Drug Metabolism Pathways Linked to Prognosis and Resistance in Multiple Myeloma

Scientific Contributions by Alboukadel Kassambara

This study shows how gene expression profiles of drug metabolism and clearance systems are linked to prognosis in multiple myeloma, identifying molecular signatures associated with chemoresistance.

Scientific Abstracts
Author
Affiliation
Published

March 1, 2015

Modified

May 21, 2025

Keywords

Alboukadel Kassambara, multiple myeloma, drug resistance, gene expression, xenobiotic receptors, ABC transporters, Oncotarget

Hassen W, Kassambara A, Reme T, Sahota S, Seckinger A, Vincent L, Cartron G, Moreaux J, Hose D, Klein B. Drug metabolism and clearance system in tumor cells of patients with multiple myeloma. Oncotarget. 2015. Download the PDF

Summary of the Study

Resistance to chemotherapy in multiple myeloma (MM) is a major therapeutic challenge. This study investigates the expression of 350 genes related to drug uptake, metabolism, and clearance in newly diagnosed MM cells (MMCs) and their association with clinical outcome.

Key findings

  • Favorable prognosis patients exhibited:

    • Higher expression of xenobiotic receptors (RXRα, LXR, CAR, FXR)
    • Upregulated influx transporters and phase I/II drug-metabolizing enzymes (DMEs)
  • Poor prognosis patients showed:

    • Global downregulation of xenobiotic receptor pathways
    • Upregulation of ARNT, Nrf2, and ABC transporter genes
  • Suggests potential for therapeutic targeting of RXRα, PXR, LXR, and FXR with agonists to reverse resistance

Important

In this study, Alboukadel Kassambara contributed to the gene expression and survival data analysis, establishing the link between drug metabolism gene signatures and patient outcome in multiple myeloma.

Citation

Publication: In Oncotarget Date: March 1, 2015 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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