Dataset Information

Genetic variants and family history predict prostate cancer similar to prostate-specific antigen.

ABSTRACT: Although prostate-specific antigen (PSA) is the best biomarker for predicting prostate cancer, its predictive performance needs to be improved. Results from the Prostate Cancer Prevention Trial revealed the overall performance measured by the areas under curve of the receiver operating characteristic at 0.68. The goal of the present study is to assess the ability of genetic variants as a PSA-independent method to predict prostate cancer risk.We systematically evaluated all prostate cancer risk variants that were identified from genome-wide association studies during the past year in a large population-based prostate cancer case-control study population in Sweden, including 2,893 prostate cancer patients and 1,781 men without prostate cancer.Twelve single nucleotide polymorphisms were independently associated with prostate cancer risk in this Swedish study population. Using a cutoff of any 11 risk alleles or family history, the sensitivity and specificity for predicting prostate cancer were 0.25 and 0.86, respectively. The overall predictive performance of prostate cancer using genetic variants, family history, and age, measured by areas under curve was 0.65 (95% confidence interval, 0.63-0.66), significantly improved over that of family history and age (0.61%; 95% confidence interval, 0.59-0.62; P = 2.3 x 10(-10)).The predictive performance for prostate cancer using genetic variants and family history is similar to that of PSA. The utility of genetic testing, alone and in combination with PSA levels, should be evaluated in large studies such as the European Randomized Study for Prostate Cancer trial and Prostate Cancer Prevention Trial.

SUBMITTER: Zheng SL

PROVIDER: S-EPMC3187807 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Genetic variants and family history predict prostate cancer similar to prostate-specific antigen.

Zheng S Lilly SL Sun Jielin J Wiklund Fredrik F Gao Zhengrong Z Stattin Pär P Purcell Lina D LD Adami Hans-Olov HO Hsu Fang-Chi FC Zhu Yi Y Adolfsson Jan J Johansson Jan-Erik JE Turner Aubrey R AR Adams Tamara S TS Liu Wennuan W Duggan David D Carpten John D JD Chang Bao-Li BL Isaacs William B WB Xu Jianfeng J Grönberg Henrik H

Clinical cancer research : an official journal of the American Association for Cancer Research 20090201 3

<h4>Purpose</h4>Although prostate-specific antigen (PSA) is the best biomarker for predicting prostate cancer, its predictive performance needs to be improved. Results from the Prostate Cancer Prevention Trial revealed the overall performance measured by the areas under curve of the receiver operating characteristic at 0.68. The goal of the present study is to assess the ability of genetic variants as a PSA-independent method to predict prostate cancer risk.<h4>Experimental design</h4>We systema ...[more]

PMID: 19188186

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Project description:BACKGROUND:For men on active surveillance for prostate cancer, utility of prostate-specific antigen (PSA) kinetics (PSAk) in predicting pathologic reclassification remains controversial. OBJECTIVE:To develop prediction methods for utilizing serial PSA and evaluate frequency of collection. DESIGN, SETTING, AND PARTICIPANTS:Data were collected from men enrolled in the multicenter Canary Prostate Active Surveillance Study, for whom PSA data were measured and biopsies performed on prespecified schedules. We developed a PSAk parameter based on a linear mixed-effect model (LMEM) that accounted for serial PSA levels. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS:The association of diagnostic PSA and/or PSAk with time to reclassification (increase in cancer grade and/or volume) was evaluated using multivariable Cox proportional hazards models. RESULTS AND LIMITATIONS:A total of 851 men met the study criteria; 255 (30%) had a reclassification event within 5 yr. Median follow-up was 3.7 yr. After adjusting for prostate size, time since diagnosis, biopsy parameters, and diagnostic PSA, PSAk was a significant predictor of reclassification (hazard ratio for each 0.10 increase in PSAk=1.6 [95% confidence interval 1.2-2.1, p<0.001]). The PSAk model improved stratification of risk prediction for the top and bottom deciles of risk over a model without PSAk. Model performance was essentially identical using PSA data measured every 6 mo to those measured every 3 mo. The major limitation is the reliability of reclassification as an end point, although it drives most treatment decisions. CONCLUSIONS:PSAk calculated using an LMEM statistically significantly predicts biopsy reclassification. Models that use repeat PSA measurements outperform a model incorporating only diagnostic PSA. Model performance is similar using PSA assessed every 3 or 6 mo. If validated, these results should inform optimal incorporation of PSA trends into active surveillance protocols and risk calculators. PATIENT SUMMARY:In this report, we looked at whether repeat prostate-specific antigen (PSA) measurements, or PSA kinetics, improve prediction of biopsy outcomes in men using active surveillance to manage localized prostate cancer. We found that in a large multicenter active surveillance cohort, PSA kinetics improves the prediction of surveillance biopsy outcome.

Project description:ObjectiveTo validate six previously identified markers among men at increased risk of prostate cancer (African-American men and those with a family history of prostate cancer) enrolled in the Prostate Cancer Risk Assessment Program (PRAP), a prostate cancer screening study.Patients and methodsEligibility criteria for PRAP include age 35-69 years with a family history of prostate cancer, African-American ethnicity regardless of family history, and known BRCA gene mutations. The genome-wide association study markers assessed included rs2736098 (5p15.33), rs10993994 (10q11), rs10788160 (10q26), rs11067228 (12q24), rs4430796 (17q12) and rs17632542 (19q13.33). Genotyping methods included either the Taqman(®) single nucleotide polymorphism (SNP) genotyping assay (Applied Biosystems, Foster City, CA, USA) or pyrosequencing. Linear regression models were used to evaluate the association between individual markers and log-transformed baseline PSA levels, while adjusting for potential confounders.ResultsA total of 707 participants (37% Caucasian, 63% African-American) with clinical and genotype data were included in the analysis. Rs10788160 (10q26) was strongly associated with PSA levels among Caucasian participants in the high-risk group (P < 0.01), with a 33.2% increase in PSA level with each A-allele carried. Furthermore, rs10993994 (10q11) was found to be associated with PSA level (P = 0.03) in Caucasian men in the high-risk group, with a 15% increase in PSA level with each T-allele carried. A PSA adjustment model based on allele carrier status at rs10788160 and rs10993994 was proposed, specific to high-risk Caucasian men.ConclusionsGenetic variation at 10q may be particularly important in personalizing the interpretation of PSA level for Caucasian men in the high-risk group. Such information may have clinical relevance in shared decision-making and individualized prostate cancer screening strategies for Caucasian men in the high-risk group, although further study is warranted.

Dataset Information

Genetic variants and family history predict prostate cancer similar to prostate-specific antigen.

Publications

Genetic variants and family history predict prostate cancer similar to prostate-specific antigen.

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