Catalano et al. Blood Cancer Journal (2021) 11:33 https://doi.org/10.1038/s41408-021-00422-6 Blood Cancer Journal

CORRESPONDENCE Open Access Characterization of rare germline variants in familial multiple myeloma Calogerina Catalano1,2, Nagarajan Paramasivam3,JoannaBlocka2, Sara Giangiobbe1,2,StefanieHuhn2,4, Matthias Schlesner 5, Niels Weinhold2, Rolf Sijmons6, Mirjam de Jong6, Christian Langer7, Klaus-Dieter Preuss8, Björn Nilsson 9, Brian Durie10, Hartmut Goldschmidt 2,4, Obul Reddy Bandapalli1,11,12,KariHemminki1,13,14 and Asta Försti 1,11,12

Dear Editor, exome sequencing9. Altogether, 21 families with 46 Multiple myeloma (MM) is a malignancy of plasma affected and 20 unaffected family members were recruited cells, characterized by the presence of monoclonal (Supplementary Information, Supplementary Fig. 1). Each immunoglobulin, known as M protein1. MM is preceded family had at least two individuals diagnosed with MM or by monoclonal gammopathy of undetermined significance its precursors MGUS and smoldering MM (SMM). After (MGUS) which is also a precursor of immunoglobulin sequencing, detailed bioinformatics analyses using our in- light chain (AL) amyloidosis1. Previous studies have house developed Familial Cancer Variant Prioritization reported a 2- to 4-fold increased risk of MGUS or MM in Pipeline version-2 (FCVPPv2) were conducted to prior- first-degree relatives of MM or MGUS patients, suggest- itize the most likely candidates (Fig. 1 and Supplementary ing the existence of inherited susceptibility2,3. For many Methods). Gradual filtering of variants after sequencing is years, high-risk germline predisposing have been shown for each family in Supplementary Table 1. The lacking for MM. However, recent sequencing efforts have functions of the products were collected from the

1234567890():,; 1234567890():,; 1234567890():,; 1234567890():,; proposed a few novel candidates, most notably loss-of- UniProtKB database (https://www.uniprot.org/) and function (LoF) variants in the tumor suppressor gene literature search. They are summarized in Fig. 2 and – DIS3 and in the histone demethylase gene KDM1A4 6, details are shown in Supplementary Information. and others as recently reviewed in detail in Pertesi et al. 7. We identified 109 potential pathogenic missense In addition to the suspected rare, high-penetrance var- variants; in most families, several candidates were found, iants, genome-wide association studies have identified and in four families none (Supplementary Table 2). All over 20 common, low-penetrance variants associated with variants were private for each family, except for genes the risk of MM; these were estimated to account for about KIF1B and DCHS1,inwhichtwodifferentmissense 15% of the familial MM risk8. variants were found in two unrelated families (families As the genetic basis of most MM families remains 10 and 18 for KIF1B and 15 and 17 for DCHS1). KIF1B is unexplained, our study aimed at identifying germline involved in the transport of mitochondria and synaptic predisposition genes in familial MM from Germany, vesicles and DCHS1 is a calcium-dependent cell Sweden, and the Netherlands, through whole genome and adhesion . Among the other genes harboring missense variants, tumor suppressor function was indicated for DAB2IP and oncogene function for ABL2. Correspondence: Asta Försti ([email protected]) The former had diverse signal transduction functions 1Division of Molecular Genetic Epidemiology, German Cancer Research Center and it is implicated in immune processes, as are TLN1, (DKFZ), Heidelberg, Germany ZFAT, CLCF1, IL11RA, SEC14L1, SAMHD1, DCST1, 2Department of Internal Medicine V, University of Heidelberg, Heidelberg, Germany TPP2, and MYO1G. Full list of author information is available at the end of the article Another group of genes with key regulatory functions These authors contributed equally: Calogerina Catalano, Nagarajan constituted FOXO1, B4GALT1, and NKX3-2. Transcrip- Paramasivam. These authors jointly supervised this work: Obul Reddy Bandapalli, Kari tion factor FOXO1 is a protein, which is the main target Hemminki and Asta Försti.

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Idenficaon of MM families DNA isolaon from blood samples

Whole genome/exome sequencing, variant calling, filtering and annotaon

Variants with MAF < 0.001

Familial Cancer Variant Prioritization Pipeline (FCVPP) version 2

Pedigree segregaon

CADD score >20

MutPred-LOF Missense variants Loss-of-funcon variants Human Splicing Stop, frameshi , splice site NHLBI-ESP6500 Finder dataset ExAC dataset Intolerance scores Local dataset ExAC Z-score GERP >2.0 Evoluonary conservaon PhastCons >0.3 SIFT, PolyPhen-2 HDIV, PhyloP ≥3.0 PolyPhen-2 HVAR, LRT, Mutaon Taster, Deleteriousness tools Mutaon Assessor, FATHMM, MetaSVM, MetaLR, PROVEAN

Fig. 1 Pipeline for identification of missense and loss-of-function variants in the multiple myeloma families. After identification of the families, DNA isolation from the blood samples and whole genome or exome sequencing, variant calling, filtering, and annotation, we used our in- house developed Familial Cancer Variant Prioritization Pipeline v.2 to identify the most likely cancer predisposition variants for multiple myeloma. All variants with minor allele frequency (MAF) < 0.001 that segregated with the disease in the families were filtered by CADD score >20, which indicates the top 1% of potentially deleterious variants in the . For missense variants, the corresponding genes were screened for their intolerance against functional variants using the NHLBI-ESP6500, ExAc, and local data sets as well the ExAC Z-score. The location of the variants was checked for evolutionary conservation using GERP (>2.0), PhastCons (>0.3), and PhyloP (≥3.0). Ten tools were used to predict the deleteriousness of the variants: Sorting Intolerant from Tolerant (SIFT), Polymorphism Phenotyping version-2 (PolyPhen-2) HDIV (HumDiv), PolyPhen-v2 HVAR (HumVar), Log ratio test (LRT), MutationTaster, Mutation Assessor, Functional Analysis Through Hidden Markov Models (FATHMM), MetaSVM, MetaLR, and Protein Variation Effect Analyzer (PROVEAN). For loss-of-function variants (frameshift and stopgain), pathogenic and neutral variants were predicted using MutPred-LOF with a threshold score of 0.50 at a 5% false-positive rate. Human Splicing Finder was used to evaluate the effect of splice site variants, with a yes/no score. of insulin signaling, it increases osteoblast numbers and We checked our gene list also for the presence of the regulates B cell development. B4GALT1 is involved 82 somatically mutated driver genes in MM, described in in the glycosylation of immunoglobulin G (IgG) and Walker et al. 10 and Maura et al. 11, but only SAMHD1 variants in this gene have been associated with IgG levels passed all our in-house pipeline filters. SAMHD1 is a and hematological neoplasms, including MM. NKX3-2 somatic driver in MM and the protein plays a role in (homeobox protein Nkx-3.2) is a member of the HOX maintaining dNTP levels in regulating DNA replication gene transcription factors family, which are frequently and damage repair. It enhances immunoglobulin hyper- dysregulated in hematologic malignancies. mutation in B-lymphocyte development. Our candidate list included two genes, KMT2A and We also identified 36 loss-of-function (LoF) variants in USP28, functionally related to the recently reported MM the MM families (Supplementary Table 3). If we would predisposing genes, LSD1/KDM1A, encoding a lysine- apply a MutPred-LOF (http://mutpredlof.cs.indiana.edu/ specific demethylase, and USP45, an apoptosis-related index.html) score higher than 0.50 at a 5% false-positive gene-regulating DNA repair5,6. KMT2A (alias MLL1) is a rate, only two frameshift variants, in the genes SLC30A5 histone H3 lysine 4 (H3K4) methyltransferase, which and LONP2, and six stop codon variants would pass the plays an essential role in early development and hema- threshold. None of these had an apparent relationship to topoiesis and which mediates chromatin modifications MM. Of the eight splice site variants, five were predicted associated with epigenetic transcriptional activation. by Human Splicing Finder (http://www.umd.be/HSF/ USP28 is a deubiquitinase involved in DNA damage- HSF.shtml) to alter the splicing motifs (indicated by induced apoptosis. It regulates MYC protein stability in “yes” in Supplementary Table 3), however with no link to response to DNA damage. MM. Many of the genes with LoF mutations encode

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Hematological malignancies Somac driver in MM FOXO1 (B-cell development) SAMHD1 (Ig hypermutaon in Oncogene B4GALT1 (IgG glycosylaon) B-lymphocyte development) ABL2 NKX3-2 (HOX transcripon factor family)

Tumor suppressor DAB2IP 109 missense variants Signal transducon Funconal relaonship with previously Immune processes suggested MM predisposion genes KMT2A (H3K4 methyltransferase) USP28 (DNA damage induced apoptosis) Immune processes Genes with variants in TLN1, ZFAT, CLCF1 2 families IL11RA, SEC14L1 KIF1B (cellular transporter) SAMHD1, DCST1 DCHS1 (calcium-dependent TPP2, MYO1G cell adhesion)

7 copy number variants 36 loss-of-funcon variants

FGFBP1 and FGFBP2 (FGF signaling, enhanced MM No apparent relaonship to MM cell proliferaon and survival, bone homeostasis) PROM1 (maintenance of stem cell properes)

Fig. 2 Summary of the identified variants in the multiple myeloma families. Total number of missense, loss-of-function, and copy-number variants are shown, and for the most intersting variants the function of the corresponding genes.

with housekeeping functions, including LONP2, In a review of cancer-predisposing genes, it was CSGALNACT2, HMGCLL1, and FUK. observed that over 40% of germline variants were in genes We identified seven copy-number variants (CNVs) that that functioned also as somatic drivers13. In the above, we segregated with MM in the families (Supplementary Table referred to some somatic drivers, and some of the 4). These CNVs affected the coding regions of 11 genes. observed genes are known to interact with key signaling Duplication of chr4:15936942-16178663 in Family 5 cov- pathways in MM, including PI3K/Akt/mTOR, Ras/Raf/ ered the genes encoding fibroblast growth factor binding MEK/MAPK, JAK/STAT, NF-κB, Wnt/β-, and proteins FGFBP1 and FGFBP2, prominin 1 (PROM1), and RANK/RANKL/OPG14. Among the relevant genes in our transmembrane anterior posterior transformation protein list, DAB2IP, encoding a Ras-GTPase activating protein, 1 homolog (TAPT1). One of the primary genetic events in modulates key oncogenic pathways such as PI3K/Akt, NF- MM is t(4:14) translocation, creating a fusion between the κB, and Wnt/β-catenin; FOXO1 encodes for a down- immunoglobulin heavy chain (IGH) enhancer and FGFR3 stream effector of Akt signaling; the LRP1B gene product and leading to overexpression of FGFR312. FGFBP1 and negatively regulates the Wnt/β-catenin/TCF signaling, FGFBP2 encode proteins that are involved in FGF ligand through its interaction with DVL2. bioactivation by releasing them from the extracellular A somewhat surprising finding was that none of the 158 matrix. Thus, duplication of these two genes may lead to candidate genes matched with the genes linked to the 23 activation of the FGF signaling, enhanced MM cell pro- common, low-risk MM variants8. It is true that an over- liferation and survival, and affect bone homeostasis. whelming number of the published MM-related low-risk PROM1 is involved in the suppression of cell differentia- variants were located in the non-protein-coding region tion and maintenance of stem cell properties. and some were distant from the coding regions8. The All the identified variants were rare (allele frequency < associated relative risks were mostly below 1.5, which 0.001) in the gnomAD database (https://gnomad. would not be compatible with strong familial clustering. broadinstitute.org) and none of them was found as a However, as many of the present families included only germline variant in any cancer patient. However, COS- two affected members, chance clustering cannot be MIC (https://cancer.sanger.ac.uk/cosmic) reported some excluded. A further factor is that by the inclusion of of the variants as rare somatic mutations, mainly in cancer persons with MGUS, which is more than one order of entities with a high mutation load, such as malignant magnitude more prevalent than MM, certain genetic melanoma and adenocarcinoma of the large intestine, but heterogeneity was introduced, in spite of the known not in any hematological malignancies. shared genetic background3. Of note, the candidates that

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passed the pipeline included two genes, KMT2A and Department of Internal Medicine I, Saarland University Medical School, 9 USP28, functionally related to the recently proposed Homburg (Saar), Germany. Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden. 10Cedars high/moderate penetrance MM predisposing genes, Sinai Cancer Center, Los Angeles, CA, USA. 11Hopp Children’s Cancer Center 4–6 LSD1/KDM1A, USP45, ARID1A, and DIS3 . (KiTZ), Heidelberg, Germany. 12Division of Pediatric Neurooncology, German In few families, no candidate variants were found using Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany. 13Division of Cancer Epidemiology, German Cancer the present criteria. Some possible reasons were explained Research Center (DKFZ), Heidelberg, Germany. 14Faculty of Medicine and in the above paragraph, and there are more possible Biomedical Center in Pilsen, Charles University in Prague, Pilsen, Czech reasons. In families of two affected individuals, polygenic Republic risks would be more likely than in multiplex families of Data availability many affected individuals. In a previous study, evidence of Whole-genome sequencing data sets have been deposited to the European enrichment of the common MM risk alleles among Genomephenome Archive (EGA) with accession number EGAS00001004734. familial cases compared to sporadic cases or population- 15 Conflict of interest based controls was reported . Our search did not con- The authors declare no competing interests. sider polygenic risk. Even though the controls were tested for the presence of the M protein, a negative result sug- Publisher’s note gests that the person would remain disease free only the Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. next decade or two9. Finally, the present bioinformatics analysis was limited to coding variants. Supplementary information The online version contains supplementary In conclusion, we report here curated sequencing material available at https://doi.org/10.1038/s41408-021-00422-6. results from 21 MM/MGUS families. While most of the Received: 2 October 2020 Revised: 4 January 2021 Accepted: 18 January 154 presented candidate genes are unlikely to have a 2021 causal relationship to MM, the identified genes could be a valuable contribution to forthcoming, pooled sequencing efforts. Familial clustering of MM is rare and the set of 21 families was only possible through multicenter efforts. References 1. Kumar, S. K. et al. Multiple myeloma. Nat. Rev. Dis. Primers 3, 17046 (2017). Based on the functional characterization and the literature 2. Frank, C. et al. Search for familial clustering of multiple myeloma with any review the strong candidates included DAB2IP, ABL2, cancer. Leukemia 30,627–632 (2016). SAMHD1, KMT2A, USP28, FOXO1, B4GALT1, NKX3-2, 3. Vachon,C.M.etal.Increasedrisk of monoclonal gammopathy in first-degree FGFBP1 FGFBP2 relatives of patients with multiple myeloma or monoclonal gammopathy of several immune-related genes, and , , and undetermined significance. Blood 114,785–790 (2009). PROM1 within the CNV in 4. Interestingly, 4. Pertesi, M. et al. Exome sequencing identifies germline variants in DIS3 in many of these are somatic driver genes in cancer. familial multiple myeloma. Leukemia 33,2324–2330 (2019). 5. Waller, R. G. et al. Novel pedigree analysis implicates DNA repair and chro- matin remodeling in multiple myeloma risk. PLoS Genet. 14, e1007111 (2018). 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Diagnostics Program, National Center for Tumor Diseases (NCT), Heidelberg, 13. Rahman, N. Realizing the promise of cancer predisposition genes. Nature 505, Germany. 4National Center for Tumor Diseases Heidelberg (NCT), Heidelberg, 302–308 (2014). Germany. 5Bioinformatics and Omics Data Analytics, German Cancer Research 14. Hu, J. et al. Targeting signaling pathways in multiple myeloma: Pathogenesis Center (DKFZ), Heidelberg, Germany. 6University Medical Center Groningen, and implication for treatments. Cancer Lett. 414, 214–221 (2018). University of Groningen, Groningen, The Netherlands. 7Kempten Clinic, 15. Halvarsson, B. M. et al. Direct evidence for a polygenic etiology in familial Kempten, Germany. 8José Carreras Center for Immuno and Gene Therapy, multiple myeloma. Blood Adv. 1,619–623 (2017).

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