Prostate cancer specific mortality and Gleason 7 disease differences in prostate cancer outcomes between cases with Gleason 4 + 3 and Gleason 3 + 4 tumors in a population based cohort

2D:4D digit ratio as a potential marker for prostate cancer risk

BACKGROUND

The second-to-fourth digit ratio (2D:4D) is thought to reflect prenatal exposure to sex steroids. We investigated the relationship between 2D:4D and odds of prostate cancer.

METHOD

Data were collected in PROtEuS, a population-based case-control study conducted in Montréal, Canada (2005-2012), including 1931 incident prostate cancer cases aged < 76 years and 1994 population controls. In-person interviews elicited information on potential risk factors. Digit lengths were measured by interviewers applying a standard protocol. Odds ratios (OR) and 95 % confidence intervals (CI) were estimated using unconditional logistic regression adjusting for potential confounders.

RESULTS

The OR of prostate cancer for a standard deviation increase in 2D:4D was 0.91 (95 % CI: 0.85-0.98). For less and more aggressive cancers, ORs were 0.93 (95 % CI: 0.87-1.00) and 0.85 (95 % CI: 0.77-0.93), respectively. There was an interaction with ancestry (p=0.04), whereas the OR among men of African descent was 1.23 (95 % CI: 0.96-1.57, based on 128 cases).

CONCLUSION

Findings suggest an inverse association between 2D:4D and odds of overall prostate cancer, more pronounced for aggressive cancers. This supports the notion that high levels of testosterone in utero, estimated by a low 2D:4D ratio, are associated with a higher risk of prostate cancer. Contrastingly, a high digit ratio was associated with greater cancer odds among participants of African descent. Upon replication, 2D:4D could prove to be an easily measured marker of prostate cancer risk.

Cigarette smoking and prostate cancer aggressiveness among African and European American men

PURPOSE

Smoking is a modifiable lifestyle factor that has not been established as a prostate cancer risk factor, nor emphasized in prostate cancer prevention. Studies have shown that African American (AA) smokers have a poorer cancer prognosis than European Americans (EAs), while having a lower prevalence of heavy smoking. We examined the relationship between cigarette smoking and prostate cancer aggressiveness and assessed racial differences in smoking habits on the probability of high-aggressive prostate cancer.

METHODS

Using data from the North Carolina-Louisiana Prostate Cancer Project (n = 1,279), prostate cancer aggressiveness was defined as high or low based on Gleason scores, serum prostate-specific antigen levels, and tumor stage. Cigarette smoking was categorized as current, former, or never smokers. Multivariable logistic regression was used to estimate adjusted odds ratios (OR) and 95% confidence intervals (CI).

RESULTS

Self-reported current (OR = 1.99; 95% CI 1.30-3.06) smoking was associated with high-aggressive prostate cancer relative to never smokers. When stratified by self-reported race, the odds of having high-aggressive cancer increased among AA current (OR = 3.58; 95% CI 2.04-6.28) and former smokers (OR = 2.21; 95% CI 1.38-3.53) compared to AA never smokers, but the odds were diminished among the EA stratum (Pself-reported race x smoking status = 0.003).

CONCLUSION

Cigarette smoking is associated with prostate cancer aggressiveness, a relationship modulated by self-reported race. Future research is needed to investigate types of cigarettes smoked and metabolic differences that may be contributing to the racial disparities observed.

Radiomic nomogram based on bi-parametric magnetic resonance imaging to predict the International Society of Urological Pathology grading ≥ 3 prostate cancer: a multicenter study

PURPOSE

To create a reliable radiomic nomogram for the prediction of the International Society of Urological Pathology (ISUP) grading ≥ 3 prostate cancer (PCa) patients.

METHODS

patients with verified PCa were obtained from three different hospitals. The patients were divided into training, internal validation, and two external validation groups. A radiomic signature (rad-score) extracted from T2WI, diffusion-weighted imaging, and apparent diffusion coefficient (ADC) maps were constructed in the training cohort. Eight clinical features were performed to develop a clinical model using univariate and multivariate logistic regression. The combined model incorporated the radiomic signature and clinical model. The model's performance was assessed by the receiver operating characteristic (ROC) curve.

RESULTS

Rad-score, magnetic resonance imaging T-stage, and ADC value were significant predictors of ISUP ≥ 3 PCa. A nomogram of these three factors was shown to have greater diagnostic accuracy than using only the radiomic signature or clinical model alone. The area under the ROC curve was 0.85, 0.88, 0.81, 0.81 for the training, internal, and two external validation cohorts, respectively. In the stratified analysis based on the MR scanner model, the area under the ROC curve of predicting ISUP ≥ 3 PCa for GE, Siemens, and combined groups were 0.84, 0.83, and 0.84, respectively, in the combined training group and an internal validation group.

CONCLUSIONS

The proposed nomogram has the potential to predict the differentiation degree of ISUP PCa patients.

Prediction of prostate cancer aggressiveness using magnetic resonance imaging radiomics: a dual-center study

PURPOSE

The Gleason score (GS) and positive needles are crucial aggressive indicators of prostate cancer (PCa). This study aimed to investigate the usefulness of magnetic resonance imaging (MRI) radiomics models in predicting GS and positive needles of systematic biopsy in PCa.

MATERIAL AND METHODS

A total of 218 patients with pathologically proven PCa were retrospectively recruited from 2 centers. Small-field-of-view high-resolution T2-weighted imaging and post-contrast delayed sequences were selected to extract radiomics features. Then, analysis of variance and recursive feature elimination were applied to remove redundant features. Radiomics models for predicting GS and positive needles were constructed based on MRI and various classifiers, including support vector machine, linear discriminant analysis, logistic regression (LR), and LR using the least absolute shrinkage and selection operator. The models were evaluated with the area under the curve (AUC) of the receiver-operating characteristic.

RESULTS

The 11 features were chosen as the primary feature subset for the GS prediction, whereas the 5 features were chosen for positive needle prediction. LR was chosen as classifier to construct the radiomics models. For GS prediction, the AUC of the radiomics models was 0.811, 0.814, and 0.717 in the training, internal validation, and external validation sets, respectively. For positive needle prediction, the AUC was 0.806, 0.811, and 0.791 in the training, internal validation, and external validation sets, respectively.

CONCLUSIONS

MRI radiomics models are suitable for predicting GS and positive needles of systematic biopsy in PCa. The models can be used to identify aggressive PCa using a noninvasive, repeatable, and accurate diagnostic method.

Differential association between dairy intake patterns and incident prostate cancer: a potential dairy matrix effect

OBJECTIVE

To evaluate the association between dairy intake patterns and the risk of prostate cancer (PC), and its histological differentiation, among men from Mexico City.

METHODS

We analyzed the information from 394 incident PC cases paired by age (± 5 years) with 794 population controls. According to the Gleason score at diagnosis, cases were classified as well- (≤ 6), moderately- (= 7), and poorly differentiated PC (≥ 8). Based on a semiquantitative-food frequency questionnaire and using energy-density approach, we estimated the energy-adjusted daily intake of whole milk, cheese (fresh, Oaxaca, and Manchego), cream, and yogurt. Through a principal component analysis, we identified three dairy intake patterns: whole milk, cheese, and yogurt. The association between each dairy intake pattern and PC was evaluated from independent nonconditional logistic regression models. We also evaluated the mediator role of calcium and saturated fat intake.

RESULTS

After adjustment, a high intake of whole milk pattern was associated with a 63% increased risk of PC (ORhigh vs low: 1.63; 95% CI 1.17-2.25, p trend = 0.002); at expenses of moderately (ORhigh vs low: 1.77; 95% CI 1.09-2.85, p trend = 0.015) and poorly differentiated PC (ORhigh vs low: 1.75; 95% CI 1.05- 2.92, p trend = 0.031). The association was mainly mediated by calcium intake (proportion mediated = 1.17; p < 0.01). No associations were found between cream and yogurt intake patterns with risk of PC, and its histological grade.

CONCLUSIONS

A differential association of dairy intake patterns with risk of PC, and the poorly differentiated PC, was identified. This association seems to be determined by different dairy matrices and it is mediated by calcium content. Longitudinal studies are needed to confirm these findings and be able to identify other potential mediators in the etiology of PC.

Intra-prostatic tumour evolution, steps in metastatic spread and histogenomic associations revealed by integration of multi-region whole-genome sequencing with histopathological features

BACKGROUND

Extension of prostate cancer beyond the primary site by local invasion or nodal metastasis is associated with poor prognosis. Despite significant research on tumour evolution in prostate cancer metastasis, the emergence and evolution of cancer clones at this early stage of expansion and spread are poorly understood. We aimed to delineate the routes of evolution and cancer spread within the prostate and to seminal vesicles and lymph nodes, linking these to histological features that are used in diagnostic risk stratification.

METHODS

We performed whole-genome sequencing on 42 prostate cancer samples from the prostate, seminal vesicles and lymph nodes of five treatment-naive patients with locally advanced disease. We spatially mapped the clonal composition of cancer across the prostate and the routes of spread of cancer cells within the prostate and to seminal vesicles and lymph nodes in each individual by analysing a total of > 19,000 copy number corrected single nucleotide variants.

RESULTS

In each patient, we identified sample locations corresponding to the earliest part of the malignancy. In patient 10, we mapped the spread of cancer from the apex of the prostate to the seminal vesicles and identified specific genomic changes associated with the transformation of adenocarcinoma to amphicrine morphology during this spread. Furthermore, we show that the lymph node metastases in this patient arose from specific cancer clones found at the base of the prostate and the seminal vesicles. In patient 15, we observed increased mutational burden, altered mutational signatures and histological changes associated with whole genome duplication. In all patients in whom histological heterogeneity was observed (4/5), we found that the distinct morphologies were located on separate branches of their respective evolutionary trees.

CONCLUSIONS

Our results link histological transformation with specific genomic alterations and phylogenetic branching. These findings have implications for diagnosis and risk stratification, in addition to providing a rationale for further studies to characterise the genetic changes causally linked to morphological transformation. Our study demonstrates the value of integrating multi-region sequencing with histopathological data to understand tumour evolution and identify mechanisms of prostate cancer spread.

Male-Pattern Vertex Baldness Trajectories, Chest Hair Patterns, and Odds of Overall and Aggressive Prostate Cancer

BACKGROUND

The link between hormones and hair growth is well established. Inconsistent associations have been found between hair patterns and cancer of the prostate, a hormone-dependent organ. We assessed vertex baldness trajectories, chest hair amount, and their relationships with the odds of developing prostate cancer in a large case-control study in Montreal, Canada.

METHODS

In-person interviews were conducted with 1,931 incident prostate cancer cases and 1,994 population-based age-matched (±5 years) controls. Participants reported their hair patterns using the validated Hamilton-Norwood scale of baldness for 10-year increments starting at age 30, and their current amount of chest hair. Group-based trajectories were used to identify men sharing similar patterns of vertex baldness severity over adulthood. Multivariable logistic regression assessed associations between indicators of baldness (frontal, vertex, age at onset, severity, and trajectories), chest hair, and odds of prostate cancer.

RESULTS

Vertex balding onset at age 30 was associated with increased odds of overall prostate cancer [Odds ratio (OR), 1.30; 95% confidence interval (CI), 1.03-1.64]. Men in the trajectory characterized by early moderate vertex baldness and developing severe baldness had increased odds of overall (OR, 1.42; 95% CI, 1.03-1.96) and especially aggressive prostate cancer (OR, 1.98; 95% CI, 1.21-3.22) compared with men without baldness. Men with little chest hair had higher odds of aggressive tumors than those with a moderate amount/a lot of chest hair.

CONCLUSIONS

Early-onset moderate vertex baldness that progresses and having little chest hair may be useful biomarkers of aggressive prostate cancer.

IMPACT

Integration of early-onset vertex balding patterns into risk prediction models of aggressive prostate cancer should be envisaged.

Development and Validation of an Explainable Radiomics Model to Predict High-Aggressive Prostate Cancer: A Multicenter Radiomics Study Based on Biparametric MRI

In the last years, several studies demonstrated that low-aggressive (Grade Group (GG) ≤ 2) and high-aggressive (GG ≥ 3) prostate cancers (PCas) have different prognoses and mortality. Therefore, the aim of this study was to develop and externally validate a radiomic model to noninvasively classify low-aggressive and high-aggressive PCas based on biparametric magnetic resonance imaging (bpMRI). To this end, 283 patients were retrospectively enrolled from four centers. Features were extracted from apparent diffusion coefficient (ADC) maps and T2-weighted (T2w) sequences. A cross-validation (CV) strategy was adopted to assess the robustness of several classifiers using two out of the four centers. Then, the best classifier was externally validated using the other two centers. An explanation for the final radiomics signature was provided through Shapley additive explanation (SHAP) values and partial dependence plots (PDP). The best combination was a naïve Bayes classifier trained with ten features that reached promising results, i.e., an area under the receiver operating characteristic (ROC) curve (AUC) of 0.75 and 0.73 in the construction and external validation set, respectively. The findings of our work suggest that our radiomics model could help distinguish between low- and high-aggressive PCa. This noninvasive approach, if further validated and integrated into a clinical decision support system able to automatically detect PCa, could help clinicians managing men with suspicion of PCa.

Metabolomic profiles of intact tissues reflect clinically relevant prostate cancer subtypes

BACKGROUND

Prostate cancer (PC) is a heterogenous multifocal disease ranging from indolent to lethal states. For improved treatment-stratification, reliable approaches are needed to faithfully differentiate between high- and low-risk tumors and to predict therapy response at diagnosis.

METHODS

A metabolomic approach based on high resolution magic angle spinning nuclear magnetic resonance (HR MAS NMR) analysis was applied on intact biopsies samples (n = 111) obtained from patients (n = 31) treated by prostatectomy, and combined with advanced multi- and univariate statistical analysis methods to identify metabolomic profiles reflecting tumor differentiation (Gleason scores and the International Society of Urological Pathology (ISUP) grade) and subtypes based on tumor immunoreactivity for Ki67 (cell proliferation) and prostate specific antigen (PSA, marker for androgen receptor activity).

RESULTS

Validated metabolic profiles were obtained that clearly distinguished cancer tissues from benign prostate tissues. Subsequently, metabolic signatures were identified that further divided cancer tissues into two clinically relevant groups, namely ISUP Grade 2 (n = 29) and ISUP Grade 3 (n = 17) tumors. Furthermore, metabolic profiles associated with different tumor subtypes were identified. Tumors with low Ki67 and high PSA (subtype A, n = 21) displayed metabolite patterns significantly different from tumors with high Ki67 and low PSA (subtype B, n = 28). In total, seven metabolites; choline, peak for combined phosphocholine/glycerophosphocholine metabolites (PC + GPC), glycine, creatine, combined signal of glutamate/glutamine (Glx), taurine and lactate, showed significant alterations between PC subtypes A and B.

CONCLUSIONS

The metabolic profiles of intact biopsies obtained by our non-invasive HR MAS NMR approach together with advanced chemometric tools reliably identified PC and specifically differentiated highly aggressive tumors from less aggressive ones. Thus, this approach has proven the potential of exploiting cancer-specific metabolites in clinical settings for obtaining personalized treatment strategies in PC.

Neighbourhood social deprivation and risk of prostate cancer

BACKGROUND

Striking geographic variations in prostate cancer incidence suggest an aetiological role for spatially-distributed factors. We assessed whether neighbourhood social deprivation, which can reflect limited social contacts, unfavourable lifestyle and environmental exposures, is associated with prostate cancer risk.

METHODS

In 2005-2012, we recruited 1931 incident prostate cancer cases and 1994 controls in a case-control study in Montreal, Canada. Lifetime residential addresses were linked to an area-based social deprivation index around recruitment (2006) and about 10 years earlier (1996). Logistic regression estimated adjusted odds ratios (ORs) and 95% confidence intervals (CIs).

RESULTS

Men residing in areas characterised by greater social deprivation had elevated prostate cancer risks (ORs of 1.54 and 1.60 for recent and past exposures, respectively; highest vs lowest quintiles), independently from area- and individual-level confounders and screening patterns. The increase in risk with recent high social deprivation was particularly elevated for high-grade prostate cancer at diagnosis (OR 1.87, 95% CI 1.32-2.64). Associations were more pronounced for neighbourhoods with higher proportions of separated/divorced or widowed individuals in the past, and with higher percentages of residents living alone recently.

CONCLUSIONS

These novel findings, suggesting that neighbourhood-level social deprivation increases the risk of prostate cancer, point out to potential targeted public health interventions.

Topology-Guided Multi-Class Cell Context Generation for Digital Pathology

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statistics and topological data analysis. We incorporate such structural descriptors into a deep generative model as both conditional inputs and a differentiable loss. This way, we are able to generate high quality multi-class cell layouts for the first time. We show that the topology-rich cell layouts can be used for data augmentation and improve the performance of downstream tasks such as cell classification.

Is there more than meets the eye in PSMA imaging in prostate cancer with PET/MRI? Looking closer at uptake time, correlation with PSA and Gleason score

BACKGROUND

In patients with increasing PSA and suspicion for prostate cancer, but previous negative biopsies, PET/MRI is used to test for tumours and target potential following biopsy. We aimed to determine different PSMA PET timing effects on signal kinetics and test its correlation with the patients' PSA and Gleason scores (GS).

METHODS

A total of 100 patients were examined for 900 s using PET/MRI approximately 1-2 h p.i. depending on the tracer used (⁶⁸Ga-PSMA-11, ¹⁸F-PSMA-1007 or ¹⁸F-rhPSMA7). The scans were reconstructed in static and dynamic mode (6 equal frames capturing "late" PSMA dynamics). TACs were computed for detected lesions as well as linear regression plots against time for static (SUV) and dynamic (SUV, SUL, and percent injected dose per gram) parameters. All computed trends were tested for correlation with PSA and GS.

RESULTS

Static and dynamic scans allowed unchanged lesion detection despite the difference in statistics. For all tracers, the lesions in the pelvic lymph nodes and bones had a mostly negative activity concentration trend (78% and 68%, resp.), while a mostly positive, stronger trend was found for the lesions in the prostate and prostatic fossa following RPE (84% and 83%, resp.). In case of ⁶⁸Ga-PSMA-11, a strong negative (R_min = - 0.62, R_max = - 0.73) correlation was found between the dynamic parameters and the PSA. ¹⁸F-PSMA-1007 dynamic data showed no correlation with PSA, while for ¹⁸F-rhPSMA7 dynamic data, it was consistently low positive (R_min = 0.29, R_max = 0.33). All tracers showed only moderate correlation against GS (R_min = 0.41, R_max = 0.48). The static parameters showed weak correlation with PSA (R_min = 0.24, R_max = 0.36) and no correlation with GS.

CONCLUSION

"Late" dynamic PSMA data provided additional insight into the PSMA kinetics. While a stable moderate correlation was found between the PSMA kinetics in pelvic lesions and GS, a significantly variable correlation with the PSA values was shown depending on the radiotracer used, the highest being consistently for ⁶⁸Ga-PSMA-11. We reason that with such late dynamics, the PSMA kinetics are relatively stable and imaging could even take place at earlier time points as is now in the clinical routine.

Motivation and family communication in hereditary prostate cancer genetic testing: Survey of patients from a US tertiary medical center

Identification of a hereditary prostate cancer in an affected individual can guide treatment and may also impact cancer screening and surveillance for patients and their relatives. This study aimed to determine the factors that are associated with the decision-making process of individuals with prostate cancer regarding whether to pursue genetic testing as well as how, why, and with whom genetic test results are shared. We surveyed 113 patients diagnosed with prostate cancer who received cancer genetic counseling through a United States tertiary medical center, inquiring about genetic testing motivations and family communication about results. Among those who pursued genetic testing, (1) learning about my family's possible cancer risk (98%), (2) learning information that may guide cancer treatment (93%), and (3) learning if I am at risk for future cancers (92%) were most frequently identified as slightly or very important factors in their decision. Participants shared their genetic test results in a higher proportion to male first-degree relatives than female first-degree relatives; however, no significant difference was found (p = 0.103). Our study may suggest sex differences related to family communication about genetic testing results. Such findings indicate a critical need for genetic counselors to clearly communicate the impact of genetic test results on both male and female relatives. Further research on motivation and family communication about genetic test results in diverse cohorts is needed.

Modified the 8th AJCC staging system for patients with advanced prostate cancer: a study based on SEER database

Background

The American Joint Committee on Cancer (AJCC) 8th staging system of prostate cancer may be insufficient in predicting the prognosis of some staged patients. This study aimed to modify the AJCC 8th staging system in patients with advanced prostate cancer.

Methods

Data of patients with advanced prostate cancer from the Surveillance, Epidemiology, and End Results (SEER) database between 2004 and 2016 were enrolled in this cohort study. All patients were divided into the training set and the testing set with a ratio of 6:4. Multivariate Cox survival model was utilized to obtain the nomogram score for each stage variable. The modified staging system was based on the total nomogram score. The C-index and Kaplan-Meier (K-M) curves were used to show the prognostic prediction effect of patients with different staging systems.

Results

A total of 28,006 patients were included for analysis. T stage, N stage, M stage, primary Gleason pattern score, secondary Gleason pattern score, and PSA level were included as stage variables. Patients with AJCC stage III C [hazard ratio (HR) = 4.17, 95% confidence interval (CI), 3.39–5.13] and AJCC stage IV B (HR = 3.19, 95%CI, 1.79–5.69) were associated with worse prognosis compared with those of AJCC stage III B, while no statistical significance was found in patients with stage IV A (P > 0.05). In terms of the modified staging system, patients with modified stage III C (HR = 2.06, 95%CI, 1.46–2.92), modified stage IV A (HR = 6.91, 95%CI, 4.81–9.94), and modified stage IV B (HR = 21.89, 95%CI, 14.76–32.46) were associated with a poorer prognosis compared with patients with modified stage III B. The prognostic ability (C-index) of the modified staging system (0.789; 95%CI, 0.777–0.801) was better than that of the AJCC 8th edition system (0.762; 95%CI, 0.748–0.776) (0.789 vs. 0.762, P = 0.004). The K-M curves indicated that the modified staging system may be distinguished prognostic differences in patients with different stages.

Conclusion

Modified staging system may be better than AJCC 8th staging system for predicting prognosis in prostate cancer patients. The AJCC 8th staging system should be further optimized.

Oncologic Outcomes of Total Length Gleason Pattern 4 on Biopsy in Men with Grade Group 2 Prostate Cancer

PURPOSE

Gleason Score 7 prostate cancer comprises a wide spectrum of disease risk, and precise substratification is paramount. Our group previously demonstrated that the total length of Gleason pattern (GP) 4 is a better predictor than %GP4 for adverse pathological outcomes at radical prostatectomy. We aimed to determine the association of GP4 length on prostate biopsy with post-prostatectomy oncologic outcomes.

MATERIALS AND METHODS

We compared 4 GP4 quantification methods-including maximum %GP4 in any single core, overall %GP4, total length GP4 (mm) across all cores and length GP4 (mm) in the highest volume core-for prediction of biochemical recurrence-free survival after radical prostatectomy using multivariable Cox proportional hazards regression.

RESULTS

A total of 457 men with grade group 2 prostate cancer on biopsy subsequently underwent radical prostatectomy. The 3-year biochemical recurrence-free survival probability was 85% (95% CI 81-88). On multivariable analysis, all 4 GP4 quantification methods were associated with biochemical recurrence-maximum %GP4 (HR=1.30; 95% CI 1.07-1.59; p=0.009), overall %GP4 (HR=1.61; 95% CI 1.21-2.15; p=0.001), total length GP4 (HR=2.48; 95% CI 1.36-4.52; p=0.003) and length GP4 in highest core (HR=1.32; 95% CI 1.11-1.57; p=0.001). However, we were unable to identify differences between methods of quantification with a relatively low event rate.

CONCLUSIONS

These findings support further studies on GP4 quantification in addition to the ratio of GP3 and GP4 to classify prostate cancer risk. Research should also be conducted on whether GP4 quantification could provide a surrogate endpoint for disease progression for trials in active surveillance.

AI Model for Prostate Biopsies Predicts Cancer Survival

An artificial intelligence (AI) algorithm for prostate cancer detection and grading was developed for clinical diagnostics on biopsies. The study cohort included 4221 scanned slides from 872 biopsy sessions at the HUS Helsinki University Hospital during 2016–2017 and a subcohort of 126 patients treated by robot-assisted radical prostatectomy (RALP) during 2016–2019. In the validation cohort (n = 391), the model detected cancer with a sensitivity of 98% and specificity of 98% (weighted kappa 0.96 compared with the pathologist’s diagnosis). Algorithm-based detection of the grade area recapitulated the pathologist’s grade group. The area of AI-detected cancer was associated with extra-prostatic extension (G5 OR: 48.52; 95% CI 1.11–8.33), seminal vesicle invasion (cribriform G4 OR: 2.46; 95% CI 0.15–1.7; G5 OR: 5.58; 95% CI 0.45–3.42), and lymph node involvement (cribriform G4 OR: 2.66; 95% CI 0.2–1.8; G5 OR: 4.09; 95% CI 0.22–3). Algorithm-detected grade group 3–5 prostate cancer depicted increased risk for biochemical recurrence compared with grade groups 1–2 (HR: 5.91; 95% CI 1.96–17.83). This study showed that a deep learning model not only can find and grade prostate cancer on biopsies comparably with pathologists but also can predict adverse staging and probability for recurrence after surgical treatment.

Risk subtyping and prognostic assessment of prostate cancer based on consensus genes

Prostate cancer (PCa) is the most frequent malignancy in male urogenital system around worldwide. We performed molecular subtyping and prognostic assessment based on consensus genes in patients with PCa. Five cohorts containing 1,046 PCa patients with RNA expression profiles and recorded clinical follow-up information were included. Univariate, multivariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression were used to select prognostic genes and establish the signature. Immunohistochemistry staining, cell proliferation, migration and invasion assays were used to assess the biological functions of key genes. Thirty-nine intersecting consensus prognostic genes from five independent cohorts were identified. Subsequently, an eleven-consensus-gene classifier was established. In addition, multivariate Cox regression analyses showed that the classifier served as an independent indicator of recurrence-free survival in three of the five cohorts. Combined receiver operating characteristic (ROC) analysis achieved synthesized effects by combining the classifier with clinicopathological features in four of five cohorts. SRD5A2 inhibits cell proliferation, while ITGA11 promotes cell migration and invasion, possibly through the PI3K/AKT signaling pathway. To conclude, we established and validated an eleven-consensus-gene classifier, which may add prognostic value to the currently available staging system.

By analysis of gene expression profiles of prostate cancer patients from multiple platforms, an eleven-consensus-gene classifier is constructed to provide a robust tool for the prediction of recurrence-free survival.

Improving the detection of aggressive prostate cancer using immunohistochemical staining of protein marker panels

Prostate cancer (PCa) is a heterogeneous group of tumors, including non-aggressive (NAG) and aggressive (AG) cancer, with variable clinical outcomes. Clinically, in order to assess the aggressiveness of a PCa, a core needle biopsy of a tumor is usually obtained to evaluate the Gleason pattern and score of the tumor. However, it may be difficult to assign on a small biopsy sample using histology. Therefore, additional tool is needed to aid in the assessment. We studied the diagnostic utility of 12 protein markers to identify AG tumors using immunohistochemistry (IHC) and tumor tissue microarray (TMA), including 215 cores of PCa and 111 cores of tumor-matched normal adjacent tissue (NAT). Protein markers were evaluated for their potential utility as single or combined panels for identification of AG. Of 12 proteins, PSMA, phospho-EGFR, AR and P16 were over-expressed in AG. Galectin-3, DPP4 and MAN1B1 revealed stronger staining patterns in NAG. The sensitivity and specificity of individual marker varied widely. Based on AUC values of individual marker, we constructed two- and three-marker panels. In two-marker panels, especially in the panel of DPP4 and PSMA, the AUC value reached 0.83 (ranging from 0.76 to 0.83). In three-marker panels, containing both DPP4 and PSMA with either Galectin-3 or phospho-EGFR, the AUC value reached 0.86 (ranging from 0.83 to 0.86). The specificities at 95% sensitivity of three-marker panels were also significantly improved. In addition to Gleason score, our IHC panels provide a practical tool to assess the aggressiveness of PCa.

Prostate heterogeneity correlates with clinical features on multiparametric MRI

BACKGROUND

Prostate heterogeneity on multi-parametric MRI (mpMRI) may confound image interpretation by obscuring lesions; systematic biopsy may have a role in this context.

PURPOSE

To determine if prostate heterogeneity (1) correlates with clinical risk factors for prostate cancer and (2) associates with higher-grade tumor in systematic biopsy (SB), compared with MRI-directed target biopsy (MDTB), i.e., SB > MDTB, thus providing a rationale for combined biopsy.

METHODS

IRB-approved retrospective study included men who underwent mpMRI, SB, and MDTB between 2015 and 2017. Regions of interest were applied to the entire transition zone (TZ) and peripheral zone (PZ) on T2-weighted imaging (T2WI), apparent diffusion coefficient maps (ADC), and early dynamic contrast-enhanced (DCE) images on the midgland slice. Mean signal intensities and standard deviation (SD) of each zone were calculated. SD served as a measure of heterogeneity. Spearman's rank correlation analysis of clinical and imaging variables was performed. Univariate logistic regression was used to determine if any imaging variable associated with SB > MDTB.

RESULTS

93 patients were included. Significant correlations included age and TZ ADC heterogeneity (rho = 0.34, p = 0.013), PSA density, and mean TZ ADC (rho = - 0.29, p = 0.049). PZ T2WI heterogeneity correlated with PZ ADC heterogeneity (rho = 0.48, p < 0.001). PZ DCE heterogeneity correlated with TZ DCE heterogeneity (rho = 0.46, p < 0.001). TZ ADC heterogeneity was associated with SB > MDTB prior to multiple comparison correction (p = 0.032). p value after correction was 0.24.

CONCLUSION

TZ ADC heterogeneity correlated with age and may reflect prostatic hyperplasia and/or prostate cancer. PZ heterogeneity, possibly a measure of prostatitis, correlated with TZ hyperplasia and/or inflammation. TZ ADC heterogeneity was associated with SB > MDTB with p value of < 0.05 prior to multiple correction; future investigation is needed to further elucidate significance of ADC heterogeneity in prostate imaging.

Virtual biopsy in prostate cancer: can machine learning distinguish low and high aggressive tumors on MRI?

In the last decades, MRI was proven a useful tool for the diagnosis and characterization of Prostate Cancer (PCa). In the literature, many studies focused on characterizing PCa aggressiveness, but a few have distinguished between low-aggressive (Gleason Grade Group (GG) <=2) and high-aggressive (GG>=3) PCas based on biparametric MRI (bpMRI). In this study, 108 PCas were collected from two different centers and were divided into training, testing, and validation set. From Apparent Diffusion Coefficient (ADC) map and T2-Weighted Image (T2WI), we extracted texture features, both 3D and 2D, and we implemented three different methods of Feature Selection (FS): Minimum Redundance Maximum Relevance (MRMR), Affinity Propagation (AP), and Genetic Algorithm (GA). From the resulting subsets of predictors, we trained Support Vector Machine (SVM), Decision Tree, and Ensemble Learning classifiers on the training set, and we evaluated their prediction ability on the testing set. Then, for each FS method, we chose the best classifier, based on both training and testing performances, and we further assessed their generalization capability on the validation set. Between the three best models, a Decision Tree was trained using only two features extracted from the ADC map and selected by MRMR, achieving, on the validation set, an Area Under the ROC (AUC) equal to 81%, with sensitivity and specificity of 77% and 93%, respectively.Clinical Relevance- Our best model demonstrated to be able to distinguish low-aggressive from high-aggressive PCas with high accuracy. Potentially, this approach could help clinician to noninvasively distinguish between PCas that might need active treatment and those that could potentially benefit from active surveillance, avoiding biopsy-related complications.

De Novo Radiomics Approach Using Image Augmentation and Features From T1 Mapping to Predict Gleason Scores in Prostate Cancer

OBJECTIVES

The aims of this study were to discriminate among prostate cancers (PCa's) with Gleason scores 6, 7, and ≥8 on biparametric magnetic resonance imaging (bpMRI) of the prostate using radiomics and to evaluate the added value of image augmentation and quantitative T1 mapping.

MATERIALS AND METHODS

Eighty-five patients with subsequently histologically proven PCa underwent bpMRI at 3 T (T2-weighted imaging, diffusion-weighted imaging) with 66 patients undergoing additional T1 mapping at 3 T. The PCa lesions as well as the peripheral and transition zones were segmented pixel by pixel in multiple slices of the 3D MRI data sets (T2-weighted images, apparent diffusion coefficient, and T1 maps). To increase the size of the data set, images were augmented for contrast, brightness, noise, and perspective multiple times, effectively increasing the sample size 10-fold, and 322 different radiomics features were extracted before and after augmentation. Four different machine learning algorithms, including a random forest (RF), stochastic gradient boosting (SGB), support vector machine (SVM), and k-nearest neighbor, were trained with and without features from T1 maps to differentiate among 3 different Gleason groups (6, 7, and ≥8).

RESULTS

Support vector machine showed the highest accuracy of 0.92 (95% confidence interval [CI], 0.62-1.00) for classifying the different Gleason scores, followed by RF (0.83; 95% CI, 0.52-0.98), SGB (0.75; 95% CI, 0.43-0.95), and k-nearest neighbor (0.50; 95% CI, 0.21-0.79). Image augmentation resulted in an average increase in accuracy between 0.08 (SGB) and 0.48 (SVM). Removing T1 mapping features led to a decline in accuracy for RF (-0.16) and SGB (-0.25) and a higher generalization error.

CONCLUSIONS

When data are limited, image augmentations and features from quantitative T1 mapping sequences might help to achieve higher accuracy and lower generalization error for classification among different Gleason groups in bpMRI by using radiomics.

Marital status and prostate cancer incidence: a pooled analysis of 12 case-control studies from the PRACTICAL consortium

While being in a committed relationship is associated with a better prostate cancer prognosis, little is known about how marital status relates to its incidence. Social support provided by marriage/relationship could promote a healthy lifestyle and an increased healthcare seeking behavior. We investigated the association between marital status and prostate cancer risk using data from the PRACTICAL Consortium. Pooled analyses were conducted combining 12 case-control studies based on histologically-confirmed incident prostate cancers and controls with information on marital status prior to diagnosis/interview. Marital status was categorized as married/partner, separated/divorced, single, or widowed. Tumours with Gleason scores ≥ 8 defined high-grade cancers, and low-grade otherwise. NCI-SEER's summary stages (local, regional, distant) indicated the extent of the cancer. Logistic regression was used to derive odds ratios (ORs) and 95% confidence intervals (CI) for the association between marital status and prostate cancer risk, adjusting for potential confounders. Overall, 14,760 cases and 12,019 controls contributed to analyses. Compared to men who were married/with a partner, widowed men had an OR of 1.19 (95% CI 1.03-1.35) of prostate cancer, with little difference between low- and high-grade tumours. Risk estimates among widowers were 1.14 (95% CI 0.97-1.34) for local, 1.53 (95% CI 1.22-1.92) for regional, and 1.56 (95% CI 1.05-2.32) for distant stage tumours. Single men had elevated risks of high-grade cancers. Our findings highlight elevated risks of incident prostate cancer among widowers, more often characterized by tumours that had spread beyond the prostate at the time of diagnosis. Social support interventions and closer medical follow-up in this sub-population are warranted.

Occupational exposure to polycyclic aromatic hydrocarbons and risk of prostate cancer

BACKGROUND

Several industries entailing exposure to polycyclic aromatic hydrocarbons (PAHs) are known or suspected carcinogens. A handful of studies have assessed the role of PAHs exposure in prostate cancer risk, but none has examined tumor aggressiveness or the influence of screening practices and detection issues. We aimed to examine the association between lifetime occupational exposure to PAHs and prostate cancer risk.

METHODS

Detailed work histories were collected from 1,929 prostate cancer cases (436 aggressive) and 1,994 controls from Montreal, Canada (2005-2012). Industrial hygienists applied the hybrid expert approach to assign intensity, frequency and certainty of exposure to benzo[a]pyrene, PAHs from wood, coal, petroleum, other sources, and any source, in each job held. Odds ratios (ORs) for prostate cancer risk associated with lifetime PAHs exposure, adjusted for age, ancestry, education, lifestyle and occupational factors, and 95% confidence intervals (CI), were estimated using unconditional logistic regression.

RESULTS

After restriction to probable and definite exposures, and application of a 5-year lag, no clear association emerged for any of the PAHs, although small excesses in risk were apparent with 5-year increments in exposure to PAHs from wood (OR = 1.06, 95%CI 0.95 to 1.18). While analyses by cancer aggressiveness suggested no major differences, some elevated risk of high-grade cancer was observed for exposure to PAHs from wood (OR = 1.37, 95%CI 0.65 to 2.89), frequently occurring among firefighters.

CONCLUSION

Findings provide weak support for an association between occupational exposure to PAHs from wood and prostate cancer risk.

Cribriform Prostate Cancer: Clinical Pathologic and Molecular Considerations

Intraductal cribriform (IDC) and invasive cribriform morphologies are associated with worse prostate cancer outcomes. Limited retrospective studies have associated IDC and cribriform morphology with germline mutations in DNA repair genes, particularly BRCA2. These findings, which prompted the National Comprehensive Cancer Network (NCCN) Guidelines for Prostate Cancer and Genetic/Familial High- Risk Assessment to consider germline testing for individuals with IDC/cribriform histology, have been questioned in a recent prospective study. A deepened understanding of the molecular mechanisms driving disease aggressiveness in cribriform morphology is critical to provide more clarity in clinical decision making. This review summarizes the current understanding of IDC and cribriform prostate cancer, with an emphasis on clinical outcomes and molecular alterations.

Rapid Diagnosis of Prostate Cancer Disease Progression Using Paper Spray Ionization Mass Spectrometry

The limitation of prostate specific antigen (PSA) for prostate cancer (PC) diagnosis is well-recognized. The Gleason score (GS) has been the most widely used grading system for prostate tumor differentiation and represents the best-established prognostic indicator for prostate cancer progression. However, a rapid and sensitive noninvasive diagnostic marker that differentiates GS-based prostate cancer disease progression is needed. As PC is becoming a leading cause of cancer related death for men in the U.S. and worldwide, an immediate need exists for an improved, sensitive, noninvasive, and rapid diagnostic test for PC screening. Here, we employed paper spray ionization-mass spectrometry (PSI MS)-based global metabolomics of urine liquid biopsies to distinguish between healthy (negative for any prostate specific health problems) and progressive PC states (low grade PC such as GS6 and high-grade PC such as GS7, GS8, and GS9). For PSI-MS-based direct untargeted metabolic investigation, a raw urine sample was directly pipetted onto a triangular paper substrate, without any additional sample preparation. Multivariate statistical analysis revealed distinct GS-specific metabolic signatures compared to a healthy control. Variable importance in projection from partial least-squares-discriminant analysis showed distinct metabolic patterns that were correlatively elevated with progressive disease and could serve as biomarkers for diagnosis of prostate cancer risk categorization.

General and abdominal obesity trajectories across adulthood, and risk of prostate cancer: results from the PROtEuS study, Montreal, Canada

PURPOSE

Greater body fatness is a probable cause of advanced prostate cancer (PCa). Body fat distribution and timing of exposure may be relevant. We investigated associations between body size trajectories and PCa incidence in a population-based case-control study in Montreal, Canada.

METHODS

Cases (n = 1,931), aged ≤ 75 years, were diagnosed with PCa in 2005-2009; 1,994 controls were selected from the electoral list. Interviews were conducted to assess body mass index (BMI) and Stunkard's silhouette at ages 20, 40, 50, 60 years, and before interview. Current waist and hip circumferences were measured, and a predictive model estimated waist circumference in the past. BMI and waist circumference trajectories were determined to identify subgroups. Logistic regression estimated odds ratios (OR) and 95% confidence intervals (CI) for the association between anthropometric indicators and PCa.

RESULTS

Subjects with a current BMI ≥ 30 kg/m² had a lower risk of overall PCa (OR 0.71, 95% CI 0.59-0.85). Associations with adult BMI followed similar trends for less and more aggressive tumors, with stronger inverse relationships in early adulthood. Contrastingly, current waist circumference ≥ 102 cm was associated with elevated risk of high-grade PCa (OR 1.33, 95% CI 1.03-1.71). Men with increasing BMI or waist circumference adult trajectories had a lower risk of PCa, especially low-grade, than those in the normal-stable range. This was especially evident among men in the obese-increase group for BMI and waist circumference.

CONCLUSION

Abdominal obesity increased the risk of aggressive PCa. The inverse relationship between body size trajectories and PCa may reflect PSA hemodilution, lower detection, and/or a true etiological effect.

Prediction of Pathological Upgrading at Radical Prostatectomy in Prostate Cancer Eligible for Active Surveillance: A Texture Features and Machine Learning-Based Analysis of Apparent Diffusion Coefficient Maps

Objective

To evaluate a combination of texture features and machine learning-based analysis of apparent diffusion coefficient (ADC) maps for the prediction of Grade Group (GG) upgrading in Gleason score (GS) ≤6 prostate cancer (PCa) (GG1) and GS 3 + 4 PCa (GG2).

Materials and methods

Fifty-nine patients who were biopsy-proven to have GG1 or GG2 and underwent MRI examination with the same MRI scanner prior to transrectal ultrasound (TRUS)-guided systemic biopsy were included. All these patients received radical prostatectomy to confirm the final GG. Patients were divided into training cohort and test cohort. 94 texture features were extracted from ADC maps for each patient. The independent sample t-test or Mann−Whitney U test was used to identify the texture features with statistically significant differences between GG upgrading group and GG non-upgrading group. Texture features of GG1 and GG2 were compared based on the final pathology of radical prostatectomy. We used the least absolute shrinkage and selection operator (LASSO) algorithm to filter features. Four supervised machine learning methods were employed. The prediction performance of each model was evaluated by area under the receiver operating characteristic curve (AUC). The statistical comparison between AUCs was performed.

Results

Six texture features were selected for the machine learning models building. These texture features were significantly different between GG upgrading group and GG non-upgrading group (P < 0.05). The six features had no significant difference between GG1 and GG2 based on the final pathology of radical prostatectomy. All machine learning methods had satisfactory predictive efficacy. The diagnostic performance of nearest neighbor algorithm (NNA) and support vector machine (SVM) was better than random forests (RF) in the training cohort. The AUC, sensitivity, and specificity of NNA were 0.872 (95% CI: 0.750−0.994), 0.967, and 0.778, respectively. The AUC, sensitivity, and specificity of SVM were 0.861 (95%CI: 0.732−0.991), 1.000, and 0.722, respectively. There had no significant difference between AUCs in the test cohort.

Conclusion

A combination of texture features and machine learning-based analysis of ADC maps could predict PCa GG upgrading from biopsy to radical prostatectomy non-invasively with satisfactory predictive efficacy.

Differences in survival of prostate cancer Gleason 8-10 disease and the establishment of a new Gleason survival grading system

Background

Although the latest Gleason grading system in 2014 has distinguished between Gleason 3 + 4 and 4 + 3, Gleason 8 and Gleason 9–10 are remained systemically classified.

Methods

A total of 261,125 patients diagnosed with prostate cancer (PCa) were selected between 2005 and 2015 from the Surveillance, Epidemiology, and End Results (SEER) database. We used propensity score matching to balance clinical variables and then compared overall survival (OS) and cancer‐specific survival (CSS) between Gleason score subgroups. We further establish a new Gleason survival grading system based on the hazard ratio (HR) values of each Gleason subgroup. Cox proportional hazards models and Kaplan–Meier curves were used to compare patient survival.

Results

Among PCa patients with Gleason score 8 disease, patients with Gleason 5 + 3 had significantly worse OS and CSS than those with Gleason 3 + 5 (OS: HR = 1.26, p = 0.042; CSS: HR = 1.42, p = 0.005) and 4 + 4 (HR = 1.50 for OS and HR = 1.69 for CSS, p < 0.001 for all). PCa patients with Gleason 5 + 3 and Gleason 4 + 5 may have the similar OS and CSS (reference Gleason score <=6, 5 + 3: OS HR = 2.44, CSS HR = 7.63; 4 + 5: OS HR = 2.40, CSS HR = 8.92; p < 0.001 for all). The new Gleason survival grading system reclassified the grades 4 and 5 of the 2014 updated Gleason grading system into three hierarchical grades, which makes the classification of grades more detailed and accurate.

Conclusion

PCa patients with Gleason 8–10 may have three different survival subgroups, Gleason 3 + 5 and 4 + 4, Gleason 5 + 3 and 4 + 5, and Gleason 5 + 4 and 5 + 5. Our results maximize risk stratification for PCa patients, provide guidance for clinicians to assess their survival and clinical management, and make a recommendation for the next Gleason grading system update.

Dietary Patterns Are Associated with Risk of Prostate Cancer in a Population-Based Case-Control Study in Montreal, Canada

This study describes the association between dietary patterns and prostate cancer (PCa) risk in a population-based case-control study conducted in Montreal, Canada (2005-2012). Cases (n = 1919) were histologically confirmed, aged ≤75 years. Concomitantly, controls (n = 1991) were randomly selected from the electoral list and frequency-matched to cases by age (±5 years). During face-to-face interviews, a 63-item food frequency questionnaire focusing on the two years before diagnosis/interview was administered. Three dietary patterns were identified from principal component analysis. Unconditional logistic regression estimated the association between dietary patterns and PCa, adjusting for age, ethnicity, education, family history, and timing of last PCa screening. When comparing scores in the highest vs. lowest quartiles, the Healthy Eating pattern was associated with a decreased risk of overall PCa (Odds ratio (OR) = 0.76, 95% confidence interval (CI) = 0.61, 0.93); this association was stronger for high-grade cancers (OR = 0.66, 95% CI = 0.48, 0.89). By contrast, the Western Sweet and Beverages pattern was associated with an elevated risk of overall PCa (OR = 1.35, 95% CI = 1.10, 1.66). The Western Salty and Alcohol pattern was not associated with PCa risk. These findings suggest that some dietary patterns influence PCa development.

Luminal Water Imaging: Comparison With Diffusion-Weighted Imaging (DWI) and PI-RADS for Characterization of Prostate Cancer Aggressiveness

BACKGROUND

Luminal water imaging (LWI), a multicomponent T₂ mapping technique, has shown promise for prostate cancer (PCa) detection and characterization.

PURPOSE

To 1) quantify LWI parameters and apparent diffusion coefficient (ADC) in PCa and benign peripheral zone (PZ) tissues; and 2) evaluate the diagnostic performance of LWI, ADC, and PI-RADS parameters for differentiation between low- and high-grade PCa lesions.

STUDY TYPE

Prospective.

SUBJECTS

Twenty-six PCa patients undergoing prostatectomy (mean age 59 years, range 46-72 years).

FIELD STRENGTH/SEQUENCE

Multiparametric MRI at 3.0T, including diffusion-weighted imaging (DWI) and LWI T₂ mapping.

ASSESSMENT

LWI parameters and ADC were quantified in index PCa lesions and benign PZ.

STATISTICAL TESTS

Differences in MRI parameters between PCa and benign PZ were assessed using Wilcoxon signed tests. Spearman correlation of pathological grade group (GG) with LWI parameters, ADC, and PI-RADS was evaluated. The utility of each of the parameters for differentiation between low-grade (GG ≤2) and high-grade (GG ≥3) PCa was determined by Mann-Whitney U tests and ROC analyses.

RESULTS

Twenty-six index lesions were analyzed (mean size 1.7 ± 0.8 cm, GG: 1 [n = 1; 4%], 2 [n = 14, 54%], 3 [n = 8, 31%], 5 [n = 3, 12%]). LWI parameters and ADC both showed high diagnostic performance for differentiation between benign PZ and PCa (highest area under the curve [AUC] for LWI parameter T_2,short [AUC = 0.98, P < 0.001]). The LWI parameters luminal water fraction (LWF) and amplitude of long T₂ component A_long significantly correlated with GG (r = -0.441, P = 0.024 and r = -0.414, P = 0.036, respectively), while PI-RADS, ADC, and the other LWI parameters did not (P = 0.132-0.869). LWF and A_long also showed significant differences between low-grade and high-grade PCa (AUC = 0.776, P = 0.008 and AUC = 0.758, P = 0.027, respectively). Maximum diagnostic performance for discrimination of high-grade PCa was found with combined LWI parameters (AUC 0.891, P = 0.001).

DATA CONCLUSION

LWI parameters, in particular in combination, showed superior diagnostic performance for differentiation between low-grade and high-grade PCa compared to ADC and PI-RADS assessment. J. Magn. Reson. Imaging 2020;52:271-279.

The prediction value of PI-RADS v2 score in high-grade Prostate Cancer: a multicenter retrospective study

Background: To explore the prediction value of PI-RADS v2 in high-grade prostate cancer and establish a prediction model combined with related variables of prostate cancer. Material and Methods: A total of 316 patients with newly discovered prostate cancer at Zhongnan Hospital of Wuhan University and Renmin Hospital of Wuhan University from December 2017 to August 2019 were enrolled in this study. The clinic information as age, tPSA, fPSA, prostate volume, Gleason score and PI-RADS v2 score have been collected. Univariate analysis was performed based on every variable to investigate the risk factors of high-grade prostate cancer. ROC curves were generated for the risk factors to distinguish the cut-off points. Logistic regression analyses were used to investigate the independent risk factors of high-grade prostate cancer. Nomogram prediction model was generated based on multivariate logistic regression analysis. The calibration curve, ROC curve, leave-one-out cross validation and independent external validation were performed to evaluate the discriminative ability, accuracy and stability of the nomogram prediction model. Results: Of 316 patients, a total of 187 patients were diagnosed as high-grade prostate cancer. Univariate analysis showed tPSA, fPSA, prostate volume, PSAD and PI-RADS v2 score were significantly different between the high- and low-grade prostate cancer patients. Univariate and multivariate logistic regression analyses showed only tPSA, prostate volume and PI-RADS v2 score were the independent risk factors of high-grade prostate cancer. The nomogram could predict the probability of high-grade prostate cancer, with a sensitivity of 79.4% and a specificity of 77.6%. The calibration curve displayed good agreement of the predicted probability with the actual observed probability. AUC of the ROC curve was 0.840 (0.797-0.884). Leave-one-out cross validation indicated the nomogram prediction model could classify 81.4% cases accurately. External data validation was performed with a sensitivity of 80.6% and a specificity of 77.3%, the Kappa value was 0.5755. Conclusions: PI-RADS v2 score had the value in predicting high-grade prostate cancer and the nomogram prediction model may help early diagnose the high risk prostate cancer.

Night-Shift Work and Risk of Prostate Cancer: Results From a Canadian Case-Control Study, the Prostate Cancer and Environment Study

Night-shift work involving disruption of circadian rhythms has been associated with breast cancer risk. A role in prostate cancer is also suspected, but evidence is limited. We investigated the association between night-shift work and prostate cancer incidence in the Prostate Cancer and Environment Study (PROtEuS), a population-based case-control study conducted in 2005-2012 in Montreal, Quebec, Canada. Participants were 1,904 prostate cancer cases (432 high-grade cancers) and 1,965 population controls. Detailed work schedules for each job held for at least 2 years (n = 15,724) were elicited in face-to-face interviews. Night-shift work was defined as having ever worked ≥3 hours between midnight and 5:00 am ≥3 nights/month for ≥1 year. Unconditional logistic regression was used to estimate odds ratios and 95% confidence intervals for the association between night-shift work and prostate cancer, adjusting for age, ancestry, and education. No association was found between overall prostate cancer and night-shift work metrics, including ever exposure, duration, intensity, cumulative exposure, rotating shifts, and early-morning shifts. For none of the exposure indices was there evidence of heterogeneity in odds ratios between low- and high-grade cancers. Sensitivity analyses restricting exposures to ≥7 nights/month or considering screening history yielded similar results. Our findings lend no support for a major role of night-shift work in prostate cancer development.

Cartilage oligomeric matrix protein in patients with osteoarthritis is independently associated with metastatic disease in prostate cancer

Metastatic prostate cancer has a 5-year survival rate of 30%. Identifying predictors of metastasis outcome could potentially reduce patient mortality. The objective of this study was to determine whether osteoarthritis had an impact on outcomes of prostate cancer including death, local recurrence and/or metastasis and to determine whether cartilage oligomeric matrix protein was involved. We performed a retrospective case-control study of patients with prostate cancer with and without the diagnosis of osteoarthritis and completed immunohistochemistry (IHC) analysis of prostate (n=20) and lymph node (n=7) surgical specimens. We evaluated death, local recurrence and metastatic disease by various IHC biomarkers including prostate specific membrane antigen (PSMA), cartilage oligomeric matrix protein (COMP), CD31, and Ki-67. Our model identified osteoarthritis as an independent risk factor for metastatic disease (OR 5.24, 95% CI 1.49 - 18.41). Most notably, when joint arthroplasty was included in the model, osteoarthritis was no longer an independent risk factor for this outcome (p=0.071). IHC demonstrated that those with osteoarthritis, had greater expression of COMP in the prostate samples (mean 23.9% vs 5.84%, p<0.05) but not of Ki-67, CD31, or PSMA. This study identified and quantified increased metastatic disease in patients with osteoarthritis. Also, patients with osteoarthritis expressed increased COMP levels in the prostate and most likely in distant lymphatic nodes. Moreover, our findings suggest that joint arthroplasty may affect the ability of osteoarthritis to promote metastasis, which could impact treatment protocols and survival outcomes of the most common cause of cancer-related death (metastasis) in the United States.

The validation of the 2014 International Society of Urological Pathology (ISUP) grading system for patients with high-risk prostate cancer: a single-center retrospective study

Introduction

Since the new 2014 grading system was recommended by the International Society of Urological Pathology (ISUP), it has been validated in patients with localized prostate cancer (PCa) and it has shown excellent prognostic value. However, its predictive power in high-risk PCa remains unclear.

Methods

A total of 420 patients with high-risk PCa who underwent radical prostatectomy (RP) were included in this study. Biochemical recurrence-free survival (BRFS) was set as the endpoint.

Results

Biochemical recurrence occurred in 84/420 (20.0%) patients at the end of follow-up. Compared to the three-tier grouping system, the five-tier grouping system could more effectively distinguish the BRFS of patients with higher predictive accuracy (C-index: 0.599 vs 0.646). The BRFS of patients with grade group (GG) 1 and GG 2 was similar (P=0.593). Also, the prognosis between those with GG 2 and GG 3 could be clearly distinguished (P=0.001). However, the discrimination capacity between patients with GG 3 and GG 4 was limited (P=0.681). When tertiary Gleason pattern (TGP5) and intraductal carcinoma of the prostate (IDC-P) were excluded, the HR value of the GG 4 group vs the GG 3 group increased from 1.15 (95% CI: 0.59–2.22) to 1.49 (95% CI: 0.72–3.10) and 1.36 (95% CI:0.65–2.83), respectively.

Conclusions

This study is the first to validate the new 2014 ISUP grading system in patients with high-risk PCa who underwent RP. The 2014 system could effectively classify patients into five groups with high predictive accuracy. Notably, the existence of TGP5 and IDC-P needs to be routinely reported in clinical practice, which could help to support the predictive value of the new grading system.

Radiomics and machine learning of multisequence multiparametric prostate MRI: Towards improved non-invasive prostate cancer characterization

Purpose

To develop and validate a classifier system for prediction of prostate cancer (PCa) Gleason score (GS) using radiomics and texture features of T₂-weighted imaging (T₂w), diffusion weighted imaging (DWI) acquired using high b values, and T₂-mapping (T₂).

Methods

T₂w, DWI (12 b values, 0–2000 s/mm²), and T₂ data sets of 62 patients with histologically confirmed PCa were acquired at 3T using surface array coils. The DWI data sets were post-processed using monoexponential and kurtosis models, while T₂w was standardized to a common scale. Local statistics and 8 different radiomics/texture descriptors were utilized at different configurations to extract a total of 7105 unique per-tumor features. Regularized logistic regression with implicit feature selection and leave pair out cross validation was used to discriminate tumors with 3+3 vs >3+3 GS.

Results

In total, 100 PCa lesions were analysed, of those 20 and 80 had GS of 3+3 and >3+3, respectively. The best model performance was obtained by selecting the top 1% features of T₂w, ADC_m and K with ROC AUC of 0.88 (95% CI of 0.82–0.95). Features from T₂ mapping provided little added value. The most useful texture features were based on the gray-level co-occurrence matrix, Gabor transform, and Zernike moments.

Conclusion

Texture feature analysis of DWI, post-processed using monoexponential and kurtosis models, and T₂w demonstrated good classification performance for GS of PCa. In multisequence setting, the optimal radiomics based texture extraction methods and parameters differed between different image types.

Circulating blood miRNAs for prostate cancer risk stratification: miRroring the underlying tumor biology with liquid biopsies

Current risk stratification methods for prostate cancer – although they have seen marked improvements over the past decades – are far from perfect. Despite the significant utility of prostate-specific antigen as a biomarker to monitor for disease recurrence, it cannot predict which tumors will recur or recommend the best treatment for patients. Similarly, although biopsies are imperative for diagnosis and staging, they are saddled with limitations and risks. We must move toward a noninvasive biomarker that has predictive and prognostic efficacy. We therefore review the current literature on circulating miRNA biomarkers, apply their use to two significant clinical problems (ie, how limitations of prostate biopsies can impact diagnosis and treatment management, and the need to tailor treatment for a clinically heterogeneous disease), and evaluate how circulating miRNAs have inherent properties that make them ideal liquid biomarkers. We also outline current gaps in knowledge that must be addressed before they can be implemented into routine clinical practice. With further research on their function and validation of their biomarker utility in large prospective cohorts, circulating miRNAs will likely prove to be the liquid biopsies of tomorrow.

Vitamin K intake and prostate cancer risk in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial

Background

Vitamin K inhibits prostate cancer cells, and an altered expression of vitamin K-dependent proteins in prostate tumors has been linked to their aggressiveness and progression. However, little is known about the effect of vitamin K intake on prostate cancer in human populations.

Objectives

We evaluated the associations of dietary intake of phylloquinone (vitamin K-1), menaquinones (vitamin K-2), and total vitamin K with the development of prostate cancer among participants in the Prostate, Lung, Colorectal, and Ovarian Cancer (PLCO) Screening Trial.

Design

Dietary intake of vitamin K was assessed with the Dietary Questionnaire (DQX) at baseline and the Dietary History Questionnaire (DHQ) at the third anniversary of randomization by using high-performance liquid chromatography-based food-composition data obtained from the USDA and published studies. During a median follow-up of 11.8 y, 2978 cases of prostate cancer (including 490 advanced cases) were identified from the 28,356 men who completed DQX. Similarly, 2973 cases of prostate cancer (including 647 advanced cases) were documented from the 48,090 men who completed DHQ. Cox proportional hazards regression was used to estimate prostate cancer risk in relation to the dietary intake of vitamin K.

Results

After adjustment for confounders, dietary intakes of phylloquinone, menaquinones, and total vitamin K, assessed with either the DQX or DHQ, were not significantly associated with the risk of advanced, nonadvanced, and total prostate cancer. These results remained virtually the same when vitamin K intake was modeled as a categorical (divided into quintiles) or continuous (per IQR increase) variable or after outliers of total vitamin K intake (defined as a value that falls above the sum of third quartile and twice the IQR) were excluded.

Conclusions

The present study does not suggest that vitamin K intake influences the occurrence of total and advanced prostate cancer in the general US population.

Association Analysis of a Microsatellite Repeat in the TRIB1 Gene With Prostate Cancer Risk, Aggressiveness and Survival

With an estimated 1.1 million men worldwide diagnosed with prostate cancer yearly, effective and more specific biomarkers for early diagnosis could lead to better patient outcome. As such, novel genetic markers are sought for this purpose. The tribbles homologue 1 gene (TRIB1) has recently shown to have a role in prostate tumorigenesis and data-mining of prostate cancer expression data confirmed clinical significance of TRIB1 in prostate cancer. For the first time, a polymorphic microsatellite in this gene was studied for its potential association with prostate cancer risk and aggressiveness. Genomic DNA was extracted from a cohort of 1,152 prostate cancer patients and 1,196 cancer-free controls and the TTTTG-TRIB1 microsatellite was genotyped. The socio-demographic and clinical characteristics were analyzed using the non-parametric t-test and two-way ANOVA. Association of the TTTTG-TRIB1 microsatellite and prostate cancer risk and aggressiveness were analyzed by binary logistic regression and confirmed by bootstrapping. Total and prostate cancer mortality was analyzed using the Kaplan Meier test. Genotype and allele correlation with TRIB1 mRNA levels was analyzed using the non-parametric Kolmogorov-Smirnov test. To predict the effect that the TTTTG-TRIB1 polymorphisms had on the mRNA structure, the in silico RNA folding predictor tool, mfold, was used. By analyzing the publicly available data, we confirmed a significant over-expression of TRIB1 in prostate cancer compared to other cancer types, and an over-expression in prostate cancerous tissue compared to adjacent benign. Three alleles (three-five repeats) were observed for TTTTG-TRIB1. The three-repeat allele was associated with prostate cancer risk at the allele (OR = 1.16; P = 0.044) and genotypic levels (OR = 1.70; P = 0.006) and this association was age-independent. The four-repeat allele was inversely associated with prosatet cancer risk (OR = 0.57; P < 0.0001). TRIB1 expression was upregulated in tumors when compared to adjacent cancer-free tissue but was not allele specific. In silico analysis suggested that the TTTTG-TRIB1 alleles may alter TRIB1 mRNA structure. In summary, the three-repeat allele was significantly associated with prostate cancer risk, suggesting a biomarker potential for this microsatellite to predict prostate cancer. Further studies are needed to elucidate the functional role of this microsatellite in regulating TRIB1 expression, perhaps by affecting the TRIB1 mRNA structure and stability.

Recurrence analysis on prostate cancer patients with Gleason score 7 using integrated histopathology whole-slide images and genomic data through deep neural networks

Prostate cancer is the most common nonskin-related cancer, affecting one in seven men in the United States. Gleason score, a sum of the primary and secondary Gleason patterns, is one of the best predictors of prostate cancer outcomes. Recently, significant progress has been made in molecular subtyping prostate cancer through the use of genomic sequencing. It has been established that prostate cancer patients presented with a Gleason score 7 show heterogeneity in both disease recurrence and survival. We built a unified system using publicly available whole-slide images and genomic data of histopathology specimens through deep neural networks to identify a set of computational biomarkers. Using a survival model, the experimental results on the public prostate dataset showed that the computational biomarkers extracted by our approach had hazard ratio as 5.73 and C -index as 0.74, which were higher than standard clinical prognostic factors and other engineered image texture features. Collectively, the results of this study highlight the important role of neural network analysis of prostate cancer and the potential of such approaches in other precision medicine applications.

Vitamin C Intake and Risk of Prostate Cancer: The Montreal PROtEuS Study

Background: Vitamin C is a reducing agent and free radical scavenger, acting as antioxidant in plasma membranes and within cells. Based on these properties, a role for vitamin C in cancer incidence has been suspected. There are as yet few large population-based studies focusing on prostate cancer, with the preponderant evidence leaning toward the absence of an association. Nevertheless, many previous studies overlooked prostate cancer aggressiveness, as well as screening and detection issues, which could bias potential associations.

Methods: The Prostate Cancer and Environment Study (PROtEuS) is a population-based case-control study conducted in Montreal, Canada. In-person interviews, conducted with 1,916 histologically confirmed prostate cancer cases and 1,985 population controls, elicited information on a wide range of socio-demographic, lifestyle, and medical factors, including PSA screening. Usual frequency of consumption of 63 food items two years prior to diagnosis/interview was collected, along with use of dietary supplements. Odds ratios (OR) and 95% confidence intervals (CI) between vitamin C intake and prostate cancer were estimated using logistic or polytomous regression, adjusting for potential confounders.

Results: We observed no association between dietary intakes of vitamin C (OR for upper vs. lower tertile: 0.95, 95%CI 0.77, 1.18), estimated using the residual method to account for energy intake, or between regular use of vitamin C supplements and/or multivitamins (OR 0.90, 95%CI 0.76–1.05), and overall prostate cancer. Analyses considering disease aggressiveness, or restricted to subjects recently screened with PSA, thereby limiting the potential for undiagnosed cancers in non-cases, generated results consistent with those from the main analyses.

Conclusion: Our findings document the absence of an association between recent dietary vitamin C intake, or supplementation, and prostate cancer incidence overall or prostate cancer grade at diagnosis. Based on this, and other available evidence, vitamin C intake does not seem to hold promises with regard to prostate cancer prevention.

How Are Gleason Scores Categorized in the Current Literature: An Analysis and Comparison of Articles Published in 2016-2017

BACKGROUND

A new prostate cancer grading system was proposed in 2013 and endorsed by major journals and societies in 2014, in part because of anecdotal evidence that Gleason scores (GSs) were incorrectly combined in the literature.

OBJECTIVE

To examine how published studies categorized GSs in current practice.

DESIGN, SETTING, AND PARTICIPANTS

A PubMed search was conducted on articles published in 2016-2017 using the search terms "Gleason" and "prostate". This literature review included 1576 articles after exclusions.

RESULTS

(1) Separating GS 7: pathology journals were more likely than non-pathology journals to grade GS 7 separately (56.9% vs 40.0%, p<0.05). Articles co-authored by a pathologist separated GS 7 more than those without a pathologist (53.2% vs 32.9%, p<0.001). North American and European studies separated GS 7 more than Asian studies (47.6% and 44.1% vs 24.1%, p<0.001). Clinical articles separated GS 7 more than research articles (43.7% vs 32.9%, p<0.001). (2) Separating GS 8 from GS 9-10: pathology journals separated GS 8 from GS 9-10 more than non-pathology journals (55.2% vs 34.4%, p=0.001). Articles co-authored by a pathologist separated GS 8 from GS 9-10 more often than those without a pathologist (44.9% vs 29.5%, p<0.001). (3) Using grade groups as "ideal" with all other groupings "non-ideal": pathology journals used ideal more than non-pathology journals (32.2% vs 15.9%, p<0.001). Ideal grouping is more likely in articles co-authored by a pathologist than in those without a pathologist (20.6% vs 11.0%, p<0.001). North American and European studies used ideal grouping more than Asian studies (17.6% and 14.0% vs 9.1%, p<0.05). (4) Arranging groupings in decreasing order from ideal to non-ideal: pathology journals were closer to ideal than non-pathology journals (p=0.002). Articles co-authored by a pathologist were classified closer to ideal than those without a pathologist (p<0.001). North American (p<0.001) and European (p=0.02) studies were closer to ideal than Asian studies.

CONCLUSIONS

There is still wide variation in how GSs are grouped world-wide. Only a minority of published articles group GSs accurately.

PATIENT SUMMARY

In this report, we looked at how GSs were grouped world-wide. We found that only a minority of published articles on prostate cancer were grouping GSs accurately, which could lead to inaccurate results and affect patient care with different prostate cancer grades. Our study calls for more widespread adoption of the new prostate cancer grading system composed of five grade groups to minimize incorrect grouping for future studies.

Occupational exposure to benzene, toluene, xylene and styrene and risk of prostate cancer in a population-based study

OBJECTIVES

While several monocyclic aromatic hydrocarbons are classified as definite or possible carcinogens to humans, little data exist on their role in prostate cancer (PCa). We examined occupational exposure to benzene, toluene, xylene (BTX) and styrene and PCa risk in a population-based case-control study in Montreal, Canada.

METHODS

Cases aged ≤75 years diagnosed with PCa in 2005-2009 (n=1920) and population controls frequency-matched on age (n=1989) provided detailed work histories. Experts evaluated the certainty, frequency and concentration of exposure to monocyclic aromatic hydrocarbons in each job lasting ≥2 years. Logistic regression estimated OR and 95% CIs for PCa risk, adjusting for potential confounders.

RESULTS

Exposures to BTX were highly intercorrelated, except for durations of exposure at substantial levels. Ever exposure to any BTX was associated with overall PCa (OR 1.27, 95% CI 1.05 to 1.53), while the OR for styrene was 1.19. However, increases in risk were largely confined to low-grade tumours, with ORs of 1.33 (95%CI 1.08 to 1.64) and 1.41 (95% CI 0.85 to 2.31) for ever exposure to any BTX and styrene, respectively, and a duration response pattern for any BTX. Risks for low-grade tumours were elevated among men exposed ≥25 years at substantial levels of benzene (OR 2.32) and styrene (OR 2.44). Some cumulative exposure categories showed increased risks but without clear trends.

CONCLUSION

Exposure to any BTX was associated with higher risks of overall PCa. Prolonged exposures at the substantial level to benzene and styrene increased risks of low-grade tumours. These novel findings were independent from PCa screening.

PBOV1 as a potential biomarker for more advanced prostate cancer based on protein and digital histomorphometric analysis

BACKGROUND

There are few tissue-based biomarkers that can accurately predict prostate cancer (PCa) progression and aggressiveness. We sought to evaluate the clinical utility of prostate and breast overexpressed 1 (PBOV1) as a potential PCa biomarker.

METHODS

Patient tumor samples were designated by Grade Groups using the 2014 Gleason grading system. Primary radical prostatectomy tumors were obtained from 48 patients and evaluated for PBOV1 levels using Western blot analysis in matched cancer and benign cancer-adjacent regions. Immunohistochemical evaluation of PBOV1 was subsequently performed in 80 cancer and 80 benign cancer-adjacent patient samples across two tissue microarrays (TMAs) to verify protein levels in epithelial tissue and to assess correlation between PBOV1 proteins and nuclear architectural changes in PCa cells. Digital histomorphometric analysis was used to track 22 parameters that characterized nuclear changes in PBOV1-stained cells. Using a training and test set for validation, multivariate logistic regression (MLR) models were used to identify significant nuclear parameters that distinguish Grade Group 3 and above PCa from Grade Group 1 and 2 PCa regions.

RESULTS

PBOV1 protein levels were increased in tumors from Grade Group 3 and above (GS 4 + 3 and ≥ 8) regions versus Grade Groups 1 and 2 (GS 3 + 3 and 3 + 4) regions (P = 0.005) as assessed by densitometry of immunoblots. Additionally, by immunoblotting, PBOV1 protein levels differed significantly between Grade Group 2 (GS 3 + 4) and Grade Group 3 (GS 4 + 3) PCa samples (P = 0.028). In the immunohistochemical analysis, measures of PBOV1 staining intensity strongly correlated with nuclear alterations in cancer cells. An MLR model retaining eight parameters describing PBOV1 staining intensity and nuclear architecture discriminated Grade Group 3 and above PCa from Grade Group 1 and 2 PCa and benign cancer-adjacent regions with a ROC-AUC of 0.90 and 0.80, respectively, in training and test sets.

CONCLUSIONS

Our study demonstrates that the PBOV1 protein could be used to discriminate Grade Group 3 and above PCa. Additionally, the PBOV1 protein could be involved in modulating changes to the nuclear architecture of PCa cells. Confirmatory studies are warranted in an independent population for further validation.

The performance of the new prognostic grade and stage groups in conservatively treated prostate cancer

We evaluated the prognosis of the new grade groups and American Joint Committee on Cancer (AJCC) stage groups in men with prostate cancer (PCa) who were treated conservatively. A total of 13 798 eligible men were chosen from the Surveillance Epidemiology and End Results database. The new grade and AJCC stage groups were investigated on prostate biopsy specimens. Kaplan–Meier survival analysis and multivariable hazards models were applied to estimate the association of new grade and stage groups with overall survival (OS) and PCa-specific survival (CSS). Mean follow-up was 42.65 months (95% confidence interval: 42.47–42.84) in the entire cohort. The 3-year OS and CSS rates stepped down for grade groups 1–5 and AJCC stage groups I–IVB, respectively. After adjusting for clinical and pathological characteristics, all grade groups and AJCC stage groups were associated with higher all-cause and PCa-specific mortality compared to the reference group (all P ≤ 0.003). In conclusion, we evaluated the oncological outcome of the new grade and AJCC stage groups on biopsy specimens of conservatively treated PCa. These two novel clinically relevant classifications can assist physicians to determine different therapeutic strategies for PCa patients.