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Polygenic risk prediction models for colorectal cancer: a systematic review

BACKGROUND: Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to...

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Autores principales: Sassano, Michele, Mariani, Marco, Quaranta, Gianluigi, Pastorino, Roberta, Boccia, Stefania
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760647/
https://www.ncbi.nlm.nih.gov/pubmed/35030997
http://dx.doi.org/10.1186/s12885-021-09143-2
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author Sassano, Michele
Mariani, Marco
Quaranta, Gianluigi
Pastorino, Roberta
Boccia, Stefania
author_facet Sassano, Michele
Mariani, Marco
Quaranta, Gianluigi
Pastorino, Roberta
Boccia, Stefania
author_sort Sassano, Michele
collection PubMed
description BACKGROUND: Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. METHODS: We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. RESULTS: We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. CONCLUSIONS: Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09143-2.
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spelling pubmed-87606472022-01-18 Polygenic risk prediction models for colorectal cancer: a systematic review Sassano, Michele Mariani, Marco Quaranta, Gianluigi Pastorino, Roberta Boccia, Stefania BMC Cancer Research BACKGROUND: Risk prediction models incorporating single nucleotide polymorphisms (SNPs) could lead to individualized prevention of colorectal cancer (CRC). However, the added value of incorporating SNPs into models with only traditional risk factors is still not clear. Hence, our primary aim was to summarize literature on risk prediction models including genetic variants for CRC, while our secondary aim was to evaluate the improvement of discriminatory accuracy when adding SNPs to a prediction model with only traditional risk factors. METHODS: We conducted a systematic review on prediction models incorporating multiple SNPs for CRC risk prediction. We tested whether a significant trend in the increase of Area Under Curve (AUC) according to the number of SNPs could be observed, and estimated the correlation between AUC improvement and number of SNPs. We estimated pooled AUC improvement for SNP-enhanced models compared with non-SNP-enhanced models using random effects meta-analysis, and conducted meta-regression to investigate the association of specific factors with AUC improvement. RESULTS: We included 33 studies, 78.79% using genetic risk scores to combine genetic data. We found no significant trend in AUC improvement according to the number of SNPs (p for trend = 0.774), and no correlation between the number of SNPs and AUC improvement (p = 0.695). Pooled AUC improvement was 0.040 (95% CI: 0.035, 0.045), and the number of cases in the study and the AUC of the starting model were inversely associated with AUC improvement obtained when adding SNPs to a prediction model. In addition, models constructed in Asian individuals achieved better AUC improvement with the incorporation of SNPs compared with those developed among individuals of European ancestry. CONCLUSIONS: Though not conclusive, our results provide insights on factors influencing discriminatory accuracy of SNP-enhanced models. Genetic variants might be useful to inform stratified CRC screening in the future, but further research is needed. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-021-09143-2. BioMed Central 2022-01-15 /pmc/articles/PMC8760647/ /pubmed/35030997 http://dx.doi.org/10.1186/s12885-021-09143-2 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sassano, Michele
Mariani, Marco
Quaranta, Gianluigi
Pastorino, Roberta
Boccia, Stefania
Polygenic risk prediction models for colorectal cancer: a systematic review
title Polygenic risk prediction models for colorectal cancer: a systematic review
title_full Polygenic risk prediction models for colorectal cancer: a systematic review
title_fullStr Polygenic risk prediction models for colorectal cancer: a systematic review
title_full_unstemmed Polygenic risk prediction models for colorectal cancer: a systematic review
title_short Polygenic risk prediction models for colorectal cancer: a systematic review
title_sort polygenic risk prediction models for colorectal cancer: a systematic review
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8760647/
https://www.ncbi.nlm.nih.gov/pubmed/35030997
http://dx.doi.org/10.1186/s12885-021-09143-2
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