期刊
BLOOD CANCER DISCOVERY
卷 1, 期 3, 页码 258-273出版社
AMER ASSOC CANCER RESEARCH
DOI: 10.1158/2643-3230.BCD-20-0132
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资金
- Memorial Sloan Kettering Cancer Center NCI Core Grant [P30 CA 008748]
- Multiple Myeloma Research Foundation (MMRF)
- Perelman Family Foundation
- Swedish Blood Cancer Foundation
- Karolinska Institute Foundations
- American Society of Hematology
- International Myeloma Foundation
- Society of Memorial Sloan Kettering Cancer Center
- Haematology Society of Australia and New Zealand New Investigator Scholarship
- Leukemia & Lymphoma Society
- Royal College of Pathologists of Australasia Mike and Carole Ralston Travelling Fellowship Award
The landscape of structural variants (SV) in multiple myeloma remains poorly understood. Here, we performed comprehensive analysis of SVs in a large cohort of 752 patients with multiple myeloma by low-coverage long-insert whole-genome sequencing. We identified 68 SV hotspots involving 17 new candidate driver genes, including the therapeutic targets BCMA (TNFRSF17), SLAM7, and MCL1. Catastrophic complex rearrangements termed chromothripsis were present in 24% of patients and independently associated with poor clinical outcomes. Templated insertions were the second most frequent complex event (19%), mostly involved in super-enhancer hijacking and activation of oncogenes such as CCND1 and MYC. Importantly, in 31% of patients, two or more seemingly independent putative driver events were caused by a single structural event, demonstrating that the complex genomic landscape of multiple myeloma can be acquired through few key events during tumor evolutionary history. Overall, this study reveals the critical role of SVs in multiple myeloma pathogenesis. SIGNIFICANCE: Previous genomic studies in multiple myeloma have largely focused on single-nucleotide variants, recurrent copy-number alterations, and recurrent translocations. Here, we demonstrate the crucial role of SVs and complex events in the development of multiple myeloma and highlight the importance of whole-genome sequencing to decipher its genomic complexity.
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