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Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma

BACKGROUND: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follo...

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Autores principales: Andersson-Evelönn, Emma, Vidman, Linda, Källberg, David, Landfors, Mattias, Liu, Xijia, Ljungberg, Börje, Hultdin, Magnus, Rydén, Patrik, Degerman, Sofie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666468/
https://www.ncbi.nlm.nih.gov/pubmed/33187526
http://dx.doi.org/10.1186/s12967-020-02608-1
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author Andersson-Evelönn, Emma
Vidman, Linda
Källberg, David
Landfors, Mattias
Liu, Xijia
Ljungberg, Börje
Hultdin, Magnus
Rydén, Patrik
Degerman, Sofie
author_facet Andersson-Evelönn, Emma
Vidman, Linda
Källberg, David
Landfors, Mattias
Liu, Xijia
Ljungberg, Börje
Hultdin, Magnus
Rydén, Patrik
Degerman, Sofie
author_sort Andersson-Evelönn, Emma
collection PubMed
description BACKGROUND: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables. METHODS: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression. RESULTS: The “triple classifier” which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress ((p)CIP(5yr)) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis. CONCLUSIONS: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC.
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spelling pubmed-76664682020-11-16 Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma Andersson-Evelönn, Emma Vidman, Linda Källberg, David Landfors, Mattias Liu, Xijia Ljungberg, Börje Hultdin, Magnus Rydén, Patrik Degerman, Sofie J Transl Med Research BACKGROUND: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost one-third of patients with non-metastatic tumors at diagnosis will later progress with metastatic disease. These patients need to be identified already at diagnosis, to undertake closer follow up and/or adjuvant treatment. Today, clinicopathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classification to identify the high-risk patients which will benefit most from modern adjuvant therapies. Interestingly, DNA methylation profiling has emerged as a promising prognostic biomarker in ccRCC. This study aimed to derive a model for prediction of tumor progression after nephrectomy in non-metastatic ccRCC by combining DNA methylation profiling with clinicopathological variables. METHODS: A novel cluster analysis approach (Directed Cluster Analysis) was used to identify molecular biomarkers from genome-wide methylation array data. These novel DNA methylation biomarkers, together with previously identified CpG-site biomarkers and clinicopathological variables, were used to derive predictive classifiers for tumor progression. RESULTS: The “triple classifier” which included both novel and previously identified DNA methylation biomarkers together with clinicopathological variables predicted tumor progression more accurately than the currently used Mayo scoring system, by increasing the specificity from 50% in Mayo to 64% in our triple classifier at 85% fixed sensitivity. The cumulative incidence of progress ((p)CIP(5yr)) was 7.5% in low-risk vs 44.7% in high-risk in M0 patients classified by the triple classifier at diagnosis. CONCLUSIONS: The triple classifier panel that combines clinicopathological variables with genome-wide methylation data has the potential to improve specificity in prognosis prediction for patients with non-metastatic ccRCC. BioMed Central 2020-11-13 /pmc/articles/PMC7666468/ /pubmed/33187526 http://dx.doi.org/10.1186/s12967-020-02608-1 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
Andersson-Evelönn, Emma
Vidman, Linda
Källberg, David
Landfors, Mattias
Liu, Xijia
Ljungberg, Börje
Hultdin, Magnus
Rydén, Patrik
Degerman, Sofie
Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
title Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
title_full Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
title_fullStr Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
title_full_unstemmed Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
title_short Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
title_sort combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7666468/
https://www.ncbi.nlm.nih.gov/pubmed/33187526
http://dx.doi.org/10.1186/s12967-020-02608-1
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