Prostate Cancer Risk Stratification Research Articles

Navigating the evolving diagnostic and therapeutic landscape of low- and intermediate-risk prostate cancer

Abstract In this nonsystematic review of the literature, we explored the changing landscape of detection and treatment of low- and intermediate-risk prostate cancer (PCa). Through emphasizing improved cancer assessment with histology classification and genomics, we investigated key developments in PCa detection and risk stratification. The pivotal role of prostate magnetic resonance imaging (MRI) in the novel diagnostic pathway is examined, alongside the benefits and drawbacks of MRI-targeted biopsies for detection and tumor characterization. We also delved into treatment options, particularly active surveillance for intermediate-risk PCa. Outcomes are compared between intermediate- and low-risk patients, offering insights into tailored management. Surgical techniques, including Retzius-sparing surgery, precision prostatectomy, and partial prostatectomy for anterior cancer, are appraised. Each technique has the potential to enhance outcomes and minimize complications. Advancements in technology and radiobiology, including computed tomography (CT)/MRI imaging and positron emission tomography (PET) fusion, allow for precise dose adjustment and daily target monitoring with imaging-guided radiotherapy, opening new ways of tailoring patients’ treatments. Finally, experimental therapeutic approaches such as focal therapy open new treatment frontiers, although they create new needs in tumor identification and tracking during and after the procedure.

Molecular analysis of archival diagnostic prostate cancer biopsies identifies genomic similarities in cases with progression post-radiotherapy, and those with de novo metastaticdisease.

It is important to identify molecular features that improve prostate cancer (PCa) risk stratification before radical treatment with curative intent. Molecular analysis of historical diagnostic formalin-fixed paraffin-embedded (FFPE) prostate biopsies from cohorts with post-radiotherapy (RT) long-term clinical follow-up has been limited. Utilizing parallel sequencing modalities, we performed a proof-of-principle sequencing analysis of historical diagnostic FFPE prostate biopsies. We compared patients with (i) stable PCa (sPCa)postprimary or salvage RT,(ii) progressing PCa (pPCa) post-RT,and (iii) de novo metastatic PCa(mPCa). A cohort of 19 patients with diagnostic prostate biopsies (n = 6 sPCa, n = 5 pPCa, n = 8 mPCa) and mean 4 years 10 months follow-up (diagnosed 2009-2016) underwent nucleic acid extraction from demarcated malignancy. Samples underwent 3'RNA sequencing (3'RNAseq) (n = 19), nanoString analysis (n = 12), and Illumina 850k methylation (n = 8) sequencing. Bioinformatic analysis was performed to coherently identify differentially expressed genes and methylated genomic regions (MGRs). Eighteen of 19 samples provided useable 3'RNAseq data. Principal component analysis (PCA) demonstrated similar expression profiles between pPCa and mPCa cases, versus sPCa. Coherently differentially methylated probes between these groups identified ~600 differentially MGRs. The top 50 genes with increased expression in pPCa patients were associated with reduced progression-free survival post-RT (p < 0.0001) in an external cohort. 3'RNAseq, nanoString and 850k-methylation analyses are each achievable from historical FFPE diagnostic pretreatment prostate biopsies, unlocking the potential to utilize large cohorts of historic clinical samples. Profiling similarities between individuals with pPCa and mPCa suggests biological similarities and historical radiological staging limitations, which warrant further investigation.

An early-onset specific polygenic risk score optimizes age-based risk estimate and stratification of prostate cancer: population-based cohort study

BackgroundEarly-onset prostate cancer (EOPC, ≤ 55 years) has a unique clinical entity harboring high genetic risk, but the majority of EOPC patients still substantial opportunity to be early-detected thus suffering an unfavorable prognosis. A refined understanding of age-based polygenic risk score (PRS) for prostate cancer (PCa) would be essential for personalized risk stratification.MethodsWe included 167,517 male participants [4882 cases including 205 EOPC and 4677 late-onset PCa (LOPC)] from UK Biobank. A General-, an EOPC- and an LOPC-PRS were derived from age-specific genome-wide association studies. Weighted Cox proportional hazard models were applied to estimate the risk of PCa associated with PRSs. The discriminatory capability of PRSs were validated using time-dependent receiver operating characteristic (ROC) curves with additional 4238 males from PLCO and TCGA. Phenome-wide association studies underlying Mendelian Randomization were conducted to discover EOPC linking phenotypes.ResultsThe 269-PRS calculated via well-established risk variants was more strongly associated with risk of EOPC [hazard ratio (HR) = 2.35, 95% confidence interval (CI) 1.99–2.78] than LOPC (HR = 1.95, 95% CI 1.89–2.01; I2 = 79%). EOPC-PRS was dramatically related to EOPC risk (HR = 4.70, 95% CI 3.98–5.54) but not to LOPC (HR = 0.98, 95% CI 0.96–1.01), while LOPC-PRS had similar risk estimates for EOPC and LOPC (I2 = 0%). Particularly, EOPC-PRS performed optimal discriminatory capability for EOPC (area under the ROC = 0.613). Among the phenomic factors to PCa deposited in the platform of ProAP (Prostate cancerAge-basedPheWAS; https://mulongdu.shinyapps.io/proap), EOPC was preferentially associated with PCa family history while LOPC was prone to environmental and lifestyles exposures.ConclusionsThis study comprehensively profiled the distinct genetic and phenotypic architecture of EOPC. The EOPC-PRS may optimize risk estimate of PCa in young males, particularly those without family history, thus providing guidance for precision population stratification.

Proteomic Identification of Small Extracellular Vesicle Proteins LAMB1 and Histone H4 for Prostate Cancer Diagnosis and Risk Stratification.

Diagnosis and stratification of prostate cancer (PCa) patients using the prostate-specific antigen (PSA) test is challenging. Extracellular vesicles (EVs), as a new star of liquid biopsy, has attracted interest to complement inaccurate PSA screening and invasiveness of tissue biopsy. In this study, a panel of potential small EV (sEV) protein biomarkers is identified from PCa cell lines using label-free LC-MS/MS proteomics. These biomarkers underwent further validation with plasma and urine samples from different PCa stages through parallel reaction monitoring-based targeted proteomics, western blotting, and ELISA. Additionally, a tissue microarray containing cancerous and noncancerous tissues is screened to provide additional evidence of selected sEV proteins associated with cancer origin. Results indicate that sEV protein LAMB1 is highly expressed in human plasma of metastatic PCa patients compared with localised PCa patients and control subjects, while sEV protein Histone H4 is highly expressed in human urine of high-risk PCa patients compared to low-risk PCa patients and control subjects. These two sEV proteins demonstrate higher specificity and sensitivity than the PSA test and show promise for metastatic PCa diagnosis, progression monitoring, and risk stratification.

BPI24-026: Utilization of ExoDx Prostate Test for Prostate Cancer Risk Stratification in the African American Population.

BPI24-026: Utilization of ExoDx Prostate Test for Prostate Cancer Risk Stratification in the African American Population.

Prediction of Prostate Cancer Risk Stratification Based on A Nonlinear Transformation Stacking Learning Strategy.

Prostate cancer (PCa) is an epithelial malignancy that originates in the prostate gland and is generally categorized into low, intermediate, and high-risk groups. The primary diagnostic indicator for PCa is the measurement of serum prostate-specific antigen (PSA) values. However, reliance on PSA levels can result in false positives, leading to unnecessary biopsies and an increased risk of invasive injuries. Therefore, it is imperative to develop an efficient and accurate method for PCa risk stratification. Many recent studies on PCa risk stratification based on clinical data have employed a binary classification, distinguishing between low to intermediate and high risk. In this paper, we propose a novel machine learning (ML) approach utilizing a stacking learning strategy for predicting the tripartite risk stratification of PCa. Clinical records, featuring attributes selected using the lasso method, were utilized with 5 ML classifiers. The outputs of these classifiers underwent transformation by various nonlinear transformers and were then concatenated with the lasso-selected features, resulting in a set of new features. A stacking learning strategy, integrating different ML classifiers, was developed based on these new features. Our proposed approach demonstrated superior performance, achieving an accuracy of 0.83 and an area under the receiver operating characteristic curve value of 0.88 in a dataset comprising 197 PCa patients with 42 clinical characteristics. This study aimed to improve clinicians' ability to rapidly assess PCa risk stratification while reducing the burden on patients. This was achieved by using artificial intelligence-related technologies as an auxiliary method for diagnosing PCa.

Abstract 4570: Prostate-specific membrane antigen targeted chemo-photodynamic therapy for the treatment of prostate cancer

Abstract Introduction: Prostate cancer is the most prevalent cancer among men in the United States and is their second leading cause of cancer death. During initial screen, about 17-31% of patients are diagnosed with non-metastatic high-risk localized prostate cancer. These patients have an increased risk of treatment failure and oncological progression. The contemporary treatment paradigm of high risk localized prostate cancer is moving toward a multidisciplinary integrated approach of systemic and local therapy. In addition, incorporation of molecular biomarkers can also improve the risk stratification and treatment of prostate cancer. Among the biomarkers for prostate cancer, prostate specific membrane antigen (PSMA) has been proven to be an excellent target for diagnosis and treatment of prostate cancer. We have generated a PSMA-targeted photodynamic therapy (PDT) agent, PSMA-1-Pc413, and a PSMA-targeted chemotherapeutic agent, PSMA-1-MMAE. The objective of this study is to generate a new molecule that combines PDT and chemotherapy to achieve improved antitumor activity. Methods: Using a bifunctional PSMA ligand, a novel theranostic PSMA-targeting conjugate was synthesized, which integrated both the photosensitizer Pc413 and the microtubular inhibitor monomethyl auristatin E (MMAE) in one simple molecule, PSMA-1-MMAE-Pc413. This molecule was first tested for in vitro uptake and cytotoxicity studies using PSMA-positive PC3pip and PSMA-negative PC3flu cells. The conjugate was then tested for in vivo fluorescence imaging using mice bearing both PC3pip and PC3flu tumors. Finally, the antitumor activity of PSMA-1-MMAE-Pc413 was tested in mice with PC3pip tumors with and without light irradiation. Results: In in vitro uptake studies, the uptake of PSMA-1-MMAE-Pc413 in PC3pip tumor was about 3-fold higher than that in PC3flu cells. In in vitro cytotoxicity studies, when exposed to 5 J/cm2 of 672nm light, PSMA-1-MMAE-Pc413 demonstrated a synergistic effect leading to greater cytotoxicity for PC3pip cells (IC50=2.77nM) when compared to PSMA-1-Pc413 with light irradiation (IC50=372nM) or PSMA-1-MMAE-Pc413 without light irradiation (IC50=12.6nM). In in vivo imaging studies, PSMA-1-MMAE-Pc413 demonstrated selective uptake in PC3pip tumors. Further, antitumor activity studies showed that PSMA-1-MMAE-Pc413 with light irradiation significantly inhibited PC3pip tumor growth and prolonged survival time as compared to mice receiving equimolar amounts of PSMA-1-Pc413 with light irradiation or PSMA-1-MMAE-Pc413 without light irradiation. Conclusion: We have synthesized a new theranostic agent PSMA-1-MMAE-Pc413 that combines imaging, chemotherapy and PDT. The new molecule dramatically improves the treatment effect as compared to each single treatment alone. (The work is supported by NIH 1RO1CA255925-01A.) Citation Format: Xinning Wang, Aditi Shirke, Sriprada Chavali, Lifang Zhang, Ethan Walker, Gopolakrishnan Ramamurthy, James Basilion. Prostate-specific membrane antigen targeted chemo-photodynamic therapy for the treatment of prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4570.

Abstract 1853: Prostate specific membrane antigen (PSMA) glycoforms and prostate cancer progression

Abstract Introduction: Prostate cancer (PCa) is the most common non-skin malignancy and the second leading cause of cancer related death in men. Prostate cancer is a heterogeneous disease with diverse clinical outcomes ranging from indolent asymptomatic cases to very aggressive and potentially lethal metastatic forms of the disease. Early detection of PCa is predominantly through determination of serum PSA levels and digital rectal examinations (DRE). Subsequently, biopsy based Gleason Grade Scores and TNM staging, tumor size or mpMRI are used to assess disease severity and progression. However, due to the low specificity of PSA concentration in diagnosis and prognosis, biopsy complications, and high mpMRI costs, there is still a critical need to identify and develop novel noninvasive biomarkers that discriminate indolent from aggressive PCa upon diagnosis. Our long-term objective is to discover biomarkers for aggressive PCa, develop and to implement them into robust clinical testing minimally invasive assays for PCa risk stratification at diagnosis. The current focus is on Prostate-Specific Membrane Antigen (PSMA), a membrane bound glycoprotein, that is significantly over-expressed in aggressive and metastatic prostate carcinomas and with poor patient prognosis. The role of its glycosylation status and composition in disease progression and its glycoforms on biomarker development and therapeutics is unknown. Aberrant glycosylation of glycoproteins occurs in many cancers, and it influences disease progression. We rationalize that differential glycosylation of PSMA correlate with progression to aggressive PCa and that PSMA glycopeptides and glycoforms can be used as diagnostics and prognosticators for aggressive disease. Methods/Results: PSMA was isolated from risk Grade Group stratified post-DRE urines, seminal plasma and prostate cancer cell lines with different metastatic phenotypes cell line lysates. Intact glycopeptides were isolated, purified and analyzed by qualitative and quantitative mass spectrometry to identify site specific glycoforms. Our data demonstrate differential expression of unique PSMA glycoforms that correlate with disease risk and severity. Conclusions: Differential glycosylation of PSMA correlates with PCa disease progression to aggressive phenotypes and may have utility in disease stratification. Ongoing studies focus on determining if these qualitative and quantitative changes have diagnostic/prognostic value and if these be integrated with other PCa biomarkers to improve their prognostic power in discriminating aggressive PCa. Acknowledgements: This work was supported by the DOD, USAMRAA, through the Prostate Cancer Research Program under Award No. W81XWH18-1-0228. Opinions, interpretations, and conclusions are those of the author and are not necessarily endorsed by the USAMRAA. Citation Format: Stephen Mackay, Ian O. Oduor, Tanya Burch, Dean A. Troyer, Oliver J. Semmes, Julius O. Nyalwidhe. Prostate specific membrane antigen (PSMA) glycoforms and prostate cancer progression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1853.

Prostate cancer risk stratification via eNose urine odor analysis: a preliminary report.

Prostate cancer (PCa) is known for its highly diverse clinical behavior, ranging from low-risk, slow-growing tumors to aggressive and life-threatening forms. To avoid over-treatment of low-risk PCa patients, it would be very important prior to any therapeutic intervention to appropriately classify subjects based on tumor aggressiveness. Unfortunately, there is currently no reliable test available for this purpose. The aim of the present study was to evaluate the ability of risk stratification of PCa subjects using an electronic nose (eNose) detecting PCa-specific volatile organic compounds (VOCs) in urine samples. The study involved 120 participants who underwent diagnostic prostate biopsy followed by robot assisted radical prostatectomy (RARP). PCa risk was categorized as low, intermediate, or high based on the D'Amico risk classification and the pathological grade (PG) assessed after RARP. The eNose's ability to categorize subjects for PCa risk stratification was evaluated based on accuracy and recall metrics. The study population comprised 120 participants. When comparing eNose predictions with PG an accuracy of 79.2% (95%CI 70.8 - 86%) was found, while an accuracy of 74.2% (95%CI 65.4 - 81.7%) was found when compared to D'Amico risk classification system. Additionally, if compared low- versus -intermediate-/high-risk PCa, the eNose achieved an accuracy of 87.5% (95%CI 80.2-92.8%) based on PG or 90.8% (95%CI 84.2-95.3%) based on D'Amico risk classification. However, when using low-/-intermediate versus -high-risk PCa for PG, the accuracy was found to be 91.7% (95%CI 85.2-95.9%). Finally, an accuracy of 80.8% (95%CI72.6-87.4%) was found when compared with D'Amico risk classification. The findings of this study indicate that eNose may represent a valid alternative not only for early and non-invasive diagnosis of PCa, but also to categorize patients based on tumor aggressiveness. Further studies including a wider sample population will be necessary to confirm the potential clinical impact of this new technology.

Precise diagnosis and risk stratification of prostate cancer by comprehensive serum metabolic fingerprints: a prediction model study.

Prostate cancer (PCa) is one of the most common malignancies in men worldwide and has caused increasing clinical morbidity and mortality, making timely diagnosis and accurate staging crucial. The authors introduced a novel approach based on mass spectrometry for precise diagnosis and stratification of PCa to facilitate clinical decision-making. Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry analysis of trace blood samples was combined with machine learning algorithms to construct diagnostic and stratification models. A total of 367 subjects, comprising 181 with PCa and 186 with non-PCa were enrolled. Additional 60 subjects, comprising 30 with PCa and 30 with non-PCa were enrolled as an external cohort for validation. Subsequent metabolomic analysis was carried out using Autoflex MALDI-TOF, and the mass spectra were introduced into various algorithms to construct different models. Serum metabolic fingerprints were successfully obtained from 181 patients with PCa and 186 patients with non-PCa. The diagnostic model based on the eight signals demonstrated a remarkable area under curve of 100% and was validated in the external cohort with the area under curve of 87.3%. Fifteen signals were selected for enrichment analysis, revealing the potential metabolic pathways that facilitate tumorigenesis. Furthermore, the stage prediction model with an overall accuracy of 85.9% precisely classified subjects with localized disease and those with metastasis. The risk stratification model, with an overall accuracy of 89.6%, precisely classified the subjects as low-risk and high-risk. Our study facilitated the timely diagnosis and risk stratification of PCa and provided new insights into the underlying mechanisms of metabolic alterations in PCa.

Direct three-dimensional segmentation of prostate glands with nnU-Net.

In recent years, we and others have developed non-destructive methods to obtain three-dimensional (3D) pathology datasets of clinical biopsies and surgical specimens. For prostate cancer risk stratification (prognostication), standard-of-care Gleason grading is based on examining the morphology of prostate glands in thin 2D sections. This motivates us to perform 3D segmentation of prostate glands in our 3D pathology datasets for the purposes of computational analysis of 3D glandular features that could offer improved prognostic performance. To facilitate prostate cancer risk assessment, we developed a computationally efficient and accurate deep learning model for 3D gland segmentation based on open-top light-sheet microscopy datasets of human prostate biopsies stained with a fluorescent analog of hematoxylin and eosin (H&E). For 3D gland segmentation based on our H&E-analog 3D pathology datasets, we previously developed a hybrid deep learning and computer vision-based pipeline, called image translation-assisted segmentation in 3D (ITAS3D), which required a complex two-stage procedure and tedious manual optimization of parameters. To simplify this procedure, we use the 3D gland-segmentation masks previously generated by ITAS3D as training datasets for a direct end-to-end deep learning-based segmentation model, nnU-Net. The inputs to this model are 3D pathology datasets of prostate biopsies rapidly stained with an inexpensive fluorescent analog of H&E and the outputs are 3D semantic segmentation masks of the gland epithelium, gland lumen, and surrounding stromal compartments within the tissue. nnU-Net demonstrates remarkable accuracy in 3D gland segmentations even with limited training data. Moreover, compared with the previous ITAS3D pipeline, nnU-Net operation is simpler and faster, and it can maintain good accuracy even with lower-resolution inputs. Our trained DL-based 3D segmentation model will facilitate future studies to demonstrate the value of computational 3D pathology for guiding critical treatment decisions for patients with prostate cancer.

Deep Semisupervised Transfer Learning for Fully Automated Whole-Body Tumor Quantification and Prognosis of Cancer on PET/CT.

Automatic detection and characterization of cancer are important clinical needs to optimize early treatment. We developed a deep, semisupervised transfer learning approach for fully automated, whole-body tumor segmentation and prognosis on PET/CT. Methods: This retrospective study consisted of 611 18F-FDG PET/CT scans of patients with lung cancer, melanoma, lymphoma, head and neck cancer, and breast cancer and 408 prostate-specific membrane antigen (PSMA) PET/CT scans of patients with prostate cancer. The approach had a nnU-net backbone and learned the segmentation task on 18F-FDG and PSMA PET/CT images using limited annotations and radiomics analysis. True-positive rate and Dice similarity coefficient were assessed to evaluate segmentation performance. Prognostic models were developed using imaging measures extracted from predicted segmentations to perform risk stratification of prostate cancer based on follow-up prostate-specific antigen levels, survival estimation of head and neck cancer by the Kaplan-Meier method and Cox regression analysis, and pathologic complete response prediction of breast cancer after neoadjuvant chemotherapy. Overall accuracy and area under the receiver-operating-characteristic (AUC) curve were assessed. Results: Our approach yielded median true-positive rates of 0.75, 0.85, 0.87, and 0.75 and median Dice similarity coefficients of 0.81, 0.76, 0.83, and 0.73 for patients with lung cancer, melanoma, lymphoma, and prostate cancer, respectively, on the tumor segmentation task. The risk model for prostate cancer yielded an overall accuracy of 0.83 and an AUC of 0.86. Patients classified as low- to intermediate- and high-risk had mean follow-up prostate-specific antigen levels of 18.61 and 727.46 ng/mL, respectively (P < 0.05). The risk score for head and neck cancer was significantly associated with overall survival by univariable and multivariable Cox regression analyses (P < 0.05). Predictive models for breast cancer predicted pathologic complete response using only pretherapy imaging measures and both pre- and posttherapy measures with accuracies of 0.72 and 0.84 and AUCs of 0.72 and 0.76, respectively. Conclusion: The proposed approach demonstrated accurate tumor segmentation and prognosis in patients across 6 cancer types on 18F-FDG and PSMA PET/CT scans.

The effect of human leukocyte antigen genotype on survival in advanced prostate cancer treated with primary androgen deprivation therapy: the KYUCOG-1401-A study.

Immune editing, in which human leukocyte antigens (HLA) have critical roles, has been suggested to shape the landscape of human cancer. This study prospectively investigated whether HLA gene zygosity is associated with the prognosis of primary androgen deprivation therapy in advanced prostate cancer. KYUCOG-1401-A was conducted in conjunction with a prospective clinical trial (KYUCOG-1401). Among the patients enrolled in KYUCOG-1401 and treated with primary androgen deprivation therapy, only Japanese patients were included. HLA genotypes of HLA-A, B, C, DRB1, DQB1, and DPB1 were determined. The effect of divergence of HLA genotypes on time to progression, prostate cancer-specific survival, and overall survival was evaluated. Among 127 patients, homozygosity for HLA-DRB1 (HR, 95% CI; 4.05, 1.54-10.7, P = 0.0047) and HLA-DQB1 (HR, 95% CI; 3.75, 1.47-9.58, P = 0.0058) was associated with an increased risk of prostate cancer-specific mortality. Patients with higher HLA evolutionary divergence scores at HLA-DQB1 (HR, 95% CI; 0.90, 0.82-0.97, P = 0.0093) had lower risks of prostate cancer-specific mortality. Androgen-responsive gene sets were upregulated in CD4low and CD8low tumors in the prostate cancer cohort, but not in the bladder and kidney cancer cohorts. This study suggested that the diversity of HLA-II loci including HLA-DRB1 and HLA-DQB1 plays an important role in advanced prostate cancer survival, contributing to improved risk stratification in advanced prostate cancer. Moreover, it was shown that CD4+ T cells play an important role in androgen deprivation therapy, suggesting that immunotherapy targeting CD4+ T cells is promising for prostate cancer.

MRI-based prostate cancer classification using 3D efficient capsule network.

Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer-related death. Gleason score (GS) is the primary driver of PCa risk-stratification and medical decision-making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non-invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi-quantitative interpretation criteria, leading to inter-reader variability. Computer-aided diagnosis employing quantitative MRI analysis has yielded promising results in non-invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS-derived PCa risk using T2-weighted (T2W) MRI images. In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully-connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Two groups of experiments were performed: (1) we first identified high-risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one-versus-one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five-fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1-score for low versus high grade, 0.79 AUC and 0.75 F1-score for low + medium versus high grade, 0.75 AUC and 0.69 F1-score for medium versus high grade and 0.59 AUC and 0.57 F1-score for low versus medium grade. Our method outperformed state-of-the-art radiomics-based classification and deep learning methods with the highest metrics for each experiment. Our divide-and-conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non-invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies.

Prevalence of variant of unknown significance (VUS) in men with prostate cancer who serve as a benchmark for enhanced screening and for personalized therapy.

342 Background: Pathogenic variants (PVs) in DNA repair genes (DRG), such as in BRCA1, BRCA2, ATM, CHEK2, etc., have implications for prostate cancer (PCa) family genetic counselling, testing and risk stratification in men. However, understanding how to counsel patients with variants of unknown significance (VUS) is an unmet clinical need. On the other hand, there is the necessity to cautiously interpret VUS findings and to bolster VUS evaluations with additional evidence for pathogenicity. In this study, we aim to investigate the prevalence of germline VUS in men with PCa and their association with outcomes after robot-assisted radical prostatectomy (RARP). Methods: This is a nested cohort case study from an ongoing PCa screening, set to identify and screen men in the Italian population with a genetic predisposition (AIRC - Fondazione AIRC per la Ricerca sul Cancro project IG 2020 ID 25027). Cases for the current analysis were selected by “Adam's rib strategy” searching for germline DRG variant in men with a diagnosis of high-risk PCa who were scheduled for RARP. After informed consent was signed, a blood sample was obtained. A previously validated multigene panel test was used to assess the presence of germline variants. PVs and VUS were classified according to the American College of Medical Genetics and Genomics and Association for Molecular Pathology consensus criteria and International Agency for Research on Cancer guidelines. The primary endpoint was to describe the prevalence of VUS and the secondary to correlate VUS with pathological outcomes after RARP. Results: A total of 118 men with diagnosis of PCa who underwent RP were enrolled. The mean age at diagnosis of PCa was 65.9 (±6.42) years; pathological grade was ISUP 1-2 in 58.0% patients and 3-5 in 42.0%. Of the 76 men evaluated, 18 (23.7%) had a VUS and 1 (1.32%) a pathogenic germline variant in BRCA1 and PALB2. Among those with VUS younger men at diagnosis were more likely to carry a VUS than older at diagnosis mean (age 64 ± 6.41 vs. 66 ± 7.20 years). Additionally, 33.3% vs 16.2 % were carrier of VUS and N+ and required adjuvant androgen deprivation therapy. No difference between ISUP at final pathology and positive margin rate were found. Conclusions: Our findings showed a high prevalence of VUS germline mutations in PCa patients undergoing RARP. These mutations seem to be associated with worse pathological outcomes. Limitations includes short follow-up and absence of non-Caucasian patients.

Prostate Cancer Risk Stratification by Simple Scoring of the Current pT3 Lesions: A Proposal for a New Pathologic T-Staging System

Cancer spread beyond the prostate, including extraprostatic extension (other than seminal vesicle or bladder invasion; EPE)/microscopic bladder neck invasion and seminal vesicle invasion (SVI) currently classified as pT3a and pT3b lesions, respectively, does not uniformly indicate poor oncologic outcomes. Accurate risk stratification of current pT3 disease is therefore required. We herein further determined the prognostic impact of these histopathologic lesions routinely assessed and reported by pathologists, particularly their combinations. We assessed consecutive 2892 patients undergoing radical prostatectomy for current pT2 (n = 1692), pT3a (n = 956), or pT3b (n = 244) disease at our institution between 2009 and 2018. Based on our preliminary findings, point(s) were given (1 point to focal EPE, microscopic bladder neck invasion, or unilateral SVI; 2 points to nonfocal/established EPE or bilateral SVI) and summed up in each case. Our cohort had 0 point (n = 1692, 58.5%; P0), 1 point (n = 243, 8.4%; P1), 2 points (n = 657, 22.7%; P2), 3 points (n = 192, 6.6%; P3), 4 points (n = 76, 2.6%; P4), and 5 points (n = 32, 1.1%; P5). Univariate analysis revealed associations of higher points with significantly worse biochemical progression-free survival, particularly when P4 and P5 were combined. In multivariable analysis (P0 as a reference), P1 (hazard ratio [HR], 1.57; P = .033), P2 (HR, 3.25; P < .001), P3 (HR, 4.01; P < .001), and P4 + P5 (HR, 5.99; P < .001) showed significance for the risk of postoperative progression. Meanwhile, Harrell C-indexes for the current pT staging, newly developed point system, and the Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score were 0.727 (95% CI, 0.706-0.748), 0.751 (95% CI, 0.729-0.773), and 0.774 (95% CI, 0.755-0.794), respectively, for predicting progression. We believe our data provide a logical rationale for a novel pathologic T-staging system based on the summed points, pT1a (0 point), pT1b (1 point), pT2 (2 points), pT3a (3 points), and pT3b (4 or 5 points), which more accurately stratifies the prognosis of prostate cancer.

Tumor-associated macrophages and PD-L1 in prostate cancer: a possible key to unlocking immunotherapy efficacy.

Prostate cancer (PCa) is often considered as a "cold" tumor with low responsiveness to immunotherapy. Recent evidence suggests the activation of specific immune cells, such as tumor-associated macrophages (TAMs), could potentially influence the efficacy of immunotherapy in PCa. However, the relationship between TAMs and PD-L1, a significant regulator in immunotherapy, within PCa remains unexplored. In this study, we assessed TAM infiltration and PD-L1 expression levels in a local cohort of 95 PCa tissue samples and two publicly available PCa datasets. We employed a combination of bioinformatics and experimental techniques, including gene set enrichment analysis, CIBERSORTx, tissue microarray, immunohistochemistry staining, and analysis of single-cell sequencing datasets, to provide a comprehensive understanding of the association between PD-L1 and TAMs in the PCa microenvironment. The study showed that CD68+ TAMs and CD163+ TAMs (M2-TAMs) were more abundant in the tumor microenvironment than in non-cancerous surrounding tissues. The infiltration of CD163+ TAMs was significantly associated with the Gleason score and risk stratification of PCa. Importantly, elevated PD-L1 expression correlated significantly with high infiltration of CD163+ TAMs. Furthermore, patients displaying high levels of CD163+ TAMs and PD-L1 expression exhibited shorter times to biochemical recurrence-free survival. Our study suggests that CD163+ TAMs are closely associated with PD-L1 expression and can act as a valuable prognostic indicator for PCa. The high infiltration of M2-TAMs, coupled with the overexpression of PD-L1, may contribute to immune escape mechanisms in PCa, thereby influencing disease prognosis.

Less qualitative multiparametric magnetic resonance imaging in prostate cancer can underestimate extraprostatic extension in higher grade tumors.

Multiparametric magnetic resonance imaging (mpMRI) is increasingly used for risk stratification and preoperative staging of prostate cancer. It remains unclear how Grade Group (GG) interacts with the ability of mpMRI to determine the presence of extraprostatic extension (EPE) on surgical pathology. A retrospective review of a robotic assisted laparoscopic radical prostatectomy (RALP) database from 2016-2020 was performed. Radiology mpMRI reports by multiple attending radiologists and without clear standardization or quality control were retrospectively assessed for EPE findings and compared with surgical pathology reports. The data were stratified by biopsy-based GG and a multivariable cluster analysis was performed to incorporate additional preoperative variables (age at diagnosis, PSA, etc.). Hazard ratios were calculated to determine how mpMRI findings and radiographic EPE relate to positive surgical margins. 289 patients underwent at least one mpMRI prior to RALP. Preoperative mpMRI demonstrated sensitivity of 39.3% and specificity of 88.8% for pathological EPE and had a negative predictive value (NPV) of 49.5%, and positive predictive value (PPV) of 84.0%. Stratification of NPV by GG yielded the following values: GG 1-5 (49.5%), GG 3-5 (40.8%), GG 4-5 (43.4%), and GG 5 (30.4%). Additionally, positive EPE on preoperative mpMRI was associated with a significantly decreased risk of positive surgical margins (RR: 0.655; 95% CI: 0.557-0.771). NPV of prostate mpMRI for EPE may be decreased for higher grade tumors. A detailed reference reading and image quality optimization may improve performance. However, urologists should exercise caution in nerve sparing approaches in these patients.

The Role of Multiparametric MRI (mpMRI) in the Prediction of Adverse Prostate Cancer Pathology in Radical Prostatectomy Specimen.

Prostate cancer (PCa) risk stratification is essential in guiding therapeutic decision. Multiparametric magnetic resonance tomography (mpMRI) holds promise in the prediction of adverse pathologies (AP) after prostatectomy (RP). This study aims to identify clinical and imaging markers in the prediction of adverse pathology. Patients with PCa, diagnosed by targeted biopsy after mpMRI and undergoing RP, were included. The predictive accuracy of mpMRI for extraprostatic extension (ECE), seminal vesicle infiltration (SVI), and lymph node positivity was calculated from the final histopathology. 846 patients were involved. Independent risk parameters include imaging findings such as ECE (OR 3.12), SVI (OR 2.55), and PI-RADS scoring (4: OR 2.01 and 5: OR 4.34). mpMRI parameters such as ECE, SVI, and lymph node metastases showed a high prognostic accuracy (73.28% vs. 95.35% vs. 93.38%) with moderate sensitivity compared to the final histopathology. The ROC analysis of our combined scoring system (D'Amico classification, PSA density, and MRI risk factors) improves the prediction of adverse pathology (AUC: 0.73 vs. 0.69). Our study supports the use of mpMRI for comprehensive pretreatment risk assessment in PCa. Due to the high accuracy of factors like ECE, SVI, and PI-RADS scoring, utilizing mpMRI data enabled accurate prediction of unfavorable pathology after RP.

The Potential of Extracellular Matrix- and Integrin Adhesion Complex-Related Molecules for Prostate Cancer Biomarker Discovery.

Prostate cancer is among the top five cancer types according to incidence and mortality. One of the main obstacles in prostate cancer management is the inability to foresee its course, which ranges from slow growth throughout years that requires minimum or no intervention to highly aggressive disease that spreads quickly and resists treatment. Therefore, it is not surprising that numerous studies have attempted to find biomarkers of prostate cancer occurrence, risk stratification, therapy response, and patient outcome. However, only a few prostate cancer biomarkers are used in clinics, which shows how difficult it is to find a novel biomarker. Cell adhesion to the extracellular matrix (ECM) through integrins is among the essential processes that govern its fate. Upon activation and ligation, integrins form multi-protein intracellular structures called integrin adhesion complexes (IACs). In this review article, the focus is put on the biomarker potential of the ECM- and IAC-related molecules stemming from both body fluids and prostate cancer tissue. The processes that they are involved in, such as tumor stiffening, bone turnover, and communication via exosomes, and their biomarker potential are also reviewed.