Risk Model for Prostate Cancer Using Environmental and Genetic Factors in the Spanish Multi-Case-Control (MCC) Study

Gómez-Acebo, Inés; Dierssen Sotos, Trinidad; Fernández-Navarro, Pablo; Palazuelos, Camilo; Moreno, Víctor; Aragonés, Núria; Castaño Vinyals, Gemma; Jiménez-Moleón, José Juan; Ruiz-Cerdá, Jose Luis; Pérez-Gómez, Beatriz; Ruiz Dominguez, José Manuel; Alonso Molero, Jessica; Pollán, Marina; Kogevinas, Manolis; Llorca, Javier

doi:http://dx.doi.org/10.1038/s41598-017-09386-9

Risk Model for Prostate Cancer Using Environmental and Genetic Factors in the Spanish Multi-Case-Control (MCC) Study

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Gómez-Acebo I, Dierssen-Sotos T, Fernandez-Navarro P, Palazuelos C, Moreno V, Aragonés N et al. Risk Model for Prostate Cancer Using Environmental and Genetic Factors in the Spanish Multi-Case-Control (MCC) Study. Sci Rep. 2017 Aug 21;7(1):8994. DOI: 10.1038/s41598-017-09386-9

Abstract

Prostate cancer (PCa) is the second most common cancer among men worldwide. Its etiology remains largely unknown compared to other common cancers. We have developed a risk stratification model combining environmental factors with family history and genetic susceptibility. 818 PCa cases and 1,006 healthy controls were compared. Subjects were interviewed on major lifestyle factors and family history. Fifty-six PCa susceptibility SNPs were genotyped. Risk models based on logistic regression were developed to combine environmental factors, family history and a genetic risk score. In the whole model, compared with subjects with low risk (reference category, decile 1), those carrying an intermediate risk (decile 5) had a 265% increase in PCa risk (OR = 3.65, 95% CI 2.26 to 5.91). The genetic risk score had an area under the ROC curve (AUROC) of 0.66 (95% CI 0.63 to 0.68). When adding the environmental score and family history to the genetic risk score, the AUROC increased by 0.05, reaching 0.71 (95% CI 0.69 to 0.74). Genetic susceptibility has a stronger risk value of the prediction that modifiable risk factors. While the added value of each SNP is small, the combination of 56 SNPs adds to the predictive ability of the risk model.