Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal
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rnal of Translational Medicine Open Access
RESEARCH
Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma Emma Andersson‑Evelönn1†, Linda Vidman2†, David Källberg2,3, Mattias Landfors1, Xijia Liu2, Börje Ljungberg4, Magnus Hultdin1, Patrik Rydén2*† and Sofie Degerman1,5*†
Abstract Background: Metastasized clear cell renal cell carcinoma (ccRCC) is associated with a poor prognosis. Almost onethird 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, clinico‑ pathological variables are used to risk classify patients, but molecular biomarkers are needed to improve risk classifi‑ cation 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 sensi‑ tivity. 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. Keywords: Clear cell renal cell carcinoma, Classification, DNA methylation, Prognosis, Directed cluster analysis
*Correspondence: [email protected]; [email protected] † Emma Andersson-Evelönn and Linda Vidman contributed equally as first authors, and Patrik Rydén and Sofie Degerman contributed equally as last authors 1 Department of Medical Biosciences, Pathology, Umeå University, 901 87 Umeå, Sweden 2 Department of Mathematics and Mathematical Statistics, Umeå University, 901 87 Umeå, Sweden Full list of author information is available at the end of the article © The Author(s) 2020. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any
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