International Journal of Molecular Sciences Brief Report Do GWAS-Identified Risk Variants for Chronic Lymphocytic Leukemia Influence Overall Patient Survival and Disease Progression? Antonio José Cabrera-Serrano 1,2 , José Manuel Sánchez-Maldonado 1,2 , Rob ter Horst 3 , Angelica Macauda 4 , Paloma García-Martín 5 , Yolanda Benavente 6,7,8 , Stefano Landi 9 , Alyssa Clay-Gilmour 10 , Yasmeen Niazi 11,12 , Blanca Espinet 13,14 , Juan José Rodríguez-Sevilla 15 , Eva María Pérez 5 , Rossana Maffei 16 , Gonzalo Blanco 13,14 , Matteo Giaccherini 9 , James R. Cerhan 17 , Roberto Marasca 16 , Miguel Ángel López-Nevot 18 , Tzu Chen-Liang 19 , Hauke Thomsen 20 , Irene Gámez 19 , Daniele Campa 9 , Víctor Moreno 6,21,22 , Silvia de Sanjosé 6,7 , Rafael Marcos-Gragera 7,23,24,25 , María García-Álvarez 26 , Trinidad Dierssen-Sotos 7,27 , Andrés Jerez 28 , Aleksandra Butrym 29 , Aaron D. Norman 17 , Mario Luppi 16 , Susan L. Slager 30,31 , Kari Hemminki 32,33 , Yang Li 34,35 , Sonja I. Berndt 36 , Delphine Casabonne 6,7 , Miguel Alcoceba 26 , Anna Puiggros 13,14 , Mihai G. Netea 34,37 , Asta Försti 11,12 , Federico Canzian 4 and Juan Sainz 1,2,7,38, * 1 Genomic Oncology Area, GENYO, Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, PTS, 18016 Granada, Spain; antonio.cabrera@genyo.es (A.J.C.-S.); josemanuel.sanchez@genyo.es (J.M.S.-M.) 2 Instituto de Investigación Biosanitaria IBs.Granada, 18012 Granada, Spain 3 CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria; rob.terhorst@radboudumc.nl 4 Genomic Epidemiology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; angelicamacauda@gmail.com (A.M.); f.canzian@dkfz.de (F.C.) Citation: Cabrera-Serrano, A.J.; 5 Hospital Campus de la Salud, PTS, 18016 Granada, Spain; palomagrm@hotmail.com (P.G.-M.); Sánchez-Maldonado, J.M.; ter Horst, evam.perez.sspa@juntadeandalucia.es (E.M.P.) 6 Catalan Institute of Oncology, Bellvitge Biomedical Research Institute (IDIBELL), 08908 Barcelona, Spain; R.; Macauda, A.; García-Martín, P.; Benavente, Y.; Landi, S.; ybenavente@iconcologia.net (Y.B.); v.moreno@iconcologia.net (V.M.); s.sanjose@iconcologia.net (S.d.S.); Clay-Gilmour, A.; Niazi, Y.; Espinet, dcasabonne@iconcologia.net (D.C.) 7 Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), B.; et al. Do GWAS-Identified Risk University of Barcelona, 08908 Barcelona, Spain; rmarcos@iconcologia.net (R.M.-G.); Variants for Chronic Lymphocytic trinidad.dierssen@unican.es (T.D.-S.) Leukemia Influence Overall Patient 8 CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain Survival and Disease Progression? Int. 9 Department of Biology, University of Pisa, 56126 Pisa, Italy; stefano.landi@unipi.it (S.L.); J. Mol. Sci. 2023, 24, 8005. matteo.giaccherini@phd.unipi.it (M.G.); daniele.campa@unipi.it (D.C.) https://doi.org/10.3390/ 10 Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, ijms24098005 Greenville, SC 29208, USA; claygila@mailbox.sc.edu 11 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Academic Editors: Yasuharu Sato Consortium (DKTK), 69120 Heidelberg, Germany; y.niazi@dkfz.de (Y.N.); a.foersti@kitz-heidelberg.de (A.F.) and Yoshito Nishimura 12 Hopp Children’s Cancer Center (KiTZ), 69120 Heidelberg, Germany 13 Molecular Cytogenetics Laboratory, Pathology Department, Hospital del Mar, 08003 Barcelona, Spain; Received: 12 March 2023 bespinet@parcdesalutmar.cat (B.E.); gblancoares@northwell.edu (G.B.); apuiggros@psmar.cat (A.P.) Revised: 14 April 2023 14 Translational Research on Hematological Neoplasms Group, Cancer Research Program, Accepted: 23 April 2023 Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), 08003 Barcelona, Spain Published: 28 April 2023 15 Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; jrodsevilla@gmail.com 16 Department of Medical and Surgical Sciences, University of Modena and Reggio Emilia, AOU Policlinico, 41124 Modena, Italy; maffei.rossana@aou.mo.it (R.M.); roberto.marasca@unimore.it (R.M.); Copyright: © 2023 by the authors. mario.luppi@unimore.it (M.L.) 17 Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55905, USA; Licensee MDPI, Basel, Switzerland. buehler@mayo.edu (J.R.C.); aarondeannorman@gmail.com (A.D.N.) This article is an open access article 18 Immunology Department, Virgen de las Nieves University Hospital, 18014 Granada, Spain; manevot@ugr.es distributed under the terms and 19 Hematology Department, Morales Meseguer University Hospital, 30008 Murcia, Spain; conditions of the Creative Commons tzuchen82@gmail.com (T.C.-L.); igamez50@gmail.com (I.G.) Attribution (CC BY) license (https:// 20 MSB Medical School Berlin, D-14197 Berlin, Germany; hauke.thomsen@medicalschool-berlin.de creativecommons.org/licenses/by/ 21 Cancer Prevention and Control Program, Unit of Biomarkers and Susceptibility, Bellvitge Biomedical 4.0/). Research Institute (IDIBELL), Catalan Institute of Oncology, 08907 Barcelona, Spain Int. J. Mol. Sci. 2023, 24, 8005. https://doi.org/10.3390/ijms24098005 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2023, 24, 8005 2 of 11 22 Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, 08907 Barcelona, Spain 23 Epidemiology Unit and Girona Cancer Registry, Oncology Coordination Plan, Department of Health, Autonomous Government of Catalonia, Catalan Institute of Oncology, Girona Biomedical Research Institute (IdiBGi), 17190 Girona, Spain 24 Department of Nursing, Universitat de Girona, 17007 Girona, Spain 25 Josep Carreras Leukemia Research Institute, 08916 Girona, Spain 26 Department of Hematology, University Hospital of Salamanca (HUS/IBSAL), CIBERONC and Cancer Research Institute of Salamanca-IBMCC (USAL-CSIC), 37007 Salamanca, Spain; mgarca1991@gmail.com (M.G.-Á.); migalcoceba@gmail.com (M.A.) 27 Faculty of Medicine, University of Cantabria, 39011 Santander, Spain 28 Department of Hematology, Experimental Hematology Unit, Vall d’Hebron Institute of Oncology (VHIO), University Hospital Vall d’Hebron, 08035 Barcelona, Spain; anjecayu@gmail.com 29 Department of Cancer Prevention and Therapy, Medical University of Wrocław, 50-556 Wrocław, Poland; aleksandra.butrym@gmail.com 30 Division of Computational Genomics, Mayo Clinic, Rochester, MN 85054, USA; slager.susan@mayo.edu 31 Division of Hematology, Mayo Clinic, Rochester, MN 55905, USA 32 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; k.hemminki@dkfz-heidelberg.de 33 Faculty of Medicine and Biomedical Center in Pilsen, Charles University in Prague, 30605 Pilsen, Czech Republic 34 Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; yang.li@helmholtz-hzi.de (Y.L.); mihai.netea@radboudumc.nl (M.G.N.) 35 Centre for Individualised Infection Medicine (CiiM) & TWINCORE, Joint Ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), 30625 Hannover, Germany 36 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA; berndts@mail.nih.gov 37 Department for Immunology & Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115 Bonn, Germany 38 Department of Biochemistry and Molecular Biology I, Faculty of Sciences, University of Granada (UGR), 18012 Granada, Spain * Correspondence: juan.sainz@genyo.es; Tel.: +34-95871-5500 (ext. 126); Fax: +34-9-5863-7071 Abstract: Chronic lymphocytic leukemia (CLL) is the most common leukemia among adults world- wide. Although genome-wide association studies (GWAS) have uncovered the germline genetic component underlying CLL susceptibility, the potential use of GWAS-identified risk variants to predict disease progression and patient survival remains unexplored. Here, we evaluated whether 41 GWAS-identified risk variants for CLL could influence overall survival (OS) and disease progres- sion, defined as time to first treatment (TTFT) in a cohort of 1039 CLL cases ascertained through the CRuCIAL consortium. Although this is the largest study assessing the effect of GWAS-identified susceptibility variants for CLL on OS, we only found a weak association of ten single nucleotide polymorphisms (SNPs) with OS (p < 0.05) that did not remain significant after correction for multiple testing. In line with these results, polygenic risk scores (PRSs) built with these SNPs in the CRuCIAL cohort showed a modest association with OS and a low capacity to predict patient survival, with an area under the receiver operating characteristic curve (AUROC) of 0.57. Similarly, seven SNPs were associated with TTFT (p < 0.05); however, these did not reach the multiple testing significance thresh- old, and the meta-analysis with previous published data did not confirm any of the associations. As expected, PRSs built with these SNPs showed reduced accuracy in prediction of disease progression (AUROC = 0.62). These results suggest that susceptibility variants for CLL do not impact overall survival and disease progression in CLL patients. Keywords: chronic lymphocytic leukemia; overall survival; TTFT; genetic variants; susceptibility; polygenic risk score Int. J. Mol. Sci. 2023, 24, 8005 3 of 11 1. Introduction Chronic lymphocytic leukemia (CLL) is the most common form of leukemia among adults worldwide [1], and its global health burden has risen substantially over the past 30 years [2]. CLL remains an incurable disease [3] with a heterogeneous clinical course and a 10-year survival rate ranging from 47.3% to 72.5% in males and 58.2% to 78.7% in females [4]. Traditional clinical prognostic factors include Rai and Binet staging systems, lymphocyte doubling time, cytogenetic alterations, and point mutations, which are used for patient risk stratification and clinical management [5]. Although age, sex, exposure to chemicals, race/ethnicity, and family history of hematological cancers influence the risk of CLL, recent studies have suggested that the combination of these classical factors with genetic markers might help in predicting disease onset and clinical outcome [6]. However, despite the overall success of genome-wide association studies (GWAS) in identifying susceptibility loci for CLL [7–10], there remains an unmet need to characterize genetic markers associated with disease progression and overall patient survival. Considering this background, we investigated whether GWAS-identified susceptibility variants for CLL could influence the overall survival (OS) of CLL patients and their disease progression, defined as time to first treatment (TTFT). Finally, we explored whether the effect of selected variants on OS and TTFT could be mediated by immune-related processes through a comprehensive battery of functional experiments developed in the 500FG cohort recruited in the context of the Human Functional Project (HFGP). 2. Results CLL patients from the CRuCIAL cohort had a mean age of 65.87 and a male/female ratio of 1.57, which is in line with the worldwide median age of diagnosis and gender distributions (Table 1) [11]. Median follow-up time was 76.77 months without variation of follow-up statistics for censored patients, and the total number of deceased patients was 287. The OS did not differ significantly by country, ruling out the possibility of any deviation due to multicenter randomized patient recruitment. On the other hand, CLL patients with TTFT data in the CRuCIAL cohort had a mean age of 65.03 and a male/female ratio of 1.62. The median time to first treatment was 759.49 days and the number of deceased patients was 111 (Table 1). Table 1. Patient characteristics of the CRuCIAL cohort used for OS and TTFT analyses. CRuCIAL Cohort (1039 CLL Cases for OS Analysis) CRuCIAL Cohort (354 CLL Cases for TTFT Analysis) Age (years) 65.87 ± 11.05 Age (years) 65.03 ± 10.94 Sex ratio (male/female) 1.57 (636/403) Sex ratio (male/female) 1.62 (219/135) Country of origin Country of origin (alive/decesead) Spain 754 (536/218) Spain 227 (174/73) Italy 285 (216/69) Italy 127 (86/41) Median follow-up time (months) 76.77 (50–123) Median TTFT (days) 759.49 (31–1148.74) Alive 76 (52.1–123) Alive 830.81 (44.13–1308.93) Deceased 77 (44–123) Deceased 564.83 (13.10–800) Status at follow-up Status at follow-up Alive 752 (72.38) Alive 239 (68.29) Deceased 287 (27.62) Deceased 111 (31.71) Binet Stage Binet stage A 647 (81.48) A 201 (64.84) B 99 (12.46) B 68 (21.94) C 48 (06.04) C 41 (13.23) Rai Stage Rai stage 0 597 (65.67) 0 124 (37.35) I 152 (16.72) I 105 (31.63) II 114 (12.57) II 63 (18.98) III 12 (01.31) III 16 (04.82) IV 34 (03.73) IV 24 (07.23) Data are mean ± standard deviation, n (%), or percentiles (25th–75th percentiles). I 152 (16.72) I 105 (31.63) II 114 (12.57) II 63 (18.98) III 12 (01.31) III 16 (04.82) IV Sci. 2023, 24, 8005 Int. J. Mol. 34 (03.73) IV 24 (07.23) 4 of 11 Data are mean ± standard deviation, n (%), or percentiles (25th–75th percentiles). CoxCoxregression regressionanalyses analysesshowed that ten showed that tengenetic geneticvariants variants within within the CAMK2D, the CAMK2D, CASP8, CASP8, CFLAR, CXXC1, GPR37, IRF8, LEF1, MYNN, PRKD2, and CFLAR, CXXC1, GPR37, IRF8, LEF1, MYNN, PRKD2, and TERC loci were associatedTERC loci were associ-with ated OSwith at p OS at plevel < 0.05 < 0.05 level (Table 2).(Table 2). Although Although potentially potentially interesting, interesting, noneassociations none of these of these associations remained remained significantsignificant after correction after correction for multiplefor multiple testing, testing, which whichasuggested suggested weak effecta (if weak effect any) (if any) of these genesof in these genes in patient determining determining patient survival. The survival. The lack lack of previous of previous studies assessing studies assessing the impact the impact of GWAS-identified of GWAS-identified risk variants on risk variants on OS hampered theOS hampered of performance theeventual per- formance of eventual meta-analyses. meta-analyses. Table 2. Association Table of GWAS-identified 2. Association riskrisk of GWAS-identified variants for for variants CLL andand CLL OS.OS. Risk SNP SNP Chr.Chr. Nearby Gene Nearby Gene Risk AlleleHR HR (95%CI) (95%CI) δδ p p HR HR (95%CI) Ϯ (95%CI) p p HR HR (95%CI) (95%CI) ¥¥ pp Allele rs4368253 18 AC107990.1||NFE2L3P1 C 0.94 (0.78–1.14) 0.539 0.93 (0.59–1.45) 0.746 0.93 (0.73–1.18) 0.545 rs4368253 rs1439287 18 2AC107990.1||NFE2L3P1 ACOXL CT 0.94 1.03(0.78–1.14) (0.88–1.21) 0.5390.7000.93 0.99 (0.59–1.45) (0.76–1.29) 0.7460.9570.93 1.10 (0.73–1.18) (0.84–1.44) 0.545 0.481 rs58055674 2 ACOXL C 1.08 (0.90–1.30) 0.424 1.12 (0.89–1.42) 0.335 1.02 (0.64–1.61) 0.936 rs1439287 rs7944004 2 11 ACOXL ASCL2||C11orf21 T T 1.03 (0.88–1.21) 0.97 (0.82–1.15) 0.700 0.7350.99 (0.76–1.29) 1.06 (0.79–1.43) 0.957 0.6991.10 (0.84–1.44) 0.89 (0.69–1.16) 0.481 0.394 rs4987855 rs58055674 2 18 ACOXL BCL2 CG 1.08 0.97 (0.71–1.32) 0.424 (0.90–1.30) 0.8481.12 0.68 (0.10–4.87) 0.335 (0.89–1.42) 0.7011.02 0.98 (0.71–1.34) 0.936 (0.64–1.61) 0.886 rs2651823 11 C11orf21|TSPAN32 A 0.95 (0.81–1.12) 0.523 1.03 (0.78–1.36) 0.857 0.85 (0.65–1.11) 0.233 rs7944004 rs1476569 11 4 ASCL2||C11orf21CAMK2D TG 0.97 1.12(0.82–1.15) (0.95–1.32) 0.7350.1761.06 1.31 (0.79–1.43) (1.03–1.67) 0.6990.0280.89 0.92 (0.69–1.16) (0.64–1.32) 0.394 0.643 rs3769825 2 CASP8 T 1.20 (1.02–1.43) 0.033 1.23 (0.93–1.63) 0.150 1.32 (1.01–1.73) 0.041 rs4987855 rs7558911 18 2 BCL2 CFLAR GA 0.97 (0.71–1.32) 1.19 (1.01–1.41) 0.848 0.0400.68 (0.10–4.87) 1.22 (0.91–1.63) 0.701 0.1750.98 (0.71–1.34) 1.30 (1.00–1.67) 0.886 0.046 rs1036935 rs2651823 11 CXXC1 18 C11orf21|TSPAN32 AA 0.95 1.27 (1.07–1.51) 0.523 (0.81–1.12) 0.0081.03 1.34 (1.06–1.70) 0.857 (0.78–1.36) 0.0150.85 1.43 (0.98–2.10) 0.233 (0.65–1.11) 0.066 rs1359742 9 DMRTA1 G 1.05 (0.89–1.23) 0.575 1.02 (0.78–1.34) 0.875 1.11 (0.85–1.45) 0.441 rs1476569 rs6546149 4 2 CAMK2D DTNB GG 1.12 1.09(0.95–1.32) (0.91–1.31) 0.1760.3561.31 1.06 (1.03–1.67) (0.84–1.35) 0.0280.6070.92 1.29 (0.64–1.32) (0.86–1.93) 0.643 0.222 EOMES|LINC01980 l. Sci. 2023, 24,rs9880772 x FOR PEER rs3769825 3 2 REVIEW CASP8 T TA 1.20 1.06 0.98 (0.84–1.14) (1.02–1.43) 0.787 1.04 (0.80–1.34) 0.787 50.90 of (0.69–1.18) 13 0.455 rs13015798 2 FAM126B (0.88–1.28) 0.0330.5481.23 1.00 (0.93–1.63) (0.64–1.54) 0.1500.9891.32 1.10 (1.01–1.73) (0.87–1.39) 0.041 0.444 rs6586163 FAS rs7558911 rs2267708 2 10 7 CFLARGPR37 AAT 1.19 0.86 0.99 (0.84–1.17) (1.01–1.41) (0.72–1.02) 0.916 0.040 0.081 1.22 1.08 (0.80–1.45) (0.91–1.63) 0.91 (0.70–1.19) 0.631 0.175 0.504 0.92 (0.71–1.20) 1.30 0.70 (1.00–1.67) (0.51–0.97) 0.554 0.046 0.030 rs1036935 rs2953196 18 11 CXXC1 GRAMD1B AG 1.27 0.85(1.07–1.51) (0.70–1.04) 0.0080.1231.34 0.70 (1.06–1.70) (0.42–1.17) 0.0150.1761.43 0.85 (0.98–2.10) (0.67–1.09) 0.066 0.203 rs35923643 11 GRAMD1B G 0.88 (0.72–1.07) 0.192 0.80 (0.63–1.02) 0.068 1.12 (0.71–1.78) 0.631 522 rs1359742 22 rs3800461 9ODF3B 6 DMRTA1ILRUN T GC 1.050.854 1.02 (0.86–1.20) (0.89–1.23) 0.92 0.575 0.6201.020.410 1.11 (0.87–1.41) (0.67–1.27) (0.78–1.34) 0.91 0.875 0.5621.110.431 0.87 (0.61–1.24) (0.65–1.27) (0.85–1.45) 1.20 (0.30–4.86) 0.441 0.795 IRF4 6 rs9392504 6256 rs6546149 2 16 6 OPRM1||IPCEF1 rs391855 DTNB IRF8C GA 1.03 (0.87–1.21) A 0.88 (0.74–1.04) 1.090.752 (0.91–1.31) 0.87 0.143 0.356 0.96 (0.74–1.23) (0.74–1.03) 0.80 (0.60–1.08) 0.1041.060.729 (0.84–1.35) 0.99 0.145 0.607 0.88 (0.68–1.13) 0.9461.290.348 1.14 (0.86–1.51) (0.72–1.36) (0.86–1.93) 0.74 0.322 (0.58–0.96) 0.222 0.021 7565 rs9880772 15 rs898518 3 4 EOMES|LINC01980 PCAT29|LOC107984788 LEF1G TA 0.980.753 0.97 (0.82–1.16) (0.84–1.14) 1.12 (0.94–1.33) 0.787 0.2151.040.753 0.96 (0.74–1.25) (0.80–1.34) 1.50 (1.00–2.26) 0.787 0.0490.900.842 0.97 (0.71–1.33) (0.69–1.18) 1.04 (0.82–1.33) 0.455 0.721 rs34676223 1 MDS2 C 0.99 (0.83–1.18) 0.899 0.88 (0.61–1.27) 0.491 1.03 (0.81–1.30) 0.803 6404 rs13015798 7 rs57214277 2POT1 4 FAM126B C MYL12BP2||LINC02363 AT 1.060.572 0.95 (0.79–1.14) (0.88–1.28) 1.02 0.548 0.8051.000.818 0.95 (0.61–1.47) (0.86–1.21) (0.64–1.54) 1.01 0.989 0.9261.100.557 0.93 (0.74–1.18) (0.79–1.29) (0.87–1.39) 1.06 (0.77–1.46) 0.444 0.729 rs10936599 3 MYNN C 0.91 (0.74–1.11) 0.344 0.57 (0.36–0.90) 0.016 0.99 (0.77–1.26) 0.922 1714 rs6586163 8 rs11715604 10 3 POU5F1P2||ODF1 FAS NCK1 G 0.91 A (0.77–1.08) T 0.99 (0.84–1.17) 0.268 0.92 0.916 (0.72–1.17) 0.98 (0.80–1.20) 0.8311.08 (0.80–1.45) 0.485 0.82 1.06 (0.61–1.85) 0.631 (0.59–1.14) 0.8420.92 (0.71–1.20) 0.240 0.96 (0.74–1.23) 0.554 0.723 19 rs6489882 3846 rs2267708 12 7PRKD2 GPR37OAS3A 1.20 TG 0.860.061 (0.99–1.45) 1.09 (0.92–1.29) (0.72–1.02) 1.16 0.3090.910.209 0.081 (0.92–1.47) 1.16 (0.90–1.50) (0.70–1.19) 1.62 0.2400.700.027 0.504 (1.06–2.50) 1.06 (0.78–1.45) 0.030 (0.51–0.97) 0.696 rs140522 22 ODF3B T 1.02 (0.86–1.20) 0.854 1.11 (0.87–1.41) 0.410 0.87 (0.61–1.24) 0.431 096 rs2953196 2 rs2236256 11 6 QPCT||RNU6-1116P GRAMD1B GC 0.850.590 A 0.95 (0.81–1.13) OPRM1||IPCEF1 (0.70–1.04) 1.03 0.123 0.7520.700.837 0.97 (0.76–1.24) (0.87–1.21) (0.42–1.17) 0.96 0.176 0.7290.850.454 0.88 (0.63–1.23) (0.74–1.23) (0.67–1.09) 1.14 (0.86–1.51) 0.203 0.348 rs11637565 15 PCAT29|LOC107984788 G 0.97 (0.82–1.16) 0.753 0.96 (0.74–1.25) 0.753 0.97 (0.71–1.33) 0.842 1473 rs35923643 1 rs17246404 11 RHOU7 GRAMD1B POT1 G 0.96 G (0.74–1.25) C 0.88 (0.72–1.07) 0.770 1.46 0.192 (0.64–3.32) 0.95 (0.79–1.14) 0.5720.80 (0.63–1.02) 0.366 0.88 0.95 (0.61–1.47) 0.068 (0.64–1.21) 0.8181.12 (0.71–1.78) 0.432 0.93 (0.74–1.18) 0.631 0.557 rs2511714 8 POU5F1P2||ODF1 G 0.91 (0.77–1.08) 0.6200.2680.91 0.92 (0.72–1.17) 0.5620.4851.20 0.82 (0.59–1.14) 0.795 0.240 8779 rs3800461 6 rs11083846 6 19 SERPINB6 ILRUNPRKD2 CA A 1.03 (0.78–1.37) 0.920.823 (0.67–1.27) 1.04 (0.77–1.40) 1.20 (0.99–1.45) 0.061 (0.65–1.27) 0.806 0.97 (0.24–3.94) 1.16 (0.92–1.47) 0.209 (0.30–4.86) 0.970 1.62 (1.06–2.50) 0.027 8862 rs9392504 3 rs888096 6TERC2 IRF4 A 0.89 (0.73–1.08) QPCT||RNU6-1116P AA 0.880.229(0.74–1.04) 0.95 0.143 0.5900.800.037 0.62 (0.40–0.97) (0.81–1.13) (0.60–1.08) 0.97 0.145 0.8370.880.602 0.94 (0.74–1.19) (0.76–1.24) (0.68–1.13) 0.88 (0.63–1.23) 0.322 0.454 rs41271473 1 RHOU G 0.96 (0.74–1.25) 0.770 1.46 (0.64–3.32) 0.366 0.88 (0.64–1.21) 0.432 5526 rs391855 5 rs73718779 16TERT 6 IRF8 SERPINB6A 1.07 A (0.90–1.27) A 0.87 (0.74–1.03) 0.468 1.00 0.104 (0.78–1.28) 1.03 (0.78–1.37) 0.8230.99 (0.72–1.36) 0.997 1.25 1.04 (0.77–1.40) 0.946 (0.91–1.70) 0.8060.74 (0.58–0.96) 0.163 0.97 (0.24–3.94) 0.021 0.970 TERC 11 rs12638862 4987 rs898518 4 35 TMPRSS5||DRD2 rs7705526 LEF1TERT T AA 0.94 (0.71–1.23) A 0.89 (0.73–1.08) 1.120.632 (0.94–1.33) 0.229 0.215 0.94 (0.70–1.28) 1.07 (0.90–1.27) 0.468 0.62 (0.40–0.97) 1.500.712 (1.00–2.26) 1.00 (0.78–1.28) 0.037 0.049 0.73 (0.23–2.30) 0.997 0.94 (0.74–1.19) 1.040.588 (0.82–1.33) 0.721 1.25 (0.91–1.70) 0.602 0.163 070 rs34676223 1 11 6 rs61904987 TSBP1-AS1 MDS2A 1.11 (0.92–1.33) TMPRSS5||DRD2 CT 0.990.285(0.83–1.18) 0.94 0.899 0.6320.880.433 1.19 (0.77–1.82) (0.71–1.23) (0.61–1.27) 0.94 0.491 0.7121.030.343 1.12 (0.88–1.42) (0.70–1.28) (0.81–1.30) 0.73 (0.23–2.30) 0.803 0.588 rs926070 6 TSBP1-AS1 A 1.11 (0.92–1.33) 0.285 1.19 (0.77–1.82) 0.433 1.12 (0.88–1.42) 0.343 4272 rs57214277 19 rs7254272 4 19MYL12BP2||LINC02363 ZBTB7A|MAP2K2 A ZBTB7A|MAP2K2 0.82 T (0.66–1.02) A 1.02 (0.86–1.21) 0.078 0.78 0.805 (0.61–1.00) 0.82 (0.66–1.02) 0.0781.01 (0.79–1.29) 0.051 0.92 0.78 (0.61–1.00) 0.926 (0.50–1.69) 0.0511.06 (0.77–1.46) 0.794 0.92 (0.50–1.69) 0.729 0.794 rs10936599 3 Abbreviations:MYNN SNP, Abbreviations: C single single nucleotide SNP, 0.91 (0.74–1.11) polymorphism; nucleotide HR, 0.344 0.57 hazards polymorphism; (0.36–0.90) ratio. HR, Significant hazards 0.016 ratio. 0.99in(0.77–1.26) results Significant bold in bold0.922 results (p < 0.05). Ϯ Cox rs11715604 3 (p < 0.05). δ Cox NCK1 regression analysis δ Cox regression T assuming analysis0.98 a log-additive (0.80–1.20) assuming model 0.831 a log-additive 1.06of(0.61–1.85) model ofinheritance. inheritance.0.842 regression 0.96 (0.74–1.23) Cox regression 0.723 a analysis assuming dominant model. ¥ Cox regression analysis assuming a recessive model. rs6489882 12 analysis assuming OAS3 a dominant model. ¥ Cox regression analysis assuming a recessive model. G 1.09 (0.92–1.29) 0.309 1.16 (0.90–1.50) 0.240 1.06 (0.78–1.45) 0.696 As expected, we Asfound expected, we found a weak a weakbetween association association thebetween weighted theand weighted and unweighted PRSs unweighted and OS of CLL patients (HR = 1.22, p = 1.80 × 10 −5 and HR = 1.19,×p = 7.61 × 10−5 ). PRSs and OS of CLL patients (HR = 1.22, p = 1.80 × 10 and HRScaled_80% Scaled_80% −5 = 1.19, p = 7.61 10−5). Weighted Weighted and unweighted and unweighted PRSs increased PRSs increased the capacity the capacity to predictto OS predict OS by by only only 6–7%, with an 6–7%, area under with an area under the receiver the receiver operating operating characteristic characteristic curvecurve (AUROC) (AUROC) for for thethe unweighted and un- weightedPRS weighted and weighted PRSofof0.56 0.56and and0.57, 0.57,respectively respectively(Table (Table3). 3). In agreement with these findings, we found that none of these SNPs were correlated withbetween Table 3. Associations host immune parameters unweighted (cQTL and weighted PRSsdata, and absolute OS. numbers of 91 blood-derived cell populations, 106 serum immunological proteins, or 7 steroid hormones), which reinforced Polygenic Risk theScores (n = 891) hypothesis of a null effect of these markers in determining AUROC overall patient survival. Quintiles On HRthe 95%CI a other hand, Cox regression p analyses adjusted AUROC for(95%CI) age, sex, and country of ighted, subjects origin revealed that seven genetic variants within the ACOXL, CASP8, GRAMD1B, MYNN, 1 1.00 - 100% call rate PRKD2, TERC, and ZBTB7A|MAP2K2 loci were associated with TTFT at p < 0.05 level 2 (Table 4). However, 1.06 none of the associations (0.70–1.60) 0.787 with TTFT remained significant after correction 3 1.67 (1.15–2.43) 0.007 4 1.42 (0.99–2.03) 0.053 5 2.36 (1.56–3.58) 5.30 × 10−5 Continuous b 1.20 (1.09–1.31) 8.70 × 10−5 0.56 (0.52–0.60) ghted, subjects Int. J. Mol. Sci. 2023, 24, 8005 5 of 11 for multiple testing, suggesting that these susceptibility variants for CLL do not have a relevant role in determining disease progression. Table 3. Associations between unweighted and weighted PRSs and OS. Polygenic Risk Scores (n = 891) AUROC Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW Quintiles HR 95%CI a p AUROC (95%CI) 4 of 13 Unweighted, subjects with 100% call rate 1 1.00 - 2 1.06 (0.70–1.60) 0.787 3 1.67 (1.15–2.43) 0.007 A 4 647 (81.48) 1.42 (0.99–2.03) A 0.053 201 (64.84) 5 2.36 (1.56–3.58) 5.30 × 10−5 B 99 (12.46) b Continuous B 1.20 (1.09–1.31) 8.70 × 10−5 68 (21.94) 0.56 (0.52–0.60) Weighted, subjects with 100% call rate 1 1.00 - C 48 (06.04) 2 C 1.33 (0.85–2.09) 0.206 41 (13.23) Rai Stage 3 2.05 (1.34–3.15) Rai stage 0.001 4 1.68 (1.08–2.59) 0.020 0 597 (65.67) 5 0 2.50 (1.63–3.83) 2.40 × 10−5 124 (37.35) Continuous b 1.22 (1.11–1.33) 1.80 × 10−5 0.57 (0.53–0.61) I 152 (16.72) I 105 (31.63) Polygenic Risk Scores (n = 1003) AUROC II 114 (12.57) II 63 (18.98) Quintiles HR 95%CI a p AUROC (95%CI) III Unweighted, subjects with 80% call rate 12 (01.31) 1 III 1.00 - 16 (04.82) IV 2 34 (03.73) 0.99 (0.66–1.46) IV 0.948 24 (07.23) 3 1.48 (1.05–2.11) 0.027 Data are mean ± standard 4 deviation, n 1.36 (%),(0.98–1.90) or percentiles (25th–75th percentiles). 0.066 5 2.08 (1.41–3.07) 2.41 × 10−4 Continuous Cox regression b analyses showed1.17 (1.08–1.28) that ten genetic 2.32 × 10−4 within the variants 0.55CAMK2D, (0.51–0.59) Weighted, subjects with 80% call rate 1 1.00 - CASP8, CFLAR, CXXC1, 2 GPR37, IRF8, LEF1, MYNN, PRKD2, 1.29 (0.85–1.95) and TERC loci were associ- 0.224 ated with OS at p <30.05 level (Table 1.78 2). Although potentially (1.19–2.67) 0.005interesting, none of these associations remained4 significant after 1.57 (1.05–2.35) correction for multiple0.028testing, which suggested a 5 2.19 (1.48–3.26) 9.80 × 10−5 weak effect (if Continuous any) of these b genes 1.19 in determining (1.09–1.29) patient survival. 7.61 × 10−5 The lack of(0.52–0.60) 0.56 previous studies assessing the impact of GWAS-identified risk variants on OS hampered the per- a HR, hazards ratio; CI, confidence interval. All analyses were adjusted for age, sex, and country of origin; b The formance of analysis unit for the eventual meta-analyses. with the continuous variable was the increment of one quintile. Table 2. Association Table of GWAS-identified 4. Association riskrisk of GWAS-identified variants for for variants CLL andand CLL OS.TTFT. Risk SNP SNP Chr.Chr. Nearby Gene Nearby Gene Risk AlleleHR HR (95%CI) (95%CI) δδ p p HR HR (95%CI) Ϯ (95%CI) p p HR HR (95%CI) (95%CI) ¥¥ p p Allele rs4368253 18 AC107990.1||NFE2L3P1 C 1.03 (0.74–1.42) 0.871 0.62 (0.31–1.25) 0.184 1.18 (0.80–1.74) 0.413 rs4368253 rs1439287 18 2AC107990.1||NFE2L3P1 ACOXL CT 0.94 1.05(0.78–1.14) (0.81–1.37) 0.539 0.7080.93 0.98 (0.59–1.45) (0.65–1.50) 0.746 0.9430.93 1.18 (0.73–1.18) (0.76–1.83) 0.545 0.469 rs58055674 2 ACOXL C 1.39 (1.02–1.90) 0.036 1.60 (1.08–2.38) 0.019 1.18 (0.51–2.73) 0.696 rs1439287 rs7944004 2 11 ACOXL ASCL2||C11orf21 T T 1.03 (0.88–1.21) 1.04 (0.78–1.39) 0.700 0.7690.99 (0.76–1.29) 1.01 (0.61–1.67) 0.957 0.9671.10 (0.84–1.44) 1.09 (0.71–1.68) 0.481 0.684 rs4987855 rs58055674 2 18 ACOXL BCL2 CG 1.08 0.75 (0.44–1.27) (0.90–1.30) 0.2811.12 (0.89–1.42) 0.424 NA NA 1.02 0.73 0.335 (0.43–1.25) (0.64–1.61) 0.256 0.936 rs2651823 11 C11orf21|TSPAN32 A 1.12 (0.85–1.46) 0.418 1.13 (0.73–1.76) 0.591 1.20 (0.77–1.85) 0.423 rs7944004 rs1476569 11 4 ASCL2||C11orf21 CAMK2D TG 0.97 1.01(0.82–1.15) (0.76–1.35) 0.735 0.9511.06 1.02 (0.79–1.43) (0.69–1.51) 0.699 0.9150.89 0.99 (0.69–1.16) (0.54–1.82) 0.394 0.971 rs3769825 2 CASP8 T 1.41 (1.06–1.87) 0.017 1.56 (0.98–2.47) 0.059 1.58 (1.00–2.48) 0.048 rs4987855 rs7558911 18 2 BCL2 CFLAR G A 0.97 (0.71–1.32) 1.21 (0.93–1.58) 0.848 0.1630.68 (0.10–4.87) 1.23 (0.78–1.95) 0.701 0.3750.98 (0.71–1.34) 1.35 (0.89–2.05) 0.886 0.155 rs1036935 rs2651823 11 CXXC1 18 C11orf21|TSPAN32 A A 0.95 1.13 (0.83–1.53) (0.81–1.12) 0.4461.03 1.14 0.523 (0.77–1.69) (0.78–1.36) 0.5030.85 1.24 0.857 (0.59–2.61) (0.65–1.11) 0.568 0.233 rs1359742 9 DMRTA1 G 0.98 (0.74–1.30) 0.884 0.85 (0.54–1.32) 0.468 1.11 (0.72–1.73) 0.636 rs1476569 rs6546149 4 2 CAMK2D DTNB GG 1.12 0.95 (0.95–1.32) (0.69–1.31) 0.176 0.7691.31 0.90 (1.03–1.67) (0.61–1.33) 0.028 0.5870.92 1.14 (0.64–1.32) (0.55–2.36) 0.643 0.723 rs9880772 3 EOMES|LINC01980 T 0.96 (0.73–1.24) 0.735 0.96 (0.64–1.43) 0.831 0.91 (0.56–1.48) 0.717 rs3769825 rs13015798 2 2 CASP8 FAM126B TA 1.20 (1.02–1.43) 0.92 (0.68–1.23) 0.033 0.5671.23 (0.93–1.63) 0.88 (0.45–1.70) 0.150 0.6951.32 (1.01–1.73) 0.90 (0.61–1.33) 0.041 0.605 rs6586163 10 FAS A 0.81 (0.62–1.07) 0.139 0.80 (0.51–1.26) 0.339 0.72 (0.45–1.13) 0.150 rs7558911 rs2267708 2 7 CFLARGPR37 A T 1.19 (1.01–1.41) 0.76 (0.58–1.00) 0.040 0.052 1.22 (0.91–1.63) 0.69 (0.46–1.03) 0.175 0.069 1.30 (1.00–1.67) 0.71 (0.43–1.16) 0.046 0.172 rs1036935 rs2953196 18 11 CXXC1 GRAMD1B AG 1.27 0.82 (1.07–1.51) (0.59–1.14) 0.008 0.2401.34 0.85 (1.06–1.70) (0.34–2.10) 0.015 0.7241.43 0.76 (0.98–2.10) (0.50–1.16) 0.066 0.202 rs35923643 11 GRAMD1B G 0.71 (0.52–0.98) 0.040 0.68 (0.46–1.02) 0.061 0.54 (0.23–1.28) 0.164 rs1359742 rs3800461 9 6 DMRTA1ILRUN GC 1.05 0.97 (0.89–1.23) (0.61–1.54) 0.575 0.8811.02 0.94 (0.78–1.34) (0.55–1.60) 0.875 0.8081.11 1.17 (0.85–1.45) (0.27–5.08) 0.441 0.832 rs9392504 6 IRF4 A 0.94 (0.72–1.23) 0.648 1.07 (0.63–1.81) 0.800 0.83 (0.55–1.26) 0.380 rs6546149 rs391855 2 16 DTNB IRF8 G A 1.09 (0.91–1.31) 1.14 (0.87–1.49) 0.356 0.3361.06 (0.84–1.35) 1.28 (0.77–2.14) 0.607 0.3411.29 (0.86–1.93) 1.15 (0.77–1.72) 0.222 0.503 rs9880772 rs898518 3 4 EOMES|LINC01980 LEF1 TA 0.98 1.16(0.84–1.14) (0.87–1.54) 0.787 0.3101.04 1.58 (0.80–1.34) (0.82–3.04) 0.787 0.1720.90 1.09 (0.69–1.18) (0.74–1.62) 0.455 0.650 rs34676223 1 MDS2 C 1.28 (0.96–1.71) 0.098 1.96 (0.94–4.07) 0.073 1.24 (0.84–1.82) 0.280 rs13015798 rs57214277 2 4 FAM126B MYL12BP2||LINC02363 AT 1.06 1.10 (0.88–1.28) (0.85–1.43) 0.548 0.4561.00 1.28 (0.64–1.54) (0.86–1.91) 0.989 0.2251.10 0.97 (0.87–1.39) (0.58–1.61) 0.444 0.897 rs10936599 3 MYNN C 1.03 (0.73–1.46) 0.866 0.42 (0.21–0.83) 0.013 1.30 (0.85–1.98) 0.222 rs6586163 rs11715604 10 3 FAS NCK1 A T 0.99 (0.84–1.17) 0.78 (0.55–1.10) 0.916 0.1581.08 (0.80–1.45) 0.59 (0.24–1.48) 0.631 0.2630.92 (0.71–1.20) 0.77 (0.51–1.17) 0.554 0.225 rs6489882 rs2267708 7 12 GPR37OAS3 TG 0.86 1.01 (0.76–1.34) (0.72–1.02) 0.9290.91 1.06 0.081 (0.70–1.59) (0.70–1.19) 0.7970.70 0.96 0.504 (0.56–1.63) (0.51–0.97) 0.868 0.030 rs140522 22 ODF3B T 0.88 (0.66–1.18) 0.391 0.86 (0.58–1.28) 0.452 0.81 (0.43–1.53) 0.523 rs2953196 rs2236256 11 6 GRAMD1B OPRM1||IPCEF1 GC 0.85 1.06 (0.70–1.04) (0.79–1.41) 0.123 0.7040.70 0.79 (0.42–1.17) (0.52–1.21) 0.176 0.2790.85 1.50 (0.67–1.09) (0.97–2.33) 0.203 0.070 rs11637565 15 PCAT29|LOC107984788 G 0.90 (0.68–1.20) 0.482 1.04 (0.67–1.61) 0.874 0.67 (0.38–1.16) 0.155 rs35923643 11 GRAMD1B G 0.88 (0.72–1.07) 0.192 0.80 (0.63–1.02) 0.068 1.12 (0.71–1.78) 0.631 rs3800461 6 ILRUN C 0.92 (0.67–1.27) 0.620 0.91 (0.65–1.27) 0.562 1.20 (0.30–4.86) 0.795 rs9392504 6 IRF4 A 0.88 (0.74–1.04) 0.143 0.80 (0.60–1.08) 0.145 0.88 (0.68–1.13) 0.322 rs391855 16 IRF8 A 0.87 (0.74–1.03) 0.104 0.99 (0.72–1.36) 0.946 0.74 (0.58–0.96) 0.021 rs898518 4 LEF1 A 1.12 (0.94–1.33) 0.215 1.50 (1.00–2.26) 0.049 1.04 (0.82–1.33) 0.721 rs34676223 1 MDS2 C 0.99 (0.83–1.18) 0.899 0.88 (0.61–1.27) 0.491 1.03 (0.81–1.30) 0.803 l. Sci. 2023, 24, x FOR PEER REVIEW 5 of 13 CASP8, CFLAR, CXXC1, GPR37, IRF8, LEF1, MYNN, PRKD2, and TERC loci were associ- ated with OS at p < 0.05 level (Table 2). Although potentially interesting, none of these associations remained significant after correction for multiple testing, which suggested a 522 22 ODF3B T 1.02 (0.86–1.20) 0.854 1.11 (0.87–1.41) 0.410 0.87 (0.61–1.24) 0.431 weak effect (if any) of these genes in determining patient survival. The lack of previous 6256 6 OPRM1||IPCEF1 C 1.03 (0.87–1.21) 0.752 0.96 (0.74–1.23) 0.729 1.14 (0.86–1.51) 0.348 Int. J. Mol. Sci. 2023, 24, 8005 studies assessing the impact of GWAS-identified risk variants on OS hampered the per- 6 of 11 7565 15 PCAT29|LOC107984788 G 0.97 (0.82–1.16) 0.753 0.96 (0.74–1.25) 0.753 0.97 (0.71–1.33) 0.842 formance of eventual meta-analyses. 6404 7 POT1 C 0.95 (0.79–1.14) 0.572 0.95 (0.61–1.47) 0.818 0.93 (0.74–1.18) 0.557 1714 8 POU5F1P2||ODF1 TableG 0.91 (0.77–1.08) 2. Association 0.268 0.92 (0.72–1.17) of GWAS-identified risk variants 0.485for CLL 0.82 and(0.59–1.14) OS. 0.240 Table 4. Cont. 3846 19 PRKD2 A 1.20 (0.99–1.45) 0.061 1.16 (0.92–1.47) 0.209 1.62 (1.06–2.50) 0.027 096 2 SNP QPCT||RNU6-1116P Risk A 0.95 (0.81–1.13) SNP Chr.Chr. NearbyNearby Gene Gene Risk AlleleHR0.590(95%CI) HR 0.97δδ(0.76–1.24) (95%CI) p p HR0.837 (95%CI) HR 0.88Ϯ(0.63–1.23) (95%CI) p p HR0.454 (95%CI) HR (95%CI) ¥¥ pp 1473 1 rs17246404 RHOU G 0.96 Allele (0.74–1.25) 0.770 1.46 (0.64–3.32) 0.366 0.88 (0.64–1.21) 0.432 7 POT1 C 1.14 (0.83–1.56) 0.425 1.63 (0.70–3.79) 0.257 1.08 (0.72–1.61) 0.702 8779 rs4368253 rs2511714 6 rs11083846 18 8AC107990.1||NFE2L3P1 SERPINB619 POU5F1P2||ODF1 PRKD2 A 1.03 CG 0.940.823 (0.78–1.37) A (0.78–1.14) 0.97 (0.72–1.31) 0.539 1.04 0.8380.93 0.93 (0.77–1.40) 1.34 (1.00–1.80) 0.050 (0.59–1.45) 0.806 (0.61–1.40) 0.746 0.97 1.21 (0.82–1.78) 0.7210.93 1.03 (0.24–3.94) 0.331 (0.73–1.18) 0.970 (0.57–1.86) 0.545 2.31 (1.31–4.08) 0.914 0.004 8862 rs1439287 3 rs888096 2 TERC 2 ACOXL A 0.89 (0.73–1.08) QPCT||RNU6-1116P T A 1.03 (0.88–1.21) 0.229 0.700 0.62 (0.40–0.97) 1.02 (0.76–1.35) 0.912 0.99 (0.76–1.29) 0.037 0.957 0.94 (0.74–1.19) 0.96 (0.64–1.44) 0.8511.10 (0.84–1.44) 0.602 1.14 (0.66–1.96) 0.481 0.631 rs41271473 1 RHOU CG 1.080.468 0.76 (0.50–1.13) 0.424 0.1761.12 0.85 (0.26–2.76) 0.335 0.7861.02 0.69 (0.42–1.13) 0.936 0.139 5526 rs58055674 5 rs73718779 2TERT 6 ACOXL A SERPINB6 1.07 (0.90–1.27) A (0.90–1.30) 1.00 (0.78–1.28) 1.15 (0.74–1.78) 0.536 (0.89–1.42) 0.997 1.25 1.09 (0.68–1.74)(0.91–1.70) 0.734 (0.64–1.61) 0.163 2.86 (0.69–11.9) 0.148 4987 rs7944004 11 rs12638862 rs7705526 11 3 ASCL2||C11orf21 TMPRSS5||DRD2 5 TERC TERT T 0.94 TA 0.970.632 (0.71–1.23) A (0.82–1.15) 0.94 (0.66–1.33) 0.735 0.94 0.7151.06 0.40 (0.70–1.28) 0.92 (0.70–1.23) 0.589 (0.79–1.43) 0.712 (0.19–0.84) 0.699 0.73 0.79 (0.53–1.17) 0.0150.89 1.12 (0.23–2.30) 0.240 (0.69–1.16) 0.588 (0.74–1.69) 0.394 1.13 (0.69–1.86) 0.601 0.633 070 rs4987855 18 6 rs61904987 TSBP1-AS1 11 BCL2 TMPRSS5||DRD2A 1.11 (0.92–1.33)G T 0.97 (0.71–1.32) 0.285 0.848 1.19 (0.77–1.82) 1.28 (0.86–1.92) 0.2280.68 (0.10–4.87) 0.433 0.701 1.12 (0.88–1.42) 1.35 (0.85–2.16) 0.2030.98 (0.71–1.34) 0.343 1.23 (0.30–5.10) 0.886 0.776 rs926070 6 TSBP1-AS1 A 1.00 (0.74–1.35) 0.523 0.9871.03 0.87 (0.45–1.69) 0.857 0.6840.85 1.04 (0.71–1.54) 0.233 0.835 4272 rs2651823 19 rs7254272 11 19 ZBTB7A|MAP2K2 C11orf21|TSPAN32 ZBTB7A|MAP2K2A 0.82 A (0.66–1.02) A 0.95 (0.81–1.12) 0.078 0.78 (0.61–1.00) 0.74 (0.51–1.07) 0.110 (0.78–1.36) 0.051 0.92 0.62 (0.41–0.96)(0.50–1.69) 0.030 (0.65–1.11) 0.794 1.34 (0.58–3.09) 0.497 rs1476569 4 Abbreviations:CAMK2D SNP, G polymorphism; single nucleotide Abbreviations: SNP, single 1.12 (0.95–1.32) HR,0.176 nucleotide polymorphism;hazards1.31 (1.03–1.67) ratio. HR, Significant hazards 0.028 0.92in(0.64–1.32) results ratio. Significant bold in bold0.643 results (p < 0.05). rs3769825 2 (p < 0.05). δ Cox CASP8 regression analysis δ Cox regression T assuming analysis 1.20 (1.02–1.43) assuminga log-additive 0.033 a log-additive model 1.23of(0.93–1.63) model inheritance.0.150 ofinheritance. Ϯ Cox 1.32 (1.01–1.73) regression Cox regression 0.041 a analysis assuming ¥ Cox regression analysis assuming a recessive model of inheritance. rs7558911 2 analysis CFLAR assuming dominant a dominantmodel ofA 1.19 (1.01–1.41) 0.040 1.22 (0.91–1.63) 0.175 1.30 (1.00–1.67) 0.046 inheritance. model. ¥ Cox regression analysis assuming a recessive model. rs1036935 18 CXXC1 A 1.27 (1.07–1.51) 0.008 1.34 (1.06–1.70) 0.015 1.43 (0.98–2.10) 0.066 rs1359742 9 As expected, DMRTA1we Infound line with aGweakthese data, association 1.05 a meta-analysis (0.89–1.23) between 0.575 1.02 theof(0.78–1.34) our data and weighted including 0.875 1.11data unweighted from a previous (0.85–1.45) 0.441 PRSs and OS ofGWAS CLL confirmed patients (HR that= none 1.22, pof= these 1.80 loci × 10 have −5 and a significant HR Scaled_80% = 1.19, p = 7.61 × (Table 5). These impact on TTFT rs6546149 2 DTNB G 1.09 (0.91–1.31) 0.356 1.06 (0.84–1.35) 0.607 1.29 (0.86–1.93) 0.222 −5 Weighted findings support the notion of a null effect of susceptibility variants on disease progression rs9880772 3 10 ).EOMES|LINC01980 and unweighted T PRSs 0.98increased (0.84–1.14) the0.787 capacity 1.04to predict OS0.787 (0.80–1.34) by only0.906–7%, (0.69–1.18) 0.455 in CLL. rs13015798 2 with an area FAM126B under the receiverA operating 1.06 (0.88–1.28) characteristic 0.548 1.00 curve (AUROC) (0.64–1.54) 0.989for1.10 the(0.87–1.39) un- 0.444 weighted and weighted PRS of 0.56 and 0.57, respectively (Table 3). rs6586163 10 FAS A 0.99 Table 5. Meta-analysis (0.84–1.17) of association 0.916 of estimates 1.08 (0.80–1.45) 0.631 GWAS-identified 0.92 (0.71–1.20) risk variants for CLL and 0.554 disease rs2267708 7 Table 3. Associations GPR37 progression in the T 0.86 CRuCIAL (0.72–1.02) cohorts with between unweighted and weighted PRSs and OS. 0.081 0.91 a previous (0.70–1.19) GWAS. 0.504 0.70 (0.51–0.97) 0.030 rs2953196 11 GRAMD1B G 0.85 (0.70–1.04) 0.123 0.70 (0.42–1.17) 0.176 0.85 (0.67–1.09) 0.203 CRuCIAL Consortium Lin et al. (2021) [12]AUROC Meta-Analysis rs35923643 11 Polygenic GRAMD1BRisk Scores (n = 891) G 0.88 (354 (0.72–1.07) CLL Cases) 0.192 0.80 (0.63–1.02) (755 CLL Cases) 0.068 1.12 (0.71–1.78) (1109 CLL Cases)0.631 SNP Chr. Nearby Gene Risk Allele a rs3800461 6 QuintilesILRUN HR 95%CI C 0.92 (0.67–1.27) HR (95%CI) δ pp 0.620 0.91 (0.65–1.27) HR (95%CI) δ AUROC 0.562 p (95%CI) 1.20 (0.30–4.86) HR (95%CI) δ p 0.795 phet ighted,rs9392504 subjects rs4368253 6 18 IRF4 AC107990.1||NFE2L3P1 CA 0.88 1.03(0.74–1.04) (0.74–1.42) 0.143 0.80 0.871 (0.60–1.08)0.684 1.03 (0.87–1.17) 0.145 1.030.88 (0.68–1.13) (0.85–1.19) 0.7260.322 0.985 rs1439287 1 ACOXL 1.00 - 100% call rate rs391855 rs58055674 16 22 IRF8 ACOXL T C A 0.87 1.05 (0.81–1.37) (0.74–1.03) 0.036 1.39 (1.02–1.90) 0.708 0.104 0.99 1.00 (0.86–1.12) (0.72–1.36)0.837 0.98 (0.84–1.15) 0.990 1.01 (1.16–0.88) 0.946 1.080.74 (0.58–0.96) (0.89–1.23) 0.858 0.398 0.765 0.021 0.096 rs898518 rs7944004 4 11 2 ASCL2||C11orf21 LEF1 1.06 (0.70–1.60) TA 1.12 1.04(0.94–1.33) (0.78–1.39) 0.787 0.215 1.50 (1.00–2.26) 0.769 - 0.049 - 1.04 1.04 (0.82–1.33) (1.35–0.80) 0.7830.721 1.000 rs4987855 18 BCL2 G 0.75 (0.44–1.27) 0.281 0.82 (0.45–1.10) 0.230 0.78 (0.33–1.10) 0.205 0.734 rs34676223 rs2651823 1 11 3 MDS2 C11orf21|TSPAN32 1.67 (1.15–2.43) AC 0.99 1.12(0.83–1.18) (0.85–1.46) 0.007 0.899 0.88 0.418 (0.61–1.27)0.302 0.93 (0.80–1.07) 0.491 0.981.03 (0.81–1.30) (1.14–0.84) 0.7960.803 0.303 rs1476569 4 CAMK2D G 1.01 (0.76–1.35) 0.951 0.99 (0.85–1.15) 0.898 1.00 (0.83–1.14) 0.955 0.917 rs57214277 rs3769825 4 2 4 MYL12BP2||LINC02363 CASP8 1.42 (0.99–2.03) T T 1.02 (0.86–1.21) 1.41 (1.06–1.87) 0.053 0.805 0.017 1.01 (0.79–1.29) 1.10 (0.96–1.22) 0.926 0.149 1.06 (0.77–1.46) 1.01 (1.18–0.87) 0.8700.729 0.018 rs10936599 rs7558911 32 5 MYNN CFLAR 2.36 (1.56–3.58) AC 0.91 (0.93–1.58) 5.30 1.21(0.74–1.11) × 10−50.57 0.344 0.163 (0.36–0.90)0.019 0.81 (0.63–0.97) 0.016 0.940.99 (0.77–1.26) (1.10–0.80) 0.4210.922 0.048 rs1036935 18 CXXC1 A 1.13 (0.83–1.53) 0.446 0.96 (0.83–1.13) 0.660 1.01 (1.19–0.85) 0.949 0.444 rs11715604 rs1359742 3 Continuous 9 b NCK1 DMRTA1 1.20 (1.09–1.31) GT 0.98 (0.74–1.30) 8.70 0.98(0.80–1.20) × 10 1.06 0.831 0.884 −5 (0.61–1.85) 0.93 (0.76–1.07) 0.56 (0.52–0.60) 0.842 0.344 0.96 0.94 (0.74–1.23) (0.77–1.10) 0.5060.723 0.778 rs6546149 2 DTNB G 0.95 (0.69–1.31) 0.769 0.86 (0.73–1.01) 0.067 0.87 (0.64–1.06) 0.192 0.652 ghted, subjects rs6489882 rs9880772 12 3 OAS3 EOMES|LINC01980 TG 1.09 0.96(0.92–1.29) (0.73–1.24) 0.309 1.16 0.735 (0.90–1.50)0.954 1.00 (0.87–1.12) 0.240 0.981.06 (0.78–1.45) (1.13–0.86) 0.8280.696 0.815 1 1.00 - 100% callrs13015798 rate 2 FAM126B A 0.92 (0.68–1.23) 0.567 0.78 (0.57–0.96) 0.015 0.85 (1.02–0.71) 0.076 0.556 rs6586163 10 FAS A 0.81 (0.62–1.07) 0.139 1.00 (0.86–1.13) 0.972 0.95 (1.11–0.82) 0.527 0.249 rs2267708 7 2 GPR37 1.33 (0.85–2.09) T 0.76 (0.58–1.00) 0.206 0.98 (0.85–1.12) 0.052 0.743 0.91 (1.07–0.78) 0.273 0.176 rs2953196 rs35923643 11 11 3 GRAMD1B GRAMD1B G 2.05 (1.34–3.15) G 0.82 (0.59–1.14) 0.71 (0.52–0.98) 0.240 0.001 1.16 0.040 (0.99–1.31) 1.18 (1.00–1.39) 0.065 0.049 1.06 (0.84–1.23) 1.03 (0.84–1.19) 0.596 0.760 0.102 0.021 rs3800461 6 4 ILRUN C 1.68 (1.08–2.59) 0.97 (0.61–1.54) 0.881 0.020 1.19 (0.96–1.47) 0.105 1.12 (1.41–0.88) 0.363 0.426 rs9392504 6 IRF4 A 0.94 (0.72–1.23) 0.648 1.03 (0.88–1.16) 0.678 1.00 (1.16–0.87) 0.970 0.597 rs391855 16 5 IRF8 2.50 (1.63–3.83) A 1.14 (0.87–1.49) 2.40 × 10−51.00 (0.85–1.13) 0.336 0.967 1.04 (1.21–0.89) 0.624 0.477 rs898518 4 LEF1 A 1.16 (0.87–1.54) 0.310 0.92 (0.76–1.06) 0.282 0.98 (1.14–0.84) 0.818 0.234 rs34676223 Continuous 1 b MDS2 1.22 (1.11–1.33) C 1.28 (0.96–1.71) 1.80 × 0.098 10 −5 1.06 (0.90–1.19) 0.57 (0.53–0.61) 0.467 1.11 (0.93–1.25) 0.210 0.347 rs57214277 4 MYL12BP2||LINC02363 T 1.10 (0.85–1.43) 0.456 0.92 (0.79–1.06) 0.228 0.97 (1.13–0.84) 0.728 0.286 rs10936599 3 Polygenic Risk Scores (n = 1003) MYNN C 1.03 (0.73–1.46) 0.866 0.91 (0.70–1.08) AUROC 0.314 0.94 (0.72–1.13) 0.586 0.587 η rs11715604 3 Quintiles NCK1 HR 95%CI T a 0.78 (0.55–1.10) p 1.05 (0.87–1.27) AUROC 0.158 0.614 (95%CI) 0.96 (0.73–1.15) 0.719 0.207 rs6489882 12 OAS3 G 1.01 (0.76–1.34) 0.929 1.10 (0.95–1.27) 0.213 1.06 (0.91–1.18) 0.450 0.588 ighted, subjects rs140522 22 ODF3B T 0.88 (0.66–1.18) 0.391 1.04 (0.90–1.20) 0.641 0.99 (1.16–0.85) 0.927 0.397 rs2236256 6 1 OPRM1||IPCEF1 1.00 C 1.06 (0.79–1.41) - 0.704 1.04 (0.91–1.20) 0.568 1.04 (0.88–1.18) 0.580 0.930 h 80% call rate rs11637565 15 PCAT29|LOC107984788 G 0.90 (0.68–1.20) 0.482 0.89 (0.77–1.03) 0.112 0.88 (0.68–1.05) 0.182 0.958 rs17246404 7 2 POT1 C 0.99 (0.66–1.46) 1.14 (0.83–1.56) 0.425 0.948 1.01 (0.85–1.15) 0.872 1.04 (0.86–1.19) 0.636 0.577 rs2511714 8 POU5F1P2||ODF1 G 0.97 (0.72–1.31) 0.838 1.00 (0.87–1.14) 0.962 0.99 (0.83–1.13) 0.895 0.883 rs11083846 19 3 PRKD2 1.48 (1.05–2.11) A 1.34 (1.00–1.80) 0.027 1.11 (0.94–1.30) 0.050 0.228 1.17 (1.41–0.98) 0.088 0.342 rs888096 2 QPCT||RNU6-1116P A 1.02 (0.76–1.35) 0.912 1.08 (0.93–1.24) 0.324 1.06 (1.26–0.90) 0.484 0.801 rs41271473 1 4 RHOU 1.36 (0.98–1.90) G 0.76 (0.50–1.13) 0.066 0.176 0.92 (0.71–1.09) 0.368 0.87 (0.61–1.08) 0.247 0.453 rs73718779 rs12638862 6 3 5 SERPINB6 TERC A 2.08 (1.41–3.07) A 1.15 (0.74–1.78) 2.41 0.94 (0.66–1.33) 0.536 0.74 (0.42–0.99) × 10−40.95 0.715 (0.76–1.11) 0.040 0.567 1.23 (1.59–0.95) 0.95 (1.14–0.79) 0.113 0.582 0.750 0.958 rs7705526 Continuous 5 b TERT 1.17 (1.08–1.28) A 0.92 (0.70–1.23) 2.32 × 10 0.589 −4 - 0.55 -(0.51–0.59) 0.92 (1.27–0.67) 0.621 1.000 rs61904987 11 TMPRSS5||DRD2 T 1.28 (0.86–1.92) 0.228 1.06 (0.87–1.29) 0.570 1.11 (1.40–0.89) 0.357 0.481 rs926070 6 TSBP1-AS1 A 1.00 (0.74–1.35) 0.987 1.07 (0.91–1.19) 0.382 1.05 (1.25–0.89) 0.570 0.742 rs7254272 19 ZBTB7A|MAP2K2 A 0.74 (0.51–1.07) 0.110 1.12 (0.93–1.35) 0.242 1.00 (1.22–0.82) 0.991 0.096 Abbreviations: SNP, single nucleotide polymorphism; HR, hazards ratio. CI, confidence interval; Meta-analysis was performed assuming a fixed-effect model. Significant results in bold (p < 0.05). η Authors report the effect found for the rs62410363 (a SNP in strong linkage disequilibrium with the rs11715604, r2 = 0.97). δ Cox regression analyses were adjusted for age, sex, and country of origin and were calculated according to log-additive model of inheritance. Int. J. Mol. Sci. 2023, 24, 8005 7 of 11 As expected, the association of weighted and unweighted PRSs built with those vari- ants associated with TTFT in the CRuCIAL cohort was very modest (HRUnweighted = 1.26, p = 6.20 × 10−4 and HRWeighted = 1.32, p = 5.17 × 10−4 and HRUnweighted_Scaled_80% = 1.29, p = 4.40 × 10−5 and HRWeighted_Scaled_80% = 1.34, p = 6.60 × 10−5 ). Therefore, we were able to confirm that these PRSs were not useful in accurately predicting disease progression (AUROCUnweighted = 0.59, AUROCWeighted = 0.60, AUROCUnweighted_Scaled_80% = 0.61 and AUROCWeighted_Scaled_80% = 0.61; Table 6). Table 6. Associations between unweighted and weighted PRSs and disease progression. Polygenic Risk Scores (n = 290) AUROC Quintiles HR 95%CI a p AUROC (95%CI) Unweighted, subjects with 100% call rate 1 1.00 - 2 1.23 (0.68–2.22) 0.487 3 - - 4 1.89 (1.08–3.31) 0.026 5 2.66 (1.45–4.88) 1.50 × 10−3 Continuous b 1.26 (1.11–1.45) 6.20 × 10−4 0.59 (0.52–0.66) Weighted, subjects with 100% call rate 1 1.00 - 2 2.22 (1.05–4.71) 0.037 3 1.45 (0.66–3.16) 0.353 4 2.34 (1.14–4.79) 0.020 5 3.87 (1.89–7.94) 2.10 × 10−4 Continuous b 1.32 (1.13–1.54) 5.17 × 10−4 0.60 (0.53–0.67) Polygenic risk scores (n = 323) AUROC Quintiles HR 95%CI a p AUROC (95%CI) Unweighted, subjects with 80% call rate 1 1.00 2 1.27 (0.73–2.21) 0.392 3 - - 4 1.85 (1.07–3.19) 0.027 5 3.00 (1.75–5.12) 5.90 × 10−5 Continuous b 1.29 (1.14–1.46) 4.40 × 10−5 0.61 (0.54–0.67) Weighted, subjects with 80% call rate 1 1.00 2 1.89 (0.93–3.85) 0.080 3 1.64 (0.81–3.32) 0.172 4 2.64 (1.37–5.10) 3.80 × 10−3 5 3.58 (1.85–6.93) 1.50 × 10−4 Continuous b 1.34 (1.16–1.54) 6.60 × 10−5 0.61 (0.55–0.67) aHR, hazards ratio; CI, confidence interval. All analyses were adjusted for age, sex, and geographic region of origin. b The unit for the analysis with the continuous variable was the increment of one quintile. 3. Discussion This is the largest study evaluating the association of GWAS-identified susceptibility variants for CLL with OS, and one of the first studies assessing the effect of GWAS-identified susceptibility variants for CLL in disease progression. Even though we found potentially interesting associations between ten SNPs within the CAMK2D, CASP8, CFLAR, CXXC1, GPR37, IRF8, LEF1, MYNN, PRKD2, and TERC loci and the OS of CLL patients, none of these associations remained significant after correction for multiple testing. As expected, we found a modest association between weighted and unweighted PRSs and OS, which increased the prediction capacity by only 7%. These findings suggest that susceptibility variants for CLL do not have a large influence on OS, which is in agreement with a previous study that, using a similar approach, demonstrated that susceptibility variants do not influence the OS of patients diagnosed with multiple myeloma, another B cell malignancy [13]. This study failed to find a statistically significant association between GWAS-identified risk variants for CLL and TTFT. We found that only seven SNPs within the ACOXL, CASP8, GRAMD1B, MYNN, PRKD2, TERC, and ZBTB7A|MAP2K2 loci showed an association with TTFT at p < 0.05 level. None of these associations remained significant after correction for multiple testing, and a meta-analysis of our data with those from a previous GWAS Int. J. Mol. Sci. 2023, 24, 8005 8 of 11 on TTFT confirmed the null effect of these variants on disease progression. In agreement with these results, we found that weighted and unweighted PRSs did not have the capacity to predict TTFT. Nonetheless, in light of the relatively low power of our study (80% of power to detect an HR of 1.45 for a SNP with a frequency of 0.25) and the sparse number of studies assessing the impact of GWAS-identified risk variants for CLL on OS and TTFT, we cannot rule out the possibility that some of these SNPs might have a stronger effect on the modulation of OS and disease progression. In fact, we found it interesting that carriers of the CXXC1rs1036935A allele had poorer OS, as our team has previously reported that the presence of this allele is associated with decreased numbers of CD19+CD20+ B cells [14], a subtype of cells poorly expressed in CLL patients. The CXXC1 locus encodes for a protein of the SETD1 complex, which acts as an epigenetic transcriptional activator; if deregulated, it can lead to tumor progression and poorer survival [15]. This study has both strengths and weaknesses. The major strengths of this study are the large number of genetic markers analyzed and the relatively large size of the study population. Another strength is the comprehensive functional analysis conducted in the HFGP cohort, which included cQTL data after stimulation of whole blood, PBMCs, and MDMs with LPS, PHA, Pam3Cys, CpG, and B. burgdorferi and E. coli, as well as data on serological and plasmatic inflammatory proteins, serum steroid hormones, and blood- derived immune cell types. A limitation of this study is its multicentric nature, with inevitable drawbacks such as the impossibility of uniformly collecting medication history and Rai–Binet status for a significant proportion of the patients analyzed. In addition, considering that all study participants included in this study were of European ancestry, we could not determine the impact of GWAS-identified variants on patient survival and TTFT in other ethnic or ancestral populations. 4. Materials and Methods 4.1. Study Participants This study included 1039 CLL patients ascertained through the CRuCIAL consor- tium. CLL patients were diagnosed following the updated international criteria [5]. Study participants were of European ancestry, and provided their written informed consent to participate in the study, which was approved by the ethical review committee of partici- pant institutions: Virgen de las Nieves University Hospital (Granada, Spain, 0760-N-18); University Hospital of Salamanca (Salamanca, Spain, PI90/07/2018); Hospital del Mar (Barcelona, Spain); Catalan Institute of Oncology (Barcelona, Spain); Morales Meseguer University Hospital (Murcia, Spain); Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP) group (Spain); University of Modena and Reggio Emilia, AOU Policlinico (Modena, Italy); University of Pisa (Pisa, Italy), Wroclaw Medical Uni- versity (Wroclaw, Poland); and the Radboud University Medical Center (Nijmegen, The Netherlands, 2011/299). A detailed description of the study cohort has been reported elsewhere [14]. The main characteristics of the patients are included in Table 1. This study followed the Declaration of Helsinki. 4.2. SNP Selection and Genotyping A total of 41 single nucleotide polymorphisms (SNPs) were selected based on pub- lished GWAS, functionality data, and linkage disequilibrium between the SNPs (Supple- mentary Table S1) [14]. Genotyping of selected SNPs was carried out at GENYO (Centre for Genomics and Oncological Research: Pfizer/University of Granada/Andalusian Regional Government, Granada, Spain) using KASPar® assays (LGC Genomics, Hoddesdon, UK) according to previously reported protocols. For internal quality control, ~5% of samples were randomly selected and included as duplicates. Concordance between the original and the duplicate samples for the selected SNPs was ≥99.0%. Call rate was higher than 90%. Int. J. Mol. Sci. 2023, 24, 8005 9 of 11 4.3. Statistical Analysis and Meta-Analysis The Hardy–Weinberg Equilibrium (HWE) test was performed in the control group using a standard observed–expected chi-square (χ2 ) test. The primary outcome was OS and the endpoint was defined as death from any cause. Survival time was calculated as the time from CLL diagnosis until the occurrence of the study endpoint, censoring at the date of death or the last observed follow-up time. The second outcome was time to first treatment (TTFT), defined as the interval between CLL diagnosis and date of the first treatment or last follow-up, while the endpoint was defined as death from any cause. Association with OS and TTFT, defined as hazard ratio (HR), was calculated for each SNP using Cox regression analysis adjusted for age, sex, and country of origin. Considering the number of SNPs and the number of inheritance models tested (log-additive, dominant, and recessive), we set a significance threshold to 0.00041 (0.05/41/3) using the Bonferroni correction. Association analyses were performed using STATA (v12.1; Stata Corp, College Station, TX, USA) and power calculations were estimated using the survSNP package in R (v4.1.1; R Core Team, 2018). In order to confirm potentially interesting associations with disease progression, we conducted a meta-analysis of the CRuCIAL data with those from a previous GWAS [12] using METAL and assuming a fixed-effect model; the I2 statistic was used to assess sta- tistical heterogeneity between cohorts. Next, in order to confirm whether susceptibility variants could predict OS and disease progression, we computed weighted and unweighted polygenic risk scores (PRSs) considering those SNPs associated with OS and TTFT at a threshold of p < 0.05. We built PRSs considering either subjects with a call rate of 100% (n = 891 and 290) or 80% (n = 1003 and 323) for OS and TTFT, respectively. PRS is an ap- proach that estimates the individual risk to a phenotype or disease, which is calculated as a sum of their genotypes weighted by corresponding genotype effect sizes from summary statistic data. A detailed explanation of how the PRS scores were generated has been published in [16]. 4.4. Functional Effect of the GWAS-Identified Risk Variants on Immune Responses Mechanistically, we investigated whether selected SNPs were correlated with produc- tion of nine cytokines after in vitro stimulation of peripheral mononuclear cells (PBMCs) from 408 healthy subjects from the Human Functional Genomic Project (HFGP) cohort with LPS (100 ng/mL, Sigma-Aldrich, St. Louis, MO, USA), PHA (10 µg/mL, Sigma-Aldrich, St. Louis, MO, USA), Pam3Cys (10 µg/mL, EMC microcollections, Tuebingen, Germany), CpG (100 ng/mL, InvivoGen, San Diego, CA, USA), and B. burgdorferi (ATCC strain 35210) and E. coli (ATCC 25922). In addition, we investigated the correlation between SNPs and circulating concentrations of 103 serum and plasmatic inflammatory proteins, absolute numbers of 91 blood-derived immune cell populations (Supplementary Tables S2 and S3) and 7 plasma steroid hormones. Experimental protocols for the functional experiments have been previously described in detail [17,18]. Functional results for selected SNPs have been previously published by our team as part of a study in the context of the CRuCIAL consortium that aimed at validating the associations of GWAS-identified risk variants for CLL [14]. 5. Conclusions This study suggests that susceptibility variants for CLL do not substantially impact the overall survival of CLL patients, and confirms previous results suggesting the null effect of these variants on TTFT. Supplementary Materials: The supporting information can be downloaded at: https://www.mdpi. com/article/10.3390/ijms24098005/s1. Author Contributions: J.S. designed and coordinated the study. A.J.C.-S., J.M.S.-M. and P.G.-M. were involved in the generation of genetic data from the CRuCIAL cohort. A.J.C.-S. and J.S. performed genetic association analyses, computed PRSs and drafted the manuscript. J.S. obtained funding and Int. J. Mol. Sci. 2023, 24, 8005 10 of 11 performed data quality control. M.G.N., R.t.H. and Y.L. provided the functional raw data from the HFGP cohort and J.S. performed the statistical analysis of functional data. A.M., Y.B., S.L., A.C.-G., Y.N., B.E., J.J.R.-S., E.M.P., R.M. (Rossana Maffei), G.B., M.G., J.R.C., R.M. (Roberto Marasca), M.Á.L.- N., T.C.-L., H.T., I.G., D.C. (Daniele Campa), V.M., S.d.S., R.M.-G., M.G.-Á., T.D.-S., A.J., A.B., A.D.N., M.L., S.L.S., K.H., Y.L., S.I.B., D.C. (Delphine Casabonne), M.A., A.P., M.G.N., A.F. and F.C. provided samples and data from CLL patients. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the European Union’s Horizon 2020 research and innovation program, N◦ 856620 and by grants from the Instituto de Salud Carlos III and FEDER (Madrid, Spain; PI17/02256 and PI20/01845) and from the Consejería de Transformación Económica, Industria, Conocimiento y Universidades y FEDER (PY20/01282). “The Mayo studies in InterLymph were supported in part by the US National Cancer Institute grants P50 CA97274 and R01 CA92153.” Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Boards of all institutions participat- ing in the recruitment of CLL patients and/or healthy donors (Virgen de las Nieves University Hospi- tal (Granada, Spain, 0760-N-18); University Hospital of Salamanca (Salamanca, Spain, PI90/07/2018); Hospital del Mar (Barcelona, Spain); Catalan Institute of Oncology (Barcelona, Spain); Morales Meseguer University Hospital (Murcia, Spain); Consortium for Biomedical Research in Epidemiol- ogy and Public Health (CIBERESP) group (Spain); University of Modena and Reggio Emilia, AOU Policlinico (Modena, Italy); University of Pisa (Pisa, Italy), Wroclaw Medical University (Wroclaw, Poland) and by the Radboud University Medical Center (Nijmegen, The Netherlands, 2011/299)). Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: Genetic data from the CRuCIAL population can be accessed at https: //ftp.genyo.es/ (accessed on 28 February 2023) and are available upon reasonable request. Func- tional data used in this study are available at BBMRI-NL data infrastructure (https://hfgp.bbmri.nl/, accessed on 28 February 2023), where they have been meticulously catalogued and archived using the MOLGENIS open-source platform for scientific data. This allows flexible data querying and down- load, including sufficiently rich metadata and interfaces for machine processing (R statistics, REST API) using FAIR principles to optimize Findability, Accessibility, Interoperability, and Reusability. These datasets are not publicly available because they contain information that could compromise the research participants’ privacy. 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