# Identification of novel genetic loci for risk of multiple myeloma by functional annotation

Angelica Macauda, Klara Briem, Alyssa Clay-Gilmour, Wendy Cozen, Asta Försti, Matteo Giaccherini, Chiara Corradi, Juan Sainz, Yasmeen Niazi, Rob ter Horst, et al.

► To cite this version:

Angelica Macauda, Klara Briem, Alyssa Clay-Gilmour, Wendy Cozen, Asta Försti, et al. Identification of novel genetic loci for risk of multiple myeloma by functional annotation. Leukemia, 2023, 10.1038/s41375-023-04914257

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EPIDEMIOLOGY

# Identification of novel genetic loci for risk of multiple myeloma by functional annotation

© The Author(s) 2023

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Leukemia (2023) 37:2326–2329; https://doi.org/10.1038/s41375-023-02022-8

**TO THE EDITOR:**

Multiple myeloma (MM) is one of the most common hematological malignancies, accounting for 20% of all newly diagnosed hematological cancers [1]. The most recent data from Cancer Today show that in 2020 the number of new MM cases was 176,404 worldwide (https://gco.iarc.fr/today/home).

Established risk factors for MM include age, male sex, African ancestry, obesity, chronic inflammation, exposure to pesticides, organic solvents, and radiation [2]. Familial aggregation of MM and its precursor monoclonal gammopathy of undetermined significance (MGUS) suggests that genetic factors play a role in risk of MM as well [3]. Genetic variability has been identified as a risk factor for MM, including 25 common genetic loci identified in genome-wide association studies (GWAS). However, estimates of heritability show that many more loci remain to be found [4].

A key question is therefore how to find new causative variants. The stringent significance threshold usually used in GWAS ($p < 5 \times 10^{-8}$) accounts for the many statistical tests being performed but may result in false negatives. Reducing the number of tests will relax the required significance threshold, thereby increasing statistical power to detect associations with MM risk for each SNP. One strategy for reducing the number of tests is to examine SNPs with higher prior probability of association according to meaningful biological criteria. We looked for novel MM risk loci using a two-phase large-scale association study, prioritizing polymorphisms with predicted functional impact, a strategy that has been used for other cancers and led to the discovery of new loci [5–7]. It is well known that functional variants are indeed more likely to be associated with disease development [8].

We used data from the International Lymphoma Epidemiology Consortium (InterLymph) for discovery and from the German-speaking Myeloma Multicenter Group (GMMG), the International Multiple Myeloma rESEarch (IMMEnSE) consortium, as well as summary statistics from the FinnGen study for replication, for a total of 5982 MM cases and 266,173 controls. Detailed characteristics of the study populations are shown in the supplementary methods and supplementary table 1.

Candidate SNPs to be replicated were selected based on their association with MM risk and their functional role. First, we obtained summary results including odds ratios (OR), 95% confidence intervals (95%-CI), and *p*-values of the top SNPs of the InterLymph GWAS. Subsequently, all SNPs in the MM data set

from InterLymph with $p < 5 \times 10^{-4}$ (N = 4396) were looked up in the first replication dataset, the GMMG GWAS. We did not consider SNPs from 15 loci that were reported to be associated at genome-wide significance level in previous GWASs. All SNPs with significant *p*-values (*p* < 0.05) in the GMMG GWAS and ORs going in the same direction in both datasets were selected. The next step was annotating the selected SNPs (N = 136) for their predicted function, using several suitable bioinformatic tools and databases. Specifically, we looked at expression and splicing quantitative trait loci (eQTLs and sQTLs), SNPs located in transcription factor binding sites (TFBS), long non-coding RNA (lncSNPs), SNPs that are located within enhancers, and polymorphisms located in gene coding regions (missense, stop-gain, stop-loss, synonymous SNPs). Supplementary Table 2 shows the details of the 136 SNPs and their predicted functional characterization. The resulting list from all annotations was pruned for linkage disequilibrium (LD) using the LDlink portal (https://ldlink.nci.nih.gov/). Only SNPs with $r^2 < 0.6$ among them were kept, resulting in a total of 12 independent loci on 9 chromosomes. Replication in IMMEnSE and FinnGen was attempted for SNPs showing association with risk in the meta-analysis between InterLymph and GMMG GWAS and at least one in silico functional annotation. After exclusion of SNPs that had already been analysed in IMMEnSE in the context of previous projects and already shown not to be significantly associated with MM risk (on chromosomes 6, 8, 12 and 21), 4 SNPs showed to have low *p*-value of association with MM risk and had at least one functional prediction annotation (rs12038685, rs2664188, rs12652920, rs28199), which were therefore chosen for replication in IMMEnSE (Supplementary Table 3). An in-depth description of the SNP functional annotation and selection, as well as the technical details of the genotyping and quality control, can be found in the Supplementary Methods.

Analysis of association between each SNP and MM risk was carried out with logistic regression models, by estimating ORs, their 95%-CI, and associated *p*-value. The analyses were adjusted for age (at diagnosis for MM cases and recruitment for controls), sex, and the 10 first principal components for GWAS data, or country of origin in IMMEnSE, which lacks GWAS data. All meta-analyses were conducted with R, using a fixed-effect model between summary statistics of the different studies. The *I*² statistic was computed to quantify heterogeneity across studies.

rs28199, on chromosome 5, was associated with MM risk in IMMEnSE (OR = 1.19, 95% C.I. = 0.72–0.97, *p* = 0.018) and FinnGen (1.17, 95% C.I. = 1.05–1.31, *p* = 0.014). The G allele of this SNP resulted to be significantly associated with increased MM risk at a genome-wide level in the meta-analysis of the four datasets (OR = 1.18, 95% C.I. = 1.11–1.23, *p* = $3.18 \times 10^{-10}$) with no heterogeneity among the studies (*I*² = 0) (Table 1, Fig. 1).

Received: 8 March 2023 Revised: 8 August 2023 Accepted: 4 September 2023

Published online: 18 September 2023
<table><caption>**Table 1.** Association results of the SNPs selected for replication in IMMENSE.</caption><thead><tr><th>SNP</th><th>Study</th><th>OR<sup>a</sup></th><th>95% CI<sup>b</sup></th><th>p-value</th><th>Functional annotation<sup>c</sup></th></tr></thead><tbody><tr><td rowspan="5">rs12038685</td><td>InterLymph</td><td>1.22</td><td>1.11-1.34</td><td>5.04 x 10<sup>-5</sup></td><td rowspan="5">TBFS</td></tr><tr><td>GMMG</td><td>1.11</td><td>1.00-1.23</td><td>0.046</td></tr><tr><td>FinnGen</td><td>1.2</td><td>1.07-1.32</td><td>0.004</td></tr><tr><td>IMMEnSE</td><td>0.94</td><td>0.81-1.09</td><td>0.409</td></tr><tr><td>Meta-analysis</td><td>1.18</td><td>1.10-1.25</td><td>2.07 x 10<sup>-7</sup></td></tr><tr><td rowspan="5">rs2664188</td><td>InterLymph</td><td>1.18</td><td>1.09-1.28</td><td>8.14 x 10<sup>-5</sup></td><td rowspan="5">eQTL</td></tr><tr><td>GMMG</td><td>1.09</td><td>1.00-1.09</td><td>0.048</td></tr><tr><td>FinnGen</td><td>1.01</td><td>0.89-1.12</td><td>0.808</td></tr><tr><td>IMMEnSE</td><td>0.96</td><td>0.84-1.10</td><td>0.58</td></tr><tr><td>Meta-analysis</td><td>0.91</td><td>0.85-0.97</td><td>0.002</td></tr><tr><td rowspan="5">rs12652920</td><td>InterLymph</td><td>0.83</td><td>0.75-0.91</td><td>1.62 x 10<sup>-4</sup></td><td rowspan="5">TBFS</td></tr><tr><td>GMMG</td><td>0.90</td><td>0.81-0.99</td><td>0.031</td></tr><tr><td>FinnGen</td><td>1.09</td><td>0.96-1.22</td><td>0.174</td></tr><tr><td>IMMEnSE</td><td>0.99</td><td>0.84-1.15</td><td>0.875</td></tr><tr><td>Meta-analysis</td><td>0.91</td><td>0.86-0.97</td><td>0.0001</td></tr><tr><td rowspan="5">rs28199</td><td>InterLymph</td><td>1.18</td><td>1.10-1.28</td><td>7.31 x 10<sup>-5</sup></td><td rowspan="5">TBFS</td></tr><tr><td>GMMG</td><td>1.14</td><td>1.04-1.25</td><td>0.004</td></tr><tr><td>FinnGen</td><td>1.18</td><td>1.05-1.31</td><td>0.014</td></tr><tr><td>IMMEnSE</td><td>1.20</td><td>1.03-1.39</td><td>0.018</td></tr><tr><td>Meta-analysis</td><td>1.18</td><td>1.11-1.23</td><td>3.18 x 10<sup>-10</sup></td></tr></tbody></table>

<figure><img src="image_3.png" /><figcaption>**Fig. 1** Meta-analysis result of rs28199. Forest plot of the meta-analysis using a fixed effects model across all four datasets. Heterogeneity was assessed using the I² statistic. OR = odds ratio, 95% CI = 95% confidence interval.</figcaption></figure>

This polymorphism was selected for being predicted to affect the binding site of three transcription factors: IRF1, STAT2_STAT1 and FOXP1. The strongest effect of the SNP was calculated for IRF1 (interferon regulatory factor 1), a protein member of the IRF family which was first recognized for its role as activator of genes involved in both innate and acquired immune responses. IRF-1 activates a set of target genes associated with regulation of cell cycle, apoptosis and the immune response [9, 10]. According to the SNP2TFBS database, rs28199 is predicted to modify a binding site of IRF1 leading to a stronger bond, which could in turn result in oncogenesis considering the set of genes that IRF1 regulates. The minor allele of rs28199 is located within a regulatory region which according to the variant effect predictor tool (https://www.ensembl.org/info/docs/tools/vep/index.html) and HaploReg (https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php)

binds the CTCF protein, a highly conserved zinc finger with various cellular regulatory role. CTCF binding perturbations cause different types of 3D genome reorganization and may cause the activation of the neighboring oncogenes [11]. Among the genes that CTCF regulates there is STK10, which encodes for a serine/threonine-protein kinase, highly expressed in hematopoietic tissue [12]. In various lymphoid cells rs28199-G is associated with an increased expression of STK10. Overexpression of STK10 has been reported in several cancer types, including acute myeloid leukemia (AML), another blood malignancy [13, 14].

We used data from the 500 Functional Genomics cohort from the Human Functional Genomics Project (HFGP; http://www.humanfunctionalgenomics.org/site/) to explore the possible role in modulating immune response of the four SNPs selected for the final replication steps. Namely, we tested if any of the SNPs of interest were cytokine expression quantitative trait loci (cQTL) using data from in vitro stimulation experiments, as well as absolute numbers of 91 blood-derived cell populations and 103 serum or plasmatic inflammatory proteins. The cQTL analyses showed that rs28199-G is also associated with an increased blood level of Interleukin-6 (IL-6) (beta=0.075, p = 0.002). IL-6 is a cytokine with a well established role as a growth and survival factor in MM [15]. Specifically, in line with our results, an increased level of IL-6 contributes to the pleiotropic effects of IL-6 regarding proliferation, survival, drug resistance, and migration of MM cells, thereby facilitating disease progression [16]. The counts of cell populations and the levels of serum or plasmatic inflammatory proteins were not significantly associated with the SNPs of interest.

In conclusion, we identified a new genetic association for MM, supported by functional biological explanations, thus highlighting the importance of secondary analysis using functional approaches for GWAS.

<footnote><sup>a</sup>OR odds ratio.</footnote>

<footnote><sup>b</sup>95%CI. 95% confidence interval.</footnote>

<footnote><sup>c</sup>TFBS: the SNP is predicted to alter one or more transcription factor binding site(s); eQTL: the SNP is predicted to be a quantitative trait locus in whole blood or EBV-transformed B-lymphocyte cell lines.</footnote>
Angelica Macauda¹, Klara Briem¹, Alyssa Clay-Gilmour²,
Wendy Cozen³, Asta Försti ID ⁴,⁵, Matteo Giaccherini⁶,
Chiara Corradi ID ⁶, Juan Sainz ID ⁷,⁸,⁹, Yasmeen Niazi ⁴,⁵,
Rob ter Horst ID ¹⁰,¹¹, Yang Li ¹⁰,¹², Mihai G. Netea ID ¹⁰,¹³,
Ulla Vogel¹⁴, Kari Hemminki¹⁵,¹⁶, Susan L. Slager ID ¹⁷,
Judit Varkonyi¹⁸, Vibeke Andersen ID ¹⁹,²⁰,²¹,
Elzbieta Iskierka-Jazdzewska²², Joaquin Martínez-Lopez ID ²³,
Jan Zaucha²⁴, Nicola J. Camp ID ²⁵, S. Vincent Rajkumar²⁶,
Agnieszka Druzd-Sitek²⁷, Parveen Bhatti²⁸,²⁹,
Stephen J. Chanock ID ³⁰, Shaji K. Kumar ID ²⁶, Edyta Subocz ID ³¹,
Grzegorz Mazur³², Stefano Landi ID ⁶, Mitchell J. Machiela ³⁰,
Andrés Jerez³³, Aaron D. Norman ¹⁷,³⁴, Michelle A. T. Hildebrandt ³⁵,
Katalin Kadar³⁶, Sonja I. Berndt ID ³⁰, Elad Ziv³⁷, Gabriele Buda³⁸,
Arnon Nagler³⁹, Charles Dumontet⁴⁰, Malgorzata Raźny ⁴¹,
Marzena Watek ID ⁴²,⁴³, Aleksandra Butrym ³²,⁴⁴, Norbert Grzasko ⁴⁵,
Marek Dudzinski⁴⁶, Malwina Rybicka-Ramos⁴⁷, Eva-Laure Matera ⁴⁰,
Ramón García-Sanz ID ⁴⁸, Hartmut Goldschmidt ID ⁴⁹,⁵⁰,⁵¹,
Krzysztof Jamroziak ⁴², Artur Jurczyszyn ID ⁵², Esther Clavero ⁵³,
Graham G. Giles ID ⁵⁴,⁵⁵,⁵⁶, Matteo Pelosi⁵⁷, Daria Zawirska ⁵⁸,
Marcin Kruszewski⁵⁹, Herlander Marques⁶⁰,⁶¹, Eva Haastrup ⁶²,
José Manuel Sánchez-Maldonado ⁷,⁸, Uta Bertsch ⁴⁹,⁵⁰,
Marcin Rymko ⁶³, Marc-Steffen Raab⁴⁹, Elizabeth E. Brown ⁶⁴,
Jonathan N. Hofmann ID ³⁰, Celine Vachon ID ¹⁷,⁶⁵,
Daniele Campa ⁶,⁶⁵ and Federico Canzian ID ¹,⁶⁵

¹Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany. ²Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, USA. ³Division of Hematology/Oncology, Division of Hematology/Oncology, Department of Medicine, School of Medicine, Department of Pathology, School of Medicine, Susan and Henry Samueli College of Health Sciences, Chao Family Comprehensive Cancer Center, University of California at Irvine, Irvine, CA, USA. ⁴Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), Heidelberg, Germany. ⁵Hopp Children's Cancer Center (KiTZ), Heidelberg, Germany Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), Heidelberg, Germany. ⁶Department of Biology, University of Pisa, Pisa, Italy. ⁷Genomic Oncology Area, GENYO. Centre for Genomics and Oncological Research: Pfizer / University of Granada / Andalusian Regional Government, PTS, Granada, Spain. ⁸Instituto de Investigación Biosanitaria IBs.Granada, Granada, Spain. ⁹Department of Biochemistry and Molecular Biology, University of Granada, Granada, Spain. ¹⁰Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands. ¹¹CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Vienna, Austria. ¹²Centre for Individualised Infection Medicine (CiiM) & TWINCORE, joint ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany. ¹³Department for Immunology & Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, Bonn, Germany. ¹⁴National Research Centre for the Working Environment, Copenhagen, Denmark. ¹⁵Biomedical Center, Faculty of Medicine, Charles University Pilsen, Pilsen, Czech Republic. ¹⁶Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany. ¹⁷Division of Biomedical Statistics & Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. ¹⁸Department of Hematology, Semmelweis University, Budapest, Hungary. ¹⁹Molecular Diagnostics and Clinical Research Unit, Institute of Regional Health Research, University Hospital of Southern Denmark, Odense, Denmark. ²⁰Institute of Regional Research, University of Southern Denmark, Odense, Denmark. ²¹Institute of Molecular Medicine, University of Southern Denmark, Odense, Denmark. ²²Department of Hematology, Transplantology and Internal Medicine, Medical University of Warsaw, Warsaw, Poland. ²³Hospital Universitario 12 de Octubre, Instituto de Investigación del Hospital Universitario 12 de Octubre, 28041 Madrid, Spain. ²⁴Department of Haematology &

Transplantology, Medical University of Gdańsk, Gdańsk, Poland.
²⁵Division of Hematology and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA. ²⁶Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN, USA.
²⁷Department of Lymphoproliferative Diseases, Maria Skłodowska-Curie National Research Institute of Oncology, Warsaw, Poland.
²⁸Cancer Control Research, BC Cancer, Vancouver, BC, Canada.
²⁹Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA. ³⁰Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. ³¹Department of Hematology, Warmian-Masurian Cancer Center of The Ministry Of The Interior And Administration's Hospital, Olsztyn, Poland.
³²Wroclaw Medical University, Wroclaw, Poland. ³³Hematology and Medical Oncology Department, University Hospital Morales Meseguer, IMIB, Murcia, Spain. ³⁴Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA. ³⁵Department of Lymphoma - Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA. ³⁶St. John's Hospital, Budapest, Hungary.
³⁷Department of Medicine, University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, CA, USA. ³⁸Hematology Unit, Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy. ³⁹Hematology Division Chaim Sheba Medical Center, Tel Hashomer, Israel. ⁴⁰INSERM 1052/CNRS 5286, CRCL, Lyon, France. ⁴¹Department of Hematology, Rydygier Specialistic Hospital, Cracow, Poland. ⁴²Department of Hematology, Institute of Hematology and Transfusion Medicine, Warsaw, Poland.
⁴³Department of Hematology, Holycross Cancer Center, Kielce, Poland. ⁴⁴Alfred Sokolowski Specialist Hospital in Walbrzych Oncology Support Centre for Clinical Trials, Walbrzych, Poland.
⁴⁵Department of Experimental Hematooncology, Medical University of Lublin, Lublin, Poland. ⁴⁶Institute of Medical Sciences, College of Medical Sciences, University of Rzeszow, Rzeszow, Poland.
⁴⁷Department of Hematology, Specialist Hospital No.1 in Bytom, Academy of Silesia, Faculty of Medicine, Katowice, Poland.
⁴⁸University Hospital of Salamanca, Diagnostic Laboratory Unit in Hematology, University Hospital of Salamanca, IBSAL, CIBERONC, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), Salamanca, Spain. ⁴⁹Department of Internal Medicine V, Heidelberg University Hospital, Heidelberg, Germany. ⁵⁰National Centre for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany.
⁵¹National Centre for Tumour Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany. GMMG Study Group at University Hospital Heidelberg, Heidelberg, Germany. ⁵²Hematology Department, Jagiellonian University Medical College, Cracow, Poland. ⁵³Hematology Department, Virgen de las Nieves University Hospital, Granada, Spain. ⁵⁴Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC, Australia. ⁵⁵Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC, Australia. ⁵⁶Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. ⁵⁷U.O. Dipartimento di Ematologia, Azienda USL Toscana Nord Ovest, Livorno, Italy.
⁵⁸Department of Hematology, University Hospital, Crakow, Poland.
⁵⁹Department of Hematology, University Hospital No. 2 in Bydgoszcz, Bydgoszcz, Poland. ⁶⁰Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, Braga, Portugal. ⁶¹ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal. ⁶²Department of Clinical Immunology, the Bloodbank, Rigshospitalet, Copenhagen University Hospital, København, Denmark. ⁶³Department of Hematology, Provincial Polyclinical Hospital in Torun, Torun, Poland. ⁶⁴Department of Pathology, School of Medicine at the University of Alabama, Birmingham, AL, USA. ⁶⁵These authors contributed equally: Celine Vachon, Daniele Campa, Federico Canzian.
⁶⁶email: f.canzian@dkfz.de
DATA AVAILABILITY

The primary data for this work will be made available to researchers who submit a
reasonable request to the corresponding author, conditional to approval by the Steering
Committees of the respective studies (InterLymph, GMMG, IMMEnSE) and Ethics
Commission of the Medical Faculty of the University of Heidelberg, Germany. Data will be
stripped from all information allowing identification of study participants. The summary
statistics of the FinnGen GWAS are publicly available at https://www.finnngen.fi. Functional
data of the Human Functional Genomics Project used in this project have been
catalogued and archived in the BBMRI-NL data infrastructure (https://hfgp.bbmri.nl/)
using the MOLGENIS open-source platform for scientific data, which allows flexible data
querying and download, including sufficiently rich metadata and interfaces for machine
processing (R statistics, REST API) and using FAIR principles to optimize Findability,
Accessibility, Interoperability and Reusability.

REFERENCES

1. Kumar SK, Rajkumar V, Kyle RA, van Duin M, Sonneveld P, Mateos MV, et al. Multiple myeloma. Nat Rev Dis Prim. 2017;3:17046.

2. Georgakopoulou R, Fiste O, Sergentanis TN, Andrikopoulou A, Zagouri F, Gav-
riatopoulou M, et al. Occupational Exposure and Multiple Myeloma Risk: An
Updated Review of Meta-Analyses. J Clin Med. 2021;10:4179.

3. Kristinsson SY, Bjorkholm M, Goldin LR, Blimark C, Mellqvist UH, Wahlin A, et al. Familial Aggregation of Multiple Myeloma and Its Precursor Monoclonal Gam-mopathy of Undetermined Significance (MGUS): A Population-Based Study in Sweden. Blood 2008;112:1678.

4. Pertesi M, Went M, Hansson M, Hemminki K, Houlston RS, Nilsson B. Genetic predisposition for multiple myeloma. Leukemia 2020;34:697–708.

5. Campa D, Gentiluomo M, Stein A, Aoki MN, Oliverius M, Vodičková L, et al. The PANcreatic Disease ReseArch (PANDoRA) consortium: Ten years' experience of association studies to understand the genetic architecture of pancreatic cancer. Crit Rev Oncol Hematol. 2023;186:104020.

6. Yu Y, Mao L, Cheng Z, Zhu X, Cui J, Fu X, et al. A novel regQTL-SNP and the risk of lung cancer: a multi-dimensional study. Arch Toxicol. 2021;95:3815-27.

7. Iversen ES, Lipton G, Clyde MA, Monteiro ANA. Functional annotation signatures of disease susceptibility loci improve SNP association analysis. BMC Genom. 2014;15:398.

8. Liu W, Li M, Zhang W, Zhou G, Wu X, Wang J, et al. Leveraging functional annotation to identify genes associated with complex diseases. PLOS Comput Biol. 2020;16:e1008315.

9. Kim PKM, Armstrong M, Liu Y, Yan P, Bucher B, Zuckerbraun BS, et al. IRF-1 expression induces apoptosis and inhibits tumor growth in mouse mammary cancer cells in vitro and in vivo. Oncogene 2004;23:1125-35.

10. Armstrong MJ, Stang MT, Liu Y, Gao J, Ren B, Zuckerbraun BS, et al. Interferon Regulatory Factor 1 (IRF-1) induces p21WAF1/CIP1 dependent cell cycle arrest and p21WAF1/CIP1 independent modulation of survivin in cancer cells. Cancer Lett. 2012;319:56-65.

11. Segueni J, Noordermeer D. CTCF: A misguided jack-of-all-trades in cancer cells. Comput Struct Biotechnol J 2022;20:2685-98.

12. Walter SA, Cutler RE, Martinez R, Gishizky M, Hill RJ. Stk10, a new member of the polo-like kinase kinase family highly expressed in hematopoietic tissue. J Biol Chem. 2003;278:18221-8.

13. Bi L, Jia S, Hu W, Su X, Chen X, Tang H. Systematic analysis of prognostic sig-nificance, functional enrichment and immune implication of STK10 in acute myeloid leukemia. BMC Med Genom. 2022;15:101.

14. Zhang L, Lu SY, Guo R, Ma JX, Tang LY, Wang JJ, et al. STK10 knockout inhibits cell migration and promotes cell proliferation via modulating the activity of ERM and p38 MAPK in prostate cancer cells. Exp Ther Med. 2021;22:851.

15. Burger R. Impact of Interleukin-6 in Hematological Malignancies. TMH. 2013; 40:336-43.

16. Harmer D, Falank C, Reagan MR. Interleukin-6 Interweaves the Bone Marrow Microenvironment, Bone Loss, and Multiple Myeloma. Front Endocrinol. 2019;9:788.

ACKNOWLEDGEMENTS

The authors wish to thank the International Myeloma Society (IMS) for its travel grant support to present this study during the 18th International Myeloma Workshop (IMW).

AUTHOR CONTRIBUTIONS

FC, DC and AM conceived and designed the study. KB and AM performed lab work. FC, AM, DC and AC performed data quality control and statistical analyses. FC, AM and DC drafted the manuscript. All other authors provided samples and data. All authors critically read, commented, and approved the manuscript.

FUNDING

This work was partially supported by: intramural funding of DKFZ; Horizon 2020 of the European Union, grant 856620; the Intramural Research Program of the National Cancer Institute, National Institutes of Health, grant number U01CA271014 and U01 CA249955. Open Access funding enabled and organized by Projekt DEAL.

COMPETING INTERESTS

The authors declare no competing interests.

ADDITIONAL INFORMATION

**Supplementary information** The online version contains supplementary material available at https://doi.org/10.1038/s41375-023-02022-8.

**Correspondence** and requests for materials should be addressed to Federico Canzian.

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