1H HRMAS NMR Derived Bio-markers Related to Tumor Grade, Tumor Cell Fraction, and Cell Proliferation in Prostate Tissue Samples

A high-resolution magic angle spinning NMR spectroscopic approach is presented for evaluating the occurrence, amount and aggressiveness of cancer in human prostate tissue samples. Using this technique, key metabolites in malignant and non-malignant samples (n = 149) were identified, and patterns of their relative abundance were analyzed by multivariate statistical methods. Ratios of various metabolites – including (glycerophophorylcholine + phosphorylcholine)/creatine, myo-inositol/scyllo-inositol, scyllo-inositol/creatine, choline/creatine, and citrate/creatine – correlated with: i) for non-malignant tissue samples, the distance to the nearest tumor and its Gleason score and; ii) the fraction of tumor cells present in the sample; and iii) tumor cell proliferation (Ki67 labelling index). This NMR-based approach allows the extraction of information that could be useful for developing novel diagnostic methods for prostate cancer

Quantifying the Transition from Active Surveillance to Watchful Waiting Among Men with Very Low-risk Prostate Cancer

AbstractBackgroundActive surveillance (AS) is commonly used for men with low-risk prostate cancer (PCa). When life expectancy becomes too short for curative treatment to be beneficial, a change from AS to watchful waiting (WW) follows. Little is known about this change since it is rarely documented in medical records.ObjectiveTo model transition from AS to WW and how this is affected by age and comorbidity among men with very low-risk PCa.Design, setting, and participantsNational population-based healthcare registers were used for analysis.Outcome measurements and statistical analysisUsing data on PCa characteristics, age, and comorbidity, a state transition model was created to estimate the probability of changes between predefined treatments to estimate transition from AS to WW.Results and limitationsOur estimates indicate that 48% of men with very low-risk PCa starting AS eventually changed to WW over a life course. This proportion increased with age at time of AS initiation. Within 10 yr from start of AS, 10% of men aged 55 yr and 50% of men aged 70 yr with no comorbidity at initiation changed to WW. Our prevalence simulation suggests that the number of men on WW who were previously on AS will eventually stabilise after 30 yr. A limitation is the limited information from clinical follow-up visits (eg, repeat biopsies).ConclusionsWe estimated that changes from AS to WW become common among men with very low-risk PCa who are elderly. This potential change to WW should be discussed with men starting on AS. Moreover, our estimates may help in planning health care resources allocated to men on AS, as the transition to WW is associated with lower demands on outpatient resources.Patient summaryChanges from active surveillance to watchful waiting will become more common among men with very low-risk prostate cancer. These observations suggest that patients need to be informed about this potential change before they start on active surveillance

Stromal PDGFRβ Expression in Prostate Tumors and Non-Malignant Prostate Tissue Predicts Prostate Cancer Survival

Background: The identification of new prognostic markers for prostate cancer is highly warranted, since it is difficult to identify patients requiring curative treatment. Data from both experimental models and clinical samples have identified important functions of PDGFRb on pericytes and fibroblasts in the tumor stroma. Methodology/Principal Findings: In this study the prognostic significance of PDGFRb in prostate cancer stroma, and in matched non-malignant tissue, was evaluated with immunohistochemistry. PDGFRb expression was analyzed in normal and tumor stroma from more than 300 prostate cancer patients. High PDGFRb expression in tumor stroma was associated with large tumor size, advanced stage, high Gleason score and high vessel density. Perivascular PDGFRb staining in tumors was also correlated with high Gleason score. Correlations were also observed between PDGFRb status in tumor stroma and nonmalignant stroma. Similarly, high PDGFRb expression in adjacent non-malignant tissue stroma correlated with large tumor size, advanced stage, high Gleason score and proliferation in non-malignant epithelium. Interestingly, high levels of PDGFRb in the stroma of tumor and non-malignant tissue were associated with shorter cancer specific survival in prostate cancer patients. Conclusions/Significance: The study revealed a number of novel associations between stromal PDGFRb expression i

Prostate specific antigen for early detection of prostate cancer: longitudinal study

Objective To evaluate if prostate specific antigen test attains validity standards required for screening in view of recent prostate cancer screening trial results

Waist circumference and a body shape index and prostate cancer risk and mortality.

We recently found a negative association between body mass index (BMI) and the risk of localised prostate cancer (PCa), no association with advanced PCa, and a positive association with PCa-specific mortality. In a 15% subpopulation of that study, we here investigated the measures of abdominal adiposity including waist circumference (WC) and A Body Shape Index (ABSI) in relation to PCa risk and mortality. We used data from 58,457 men from four Swedish cohorts to assess WC and ABSI in relation to PCa risk according to cancer risk category, including localised asymptomatic and symptomatic PCa and advanced PCa, and PCa-specific mortality. Cox regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). During, on average, 10 years of follow-up, 3290 men were diagnosed with PCa and 387 died of PCa. WC was negatively associated with the risk of total PCa (HR per 10 cm, 0.95; 95% CI 0.92-0.99), localised PCa (HR per 10 cm, 0.93, 95% CI 0.88-0.96) and localised asymptomatic PCa cases detected through a prostate-specific antigen (PSA) test (HR per 10 cm, 0.87, 95% CI 0.81-0.94). WC was not associated with the risk of advanced PCa (HR per 10 cm, 1.02, 95% CI 0.93-1.14) or with PCa-specific mortality (HR per 10 cm, 1.04, 95% CI 0.92-1.19). ABSI showed no associations with the risk of PCa or PCa-specific mortality. While the negative association between WC and the risk of localised PCa was partially driven by PSA-detected PCa cases, no association was found between abdominal adiposity and clinically manifest PCa in our population

Comparative performance and external validation of the multivariable PREDICT Prostate tool for non-metastatic prostate cancer: a study in 69,206 men from Prostate Cancer data Base Sweden (PCBaSe)

Abstract: Background: PREDICT Prostate is an endorsed prognostic model that provides individualised long-term prostate cancer-specific and overall survival estimates. The model, derived from UK data, estimates potential treatment benefit on overall survival. In this study, we externally validated the model in a large independent dataset and compared performance to existing models and within treatment groups. Methods: Men with non-metastatic prostate cancer and prostate-specific antigen (PSA) < 100 ng/ml diagnosed between 2000 and 2010 in the nationwide population-based Prostate Cancer data Base Sweden (PCBaSe) were included. Data on age, PSA, clinical stage, grade group, biopsy involvement, primary treatment and comorbidity were retrieved. Sixty-nine thousand two hundred six men were included with 13.9 years of median follow-up. Fifteen-year survival estimates were calculated using PREDICT Prostate for prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM). Discrimination was assessed using Harrell’s concordance (c)-index in R. Calibration was evaluated using cumulative available follow-up in Stata (TX, USA). Results: Overall discrimination of PREDICT Prostate was good with c-indices of 0.85 (95% CI 0.85–0.86) for PCSM and 0.79 (95% CI 0.79–0.80) for ACM. Overall calibration of the model was excellent with 25,925 deaths predicted and 25,849 deaths observed. Within the conservative management and radical treatment groups, c-indices for 15-year PCSM were 0.81 and 0.78, respectively. Calibration also remained good within treatment groups. The discrimination of PREDICT Prostate significantly outperformed the EAU, NCCN and CAPRA scores for both PCSM and ACM within this cohort overall. A key limitation is the use of retrospective cohort data. Conclusions: This large external validation demonstrates that PREDICT Prostate is a robust and generalisable model to aid clinical decision-making

Comparative performance and external validation of the multivariable PREDICT Prostate tool for non-metastatic prostate cancer: a study in 69,206 men from Prostate Cancer data Base Sweden (PCBaSe)

Abstract: Background: PREDICT Prostate is an endorsed prognostic model that provides individualised long-term prostate cancer-specific and overall survival estimates. The model, derived from UK data, estimates potential treatment benefit on overall survival. In this study, we externally validated the model in a large independent dataset and compared performance to existing models and within treatment groups. Methods: Men with non-metastatic prostate cancer and prostate-specific antigen (PSA) < 100 ng/ml diagnosed between 2000 and 2010 in the nationwide population-based Prostate Cancer data Base Sweden (PCBaSe) were included. Data on age, PSA, clinical stage, grade group, biopsy involvement, primary treatment and comorbidity were retrieved. Sixty-nine thousand two hundred six men were included with 13.9 years of median follow-up. Fifteen-year survival estimates were calculated using PREDICT Prostate for prostate cancer-specific mortality (PCSM) and all-cause mortality (ACM). Discrimination was assessed using Harrell’s concordance (c)-index in R. Calibration was evaluated using cumulative available follow-up in Stata (TX, USA). Results: Overall discrimination of PREDICT Prostate was good with c-indices of 0.85 (95% CI 0.85–0.86) for PCSM and 0.79 (95% CI 0.79–0.80) for ACM. Overall calibration of the model was excellent with 25,925 deaths predicted and 25,849 deaths observed. Within the conservative management and radical treatment groups, c-indices for 15-year PCSM were 0.81 and 0.78, respectively. Calibration also remained good within treatment groups. The discrimination of PREDICT Prostate significantly outperformed the EAU, NCCN and CAPRA scores for both PCSM and ACM within this cohort overall. A key limitation is the use of retrospective cohort data. Conclusions: This large external validation demonstrates that PREDICT Prostate is a robust and generalisable model to aid clinical decision-making

Height, body mass index and prostate cancer risk and mortality by way of detection and cancer risk category.

Obesity is a risk factor for advanced, but not localised, prostate cancer (PCa), and for poor prognosis. However, the detection of localised PCa through asymptomatic screening might influence these associations. We investigated height and body mass index (BMI) among 431 902 men in five Swedish cohorts in relation to PCa risk, according to cancer risk category and detection mode, and PCa-specific mortality using Cox regression. Statistical tests were two-sided. Height was positively associated with localised intermediate-risk PCa (HR per 5 cm, 1.03, 95% CI 1.01-1.05), while overweight and obesity were negatively associated with localised low- and intermediate-risk PCa (HRs per 5 kg/m2 , 0.86, 95% CI 0.81-0.90, and 0.92, 95% CI 0.88-0.97). However, these associations were partially driven by PCa's detected by asymptomatic screening and, for height, also by symptoms unrelated to PCa. The HR of localised PCa's, per 5 kg/m2 , was 0.88, 95% CI 0.83 to 0.92 for screen-detected PCa's and 0.96, 95% CI 0.90 to 1.01 for PCa's detected through lower urinary tract symptoms. BMI was positively associated with PCa-specific mortality in the full population and in case-only analysis of each PCa risk category (HRs per 5 kg/m2 , 1.11-1.22, P for heterogeneity = .14). More active health-seeking behaviour among tall and normal-weight men may partially explain their higher risk of localised PCa. The higher PCa-specific mortality among obese men across all PCa risk categories in our study suggests obesity as a potential target to improve the prognosis of obese PCa patients

Теоретические аспекты формирования и содержание организационно–экономического механизма функционирования интегрированных структур в АПК

Background: The presence of comorbid conditions is strongly related to survival and also affects treatment choices in cancer patients. This comorbidity is often quantified by the Charlson Comorbidity Index (CCI) using specific weights (1, 2, 3, or 6) for different comorbidities. It has been shown that the CCI increases at different times and with different sizes, so that traditional time to event analysis is not adequate to assess these temporal changes. Here, we present a method to model temporal changes in CCI in cancer patients using data from PCBaSe Sweden, a nation-wide population-based prospective cohort of men diagnosed with prostate cancer. Our proposed model is based on the assumption that a change in comorbidity, as quantified by the CCI, is an irreversible one-way process, i.e., CCI accumulates over time and cannot decrease. Methods: CCI was calculated based on 17 disease categories, which were defined using ICD-codes for discharge diagnoses in the National Patient Register. A state transition model in discrete time steps (i.e., four weeks) was applied to capture all changes in CCI. The transition probabilities were estimated from three modelling steps: 1) Logistic regression model for vital status, 2) Logistic regression model to define any changes in CCI, and 3) Poisson regression model to determine the size of CCI change, with an additional logistic regression model for CCI changes &gt;= 6. The four models combined yielded parameter estimates to calculate changes in CCI with their confidence intervals. Results: These methods were applied to men with low-risk prostate cancer who received active surveillance (AS), radical prostatectomy (RP), or curative radiotherapy (RT) as primary treatment. There were large differences in CCI changes according to treatment. Conclusions: Our method to model temporal changes in CCI efficiently captures changes in comorbidity over time with a small number of regression analyses to perform - which would be impossible with tradition time to event analyses. However, our approach involves a simulation step that is not yet included in standard statistical software packages. In our prostate cancer example we showed that there are large differences in development of comorbidities among men receiving different treatments for prostate cancer

Prostate cancer mortality in areas with high and low prostate cancer incidence

BACKGROUND: The effect of prostate-specific antigen (PSA) screening on prostate cancer mortality remains debated, despite evidence from randomized trials. We investigated the association between prostate cancer incidence, reflecting uptake of PSA testing, and prostate cancer mortality. METHODS: The study population consisted of all men aged 50 to 74 years residing in eight counties in Sweden with an early increase in prostate cancer incidence and six counties with a late increase during two time periods. Incidence of metastatic prostate cancer was investigated in the period from 2000 to 2009, and prostate cancer-specific mortality and excess mortality were investigated in the period from 1990 to 1999 and the period from 2000 to 2009 by calculating rate ratios for high- vs low-incidence counties and rate ratios for the period from 2000 to 2009 vs the period from 1990 to 1999 within these two groups. All statistical tests were two-sided. RESULTS: There were 4528134 person-years at risk, 1577 deaths from prostate cancer, and 1210 excess deaths in men with prostate cancer in high-incidence counties and 2471373 person-years at risk, 985 prostate cancer deaths, and 878 excess deaths in low-incidence counties in the period from 2000 to 2009. Rate ratios in counties with high vs low incidence adjusted for time period were 0.81 (95% confidence interval [CI] = 0.73 to 0.90) for prostate cancer- specific mortality and 0.74 (95% CI = 0.64 to 0.86) for excess mortality, and the rate ratio of metastatic prostate cancer was 0.85 (95% CI = 0.79 to 0.92). CONCLUSIONS: The lower prostate cancer mortality in high-incidence counties reflecting a high PSA uptake suggests that more-intense as compared with less-intense opportunistic PSA screening reduces prostate cancer mortality