R was compared against pre-RP, post-RP clinical variables, and extant nomograms for BCR-free survival (bFS) at 3 years.

Results

C_S+R resulted in a higher AUC (0.75) compared to C_R (0.70, p = 0.04) and C_S (0.69, p = 0.01) on D₂ in predicting BCR. On univariable analysis, C_S+R achieved a higher hazard ratio (2.89, 95% CI 0.35–12.81, p < 0.01) compared to other pre-RP clinical variables for bFS. C_S+R, pathologic Gleason grade, extraprostatic extension, and positive surgical margins were associated with bFS (p < 0.05). C_S+R resulted in a higher C-index (0.76 ± 0.06) compared to CAPRA (0.69 ± 0.09, p < 0.01) and Decipher risk (0.59 ± 0.06, p < 0.01); however, it was comparable to post-RP CAPRA-S (0.75 ± 0.02, p = 0.07).

Conclusions

Radiomic shape descriptors quantifying prostate surface distension complement texture radiomics of prostate cancer on MRI and result in an improved association with biochemical recurrence post-radical prostatectomy.

Impact of Decipher Biopsy testing on clinical outcomes in localized prostate cancer in a prospective statewide collaborative

BACKGROUND

Decipher Biopsy is a commercially available gene expression classifier used in risk stratification of newly diagnosed prostate cancer (PCa). Currently, there are no prospective data evaluating its clinical utility. We seek to assess the clinical utility of Decipher Biopsy in localized PCa patients.

METHODS

A multi-institutional study of 855 men who underwent Decipher Biopsy testing between February 2015 and October 2019. All patients were tracked through the prospective Michigan Urological Surgery Improvement Collaborative and linked to the Decipher Genomics Resource Information Database (GRID^®; NCT02609269). Patient matching was performed by an independent third-party (ArborMetrix Inc.) using two or more unique identifiers. Cumulative incidence curves for time to treatment (TTT) and time to failure (TTF) were constructed using Kaplan-Meier estimates. Multivariable Cox proportional hazard models were used to evaluate the independent association of high-risk Decipher scores with the conversion from AS to radical therapy and treatment failure (biochemical failure or receipt of salvage therapy).

RESULTS AND LIMITATIONS

Eight hundred fifty-five patients underwent Decipher Biopsy testing during the study period. Of the 855 men, 264 proceeded to AS (31%), and 454 (53%) received radical therapy. In men electing AS, after adjusting for NCCN risk group, age, PSA, prostate volume, body mass index, and percent positive cores, a high-risk Decipher score was independently associated with shorter TTT (HR 2.51, 95% CI 1.52-4.13 p < 0.001). Similarly, in patients that underwent radical therapy, a high-risk Decipher score was independently associated with TTF (HR 2.98, 95% CI 1.22-7.29, p = 0.01) on multivariable analysis. Follow-up time was a limitation.

CONCLUSION

In a prospective statewide registry, high-risk Decipher Biopsy score was strongly and independently associated with conversion from AS to definitive treatment and treatment failure. These real-world data support the clinical utility of Decipher Biopsy. An ongoing phase 3 randomized trial (NCT04396808) will provide level 1 evidence of the clinical impact of Decipher biopsy testing.

Optimal Use of Tumor-Based Molecular Assays for Localized Prostate Cancer

PURPOSEOF REVIEW

The use of genomic testing for prostate cancer continues to grow; however, utilization remains institutionally dependent. Herein, we review current tissue-based markers and comment on current use with active surveillance and prostate MRI.

RECENT FINDINGS

While data continues to emerge, several studies have shown a role for genomic testing for treatment selection. Novel testing options include ConfirmMDx, ProMark, Prolaris, and Decipher, which have shown utility in select patients. The current body of literature on this specific topic remains very limited; prospective trials with long-term follow-up are needed to improve our understanding on how these genomic tests fit when combined with our current clinical tools. As the literature matures, it is likely that newer risk calculators that combine our classic clinical variables with genomic and imaging data will be developed to bring about standard protocols for prostate cancer decision-making.

Active surveillance for intermediate-risk prostate cancer

INTRODUCTION

Active surveillance (AS) is an established approach in the management of low-risk, localized prostate cancer. While the use of AS to manage intermediate-risk (IR) disease has gradually increased over time, there remains uncertainty with regards to its safety, with only a minority of IR patients currently being managed with this approach.

MATERIALS AND METHODS

We conducted a narrative review based on an analysis of the literature focusing on articles describing AS for IR prostate cancer. We focus on the uncertainty surrounding AS in IR disease by discussing variations in the definitions and guideline recommendations associated with IR disease, and describing the limitations of the evidence from observational studies and randomized trials.

CONCLUSION

The safety of AS for IR disease remains unknown, given the lack of randomized trials and the limitations of the current observational studies. Further research is needed to identify select patients with IR prostate cancer that can be managed safely with AS.

Biomarkers for prostate cancer detection and risk stratification

Although prostate cancer (PCa) is the most commonly diagnosed cancer in men, most patients do not die from the disease. Prostate specific antigen (PSA), the most widely used oncologic biomarker, has revolutionized screening and early detection, resulting in reduced proportion of patients presenting with advanced disease. However, given the inherent limitations of PSA, additional diagnostic and prognostic tools are needed to facilitate early detection and accurate risk stratification of disease. Serum, urine, and tissue-based biomarkers are increasingly being incorporated into the clinical care paradigm, but there is still a limited understanding of how to use them most effectively. In the current article, we review test characteristics and clinical performance data for both serum [4 K score, prostate health index (phi)] and urine [SelectMDx, ExoDx Prostate Intelliscore, MyProstateScore (MPS), and PCa antigen 3 (PCA3)] biomarkers to aid decisions regarding initial or repeat biopsies as well as tissue-based biomarkers (Confirm MDx, Decipher, Oncotype Dx, and Polaris) aimed at risk stratifying patients and identifying those patients most likely to benefit from treatment versus surveillance or monotherapy versus multi-modal therapy.

Validating the Association of Adverse Pathology with Distant Metastasis and Prostate Cancer Mortality 20-Years After Radical Prostatectomy

PURPOSE

To assess the association of adverse pathology (AP), defined as high-grade (≥ Gleason Grade Group 3) and/or non-organ confined disease, with long-term oncologic outcomes after radical prostatectomy (RP).

MATERIALS AND METHODS

Using a stratified cohort sampling design, we evaluated the association of AP with the risk of distant metastasis (DM) and prostate cancer-specific mortality (PCSM) up to 20 years after RP in 428 patients treated between 1987 to 2004. Cox regression of cause-specific hazards was used to estimate the absolute risk of both endpoints, with death from other causes treated as a competing risk. Additionally, subgroup analysis in patients with low and/or intermediate-risk disease, who are potentially eligible for active surveillance (AS), was performed.

RESULTS

Within the cohort sample, 53% of men exhibited AP at time of RP, with median follow up of 15.5 years (IQR 14.6-16.6 years) thereafter. Adverse pathology was highly associated with DM and PCSM in the overall cohort (HR 12.30, 95% confidence interval [CI] 5.30-28.55, and HR 10.03, 95% CI 3.42-29.47, respectively, both P < 0.001). Adverse pathology was also highly associated with DM and PCSM in the low/intermediate-risk subgroup (HR 10.48, 95% CI 4.18-26.28, and 8.60, 95% CI 2.40-30.48, respectively, both P < 0.001).

CONCLUSIONS

Adverse pathology at the time of RP is highly associated with future development of DM and PCSM. Accurate prediction of AP may thus be useful for individualizing risk-based surveillance and treatment strategies.

Non-invasive Urine Test for Molecular Classification of Clinical Significance in Newly Diagnosed Prostate Cancer Patients

Objective: To avoid over-treatment of low-risk prostate cancer patients, it is important to identify clinically significant and insignificant cancer for treatment decision-making. However, no accurate test is currently available.

Methods: To address this unmet medical need, we developed a novel gene classifier to distinguish clinically significant and insignificant cancer, which were classified based on the National Comprehensive Cancer Network risk stratification guidelines. A non-invasive urine test was developed using quantitative mRNA expression data of 24 genes in the classifier with an algorithm to stratify the clinical significance of the cancer. Two independent, multicenter, retrospective and prospective studies were conducted to assess the diagnostic performance of the 24-Gene Classifier and the current clinicopathological measures by univariate and multivariate logistic regression and discriminant analysis. In addition, assessments were performed in various Gleason grades/ISUP Grade Groups.

Results: The results showed high diagnostic accuracy of the 24-Gene Classifier with an AUC of 0.917 (95% CI 0.892–0.942) in the retrospective cohort (n = 520), AUC of 0.959 (95% CI 0.935–0.983) in the prospective cohort (n = 207), and AUC of 0.930 (95% 0.912-CI 0.947) in the combination cohort (n = 727). Univariate and multivariate analysis showed that the 24-Gene Classifier was more accurate than cancer stage, Gleason score, and PSA, especially in the low/intermediate-grade/ISUP Grade Group 1–3 cancer subgroups.

Conclusions: The 24-Gene Classifier urine test is an accurate and non-invasive liquid biopsy method for identifying clinically significant prostate cancer in newly diagnosed cancer patients. It has the potential to improve prostate cancer treatment decisions and active surveillance.

Biomarker in Active Surveillance for Prostate Cancer: A Systematic Review

Active surveillance (AS) in prostate cancer (PCa) represents a curative alternative for men with localised low-risk PCa. Continuous improvement of AS patient's selection and surveillance modalities aims at reducing misclassification, simplifying modalities of surveillance and decreasing need for invasive procedures such repeated biopsies. Biomarkers represent interesting tools to evaluate PCa diagnosis and prognosis, of which many are readily available or under evaluation. The aim of this review is to investigate the biomarker performance for AS selection and patient outcome prediction. Blood, urinary and tissue biomarkers were studied and a brief description of use was proposed along with a summary of major findings. Biomarkers represent promising tools which could be part of a more tailored risk AS strategy aiming to offer personalized medicine and to individualize the treatment and monitoring of each patient. The usefulness of biomarkers has mainly been suggested for AS selection, whereas few studies have investigated their role during the monitoring phase. Randomized prospective studies dealing with imaging are needed as well as larger prospective studies with long-term follow-up and strong oncologic endpoints.

Tissue- and Liquid-Based Biomarkers in Prostate Cancer Precision Medicine

Worldwide, prostate cancer (PC) is the second-most-frequently diagnosed male cancer and the fifth-most-common cause of all cancer-related deaths. Suspicion of PC in a patient is largely based upon clinical signs and the use of prostate-specific antigen (PSA) levels. Although PSA levels have been criticised for a lack of specificity, leading to PC over-diagnosis, it is still the most commonly used biomarker in PC management. Unfortunately, PC is extremely heterogeneous, and it can be difficult to stratify patients whose tumours are unlikely to progress from those that are aggressive and require treatment intensification. Although PC-specific biomarker research has previously focused on disease diagnosis, there is an unmet clinical need for novel prognostic, predictive and treatment response biomarkers that can be used to provide a precision medicine approach to PC management. In particular, the identification of biomarkers at the time of screening/diagnosis that can provide an indication of disease aggressiveness is perhaps the greatest current unmet clinical need in PC management. Largely through advances in genomic and proteomic techniques, exciting pre-clinical and clinical research is continuing to identify potential tissue, blood and urine-based PC-specific biomarkers that may in the future supplement or replace current standard practices. In this review, we describe how PC-specific biomarker research is progressing, including the evolution of PSA-based tests and those novel assays that have gained clinical approval. We also describe alternative diagnostic biomarkers to PSA, in addition to biomarkers that can predict PC aggressiveness and biomarkers that can predict response to certain therapies. We believe that novel biomarker research has the potential to make significant improvements to the clinical management of this disease in the near future.

Active Surveillance in Prostate Cancer: Role of Available Biomarkers in Daily Practice

Prostate cancer (PCa) is the most commonly diagnosed cancer in men. The diagnosis is currently based on PSA levels, which are associated with overdiagnosis and overtreatment. Moreover, most PCas are localized tumours; hence, many patients with low-/very low-risk PCa could benefit from active surveillance (AS) programs instead of more aggressive, active treatments. Heterogeneity within inclusion criteria and follow-up strategies are the main controversial issues that AS presently faces. Many biomarkers are currently under investigation in this setting; however, none has yet demonstrated enough diagnostic ability as an independent predictor of pathological or clinical progression. This work aims to review the currently available literature on tissue, blood and urine biomarkers validated in clinical practice for the management of AS patients.

The 2019 Genitourinary Pathology Society (GUPS) White Paper on Contemporary Grading of Prostate Cancer

CONTEXT.—

Controversies and uncertainty persist in prostate cancer grading.

OBJECTIVE.—

To update grading recommendations.

DATA SOURCES.—

Critical review of the literature along with pathology and clinician surveys.

CONCLUSIONS.—

Percent Gleason pattern 4 (%GP4) is as follows: (1) report %GP4 in needle biopsy with Grade Groups (GrGp) 2 and 3, and in needle biopsy on other parts (jars) of lower grade in cases with at least 1 part showing Gleason score (GS) 4 + 4 = 8; and (2) report %GP4: less than 5% or less than 10% and 10% increments thereafter. Tertiary grade patterns are as follows: (1) replace "tertiary grade pattern" in radical prostatectomy (RP) with "minor tertiary pattern 5 (TP5)," and only use in RP with GrGp 2 or 3 with less than 5% Gleason pattern 5; and (2) minor TP5 is noted along with the GS, with the GrGp based on the GS. Global score and magnetic resonance imaging (MRI)-targeted biopsies are as follows: (1) when multiple undesignated cores are taken from a single MRI-targeted lesion, an overall grade for that lesion is given as if all the involved cores were one long core; and (2) if providing a global score, when different scores are found in the standard and the MRI-targeted biopsy, give a single global score (factoring both the systematic standard and the MRI-targeted positive cores). Grade Groups are as follows: (1) Grade Groups (GrGp) is the terminology adopted by major world organizations; and (2) retain GS 3 + 5 = 8 in GrGp 4. Cribriform carcinoma is as follows: (1) report the presence or absence of cribriform glands in biopsy and RP with Gleason pattern 4 carcinoma. Intraductal carcinoma (IDC-P) is as follows: (1) report IDC-P in biopsy and RP; (2) use criteria based on dense cribriform glands (>50% of the gland is composed of epithelium relative to luminal spaces) and/or solid nests and/or marked pleomorphism/necrosis; (3) it is not necessary to perform basal cell immunostains on biopsy and RP to identify IDC-P if the results would not change the overall (highest) GS/GrGp part per case; (4) do not include IDC-P in determining the final GS/GrGp on biopsy and/or RP; and (5) "atypical intraductal proliferation (AIP)" is preferred for an intraductal proliferation of prostatic secretory cells which shows a greater degree of architectural complexity and/or cytological atypia than typical high-grade prostatic intraepithelial neoplasia, yet falling short of the strict diagnostic threshold for IDC-P. Molecular testing is as follows: (1) Ki67 is not ready for routine clinical use; (2) additional studies of active surveillance cohorts are needed to establish the utility of PTEN in this setting; and (3) dedicated studies of RNA-based assays in active surveillance populations are needed to substantiate the utility of these expensive tests in this setting. Artificial intelligence and novel grading schema are as follows: (1) incorporating reactive stromal grade, percent GP4, minor tertiary GP5, and cribriform/intraductal carcinoma are not ready for adoption in current practice.

Current State of Personalized Genitourinary Cancer Radiotherapy in the Era of Precision Medicine

Radiation therapy plays a crucial role for the management of genitourinary malignancies, with technological advancements that have led to improvements in outcomes and decrease in treatment toxicities. However, better risk-stratification and identification of patients for appropriate treatments is necessary. Recent advancements in imaging and novel genomic techniques can provide additional individualized tumor and patient information to further inform and guide treatment decisions for genitourinary cancer patients. In addition, the development and use of targeted molecular therapies based on tumor biology can result in individualized treatment recommendations. In this review, we discuss the advances in precision oncology techniques along with current applications for personalized genitourinary cancer management. We also highlight the opportunities and challenges when applying precision medicine principles to the field of radiation oncology. The identification, development and validation of biomarkers has the potential to personalize radiation therapy for genitourinary malignancies so that we may improve treatment outcomes, decrease radiation-specific toxicities, and lead to better long-term quality of life for GU cancer survivors.

Transcript analysis of commercial prostate cancer risk stratification panels in hard-to-predict grade group 2-4 prostate cancers

BACKGROUND

Improved prognostication is needed to minimize overtreatment in grade group (GG) 2-4 prostate cancer. Our aim was to determine, at messenger RNA (mRNA) level, the performance of the genes in the commercial panels Decipher, Oncotype DX, Prolaris, and mutational panel MSK-IMPACT to predict metastasis-free and prostate cancer-specific death (PCSD) in patients with GG 2-4 prostate cancer at radical prostatectomy.

METHODS

The retrospective cohort consisted of GG 2-4 patients treated with radical prostatectomy (median follow-up 10.4 years). Seventy-six cases with postoperative metastasis or PCSD and 84 controls with similar clinical baseline risk, but without progression, were analyzed. Index lesion mRNA transcripts were analyzed using NanoString technology. Random forest models were trained using panel gene sets to predict clinical endpoints and area under the curve (AUC), sensitivity, specificity, Youden index, and number needed to diagnose (NND) was measured. Survival probability was assessed with Kaplan-Meier estimator.

RESULTS

All gene sets outperformed clinical parameters and predicted metastasis-free and prostate cancer-specific survival. However, there were significant differences between the panels. In metastasis prediction, the genes in Oncotype DX had inferior performance (area under the curve [AUC] = 0.65) compared to other panels (AUC = 0.73-0.74). Decipher, MSK-IMPACT and Prolaris showed similar NND (2.83-3.12) with Oncotype DX having highest NND (4.79). In PCSD prediction, the Prolaris gene set performed worse (AUC = 0.66) than MSK-IMPACT or Decipher (AUC = 0.72). Oncotype DX performed similarly to other panels (AUC = 0.69, p > .05). Oncotype DX demonstrated lowest NND (2.79) compared to other panels (4.22-5.66).

CONCLUSION

Transcript analysis of genes included in commercial panels is feasible in survival prediction of GG 2-4 patients after radical prostatectomy and may aid in clinical decision making. There were significant differences between the panels, and overall stronger predictive gene sets are needed. Prospective investigation is warranted in biopsy materials.

GPS Assay Association With Long-Term Cancer Outcomes: Twenty-Year Risk of Distant Metastasis and Prostate Cancer-Specific Mortality

PURPOSE

To assess the association between the Oncotype DX Genomic Prostate Score (GPS) result and long-term oncological outcomes following radical prostatectomy (RP).

METHODS

We evaluated the association of the GPS result assayed from the index lesion from RP tissue with the risk of distant metastases (DM) and prostate cancer–specific mortality (PCSM) over the 20 years following RP in a stratified cohort sample of 428 patients from 2,641 treated between 1987 and 2004. Cox regression of cause-specific hazards was used to estimate the absolute risk of both end points, with death from other causes treated as a competing risk. A correction for regression to the mean (RM) was applied since the GPS test was developed using this cohort. Exploratory analysis using presurgical parameters and the GPS test as prognostic variables was performed to assess the additional value of the GPS test on 20-year risk of DM and PCSM. Model discrimination was measured using the area under the receiver operating characteristic curve.

RESULTS

The GPS test appears to be independently associated with both 20-year risk of DM and PCSM with a low false discovery rate. Per 20-unit increase in GPS, multivariable analysis with RM correction estimated hazard ratios of 2.24 (95% CI, 1.49 to 3.53) and 2.30 (95% CI, 1.45 to 4.36) for DM and PCSM, respectively. Accuracy of models including clinical risk factors alone appeared to improve when including the GPS test in assessing risk of both end points.

CONCLUSION

The results suggest that the GPS test provides information on the risk for the meaningful long-term outcomes of DM and PCSM.

Grading Evolution and Contemporary Prognostic Biomarkers of Clinically Significant Prostate Cancer

Simple Summary

Prostate cancer treatment decisions are based on clinical stage and histological diagnosis, including Gleason grading assessed by a pathologist, in biopsies. Prior to staging and grading, serum or blood prostate-specific antigen (PSA) levels are measured and often trigger diagnostic examinations. However, PSA is best suited as a marker of cancer relapse after initial treatment. In this review, we first narratively describe the evolution of histological grading, the current status of Gleason pattern-based diagnostics and glance into future methodology of risk assessment by histological examination. In the second part, we systematically review the biomarkers that have been shown, independent from clinical characteristics, to correlate with clinically relevant end-points, i.e., occurrence of metastases, disease-specific mortality and overall survival after initial treatment of localized prostate cancer.

Abstract

Gleason grading remains the strongest prognostic parameter in localized prostate adenocarcinoma. We have here outlined the evolution and contemporary practices in pathological evaluation of prostate tissue samples for Gleason score and Grade group. The state of more observer-independent grading methods with the aid of artificial intelligence is also reviewed. Additionally, we conducted a systematic review of biomarkers that hold promise in adding independent prognostic or predictive value on top of clinical parameters, Grade group and PSA. We especially focused on hard end points during the follow-up, i.e., occurrence of metastasis, disease-specific mortality and overall mortality. In peripheral blood, biopsy-detected prostate cancer or in surgical specimens, we can conclude that there are more than sixty biomarkers that have been shown to have independent prognostic significance when adjusted to conventional risk assessment or grouping. Our search brought up some known putative markers and panels, as expected. Also, the synthesis in the systematic review indicated markers that ought to be further studied as part of prospective trials and in well characterized patient cohorts in order to increase the resolution of the current clinico-pathological prognostic factors.

A novel imaging based Nomogram for predicting post-surgical biochemical recurrence and adverse pathology of prostate cancer from pre-operative bi-parametric MRI

Background

We developed and validated an integrated radiomic-clinicopathologic nomogram (RadClip) for post-surgical biochemical recurrence free survival (bRFS) and adverse pathology (AP) prediction in men with prostate cancer (PCa). RadClip was further compared against extant prognostics tools like CAPRA and Decipher.

Methods

A retrospective study of 198 patients with PCa from four institutions who underwent pre-operative 3 Tesla MRI followed by radical prostatectomy, between 2009 and 2017 with a median 35-month follow-up was performed. Radiomic features were extracted from prostate cancer regions on bi-parametric magnetic resonance imaging (bpMRI). Cox Proportional-Hazards (CPH) model warped with minimum redundancy maximum relevance (MRMR) feature selection was employed to select bpMRI radiomic features for bRFS prediction in the training set (D₁, N = 71). In addition, a bpMRI radiomic risk score (RadS) and associated nomogram, RadClip, were constructed in D₁ and then compared against the Decipher, pre-operative (CAPRA), and post-operative (CAPRA-S) nomograms for bRFS and AP prediction in the testing set (D₂, N = 127).

Findings

“RadClip yielded a higher C-index (0.77, 95% CI 0.65-0.88) compared to CAPRA (0.68, 95% CI 0.57-0.8) and Decipher (0.51, 95% CI 0.33-0.69) and was found to be comparable to CAPRA-S (0.75, 95% CI 0.65-0.85). RadClip resulted in a higher AUC (0.71, 95% CI 0.62-0.81) for predicting AP compared to Decipher (0.66, 95% CI 0.56-0.77) and CAPRA (0.69, 95% CI 0.59-0.79).”

Interpretation

RadClip was more prognostic of bRFS and AP compared to Decipher and CAPRA. It could help pre-operatively identify PCa patients at low risk of biochemical recurrence and AP and who therefore might defer additional therapy.

Funding

The National Institutes of Health, the U.S. Department of Veterans Affairs, and the Department of Defense.

How should radiologists incorporate non-imaging prostate cancer biomarkers into daily practice?

OBJECTIVE

To review the current body of evidence surrounding non-imaging biomarkers in patients with known or suspected prostate cancer.

RESULTS

Several non-imaging biomarkers have been developed and are available that aim to improve risk estimates at several clinical junctures. For patients with suspicion of prostate cancer who are considering first-time or repeat biopsy, blood- and urine-based assays can improve the prediction of harboring clinically significant disease and may reduce unnecessary biopsy. Blood- and urine-based biomarkers have been evaluated in association with prostate MRI, offering insights that might augment decision-making in the pre and post-MRI setting. Tissue-based genomic and proteomic assays have also been developed that provide independent assessments of prostate cancer aggressiveness that can complement imaging.

CONCLUSION

A growing number of non-imaging biomarkers are available to assist in clinical decision-making for men with known or suspected prostate cancer. An appreciation for the intersection of imaging and biomarkers may improve clinical care and resource utilization for men with prostate cancer.

Report From the International Society of Urological Pathology (ISUP) Consultation Conference on Molecular Pathology of Urogenital Cancers. I. Molecular Biomarkers in Prostate Cancer

The combined clinical and molecular heterogeneity of prostate cancer necessitates the use of prognostic, predictive, and diagnostic biomarkers to assist the clinician with treatment selection. The pathologist plays a critical role in guiding molecular biomarker testing in prostate cancer and requires a thorough knowledge of the current testing options. In the setting of clinically localized prostate cancer, prognostic biomarkers such as Ki-67 labeling, PTEN loss or mRNA-based genomic signatures can be useful to help determine whether definitive therapy is required. In the setting of advanced disease, predictive biomarkers, such as the presence of DNA repair deficiency mediated by BRCA2 loss or mismatch repair gene defects, may suggest the utility of poly-ADP ribosylase inhibition or immune checkpoint blockade. Finally, androgen receptor-related biomarkers or diagnostic biomarkers indicating the presence of small cell neuroendocrine prostate cancer may help guide the use of androgen receptor signaling inhibitors and chemotherapy. In this review, we examine the current evidence for several prognostic, predictive and diagnostic tissue-based molecular biomarkers in prostate cancer management. For each assay, we summarize a recent survey of the International Society of Urology Pathology (ISUP) members on current testing practices and include recommendations for testing that emerged from the ISUP Working Group on Molecular Pathology of Prostate Cancer and the 2019 Consultation Conference on Molecular Pathology of Urogenital Cancers.

Genetic testing for the clinician in prostate cancer

INTRODUCTION

Prostate cancer (PCa) is one of the most common cancers worldwide and a leading cause of cancer-related mortality. Although the diagnosis and treatment of prostate cancer has improved substantially in recent years, new molecular biomarkers are needed to further prolong survival and improve the quality of life in these patients.

AREAS COVERED

This review analyzes the current evidence for prognostic and predictive molecular biomarkers that can be applied across different clinical scenarios, ranging from localized disease to metastatic castration-resistant PCa, with a particular emphasis on the biomarkers likely to become available in routine clinical practice in the near future.

EXPERT OPINION

There is a growing need for molecular testing to identify the most indolent types of prostate cancer to help optimize treatment strategies and spare treatment in these patients when possible. Current trends in the treatment of prostate cancer underscore the unmet clinical need for biomarkers to improve decision-making in a challenging clinical setting.

The development of apalutamide for the treatment of prostate cancer

INTRODUCTION

Prostate cancer progresses, despite androgen-deprivation therapy, the backbone of its treatment. This progression is mainly related to the androgen receptor (AR)-related mechanisms of resistance, and, several AR-targeted therapies have demonstrated benefit in metastatic and nonmetastatic disease. Apalutamide is a third-generation AR-targeted therapy which competitively blocks the AR and prevents AR dimerization, nuclear internalization, thereby avoiding cancer progression. Early studies have demonstrated that apalutamide was safe and demonstrated clinical benefit. Phase II and phase III studies had confirmed preliminary results of clinical benefit with apalutamide in patients with nonmetastatic castration-resistant prostate cancer (nmCRPC) and in metastatic hormone-sensitive prostate cancer (mHSPC).

AREAS COVERED

Herein, the authors discuss the development of apalutamide, from its discovery and early studies to phase III trials. They also examine new perspectives and biomarkers that may help oncologists to make decisions in patients taking apalutamide. Studies evaluating apalutamide in other settings and in combination with other therapies are also debated.

EXPERT OPINION

Apalutamide has become a relevant therapy for patients with nmCRPC and mHSPC for its benefit in delaying metastasis in addition to its improvement of overall survival, without compromising the quality of life. Apalutamide should be considered as a standard-of-care for patients with nmCRPC and patients with mHSPC.

Clinical Applications of Molecular Biomarkers in Prostate Cancer

There is clinically relevant molecular heterogeneity in prostate cancer (PCa), but this biological diversity has had only a minimal impact on clinical practice. Treatment outcomes in patients with localised PCa are often highly variable, even among patients stratified to the same risk group or disease state based on standard clinical and pathological parameters. In recent years, the development of gene panels has provided valuable data on the differential expression of genes in patients with PCa. Nevertheless, there is an urgent need to identify and validate prognostic and predictive biomarkers that can be applied across clinical scenarios, ranging from localised disease to metastatic castration-resistant PCa. The availability of such tools would allow for precision medicine to finally reach PCa patients. In this review, we evaluate current data on molecular biomarkers for PCa, with an emphasis on the biomarkers and gene panels with the most robust evidence to support their application in routine clinical practice.

Decipher identifies men with otherwise clinically favorable-intermediate risk disease who may not be good candidates for active surveillance

BACKGROUND

We aimed to validate Decipher to predict adverse pathology (AP) at radical prostatectomy (RP) in men with National Comprehensive Cancer Network (NCCN) favorable-intermediate risk (F-IR) prostate cancer (PCa), and to better select F-IR candidates for active surveillance (AS).

METHODS

In all, 647 patients diagnosed with NCCN very low/low risk (VL/LR) or F-IR prostate cancer were identified from a multi-institutional PCa biopsy database; all underwent RP with complete postoperative clinicopathological information and Decipher genomic risk scores. The performance of all risk assessment tools was evaluated using logistic regression model for the endpoint of AP, defined as grade group 3-5, pT3b or higher, or lymph node invasion.

RESULTS

The median age was 61 years (interquartile range 56-66) for 220 patients with NCCN F-IR disease, 53% classified as low-risk by Cancer of the Prostate Risk Assessment (CAPRA 0-2) and 47% as intermediate-risk (CAPRA 3-5). Decipher classified 79%, 13% and 8% of men as low-, intermediate- and high-risk with 13%, 10%, and 41% rate of AP, respectively. Decipher was an independent predictor of AP with an odds ratio of 1.34 per 0.1 unit increased (p value = 0.002) and remained significant when adjusting by CAPRA. Notably, F-IR with Decipher low or intermediate score did not associate with significantly higher odds of AP compared to VL/LR.

CONCLUSIONS

NCCN risk groups, including F-IR, are highly heterogeneous and should be replaced with multivariable risk-stratification. In particular, incorporating Decipher may be useful for safely expanding the use of AS in this patient population.

Reference-free transcriptome exploration reveals novel RNAs for prostate cancer diagnosis

The use of RNA-sequencing technologies held a promise of improved diagnostic tools based on comprehensive transcript sets. However, mining human transcriptome data for disease biomarkers in clinical specimens are restricted by the limited power of conventional reference-based protocols relying on unique and annotated transcripts. Here, we implemented a blind reference-free computational protocol, DE-kupl, to infer yet unreferenced RNA variations from total stranded RNA-sequencing datasets of tissue origin. As a bench test, this protocol was powered for detection of RNA subsequences embedded into putative long noncoding (lnc)RNAs expressed in prostate cancer. Through filtering of 1,179 candidates, we defined 21 lncRNAs that were further validated by NanoString for robust tumor-specific expression in 144 tissue specimens. Predictive modeling yielded a restricted probe panel enabling more than 90% of true-positive detections of cancer in an independent The Cancer Genome Atlas cohort. Remarkably, this clinical signature made of only nine unannotated lncRNAs largely outperformed PCA3, the only used prostate cancer lncRNA biomarker, in detection of high-risk tumors. This modular workflow is highly sensitive and can be applied to any pathology or clinical application.