Association Between Oncotype DX Genomic Prostate Score and Adverse Tumor Pathology After Radical Prostatectomy

Genomics in active surveillance and post-prostatectomy patients: A review of when and how to use effectively

PURPOSE OF REVIEW

Prostate cancer (PCa) represents a significant health burden globally, ranking as the most diagnosed cancer among men and a leading cause of cancer-related mortality. Conventional treatment methods such as radiation therapy or radical prostatectomy have significant side effects which often impact quality of life. As our understanding of the natural history and progression of PCa has evolved, so has the evolution of management options.

RECENT FINDINGS

Active surveillance (AS) has become an increasingly favored approach to the management of very low, low, and properly selected favorable intermediate risk PCa. AS permits ongoing observation and postpones intervention until definitive treatment is required. There are, however, challenges with selecting patients for AS, which further emphasizes the need for more precise tools to better risk stratify patients and choose candidates more accurately. Tissue-based biomarkers, such as ProMark, Prolaris, GPS (formerly Oncotype DX), and Decipher, are valuable because they improve the accuracy of patient selection for AS and offer important information on the prognosis and severity of disease. By enabling patients to be categorized according to their risk profiles, these biomarkers help physicians and patients make better informed treatment choices and lower the possibility of overtreatment. Even with their potential, further standardization and validation of these biomarkers is required to guarantee their broad clinical utility. Active surveillance has emerged as a preferred strategy for managing low-risk prostate cancer, and tissue-based biomarkers play a crucial role in refining patient selection and risk stratification. Standardization and validation of these biomarkers are essential to ensure their widespread clinical use and optimize patient outcomes.

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66-6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05-14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

An integrated radiology-pathology machine learning classifier for outcome prediction following radical prostatectomy: Preliminary findings

Objectives

To evaluate the added benefit of integrating features from pre-treatment MRI (radiomics) and digitized post-surgical pathology slides (pathomics) in prostate cancer (PCa) patients for prognosticating outcomes post radical-prostatectomy (RP) including a) rising prostate specific antigen (PSA), and b) extraprostatic-extension (EPE).

Methods

Multi-institutional data (N = 58) of PCa patients who underwent pre-treatment 3-T MRI prior to RP were included in this retrospective study. Radiomic and pathomic features were extracted from PCa regions on MRI and RP specimens delineated by expert clinicians. On training set (D1, N = 44), Cox Proportional-Hazards models MR, MP and MRaP were trained using radiomics, pathomics, and their combination, respectively, to prognosticate rising PSA (PSA > 0.03 ng/mL). Top features from MRaP were used to train a model to predict EPE on D1 and test on external dataset (D2, N = 14). C-index, Kalplan-Meier curves were used for survival analysis, and area under ROC (AUC) was used for EPE. MRaP was compared with the existing post-treatment risk-calculator, CAPRA (MC).

Results

Patients had median follow-up of 34 months. MRaP (c-index = 0.685 ± 0.05) significantly outperformed MR (c-index = 0.646 ± 0.05), MP (c-index = 0.631 ± 0.06) and MC (c-index = 0.601 ± 0.071) (p < 0.0001). Cross-validated Kaplan-Meier curves showed significant separation among risk groups for rising PSA for MRaP (p < 0.005, Hazard Ratio (HR) = 11.36) as compared to MR (p = 0.64, HR = 1.33), MP (p = 0.19, HR = 2.82) and MC (p = 0.10, HR = 3.05). Integrated radio-pathomic model MRaP (AUC = 0.80) outperformed MR (AUC = 0.57) and MP (AUC = 0.76) in predicting EPE on external-data (D2).

Conclusions

Results from this preliminary study suggest that a combination of radiomic and pathomic features can better predict post-surgical outcomes (rising PSA and EPE) compared to either of them individually as well as extant prognostic nomogram (CAPRA).

Biomarkers for Prostate Cancer: From Diagnosis to Treatment

Prostate cancer (PCa) is a widespread malignancy with global significance, which substantially affects cancer-related mortality. Its spectrum varies widely, from slow-progressing cases to aggressive or even lethal forms. Effective patient stratification into risk groups is crucial to therapeutic decisions and clinical trials. This review examines a wide range of diagnostic and prognostic biomarkers, several of which are integrated into clinical guidelines, such as the PHI, the 4K score, PCA3, Decipher, and Prolaris. It also explores the emergence of novel biomarkers supported by robust preclinical evidence, including urinary miRNAs and isoprostanes. Genetic alterations frequently identified in PCa, including BRCA1/BRCA2, ETS gene fusions, and AR changes, are also discussed, offering insights into risk assessment and precision treatment strategies. By evaluating the latest developments and applications of PCa biomarkers, this review contributes to an enhanced understanding of their role in disease management.

Impact of genomic testing on urologists' treatment preference in favorable risk prostate cancer: A randomized trial

INTRODUCTION

The Oncotype Dx Genomic Prostate Score (GPS) is a 17-gene relative expression assay that predicts adverse pathology at prostatectomy. We conducted a novel randomized controlled trial to assess the impact of GPS on urologist's treatment preference for favorable risk prostate cancer (PCa): active surveillance versus active treatment (i.e., prostatectomy/radiation). This is a secondary endpoint from the ENACT trial which recruited from three Chicago hospitals from 2016 to 2019.

METHODS

Ten urologists along with men with very low to favorable-intermediate risk PCa were included in the study. Participants were randomly assigned to standardized counseling with or without GPS assay. The main outcome was urologists' preference for active treatment at Visit 2 by study arm (GPS versus Control). Multivariable best-fit binary logistic regressions were constructed to identify factors independently associated with urologists' treatment preference.

RESULTS

Two hundred men (70% Black) were randomly assigned to either the Control (96) or GPS arm (104). At Visit 2, urologists' preference for prostatectomy/radiation almost doubled in the GPS arm to 29.3% (29) compared to 14.1% (13) in the Control arm (p = 0.01). Randomization to the GPS arm, intermediate NCCN risk level, and lower patient health literacy were predictors for urologists' preference for active treatment.

DISCUSSION

Limitations included sample size and number of urologists. In this study, we found that GPS testing reduced urologists' likelihood to prefer active surveillance.

CONCLUSIONS

These findings demonstrate how obtaining prognostic biomarkers that predict negative outcomes before treatment decision-making might influence urologists' preference for recommending aggressive therapy in men eligible for active surveillance.

The 17-Gene Genomic Prostate Score Assay Is Prognostic for Biochemical Failure in Men With Localized Prostate Cancer After Radiation Therapy at a Community Cancer Center

Purpose

The objective of this study was to assess the association between the Oncotype DX Genomic Prostate Score (GPS) assay and long-term outcomes in men with localized prostate cancer (PCa) after radiation therapy (RT). We hypothesized that the GPS assay is prognostic for biochemical failure (BCF), along with distant metastasis (DM) and PCa-specific mortality in patients with PCa receiving RT.

Methods and Materials

We retrospectively studied men with localized PCa treated with definitive RT at Georgia Urology from 2010 to 2016. The primary objective was to assess the association between GPS results and time to BCF per the Phoenix criteria; we also assessed time to DM and PCa-specific mortality. We used Cox proportional hazards regression models for all analyses, with clinicopathologic covariates determined a priori for multivariable modeling.

Results

A total of 450 patients (median age, 65 years; 35% Black) met eligibility criteria. There was a strong univariable association between GPS result and time to BCF (hazard ratio [HR] per 20-unit increase = 3.08; 95% confidence interval [CI], 2.11-4.46; P < .001), which persisted after adjusting for clinicopathologic characteristics in multivariable analyses. We also observed this association for time to DM (HR = 5.19; 95% CI, 3.06-8.77; P < .001) and PCa-specific mortality (HR = 13.07; 95% CI, 4.42-49.39; P < .001). Race was not a predictor of time to BCF or DM, and the GPS assay was strongly prognostic for all endpoints in Black and White patients.

Conclusions

In a community-based cohort, the GPS assay was strongly prognostic for time to BCF as well as long-term outcomes in men treated with RT for localized PCa.

MRI-derived tumor volume as a predictor of biochemical recurrence and adverse pathology in patients after radical prostatectomy: a propensity score matching study

PURPOSE

To investigate the predictive value of MRI-derived tumor volume (TV) of biochemical recurrence (BCR) and adverse pathology (AP) in patients following radical prostatectomy (RP).

METHODS

The data of 565 patients receiving RP in a single institution between 2010 and 2021 were retrospectively analyzed. All suspicious tumor foci were delineated manually using ITK-SNAP software as the regions of interest (ROIs). The sum of the TV of all lesions was calculated automatically based on the voxel in the ROIs to acquire the final TV parameter. TV was categorized as low-volume (≤ 6.5 cm³) and high-volume (> 6.5 cm³) based on the cut-off value. Univariate and multivariate Cox and logistic regression analyses were performed to identify independent predictors of BCR and AP. The Kaplan-Meier with the log-rank test was conducted to compare the BCR-free survival (BFS) between the low and high-volume groups.

RESULTS

All the included patients were divided into the low-volume group (n = 337) and the high-volume group (n = 228). The TV was an independent predictor of BFS in the multivariate Cox regression analysis (Hazard Ratio (HR) [95% CI]: 1.550 [1.066-2.256], P = 0.022). The Kaplan-Meier analysis demonstrated that low volume was associated with a better BFS than high volume before propensity score matching (PSM) (P < 0.001). One hundred and fifty-eight pairs were obtained by 1:1 PSM to balance the baseline parameters between the two groups. After the PSM, low-volume remained to be associated with a better BFS than high-volume (P = 0.006). TV as a categorical variable was an independent factor of AP in multivariate logistic regression analysis (Odd ratio (OR) [95% CI]: 1.821 [1.064-3.115], P = 0.029). After balancing the potential factors influencing AP by 1:1 PSM, 162 new pairs were identified. The high-volume group had a higher AP rate than the low-volume group after PSM (75.9 vs. 64.8%, P = 0.029).

CONCLUSION

We adopted a novel approach to acquiring the TV on preoperative MRI. TV was significantly associated with BFS and AP of patients undergoing RP, which was further illustrated by PSM analysis. MRI-derived TV may serve as a predictive marker for assessing BFS and AP in further studies, which will facilitate clinical decision-making and patient counseling.

A Unique Gene Signature Predicting Recurrence Free Survival in Stage IA Lung Adenocarcinoma

Objective

Resected stage IA lung adenocarcinoma (LUAD) has a reported 5-year recurrence free survival (RFS) of 63-81%. A unique gene signature stratifying patients with early stage LUAD as high or low-risk of recurrence would be valuable.

Methods

GEO datasets combining European and North American LUAD patients (n=684) were filtered for stage IA (n=105) to develop a robust signature for recurrence (RFSscore). Univariate Cox proportional hazard regression model was used to assess associations of gene expression with RFS and OS. Leveraging a bootstrap approach of these identified upregulated genes allowed construction of a model which was evaluated by Area Under the Received Operating Characteristics. The optimal signature has RFSscore calculated via a linear combination of expression of selected genes weighted by the corresponding Cox regression derived coefficients. Log-rank analysis calculated RFS and OS. Results were validated using the LUAD TCGA transcriptomic NGS based dataset.

Results

Rigorous bioinformatic analysis identified a signature of 4 genes: KNSTRN, PAFAH1B3, MIF, CHEK1. Kaplan-Meier analysis of stage IA LUAD with this signature resulted in 5-year RFS for low-risk of 90% compared to 53% for high-risk (HR 6.55, 95%CI 2.65-16.18, p-value <0.001), confirming the robustness of the gene signature with its clinical significance. Validation of the signature using TCGA dataset resulted in an AUC of 0.797 and 5-year RFS for low and high-risk stage IA patients being 91% and 67%, respectively (HR 3.44, 95%CI 1.16-10.23, p-value=0.044).

Conclusions

This 4 gene signature stratifies European and North American patients with pathologically confirmed stage IA LUAD into low and high-risk groups for OS and more importantly RFS.

The Role οf Ion Channels in the Development and Progression of Prostate Cancer

Ion channels have major regulatory functions in living cells. Apart from their role in ion transport, they are responsible for cellular electrogenesis and excitability, and may also regulate tissue homeostasis. Although cancer is not officially classified as a channelopathy, it has been increasingly recognized that ion channel aberrations play an important role in virtually all cancer types. Ion channels can exert pro-tumorigenic activities due to genetic or epigenetic alterations, or as a response to molecular signals, such as growth factors, hormones, etc. Increasing evidence suggests that ion channels and pumps play a critical role in the regulation of prostate cancer cell proliferation, apoptosis evasion, migration, epithelial-to-mesenchymal transition, and angiogenesis. There is also evidence suggesting that ion channels might play a role in treatment failure in patients with prostate cancer. Hence, they represent promising targets for diagnosis, staging, and treatment, and their effects may be of particular significance for specific patient populations, including those undergoing anesthesia and surgery. In this article, the role of major types of ion channels involved in the development and progression of prostate cancer are reviewed. Identifying the underlying molecular mechanisms of the pro-tumorigenic effects of ion channels may potentially inform the development of novel therapeutic strategies to counter this malignancy.

Urinary extracellular vesicles miRNA-A new era of prostate cancer biomarkers

Prostate cancer is the second most common male cancer worldwide showing the highest rates of incidence in Western Europe. Although the measurement of serum prostate-specific antigen levels is the current gold standard in PCa diagnosis, PSA-based screening is not considered a reliable diagnosis and prognosis tool due to its lower sensitivity and poor predictive score which lead to a 22%-43% overdiagnosis, unnecessary biopsies, and over-treatment. These major limitations along with the heterogeneous nature of the disease have made PCa a very unappreciative subject for diagnostics, resulting in poor patient management; thus, it urges to identify and validate new reliable PCa biomarkers that can provide accurate information in regard to disease diagnosis and prognosis. Researchers have explored the analysis of microRNAs (miRNAs), messenger RNAs (mRNAs), small proteins, genomic rearrangements, and gene expression in body fluids and non-solid tissues in search of lesser invasive yet efficient PCa biomarkers. Although the presence of miRNAs in body fluids like blood, urine, and saliva initially sparked great interest among the scientific community; their potential use as liquid biopsy biomarkers in PCa is still at a very nascent stage with respect to other well-established diagnostics and prognosis tools. Up to date, numerous studies have been conducted in search of PCa miRNA-based biomarkers in whole blood or blood serum; however, only a few studies have investigated their presence in urine samples of which less than two tens involve the detection of miRNAs in extracellular vesicles isolated from urine. In addition, there exists some discrepancy around the identification of miRNAs in PCa urine samples due to the diversity of the urine fractions that can be targeted for analysis such as urine circulating cells, cell-free fractions, and exosomes. In this review, we aim to discuss research output from the most recent studies involving the analysis of urinary EVs for the identification of miRNA-based PCa-specific biomarkers.

Intraductal Carcinoma of the Prostate as a Cause of Prostate Cancer Metastasis: A Molecular Portrait

Simple Summary

Most men with prostate cancer will live as long as those who do not have prostate cancer. However, some men will die early of their disease due to a particular type of prostate cancer associated with recurrence and metastasis: intraductal carcinoma of the prostate. In this review, we discuss the associations between intraductal carcinoma of the prostate and metastasis, and the contemporary knowledge about the molecular alterations of intraductal carcinoma of the prostate.

Abstract

Intraductal carcinoma of the prostate (IDC-P) is one of the most aggressive types of prostate cancer (PCa). IDC-P is identified in approximately 20% of PCa patients and is associated with recurrence, metastasis, and PCa-specific death. The main feature of this histological variant is the colonization of benign glands by PCa cells. Although IDC-P is a well-recognized independent parameter for metastasis, mechanisms by which IDC-P cells can spread and colonize other tissues are not fully known. In this review, we discuss the molecular portraits of IDC-P determined by immunohistochemistry and genomic approaches and highlight the areas in which more research is needed.

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.

[Robot-assisted surgery as an elective-fascinating lesson(s)?]

Hintergrund

Auch wenn sich roboterassistiertes Operieren zu einem verbreiteten Standardverfahren in einigen chirurgischen Fächern entwickelt hat, ist es im Lehrplan heutiger Medizinstudierender unterrepräsentiert.

Fragestellung

Wir berichten vom deutschlandweit ersten Wahlfach „Robotische Chirurgie“ für Studierende an einer urologischen Universitätsklinik.

Material und Methoden

In einer Kleingruppe mit zehn Studierenden wurden in sechs Treffen à 2 h theoretische Grundlagen und praktische Fertigkeiten in der robotischen Chirurgie vermittelt, inklusive einer Hospitation während einer urologischen roboterassistierten Operation. Der Zuwachs an Wissen (10 MCQ-Fragen) und Fähigkeiten (Übungen Camera 0, Clutch, Sea Spikes 1) an einem robotischen Simulationssystem wurde quantifiziert und die studentische Einschätzung evaluiert.

Ergebnisse

Bei den 10 Teilnehmenden war ein signifikanter Wissenszuwachs messbar, am Ende wurden in derselben theoretischen Prüfung im Median 3,5 mehr korrekte Antworten gegeben (p = 0,011). In zwei von drei praktischen Übungen stieg die Gesamtpunktzahl signifikant an (Camera 0 und Sea Spikes 1, für beide p < 0,05), in der Übung „Clutch“ verbesserte sich nur die Bewegungsökonomie (p = 0,028). Das Modul wurde (sehr) gut bewertet und die Teilnehmenden konnten sich am Ende deutlich stärker vorstellen, später Urologe/in zu werden (p = 0,007).

Schlussfolgerungen

Bei einem Bedarf von studentischer Seite, mehr über roboterassistierte Operationen zu lernen, erscheint ein Wahlfach als geeignetes Format, um theoretische Grundlagen und praktische Fertigkeiten in der robotischen (urologischen) Chirurgie zu vermitteln. Zusätzlich hat es das Potenzial, auf das Fachgebiet Urologie aufmerksam zu machen und könnte potenziell neue Kolleginnen und Kollegen gewinnen.

Zusatzmaterial online

Die Online-Version dieses Beitrags (10.1007/s00120-021-01756-6) enthält als zusätzliches Material das in diesem Beitrag durchgeführte Eingangstestat zur Prüfung des Vorwissens der Studierenden in einer theoretischen Prüfung (10 Multiple-choice-Fragen).

Prognostic Genomic Tissue-Based Biomarkers in the Treatment of Localized Prostate Cancer

In localized prostate cancer clinicopathologic variables have been used to develop prognostic nomograms quantifying the probability of locally advanced disease, of pelvic lymph node and distant metastasis at diagnosis or the probability of recurrence after radical treatment of the primary tumor. These tools although essential in daily clinical practice for the management of such a heterogeneous disease, which can be cured with a wide spectrum of treatment strategies (i.e., active surveillance, RP and radiation therapy), do not allow the precise distinction of an indolent instead of an aggressive disease. In recent years, several prognostic biomarkers have been tested, combined with the currently available clinicopathologic prognostic tools, in order to improve the decision-making process. In the following article, we reviewed the literature of the last 10 years and gave an overview report on commercially available tissue-based biomarkers and more specifically on mRNA-based gene expression classifiers. To date, these genomic tests have been widely investigated, demonstrating rigorous quality criteria including reproducibility, linearity, analytical accuracy, precision, and a positive impact in the clinical decision-making process. Albeit data published in literature, the systematic use of these tests in prostate cancer is currently not recommended due to insufficient evidence.

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.