Prostate cancer polygenic risk score and prediction of lethal prostate cancer

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作者
Robert J. Klein
Emily Vertosick
Dan Sjoberg
David Ulmert
Ann-Charlotte Rönn
Christel Häggström
Elin Thysell
Göran Hallmans
Anders Dahlin
Pär Stattin
Olle Melander
Andrew Vickers
Hans Lilja
机构
[1] Icahn School of Medicine at Mount Sinai,Department of Genetics and Genomic Sciences and Icahn Genomics Institute
[2] Memorial Sloan Kettering Cancer Center,Department of Epidemiology and Biostatistics
[3] University of California Los Angeles,Department of Molecular and Medical Pharmacology
[4] David Geffen UCLA School of Medicine,Ahmanson Translational Imaging Division
[5] David Geffen UCLA School of Medicine,Jonsson Comprehensive Cancer Center
[6] University of California Los Angeles, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research
[7] University of California Los Angeles,Institute of Urologic Oncology
[8] Karolinska University Hospital,Clinical Research Center
[9] Umeå University,Department of Biobank Research
[10] Uppsala University,Department of Surgical Sciences
[11] Umeå University,Department of Medical Biosciences
[12] Umeå University,Department of Public Health and Clinical Medicine
[13] Lund University,Department of Clinical Sciences
[14] Uppsala University Hospital,Department of Surgical Sciences
[15] Skåne University Hospital,Department of Emergency and Internal Medicine
[16] Memorial Sloan Kettering Cancer Center,Departments of Laboratory Medicine, Surgery, and Medicine
[17] Lund University,Department of Translational Medicine
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摘要
Polygenic risk scores (PRS) for prostate cancer incidence have been proposed to optimize prostate cancer screening. Prediction of lethal prostate cancer is key to any stratified screening program to avoid excessive overdiagnosis. Herein, PRS for incident prostate cancer was evaluated in two population-based cohorts of unscreened middle-aged men linked to cancer and death registries: the Västerbotten Intervention Project (VIP) and the Malmö Diet and Cancer study (MDC). SNP genotypes were measured by genome-wide SNP genotyping by array followed by imputation or genotyping of selected SNPs using mass spectrometry. The ability of PRS to predict lethal prostate cancer was compared to PSA and a commercialized pre-specified model based on four kallikrein markers. The PRS was associated with incident prostate cancer, replicating previously reported relative risks, and was also associated with prostate cancer death. However, unlike PSA, the PRS did not show stronger association with lethal disease: the hazard ratio for prostate cancer incidence vs. prostate cancer metastasis and death was 1.69 vs. 1.65 in VIP and 1.25 vs. 1.25 in MDC. PSA was a much stronger predictor of prostate cancer metastasis or death with an area-under-the-curve of 0.78 versus 0.63 for the PRS. Importantly, addition of PRS to PSA did not contribute additional risk stratification for lethal prostate cancer. We have shown that a PRS that predicts prostate cancer incidence does not have utility above and beyond that of PSA measured at baseline when applied to the clinically relevant endpoint of prostate cancer death. These findings have implications for public health policies for delivery of prostate cancer screening. Focusing polygenic risk scores on clinically significant endpoints such as prostate cancer metastasis or death would likely improve clinical utility.
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