Histone methylation-modifiers, such as EZH2 and KMT2D, are recurrently altered in B-cell lymphomas. To comprehensively describe the landscape of alterations affecting genes encoding histone methylation-modifiers in lymphomagenesis we investigated whole genome and transcriptome data of 186 mature B-cell lymphomas sequenced in the ICGC MMML-Seq project. Besides confirming common alterations of KMT2D (47% of cases), EZH2 (17%), SETD1B (5%), PRDM9 (4%), KMT2C (4%), and SETD2 (4%), also identified by prior exome or RNA-sequencing studies, we here found recurrent alterations to KDM4C in chromosome 9p24, encoding a histone demethylase. Focal structural variation was the main mechanism of KDM4C alterations, and was independent from 9p24 amplification. We also identified KDM4C alterations in lymphoma cell lines including a focal homozygous deletion in a classical Hodgkin lymphoma cell line. By integrating RNA-sequencing and genome sequencing data we predict that KDM4C structural variants result in loss-offunction. By functional reconstitution studies in cell lines, we provide evidence that KDM4C can act as a tumor suppressor. Thus, we show that identification of structural variants in whole genome sequencing data adds to the comprehensive description of the mutational landscape of lymphomas and, moreover, establish KDM4C as a putative tumor suppressive gene recurrently altered in subsets of B-cell derived lymphomas.
The majority of mature B-cell malignancies originates from the germinal center (GC) B cell or the post-GC stage.1 Historically, Hodgkin lymphoma (HL) and non-Hodgkin lymphomas (NHL) are distinguished. The neoplastic Hodgkin-/Reed-Sternberg cells in classical HL (cHL) are supposed to be derived from pre-apoptotic GC B cell.1 The most prevalent types of GC-derived NHL are diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL).2 DLBCL is an aggressive disease, composed of various subtypes including the gene expression-based GC B-cell like (GCB) and activated B-cell like (ABC) types,2,3 or recently genomically defined groups.4–10 FL is a more indolent disease which occasionally transforms into DLBCL.11
The immanent genomic instability of B cells during the GC reaction, which is required for the formation of antibody diversity, is assumed to be causative for malignant transformation of GC or post-GC B cells.12 The GC reaction requires a tightly controlled balance between proliferation and growth arrest, and between full cellular activation or a rather resting stage. Epigenetic modifiers are key regulators of these “on-off“ stages.12 In line, they are common targets of genomic alterations in GC-derived B-cell lymphomas,11 including the genes encoding the histone methyltransferases KMT2D, KDMT2C and EZH2, the histone acetyltransferases CREBBP and EP300,6,13–15 and the chromatin remodelers including members of the SWI/SNF complex.16 A series of recent studies investigated genomic alteration frequencies in oncogenic drivers including epigenetic modifiers in huge series of patients with DLBCL, but also FL. These studies, investigating in total more than 1,800 DLBCL (Chapuy et al., n=304;5 Schmitz et al., n=574;6 Reddy et al., n= 1,00114), found aberrations in KMT2D (24-31%), CREBBP (11-17%), and EP300 (6-8%).5,6,14 Consequently, alterations in these histone modifiers contribute to the definition of genetic subgroups of DLBCL, such as cluster 3 in Chapuy et al.,5 or the EZB group in Schmitz et al.6 and Wright et al.,4 the BCL2 group in Lacy et al.8 and the EZH2 group in Hübschmann et al.7 Most of these studies characterized the genomic alteration landscape predominantly by exome or otherwise targeted sequencing, in part combined with transcriptome sequencing.10 Methodically, however, although this approach detects single nucleotide variants (SNV), small insertions and deletions (indels) and gross imbalances, it has an inherent weakness in the detection of some structural variants such as intragenic deletions, chromosomal translocations, and inversions.17
To overcome this shortcoming, we here mined data from whole genome sequencing (WGS) of 186 GC-derived B-cell lymphomas and 183 corresponding germline DNA samples generated by us in the framework of the International Cancer Genome Consortium (ICGC) Molecular Mechanisms in Malignant Lymphoma by Sequencing (MMML-Seq) project (https://dcc.icgc.org).7,18–20 Given the increasing pathogenic, diagnostic and therapeutic importance of altered histone methylation in B-cell lymphomas,11,13,21 we focused here on 79 genes encoding histone methylation modifiers.
Whole genome and transcriptome sequencing data
We mined WGS data and available RNA-sequencing data of 186 GC-derived B-cell lymphomas from the ICGC MMML-Seq network7 (Online Supplementary Methods). The ICGC MMML-Seq study has been approved by the Institutional Review Board of the Medical Faculties of the University of Kiel (A150/10) and Ulm (349/11), and of the recruiting centers. Methods and procedures used in the ICGC MMML-Seq project have been detailed in various publications7,18,20,22,23 of the network. Sequencing data are available from the European Genome-phenome Archive (EGA) (accession number EGAS00001002199). The genomic status of 79 genes encoding histone methylation modifiers (selected based on http://crdd.osdd.net/raghava/dbem/index.php, accessed 01/03/2018; Online Supplementary Table S1) was investigated.
Cell lines and cell line data
Nineteen B- and T-cell NHL and four cHL-derived cell lines were used in the study (Online Supplementary Table S2). The identity of the cell lines used was confirmed by short tandem repeat analysis using the StemElite ID System (Promega). Copy number data24 and/or exome data25 from previously published studies on the four cHL cell lines herein analyzed and two additional cHL cell lines were included as well as previously reported WGS data from the cHL cell line L1236.26
The computational approaches for the analysis of WGS and transcriptome data were recently described20 (Online Supplementary Materials and Methods). Briefly, WGS data were analyzed using the DKFZ core variant calling workflows of the ICGC Pan-Cancer Analysis of Whole Genomes (PCAWG) project. Allele-specific copy-number alterations were analyzed using ACE-seq and structural variants were called using the SOPHIA algorithm20 and DELLY v0.5.9.27,28 To determine the incidence of structural variants in the PCWAG, filtered structural variant calls were generated by SOPHIA for the PCAWG cohorts processed with the same tools and settings as with the lymphoma cohort used in this study.
Transcriptome data were mapped with segemehl 0.2.0.29 Gene expression values were counted by the RNAcounter 1.5.2, using the “--nh” option and counting only exonic reads (-t exon).
Mechismo (http://mechismo.russelllab.org/) was used to predict the potential effect of structural variants and SNV detected by WGS.
Verification of KDM4C alterations by polymerase chain reaction-based Sanger sequencing and fluorescence in situ hybridization
Verification analyses on a DNA level included polymerase chain reaction (PCR) amplification with subsequent Sanger sequencing and fluorescent in situ hybridization (FISH) (Online Supplementary Materials and Methods). Alternative KDM4C fusion transcripts were validated using specific primers to amplify breakpoint fusion sequences from tumor RNA-derived cDNA (Online Supplementary Materials and Methods) and Sanger sequencing. Verifications using FISH were done using two home-made FISH-probes, i.e., using locus-specific and break-apart KDM4C probes (Online Supplementary Material and Methods). Digital image acquisition, processing, and evaluation of FISH assays were performed using ISIS digital image analysis version 5.0 (MetaSystems, Altussheim, Germany). The same FISH approaches were used to evaluate the genomic status of KDM4C in lymphoma cell lines.
For functional analyses, the respective cells were transfected with doxycycline-inducible KDM4C expression constructs or transduced with KDM4C-encoding lentiviruses (Online Supplementary Materials and Methods). For generation of KDM4C-inducible cells, cells were electroporated in OPTI-MEM I using Gene-Pulser II (Bio-Rad). Twenty-four hours after transfection, hygromycin B (Sigma-Aldrich, Taufkirchen, Germany) was added. After 21–28 days of culture in the presence of hygromycin B, cells were suitable for functional assays. Where indicated, green fluorescent protein-positive (GFP+) cells were enriched 72 h after doxycycline-induction using a FACS Aria. Production of lentiviruses and lentiviral transduction of cells was performed as described previously31 (Online Supplementary Materials and Methods). KDM4C protein expression was assessed by western blot, and immunohistochemistry using a rabbit polyclonal anti-KDM4C antibody raised against amino acids 1007-1056.
Aberrations in genes encoding histone methylation modifiers in the ICGC MMML-Seq cohort determined by whole genome sequencing
We analyzed WGS data of 186 GC-derived B-cell lymphomas7 for somatic aberrations potentially perturbing gene function, including SNV, indels, structural variants and focal copy number aberrations affecting at least two cases in 79 genes encoding histone methylation modifiers. The genes most commonly affected were KMT2D (47% of cases), EZH2 (17%), SETD1B (5%), KDM4C (4%), PRDM9 (4%), KMT2C (4%), and SETD2 (4%) (Figure 1A).
The genes identified and their frequency of alteration in our cohort are grossly in line with previous analyses of DLBCL cohorts.5,6,14 However, alterations of KDM4C have not been emphasized as a recurrent finding in previous whole exome studies of GC B-cell lymphomas. KDM4C (also called JMJD2C) encodes a member of the Jumonji family of demethylases, which activate genes by removing methyl groups from histones H3K9 and H3K36.32,33
Genomic alterations affecting the KDM4C gene locus
We detected aberrations of KDM4C in 7/186 (3.7%) of all cases, and in 4/75 (5.3%) of bona fide DLBCL. In detail, we detected a total of seven focal aberrations in KDM4C, including four heterozygous deletions, one duplication, one translocation and one non-synonymous SNV each, affecting seven patients (Figure 1B; Online Supplementary Table S2). The minimum read number supporting KDM4C alterations was four reads. The cancer cell fraction or the proportion of cancer cells with a KDM4C alteration among all cancer cells suggests that these alterations are clonal in GC B-cell lymphomas (Online Supplementary Table S2). We verified all KDM4C aberrations by FISH using homemade FISH assays and/or PCR and Sanger sequencing (Online Supplementary Table S2).
Pathological and genetic features of the lymphomas with KDM4C gene alterations
The cases displaying KDM4C alterations included four of 75 DLBCL (5.3%), one of 17 FL-DLBCL (5.9%), one of four (25%) large B-cell lymphomas with IRF4-rearrangement, and the one primary mediastinal B-cell lymphoma (Table 1). We explored whether cases with KDM4C aberrations diagnosed as DLBCL (n=4) cluster in a specific genomic subgroup according to the non-negative matrix factorization (NMF) clustering described previously by our group,7 which resembles features of genetic DLBCL subgroups previously also described by others4,5 (Table 1). Using the described four-cluster classifier limited to DLBCL cases, we identified two MYD88-like and two TP53-like cases. Using the nine-subcluster classifier established for the whole ICGC MMML-Seq FL and DLBCL cohort, we assigned two cases to the PIM1-like cluster, two cases to the PAX5-like cluster and one case to the MYD88-like cluster (2 cases other than DLBCL or FL not included in the previous NMF analyses, i.e. 1 primary mediastinal B-cell lymphoma and 1 IRF4 rearrangement-positive large B-cell lymphoma). Furthermore, we explored the distribution of KDM4C-altered cases diagnosed as DLBCL (n=4) among transcriptional subgroups based on the cell-of-origin signature. Two cases were classified as GCB-like and two cases as ABC-like lymphomas. Thereafter, we extended the analysis to the whole cohort and we observed that four of seven cases with KDM4C aberrations displayed a GCB signature and two of seven cases an ABC signature (1 case assigned to Type III/unclassified). Overall, KDM4C aberrations were not significantly enriched in any specific genetic or transcriptional subtype of the analyzed lymphomas.
In addition, we explored whether KDM4C aberrations were associated with the 9p24.1 amplification, as described previously.34 In five out of seven cases with KDM4C focal aberrations, we did not detect 9p24.1 gain (Online Supplementary Figures S1 and S2). This indicates that the vast majority of focal KDM4C aberrations occurred independently of 9p24 amplifications. We also explored aneuploidy of chromosome 9 and the general genomic ploidy of the KDM4C-altered cases. We did not observe a significant increase of chromosome 9 gains (Online Supplementary Figure S2) or polyploid genome contents (Online Supplementary Figure S3) in cases with KDM4C aberrations as compared to cases without such aberrations. Furthermore, we investigated whether the KDM4C aberrations occurred preferentially in lymphomas with highly rearranged genomes that were predisposed to carry these events by chance. We identified a mean of 73.85 structural variants per case (range, 34-144) in lymphomas with KDM4C aberration compared to a mean of 83.24 structural variants per case (range, 5-1696) in lymphomas lacking KDM4C aberrations. The mean number of structural variants was not significantly different between the two groups of patients (Wilcoxon test, P=0.125) (Online Supplementary Figure S4).
KDM4C alterations as potential tumor drivers
KDM4C is located in a early replicating genomic region in B cells (at the border of a late replicating region) based on the ENCODE Repli-seq data from different lymphoblastoid cell lines35 (Online Supplementary Figure S5). This fact suggests that mutation of the gene is not a passenger effect caused by late replication. Furthermore, we examined whether the breakpoints were located in the RGYW/WRCY motif typically associated with the activation-induced cytidine deaminase (AID) enzyme36 that is active in GC B cells. However, none of the breakpoints directly hits these motifs. These facts suggest that mutation of the KDM4C gene in lymphoma is not a bystander effect caused by late replication or aberrant somatic hypermutation.
Next we analyzed the incidence of structural variants affecting the KDM4C locus in the 45 tumor datasets with WGS available included in the PCWAG.19 We identified 14 cohorts with an incidence of KDM4C alterations equal to or higher than that reported herein (Online Supplementary Figure S6). Importantly, the cohort with the highest incidence of structural variant breakpoints affecting KDM4C was the DLBCL-US cohort, corroborating our findings and highlighting the relevance of KDM4C alterations in this subgroup of B-cell lymphomas. Other types of cancers harboring structural variants in KDM4C were renal, head and neck, ovarian, hepatocarcinoma, esophageal, bladder, prostate, osteosarcoma, and gastric cancer, suggesting that KDM4C alterations might not be restricted to lymphomas. A role of KDM4C in these tumors has been discussed previously.37–44
Molecular consequence of KDM4C aberrations
The KDM4C protein consists of an N-terminal catalytic domain (JmjN and JmjC) followed by three zinc-fingers (PHD or C2H2) and two C-terminal Tudor domains.45 By integrating RNA-sequencing and WGS data in the six cases with focal structural variants and available transcriptome data (4 focal heterozygous deletions, 1 translocation, and 1 duplication), we detected alternative KDM4C transcripts in five of them, which were verified by reverse transcriptase PCR and sequencing (Online Supplementary Table S2). In silico analyses using Mechismo (http://mechismo.russelllab.org/) predicted that these alternative transcripts would result in altered proteins lacking the catalytic or recognition (epigenetic readers) domains (Figure 2A). In silico modeling of the non-synonymous somatic SNV (c.G80A, p.R27Q) detected in case 4177842, which lies in the JmJN domain, suggests a possible functional consequence on protein function (Online Supplementary Figure S1).
Lack of epigenetic alterations at the KDM4C locus due to KDM4C mutation
As to the recently proposed role of a circular RNA (circRNA) derived from the KDM4C locus (circKDM4C) in repression of proliferation and metastasis in breast cancer,46 we also explored expression of circKDM4C in the RNA-sequencing data of our cohort. We identified circKDM4C expression in three cases (3/180, 1.7%), but none of them showed an alteration of the KDM4C locus (data not shown). In addition, we investigated the epigenetic architecture at the KDM4C locus by mining previously published whole genome bisulfite sequencing and array based DNA methylation, as well as chromatin state data.22 The promoter and transcription start site region showed strong hypomethylation in all subtypes of lymphomas and B-cell controls (Online Supplementary Figure S7). We did not observe any differential DNA methylation with regard to expression or alteration of the KDM4C locus with the notable exception of CpG cg13880654 associated with an intronic enhancer site which was hypermethylated in the majority of lymphoma cell lines other than Burkitt lymphoma cell lines. Together, the pattern of alterations strongly suggests KDM4C protein loss-of-function as a common effect of the KDM4C gene aberrations (Figure 2A, Online Supplementary Figure S1), indicative of a tumor suppressor function of KDM4C protein.
Using the RNA-sequencing data, we investigated KDM4C transcript expression in the seven cases with KDM4C alterations compared to 173 GC B-cell lymphomas without KDM4C aberrations. To reduce confounders, we performed these analyses for each lymphoma subtype separately. No statistically significant difference in KDM4C transcript expression was observed between samples with altered as compared to wildtype KDM4C in FL-DLBCL (P=0.4706), in DLBCL (P=0.2095), or in large B-cell lymphomas with IRF4 breaks (P=1), although this analysis was clearly limited by the low number of KDM4C-altered cases per group (Online Supplementary Figure S8).
Next, we performed differential expression analyses of RNA-sequencing data comparing cases with and without KDM4C alterations. There were only two morphological subgroups for which sufficient numbers of cases with and without KDM4C alterations with RNA-sequencing data were available, namely DLBCL and FL-DLBCL. We performed the differential expression analyses in both of these groups separately. None of the previously described target genes of KDM4C (Online Supplementary Table S3) was among the 107 differentially expressed genes between KDM4C mutated and wildtype cases in the DLBCL group or the 19 differentially expressed genes in the FL-DLBCL group (Online Supplementary Table S4). Notably, there appears to be a small but significant difference in gene expression between the KDM4C-mutated and KDM4C-wildtype cases in the DLBCL group. In contrast, the results in the FL-DLBCL group are in line with common fluctuations that may or may not be due to KDM4C status, as this is only one against all other comparisons, and comparing a random FL-DLBCL case against all others often shows even bigger differences. We analyzed the genes differentially expressed between mutated and unmutated KDM4C in DLBCL using string-db.47 While there are more interactions than randomly expected between the 64 proteins known to string-db (14 vs. 7; P<0001), the only enrichment found was in signal peptide domain from UniProt keywords (25 of 64, false discovery rate=0.0135). Next, we intersected the differentially expressed genes based on KDM4C mutation status in the DLBCL group with genes differentially expressed between subtypes of DLBCL (ABC, GCB and Type III). However, no significant enrichment in the number of overlapping genes was detected.
Finally, we examined the expression levels of H3 in the cases harboring KDM4C aberrations as compared to the cases lacking KDM4C alterations, but no significantly differential expression on H3 was detected (P>0.1).
Functional analysis of KDM4C
We explored public data and screened a total of 23 lymphoma cell lines for the presence of inactivating KDM4C alterations using the same approach employed to validate the structural variants described above and combined this with published genomic data from two additional cHL cell lines (Online Supplementary Table S2). We detected KDM4C deletions in the mycosis fungoides-derived cell line MyLa, and the cHL-derived cell line L1236 (Online Supplementary Table S2). More specifically, in WGS data of L123626 we identified a KDM4C deletion of approximately 60 kb (chr9: 6,775,810-6,836,328 bp [hg19], comprising exons 2, 3 and 4) at one allele, with an approximately 32 kb internal deletion of the second allele (chr9: 6,795,791-6,828,233 bp [hg19], affecting exons 3 and 4), resulting in homozygous loss of exons 3 and 4 of KDM4C (Figure 1B). Whereas the larger heterozygous deletion ablates the canonical translation initiation codon, the 32 kb deletion is predicted to encode an (if translated) non-functional protein in L1236 lacking the N-terminus with the catalytic JmjN domain (Figure 2A). The KDM4C heterozygous deletion in My-La cells is in agreement with the conventional cytogenetic analysis describing a del(9)(p21).48 In addition, SUP-HD1 and KARPAS-422 cell lines carry the SNV c.G1713A, p.W571*, and c.C2498T, p.P833L, respectively (Figure 2A, Online Supplementary Table S2) reported in the COSMIC cell lines database (cancer.sanger.ac.uk/cell_lines), which we validated by PCR and Sanger sequencing and which are predicted as pathogenic variants by FATHMM. Evolutionary and protein structure predictions also suggest that p.P833L is a loss-of-function variant (Online Supplementary Figure S1). Thus, remarkably, with L1236 and SUP-HD1 two out of six bona fide cHL cell lines show potential inactivating changes in the KDM4C gene suggesting a tumor suppressive role in both GC B-cell lymphomas and cHL.
In agreement with the genomic data, KDM4C immunoblotting of the various cell lines revealed a complete loss of KDM4C protein expression in L1236 cells, and a strong reduction in My-La, but also in the cell lines Se-Ax and SUDHL-1 using a homemade rabbit polyclonal anti-KDM4C antibody (against amino acids 1007-1056) (Figure 2B). To address the functional consequences of KDM4C deletions, we constructed an episomally replicating vector for doxycycline-inducible KDM4C re-expression in L1236 cells as well as KDM4C lentiviruses for re-expression in SU-DHL-1 cells (Figure 2C). In both cell lines, KDM4C re-expression resulted in a loss of KDM4C-expressing cells over time, in agreement with a tumor suppressor function in these cells. Such an effect was not observed in either KARPAS-422 (p.P833L) or in NAMALWA (no KDM4C aberration) (Figure 2D).
In addition, we aimed to investigate the expression of KDM4C by immunohistochemistry in primary tissues as well as cell lines using the anti-KDM4C antibody used for the immunoblotting technique. We were able to prove the lack of expression of KDM4C protein in L1236 and to specifically detect ectopically expressed KDM4C in formalinfixed and paraffin-embedded transfected HEK293 cells (data not shown). However, this as well as several commercially available anti-KDM4C antibodies failed in our hands to reliably quantify KDM4C protein expression in primary tissues (data not shown).
Here, we analyzed WGS data of 186 GC B-cell lymphomas to investigate somatic aberrations, including SNV, indels, structural variants, and focal copy number aberrations in 79 genes encoding histone methylation regulators, to identity epigenetic modifiers potentially involved in GC B-cell lymphomagenesis.6,11–16 We identified KDM4C, encoding a histone demethylase, as being recurrently altered in B-cell lymphomas (7/186, 4%).
Integrating RNA-sequencing and WGS data in the six cases with focal structural variants, we detected alternative KDM4C transcripts in five of them. In silico analyses using Mechismo predicted that these alternative transcripts result in altered proteins lacking the catalytic or recognition (epigenetic readers) domains, suggesting a loss of function. In contrast, KDM4C has been previously described as an oncogene in lymphomas, which is activated by large chromosome 9p gains. These gains mostly derive from co-amplification with JAK2, CD274, and PDCD1LG2, recurrently found in cHL and primary mediastinal B-cell lymphoma.34 Nonetheless, recent studies in these lymphoma subtypes refined the minimally gained region and point to CD274 and PDCD1LG2 as main targets, whereas KDM4C is not consistently gained (Online Supplementary Figure S9). We here detected KDM4C to be altered due to focal structural variants (6/7) rather than gross imbalances. Moreover, we observed that the vast majority of focal KDM4C aberrations occurred independently of 9p24 amplifications. In brief, KDM4C might belong to the increasing list of genes with oncogenic and tumor suppressive function depending on the cellular context and the type of aberration. Examples from hematologic neoplasms include EZH2 and the CEBP gene family.49-51
In addition to genomic mutations, we explored different other layers that could contribute to dysregulated KDM4C in GC B-cell lymphomas. We used previously published data derived from whole genome bisulfite sequencing, array-based DNA methylation, and chromatin state data22 to investigate epigenetic alterations at the KDM4C locus. However, we did not find evidence for epigenetic inactivation of the KDM4C locus. Furthermore, we investigated the role of expression of circKDM4C, a circRNA from the KDM4C locus recently described to be of pathogenic relevance in solid tumors.46,52 Although we detected circKDM4C expression in 1.7% of the cases, none of them showed an alteration of the KDM4C locus. Taken together, these results suggest that genomic alterations are the main mechanism involved in KDM4C gene dysregulation in GC B-cell lymphomas. The fact that these genomic alterations are mostly focal structural variants and that SNV of KDM4C (1/7) seem to be rare in GC B-cell lymphomas likely explains why alterations of this gene have been underestimated in previous whole exome analyses.5,6
KDM4C alterations are not exclusive to GC B-cell NHL. Exploring a set of cell lines we observed that L123626 and SUP-HD1, and thus two out of six (33.3%) bona fide cHL cell lines, show potentially inactivating changes in the KDM4C gene, suggesting a tumor suppressive role in both GC B-cell NHL and cHL. We could not detect changes in KDM4C transcript expression between cases with altered and wildtype KDM4C using RNA-sequencing data of the GC B-cell derived lymphomas in the ICGC cohort. In contrast, using KDM4C immunoblotting in the cell lines we observed a complete loss of KDM4C protein expression in L1236 cells, and a strong reduction in My-La, which is in line with the genomic analysis. Subsequent functional analyses using KDM4C re-expression in cell lines with reduced or lack of KDM4C expression support the hypothesis of a tumor suppressor function of KDM4C at least in a subset of lymphomas.
In conclusion, our work not only adds KDM4C to the list of histone methylation modifiers recurrently altered in B-cell lymphomas, but it also supports a function of KDM4C as a tumor suppressor at least in a subset of lymphoma types. Moreover, our data demonstrate that focal structural variants contribute to the mutational burden of distinct genes, which might be missed by pure exome and/or RNA-sequencing approaches. This has to be considered if mutational landscapes are defined for classification schemes, such as those proposed for lymphomas.
- Received September 13, 2021
- Accepted March 18, 2022
No conflicts of interest to disclose.
CL performed PCR and Sanger sequencing validations. CL, SB and RSi performed FISH validation analyses. NS, MJ and SM realized the functional analyses. SS, UHT, JS, KK, SHB, DH, MK, and MS performed analysis of next-generation sequencing data. JMM and RSc developed the KDM4C antibody. MG provided the genomic data of cHL cell lines. MS, SH and JOK supervised next-generation sequencing analysis and interpreted data. GA, RBR and JCG generated the protein modeling. AM and WK performed immunohistochemistry analyses. HE, SG, OA, BR and PL analyzed the whole genome bisulfite sequencing and methylation arrays. HLS and MJ provided KDM4C-knockout data of cHL cell lines. NS and HLS cloned KDM4C expression vectors. RK provided normal B-cell samples. CL, RSi and SM interpreted data and wrote the manuscript. RSi and SM designed the study. RW and CL supported coordination of the project. LT performed the clinical coordination of the project. RSi coordinated the ICGC MMML-Seq network. All authors read and approved the final manuscript.
Sequencing data are available from the European Genome-phenome Archive (EGA) (accession number EGAS00001002199)
This study was supported by the German Ministry of Science and Education (BMBF) in the framework of the ICGC MMML-Seq (01KU1002A-J) and ICGC DE-Mining (01KU1505G and 01KU1505E) projects. This work was also supported by the BMBF-funded Heidelberg Center for Human Bioinformatics (HD-HuB) within the German Network for Bioinformatics Infrastructure (de.NBI) (#031A537A, #031A537C). Former grant support for MMML by the Deutsche Krebshilfe (2003-2011) is gratefully acknowledged. CL was supported by an Alexander von Humboldt Foundation post-doctoral fellowship. RSi and MG received funding from the European Union's Horizon 2020 research and innovation program under grant agreement n. 952304. Former support to MG in the form of a FEBS long-term fellowship and a “Support for International Mobility of Scientists” fellowship of the Polish Ministry of Science and Higher Education is gratefully acknowledged.
We thank the High Throughput Sequencing Unit of the DKFZ Genomics and Proteomics Core Facility for providing whole-genome sequencing services. We thank Prof. Elias Campo and Blanca González-Farré for support with immunohistochemistry experiments, and Prof. Georg Bornkamm for providing pRTS-1. The support of the technical staf of the Institutes of Human Genetics in Kiel and Ulm, as well as the former masters student, Ivan Potreba, are gratefully acknowledged.
- Swerdlow SH, Campo E, Harris NL, Jaffe ES, Pileri SA, Stein H TJ. WHO Classification of Tumours of Haematopoietic and Lymphoid Tissues. 4th ed. 2017. Google Scholar
- Rosenwald A, Wright G, Chan WC. The use of molecular profiling to predict survival after chemotherapy for diffuse large-B-cell lymphoma. N Engl J Med. 2002; 346(25):1937-1947. https://doi.org/10.1056/NEJMe020050PubMedGoogle Scholar
- Hummel M, Bentink S, Berger H. A biologic definition of Burkitt’s lymphoma from transcriptional and genomic profiling. N Engl J Med. 2006; 354(23):2419-2430. https://doi.org/10.1056/NEJMoa055351PubMedGoogle Scholar
- Wright GW, Huang DW, Phelan JD. A probabilistic classification tool for genetic subtypes of diffuse large B cell lymphoma with therapeutic implications. Cancer Cell. 2020; 37(4):551-568.e14. https://doi.org/10.1016/j.ccell.2020.03.015PubMedPubMed CentralGoogle Scholar
- Chapuy B, Stewart C, Dunford AJ. Molecular subtypes of diffuse large B cell lymphoma are associated with distinct pathogenic mechanisms and outcomes. Nat Med. 2018; 24(5):679-690. https://doi.org/10.1038/s41591-018-0016-8PubMedPubMed CentralGoogle Scholar
- Schmitz R, Wright GW, Huang DW. Genetics and pathogenesis of diffuse large B-cell lymphoma. N Engl J Med. 2018; 378(15):1396-1407. https://doi.org/10.1056/NEJMoa1801445PubMedPubMed CentralGoogle Scholar
- Hübschmann D, Kleinheinz K, Wagener R. Mutational mechanisms shaping the coding and noncoding genome of germinal center derived B-cell lymphomas. Leukemia. 2021; 35(7):2002-2016. https://doi.org/10.1038/s41375-021-01251-zPubMedPubMed CentralGoogle Scholar
- Lacy SE, Barrans SL, Beer PA. Targeted sequencing in DLBCL, molecular subtypes, and outcomes: a Haematological Malignancy Research Network report. Blood. 2020; 135(20):1759-1771. https://doi.org/10.1182/blood.2019003535PubMedPubMed CentralGoogle Scholar
- Runge HFP, Lacy S, Barrans S. Application of the LymphGen classification tool to 928 clinically and genetically-characterised cases of diffuse large B cell lymphoma (DLBCL). Br J Haematol. 2021; 192(1):216-220. https://doi.org/10.1111/bjh.17132PubMedGoogle Scholar
- Morin RD, Arthur SE, Hodson DJ. Molecular profiling in diffuse large B-cell lymphoma: why so many types of subtypes?. Br J Haematol. 2022; 196(4):814-829. https://doi.org/10.1111/bjh.17811PubMedGoogle Scholar
- Pasqualucci L, Dalla-Favera R. Genetics of diffuse large B-cell lymphoma. Blood. 2018; 131(21):2307-2319. https://doi.org/10.1182/blood-2017-11-764332PubMedPubMed CentralGoogle Scholar
- De Silva NS, Klein U. Dynamics of B cells in germinal centres. Nat Rev Immunol. 2015; 15(3):137-148. https://doi.org/10.1038/nri3804PubMedPubMed CentralGoogle Scholar
- Morin RD, Johnson NA, Severson TM. Somatic mutations altering EZH2 (Tyr641) in follicular and diffuse large B-cell lymphomas of germinal-center origin. Nat Genet. 2010; 42(2):181-185. https://doi.org/10.1038/ng.518PubMedPubMed CentralGoogle Scholar
- Reddy A, Zhang J, Davis NS. Genetic and functional drivers of diffuse large B cell lymphoma. Cell. 2017; 171(2):481-494.e15. Google Scholar
- Hashwah H, Schmid CA, Kasser S. Inactivation of CREBBP expands the germinal center B cell compartment, down-regulates MHCII expression and promotes DLBCL growth. Proc Natl Acad Sci U S A. 2017; 114(36):9701-9706. https://doi.org/10.1073/pnas.1619555114PubMedPubMed CentralGoogle Scholar
- Krysiak K, Gomez F, White BS. Recurrent somatic mutations affecting B-cell receptor signaling pathway genes in follicular lymphoma. Blood. 2017; 129(4):473-483. https://doi.org/10.1182/blood-2016-07-729954PubMedPubMed CentralGoogle Scholar
- Singleton AB. Exome sequencing: a transformative technology. Lancet Neurol. 2011; 10(10):942-946. https://doi.org/10.1016/S1474-4422(11)70196-XPubMedPubMed CentralGoogle Scholar
- Richter J, Schlesner M, Hoffmann S. Recurrent mutation of the ID3 gene in Burkitt lymphoma identified by integrated genome, exome and transcriptome sequencing. Nat Genet. 2012; 44(12):1316-1320. https://doi.org/10.1038/ng.2469PubMedGoogle Scholar
- ICGC/TCGA Pan-Cancer Analysis of Whole Genomes Consortium. Pan-cancer analysis of whole genomes. Nature. 2020; 578(7793):82-93. Google Scholar
- López C, Kleinheinz K, Aukema SM. Genomic and transcriptomic changes complement each other in the pathogenesis of sporadic Burkitt lymphoma. Nat Commun. 2019; 10(1):1459. https://doi.org/10.1038/s41467-019-08578-3PubMedPubMed CentralGoogle Scholar
- Li H, Kaminski MS, Li Y. Mutations in linker histone genes HIST1H1 B, C, D, and E; OCT2 (POU2F2); IRF8; and ARID1A underlying the pathogenesis of follicular lymphoma. Blood. 2014; 123(10):1487-1498. https://doi.org/10.1182/blood-2013-05-500264PubMedPubMed CentralGoogle Scholar
- Kretzmer H, Bernhart SH, Wang W. DNA methylome analysis in Burkitt and follicular lymphomas identifies differentially methylated regions linked to somatic mutation and transcriptional control. Nat Genet. 2015; 47(11):1316-1325. https://doi.org/10.1038/ng.3413PubMedPubMed CentralGoogle Scholar
- Doose G, Haake A, Bernhart SH. MINCR is a MYC-induced lncRNA able to modulate MYC’s transcriptional network in Burkitt lymphoma cells. Proc Natl Acad Sci U S A. 2015; 112(38):E5261-5270. Google Scholar
- Otto C, Giefing M, Massow A. Genetic lesions of the TRAF3 and MAP3K14 genes in classical Hodgkin lymphoma. Br J Haematol. 2012; 157(6):702-708. https://doi.org/10.1111/j.1365-2141.2012.09113.xPubMedGoogle Scholar
- Liu Y, Abdul Razak FR, Terpstra M. The mutational landscape of Hodgkin lymphoma cell lines determined by whole-exome sequencing. Leukemia. 2014; 28(11):2248-2251. https://doi.org/10.1038/leu.2014.201PubMedGoogle Scholar
- Schneider M, Schneider S, Zühlke-Jenisch R. Alterations of the CD58 gene in classical Hodgkin lymphoma. Genes Chromosomes Cancer. 2015; 54(10):638-645. https://doi.org/10.1002/gcc.22276PubMedGoogle Scholar
- Rausch T, Zichner T, Schlattl A, Stütz AM, Benes V, Korbel JO. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics. 2012; 28(18):i333-i339. https://doi.org/10.1093/bioinformatics/bts378PubMedPubMed CentralGoogle Scholar
- Northcott PA, Buchhalter I, Morrissy AS. The whole-genome landscape of medulloblastoma subtypes. Nature. 2017; 547(7663):311-317. https://doi.org/10.1038/nature23095PubMedGoogle Scholar
- Hoffmann S, Otto C, Kurtz S. Fast mapping of short sequences with mismatches, insertions and deletions using index structures. PLoS Comput Biol. 2009; 5(9):e1000502. https://doi.org/10.1371/journal.pcbi.1000502PubMedPubMed CentralGoogle Scholar
- Betts MJ, Lu Q, Jiang Y. Mechismo: predicting the mechanistic impact of mutations and modifications on molecular interactions. Nucleic Acids Res. 2015; 43(2):e10. https://doi.org/10.1093/nar/gku1094PubMedPubMed CentralGoogle Scholar
- Schleussner N, Merkel O, Costanza M. The AP-1-BATF and -BATF3 module is essential for growth, survival and TH17/ILC3 skewing of anaplastic large cell lymphoma. Leukemia. 2018; 32(9):1994-2007. https://doi.org/10.1038/s41375-018-0045-9PubMedPubMed CentralGoogle Scholar
- Cloos PAC, Christensen J, Agger K. The putative oncogene GASC1 demethylates tri- and dimethylated lysine 9 on histone H3. Nature. 2006; 442(7100):307-311. https://doi.org/10.1038/nature04837PubMedGoogle Scholar
- Loh Y-H, Zhang W, Chen X, George J, Ng H-H. Jmjd1a and Jmjd2c histone H3 Lys 9 demethylases regulate self-renewal in embryonic stem cells. Genes Dev. 2007; 21(20):2545-2557. https://doi.org/10.1101/gad.1588207PubMedPubMed CentralGoogle Scholar
- Rui L, Emre NCT, Kruhlak MJ. Cooperative epigenetic modulation by cancer amplicon genes. Cancer Cell. 2010; 18(6):590-605. https://doi.org/10.1016/j.ccr.2010.11.013PubMedPubMed CentralGoogle Scholar
- Hansen RS, Thomas S, Sandstrom R. Sequencing newly replicated DNA reveals widespread plasticity in human replication timing. Proc Natl Acad Sci U S A. 2010; 107(1):139-144. https://doi.org/10.1073/pnas.0912402107PubMedPubMed CentralGoogle Scholar
- Yu K, Huang FT, Lieber MR. DNA substrate length and surrounding sequence affect the activation-induced deaminase activity at cytidine. J Biol Chem. 2004; 279(8):6496-6500. https://doi.org/10.1074/jbc.M311616200PubMedGoogle Scholar
- Krill-Burger JM, Lyons MA, Kelly LA. Renal cell neoplasms contain shared tumor type-specific copy number variations. Am J Pathol. 2012; 180(6):2427-2439. https://doi.org/10.1016/j.ajpath.2012.01.044PubMedPubMed CentralGoogle Scholar
- Lee DH, Kim GW, Jeon YH, Yoo J, Lee SW, Kwon SH. Advances in histone demethylase KDM4 as cancer therapeutic targets. FASEB J. 2020; 34(3):3461-3484. https://doi.org/10.1096/fj.201902584RPubMedGoogle Scholar
- Shao N, Cheng J, Huang H. GASC1 promotes hepatocellular carcinoma progression by inhibiting the degradation of ROCK2. Cell Death Dis. 2021; 12(3):253. https://doi.org/10.1038/s41419-021-03550-wPubMedPubMed CentralGoogle Scholar
- Ma X, Ying Y, Sun J. circKDM4C enhances bladder cancer invasion and metastasis through miR-200bc-3p/ZEB1 axis. Cell Death Discov. 2021; 7(1):365. https://doi.org/10.1038/s41420-021-00712-9PubMedPubMed CentralGoogle Scholar
- Chen GQ, Ye P, Ling RS. Histone demethylase KDM4C is required for ovarian cancer stem cell maintenance. Stem Cells Int. 2020; 2020:8860185. https://doi.org/10.1155/2020/8860185PubMedPubMed CentralGoogle Scholar
- Kleszcz R, Skalski M, Krajka-Kuźniak V, Paluszczak J. The inhibitors of KDM4 and KDM6 histone lysine demethylases enhance the anti-growth effects of erlotinib and HS-173 in head and neck cancer cells. Eur J Pharm Sci. 2021; 166:105961. https://doi.org/10.1016/j.ejps.2021.105961PubMedGoogle Scholar
- Yang D, Xu T, Fan L, Liu K, Li G. microRNA-216b enhances cisplatin-induced apoptosis in osteosarcoma MG63 and SaOS-2 cells by binding to JMJD2C and regulating the HIF1α/HES1 signaling axis. J Exp Clin Cancer Res. 2020; 39(1):201. https://doi.org/10.1186/s13046-020-01670-3PubMedPubMed CentralGoogle Scholar
- Lang T, Xu J, Zhou L. Disruption of KDM4C-ALDH1A3 feedforward loop inhibits stemness, tumorigenesis and chemoresistance of gastric cancer stem cells. Signal Transduct Target Ther. 2021; 6(1):336. https://doi.org/10.1038/s41392-021-00674-5PubMedPubMed CentralGoogle Scholar
- Huang Y, Fang J, Bedford MT, Zhang Y, Xu R-M. Recognition of histone H3 lysine-4 methylation by the double tudor domain of JMJD2A. Science. 2006; 312(5774):748-751. https://doi.org/10.1126/science.1125162PubMedGoogle Scholar
- Liang Y, Song X, Li Y. circKDM4C suppresses tumor progression and attenuates doxorubicin resistance by regulating miR-548p/PBLD axis in breast cancer. Oncogene. 2019; 38(42):6850-6866. https://doi.org/10.1038/s41388-019-0926-zPubMedGoogle Scholar
- Jensen LJ, Kuhn M, Stark M. STRING 8--a global view on proteins and their functional interactions in 630 organisms. Nucleic Acids Res. 2009; 37(Database issue):D412-416. https://doi.org/10.1093/nar/gkn760PubMedPubMed CentralGoogle Scholar
- Netchiporouk E, Gantchev J, Tsang M. Analysis of CTCL cell lines reveals important differences between mycosis fungoides/Sézary syndrome vs. HTLV-1(+) leukemic cell lines. Oncotarget. 2017; 8(56):95981-95998. https://doi.org/10.18632/oncotarget.21619PubMedPubMed CentralGoogle Scholar
- Gan L, Yang Y, Li Q, Feng Y, Liu T, Guo W. Epigenetic regulation of cancer progression by EZH2: from biological insights to therapeutic potential. Biomark Res. 2018; 6(1):10. https://doi.org/10.1186/s40364-018-0122-2PubMedPubMed CentralGoogle Scholar
- Tolomeo M, Grimaudo S. The “Janus” role of C/EBPs family members in cancer progression. Int J Mol Sci. 2020; 21(12):1-18. https://doi.org/10.3390/ijms21124308PubMedPubMed CentralGoogle Scholar
- Shaulian E. AP-1 - the Jun proteins: oncogenes or tumor suppressors in disguise?. Cell Signal. 2010; 22(6):894-899. https://doi.org/10.1016/j.cellsig.2009.12.008PubMedGoogle Scholar
- Ma SD, Xu X, Jones R. PD-1/CTLA-4 blockade inhibits Epstein-Barr virus-induced lymphoma growth in a cord blood humanized-mouse model. PLoS Pathog. 2016; 12(5):e1005642. https://doi.org/10.1371/journal.ppat.1005642PubMedPubMed CentralGoogle Scholar
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