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. 2019 May 17;21(8):1039–1048. doi: 10.1093/neuonc/noz088

A genome-wide association study identifies susceptibility loci for primary central nervous system lymphoma at 6p25.3 and 3p22.1: a LOC Network study

Karim Labreche 1,2, Mailys Daniau 2,3, Amit Sud 1, Philip J Law 1, Louis Royer-Perron 2,4, Amy Holroyd 1, Peter Broderick 1, Molly Went 1, Marion Benazra 2,3, Guido Ahle 5, Pierre Soubeyran 6,7, Luc Taillandier 8, Olivier L Chinot 9,10, Olivier Casasnovas 11, Jacques-Olivier Bay 12, Fabrice Jardin 13, Lucie Oberic 14, Michel Fabbro 15, Gandhi Damaj 16, Annie Brion 17, Karima Mokhtari 2,18,19, Cathy Philippe 20, Marc Sanson 2,4,19, Caroline Houillier 2, Carole Soussain 21, Khê Hoang-Xuan 2,4,2, Richard S Houlston 1,2, Agusti Alentorn 2,4,2,✉; LOC Network
PMCID: PMC6682213  PMID: 31102405

Abstract

Background

Primary central nervous system lymphoma (PCNSL) is a rare form of extra-nodal non-Hodgkin lymphoma. PCNSL is a distinct subtype of non-Hodgkin lymphoma, with over 95% of tumors belonging to the diffuse large B-cell lymphoma (DLBCL) group. We have conducted a genome-wide association study (GWAS) on immunocompetent patients to address the possibility that common genetic variants influence the risk of developing PCNSL.

Methods

We performed a meta-analysis of 2 new GWASs of PCNSL totaling 475 cases and 1134 controls of European ancestry. To increase genomic resolution, we imputed >10 million single nucleotide polymorphisms using the 1000 Genomes Project combined with UK10K as reference. In addition we performed a transcription factor binding disruption analysis and investigated the patterns of local chromatin by Capture Hi-C data.

Results

We identified independent risk loci at 3p22.1 (rs41289586, ANO10, P = 2.17 × 10−8) and 6p25.3 near EXOC2 (rs116446171, P = 1.95 x 10−13). In contrast, the lack of an association between rs41289586 and DLBCL suggests distinct germline predisposition to PCNSL and DLBCL. We found looping chromatin interactions between noncoding regions at 6p25.3 (rs11646171) with the IRF4 promoter and at 8q24.21 (rs13254990) with the MYC promoter, both genes with strong relevance to B-cell tumorigenesis.

Conclusion

To our knowledge this is the first study providing insight into the genetic predisposition to PCNSL. Our findings represent an important step in defining the contribution of common genetic variation to the risk of developing PCNSL.

Keywords: cancer susceptibility, GWAS, primary CNS lymphoma

Key Points.

1. To our knowledge, this is the first study providing insight into the genetic predisposition to PCNSL.

2. We identified independent risk loci at 3p22.1 (rs41289586, ANO10, P = 2.17 x 10-8) and 6p25.3 near EXOC2 (rs116446171, P = 1.95 x 10-13).

Importance of the Study.

PCNSLs are a rare type of DLBCL. Molecular studies in PCNSL patients have revealed similar patterns of molecular characteristics as those in nodal DLBCL. However, it is unknown whether there is a genetic predisposition to PCNSL. We performed a meta-analysis of 2 new GWASs of PCNSL analyzing the genotype of 475 patients and over 1000 healthy subjects to identify common genetic variants influencing the risk of PCNSL. We have identified independent risk loci associated with PCNSL. This finding advances our understanding of the genetic basis of PCNSL development.

Primary CNS lymphoma (PCNSL), a diffuse large B-cell lymphoma, is a rare tumor that accounts for ≤1% of all lymphomas, and approximately 2% of all primary CNS tumors.1 The World Health Organization (WHO) classification of tumors of hematopoietic and lymphoid tissues recognizes PCNSL as a distinct subtype of non-Hodgkin lymphoma,2 with over 95% of tumors having comparative histology to diffuse large B-cell lymphoma (DLBCL).3

Immunocompromised individuals are considered most at risk of PCNSL; however, the incidence of PCNSL is increasing in the immunocompetent populations and now represents the vast majority of patients.4–6 The disease typically follows an aggressive course and, despite advances in the treatment of PCNSL, is still associated with very high mortality.3

Although PCNSL is strongly linked to Epstein–Barr virus (EBV) infection in immunocompromised patients, its detection is virtually absent in PCNSL from immunocompetent patients and little else is known about its etiology or risk factors in the population.7 To address the possibility that common genetic variants influence the risk of developing PCNSL, we have conducted a genome-wide association study (GWAS) on immunocompetent patients. Specifically, we performed a meta-analysis of 2 new GWASs of PCNSL and identify independent single nucleotide polymorphisms (SNPs) at 3p22.1 and 6p25.3 associated with risk.

Materials and Methods

Subjects and Ethics

This study was based on 2 primary GWAS datasets. GWAS-1 comprised 346 immunocompetent HIV negative patients (184 male; median age 68 y) with PCNSL ascertained through the Neurology Service Mazarin of the Hospital Group of Pitié-Salpêtrière and the Lymphoma Oculo-Cerebral Network (LOC) between 2008 and 2017, which serves all of France. For controls we made use of Illumina HumanHap 660 data on 788 healthy subjects from the SU.VI.MAX study (SUpplementation en VItamines et MinerauxAntioXydants) (women aged 35–60 y; men aged 45–60 y). GWAS-2 comprised 129 immunocompetent HIV negative patients (76 males; median age 69 y) with primary DLBCL CNS tumors ascertained through the Neurology Service Mazarin and LOC from 2001–2007. For controls, we made use of second series of Illumina HumanHap 660 data generated on 346 individuals from SU.VI.MAX. Collection of patient samples and associated clinico-pathological information was undertaken with written informed consent and ethical review board approval in accordance with the tenets of the Declaration of Helsinki. The diagnosis of PCNSL (ICD-10 C83.3; WHO 9690/3) was established in accordance with WHO guidelines and all patient samples were obtained at first diagnosis.

Genotyping and Quality Control

Constitutional DNA was extracted from venous blood samples using the QIAamp DNA Blood Mini Kit (Qiagen) (OncoNeuroTek, Paris) and quantified using Caliper LabchipGX and Nanodrop. Cases were genotyped using the Infinium OmniExpress 24 v1.2 BeadChip array according to the manufacturer’s recommendations (Illumina). Standard quality control measures were applied to the GWAS.8 Specifically, individuals with low call rate (<99%) as well as all individuals with non-European ancestry (using the HapMap version 2 CEU, JPT/CHB, and YRI populations as a reference) were excluded. SNPs with a call rate <90% were excluded, as were those with a minor allele frequency (MAF) <0.01 or displaying significant deviation from Hardy–Weinberg equilibrium (ie, P < 10−6). GWAS data were imputed to >10 million SNPs with IMPUTE2 v2.39 software using a merged reference panel consisting of data from the 1000 Genomes Project (phase 1 integrated release 3, March 2012)10 and UK10K.11 Genotypes were aligned to the positive strand in both imputation and genotyping. Imputation was conducted separately for each GWAS, and in each, the data were pruned to a common set of SNPs between cases and controls before imputation. Poorly imputed SNPs defined by an information measure <0.80 were excluded. Tests of association between imputed SNPs and P-values were calculated using logistic regression under an additive genetic model in SNPTEST v2.5.12 The adequacy of case-control matching and the possibility of differential genotyping of cases and controls were evaluated using Quantile-Quantile (Q-Q) plots of test statistics (Supplementary Fig. 1). The fidelity of rs116446171, rs41289586, and rs13254990, and rs10806525 imputation was confirmed by direct genotyping either by sequencing or by KasPar allele-specific PCR (Supplementary Table 1).

HLA Imputation and Analysis

To examine if specific coding variants within human leukocyte antigen (HLA) genes contributed to association signals, we imputed the classical HLA alleles (A, B, C, DQA1, DQB1, DRB1) and coding variants across the HLA region (chr6:29–34 Mb) using SNP2HLA13 (http://www.broadinstitute.org/mpg/snp2hla/). Imputation was based on a reference panel from the Type 1 Diabetes Genetics Consortium, which comprises genotype data from 5225 individuals of European descent typed for HLA-A, B, C, DRB1, DQA1, DQB1, DPB1, and DPA1 4-digit alleles. A total of 8961 classical HLA alleles (2- and 4-digit resolution) and 1873 AA markers including 580 AA positions that were “multi-allelic” were successfully imputed (info score >0.8 for variant). Multi-allelic markers were analyzed as binary markers and a meta-analysis was conducted where we tested SNPs, HLA alleles, and AAs across the HLA region for association with PCNSL using SNPTEST v2.5.12

Meta-analysis

Meta-analyses were performed using the fixed-effects inverse-variance method based on the β estimates and standard errors from each study using META v1.6.14 Calculated were Cochran’s Q-statistic to test for heterogeneity and the I2 statistic to quantify the proportion of the total variation due to heterogeneity.15

Expression Quantity Trait Loci Analysis

To examine the relationship between SNP genotype and gene expression we carried out summary data–based Mendelian randomization (SMR) analysis as per Zhu et al (http://cnsgenomics.com/software/smr/index.html).16 We used publicly available lymphoblastoid cell line (LCL) data from the Genotype-Tissue Expression (GTEx)17 (http://www.gtexportal.org) v6p release and Multiple Tissue Human Expression Resource (MuTHER).18 Briefly, GWAS summary statistics files were generated from the meta-analysis. Reference files were generated from merging 1000 Genomes phase 3 and UK10K (ALSPAC and TwinsUK) variant call formats. Results from the SMR test for each of the 5 risk loci are reported in Supplementary Data 1. As previously advocated, only probes with at least one expression quantity trait loci (eQTL) P-value of <5.0 × 10−8 were considered for SMR analysis. We set a threshold for the SMR test of PSMR < 7.57 × 10−4 and PSMR < 2.5 × 10−3 corresponding to a Bonferroni correction for 66 tests (66 probes with a top eQTL P < 5.0 × 10−8 across the 5 loci and 2 LCL eQTL dataset) and 20 tests (20 probes with a top eQTL P < 5.0 × 10−8 across the 5 loci and MuTHER eQTL dataset), respectively.

Functional Annotation

Novel risk SNPs and their proxies (ie, r2 > 0.2 in the 1000 Genomes EUR reference panel) were annotated for putative functional effect based upon histone mark chromatin immunoprecipitation sequencing (ChIP-Seq) data for H3K27ac, H3K4Me1, and H3K27Me3 from GM12878 (LCL)19 and primary B cells.20 We searched for overlap with “super-enhancer” regions as defined by Hnisz et al,21 restricting the analysis to the GM12878 cell line and CD19+ B cells. The novel-risk SNPs and their proxies (r2 > 0.2 as above) were intersected with regions of accessible chromatin in chronic lymphocytic leukemia cells, as defined by Rendeiro et al,20 which were used as a surrogate for likely sites of transcription factor (TF) binding. SNPs falling within accessible sites (n = 47) were taken forward to TF binding motif analysis and were also annotated for genomic evolutionary rate profiling score22 as well as bound TFs based on ENCODE project19 ChIP-Seq data.

Transcription Factor Binding Disruption Analysis

To examine enrichment in specific TF binding across risk loci, we adapted the variant set enrichment method of Cowper-Sal lari et al.23 Briefly, for each risk locus, a region of strong linkage disequilibrium (defined as r2 > 0.8 and D′ > 0.8) was determined, and SNPs within were termed the associated variant set (AVS). TF ChIP-Seq uniform peak data were obtained from ENCODE for the GM12878 cell line, which included data for 82 TFs. For each of these marks, the overlap of the SNPs in the AVS and the binding sites was determined to produce a mapping tally. A null distribution was produced by randomly selecting SNPs with the same characteristics as the risk-associated SNPs, and the null mapping tally calculated. This process was repeated 10 000 times, and approximate P-values were calculated as the proportion of permutations where the null mapping tally was greater or equal to the AVS mapping tally. An enrichment score was calculated by normalizing the tallies to the median of the null distribution. Thus, the enrichment score is the number of standard deviations of the AVS mapping tally from the mean of the null distribution tallies.

Results

Association Analysis

After quality control, the 2 GWASs provided SNP genotypes on a total of 475 cases and 1134 controls (Supplementary Fig. 1 and 2; Supplementary Tables 2 and 3). To increase genomic resolution, we imputed >10 million SNPs using the 1000 Genomes Project10 combined with UK10K11 as reference. Q-Q plots for SNPs with MAF >0.5% post imputation showed only minimal evidence of overdispersion (λ values for both GWASs = 1.00; Supplementary Fig. 3). Meta-analyzing test results from the 2 GWASs, we derived joint odds ratios per allele and 95% CIs under a fixed-effects model for each SNP and associated P-values.

Genome-wide significant associations (P < 5 × 10−8) were shown for loci at 3p22.1 (rs41289586, P = 2.17 × 10−8) and 6p25.3 (rs116446171, P = 1.95 × 10−13) (Fig. 1, Table 1). Conditional analysis of GWAS data showed no evidence for additional independent signals at either of the 2 risk loci.

Fig. 1.

Fig. 1

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Manhattan plot of association P-values. Shown are the genome-wide −log10P-values (two-sided) of >10 million successfully imputed autosomal SNPs in 475 cases and 1134 controls. The red horizontal line represents the genome-wide significance threshold of P = 5.0 × 10−8.

Table 1.

Summary results for SNPs associated with primary central nervous system lymphoma risk

Locus Nearest Gene(s) SNP Position (bp, hg19) Risk Allele Dataset RAF (case;control) Imputation Info Score OR 95% CI P-value
6p25.3 EXOC2 rs116446171 484,453 G GWAS-1 (0.066;0.022) 0.85 4.11 (2.47–6.85) 5.13 × 10-8
GWAS-2 (0.088;0.019) 0.84 7.87 (3.59–17.21) 2.36 × 10-7
Combined 4.99 (3.26–7.65) 1.53 × 10-13
I 2 = 46% P het = 0.17
3p22.1 ANO10 rs41289586 43,618,558 T GWAS-1 (0.048;0.017) 0.96 3.42 (1.94–6.02) 1.90 × 10-5
GWAS-2 (0.065;0.019) 0.98 4.84 (2.10–11.13) 2.05 × 10-4
Combined 3.82 (2.39–6.09) 1.87 × 10-8
I 2 = 0% P het = 0.50
8q24.21 PTV1 rs13254990 129,076,451 T GWAS-1 (0.43;0.33) 0.98 1.58 (1.31–1.91) 2.21 × 10-6
GWAS-2 (0.40;0.32) 0.98 1.44 (1.05–1.96) 0.021
Combined 1.54 (1.31–1.81) 1.33 × 10-7
I 2 = 0% P het = 0.60
6q15 BACH2 rs10806425 90,926,612 C GWAS-1 (0.68;0.58) 1 1.50 (1.25–1.80) 8.93 × 10-6
GWAS-2 (0.69;0.59) 1 1.53 (1.14–2.05) 0.0045
Combined 1.51 (1.30–1.77) 1.36 × 10-7
I 2 = 0% P het = 0.93
6p21.32 HLA-DRA rs2395192 32,447,644 C GWAS-1 (0.48;0.59) 0.96 1.56 (1.30–1.88) 1.65 × 10-6
GWAS-2 (0.52;0.60) 0.97 1.38 (1.03–1.84) 0.029
Combined 1.51 (1.29–1.76) 1.81 × 10-7
I 2 = 0% P het = 0.47
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Abbreviations: bp, base pair; OR, odds ratio; 95% CI, 95% confidence interval; Phet, P-value for heterogeneity; I2, proportion of the total variation due to heterogeneity.

RAF is risk allele frequency across all of the GWAS-1 and GWAS-2 datasets, respectively.

Following on from this we examined whether other reported risk loci for DLBCL influenced PCNSL risk. Respective association P-values for the 6p21.22-HLA (rs2523607) and 2p23.3 (rs79480871) risk SNPs were 0.023 and 0.14 (Supplementary Table 4).

HLA Alleles

Variation at HLA has been linked to risk of DLCBL and a number of other B-cell tumors.24–28 The strongest SNP association at 6p21 (HLA) for PCNSL was provided by rs2395192 (P = 1.81 × 10−7), which maps between HLA-DRA and HLA-DRB5 (Supplementary Fig. 5, Table 1). To obtain additional insight into plausible functional variants within the HLA region, we imputed the classical HLA alleles and amino acid residues using SNP2HLA.13 No imputed HLA alleles or amino acid positions reached genome-wide significance (Supplementary Fig. 5). The strongest coding changes within the HLA region were observed for the HLA class II alleles DRB1 Ser11Pro (AA_DRB1_11_32660115_SP, P = 3.35 × 10−6) and presence of the haplotype SRG (DRB1_13_32660109_SRG, P = 3.35 × 10−6) (Supplementary Table 5).

Functional Annotation of Risk Loci

To gain insight into the biological basis underlying associations at 6p25.3 and other promising risk loci, we first evaluated each of the risk SNPs as well as the correlated variants using the online resources HaploRegv4,29 RegulomeDB,30 and Fantom531 for evidence of functional effects (Supplementary Data 2). These data revealed regions of active chromatin at 6p25.3, 6q15, and 8q24 risk loci in B cells. To explore whether there was an association between SNP genotype and transcript levels we performed an eQTL analysis using the GTEx project,17 MuTHER,18 and blood eQTL data from Westra et al.32 We used SMR16 analysis to test for a concordance between signals from GWAS and cis eQTL for genes within 1 Mb of the sentinel and correlated SNPs (r2 > 0.8) at each locus (Supplementary Data 1) and derived bXY statistics, which estimate the effect of gene expression on PCNSL risk. After accounting for multiple testing we were unable to demonstrate any consistently significant eQTL for any of the risk loci examined. Chromatin looping interactions formed between enhancer elements, and the promoters of genes they regulate map within distinct chromosomal topological associating domains. To identify patterns of local chromatin, we analyzed promoter Capture Hi-C data on the LCL cell line GM12878 as a source of B-cell information.33 Looping chromatin interactions were shown between noncoding regions at 6p25.3 (rs11646171) with the interferon regulatory factor 4 (RF4) promoter (Fig. 2) and at 8q24.21 (rs13254990) with the MYC promoter, both genes with strong relevance to B-cell tumorigenesis.

Fig. 2.

Fig. 2

Fig. 2

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Regional plots of association results and recombination rates for new risk loci for primary cerebral nervous system lymphoma. Results shown for (A) 6p25 and (B) 3q21. Plots (drawn using visPig../../../../../../data_oupjournals/production/neuonc/noz088/from_client/accepted_manuscripts/Scales, - _ENREF_5857) show association results of both genotyped (triangles) and imputed (circles) SNPs in the GWAS samples and recombination rates. −log10P values (y-axis) of the SNPs are shown according to their chromosomal positions (x-axis). The sentinel SNP in each combined analysis is shown as a large circle or triangle and is labeled by its rsID. The color intensity of each symbol reflects the extent of linkage disequilibrium with the top genotyped SNP, white (r2 = 0) through to dark red (r2 = 1.0). Genetic recombination rates, estimated using 1000 Genomes Project samples, are shown with a light blue line. Physical positions are based on National Center for Biotechnology Information build 37 of the human genome. Also shown are the chromatin-state segmentation track (ChromHMM) for lymphoblastoid cells using data from the HapMap ENCODE Project, and the positions of genes and transcripts mapping to the region of association. The top track represents Capture Hi-C promoter contacts in GM12878 cells. The color intensity of each contact reflects the interaction score.

Using ChIP-Seq data on 82 TFs in GM12878 we examined for an overrepresentation of the binding of TFs at risk loci. Although not statistically significant, the strongest TF bindings were shown for TBL1XR1, which is mutated in 20% of PCNSL34 (Supplementary Fig. 6).

Discussion

To our knowledge this is the first study providing insight into the genetic predisposition to PCNSL. While PCNSL is a specific entity, it corresponds pathologically to DLBCL. Hence, it is perhaps not surprising that we identified associations in common with DLBCL24 at 6q25.3 and 8q24.21 for PCNSL. However, the absence of associations at the 8q24.21 (rs4733601) and 2p23.3 (rs79480871) risk loci suggests the existence of a distinct developmental pathway for PCNSL, possibly reflective of its etiology. Moreover, analysis of publicly accessible GWAS data on DLBCL (National Cancer Institute GWAS Stage 1) provides no support for an association between 3p22.1 (rs41289586) and DLBCL24 (Supplementary Table 6).

The 6p25.3 risk SNP rs116446171 (Fig. 2), which maps intergenic to exocyst complex component 2 (EXOC2) and IRF4, has been previously shown to influence the risk of DLBCL.24 EXOC2 is part of the multi-protein exocyst complex essential for polarized vesicle trafficking and the maintenance and intercellular transfer of viral proteins and virions.35 Furthermore, the RalB/EXOC2 effector complex is a component of the TBK1 (TANK binding kinase 1)-dependent innate signaling pathway.36 While thus far there is no evidence to implicate EXOC2 in lymphoma, the RalB/EXOC2 complex may contribute to tumor cell survival.36 In contrast, IRF4 has a well-established role in the development of many B-cell malignancies.37–39

The 3p22.1 risk SNP rs41289586 (Fig. 2) localizes to exon 6 of the anoctamin 10 gene (ANO10) and is responsible for the rare missense change (ANO10:c.788G>A, p.Arg263His). Although rs1052501 at 3p22.1 has been reported to be associated with risk of multiple myeloma,40 another B-cell malignancy, this SNP maps >1 Mb away from rs41289586 (pairwise r2 = 0.0002). Inherited defects in ANO10, which encodes a calcium-activated chloride channel transmembrane protein, are a cause of autosomal recessive spinocerebellar ataxia.41 While other anoctamins have been implicated in cancer,42 to date there is no evidence for the role of ANO10 in a B-cell malignancy. However, intriguingly, rs41289586 has been associated with regulation of macrophage response and associated with Borrelia seropositivity,43 implicating ANO10 in the innate immune defense.

In addition to the 6p25.3 and 3p22.1 risk loci, we identified promising associations (P<2 × 10−7) at 6q15 (rs10806425, P = 1.36 × 10−7) and 8q24.21 (rs13254990; P = 1.33 × 10−7) annotating genes with strong relevance to B-cell tumorigenesis (Table 1, Supplementary Fig. 4). Rs10806425 localizes to intron 1 of the gene encoding BACH2 (basic leucine zipper TF 2). Loss of heterozygosity of BACH2 has been reported at a frequency of 20% in B-cell lymphoma.44 DLBCL patients with higher BACH2 expression tend to have a better prognosis.45BACH2 is a key regulator of the pre‒B-cell receptor checkpoint as well as a tumor suppressor in pre-B acute lymphoblastic leukemia.46 One mechanism of BACH2 downregulation in leukemia is the loss of the transcription factor PAX5, which is intriguingly, commonly mutated in both PCNSL47 and B-cell acute lymphoblastic leukemia.46

The 8q24 SNP rs13254990 localizes to intron 4 of PVT1, a noncoding RNA affecting the activation of MYC. Two independent risk loci at 8q24 defined by SNPs rs13255592 and rs4733601 have previously been shown to influence DLBCL.24 Rs13255592 also localizes within intron 4 of PVT1 and is highly correlated with rs13254990 (r2 = 0.98, P = 3.81 × 10−7). However, no association between rs4733601, which maps approximately 1.9 Mb telomeric to PVT1, and PCNSL risk was shown (r2 = 4.21 × 10−5, P = 0.99; Supplementary Table 4). The 8q24.21 128-130Mb genomic interval harbors multiple independent risk loci with different tumor specificities (Supplementary Table 7). The strongest additional association for PCNSL being shown by the Hodgkin lymphoma risk SNP rs2019960 (P = 4.1 × 10−5) raising the possibility of an additional risk locus for the disease at 8q24.21.28

Although in part speculative, the 6q25.3 association implicates IRF4 in the development of PCNSL. Through interaction with transcription factors including PU.1, IRF4 controls the termination of pre–B-cell receptor signaling and promotes the differentiation of pro-B cells to small B cells.48 Furthermore, via BLIMP1 and BCL6, IRF4 controls the transition of memory B cells.49 The observation that PVT1 rearrangement occurs frequently in highly aggressive B-cell lymphomas harboring an 8q24 abnormality suggests that germline variation in this region may influence PCNSL risk.50–52 The 6q15 association implicates BACH2 in the development of PCNSL. BACH2 is an attractive candidate a priori for having a role in PCNSL development being regulator of the antibody response mediating effects through BLIMP1, XBP1, LRF4, and PAX5.53. Moreover, BACH2 regulates the activity of the tumor suppressor c-Rel in lymphoma development.54 Collectively these data are consistent with aberrant B-cell developmental pathways being central for predisposition to PCNSL. The finding of a relationship between ANO10:c.788G>A, p.Arg263His with PCNSL, but not classical DLBCL highlights differences in biological etiology with this lymphoma. Intriguingly, this variant has previously shown to influence macrophage response,43 thereby implicating ANO10 in innate immune defense with the development of PCNSL. While not statistically significant, the HLA-DRA and HLA-DRB1 associations are also of relevance as these alleles have previously been shown to influence the human reaction to viral load and EBV infection, respectively.55 The linkage of these genes to the development of PCNSL is therefore entirely consistent with an infective basis to this B-cell malignancy even though none of the patients we studied were immunocompromised.

Inevitably constrained by the sheer rarity of PCNSL, we acknowledge that a limitation of our study has been an inability to replicate study findings in additional series. Another limitation is the absence of case-case study with other DLBCL. This could be performed in future analyses. Our findings are, however, based on a meta-analysis of 2 series cohorts of PCNSL. Moreover, despite the rarity of PCNSL, ascertaining patients through the LOC Network has provided us with greater power to detect associations than the smaller studies of other rare lymphomas.56

In summary, our findings represent an important step in defining the contribution of common genetic variation to the risk of developing PCNSL. Our observations are notable, since the associations highlighted define regions of the genome harboring plausible candidate genes for further investigation. Given the relatively modest size of our analysis, it is highly probable that further studies will discover additional common susceptibility loci. These coupled with functional analyses should provide for an explanation of the biological underpinnings of PCNSL.

Data Availability

Genotype data that support the findings of this study have been deposited in the database of the European Genome-phenome Archive (EGA) with accessions code PRJEB21814. NCI Non-Hodgkin Lymphoma GWAS data was obtained through dbGAP (phs000801.v2.p1). The remaining data are contained within the paper, and Supplementary files are available from the authors upon request.

Funding

The primary source of funding was provided by la Ligue Nationale Contre le Cancer-RE 2015 (K.H.X), French National Institute of Cancer (InCa) LOC Network (K.H.X and C.S). A.A has also been granted with a “poste d’accueil AP-HP-CEA.” K.H.X. and A.A. received a grant from DGOS and INCa, PRT-K 2016. K.L. is supported by l’Association pour la Recherche sur les Tumeurs Cérébrales (ARTC) and Institute CARNOT – Institut du Cerveau et de la Moelle Epinière (ICM). Finally, we also acknowledge support from Cancer Research UK and Bloodwise. A.S. is supported by a clinical fellowship from Cancer Research UK.

Supplementary Material

noz088_Suppl_Supplementary_Data_1
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noz088_Suppl_Supplementary_Data_2
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noz088_Suppl_Supplementary_information
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Acknowledgments

We are grateful to all investigators and all the patients and individuals for their participation. Samples from AP-HM were retrieved from the AP-HM tumor bank, authorization number 2013-1786. We also thank the clinicians, other hospital staff, and study staff who contributed to the blood sample and data collection for this study and OncoNeuroTek, which provided and prepared DNA samples. Genotypes from NCI Non-Hodgkin Lymphoma GWAS were accessed through dbGaP accession phs000801.v2.p1.

Contributor Information

LOC Network:

Marie-Pierre Moles-Moreau, Rémy Gressin, Vincent Delwail, Franck Morschhauser, Philippe Agapé, Arnaud Jaccard, Hervé Ghesquieres, Adrian Tempescul, Emmanuel Gyan, Jean-Pierre Marolleau, Roch Houot, Luc Fornecker, Anna-Luisa Di Stefano, Inès Detrait, Amithys Rahimian, Mark Lathrop, Diane Genet, Frédéric Davi, Nathalie Cassoux, Valérie Touitou, Sylvain Choquet, Anne Vital, Marc Polivka, Dominique Figarella-Branger, Alexandra Benouaich-Amiel, Chantal Campello, Frédéric Charlotte, Nadine Martin-Duverneuil, Loïc Feuvret, Aurélie Kas, Soledad Navarro, Chiara Villa, Franck Bielle, Fabrice Chretien, Marie Christine Tortel, Guillaume Gauchotte, Emmanuelle Uro-Coste, Catherine Godfrain, Valérie Rigau, Myrto Costopoulos, Magalie Le Garff-Tavernier, David Meyronnet, Audrey Rousseau, Clovis Adam, Thierry Lamy, Cécile Chabrot, Eileen M Boyle, Marie Blonski, and Anna Schmitt

LOC Network

Marie-Pierre Moles-Moreau,1 Rémy Gressin,2 Vincent Delwail,3 Franck Morschhauser,4 Philippe Agapé,5 Arnaud Jaccard,6 Hervé Ghesquieres,7 Adrian Tempescul,8 Emmanuel Gyan,9 Jean-Pierre Marolleau,10 Roch Houot,11 Luc Fornecker,12 Anna-Luisa Di Stefano,13 Inès Detrait,14 Amithys Rahimian,15 Mark Lathrop,16 Diane Genet,17 Frédéric Davi,18 Nathalie Cassoux,19 Valérie Touitou,20 Sylvain Choquet,21 Anne Vital,22 Marc Polivka,23 Dominique Figarella-Branger,24,25 Alexandra Benouaich-Amiel,26 Chantal Campello,27 Frédéric Charlotte,28 Nadine Martin-Duverneuil,29 Loïc Feuvret,30 Aurélie Kas,31 Soledad Navarro,32 Chiara Villa,33 Franck Bielle,34 Fabrice Chretien,35 Marie Christine Tortel,36 Guillaume Gauchotte,37 Emmanuelle Uro-Coste,38 Catherine Godfrain,39 Valérie Rigau,40 Myrto Costopoulos,18 Magalie Le Garff-Tavernier,18 David Meyronnet,41 Audrey Rousseau,42 Clovis Adam,43 Thierry Lamy,44 Cécile Chabrot,45 Eileen M. Boyle,46 Marie Blonski,47 Anna Schmitt.48

1Department of Hematology, Angers University Hospital, Angers, 49033, France.

2Department of Hematology CHU Grenoble Michallon 38043 Grenoble Cedex 02 – France

3Service d’Oncologie Hématologique et de Thérapie Cellulaire, CHU de Poitiers, INSERM, CIC 1402, Poitiers, Centre d’Investigation Clinique, Université de Poitiers, Poitiers, France

4Department of Hematology, CHRU Lille, Lille, 59037, France

5Institut de Cancérologie – 44800 Saint Herblain – France

6Department of Hematology CHU Dupuytren 87042 Limoges- France

7Department of Hematology, University Hospital of Lyon, 69002, Lyon, France

8Department of Hematology CHU Morvan 29609 Brest Cedex – France

9Department of Hematology CHU Bretonneau 34044 Tours, France

10Department of Hematology, University Hospital of Amiens, 80054, Amiens, France .

11CHU Rennes, Service Hématologie Clinique, F-35033 Rennes, France

12Department of Hematology CHU Strasbourg 67000 Strasbourg – France

13Department of Neurology Hôpital Foch 92151 Suresnes – France

14Inserm, U 1127, ICM, F-75013 Paris, France; CNRS, UMR 7225, ICM, F-75013 Paris, France; Institut du Cerveau et de la Moelle épinière ICM, Paris 75013, France; Sorbonne Universités, UPMC Université Paris 06, UMR S 1127, F-75013 Paris, France; AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, 75013 Paris, France.

15OncoNeuroTek, Institut du Cerveau et de la Moelle épinière, ICM, Paris, F-75013, France.

16McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada, H3A 0G1.

17AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de neurologie 2-Mazarin, 75013 Paris, France. Universités, UPMC Université Paris 06, UMR S 1127, F-75013 Paris, France

18Department of Biological Hematology, AP-HP, Groupe Hospitalier Pitié-Salpêtrière, 75013, Paris, France.

19Department of Oncological Ophtalmology, Institut Curie, Paris, France.

20AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Department of Ophthalmology, Paris, 75013, France.

21AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Department of Hematology, Paris, 75013, France

22CNRS, Institut des Maladies Neurodégénératives, UMR 5293, F-33000 Bordeaux, France;Department of Pathology, Bordeaux University Hospital, Bordeaux, France

23Department of Pathology, CHU Paris-GH St-Louis Lariboisière F.Widal - Hôpital Lariboisière, 75010, Paris, France.

24Department of pathology and Neuropathology, Hôpital de la Timone, Aix-Marseille Univ, AP-HM, Marseille, 13005, France.

25AMU, CRO2, 13005, Marseille, 13005, France.

26Department of Neurology, CHU Toulouse, 31059 Toulouse, France.

27Service de Neuro-Oncologie, CHU Timone,13005 Marseille – France.

28Department of Pathology, CHU Pitié-Salpêtrière, 75013 Paris, France.

29Department of Neuroradiology, CHU Pitié-Salpêtrière, 75013 Paris, France.

30Department of Radiotherapy, CHU Pitié-Salpêtrière, 75013 Paris, France.

31Department of Nuclear Medicine, CHU Pitié-Salpêtrière, 75013 Paris, France.

32Department of Neurosurgery, CHU Pitié-Salpêtrière, 75013 Paris, France.

33Department of Pathology, Hôpital Foch, 92151 Suresnes – France.

34Inserm, U 1127, ICM, F-75013 Paris, France; CNRS, UMR 7225, ICM, F-75013 Paris, France; Institut du Cerveau et de la Moelle épinière ICM, Paris 75013, France; Sorbonne Universités, UPMC Université Paris 06, UMR S 1127, F-75013 Paris, France; AP-HP, Groupe Hospitalier Pitié-Salpêtrière, Service de Neuropathologie, 75013 Paris, France.

35Department of Neuropathology, Centre Hospitalier Sainte Anne, Paris, France.

36Department of Pathology, Hôpitaux Civils de Colmar, 68024, Colmar Cedex, France.

37Department of Pathology, Nancy University Hospital, Vandoeuvre-lès-Nancy, France

38Department of Pathology, CLCC Institut Claudius Regaud, 31059 Toulouse, France.

39Department of Pathology, CHU Clermont-Ferrand, 63000 Clermont-Ferrand, France.

40Department of Pathology, CHU Montpellier, 34000 Montpellier, France.

41Department of Pathology, University Hospital of Lyon, 69002, Lyon, France.

42Department of Pathology, CHU Angers, 49933 Angers – France.

43Department of Pathology, CHU Kremlin Bicêtre, 94270 Le Kremlin Bicêtre.

44CHU Rennes, Rennes, 35033, France.

45CHU Clermont Ferrand, Clermont Ferrand, 63000, France.

46Department of Haematology, Lille University Hospital, Lille, 59037, France.

47CHU Nancy. Nancy, 54500, France.

48CHU Bordeaux. Bordeaux, 33000, France.

Conflict of interest statement. The remaining authors declare no competing financial interests.

Authorship statement. A.A. and K.H.X. developed the project and provided overall project management; K.L., M.D., R.S.H., K.H.X., and A.A. drafted the manuscript. K.L., A.S., P.J.L., and M.W. performed bioinformatic and statistical analyses. Patient samples and phenotype data were provided by C.D., D.G., K.H.X., C.S., and other members of the LOC Network. M.D., I.D., L.R.P., A.R., D.G. performed project management and supervised genotyping; M.D., I.D., A.R., M.B., and A.H. performed sequencing and genotyping. A.A., K.H.X., M.D., A.R., L.R.P., and P.B. supervised laboratory management and oversaw genotyping of cases; D.G., M.D., I.D., L.R.P. performed sample management of cases. All authors reviewed and approved the manuscript prior to submission.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

noz088_Suppl_Supplementary_Data_1
Click here for additional data file. (14.1KB, xlsx)
noz088_Suppl_Supplementary_Data_2
Click here for additional data file. (15.7KB, xlsx)
noz088_Suppl_Supplementary_information
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Data Availability Statement

Genotype data that support the findings of this study have been deposited in the database of the European Genome-phenome Archive (EGA) with accessions code PRJEB21814. NCI Non-Hodgkin Lymphoma GWAS data was obtained through dbGAP (phs000801.v2.p1). The remaining data are contained within the paper, and Supplementary files are available from the authors upon request.

Articles from Neuro-Oncology are provided here courtesy of Society for Neuro-Oncology and Oxford University Press

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