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Updated: Dec 29, 2025

VDJ-Seq: Deep Sequencing Analysis of Rearranged Immunoglobulin Heavy Chain Gene to Reveal Clonal Evolution Patterns of B Cell Lymphoma
Published on: December 28, 2015
Molecular basis of clonal evolution in multiple myeloma
Yusuke Furukawa1, Jiro Kikuchi2
1Division of Stem Cell Regulation, Center for Molecular Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi, 329-0498, Japan. furuyu@jichi.ac.jp.
Multiple myeloma (MM) treatment resistance stems from complex genomic architecture and clonal evolution. Understanding these patterns, including subclone drug sensitivities, is key to developing more effective therapies.
Area of Science:
- Oncology
- Genomics
- Cancer Biology
Background:
- Multiple myeloma (MM) exhibits worse-than-expected treatment outcomes, potentially due to its complex genomic landscape and clonal evolution.
- Disease progression involves branching subclonal evolution and the emergence of minor clones that outcompete others under therapeutic pressure.
Purpose of the Study:
- To investigate the genomic architecture and clonal evolution patterns in multiple myeloma.
- To correlate these patterns with treatment refractoriness and drug sensitivities.
Main Methods:
- Analysis of genomic data to identify subclonal architecture and evolutionary patterns.
- Correlation of identified clones with differential drug sensitivities to proteasome inhibitors (PIs) and immunomodulatory drugs (IMiDs).
Main Results:
- Mature clones are sensitive to PIs, while immature clones are resistant to PIs but sensitive to IMiDs.
- Branching evolution, influenced by microenvironment fitness and immune evasion, responds well to IMiDs.
- Approximately 20% of MM cases show neutral evolution with high-risk drivers, exhibiting resistance to IMiDs.
Conclusions:
- Understanding the genomic landscape and clonal evolution patterns is crucial for improving multiple myeloma treatment strategies.
- Differential clone sensitivities to PIs and IMiDs highlight the need for tailored therapeutic approaches based on evolutionary dynamics.
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