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Combining epigenetic and clinicopathological variables improves specificity in prognostic prediction in clear cell renal cell carcinoma
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics.
Umeå University, Faculty of Science and Technology, Department of Mathematics and Mathematical Statistics. Umeå University, Faculty of Social Sciences, Umeå School of Business and Economics (USBE), Statistics.
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology.
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2020 (English)In: Journal of Translational Medicine, ISSN 1479-5876, E-ISSN 1479-5876, Vol. 18, no 1, article id 435Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2020. Vol. 18, no 1, article id 435
Keywords [en]
Clear cell renal cell carcinoma, Classification, DNA methylation, Prognosis, Directed cluster analysis
National Category
Urology and Nephrology
Identifiers
URN: urn:nbn:se:umu:diva-176921DOI: 10.1186/s12967-020-02608-1ISI: 000594136300002PubMedID: 33187526Scopus ID: 2-s2.0-85095955809OAI: oai:DiVA.org:umu-176921DiVA, id: diva2:1502337
Funder
The Kempe FoundationsSwedish Research CouncilRegion VästerbottenAvailable from: 2020-11-19 Created: 2020-11-19 Last updated: 2023-03-24Bibliographically approved

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Andersson-Evelönn, EmmaVidman, LindaKällberg, DavidLandfors, MattiasLiu, XijiaLjungberg, BörjeHultdin, MagnusRydén, PatrikDegerman, Sofie

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Andersson-Evelönn, EmmaVidman, LindaKällberg, DavidLandfors, MattiasLiu, XijiaLjungberg, BörjeHultdin, MagnusRydén, PatrikDegerman, Sofie
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PathologyDepartment of Mathematics and Mathematical StatisticsStatisticsUrology and AndrologyDepartment of Clinical Microbiology
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Journal of Translational Medicine
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