Comparison of outcomes of different biopsy schedules among men on active surveillance for prostate cancer: An analysis of the G.A.P.3 global consortium database

BACKGROUND

The optimal interval for repeat biopsy during active surveillance (AS) for prostate cancer is yet to be defined. This study examined whether risk of upgrading (to grade group ≥ 2) or risk of converting to treatment varied according to intensity of repeat biopsy using data from the GAP3 consortium's global AS database.

MATERIALS AND METHODS

Intensity of surveillance biopsy schedules was categorized according to centers' protocols: (a) Prostate Cancer Research International Active Surveillance project (PRIAS) protocols with biopsies at years 1, 4, and 7 (10 centers; 7532 men); (b) biennial biopsies, that is, every other year (8 centers; 4365 men); and (c) annual biopsy schedules (4 centers; 1602 men). Multivariable Cox regression was used to compare outcomes according to biopsy intensity.

RESULTS

Out of the 13,508 eligible participants, 56% were managed according to PRIAS protocols (biopsies at years 1, 4, and 7), 32% via biennial biopsy, and 12% via annual biopsy. After adjusting for baseline characteristics, risk of converting to treatment was greater for those on annual compared with PRIAS biopsy schedules (hazard ratio [HR] = 1.66; 95% confidence interval [CI] = 1.51-1.83; p < 0.001), while risk of upgrading did not differ (HR = 0.96; 95% CI = 0.84-1.10).

CONCLUSION

Results suggest more frequent biopsy schedules may deter some men from continuing AS despite no evidence of grade progression.

Data Science in Healthcare: COVID-19 and Beyond

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning (ML), neural networks (NN) and artificial intelligence (AI), to create value, based on extracting knowledge and insights from available 'big' data [...].

Prostate Cancer Patients Under Active Surveillance with a Suspicious Magnetic Resonance Imaging Finding Are at Increased Risk of Needing Treatment: Results of the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) Consortium

BACKGROUND

The inclusion criterion for active surveillance (AS) is low- or intermediate-risk prostate cancer. The predictive value of the presence of a suspicious lesion at magnetic resonance imaging (MRI) at the time of inclusion is insufficiently known.

OBJECTIVE

To evaluate the percentage of patients needing active treatment stratified by the presence or absence of a suspicious lesion at baseline MRI.

DESIGN SETTING AND PARTICIPANTS

A retrospective analysis of the data from the multicentric AS GAP3 Consortium database was conducted. The inclusion criteria were men with grade group (GG) 1 or GG 2 prostate cancer combined with prostate-specific antigen <20 ng/ml. We selected a subgroup of patients who had MRI at baseline and for whom MRI results and targeted biopsies were used for AS eligibility. Suspicious MRI was defined as an MRI lesion with Prostate Imaging Reporting and Data System (PI-RADS)/Likert ≥3 and for which targeted biopsies did not exclude the patient for AS.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

The primary outcome was treatment free survival (FS). The secondary outcomes were histological GG progression FS and continuation of AS (discontinuation FS).

RESULTS AND LIMITATIONS

The study cohort included 2119 patients (1035 men with nonsuspicious MRI and 1084 with suspicious MRI) with a median follow-up of 23 (12-43) mo. For the whole cohort, 3-yr treatment FS was 71% (95% confidence interval [CI]: 69-74). For nonsuspicious MRI and suspicious MRI groups, 3-yr treatment FS rates were, respectively, 80% (95% CI: 77-83) and 63% (95% CI: 59-66). Active treatment (hazard ratio [HR] = 2.0, p < 0.001), grade progression (HR = 1.9, p < 0.001), and discontinuation of AS (HR = 1.7, p < 0.001) were significantly higher in the suspicious MRI group than in the nonsuspicious MRI group.

CONCLUSIONS

The risks of switching to treatment, histological progression, and AS discontinuation are higher in cases of suspicious MRI at inclusion.

PATIENT SUMMARY

Among men with low- or intermediate-risk prostate cancer who choose active surveillance, those with suspicious magnetic resonance imaging (MRI) at the time of inclusion in active surveillance are more likely to show switch to treatment than men with nonsuspicious MRI.

Comparison of Characteristics, Follow-up and Outcomes of Active Surveillance for Prostate Cancer According to Ethnicity in the GAP3 Global Consortium Database

BACKGROUND

Studies of active surveillance (AS) for prostate cancer (PCa) have focussed predominantly on Caucasian populations. Little is known about the experience of Asian men, while suitability for men of African descent has been questioned.

OBJECTIVE

To compare baseline characteristics, follow-up, and outcomes for men on AS for PCa, according to ethnicity.

DESIGN SETTING AND PARTICIPANTS

The study cohort included 13 centres from the GAP3 consortium that record ethnicity (categorised broadly as Caucasian/white, African/Afro-Caribbean/black, Asian, mixed/other, and unknown). Men with biopsy grade group >2, prostate-specific antigen (PSA) >20 ng/ml, T stage ≥cT3, or age >80 yr were excluded.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Clinical characteristics, follow-up schedules, outcome status, and reasons for discontinuation were compared across ethnic groups. Risk of upgrading, potential disease progression (grade group ≥3 or T stage ≥3), suspicious indications (any upgrading, number of positive cores >3, T stage ≥cT3, PSA >20 ng/ml, or PSA density >0.2 ng/ml/cc²), and conversion to treatment were assessed using mixed-effect regression models.

RESULTS AND LIMITATIONS

The eligible cohort (n = 9158) comprised 83% Caucasian men, 6% men of African descent, 5% Asian men, 2% men of mixed/other ethnicity, and 4% men of unknown ethnicity. Risks of suspicious indicators (hazard ratio = 1.27; 95% confidence interval [CI] 1.12-1.45), upgrading (odds ratio [OR] = 1.40; 95% CI 1.14-1.71), and potential progression (OR = 1.46; 95% CI 1.06-2.01) were higher among African/black than among Caucasian/white men. Risk of transitioning to treatment did not differ by ethnicity. More Asian than Caucasian men converted without progression (42% vs 26%, p < 0.001). Heterogeneity in surveillance protocols and racial makeup limit interpretation.

CONCLUSIONS

This multinational study found differences in the risk of disease progression and transitioning to treatment without signs of progression between ethnic groups. Further research is required to determine whether differences are due to biology, sociocultural factors, and/or clinical practice.

PATIENT SUMMARY

This international study compared prostate cancer active surveillance outcomes by ethnicity. Risks of upgrading and disease progression were higher among African than among Caucasian men. Transitioning to treatment without progression was highest among Asian men. Understanding of these differences requires further investigation.

The ReIMAGINE Multimodal Warehouse: Using Artificial Intelligence for Accurate Risk Stratification of Prostate Cancer

Introduction. Prostate cancer (PCa) is the most frequent cancer diagnosis in men worldwide. Our ability to identify those men whose cancer will decrease their lifespan and/or quality of life remains poor. The ReIMAGINE Consortium has been established to improve PCa diagnosis. Materials and methods. MRI will likely become the future cornerstone of the risk-stratification process for men at risk of early prostate cancer. We will, for the first time, be able to combine the underlying molecular changes in PCa with the state-of-the-art imaging. ReIMAGINE Screening invites men for MRI and PSA evaluation. ReIMAGINE Risk includes men at risk of prostate cancer based on MRI, and includes biomarker testing. Results. Baseline clinical information, genomics, blood, urine, fresh prostate tissue samples, digital pathology and radiomics data will be analysed. Data will be de-identified, stored with correlated mpMRI disease endotypes and linked with long term follow-up outcomes in an instance of the Philips Clinical Data Lake, consisting of cloud-based software. The ReIMAGINE platform includes application programming interfaces and a user interface that allows users to browse data, select cohorts, manage users and access rights, query data, and more. Connection to analytics tools such as Python allows statistical and stratification method pipelines to run profiling regression analyses. Discussion. The ReIMAGINE Multimodal Warehouse comprises a unique data source for PCa research, to improve risk stratification for PCa and inform clinical practice. The de-identified dataset characterized by clinical, imaging, genomics and digital pathology PCa patient phenotypes will be a valuable resource for the scientific and medical community.

Risk-Based Selection for Active Surveillance: Results of the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) Initiative

PURPOSE

We sought to identify and validate known predictors of disease reclassification at 1 or 4 years to support risk-based selection of patients suitable for active surveillance.

MATERIALS AND METHODS

An individual participant data meta-analysis using data from 25 established cohorts within the Movember Foundations GAP3 Consortium. In total 5,530 men were included. Disease reclassification was defined as any increase in Gleason grade group at biopsy at 1 and 4 years. Associations were estimated using random effect logistic regression models. The discriminative ability of combinations of predictors was assessed in an internal-external validation procedure using the AUC curve.

RESULTS

Among the 5,570 men evaluated at 1 year, we found 815 reclassifications to higher Gleason grade group at biopsy (pooled reclassification rate 13%, range 0% to 31%). Important predictors were age, prostate specific antigen, prostate volume, T-stage and number of biopsy cores with prostate cancer. Among the 1,515 men evaluated at 4 years, we found 205 reclassifications (pooled reclassification rates 14%, range 3% to 40%), with similar predictors. The average areas under the receiver operating characteristic curve at internal-external validation were 0.68 and 0.61 for 1-year and 4-year reclassification, respectively.

CONCLUSIONS

Disease reclassification occurs typically in 13% to 14% of biopsies at 1 and 4 years after the start of active surveillance with substantial between-study heterogeneity. Current guidelines might be extended by considering prostate volume to improve individualized selection for active surveillance. Additional predictors are needed to improve patient selection for active surveillance.

Using the Movember Foundation's GAP3 cohort to measure the effect of active surveillance on patient-reported urinary and sexual function-a retrospective study in low-risk prostate cancer patients

BACKGROUND

Active surveillance (AS) for low-risk prostate cancer (PCa) is intended to overcome potential side-effects of definitive treatment. Frequent prostate biopsies during AS may, however, impact erectile (EF) and urinary function (UF). The objective of this study was to test the influence of prostate biopsies on patient-reported EF and UF using multicenter data from the largest to-date AS-database.

METHODS

In this retrospective study, data analyses were performed using the Movember GAP3 database (v3.2), containing data from 21,169 AS participants from 27 AS-cohorts worldwide. Participants were included in the study if they had at least one follow-up prostate biopsy and completed at least one patient reported outcome measure (PROM) related to EF [Sexual Health Inventory for Men (SHIM)/five item International Index of Erectile Function (IIEF-5)] or UF [International Prostate Symptom Score (IPSS)] during follow-up. The longitudinal effect of the number of biopsies on either SHIM/IIEF-5 or IPSS were analyzed using linear mixed models to adjust for clustering at patient-level. Analyses were stratified by center; covariates included age and Gleason Grade group at diagnosis, and time on AS.

RESULTS

A total of 696 participants completed the SHIM/IIEF-5 3,175 times, with a median follow-up of 36 months [interquartile range (IQR) 20-55 months]. A total of 845 participants completed the IPSS 4,061 times, with a median follow-up of 35 months (IQR 19-56 months). The intraclass correlation (ICC) was 0.74 for the SHIM/IIEF-5 and 0.68 for the IPSS, indicating substantial differences between participants' PROMs. Limited heterogeneity between cohorts in the estimated effect of the number of biopsies on either PROM were observed. A significant association was observed between the number of biopsies and the SHIM/IIEF-5 score, but not for the IPSS score. Every biopsy was associated with a decrease in the SHIM/IIEF-5 score of an average 0.67 (95% CI, 0.47-0.88) points.

CONCLUSIONS

Repeated prostate biopsy as part of an AS protocol for men with low-risk PCa does not have a significant association with self-reported UF but does impact self-reported sexual function. Further research is, however, needed to understand whether the effect on sexual function implies a negative clinical impact on their quality of life and is meaningful from a patient's perspective. In the meantime, clinicians and patients should anticipate a potential decline in erectile function and hence consider incorporating the risk of this harm into their discussion about opting for AS and also when deciding on the stringency of follow-up biopsy schedules with long-term AS.

BioVenn – an R and Python package for the comparison and visualization of biological lists using area-proportional Venn diagrams

One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram. Venn diagrams are especially useful when they are ‘area-proportional’ i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. In 2007, the BioVenn web interface was launched, which is being used by many researchers. However, this web implementation requires users to copy and paste (or upload) lists of IDs into the web browser, which is not always convenient and makes it difficult for researchers to create Venn diagrams ‘in batch’, or to automatically update the diagram when the source data changes. This is only possible by using software such as R or Python. This paper describes the BioVenn R and Python packages, which are very easy-to-use packages that can generate accurate area-proportional Venn diagrams of two or three circles directly from lists of (biological) IDs. The only required input is two or three lists of IDs. Optional parameters include the main title, the subtitle, the printing of absolute numbers or percentages within the diagram, colors and fonts. The function can show the diagram on the screen, or it can export the diagram in one of the supported file formats. The function also returns all thirteen lists. The BioVenn R package and Python package were created for biological IDs, but they can be used for other IDs as well. Finally, BioVenn can map Affymetrix and EntrezGene to Ensembl IDs. The BioVenn R package is available in the CRAN repository, and can be installed by running ‘install.packages(“BioVenn”)’. The BioVenn Python package is available in the PyPI repository, and can be installed by running ‘pip install BioVenn’. The BioVenn web interface remains available at https://www.biovenn.nl.

A first step towards a global nomogram to predict disease progression for men on active surveillance

BACKGROUND

Signs of disease progression (28%) and conversion to active treatment without evidence of disease progression (13%) are the main reasons for discontinuation of active surveillance (AS) in men with localised prostate cancer (PCa). We aimed to develop a nomogram to predict disease progression in these patients.

METHODS

As a first step in the development of a nomogram, using data from Movembers' GAP3 Consortium (n=14,380), we assessed heterogeneity between centres in terms of risk of disease progression. We started with assessment of baseline hazards for disease progression based on grouping of centres according to follow-up protocols [high: yearly; intermediate: ~2 yearly; and low: at year 1, 4 & 7 (i.e., PRIAS)]. We conducted cause-specific random effect Cox proportional hazards regression to estimate risk of disease progression by centre in each group.

RESULTS

Disease progression rates varied substantially between centres [median hazard ratio (MHR): 2.5]. After adjustment for various clinical factors (age, year of diagnosis, Gleason grade group, number of positive cores and PSA), substantial heterogeneity in disease progression remained between centres.

CONCLUSIONS

When combining worldwide data on AS, we noted unexplained differences of disease progression rate even after adjustment for various clinical factors. This suggests that when developing a global nomogram, local adjustments for differences in risk of disease progression and competing outcomes such as conversion to active treatment need to be considered.

Personalised biopsy schedules based on risk of Gleason upgrading for patients with low-risk prostate cancer on active surveillance

OBJECTIVE

To develop a model and methodology for predicting the risk of Gleason upgrading in patients with prostate cancer on active surveillance (AS) and using the predicted risks to create risk-based personalised biopsy schedules as an alternative to one-size-fits-all schedules (e.g. annually). Furthermore, to assist patients and doctors in making shared decisions on biopsy schedules, by providing them quantitative estimates of the burden and benefit of opting for personalised vs any other schedule in AS. Lastly, to externally validate our model and implement it along with personalised schedules in a ready to use web-application.

PATIENTS AND METHODS

Repeat prostate-specific antigen (PSA) measurements, timing and results of previous biopsies, and age at baseline from the world's largest AS study, Prostate Cancer Research International Active Surveillance (PRIAS; 7813 patients, 1134 experienced upgrading). We fitted a Bayesian joint model for time-to-event and longitudinal data to this dataset. We then validated our model externally in the largest six AS cohorts of the Movember Foundation's third Global Action Plan (GAP3) database (>20 000 patients, 27 centres worldwide). Using the model predicted upgrading risks; we scheduled biopsies whenever a patient's upgrading risk was above a certain threshold. To assist patients/doctors in the choice of this threshold, and to compare the resulting personalised schedule with currently practiced schedules, along with the timing and the total number of biopsies (burden) planned, for each schedule we provided them with the time delay expected in detecting upgrading (shorter is better).

RESULTS

The cause-specific cumulative upgrading risk at the 5-year follow-up was 35% in PRIAS, and at most 50% in the GAP3 cohorts. In the PRIAS-based model, PSA velocity was a stronger predictor of upgrading (hazard ratio [HR] 2.47, 95% confidence interval [CI] 1.93-2.99) than the PSA level (HR 0.99, 95% CI 0.89-1.11). Our model had a moderate area under the receiver operating characteristic curve (0.6-0.7) in the validation cohorts. The prediction error was moderate (0.1-0.2) in theGAP3 cohorts where the impact of the PSA level and velocity on upgrading risk was similar to PRIAS, but large (0.2-0.3) otherwise. Our model required re-calibration of baseline upgrading risk in the validation cohorts. We implemented the validated models and the methodology for personalised schedules in a web-application (http://tiny.cc/biopsy).

CONCLUSIONS

We successfully developed and validated a model for predicting upgrading risk, and providing risk-based personalised biopsy decisions in AS of prostate cancer. Personalised prostate biopsies are a novel alternative to fixed one-size-fits-all schedules, which may help to reduce unnecessary prostate biopsies, while maintaining cancer control. The model and schedules made available via a web-application enable shared decision-making on biopsy schedules by comparing fixed and personalised schedules on total biopsies and expected time delay in detecting upgrading.

Comparison of biopsy under-sampling and annual progression using hidden markov models to learn from prostate cancer active surveillance studies

This study aimed to estimate the rates of biopsy undersampling and progression for four prostate cancer (PCa) active surveillance (AS) cohorts within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) consortium. We used a hidden Markov model (HMM) to estimate factors that define PCa dynamics for men on AS including biopsy under-sampling and progression that are implied by longitudinal data in four large cohorts included in the GAP3 database. The HMM was subsequently used as the basis for a simulation model to evaluate the biopsy strategies previously proposed for each of these cohorts. For the four AS cohorts, the estimated annual progression rate was between 6%-13%. The estimated probability of a biopsy successfully sampling undiagnosed non-favorable risk cancer (biopsy sensitivity) was between 71% and 80%. In the simulation study of patients diagnosed with favorable risk cancer at age 50, the mean number of biopsies performed before age 75 was between 4.11 and 12.60, depending on the biopsy strategy. The mean delay time to detection of non-favorable risk cancer was between 0.38 and 2.17 years. Biopsy undersampling and progression varied considerably across study cohorts. There was no single best biopsy protocol that is optimal for all cohorts, because of the variation in biopsy under-sampling error and annual progression rates across cohorts. All strategies demonstrated diminishing benefits from additional biopsies.

Primary lymphomas of the genitourinary tract: A population-based study

OBJECTIVE

We performed a population-based analysis focusing on primary extranodal lymphoma of either testis, kidney, bladder or prostate (PGUL).

METHODS

We identified all cases of localized testis, renal, bladder and prostate primary lymphomas (PL) versus primary testis, kidney, bladder and prostate cancers within the Surveillance, Epidemiology, and End Results database (1998-2015). Estimated annual proportion change methodology (EAPC), multivariable logistic regression models, cumulative incidence plots and multivariable competing risks regression models were used.

RESULTS

The rates of testis-PL, renal-PL, bladder-PL and prostate-PL were 3.04%, 0.22%, 0.18% and 0.01%, respectively. Patients with PGUL were older and more frequently Caucasian. Annual rates significantly decreased for renal-PL (EAPC: -5.6%; p=0.004) and prostate-PL (EAPC: -3.6%; p=0.03). In multivariable logistic regression models, older ager independently predicted testis-PL (odds ratio [OR]: 16.4; p<0.001) and renal-PL (OR: 3.5; p<0.001), while female gender independently predicted bladder-PL (OR: 5.5; p<0.001). In surgically treated patients, cumulative incidence plots showed significantly higher 10-year cancer-specific mortality (CSM) rates for testis-PL, renal-PL and prostate-PL versus their primary genitourinary tumors. In multivariable competing risks regression models, only testis-PL (hazard ratio [HR]: 16.7; p<0.001) and renal-PL (HR: 2.52; p<0.001) independently predicted higher CSM rates.

CONCLUSION

PGUL rates are extremely low and on the decrease in kidney and prostate but stable in testis and bladder. Relative to primary genitourinary tumors, PGUL are associated with worse CSM for testis-PL and renal-PL but not for bladder-PL and prostate-PL, even after adjustment for other-cause mortality.

Sharing Is Caring-Data Sharing Initiatives in Healthcare

In recent years, more and more health data are being generated. These data come not only from professional health systems, but also from wearable devices. All these 'big data' put together can be utilized to optimize treatments for each unique patient ('precision medicine'). For this to be possible, it is necessary that hospitals, academia and industry work together to bridge the 'valley of death' of translational medicine. However, hospitals and academia often are reluctant to share their data with other parties, even though the patient is actually the owner of his/her own health data. Academic hospitals usually invest a lot of time in setting up clinical trials and collecting data, and want to be the first ones to publish papers on this data. There are some publicly available datasets, but these are usually only shared after study (and publication) completion, which means a severe delay of months or even years before others can analyse the data. One solution is to incentivize the hospitals to share their data with (other) academic institutes and the industry. Here, we show an analysis of the current literature around data sharing, and we discuss five aspects of data sharing in the medical domain: publisher requirements, data ownership, growing support for data sharing, data sharing initiatives and how the use of federated data might be a solution. We also discuss some potential future developments around data sharing, such as medical crowdsourcing and data generalists.

Adherence to Active Surveillance Protocols for Low-risk Prostate Cancer: Results of the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance Initiative

BACKGROUND

Active surveillance (AS) enrolment criteria and follow-up schedules for low-risk prostate cancer vary between institutions. However, uncertainty remains about adherence to these protocols.

OBJECTIVE

To determine adherence to institution-specific AS inclusion criteria and follow-up schedules within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative.

DESIGN, SETTING, AND PARTICIPANTS

We retrospectively assessed the data of 15 101 patients from 25 established AS cohorts worldwide between 2014 and 2016.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Adherence to individual AS inclusion criteria was rated on a five-point Likert scale ranging from poor to excellent. Nonadherence to follow-up schedules was defined as absence of repeat biopsy 1 yr after the scheduled date. Cohorts were pooled into annual and Prostate Cancer Research International: Active Surveillance (PRIAS)-based biopsy schedules, and a generalised linear mixed model was constructed to test for nonadherence.

RESULTS AND LIMITATIONS

Serum prostate-specific antigen (PSA) inclusion criteria were followed in 92%, Gleason score (GS) criteria were followed in 97%, and the number of positive biopsy cores was followed in 94% of men. Both age and tumour stage (T stage) criteria had 99% adherence overall. Pooled nonadherence rates increased over time-8%, 16%, and 34% for annual schedules and 11%, 30%, and 29% for PRIAS-based schedules at 1, 4, and 7 yr, respectively-and did not differ between biopsy schedules. A limitation is that our results do not consider the use of multiparametric magnetic resonance imaging.

CONCLUSIONS

In on-going development of evidence-based AS protocols, variable adherence to PSA and GS inclusion criteria should be considered. Repeat biopsy adherence reduces with increased duration of surveillance, independent of biopsy frequency. This emphasises the importance of risk stratification at the commencement of AS.

PATIENT SUMMARY

We studied adherence to active surveillance protocols for prostate cancer worldwide. We found that inclusion criteria were generally followed well, but adherence to repeat biopsy reduced with time. This should be considered when optimising future active surveillance protocols.

Predicting Biopsy Outcomes During Active Surveillance for Prostate Cancer: External Validation of the Canary Prostate Active Surveillance Study Risk Calculators in Five Large Active Surveillance Cohorts

BACKGROUND

Men with prostate cancer (PCa) on active surveillance (AS) are followed through regular prostate biopsies, a burdensome and often unnecessary intervention, not without risks. Identifying men with at a low risk of disease reclassification may help reduce the number of biopsies.

OBJECTIVE

To assess the external validity of two Canary Prostate Active Surveillance Study Risk Calculators (PASS-RCs), which estimate the probability of reclassification (Gleason grade ≥7 with or without >34% of biopsy cores positive for PCa) on a surveillance biopsy, using a mix of months since last biopsy, age, body mass index, prostate-specific antigen, prostate volume, number of prior negative biopsies, and percentage (or ratio) of positive cores on last biopsy.

DESIGN, SETTING, AND PARTICIPANTS

We used data up to November 2017 from the Movember Foundation's Global Action Plan (GAP3) consortium, a global collaboration between AS studies.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

External validity of the PASS-RCs for estimating reclassification on biopsy was assessed by calibration, discrimination, and decision curve analyses.

RESULTS AND LIMITATIONS

Five validation cohorts (Prostate Cancer Research International: Active Surveillance, Johns Hopkins, Toronto, Memorial Sloan Kettering Cancer Center, and University of California San Francisco), comprising 5105 men on AS, were eligible for analysis. The individual cohorts comprised 429-2416 men, with a median follow-up between 36 and 84 mo, in both community and academic practices mainly from western countries. Abilities of the PASS-RCs to discriminate between men with and without reclassification on biopsy were reasonably good (area under the receiver operating characteristic curve values 0.68 and 0.65). The PASS-RCs were moderately well calibrated, and had a greater net benefit than most default strategies between a predicted 10% and 30% risk of reclassification.

CONCLUSIONS

Both PASS-RCs improved the balance between detecting reclassification and performing surveillance biopsies by reducing unnecessary biopsies. Recalibration to the local setting will increase their clinical usefulness and is therefore required before implementation.

PATIENT SUMMARY

Unnecessary prostate biopsies while on active surveillance (AS) should be avoided as much as possible. The ability of two calculators to selectively identify men at risk of progression was tested in a large cohort of men with low-risk prostate cancer on AS. The calculators were able to prevent unnecessary biopsies in some men. Usefulness of the calculators can be increased by adjusting them to the characteristics of the population of the clinic in which the calculators will be used.

Consistent Biopsy Quality and Gleason Grading Within the Global Active Surveillance Global Action Plan 3 Initiative: A Prerequisite for Future Studies

Within the Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative, 25 centers across the globe collaborate to standardize active surveillance (AS) protocols for men with low-risk prostate cancer (PCa). A centralized PCa AS database, comprising data of more than 15000 patients worldwide, was created. Comparability of the histopathology between the different cohorts was assessed by a centralized pathology review of 445 biopsies from 15 GAP3 centers. Grade group 1 (Gleason score 6) in 85% and grade group ≥2 (Gleason score ≥7) in 15% showed 89% concordance at review with moderate agreement (κ=0.56). Average biopsy core length was similar among the analyzed cohorts. Recently established highly adverse pathologies, including cribriform and/or intraductal carcinoma, were observed in 3.6% of the reviewed biopsies. In conclusion, the centralized pathology review of 445 biopsies revealed comparable histopathology among the 15 GAP3 centers with a low frequency of high-risk features. This enables further data analyses-without correction-toward uniform global AS guidelines for men with low-risk PCa. PATIENT SUMMARY: Movember Foundation's Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative combines data from 15000 men with low-risk prostate cancer (PCa) across the globe to standardize active surveillance protocols. Histopathology review confirmed that the histopathology was consistent with low-risk PCa in most men and comparable between different centers.

Reasons for Discontinuing Active Surveillance: Assessment of 21 Centres in 12 Countries in the Movember GAP3 Consortium

BACKGROUND

Careful assessment of the reasons for discontinuation of active surveillance (AS) is required for men with prostate cancer (PCa).

OBJECTIVE

Using Movember's Global Action Plan Prostate Cancer Active Surveillance initiative (GAP3) database, we report on reasons for AS discontinuation.

DESIGN, SETTING, AND PARTICIPANTS

We compared data from 10296 men on AS from 21 centres across 12 countries.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Cumulative incidence methods were used to estimate the cumulative incidence rates of AS discontinuation.

RESULTS AND LIMITATIONS

During 5-yr follow-up, 27.5% (95% confidence interval [CI]: 26.4-28.6%) men showed signs of disease progression, 12.8% (95% CI: 12.0-13.6%) converted to active treatment without evidence of progression, 1.7% (95% CI: 1.5-2.0%) continued to watchful waiting, and 1.7% (95% CI: 1.4-2.1%) died from other causes. Of the 7049 men who remained on AS, 2339 had follow-up for >5yr, 4561 had follow-up for <5yr, and 149 were lost to follow-up. Cumulative incidence of progression was 27.5% (95% CI: 26.4-28.6%) at 5yr and 38.2% (95% CI: 36.7-39.9%) at 10yr. A limitation is that not all centres were included due to limited information on the reason for discontinuation and limited follow-up.

CONCLUSIONS

Our descriptive analyses of current AS practices worldwide showed that 43.6% of men drop out of AS during 5-yr follow-up, mainly due to signs of disease progression. Improvements in selection tools for AS are thus needed to correctly allocate men with PCa to AS, which will also reduce discontinuation due to conversion to active treatment without evidence of disease progression.

PATIENT SUMMARY

Our assessment of a worldwide database of men with prostate cancer (PCa) on active surveillance (AS) shows that 43.6% drop out of AS within 5yr, mainly due to signs of disease progression. Better tools are needed to select and monitor men with PCa as part of AS.

An overview of publicly available patient-centered prostate cancer datasets

Prostate cancer (PCa) is the second most common cancer in men, and the second leading cause of death from cancer in men. Many studies on PCa have been carried out, each taking much time before the data is collected and ready to be analyzed. However, on the internet there is already a wide range of PCa datasets available, which could be used for data mining, predictive modelling or other purposes, reducing the need to setup new studies to collect data. In the current scientific climate, moving more and more to the analysis of "big data" and large, international, multi-site projects using a modern IT infrastructure, these datasets could be proven extremely valuable. This review presents an overview of publicly available patient-centered PCa datasets, divided into three categories (clinical, genomics and imaging) and an "overall" section to enable researchers to select a suitable dataset for analysis, without having to go through days of work to find the right data. To acquire a list of human PCa databases, scientific literature databases and academic social network sites were searched. We also used the information from other reviews. All databases in the combined list were then checked for public availability. Only databases that were either directly publicly available or available after signing a research data agreement or retrieving a free login were selected for inclusion in this review. Data should be available to commercial parties as well. This paper focuses on patient-centered data, so the genomics data section does not include gene-centered databases or pathway-centered databases. We identified 42 publicly available, patient-centered PCa datasets. Some of these consist of different smaller datasets. Some of them contain combinations of datasets from the three data domains: clinical data, imaging data and genomics data. Only one dataset contains information from all three domains. This review presents all datasets and their characteristics: number of subjects, clinical fields, imaging modalities, expression data, mutation data, biomarker measurements, etc. Despite all the attention that has been given to making this overview of publicly available databases as extensive as possible, it is very likely not complete, and will also be outdated soon. However, this review might help many PCa researchers to find suitable datasets to answer the research question with, without the need to start a new data collection project. In the coming era of big data analysis, overviews like this are becoming more and more useful.

From Big Data to Precision Medicine

For over a decade the term "Big data" has been used to describe the rapid increase in volume, variety and velocity of information available, not just in medical research but in almost every aspect of our lives. As scientists, we now have the capacity to rapidly generate, store and analyse data that, only a few years ago, would have taken many years to compile. However, "Big data" no longer means what it once did. The term has expanded and now refers not to just large data volume, but to our increasing ability to analyse and interpret those data. Tautologies such as "data analytics" and "data science" have emerged to describe approaches to the volume of available information as it grows ever larger. New methods dedicated to improving data collection, storage, cleaning, processing and interpretation continue to be developed, although not always by, or for, medical researchers. Exploiting new tools to extract meaning from large volume information has the potential to drive real change in clinical practice, from personalized therapy and intelligent drug design to population screening and electronic health record mining. As ever, where new technology promises "Big Advances," significant challenges remain. Here we discuss both the opportunities and challenges posed to biomedical research by our increasing ability to tackle large datasets. Important challenges include the need for standardization of data content, format, and clinical definitions, a heightened need for collaborative networks with sharing of both data and expertise and, perhaps most importantly, a need to reconsider how and when analytic methodology is taught to medical researchers. We also set "Big data" analytics in context: recent advances may appear to promise a revolution, sweeping away conventional approaches to medical science. However, their real promise lies in their synergy with, not replacement of, classical hypothesis-driven methods. The generation of novel, data-driven hypotheses based on interpretable models will always require stringent validation and experimental testing. Thus, hypothesis-generating research founded on large datasets adds to, rather than replaces, traditional hypothesis driven science. Each can benefit from the other and it is through using both that we can improve clinical practice.

The Movember Foundation's GAP3 cohort: a profile of the largest global prostate cancer active surveillance database to date

OBJECTIVES

The Movember Foundation launched the Global Action Plan Prostate Cancer Active Surveillance (GAP3) initiative to create a global consensus on the selection and monitoring of men with low-risk prostate cancer (PCa) on active surveillance (AS). The aim of this study is to present data on inclusion and follow-up for AS in this unique global AS database.

PATIENTS AND METHODS

Between 2014 and 2016, the database was created by combining patient data from 25 established AS cohorts worldwide (USA, Canada, Australasia, UK and Europe). Data on a total of 15 101 patients were included. Descriptive statistics were used to report patients' clinical and demographic characteristics at the time of PCa diagnosis, clinical follow-up, discontinuation of AS and subsequent treatment. Cumulative incidence curves were used to report discontinuation rates over time.

RESULTS

At diagnosis, the median (interquartile range [IQR]) patient age was 65 (60-70) years and the median prostate-specific antigen level was 5.4 (4.0-7.3) ng/mL. Most patients had clinical stage T1 disease (71.8%), a biopsy Gleason score of 6 (88.8%) and one tumour-positive biopsy core (60.3%). Patients on AS had a median follow-up time of 2.2 (1.0-5.0) years. After 5, 10 and 15 years of follow-up, respectively, 58%, 39% and 23% of patients were still on AS. The current version of GAP3 has limited data on magnetic resonance imaging (MRI), quality of life and genomic testing.

CONCLUSIONS

GAP3 is the largest worldwide collaboration integrating patient data from men with PCa on AS. The results will allow individual patients and clinicians to have greater confidence in the personalized decision to either delay or proceed with active treatment. Longer follow-up and the evaluation of MRI, new genomic markers and patient-related outcomes will result in even more valuable data and eventually in better patient outcomes.

Expert consensus document: Semantics in active surveillance for men with localized prostate cancer - results of a modified Delphi consensus procedure

Active surveillance (AS) is broadly described as a management option for men with low-risk prostate cancer, but semantic heterogeneity exists in both the literature and in guidelines. To address this issue, a panel of leading prostate cancer specialists in the field of AS participated in a consensus-forming project using a modified Delphi method to reach international consensus on definitions of terms related to this management option. An iterative three-round sequence of online questionnaires designed to address 61 individual items was completed by each panel member. Consensus was considered to be reached if ≥70% of the experts agreed on a definition. To facilitate a common understanding among all experts involved and resolve potential ambiguities, a face-to-face consensus meeting was held between Delphi survey rounds two and three. Convenience sampling was used to construct the panel of experts. In total, 12 experts from Australia, France, Finland, Italy, the Netherlands, Japan, the UK, Canada and the USA participated. By the end of the Delphi process, formal consensus was achieved for 100% (n = 61) of the terms and a glossary was then developed. Agreement between international experts has been reached on relevant terms and subsequent definitions regarding AS for patients with localized prostate cancer. This standard terminology could support multidisciplinary communication, reduce the extent of variations in clinical practice and optimize clinical decision making.

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining

BACKGROUND

Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g. insulin resistance, glucose intolerance and diabetes. To gain more insight into the mechanisms behind glucocorticoid induced insulin resistance, it is important to understand which genes play a role in the development of insulin resistance and which genes are affected by glucocorticoids.Medline abstracts contain many studies about insulin resistance and the molecular effects of glucocorticoids and thus are a good resource to study these effects.

RESULTS

We developed CoPubGene a method to automatically identify gene-disease associations in Medline abstracts. We used this method to create a literature network of genes related to insulin resistance and to evaluate the importance of the genes in this network for glucocorticoid induced metabolic side effects and anti-inflammatory processes.With this approach we found several genes that already are considered markers of GC induced IR, such as phosphoenolpyruvate carboxykinase (PCK) and glucose-6-phosphatase, catalytic subunit (G6PC). In addition, we found genes involved in steroid synthesis that have not yet been recognized as mediators of GC induced IR.

CONCLUSIONS

With this approach we are able to construct a robust informative literature network of insulin resistance related genes that gave new insights to better understand the mechanisms behind GC induced IR. The method has been set up in a generic way so it can be applied to a wide variety of disease networks.

The construction of genome-based transcriptional units

Gene-oriented sequence clusters (transcriptional units) have found many applications in genomics research including the construction of transcriptome maps and identification of splice variants. We developed a new method to construct transcriptional that uses the genomic sequence as a template. We present and discuss our method in detail together with an evaluation of the transcriptional units for human. We constructed 33,007 and 27,792 transcriptional units for human and mouse, respectively. The sensitivity (81%) and specificity (90%) of our method compares favorably to other established methods. We evaluated the representation of experimentally validated and predicted intergenic spliced transcripts in humans and show that we correctly represent a large fraction of these cases by single transcriptional units. Our method performs well, but the evaluation of the final set of transcriptional units show that improvements to the algorithm are still possible. However, because the precise number and types of errors are difficult to track, it is not obvious how to significantly improve the algorithm. We believe that ongoing research efforts are necessary to further improve current methods. This should include detailed documentation, comparison, and evaluation of current methods.

PhyloPat: an updated version of the phylogenetic pattern database contains gene neighborhood

Phylogenetic patterns show the presence or absence of certain genes in a set of full genomes derived from different species. They can also be used to determine sets of genes that occur only in certain evolutionary branches. Previously, we presented a database named PhyloPat which allows the complete Ensembl gene database to be queried using phylogenetic patterns. Here, we describe an updated version of PhyloPat which can be queried by an improved web server. We used a single linkage clustering algorithm to create 241,697 phylogenetic lineages, using all the orthologies provided by Ensembl v49. PhyloPat offers the possibility of querying with binary phylogenetic patterns or regular expressions, or through a phylogenetic tree of the 39 included species. Users can also input a list of Ensembl, EMBL, EntrezGene or HGNC IDs to check which phylogenetic lineage any gene belongs to. A link to the FatiGO web interface has been incorporated in the HTML output. For each gene, the surrounding genes on the chromosome, color coded according to their phylogenetic lineage can be viewed, as well as FASTA files of the peptide sequences of each lineage. Furthermore, lists of omnipresent, polypresent, oligopresent and anticorrelating genes have been included. PhyloPat is freely available at http://www.cmbi.ru.nl/phylopat.

BioVenn - a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams

BACKGROUND

In many genomics projects, numerous lists containing biological identifiers are produced. Often it is useful to see the overlap between different lists, enabling researchers to quickly observe similarities and differences between the data sets they are analyzing. One of the most popular methods to visualize the overlap and differences between data sets is the Venn diagram: a diagram consisting of two or more circles in which each circle corresponds to a data set, and the overlap between the circles corresponds to the overlap between the data sets. Venn diagrams are especially useful when they are 'area-proportional' i.e. the sizes of the circles and the overlaps correspond to the sizes of the data sets. Currently there are no programs available that can create area-proportional Venn diagrams connected to a wide range of biological databases.

RESULTS

We designed a web application named BioVenn to summarize the overlap between two or three lists of identifiers, using area-proportional Venn diagrams. The user only needs to input these lists of identifiers in the textboxes and push the submit button. Parameters like colors and text size can be adjusted easily through the web interface. The position of the text can be adjusted by 'drag-and-drop' principle. The output Venn diagram can be shown as an SVG or PNG image embedded in the web application, or as a standalone SVG or PNG image. The latter option is useful for batch queries. Besides the Venn diagram, BioVenn outputs lists of identifiers for each of the resulting subsets. If an identifier is recognized as belonging to one of the supported biological databases, the output is linked to that database. Finally, BioVenn can map Affymetrix and EntrezGene identifiers to Ensembl genes.

CONCLUSION

BioVenn is an easy-to-use web application to generate area-proportional Venn diagrams from lists of biological identifiers. It supports a wide range of identifiers from the most used biological databases currently available. Its implementation on the World Wide Web makes it available for use on any computer with internet connection, independent of operating system and without the need to install programs locally. BioVenn is freely accessible at http://www.cmbi.ru.nl/cdd/biovenn/.

Evolution of closely linked gene pairs in vertebrate genomes

The orientation of closely linked genes in mammalian genomes is not random: there are more head-to-head (h2h) gene pairs than expected. To understand the origin of this enrichment in h2h gene pairs, we have analyzed the phylogenetic distribution of gene pairs separated by less than 600 bp of intergenic DNA (gene duos). We show here that a lack of head-to-tail (h2t) gene duos is an even more distinctive characteristic of mammalian genomes, with the platypus genome as the only exception. In nonmammalian vertebrate and in nonvertebrate genomes, the frequency of h2h, h2t, and tail-to-tail (t2t) gene duos is close to random. In tetrapod genomes, the h2t and t2t gene duos are more likely to be part of a larger gene cluster of closely spaced genes than h2h gene duos; in fish and urochordate genomes, the reverse is seen. In human and mouse tissues, the expression profiles of gene duos were skewed toward positive coexpression, irrespective of orientation. The organization of orthologs of both members of about 40% of the human gene duos could be traced in other species, enabling a prediction of the organization at the branch points of gnathostomes, tetrapods, amniotes, and euarchontoglires. The accumulation of h2h gene duos started in tetrapods, whereas that of h2t and t2t gene duos only started in amniotes. The apparent lack of evolutionary conservation of h2t and t2t gene duos relative to that of h2h gene duos is thus a result of their relatively late origin in the lineage leading to mammals; we show that once they are formed h2t and t2t gene duos are as stable as h2h gene duos.

Identification of novel functional TBP-binding sites and general factor repertoires

Our current knowledge of the general factor requirement in transcription by the three mammalian RNA polymerases is based on a small number of model promoters. Here, we present a comprehensive chromatin immunoprecipitation (ChIP)-on-chip analysis for 28 transcription factors on a large set of known and novel TATA-binding protein (TBP)-binding sites experimentally identified via ChIP cloning. A large fraction of identified TBP-binding sites is located in introns or lacks a gene/mRNA annotation and is found to direct transcription. Integrated analysis of the ChIP-on-chip data and functional studies revealed that TAF12 hitherto regarded as RNA polymerase II (RNAP II)-specific was found to be also involved in RNAP I transcription. Distinct profiles for general transcription factors and TAF-containing complexes were uncovered for RNAP II promoters located in CpG and non-CpG islands suggesting distinct transcription initiation pathways. Our study broadens the spectrum of general transcription factor function and uncovers a plethora of novel, functional TBP-binding sites in the human genome.

Testing statistical significance scores of sequence comparison methods with structure similarity

BACKGROUND

In the past years the Smith-Waterman sequence comparison algorithm has gained popularity due to improved implementations and rapidly increasing computing power. However, the quality and sensitivity of a database search is not only determined by the algorithm but also by the statistical significance testing for an alignment. The e-value is the most commonly used statistical validation method for sequence database searching. The CluSTr database and the Protein World database have been created using an alternative statistical significance test: a Z-score based on Monte-Carlo statistics. Several papers have described the superiority of the Z-score as compared to the e-value, using simulated data. We were interested if this could be validated when applied to existing, evolutionary related protein sequences.

RESULTS

All experiments are performed on the ASTRAL SCOP database. The Smith-Waterman sequence comparison algorithm with both e-value and Z-score statistics is evaluated, using ROC, CVE and AP measures. The BLAST and FASTA algorithms are used as reference. We find that two out of three Smith-Waterman implementations with e-value are better at predicting structural similarities between proteins than the Smith-Waterman implementation with Z-score. SSEARCH especially has very high scores.

CONCLUSION

The compute intensive Z-score does not have a clear advantage over the e-value. The Smith-Waterman implementations give generally better results than their heuristic counterparts. We recommend using the SSEARCH algorithm combined with e-values for pairwise sequence comparisons.

PhyloPat: phylogenetic pattern analysis of eukaryotic genes

BACKGROUND

Phylogenetic patterns show the presence or absence of certain genes or proteins in a set of species. They can also be used to determine sets of genes or proteins that occur only in certain evolutionary branches. Phylogenetic patterns analysis has routinely been applied to protein databases such as COG and OrthoMCL, but not upon gene databases. Here we present a tool named PhyloPat which allows the complete Ensembl gene database to be queried using phylogenetic patterns.

DESCRIPTION

PhyloPat is an easy-to-use webserver, which can be used to query the orthologies of all complete genomes within the EnsMart database using phylogenetic patterns. This enables the determination of sets of genes that occur only in certain evolutionary branches or even single species. We found in total 446,825 genes and 3,164,088 orthologous relationships within the EnsMart v40 database. We used a single linkage clustering algorithm to create 147,922 phylogenetic lineages, using every one of the orthologies provided by Ensembl. PhyloPat provides the possibility of querying with either binary phylogenetic patterns (created by checkboxes) or regular expressions. Specific branches of a phylogenetic tree of the 21 included species can be selected to create a branch-specific phylogenetic pattern. Users can also input a list of Ensembl or EMBL IDs to check which phylogenetic lineage any gene belongs to. The output can be saved in HTML, Excel or plain text format for further analysis. A link to the FatiGO web interface has been incorporated in the HTML output, creating easy access to functional information. Finally, lists of omnipresent, polypresent and oligopresent genes have been included.

CONCLUSION

PhyloPat is the first tool to combine complete genome information with phylogenetic pattern querying. Since we used the orthologies generated by the accurate pipeline of Ensembl, the obtained phylogenetic lineages are reliable. The completeness and reliability of these phylogenetic lineages will further increase with the addition of newly found orthologous relationships within each new Ensembl release.

Benchmarking ortholog identification methods using functional genomics data

BACKGROUND

The transfer of functional annotations from model organism proteins to human proteins is one of the main applications of comparative genomics. Various methods are used to analyze cross-species orthologous relationships according to an operational definition of orthology. Often the definition of orthology is incorrectly interpreted as a prediction of proteins that are functionally equivalent across species, while in fact it only defines the existence of a common ancestor for a gene in different species. However, it has been demonstrated that orthologs often reveal significant functional similarity. Therefore, the quality of the orthology prediction is an important factor in the transfer of functional annotations (and other related information). To identify protein pairs with the highest possible functional similarity, it is important to qualify ortholog identification methods.

RESULTS

To measure the similarity in function of proteins from different species we used functional genomics data, such as expression data and protein interaction data. We tested several of the most popular ortholog identification methods. In general, we observed a sensitivity/selectivity trade-off: the functional similarity scores per orthologous pair of sequences become higher when the number of proteins included in the ortholog groups decreases.

CONCLUSION

By combining the sensitivity and the selectivity into an overall score, we show that the InParanoid program is the best ortholog identification method in terms of identifying functionally equivalent proteins.

Heavier-than-air flying machines are impossible

Many G protein-coupled receptor (GPCR) models have been built over the years. The release of the structure of bovine rhodopsin in August 2000 enabled us to analyze models built before that period to learn more about the models we build today. We conclude that the GPCR modelling field is riddled with 'common knowledge' similar to Lord Kelvin's remark in 1895 that "heavier-than-air flying machines are impossible", and we summarize what we think are the (im)possibilities of modelling GPCRs using the coordinates of bovine rhodopsin as a template. Associated WWW pages: www.gpcr.org/articles/2003_mod