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Survival outcomes in men with a positive family history of prostate cancer: a registry based study.

ABSTRACT: BACKGROUND:To investigate the correlation between family history of prostate cancer (PCa) and survival (overall and cancer specific) in patients undergoing treatment for PCa. METHODS:ine thousand four hundred fifty-nine patients with PCa were extracted from the South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC) database. Diagnosis occurred after 1998 and treatment before 2014. Cox proportional-hazards modeling was used to assess the effect of family history on overall survival after adjustment for confounders (age at diagnosis, NCCN risk category and year of treatment), and with stratification by primary treatment group. Competing risks regression modelling was used to assess PCa specific mortality. RESULTS:Men with a positive family history of PCa appear to have a lower Gleason score at the time of diagnosis (50% with Gleason

SUBMITTER: Ang M

PROVIDER: S-EPMC7499864 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Survival outcomes in men with a positive family history of prostate cancer: a registry based study.

Ang Mann M Borg Martin M O'Callaghan Michael E ME

BMC cancer 20200918 1

<h4>Background</h4>To investigate the correlation between family history of prostate cancer (PCa) and survival (overall and cancer specific) in patients undergoing treatment for PCa.<h4>Methods</h4>ine thousand four hundred fifty-nine patients with PCa were extracted from the South Australian Prostate Cancer Clinical Outcomes Collaborative (SA-PCCOC) database. Diagnosis occurred after 1998 and treatment before 2014. Cox proportional-hazards modeling was used to assess the effect of family histor ...[more]

PMID: 32948129

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Project description:A better assessment of individualized prostate cancer (PrCa) risk is needed to improve screening. The use of the prostate-specific antigen (PSA) level for screening in the general population has limitations and is not currently advocated. Approximately 100 common single nucleotide polymorphisms (SNPs) have been identified that are associated with the risk of developing PrCa. The PROFILE pilot study explored the feasibility of using SNP profiling in men with a family history (FH) of PrCa to investigate the probability of detecting PrCa at prostate biopsy (PB). The primary aim of this pilot study was to determine the safety and feasibility of PrCa screening using transrectal ultrasound-guided PB with or without diffusion-weighted magnetic resonance imaging (DW-MRI) in men with a FH. A secondary aim was to evaluate the potential use of SNP profiling as a screening tool in this population.A total of 100 men aged 40-69 years with a FH of PrCa underwent PB, regardless of their baseline PSA level. Polygenic risk scores (PRSs) were calculated for each participant using 71 common PrCa susceptibility alleles. We treated the disease outcome at PB as the outcome variable and evaluated its associations with the PRS, PSA level, and DW-MRI findings using univariate logistic regression.Of the 100 men, 25 were diagnosed with PrCa, of whom 12 (48%) had clinically significant disease. Four adverse events occurred and no deaths. The PSA level and age at study entry were associated with PrCa at PB (p = .00037 and p = .00004, respectively).The results of the present pilot study have demonstrated that PB is a feasible and safe method of PrCa screening in men with a FH, with a high proportion of PrCa identified requiring radical treatment. It is feasible to collect data on PrCa-risk SNPs to evaluate their combined effect as a potential screening tool. A larger prospective study powered to detect statistical associations is in progress.Prostate biopsy is a feasible and safe approach to prostate cancer screening in men with a family history and detects a high proportion of prostate cancer that needs radical treatment. Calculating a polygenic risk score using prostate cancer risk single nucleotide polymorphisms could be a potential future screening tool for prostate cancer.

Project description:PURPOSE:Treatment decisions about localized prostate cancer depend on accurate estimation of the patient's life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient's health. METHODS:We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model's predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS:Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION:Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.

Dataset Information

Survival outcomes in men with a positive family history of prostate cancer: a registry based study.

Publications

Survival outcomes in men with a positive family history of prostate cancer: a registry based study.

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