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. 2024 Feb 5;16(1):22.
doi: 10.1186/s13073-024-01298-4.

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification

Xinan Wang #  1 , Ziwei Zhang #  2 , Yi Ding  3 , Tony Chen  4 , Lorelei Mucci  5 , Demetrios Albanes  6 , Maria Teresa Landi  6 , Neil E Caporaso  6 , Stephen Lam  7 , Adonina Tardon  8 , Chu Chen  9 , Stig E Bojesen  10 , Mattias Johansson  11 , Angela Risch  12 , Heike Bickeböller  13 , H-Erich Wichmann  14 , Gadi Rennert  15 , Susanne Arnold  16 , Paul Brennan  11 , James D McKay  11 , John K Field  17 , Sanjay S Shete  18 , Loic Le Marchand  19 , Geoffrey Liu  20 , Angeline S Andrew  21 , Lambertus A Kiemeney  22 , Shan Zienolddiny-Narui  23 , Annelie Behndig  24 , Mikael Johansson  25 , Angie Cox  26 , Philip Lazarus  27 , Matthew B Schabath  28 , Melinda C Aldrich  29 , Rayjean J Hung  30 , Christopher I Amos  31 , Xihong Lin  4 , David C Christiani  32   33
Affiliations

Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification

Xinan Wang et al. Genome Med. .

Abstract

Background: Although polygenic risk score (PRS) has emerged as a promising tool for predicting cancer risk from genome-wide association studies (GWAS), the individual-level accuracy of lung cancer PRS and the extent to which its impact on subsequent clinical applications remains largely unexplored.

Methods: Lung cancer PRSs and confidence/credible interval (CI) were constructed using two statistical approaches for each individual: (1) the weighted sum of 16 GWAS-derived significant SNP loci and the CI through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the CI through posteriors sampling (PRS-Bayes), among 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium. Individuals were classified into different genetic risk subgroups based on the relationship between their own PRS mean/PRS CI and the population level threshold.

Results: Considerable variances in PRS point estimates at the individual level were observed for both methods, with an average standard deviation (s.d.) of 0.12 for PRS-16-CV and a much larger s.d. of 0.88 for PRS-Bayes. Using PRS-16-CV, only 25.0% of individuals with PRS point estimates in the lowest decile of PRS and 16.8% in the highest decile have their entire 95% CI fully contained in the lowest and highest decile, respectively, while PRS-Bayes was unable to find any eligible individuals. Only 19% of the individuals were concordantly identified as having high genetic risk (> 90th percentile) using the two PRS estimators. An increased relative risk of lung cancer comparing the highest PRS percentile to the lowest was observed when taking the CI into account (OR = 2.73, 95% CI: 2.12-3.50, P-value = 4.13 × 10-15) compared to using PRS-16-CV mean (OR = 2.23, 95% CI: 1.99-2.49, P-value = 5.70 × 10-46). Improved risk prediction performance with higher AUC was consistently observed in individuals identified by PRS-16-CV CI, and the best performance was achieved by incorporating age, gender, and detailed smoking pack-years (AUC: 0.73, 95% CI = 0.72-0.74).

Conclusions: Lung cancer PRS estimates using different methods have modest correlations at the individual level, highlighting the importance of considering individual-level uncertainty when evaluating the practical utility of PRS.

Keywords: Cancer control; Genetic epidemiology; Non-small cell lung cancer (NSCLC); Polygenic risk score (PRSs); Population science.

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Conflict of interest statement

M. C. Aldrich reports funding from NIH and GO2 foundation, and she serves an advisor to Guardant Health. While M. Johansson and J. D. McKay are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy, or views of the International Agency for Research on Cancer/World Health Organization. The remaining authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Overview of the study. The study was conducted in 17,166 lung cancer cases and 12,894 controls with European ancestry from the International Lung Cancer Consortium (ILCCO). The lung cancer PRSs and corresponding confidence/credible interval were constructed using two statistical approaches for each individual—(1) the weighted sum of 16 GWAS-derived significant SNP loci that have been validated in European descent population and the confidence interval through the bootstrapping method (PRS-16-CV) and (2) LDpred2 and the credible interval through posteriors sampling (PRS-Bayes). The individual-level PRS uncertainty was characterized and the impact on subsequent risk stratification and prediction were evaluated
Fig. 2
Fig. 2
Risk stratification based on PRS confidence/credible interval (CI). The large distribution illustrates the PRS distribution at the population level, and the four small ones refer to individual PRS distributions for participants with different PRS-based risks of lung cancer. The dashed horizontal lines indicate the population level thresholds for risk stratification. Individuals with their PRS CI above a pre-specified population-level threshold t at the upper tail (e.g., t = 90th percentile) were classified as certainly high genetic risk, and similarly for individuals with PRS CI below the population level threshold t at the lower tail (e.g., t = 10th percentile) as certainly low genetic risk. Individuals whose CI covered the population level threshold were considered uncertain
Fig. 3
Fig. 3
Individual-level distribution of PRS-16-CV and PRS-Bayes. A Individual-level PRS distributions obtained from PRS-16-CV. B Individual-level PRS distributions obtained from PRS-Bayes. For illustration purposes, here we only show the individual-level PRS distributions for 100 participants
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