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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.
Afiliação
  • Andersson-Evelönn E; Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden.
  • Vidman L; Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.
  • Källberg D; Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.
  • Landfors M; Department of Statistics, USBE, Umeå University, Umeå, Sweden.
  • Liu X; Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden.
  • Ljungberg B; Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden.
  • Hultdin M; Department of Surgical and Perioperative Sciences, Urology and Andrology, Umeå University, Umeå, Sweden.
  • Rydén P; Department of Medical Biosciences, Pathology, Umeå University, 901 87, Umeå, Sweden.
  • Degerman S; Department of Mathematics and Mathematical Statistics, Umeå University, 901 87, Umeå, Sweden. patrik.ryden@umu.se.
J Transl Med ; 18(1): 435, 2020 11 13.
Article em En | MEDLINE | ID: mdl-33187526
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 (pCIP5yr) 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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Texto completo: 1 Coleções: 01-internacional Temas: Geral / Tipos_de_cancer / Outros_tipos Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / Neoplasias Renais Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Transl Med Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Suécia

Texto completo: 1 Coleções: 01-internacional Temas: Geral / Tipos_de_cancer / Outros_tipos Base de dados: MEDLINE Assunto principal: Carcinoma de Células Renais / Neoplasias Renais Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Revista: J Transl Med Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Suécia