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

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Andersson-Evelönn, Emma,1983-Umeå universitet,Patologi (author)

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

Article/chapterEnglish2020

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2020-11-13
Springer Science and Business Media LLC,2020
electronicrdacarrier

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LIBRIS-ID:oai:DiVA.org:umu-176921
https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-176921URI
https://doi.org/10.1186/s12967-020-02608-1DOI

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Language:English
Summary in:English

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Subject category:art swepub-publicationtype

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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.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Urologi och njurmedicin hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Urology and Nephrology hsv//eng
Clear cell renal cell carcinoma
Classification
DNA methylation
Prognosis
Directed cluster analysis

Added entries (persons, corporate bodies, meetings, titles ...)

Vidman, LindaUmeå universitet,Institutionen för matematik och matematisk statistik(Swepub:umu)livi0018 (author)
Källberg, David,1982-Umeå universitet,Institutionen för matematik och matematisk statistik,Statistik(Swepub:umu)daka0018 (author)
Landfors, Mattias,1977-Umeå universitet,Patologi(Swepub:umu)maslas98 (author)
Liu, XijiaUmeå universitet,Institutionen för matematik och matematisk statistik(Swepub:umu)xili0017 (author)
Ljungberg, Börje,Professor,1949-Umeå universitet,Urologi och andrologi(Swepub:umu)bolj0001 (author)
Hultdin, MagnusUmeå universitet,Patologi(Swepub:umu)magn0001 (author)
Rydén, PatrikUmeå universitet,Institutionen för matematik och matematisk statistik(Swepub:umu)pary0001 (author)
Degerman, Sofie,1977-Umeå universitet,Patologi,Institutionen för klinisk mikrobiologi(Swepub:umu)soiden99 (author)
Umeå universitetPatologi (creator_code:org_t)

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