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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
Comprehensive Driver Mutation Landscape of DLBCL Informed by Integrated Tissue and Circulating DNA Analyses
Han Zhang1, Chuanyu Hong2, Xinger Gao3
1Department of Hematology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
Cancer Research and Treatment
|July 29, 2026
Summary
Integrating tumor tissue and cell-free DNA (cfDNA) sequencing reveals key driver mutations in Diffuse large B-cell lymphoma (DLBCL). This approach enhances understanding of DLBCL heterogeneity and improves prognostic accuracy.
Area of Science:
- Genomics
- Oncology
- Molecular Biology
Background:
- Diffuse large B-cell lymphoma (DLBCL) is a genetically diverse hematologic malignancy.
- Understanding the driver mutation landscape is crucial for diagnosis and treatment.
Purpose of the Study:
- To define a compartment-aware driver mutation landscape of DLBCL.
- To integrate tumor tissue and cell-free DNA (cfDNA) sequencing data.
- To assess the biological and prognostic relevance of identified driver mutations.
Main Methods:
- Somatic mutations analyzed from targeted sequencing of tumor tissue/bone marrow and cfDNA.
- Driver genes identified using four algorithms; functional enrichment and network analyses performed.
- Prognostic relevance evaluated using multigene expression-based models in independent cohorts.
Main Results:
- Driver alterations converged on key pathways: B-cell receptor signaling, NF-κB activation, epigenetic regulation, and immune escape.
- Twenty-two high-confidence driver genes identified, including PIM1, KMT2D, CD79B, B2M, TP53, MYC, and EZH2.
- cfDNA profiling showed concordance with tissue drivers, complementing tissue analysis and revealing heterogeneity; a four-gene model improved prognostic discrimination.
Conclusions:
- Integrated analysis of tumor tissue and cfDNA sequencing provides a clinically relevant driver framework for DLBCL.
- cfDNA profiling complements tissue analysis, capturing essential oncogenic signals and heterogeneity.
- This approach supports refined genomic risk stratification for DLBCL patients.
