Selective detection of histologically aggressive prostate cancer: an Early Detection Research Network Prediction model to reduce unnecessary prostate biopsies with validation in the Prostate Cancer Prevention Trial

Peripheral zone PSA density: a predominant variable to improve prostate cancer detection efficiency in men with PSA higher than 4 ng ml-1

To improve the diagnostic efficiency of prostate cancer (PCa) and reduce unnecessary biopsies, we defined and analyzed the diagnostic efficiency of peripheral zone prostate-specific antigen (PSA) density (PZ-PSAD). Patients who underwent systematic 12-core prostate biopsies in Shanghai General Hospital (Shanghai, China) between January 2012 and January 2018 were retrospectively identified (n = 529). Another group of patients with benign prostatic hyperplasia (n = 100) were randomly preselected to obtain the PSA density of the non-PCa cohort (N-PSAD). Prostate volumes and transition zone volumes were measured using multiparameter magnetic resonance imaging (mpMRI) and were combined with PSA and N-PSAD to obtain the PZ-PSAD from a specific algorithm. Receiver operating characteristic (ROC) curve analysis was used to assess the PCa detection efficiency in patients stratified by PSA level, and the area under the ROC curve (AUC) of PZ-PSAD was higher than that of PSA, PSA density (PSAD), and transition zone PSA density (TZ-PSAD). PZ-PSAD could amend the diagnosis for more than half of the patients with inaccurate transrectal ultrasonography (TRUS) and mpMRI results. When TRUS and mpMRI findings were ambiguous to predict PCa (PIRADS score ≤3), PZ-PSAD could increase the positive rate of biopsy from 21.7% to 54.7%, and help 63.8% (150/235) of patients avoid unnecessary prostate biopsy. In patients whose PSA was 4.0–10.0 ng ml⁻¹, 10.1–20.0 ng ml⁻¹, and >20.0 ng ml⁻¹, the ideal PZ-PSAD cut-off value for predicting clinically significant PCa was 0.019 ng ml⁻², 0.297 ng ml⁻², and 1.180 ng ml⁻², respectively (sensitivity >90%). Compared with PSA, PSAD, and TZ-PSAD, the efficiency of PZ-PSAD for predicting PCa is the highest, leading to fewer missed diagnoses and unnecessary biopsies.

Prostate MRI: staging and decision-making

Multi-parametric prostate MRI (mpMRI) plays a critical role in the diagnosis, staging, and evaluation of treatment response in patients with prostate cancer. Radiologists, through an accurate and standardized interpretation of mpMRI, can clinically stage prostate cancer and help to risk stratify patients who may benefit from more invasive treatment or exclude patients who may be harmed by overtreatment. The purpose of this article is to describe key findings to accurately stage prostate cancer with mpMRI and to describe the contexts in which mpMRI is best applied.

Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient's subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.

Methods for the Analysis and Interpretation for Rare Variants Associated with Complex Traits

With the advent of Next Generation Sequencing (NGS) technologies, whole genome and whole exome DNA sequencing has become affordable for routine genetic studies. Coupled with improved genotyping arrays and genotype imputation methodologies, it is increasingly feasible to obtain rare genetic variant information in large datasets. Such datasets allow researchers to gain a more complete understanding of the genetic architecture of complex traits caused by rare variants. State-of-the-art statistical methods for the statistical genetics analysis of sequence-based association, including efficient algorithms for association analysis in biobank-scale datasets, gene-association tests, meta-analysis, fine mapping methods that integrate functional genomic dataset, and phenome-wide association studies (PheWAS), are reviewed here. These methods are expected to be highly useful for next generation statistical genetics analysis in the era of precision medicine. © 2019 by John Wiley & Sons, Inc.

Developing a new PI-RADS v2-based nomogram for forecasting high-grade prostate cancer

AIM

To establish a predictive nomogram for high-grade prostate cancer (HGPCa) in biopsy-naive patients based on the Prostate Imaging-Reporting and Data System version 2 (PI-RADS v2), magnetic resonance imaging (MRI)-based prostate volume (PV), MRI-based PV-adjusted prostate-specific antigen density (PSAD), and other classical parameters.

MATERIAL AND METHODS

Between August 2014 and August 2015, 158 men who were eligible for analysis were included as the training cohort. A prediction model for HGPCa was built using backward logistic regression and was presented on a nomogram. The prediction model was evaluated by a validation cohort between September 2015 and March 2016 (n=89). Histology of all lesions was obtained with MRI-directed transrectal ultrasound (TRUS)-guided targeted and sectoral biopsy.

RESULTS

The multivariate analysis revealed that patient age, PI-RADS v2 score, and adjusted PSAD were independent predictors for HGPCa. The most discriminative cut-off value for the logistic regression model was 0.33; the sensitivity, specificity, positive predictive value, and negative predictive value were 83.3%, 87.4%, 88.4%, and 81.2%, respectively. The diagnostic performance measures retained similar values in the validation cohort (AUC=0.83).

CONCLUSION

The nomogram for forecasting HGPCa is effective and potentially reducing harm from unnecessary prostate biopsy and over-diagnosis.

Limiting overdiagnosis of low-risk prostate cancer through an evaluation of the predictive value of transrectal and power Doppler ultrasonography

PURPOSE

Overdiagnosis induced by prostate cancer screening makes necessary a better selection of candidate patients for prostate biopsy. The objective of our study is to assess the probability of having a high- or low-risk lesion that could require active surveillance (AS) after biopsies and a normal or abnormal examination, including transrectal and power Doppler ultrasonography (TRUS-PDS).

METHODS

Four hundred and twenty-nine consecutive patients with a PSA level <10 ng/ml and a normal digital rectal examination (DRE) had guided biopsies in a prospective study. We used D'Amico's criteria to assess the risk of a biological recurrence and Dall'Era's criteria to assess possible AS. The TRUS-PDS was considered positive if one biopsy was positive in the same sextant as the suspect image.

RESULTS

One hundred and seventy-seven out of 429 (41 %) T1c cancers were diagnosed; 131 out of 177 (74 %) could be qualified as low risk, and 119 out of 177 (67 %) could require AS. The TRUS-PDS was normal in 285 of 429 patients (66 %). With a normal TRUS-PDS, the probability of not having cancer with a high or intermediate risk was 96 % (negative predictive value). With an abnormal TRUS-PDS, the probability of having a positive biopsy was 59 %, and the probability of having a significant cancer was 30 %, according to the Dall'Era criteria. When TRUS-PDS was normal, these probabilities significantly decreased to 32 and 5 %, respectively (p < 0.01).

CONCLUSIONS

Patients with a PSA level <10 ng/ml, a normal DRE, and a normal TRUS-PDS have a less than 5 % risk of high- or intermediate-risk cancer.

The transcription factor ERG increases expression of neurotransmitter receptors on prostate cancer cells

Background

The TMPRSS2-ERG gene fusion occurs in about half of prostate cancer (PCa) cases and results in overexpression of the transcription factor ERG. Overexpression of ERG has many effects on cellular function. However, how these changes enhance cell growth and promote tumor development is unclear.

Methods

To investigate the role of ERG, LNCaP and PC3 cells were transfected with ERG and gene expression and metabolic profile were analyzed.

Results

Our data show that expression of ERG induces overexpression of many nicotinicacetylcholine receptors (nAChRs). In addition, metabolic profiling by LC-MS/MS revealed elevated production of several neurotransmitters in cells expressing ERG. Consistently, treatment of ERG-expressing cells with nicotine induced elevated calcium influx, GSK3β (Ser9) phosphorylation and cell proliferation. Finally, we show that PCa patientswho are smokers have larger tumors if their tumors are TMPRSS2-ERG gene fusion positive.

Conclusion

Collectively, our data suggest that ERG sensitizes prostate tumor cells to neurotransmitter receptor agonists like nicotine.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-015-1612-3) contains supplementary material, which is available to authorized users.

Population-based assessment of determining predictors for quality of prostate cancer surveillance

BACKGROUND

Despite recent interest in the overdiagnosis and overtreatment of prostate cancer, the acceptance of expectant management for patients with indolent prostate cancer has remained slow. Moreover, the intensity of surveillance strategies remains to be elucidated. The objective of this study was to determine the population-based intensity of surveillance strategy among patients diagnosed with localized prostate cancer who undergo watchful waiting/active surveillance and those who receive active treatment.

METHODS

Linked Surveillance, Epidemiology, and End Results (SEER)-Medicare data were used to identify men diagnosed with prostate cancer from 2004 to 2007 who were followed until December 31, 2009. Bivariate and multivariate regression analyses were used to quantify the use of prostate-specific antigen tests, office visits, and second prostate biopsies within 2 years of diagnosis.

RESULTS

Compared with patients who were receiving active treatment, those who were undergoing watchful waiting/active surveillance were less likely to receive prostate-specific antigen testing and to attend office visits within the 2 years after diagnosis (P < .01). Of the 3656 patients who were undergoing watchful waiting/active surveillance, only 166 (4.5%) were on active surveillance (according to the authors' a priori definition), although the number increased over the last 2 years of the study (hazard ratio, 2.18; 95% confidence interval, 1.28-3.71; P < .01). Limitations of the study included data limited to men aged 65 years or older and the lack of ability to discern between watchful waiting and active surveillance.

CONCLUSIONS

Active surveillance is underused, and there is uncertainty regarding the quality of surveillance for patients who undergo watchful waiting/active surveillance. Further research is needed into qualitatively describing the contributing factors that drive decision-making recommendations as well as improved surveillance measures for patients with prostate cancer.

Impact of obesity on outcomes after definitive dose-escalated intensity-modulated radiotherapy for localized prostate cancer

BACKGROUND

Previous publications have demonstrated conflicting results regarding body mass index (BMI) and prostate cancer (CaP) outcomes after definitive radiotherapy (RT) before the dose escalation era. The goal of the current study was to determine whether increasing BMI was associated with outcomes in men with localized CaP who were treated with dose-escalated RT.

METHODS

The authors identified patients with localized (T1b-T4N0M0) CaP who were treated with definitive intensity-modulated RT and image-guided RT from 2001 through 2010. BMI was analyzed as a continuous variable. Adjusting for confounders, multivariable competing risk and Cox proportional hazards regression models were used to assess the association between BMI and the risk of biochemical failure (BF), distant metastases (DM), cause-specific mortality (CSM), and overall mortality.

RESULTS

Of the 1442 patients identified, approximately 20% had a BMI <25 kg/m(2) , 48% had a BMI of 25 to 29.9 kg/m(2) , 23% had a BMI of 30 to 34.9 kg/m(2) , 6% had a BMI of 35 to 39.9 kg/m(2) , and 4% had a BMI of ≥40 kg/m(2) . The median follow-up was 47.6 months (range, 1-145 months), with a median age of 68 years (range, 36-89 years). The median dose was 78 grays (range, 76-80 grays) and 30% of patients received androgen deprivation therapy. Increasing BMI was found to be inversely associated with age (P<.001) and pretreatment prostate-specific antigen level (P = .018). On multivariable analysis, increasing BMI was associated with an increased risk of BF (hazard ratio [HR], 1.03; 95% confidence interval [95% CI], 1.00-1.07 [P = .042]), DM (HR, 1.07; 95% CI, 1.02-1.11 [P = .004]), CSM (HR, 1.15; 95% CI, 1.07-1.23 [P<.001]), and overall mortality (HR, 1.05; 95% CI, 1.02-1.08 [P = .004]).

CONCLUSIONS

For patients with CaP receiving dose-escalated intensity-modulated RT with daily image-guidance, increasing BMI appears to be associated with an increased risk of BF, DM, CSM, and overall mortality.

Disruption of prostate epithelial differentiation pathways and prostate cancer development

One of the foremost problems in the prostate cancer (PCa) field is the inability to distinguish aggressive from indolent disease, which leads to difficult prognoses and thousands of unnecessary surgeries. This limitation stems from the fact that the mechanisms of tumorigenesis in the prostate are poorly understood. Some genetic alterations are commonly reported in prostate tumors, including upregulation of Myc, fusion of Ets genes to androgen-regulated promoters, and loss of Pten. However, the specific roles of these aberrations in tumor initiation and progression are poorly understood. Likewise, the cell of origin for PCa remains controversial and may be linked to the aggressive potential of the tumor. One important clue is that prostate tumors co-express basal and luminal protein markers that are restricted to their distinct cell types in normal tissue. Prostate epithelium contains layer-specific stem cells as well as rare bipotent cells, which can differentiate into basal or luminal cells. We hypothesize that the primary oncogenic cell of origin is a transient-differentiating bipotent cell. Such a cell must maintain tight temporal and spatial control of differentiation pathways, thus increasing its susceptibility for oncogenic disruption. In support of this hypothesis, many of the pathways known to be involved in prostate differentiation can be linked to genes commonly altered in PCa. In this article, we review what is known about important differentiation pathways (Myc, p38MAPK, Notch, PI3K/Pten) in the prostate and how their misregulation could lead to oncogenesis. Better understanding of normal differentiation will offer new insights into tumor initiation and may help explain the functional significance of common genetic alterations seen in PCa. Additionally, this understanding could lead to new methods for classifying prostate tumors based on their differentiation status and may aid in identifying more aggressive tumors.

Impact of obesity on cancer survivorship and the potential relevance of race and ethnicity

Evidence that obesity is associated with cancer incidence and mortality is compelling. By contrast, the role of obesity in cancer survival is less well understood. There is inconsistent support for the role of obesity in breast cancer survival, and evidence for other tumor sites is scant. The variability in findings may be due in part to comorbidities associated with obesity itself rather than with cancer, but it is also possible that obesity creates a physiological setting that meaningfully alters cancer treatment efficacy. In addition, the effects of obesity at diagnosis may be distinct from the effects of weight change after diagnosis. Obesity and related comorbid conditions may also increase risk for common adverse treatment effects, including breast cancer-related lymphedema, fatigue, poor health-related quality of life, and worse functional health. Racial and ethnic groups with worse cancer survival outcomes are also the groups for whom obesity and related comorbidities are more prevalent, but findings from the few studies that have addressed these complexities are inconsistent. We outline a broad theoretical framework for future research to clarify the specifics of the biological-social-environmental feedback loop for the combined and independent contributions of race, comorbid conditions, and obesity on cancer survival and adverse treatment effects. If upstream issues related to comorbidities, race, and ethnicity partly explain the purported link between obesity and cancer survival outcomes, these factors should be among those on which interventions are focused to reduce the burden of cancer.

Overdiagnosis of prostate cancer

This chapter addresses issues relevant to prostate cancer overdiagnosis. Factors promoting the overdiagnosis of prostate cancer are reviewed. First is the existence of a relatively large, silent reservoir of this disease, as can be seen by evaluating autopsy studies and histologic step-sectioning results of prostates removed for other causes. The second main factor responsible for prostate cancer overdiagnosis is fairly widespread prostate-specific antigen and digital rectal examination-based screening, which has been fairly widely practiced in the United States for the past 20 years among heterogeneous groups of men. This has resulted in the identification of many men from this reservoir who otherwise may never have been diagnosed with symptomatic prostate cancer and is substantially responsible for the current annual incidence to mortality ratio for prostate cancer of approximately 6 to 1. Finally, the relatively indolent natural history and limited cancer-specific mortality as reported in a variety of contemporary randomized screening and treatment trials is reviewed. We attempt to quantitate the proportion of newly diagnosed prostate cancers that are overdiagnosed using various trial results and models. We explore the impact of prostate cancer overdiagnosis in terms of patient anxiety and the potential for overtreatment, with its attendant morbidity. We explore strategies to minimize overdiagnosis by targeting screening and biopsy only to men at high risk for aggressive prostate cancer and by considering the use of agents such as 5-alpha reductase inhibitors. Future prospects to prevent overtreatment, including better biopsy and molecular characterization of newly diagnosed cancer and the role of active surveillance, are discussed.

Improving prostate cancer detection in veterans through the development of a clinical decision rule for prostate biopsy

BACKGROUND

We sought to improve prostate cancer (PC) detection through developing a prostate biopsy clinical decision rule (PBCDR), based on an elevated PSA and laboratory biomarkers. This decision rule could be used after initial PC screening, providing the patient and clinician information to consider prior to biopsy.

METHODS

This case-control study evaluated men from the Tampa, Florida, James A. Haley (JH) Veteran's Administration (VA) (N = 1,378), from January 1, 1998, through April 15, 2005. To assess the PBCDR we did all of the following: 1) Identified biomarkers that are related to PC and have the capability of improving the efficiency of PC screening; 2) Developed statistical models to determine which can best predict the probability of PC; 3) Compared each potential model to PSA alone using Receiver Operator Characteristic (ROC) curves, to evaluate for improved overall effectiveness in PC detection and reduction in (negative) biopsies; and 4) Evaluated dose-response relationships between specified lab biomarkers (surrogates for extra-prostatic disease development) and PC progression.

RESULTS

The following biomarkers were related to PC: hemoglobin (HGB) (OR = 1.42 95% CI 1.27, 1.59); red blood cell (RBC) count (OR = 2.52 95% CI 1.67, 3.78); PSA (OR = 1.04 95% CI 1.03, 1.05); and, creatinine (OR = 1.55 95% CI 1.12, 2.15). Comparing all PC stages versus non-cancerous conditions, the ROC curve area under the curve (AUC) enlarged (increasing the probability of correctly classifying PC): PSA (alone) 0.59 (95% CI 0.55, 0.61); PBCDR model 0.68 (95% CI 0.65, 0.71), and the positive predictive value (PPV) increased: PSA 44.7%; PBCDR model 61.8%. Comparing PC (stages II, III, IV) vs. other, the ROC AUC increased: PSA (alone) 0.63 (95% CI 0.58, 0.66); PBCDR model 0.72 (95% CI 0.68, 0.75), and the PPV increased: 20.6% (PSA); PBCDR model 55.3%.

CONCLUSIONS

These results suggest evaluating certain common biomarkers in conjunction with PSA may improve PC prediction prior to biopsy. Moreover, these biomarkers may be more helpful in detecting clinically relevant PC. Follow-up studies should begin with replicating the study on different U.S. VA patients involving multiple practices.

The impact of prostate volume, number of biopsy cores and American Urological Association symptom score on the sensitivity of cancer detection using the Prostate Cancer Prevention Trial risk calculator

PURPOSE

We assessed the independent predictive value of prostate volume, number of biopsy cores and AUASS (American Urological Association symptom score) compared to risk factors included in the PCPTRC (Prostate Cancer Prevention Trial risk calculator for prostate cancer) and PCPTHG (Prostate Cancer Prevention Trial risk calculator for high grade cancer [Gleason grade 7 or greater]).

MATERIALS AND METHODS

Of 5,519 PCPT (Prostate Cancer Prevention Trial) participants used to construct the PCPTRC 4,958 with AUASS and prostate specific antigen 10 ng/ml or less were included on logistic regression analysis. Risk algorithms were evaluated in 571 EDRN (Early Detection Research Network) participants using the ROC AUC.

RESULTS

A total of 1,094 participants (22.1%) had prostate cancer, of whom 232 (21.2%) had high grade disease. For prostate cancer prediction higher prostate specific antigen, abnormal digital rectal examination, family history of prostate cancer and number of cores were associated with increased risk, while volume was associated with decreased risk. Excluding prostate volume and number of cores, a history of negative biopsy and increased AUASS were also associated with lower risk. For high grade cancer higher prostate specific antigen, abnormal digital rectal examination, black race and number of cores were associated with increased risk and volume, while AUASS was associated with decreased risk. The AUC of the PCPTRC adjusted for volume and number of cores was 72.7% (using EDRN data), 68.2% when adjusted for AUASS alone and 67.6% PCPTRC. For high grade disease the AUCs were 74.8%, 74.0% and 73.5% (PCPTHG), respectively.

CONCLUSIONS

Adjusted PCPT risk calculators for volume, number of cores and AUASS improve cancer detection.