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Updated: Sep 6, 2025

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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
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Correlation of changes in subclonal architecture with progression in the MMRF CoMMpass study
Gurvinder Kaur1, Lingaraja Jena1, Ritu Gupta1
1Laboratory Oncology Unit, Dr. B. R.A. IRCH, AIIMS, New Delhi.
Translational Oncology
|July 1, 2022
Summary
Multiple myeloma evolves through branching patterns, revealing unique genetic targets at diagnosis versus progression. Understanding these clonal trajectories aids early prognostication and personalized therapies for this plasma cell disorder.
Area of Science:
- Hematology
- Cancer Genomics
- Evolutionary Biology
Background:
- Multiple myeloma (MM) is a complex plasma cell disorder originating from premalignant stages.
- Disease progression involves intricate interactions between clonal mutations and the microenvironment.
- Understanding MM's evolutionary trajectory is key for early detection and treatment.
Purpose of the Study:
- To evaluate clonal evolution in multiple myeloma patients.
- To identify actionable gene targets and vulnerabilities in early disease stages.
- To explore potential for early prognostication and personalized therapies.
Main Methods:
- Analysis of clonal evolution at multiple time points.
- Study included 76 multiple myeloma patients from the MMRF CoMMpass study.
- Evaluation of molecular events, including gene mutations and cytogenetic aberrations.
Main Results:
- Multiple myeloma predominantly progresses via branching evolution.
- Heterogeneous mutational landscapes show distinct actionable targets at diagnosis versus progression.
- Unique clonal gene gains/losses correlate with cytogenetic aberrations.
- Significant correlations observed between co-occurring oncogenic mutations (e.g., TP53+SYNE1) and anticorrelative gene dependencies (e.g., FAT3+FCGBP).
Conclusions:
- Clonal evolution patterns in multiple myeloma are established early and retained.
- Identification of actionable targets and co-occurring mutation trajectories can inform risk stratification.
- These findings support the development of personalized therapies for multiple myeloma.

