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Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study

BACKGROUND: In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of in...

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Autores principales: Triviño, J. C., Ceba, A., Rubio-Solsona, E., Serra, D., Sanchez-Guiu, I., Ribas, G., Rosa, R., Cabo, M., Bernad, L., Pita, G., Gonzalez-Neira, A., Legarda, G., Diaz, J. L., García-Vigara, A., Martínez-Aspas, A., Escrig, M., Bermejo, B., Eroles, P., Ibáñez, J., Salas, D., Julve, A., Cano, A., Lluch, A., Miñambres, R., Benitez, J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654173/
https://www.ncbi.nlm.nih.gov/pubmed/33167914
http://dx.doi.org/10.1186/s12885-020-07584-9
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author Triviño, J. C.
Ceba, A.
Rubio-Solsona, E.
Serra, D.
Sanchez-Guiu, I.
Ribas, G.
Rosa, R.
Cabo, M.
Bernad, L.
Pita, G.
Gonzalez-Neira, A.
Legarda, G.
Diaz, J. L.
García-Vigara, A.
Martínez-Aspas, A.
Escrig, M.
Bermejo, B.
Eroles, P.
Ibáñez, J.
Salas, D.
Julve, A.
Cano, A.
Lluch, A.
Miñambres, R.
Benitez, J.
author_facet Triviño, J. C.
Ceba, A.
Rubio-Solsona, E.
Serra, D.
Sanchez-Guiu, I.
Ribas, G.
Rosa, R.
Cabo, M.
Bernad, L.
Pita, G.
Gonzalez-Neira, A.
Legarda, G.
Diaz, J. L.
García-Vigara, A.
Martínez-Aspas, A.
Escrig, M.
Bermejo, B.
Eroles, P.
Ibáñez, J.
Salas, D.
Julve, A.
Cano, A.
Lluch, A.
Miñambres, R.
Benitez, J.
author_sort Triviño, J. C.
collection PubMed
description BACKGROUND: In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer. METHODS: This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients. RESULTS: Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08. CONCLUSIONS: These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-020-07584-9. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-020-07584-9.
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spelling pubmed-76541732020-11-12 Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study Triviño, J. C. Ceba, A. Rubio-Solsona, E. Serra, D. Sanchez-Guiu, I. Ribas, G. Rosa, R. Cabo, M. Bernad, L. Pita, G. Gonzalez-Neira, A. Legarda, G. Diaz, J. L. García-Vigara, A. Martínez-Aspas, A. Escrig, M. Bermejo, B. Eroles, P. Ibáñez, J. Salas, D. Julve, A. Cano, A. Lluch, A. Miñambres, R. Benitez, J. BMC Cancer Research Article BACKGROUND: In recent years, the identification of genetic and phenotypic biomarkers of cancer for prevention, early diagnosis and patient stratification has been a main objective of research in the field. Different multivariable models that use biomarkers have been proposed for the evaluation of individual risk of developing breast cancer. METHODS: This is a case control study based on a population-based cohort. We describe and evaluate a multivariable model that incorporates 92 Single-nucleotide polymorphisms (SNPs) (Supplementary Table S1) and five different phenotypic variables and which was employed in a Spanish population of 642 healthy women and 455 breast cancer patients. RESULTS: Our model allowed us to stratify two groups: high and low risk of developing breast cancer. The 9th decile included 1% of controls vs 9% of cases, with an odds ratio (OR) of 12.9 and a p-value of 3.43E-07. The first decile presented an inverse proportion: 1% of cases and 9% of controls, with an OR of 0.097 and a p-value of 1.86E-08. CONCLUSIONS: These results indicate the capacity of our multivariable model to stratify women according to their risk of developing breast cancer. The major limitation of our analysis is the small cohort size. However, despite the limitations, the results of our analysis provide proof of concept in a poorly studied population, and opens up the possibility of using this method in the routine screening of the Spanish population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-020-07584-9. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12885-020-07584-9. BioMed Central 2020-11-10 /pmc/articles/PMC7654173/ /pubmed/33167914 http://dx.doi.org/10.1186/s12885-020-07584-9 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver (http://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 Article
Triviño, J. C.
Ceba, A.
Rubio-Solsona, E.
Serra, D.
Sanchez-Guiu, I.
Ribas, G.
Rosa, R.
Cabo, M.
Bernad, L.
Pita, G.
Gonzalez-Neira, A.
Legarda, G.
Diaz, J. L.
García-Vigara, A.
Martínez-Aspas, A.
Escrig, M.
Bermejo, B.
Eroles, P.
Ibáñez, J.
Salas, D.
Julve, A.
Cano, A.
Lluch, A.
Miñambres, R.
Benitez, J.
Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study
title Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study
title_full Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study
title_fullStr Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study
title_full_unstemmed Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study
title_short Combination of phenotype and polygenic risk score in breast cancer risk evaluation in the Spanish population: a case –control study
title_sort combination of phenotype and polygenic risk score in breast cancer risk evaluation in the spanish population: a case –control study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7654173/
https://www.ncbi.nlm.nih.gov/pubmed/33167914
http://dx.doi.org/10.1186/s12885-020-07584-9
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