Inhibition of DEPDC1A Delays Growth and Induces Mature Markers in Myeloma Cells

Scientific Contributions by Alboukadel Kassambara

This study highlights DEPDC1A as a bad prognostic marker in multiple myeloma, showing that its inhibition delays growth and promotes differentiation of malignant plasma cells.

Scientific Abstracts
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Affiliation
Published

April 30, 2013

Modified

May 21, 2025

Keywords

Alboukadel Kassambara, DEPDC1A, multiple myeloma, plasma cell differentiation, G2 arrest, bad prognostic marker, PLoS One

Kassambara A, Schoenhals M, Moreaux J, et al. Inhibition of DEPDC1A Delays Growth and Induces Mature Plasma Cell Markers. PLoS One. 2013. Download the PDF

Summary of the Study

High-throughput microarray profiling has enabled the identification of genes associated with poor prognosis in multiple myeloma (MM). Among these, DEPDC1A, a gene encoding a protein with a Disheveled, EGL-10, and Pleckstrin (DEP) domain, is correlated with shorter patient survival.

This study investigates the biological role of DEPDC1A using lentiviral shRNA in human myeloma cell lines. Key findings include:

  • DEPDC1A knockdown delayed cell growth by causing G2 cell cycle arrest, increasing p53 phosphorylation, and p21 accumulation
  • It induced mature plasma cell markers, such as CXCR4, IL6-R, and CD38
  • These results suggest that DEPDC1A contributes to maintaining plasmablast features in malignant plasma cells

DEPDC1A may therefore be a potential therapeutic target and a bad prognostic marker in MM.

Important

In this study, Alboukadel Kassambara was the first author, leading the experimental design, data analysis, and interpretation. He demonstrated the role of DEPDC1A in promoting a proliferative phenotype in myeloma cells, and showed that its inhibition induces **cell cycle

Citation

Publication: In PLoS One
Date: April 30, 2013
Type: Journal Article
PDF: Download the PDF

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Scientific Contributions

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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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