EpiScore Predicts Prognosis and Therapeutic Potential in DLBCL

Scientific Contributions by Alboukadel Kassambara

This study defines EpiScore, a gene expression–based risk score from epigenetic regulators, to predict survival in diffuse large B cell lymphoma and identify patients likely to benefit from epigenetic therapy.

Scientific Abstracts
Author
Affiliation
Published

April 10, 2018

Modified

May 21, 2025

Keywords

Alboukadel Kassambara, DLBCL, epigenetics, EpiScore, risk score, DNMT3A, Oncotarget

Szablewski V, Bret C, Kassambara A, Devin J, Cartron G, Costes-Martineau V, Moreaux J. An epigenetic regulator-related score (EpiScore) predicts survival in patients with diffuse large B cell lymphoma and identifies patients who may benefit from epigenetic therapy. Oncotarget. 2018. Download the PDF

Summary of the Study

Diffuse large B-cell lymphoma (DLBCL) is a biologically and clinically heterogeneous cancer. In this study, researchers developed EpiScore, a gene expression–based risk score derived from the expression of three epigenetic regulators (DNMT3A, DOT1L, SETD8) to stratify patient prognosis and therapeutic potential.

  • EpiScore was validated in two independent DLBCL cohorts (n = 414 and n = 69)

  • It classified patients into low, intermediate, and high-risk groups

  • Outperformed established prognostic tools such as:

    • IPI: International Prognostic Index
    • ABC/GCB subtype
    • GERS
    • DNA repair score
  • Immunohistochemistry confirmed that DNMT3A overexpression was associated with worse survival

  • High-risk samples were enriched for an HDAC gene signature, suggesting vulnerability to epigenetic therapies

Important

In this study, Alboukadel Kassambara contributed to the research, data analysis, and writing of the manuscript. He was specifically involved in the development and validation of the EpiScore, linking gene expression of epigenetic regulators to clinical outcomes in DLBCL.

Citation

Publication: In Oncotarget
Date: April 10, 2018
Type: Journal Article
PDF: Download the PDF

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Scientific Contributions

Note

Here are more scientific abstracts authored or co-authored by Alboukadel Kassambara. These contributions span computational biology, bioinformatics, biostatistics, machine learning, and multi-omics, with a focus on immuno-oncology and translational research.

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