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Comparative Study
. 2015 Nov 17;113(10):1512-8.
doi: 10.1038/bjc.2015.334. Epub 2015 Oct 13.

Polygenic susceptibility to testicular cancer: implications for personalised health care

Kevin Litchfield  1 , Jonathan S Mitchell  1 , Janet Shipley  2   3 , Robert Huddart  4 , Ewa Rajpert-De Meyts  5 , Niels E Skakkebæk  5 , Richard S Houlston  1 , Clare Turnbull  1   6
Affiliations
Comparative Study

Polygenic susceptibility to testicular cancer: implications for personalised health care

Kevin Litchfield et al. Br J Cancer. .

Erratum in

Abstract

Background: The increasing incidence of testicular germ cell tumour (TGCT) combined with its strong heritable basis suggests that stratified screening for the early detection of TGCT may be clinically useful. We modelled the efficiency of such a personalised screening approach, based on genetic risk profiling in combination with other diagnostic tools.

Methods: We compared the number of cases potentially detectable in the population under a number of screening models. The polygenic risk scoring (PRS) model was assumed to have a log-normal relative risk distribution across the 19 currently known TGCT susceptibility variants. The diagnostic performance of testicular biopsy and non-invasive semen analysis was also assessed, within a simulated combined screening programme.

Results: The area under the curve for the TGCT PRS model was 0.72 with individuals in the top 1% of the PRS having a nine-fold increased TGCT risk compared with the population median. Results from population-screening simulations only achieved a maximal positive predictive value (PPV) of 60%, highlighting broader clinical factors that challenge such strategies, not least the rare nature of TGCT. In terms of future improvements, heritability estimates suggest that a significant number of additional genetic risk factors for TGCT remain to be discovered, identification of which would potentially yield improvement of the PPV to 80-90%.

Conclusions: While personalised screening models may offer enhanced TGCT risk discrimination, presently the case for population-level testing is not compelling. However, future advances, such as more routine generation of whole genome data is likely to alter the landscape. More targeted screening programs may plausibly then offer clinical benefit, particularly given the significant survivorship issues associated with the successful treatment of TGCT.

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Figures

Figure 1
Figure 1
ROC curve for TGCT predisposition factors.
Figure 2
Figure 2
Population distribution of TGCT relative risk scores ordered by genetic risk (risk is relative to population median risk). The blue line plots the distribution of RR across the population; the red lines correspond to 1st, 10th, 50th, 90th and 99th centiles. The RR figures presented in black are the average in the (i) highest 10 and (ii) top 1 centile of genetic risk.
Figure 3
Figure 3
Simulation of two-stage and three-stage population-based screening for TGCT (see Materials and Methods for references underlying individual clinical assumptions applied). The improved model (far right) represents a theoretical best case scenario achievable with current technologies. Specific parameters changed in the improved scenario are: TGCT RR for top 1% of men increased to 19.2, equating to an absolute TGCT lifetime risk of 9.6%, an improved semen assay with sensitivity increased from 67 to 80%.
See this image and copyright information in PMC

References

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Publication types

Supplementary concepts