Sonja Grill

Finasteride Reduces Risk of Bladder Cancer in a Large Prospective Screening Study

UNLABELLED The androgen receptor has been implicated in the development and progression of bladder cancer (BCa), largely based on studies of animal models. We investigated whether finasteride was associated with a reduced incidence of BCa as observed by self-report in the Prostate, Lung, Colorectal, and Ovarian cancer screening trial. Cox proportional hazard regression analysis was performed to determine the association of finasteride use with time to diagnosis of BCa, controlling for age and tobacco use. Of the 72,370 male participants who met inclusion criteria, 6069 (8.4%) had reported the use of finasteride. BCa was diagnosed in 1.07% (65 of 6069) of those who reported finasteride compared with 1.46% (966 of 66,301) of those who reported no use during the trial. In a multiple Cox regression analysis, self-reported use of finasteride was associated with a decreased risk of development of BCa (hazard ratio: 0.634; 95% confidence interval, 0.493-0.816; p=0.0004), controlling for age and smoking. Limitations of this study include that it is observational and not randomized, that many of the confounding variables for BCa, such as alcohol use, were not available for use in the analysis, and that finasteride use was by annual self-report, which is subject to missing values and error. PATIENT SUMMARY Finasteride is a common medication used to reduce the size of the prostate and to promote hair growth by manipulating testosterone in men. Men are more likely than women to develop bladder cancer (BCa), but our study noted that men using finasteride were less likely to have a BCa diagnosis.

Daily growth of European beech ( Fagus sylvatica L.) on moist sites is affected by short-term drought rather than ozone uptake

Key messageDaily stem growth was reduced by drought with high significance, but not affected by ozone uptake or drought–ozone interaction. Increasing air temperature showed capacity of compensating negative drought effects.AbstractFuture increases in stress on forest trees due to rising ozone deposition and/or exacerbating drought are one of many contemporary climate change concerns. European beech (Fagus sylvatica L.) is known to be sensitive to both stressors. To date, there is limited evidence concerning the impact of ozone uptake, or its combined effect with drought, on the growth of forest trees. This study emanated from the hypothesis that high daily ozone influx potentially limits daily radial stem increment. A secondary hypothesis intimated that not only prolonged, but also short-term water limitation has the capacity for reducing intra-annual growth performance. To address these hypotheses, the concerted impacts of drought and O3 on radial stem growth were analyzed as components of multi-factorial field scenarios comprising gradients in altitude, temperature, precipitation and ozone exposure. Linear mixed models, adjusting for meteorological factors and nutrition, were fit to daily growth measurements in nine beech forest sites across Bavaria/Germany during three consecutive growing seasons. During individual years, daily ozone influx did not statistically significantly limit daily stem growth. However, short-term drought was associated with statistically significant, but minor and reversible limitations of intra-annual radial stem growth. Distinctive levels of plant-available soil water and soil water potential limited growth. Increases in air temperature were conducive to beech stem growth across the study region, apparently offering the capacity for buffering drought impact on the stem growth of beech.

Incorporation of Detailed Family History from the Swedish Family Cancer Database into the PCPT Risk Calculator

PURPOSE A detailed family history provides an inexpensive alternative to genetic profiling for individual risk assessment. We updated the PCPT Risk Calculator to include detailed family histories. MATERIALS AND METHODS The study included 55,168 prostate cancer cases and 638,218 controls from the Swedish Family Cancer Database who were 55 years old or older in 1999 and had at least 1 male first-degree relative 40 years old or older and 1 female first-degree relative 30 years old or older. Likelihood ratios, calculated as the ratio of risk of observing a specific family history pattern in a prostate cancer case compared to a control, were used to update the PCPT Risk Calculator. RESULTS Having at least 1 relative with prostate cancer increased the risk of prostate cancer. The likelihood ratio was 1.63 for 1 first-degree relative 60 years old or older at diagnosis (10.1% of cancer cases vs 6.2% of controls), 2.47 if the relative was younger than 60 years (1.5% vs 0.6%), 3.46 for 2 or more relatives 60 years old or older (1.2% vs 0.3%) and 5.68 for 2 or more relatives younger than 60 years (0.05% vs 0.009%). Among men with no diagnosed first-degree relatives the likelihood ratio was 1.09 for 1 or more second-degree relatives diagnosed with prostate cancer (12.7% vs 11.7%). Additional first-degree relatives with breast cancer, or first-degree or second-degree relatives with prostate cancer compounded these risks. CONCLUSIONS A detailed family history is an independent predictor of prostate cancer compared to commonly used risk factors. It should be incorporated into decision making for biopsy. Compared with other costly biomarkers it is inexpensive and universally available.

A simple-to-use method incorporating genomic markers into prostate cancer risk prediction tools facilitated future validation.

OBJECTIVES To incorporate single-nucleotide polymorphisms (SNPs) into the Prostate Cancer Prevention Trial Risk Calculator (PCPTRC). STUDY DESIGN AND SETTING A multivariate random-effects meta-analysis of likelihood ratios (LRs) for 30 validated SNPs was performed, allowing the incorporation of linkage disequilibrium. LRs for an SNP were defined as the ratio of the probability of observing the SNP in prostate cancer cases relative to controls and estimated by published allele or genotype frequencies. LRs were multiplied by the PCPTRC prior odds of prostate cancer to provide updated posterior odds. RESULTS In the meta-analysis (prostate cancer cases/controls = 386,538/985,968), all but two of the SNPs had at least one statistically significant allele LR (P < 0.05). The two SNPs with the largest LRs were rs16901979 [LR = 1.575 for one risk allele, 2.552 for two risk alleles (homozygous)] and rs1447295 (LR = 1.307 and 1.887, respectively). CONCLUSION The substantial investment in genome-wide association studies to discover SNPs associated with prostate cancer risk and the ability to integrate these findings into the PCPTRC allows investigators to validate these observations, to determine the clinical impact, and to ultimately improve clinical practice in the early detection of the most common cancer in men.

No Detrimental Effect of a Positive Family History on Long-Term Outcomes Following Radical Prostatectomy

PURPOSE Overall 1 in 5 patients with prostate cancer has a positive family history. In this report we evaluated the association between family history and long-term outcomes following radical prostatectomy. MATERIALS AND METHODS Patients treated with radical prostatectomy were identified from a German registry, and separated into positive first-degree family history vs negative family history (strictly negative, requiring at least 1 male first-degree relative older than 60 years and no prostate cancer in the family). Kaplan-Meier curves and Cox proportional hazards models were used for association analyses with biochemical recurrence-free and prostate cancer specific survival. RESULTS Median followup for 7,690 men included in the study was 8.4 years. Of the 754 younger patients less than 55 years old 50.9% (384) had a family history compared to 40.4% of the older patients (2,803; p <0.001). The 10-year biochemical recurrence-free (62.5%) and prostate cancer specific survival (96.1%) rates did not differ between patients with vs without a family history, nor between the younger vs older patient groups (all p >0.05). Prostate specific antigen, pathological stage, node stage and Gleason score were the only significant predictors for biochemical recurrence-free survival, while pathological stage, node stage (all p <0.005) and Gleason score (Gleason 7 vs 6 or less-HR 1.711, 95% CI 1.056-2.774, p = 0.03; Gleason 8 or greater vs 6 or less-HR 4.516, 95% CI 2.776-7.347, p <0.0001) were the only predictors for prostate cancer specific survival. CONCLUSIONS A family history of prostate cancer has no bearing on long-term outcomes after radical prostatectomy.

Statistical Methodology and Engineering for Next Generation Clinical Risk Calculators

In today’s practice of medicine, a variety of online clinical risk calculators are available to assist doctors and patients in informed decision-making. These tools may have unparalleled accuracy when founded on large cohorts or clinical trial populations; they may have passed the litmus test of multiple validations. However, evolving clinical practice, technology and population characteristics, as well as the discovery of new markers, can quickly outdate an existing risk tool, making it non-optimal for the contemporary patient. The traditional path of waiting for the next clinical trial or grant collective to end in order to amass fresh data and build a brand new model is too slow for today’s rapid science society, suggesting novel re-calibration methods applied to compartmentalized models that can be incrementally updated in real time. While Electronic Health Records promise an inexpensive, uninhibited and institution-tailored data flow, the percent usable data can be crippled by selection bias, non-ignorable missing data mechanisms and entanglement in indeterminate text fields, requiring novel big-data and record-linkage approaches to unravel. In this chapter we outline statistical methods and engineering approaches that can be used to tackle these challenges, and thereby keep risk calculators up to date in a continually evolving clinical care landscape. To illustrate we outline our experience adapting the Prostate Cancer Prevention Trial Risk Calculator during the past decade to meet the evolving challenges to risk prediction, and new research needed for the next generation of clinical risk prediction tools.