4.6 Article

Polygenic risk score as a determinant of risk of non-melanoma skin cancer in a European-descent renal transplant cohort

Journal

AMERICAN JOURNAL OF TRANSPLANTATION
Volume 19, Issue 3, Pages 801-810

Publisher

ELSEVIER SCIENCE INC
DOI: 10.1111/ajt.15057

Keywords

basic research/science; complication: malignant; dermatology; genetics; genomics; kidney transplantation/nephrology; risk assessment/risk stratification; side effects

Funding

  1. Punchestown Kidney Research Fund [EPSPG2015]
  2. Science Foundation Ireland [15/IA/3152]
  3. Northern Ireland Kidney Research Fund
  4. NHS Research Scotland
  5. Irish Research Council [EPSPG2015]
  6. Science Foundation Ireland (SFI) [15/IA/3152] Funding Source: Science Foundation Ireland (SFI)
  7. MRC [MC_PC_15025] Funding Source: UKRI

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Renal transplant recipients have an increased risk of non-melanoma skin cancer (NMSC) compared to in the general population. Here, we show polygenic risk scores (PRS) calculated from genome-wide association studies (GWAS) of NMSC in a general, nontransplant setting, can predict risk of, and time to posttransplant skin cancer. Genetic variants, reaching predefined P-value thresholds were chosen from published squamous cell carcinoma (SCC) and basal cell carcinoma (BCC) nontransplant GWAS. Using these GWAS, BCC and SCC PRS were calculated for each sample across three European ancestry renal transplant cohorts (n = 889) and tested as predictors of case:control status and time to NMSC posttransplant. BCC PRS calculated at P-value threshold 1 x 10(-5) was the most significant predictor of case:control status of NMSC posttransplant (OR = 1.61; adjusted P = .0022; AUC [full model adjusted for clinical predictors and PRS] = 0.81). SCC PRS at P-value threshold 1 x 10(-5) was the most significant predictor of time to posttransplant NMSC (adjusted P = 9.39 x 10(-7); HR = 1.41, concordance [full model] = 0.74). PRS of nontransplant NMSC is predictive of case:control status and time to NMSC posttransplant. These results are relevant to how genomics can risk stratify patients to help develop personalized treatment regimens.

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