Simulation optimization of PSA-threshold based prostate cancer screening policies

Potential of miRNAs in Plasma Extracellular Vesicle for the Stratification of Prostate Cancer in a South African Population

Prostate cancer (PCa) is the most common cause of cancer death among African men. The analysis of microRNAs (miRNAs) in plasma extracellular vesicles (EVs) can be utilized as a non-invasive tool for the diagnosis of PCa. In this study, we used small RNA sequencing to profile miRNAs cargo in plasma EVs from South African PCa patients. We evaluated the differential expression of miRNAs between low and high Gleason scores in the plasma EVs of South African patients and in the prostatic tissue from data available in the Cancer Genome Atlas (TCGA) Data Portal. We identified 7 miRNAs differently expressed in both EVs and prostatic tissues. We evaluated their expression using qPCR in a larger cohort of 10 patients with benign prostatic hyperplasia (BPH) and 24 patients with PCa. Here, we reported that the ratio between two of these miRNAs (i.e., miR-194-5p/miR-16-5p) showed a higher concentration in PCa compared to BPH and in metastatic PCa compared to localized PCa. We explored for the first time the profiling of miRNAs cargo in plasma EVs as a tool for the identification of putative markers in the South African population. Our finding indicated the ratio miR-194-5p/miR-16-5p as a non-invasive marker for the evaluation of PCa aggressiveness in this population.

Designing optimal allocations for cancer screening using queuing network models

Cancer is one of the leading causes of death, but mortality can be reduced by detecting tumors earlier so that treatment is initiated at a less aggressive stage. The tradeoff between costs associated with screening and its benefit makes the decision of whom to screen and when a challenge. To enable comparisons across screening strategies for any cancer type, we demonstrate a mathematical modeling platform based on the theory of queuing networks designed for quantifying the benefits of screening strategies. Our methodology can be used to design optimal screening protocols and to estimate their benefits for specific patient populations. Our method is amenable to exact analysis, thus circumventing the need for simulations, and is capable of exactly quantifying outcomes given variability in the age of diagnosis, rate of progression, and screening sensitivity and intervention outcomes. We demonstrate the power of this methodology by applying it to data from the Surveillance, Epidemiology and End Results (SEER) program. Our approach estimates the benefits that various novel screening programs would confer to different patient populations, thus enabling us to formulate an optimal screening allocation and quantify its potential effects for any cancer type and intervention.

We describe a mathematical modeling methodology that offers quantitative insights into the potential benefits of screening and other interventions on cancer mortality. Our queuing-theoretic approach represents a potentially useful alternative to more traditional modeling approaches, in that it can provide more detailed results and entirely circumvents the need for simulations. Our methodology can be widely applied to estimate costs and benefits of screening strategies. By providing a detailed example of our method applied to epidemiological data, we hope to encourage greater uptake of this methodology in the community.

Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes' Theorem to reduce costs and improve outcomes

Prostate cancer is the second leading cause of death from cancer, behind lung cancer, for men in the U. S, with nearly 30,000 deaths per year. A key problem is the difficulty in distinguishing, after biopsy, between significant cancers that should be treated immediately and clinically insignificant tumors that should be monitored by active surveillance. Prostate cancer has been over-treated; a recent European randomized screening trial shows overtreatment rates of 40%. Overtreatment of insignificant tumors reduces quality of life, while delayed treatment of significant cancers increases the incidence of metastatic disease and death. We develop a decision analysis approach based on simulation and probability modeling. For a given prostate volume and number of biopsy needles, our rule is to treat if total length of cancer in needle cores exceeds c, the cutoff value, with active surveillance otherwise, provided pathology is favorable. We determine the optimal cutoff value, c*. There are two misclassification costs: treating a minimal tumor and not treating a small or medium tumor (large tumors were never misclassified in our simulations). Bayes' Theorem is used to predict the probabilities of minimal, small, medium, and large cancers given the total length of cancer found in biopsy cores. A 20 needle biopsy in conjunction with our new decision analysis approach significantly reduces the expected loss associated with a patient in our target population about to undergo a biopsy. Longer needles reduce expected loss. Increasing the number of biopsy cores from the current norm of 10-12 to about 20, in conjunction with our new decision model, should substantially improve the ability to distinguish minimal from significant prostate cancer by minimizing the expected loss from over-treating minimal tumors and delaying treatment of significant cancers.

Benefits and harms of prostate cancer screening - predictions of the ONCOTYROL prostate cancer outcome and policy model

Background

A recent recalibration of the ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) Model, assuming that latent prostate cancer (PCa) detectable at autopsy might be detectable by screening as well, resulted in considerable worsening of the benefit-harm balance of screening. In this study, we used the recalibrated model to assess the effects of familial risk, quality of life (QoL) preferences, age, and active surveillance.

Methods

Men with average and elevated familial PCa risk were simulated in separate models, differing in familial risk parameters. Familial risk was assumed to affect PCa onset and progression simultaneously in the base-case, and separately in scenario analyses. Evaluated screening strategies included one-time screening at different ages, and screening at different intervals and age ranges. Optimal screening strategies were identified depending on age and individual QoL preferences. Strategies were additionally evaluated with active surveillance by biennial re-biopsy delaying treatment of localized cancer until grade progression to Gleason score ≥ 7.

Results

Screening men with average PCa risk reduced quality-adjusted life expectancy (QALE) even under favorable assumptions. Men with elevated familial risk, depending on age and disutilities, gained QALE. While for men with familial risk aged 55 and 60 years annual screening to age 69 was the optimal strategy over most disutility ranges, no screening was the preferred option for 65 year-old men with average and above disutilities.

Active surveillance greatly reduced overtreatment, but QALE gains by averted adverse events were opposed by losses due to delayed treatment and additional biopsies. The effect of active surveillance on the benefit-harm balance of screening differed between populations, as net losses and gains in QALE predicted for screening without active surveillance in men with average and familial PCa risk, respectively, were both reduced.

Conclusions

Assumptions about PCa risk and screen-detectable prevalence significantly affect the benefit-harm balance of screening. Based on the assumptions of our model, PCa screening should focus on candidates with familial predisposition with consideration of individual QoL preferences and age. Active surveillance may require treatment initiation before Gleason score progression to 7. Alternative active surveillance strategies should be evaluated in further modeling studies.

The ONCOTYROL Prostate Cancer Outcome and Policy Model: Effect of Prevalence Assumptions on the Benefit-Harm Balance of Screening

BACKGROUND

The ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model is a state-transition microsimulation model evaluating the benefits and harms of prostate cancer (PCa) screening. The natural history and detection component of the original model was based on the 2003 version of the Erasmus MIcrosimulation SCreening ANalysis (MISCAN) model, which was not calibrated to prevalence data. Compared with data from autopsy studies, prevalence of latent PCa assumed by the original model is low, which may bias the model toward screening. Our objective was to recalibrate the original model to match prevalence data from autopsy studies as well and compare benefit-harm predictions of the 2 model versions differing in prevalence.

METHODS

For recalibration, we reprogrammed the natural history and detection component of the PCOP model as a deterministic Markov state-transition cohort model in the statistical software package R. All parameters were implemented as variables or time-dependent functions and calibrated simultaneously in a single run. Observed data used as calibration targets included data from autopsy studies, cancer registries, and the European Randomized Study of Screening for Prostate Cancer. Compared models were identical except for calibrated parameters.

RESULTS

We calibrated 46 parameters. Prevalence from autopsy studies could not be fitted using the original parameter set. Additional parameters, allowing for interruption of disease progression and age-dependent screening sensitivities, were needed. Recalibration to higher prevalence demonstrated a considerable increase of overdiagnosis and decline of screening sensitivity, which significantly worsened the benefit-harm balance of screening.

CONCLUSIONS

Our calibration suggests that not all cancers are at risk of progression, and screening sensitivity may be lower at older ages. PCa screening models that use calibration to simulate disease progression in the unobservable latent phase are highly sensitive to prevalence assumptions.

Risk stratification in prostate cancer screening

Screening for prostate cancer is a controversial topic within the field of urology. The US Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial did not demonstrate any difference in prostate-cancer-related mortality rates between men screened annually rather than on an 'opportunistic' basis. However, in the world's largest trial to date--the European Randomised Study of Screening for Prostate Cancer--screening every 2-4 years was associated with a 21% reduction in prostate-cancer-related mortality rate after 11 years. Citing the uncertain ratio between potential harm and potential benefit, the US Preventive Services Task Force recently recommended against serum PSA screening. Although this ratio has yet to be elucidated, PSA testing--and early tumour detection--is undoubtedly beneficial for some individuals. Instead of adopting a 'one size fits all' approach, physicians are likely to perform personalized risk assessment to minimize the risk of negative consequences, such as anxiety, unnecessary testing and biopsies, overdiagnosis, and overtreatment. The PSA test needs to be combined with other predictive factors or be used in a more thoughtful way to identify men at risk of symptomatic or life-threatening cancer, without overdiagnosing indolent disease. A risk-adapted approach is needed, whereby PSA testing is tailored to individual risk.