The current state of genetic risk models for the development of kidney cancer: a review and validation. (7th May 2022)

• Record Type: Journal Article
• Authors: Harrison, Hannah; Li, Nicole; Saunders, Catherine L.; Rossi, Sabrina H.; Dennis, Joe; Griffin, Simon J.; Stewart, Grant D.; Usher‐Smith, Juliet A.
• Is Part Of: BJU international. Volume 130:Number 5(2022)
• Page Start: 550
• Page End: 561
• Publication Date: 2022-05-07
• Subjects: RCC -- kidney cancer -- genetics -- risk models -- risk stratification -- polygenic risk scores -- validation; Genitourinary organs -- Diseases -- Periodicals; Genitourinary organs -- Surgery -- Periodicals; Urology -- Periodicals; 616.6
• DOI: 10.1111/bju.15752
• Languages: English
• ISSNs: 1464-4096

Abstract

Abstract : Objective: To review the current state of genetic risk models for predicting the development of kidney cancer, by identifying and comparing the performance of published models. Methods: Risk models were identified from a recent systematic review and the Cancer‐PRS web directory. A narrative synthesis of the models, previous validation studies and related genome‐wide association studies (GWAS) was carried out. The discrimination and calibration of the identified models was then assessed and compared in the UK Biobank (UKB) cohort (cases, 452; controls, 487 925). Results: A total of 39 genetic models predicting the development of kidney cancer were identified and 31 were validated in the UKB. Several of the genetic‐only models (seven of 25) and most of the mixed genetic‐phenotypic models (five of six) had some discriminatory ability (area under the receiver operating characteristic curve >0.5) in this cohort. In general, models containing a larger number of genetic variants identified in GWAS performed better than models containing a small number of variants associated with known causal pathways. However, the performance of the included models was consistently poorer than genetic risk models for other cancers. Conclusions: Although there is potential for genetic models to identify those at highest risk of developing kidney cancer, their performance is poorer than the best genetic risk models for other cancers. This may be due to the comparatively small number of genetic variants associated with kidney cancer identified in GWAS to date. The development of improved genetic risk models for kidney cancer is dependent on the identification of more variants associated with this disease. Whether these will have utility within future kidney cancer screening pathways is yet to determined.