Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification
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 unex...
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| Language: | English |
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BMC
2024-02-01
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| Series: | Genome Medicine |
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| Online Access: | https://doi.org/10.1186/s13073-024-01298-4 |
| _version_ | 1797273782014967808 |
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| author | Xinan Wang Ziwei Zhang Yi Ding Tony Chen Lorelei Mucci Demetrios Albanes Maria Teresa Landi Neil E. Caporaso Stephen Lam Adonina Tardon Chu Chen Stig E. Bojesen Mattias Johansson Angela Risch Heike Bickeböller H-Erich Wichmann Gadi Rennert Susanne Arnold Paul Brennan James D. McKay John K. Field Sanjay S. Shete Loic Le Marchand Geoffrey Liu Angeline S. Andrew Lambertus A. Kiemeney Shan Zienolddiny-Narui Annelie Behndig Mikael Johansson Angie Cox Philip Lazarus Matthew B. Schabath Melinda C. Aldrich Rayjean J. Hung Christopher I. Amos Xihong Lin David C. Christiani |
| author_facet | Xinan Wang Ziwei Zhang Yi Ding Tony Chen Lorelei Mucci Demetrios Albanes Maria Teresa Landi Neil E. Caporaso Stephen Lam Adonina Tardon Chu Chen Stig E. Bojesen Mattias Johansson Angela Risch Heike Bickeböller H-Erich Wichmann Gadi Rennert Susanne Arnold Paul Brennan James D. McKay John K. Field Sanjay S. Shete Loic Le Marchand Geoffrey Liu Angeline S. Andrew Lambertus A. Kiemeney Shan Zienolddiny-Narui Annelie Behndig Mikael Johansson Angie Cox Philip Lazarus Matthew B. Schabath Melinda C. Aldrich Rayjean J. Hung Christopher I. Amos Xihong Lin David C. Christiani |
| author_sort | Xinan Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T14:49:08Z |
| format | Article |
| id | doaj.art-f2a83eace6cb48e6be31892d21e98222 |
| institution | Directory Open Access Journal |
| issn | 1756-994X |
| language | English |
| last_indexed | 2024-03-07T14:49:08Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Medicine |
| spelling | doaj.art-f2a83eace6cb48e6be31892d21e982222024-03-05T19:51:33ZengBMCGenome Medicine1756-994X2024-02-0116111110.1186/s13073-024-01298-4Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratificationXinan Wang0Ziwei Zhang1Yi Ding2Tony Chen3Lorelei Mucci4Demetrios Albanes5Maria Teresa Landi6Neil E. Caporaso7Stephen Lam8Adonina Tardon9Chu Chen10Stig E. Bojesen11Mattias Johansson12Angela Risch13Heike Bickeböller14H-Erich Wichmann15Gadi Rennert16Susanne Arnold17Paul Brennan18James D. McKay19John K. Field20Sanjay S. Shete21Loic Le Marchand22Geoffrey Liu23Angeline S. Andrew24Lambertus A. Kiemeney25Shan Zienolddiny-Narui26Annelie Behndig27Mikael Johansson28Angie Cox29Philip Lazarus30Matthew B. Schabath31Melinda C. Aldrich32Rayjean J. Hung33Christopher I. Amos34Xihong Lin35David C. Christiani36Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard UniversityDepartment of Medical Oncology, Dana-Farber Cancer InstituteBioinformatics Interdepartmental Program, University of CaliforniaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard UniversityDepartment of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard UniversityDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthDivision of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthDepartment of Medicine, British Columbia Cancer Agency, University of British ColumbiaFaculty of Medicine, University of Oviedo and CIBERESPDepartment of Epidemiology, University of Washington School of Public Health, Public Health Sciences Division, Fred Hutchinson Cancer Research CenterDepartment of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University HospitalGenomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO)Department of Biosciences and Medical Biology, Allergy-Cancer-BioNano Research Centre, University of Salzburg, and Cancer Cluster SalzburgDepartment of Genetic Epidemiology, University Medical Center, Georg August University GöttingenInstitute of Medical Informatics, Biometry and Epidemiology, Ludwig Maximilians UniversityClalit National Cancer Control Center, Carmel Medical Center and Technion Faculty of MedicineMarkey Cancer Center, University of KentuckyGenomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO)Genomic Epidemiology Branch, International Agency for Research on Cancer (IARC/WHO)Department of Molecular and Clinical Cancer Medicine, Institute of Translational Medicine, University of LiverpoolDepartment of Biostatistics, The University of Texas MD Anderson Cancer CenterEpidemiology Program, University of Hawaii Cancer CenterPrincess Margaret Cancer Centre, Dalla Lana School of Public Health, University of TorontoDepartment of Epidemiology, Department of Community and Family Medicine, Dartmouth Geisel School of MedicineDepartment for Health Evidence, Department of Urology, Radboud University Medical CenterNational Institute of Occupational HealthDepartment of Public Health and Clinical Medicine, Umeå UniversityDepartment of Radiation Sciences, Umeå UniversityDepartment of Oncology and Metabolism, The Medical School, University of SheffieldDepartment of Pharmaceutical Sciences, College of Pharmacy, Washington State UniversityDepartment of Cancer Epidemiology, H. Lee Moffitt Cancer Center and Research InstituteDepartment of Medicine, Department of Biomedical Informatics and Department of Thoracic Surgery, Vanderbilt University Medical CenterLunenfeld-Tanenbaum Research Institute, Sinai Health System, Dalla Lana School of Public Health, University of TorontoInstitute for Clinical and Translational Research, Department of Medicine, Dan L Duncan Comprehensive Cancer Center, Baylor College of MedicineDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard UniversityDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard UniversityAbstract 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.https://doi.org/10.1186/s13073-024-01298-4Non-small cell lung cancer (NSCLC)Polygenic risk score (PRSs)Cancer controlPopulation scienceGenetic epidemiology |
| spellingShingle | Xinan Wang Ziwei Zhang Yi Ding Tony Chen Lorelei Mucci Demetrios Albanes Maria Teresa Landi Neil E. Caporaso Stephen Lam Adonina Tardon Chu Chen Stig E. Bojesen Mattias Johansson Angela Risch Heike Bickeböller H-Erich Wichmann Gadi Rennert Susanne Arnold Paul Brennan James D. McKay John K. Field Sanjay S. Shete Loic Le Marchand Geoffrey Liu Angeline S. Andrew Lambertus A. Kiemeney Shan Zienolddiny-Narui Annelie Behndig Mikael Johansson Angie Cox Philip Lazarus Matthew B. Schabath Melinda C. Aldrich Rayjean J. Hung Christopher I. Amos Xihong Lin David C. Christiani Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification Genome Medicine Non-small cell lung cancer (NSCLC) Polygenic risk score (PRSs) Cancer control Population science Genetic epidemiology |
| title | Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification |
| title_full | Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification |
| title_fullStr | Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification |
| title_full_unstemmed | Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification |
| title_short | Impact of individual level uncertainty of lung cancer polygenic risk score (PRS) on risk stratification |
| title_sort | impact of individual level uncertainty of lung cancer polygenic risk score prs on risk stratification |
| topic | Non-small cell lung cancer (NSCLC) Polygenic risk score (PRSs) Cancer control Population science Genetic epidemiology |
| url | https://doi.org/10.1186/s13073-024-01298-4 |
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