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Identification of novel loci for multiple myeloma when comparing with its precursor condition monoclonal gammopathy of unknown significance

Leukemia volume 38, pages 383–385 (2024)Cite this article

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Multiple myeloma (MM) is the second most common hematologic malignancy in the US [1]. MM is preceded by an asymptomatic premalignant condition termed monoclonal gammopathy of undetermined significance (MGUS), with an annual rate of progression from MGUS to MM of about 1% [2]. While the majority of MGUS patients do not progress to overt MM, some patients could have progression rates as high as 58% in 20 years [3]. Despite differences in the risk of malignant transformation, current guidelines for MGUS recommend “watchful waiting” until the disease progresses to symptomatic MM, often manifesting as overt end-organ damage before treatment can begin. While no current treatment can prevent MGUS progressing into overt MM, identifying those patients who have a higher risk of progression could lessen the burden of the disease and extend long-term remission by early intervention. The large interpatient variability in the progression of MGUS to MM suggests an inherited genetic susceptibility. Comparing to healthy controls, genome-wide association studies have identified 23 loci significantly associated with MM risk [4] and 10 loci associated with MGUS risk at a nominal significant level (P < 10−5) [5]. However, risk loci associated with the progression from MGUS to MM remains unclear.

Here we conducted a genetic association case-control study using 246 MGUS who do not progress to MM for at least 3 years of follow-up and 654 MM patients enrolled from the Froedtert & the Medical College of Wisconsin (MCW) regional health network (Supplementary Table 1). The diagnosis of all patients was confirmed via chart review based on internationally accepted criteria. Genotyping of buffy coat samples was performed on the Infinium Global Screening Array BeadChip (Illumina, San Diego, CA, USA). After standard quality control (QC), 548,895 single nucleotide polymorphisms (SNPs) on the nuclear DNA (nDNA) and 856 patients (238 MGUS and 618 MM) were used for imputation against the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) reference panel (version R2) [6, 7]. MitoImpute pipeline was used for imputation of 97 variants on the mitochondrial DNA (mtDNA) [8]. Post-imputation QC was conducted by excluding SNPs with imputation quality score <0.3. Detailed methods are described in the Supplementary Methods.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This project was supported by the Medical College of Wisconsin Cancer Center through the Research and Education Program Fund, a component of the Advancing a Healthier Wisconsin endowment at the Medical College of Wisconsin, Drive Fore A Cure Foundation and the CTSI Team Science-Guided Integrated Clinical and Research Ensemble, National Center for Advancing Translational Sciences, National Institutes of Health, Award Number 2UL1 TR001436. JD was supported by the NHLBI (K01 HL164972 to JD). SJ was supported by NCI R01CA151354, the William G. Schuett, Jr., Multiple Myeloma Research Endowment and the Riney Family Multiple Myeloma Research Initiative.

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Authors and Affiliations

  1. Division of Hematology Oncology, Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA

    Christopher Staffi Buradagunta, Shahram Arsang-Jang, Ben Massat, Bicky Thapa, Victoria Patek, Anita D’Souza, Siegfried Janz, Binod Dhakal, Parameswaran Hari & Jing Dong

  2. Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, USA

    Paul Auer

  3. Linda T. and John A. Mellowes Center for Genomic Sciences and Precision Medicine, Medical College of Wisconsin, Milwaukee, WI, USA

    Raul Urrutia & Jing Dong

  4. Medical College of Wisconsin Cancer Center, Milwaukee, WI, USA

    Jing Dong

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  1. Christopher Staffi Buradagunta
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Contributions

JD had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Study concept and design: JD. Acquisition of data and research funding: JD, PH, and BD. Statistical analysis, and interpretation of data: CSB, SA, and JD. Data preparation: CSB, SA, BM, BT, VP, PA, BD, and JD. Drafting of the manuscript: CSB and JD. Critical review of the manuscript for important intellectual content: CSB, SA, BM, BT, VP, AD, PA, RU, SJ, BD, PH, and JD. Study supervision: JD. All authors read and approved the final version for submission.

Corresponding author

Correspondence to Jing Dong.

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Competing interests

AD reports institutional research funding from Abbie, Caelum, Janssen, Novartis, Prothena, Sanofi, TeneoBio; advisory board participation fees from BMS, Janssen, Pfizer, and Prothena; consulting fees from Janssen and Prothena.

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Buradagunta, C.S., Arsang-Jang, S., Massat, B. et al. Identification of novel loci for multiple myeloma when comparing with its precursor condition monoclonal gammopathy of unknown significance. Leukemia 38, 383–385 (2024). https://doi.org/10.1038/s41375-023-02078-6

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