The Spectrum and Clinical Impact of Epigenetic Modi er ... · Theimpactofmutationonepigeneticmodiﬁersinmyelomahas also been reported, with inactivation of the histone demethylase,

Personalized Medicine and Imaging

The Spectrum and Clinical Impact of EpigeneticModifier Mutations in MyelomaCharlotte Pawlyn1, Martin F. Kaiser1, Christoph Heuck2, Lorenzo Melchor1,Christopher P.Wardell1, AlexMurison1, Shweta S.Chavan2, DavidC. Johnson1, Dil B. Begum1,Nasrin M. Dahir1, Paula Z. Proszek1, David A. Cairns3, Eileen M. Boyle1, John R. Jones1,GordonCook4,Mark T.Drayson5, RogerG.Owen6,WalterM.Gregory3,GrahamH. Jackson7,Bart Barlogie2, Faith E. Davies1,2, Brian A.Walker1,2, and Gareth J. Morgan1,2

Abstract

Purpose: Epigenetic dysregulation is known to be an importantcontributor to myeloma pathogenesis but, unlike other B-cellmalignancies, the full spectrum of somatic mutations in epige-netic modifiers has not been reported previously. We sought toaddress this using the results from whole-exome sequencing inthe context of a large prospective clinical trial of newly diagnosedpatients and targeted sequencing in a cohort of previously treatedpatients for comparison.

Experimental Design: Whole-exome sequencing analysis of463 presenting myeloma cases entered in the UK NCRI MyelomaXI study and targeted sequencing analysis of 156 previouslytreated cases from the University of Arkansas for Medical Sciences(Little Rock, AR). We correlated the presence of mutations withclinical outcome from diagnosis and compared the mutationsfound at diagnosis with later stages of disease.

Results: In diagnostic myeloma patient samples, we identifysignificant mutations in genes encoding the histone 1 linkerprotein, previously identified in other B-cell malignancies. Ourdata suggest an adverse prognostic impact from the presence oflesions in genes encoding DNA methylation modifiers and thehistone demethylaseKDM6A/UTX. The frequency ofmutations inepigeneticmodifiers appears to increase following treatmentmostnotably in genes encoding histone methyltransferases and DNAmethylation modifiers.

Conclusions: Numerous mutations identified raise the pos-sibility of targeted treatment strategies for patients either atdiagnosis or relapse supporting the use of sequencing-baseddiagnostics in myeloma to help guide therapy as more epi-genetic targeted agents become available. Clin Cancer Res; 22(23);5783–94. �2016 AACR.

IntroductionMyeloma is a malignancy of plasma cells, terminally differ-

entiated B cells involved in the immune response. Despiteadvances in therapy over the past 10 years, subgroups of patientsdiagnosed with myeloma continue to have poor outcomes,

and most inevitably relapse. A better understanding of thegenetic and epigenetic abnormalities that contribute to diseasepathogenesis is required to develop new targeted treatmentstrategies.

The myeloma clone is thought to be immortalized followingthe acquisition of a translocation into the immunoglobulinheavy-chain locus [t(4;14), t(6;14), t(11;14), t(14;16), andt(14;20)] or hyperdiploidy. The clone then evolves via thesubsequent gain of further genetic or epigenetic events (1).Epigenetic dysregulation is known to be an important contrib-utor to myeloma progression, but the full extent of its role inthe pathogenesis of disease and high-risk behavior is uncertain(2). A key example of the relevance and role of epigeneticderegulation in myeloma comes from our understanding ofthe subgroup of patients with the t(4;14), which results in thejuxtaposition of MMSET, a H3K36 methyltransferase, to theimmunoglobulin heavy-chain locus enhancer. The resultingoverexpression of MMSET leads to a histone methylation pat-tern, characterized by increased H3K36me2 and decreasedH3K27me3, along with a distinct and aberrant DNA methyl-ation pattern (3). Downstream events occurring as a resultof this primary event include changes in expression of genesinvolved in the cell cycle, apoptosis, and cell adhesion (4–7).Patients carrying the t(4;14), around 15% of myeloma cases atclinical diagnosis (8, 9), tend to respond to treatment butrelapse early and have a shorter overall survival (OS), althoughsince the use of proteasome inhibition, there is evidence thatthis poor prognosis is ameliorated.

1The Institute of Cancer Research, London, United Kingdom. 2Myeloma Institute,University of Arkansas forMedical Sciences, Little Rock, Arkansas. 3Clinical TrialsResearch Unit, Leeds Institute of Clinical Trials Research, University of Leeds,Leeds, United Kingdom. 4University of Leeds, Leeds, United Kingdom. 5ClinicalImmunology, School of Immunity and Infection, University of Birmingham,Birmingham, United Kingdom. 6St James's University Hospital, Leeds, UnitedKingdom. 7Department of Haematology, Newcastle University, Newcastle,United Kingdom.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

Current address for P.Z. Proszek: Centre for Molecular Pathology, The RoyalMarsden NHS Foundation Trust, London, United Kingdom; and current addressfor C.P. Wardell, Laboratory for Genome Sequencing Analysis, RIKEN Center forIntegrative Medical Sciences, Tokyo, Japan.

Genome data from the Myeloma XI trial patients have been deposited at theEuropean Genome-phenome Archive (EGA, http://www.ebi.ac.uk/ega/) whichis hosted at the EBI, under accession number EGAS00001001147.

Corresponding Author: Gareth J. Morgan, University of Arkansas for MedicalSciences, 4301 W. Markham, Little Rock, AR 72205. E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-15-1790

�2016 American Association for Cancer Research.

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The impact ofmutationonepigeneticmodifiers inmyelomahasalso been reported, with inactivation of the histone demethylase,KDM6A/UTX, seen in 10% of samples (10), and mutations affect-ing the histonemethyltransferase,MLL (11) the best documented.The recognition ofmutations inMLL followed the first sequencingstudyofpatientswithmyelomawhere expressionof thehomeoboxprotein, HOX9A, was suggested to be regulated byMLL (11).Morerecently, an activatingmutationofMMSET at E1099Kwas found inthe MM.1S myeloma cell line with the same downstream effect asMMSET overexpression due to the t(4;14); (12).

On the basis of these considerations, we sought to investigatethe role of mutations in epigenetic modifiers in myeloma andhow such mutations might contribute to disease pathogenesis.We used whole-exome sequencing to examine the spectrum ofmutations in epigenetic modifiers in a series of 463 newly diag-nosed patients uniformly treated as part of theUKNCRIMyelomaXI clinical trial (MyXI) and describe the clinical implication ofmutations in terms of their effect on progression-free (PFS) andOS. Previously reported results from this dataset identified 15significantly mutated genes in myeloma, a distinct APOBECsignature associatedwithmaf translocations and the link betweenthese factors and prognosis (13, 14). Here we extend theseanalyseswith a focus onhighlighting epigeneticmutations impor-tant in the pathogenesis of myeloma and compare the frequencyof mutations at diagnosis to later stages of disease, using a datasetfrom the University of Arkansas for Medical Sciences (UAMS).This analysis comprises the largest series of newly diagnosedpatientswithmyeloma sequenced to date andprovides importantinsights into the role of epigenetics in the disease, as well ashighlighting potential avenues for future research and targetedtreatment development.

Materials and MethodsWhole-exome sequencing at diagnosis—Myeloma XI

Methods used for the analysis of patient material have beenpreviously published (13, 14) but are summarized in theinformation to follow:

Samples were taken, following informed consent and prior totreatment commencement, from 463 patients newly diagnosedwith symptomatic myeloma and enrolled in the UK NCRI Mye-loma XI trial (NCT01554852). The study was approved by theNHS Health Research Authority, National Research Ethics ServiceCommittee, and by local review committees at all participatingcenters. The study randomized patients between triplet immuno-modulatory drug inductions with thalidomide versus lenalido-mide, prior to subsequent randomizations comparing consolida-tion and maintenance approaches. The demographics of thepatients included in this analysis have been published and arereproduced in Supplementary Table S1 (14). PFS and OS, mea-sured from initial randomization, had median follow-up of 25months, 95% confidence interval (95% CI; 24.3–26.2). Themedian PFS was 26.6 months, 95% CI (23.6–29.9) and themedian OS was not reached but the 3-year OS was 66%, 95%CI (60–73).

CD138þ plasma cells were isolated from bone marrow cellsusing MACSorting (Miltenyi Biotec), lysed in RLTþ buffer andDNA/RNA extracted using the AllPrep Kit (Qiagen). White bloodcells were isolated from peripheral blood, purified by Ficoll-Pacque and DNA extracted using the QIAamp DNA Mini Kit(Qiagen).

DNA from both tumor and peripheral blood samples was usedin the exome capture protocol (13, 15). RNA baits were designedagainst the human exome with additional custom baits tiling theIGH, IGK, IGL, and MYC loci to detect the major translocations.Four exome samples were pooled and run on one lane of a HiSeq2000 (Illumina) using 76-bp paired end reads. Data quality andmetrics processing, processes for somatic mutation calling, andmolecular and copy number assessments are described fullyelsewhere (13).

Copy number estimation and cancer clonalfraction—Myeloma XI

Copy number across the exomewas determined using Control-FREEC (16) utilizing 500 bp bins, each overlapping with thesubsequent and previous 250 bp. Aminimum average read depthof 50 was required in the control samples, with at least twoneighboring bins required to show copy number aberration tocall a region as gained or lost. Copy number profiles for a series of26 chromosomal regions were compared with copy numbervalues previously estimated by multiplex-ligated probe amplifi-cation tests (17). To ensure a reliable estimation of copy number,only cases with an F correlation above 50% were used forsubsequent analyses (370/463).

Cancer clonal fraction (CCF) was calculated (18), as the pro-portion of tumor cells containing a single nucleotide variant(SNV) using the equation:

CCF ¼ min 1;CN:rR

� �

where CCF ¼ cancer clonal fraction (proportion of cells contain-ing the mutation), CN¼ copy number at that site, r¼ number ofreads containing themutation at that site, andR¼ total number ofreads at that site.

Further data analysis—Myeloma XILists of epigenetic modifiers were curated using database

searches and previous publications (Supplementary Table S2)with genes divided into eight classes: core/linker histones

Translational Relevance

Myeloma is a heterogeneous malignancy with disease indifferent subgroups of patients driven by abnormalities inmultiple genes and/ormolecular pathways. Treatment optionsand outcomes have improved over the last decade, but novelapproaches are still required. In this article, we use whole-exome sequencing results from 463 presenting cases enteredinto theUKMyelomaXI study, and targeted sequencing of 156previously treated cases, to report the wide spectrum of muta-tions in genes encoding epigenetic modifiers in myeloma.Using linked survival data from the large Myeloma XI study,we identify lesions that may have prognostic significance inKDM6A and genes encoding DNAmethylation modifiers. Wedemonstrate an increase in the frequency of epigenetic mod-ifier mutations of certain classes as disease progresses. Ouranalysis is particularly important, as numerous mutationsidentified suggest potential targeted treatment strategies withagents either currently available or known to be in develop-ment, highlighting novel treatment approaches for patients.

Pawlyn et al.

Clin Cancer Res; 22(23) December 1, 2016 Clinical Cancer Research5784




(n ¼ 79), histone demethylases (n ¼ 21), histone methyltrans-ferases (n ¼ 40), histone deacetylases (HDAC; n ¼ 20), histoneacetyltransferases (HAT; n ¼ 25), DNA methylation modifiers(n ¼ 8), readers (n ¼ 17), and chromatin assembly/remodeling(n ¼ 46). These lists were used to interrogate the results of thesequencing analysis to calculate the percentage of patients with amutation in each gene and in each class.

For all genes mutated in more than 1% of patients, mutationswere mapped to the relevant regions of the protein usingProtein Paint (19) and the Catalogue of Somatic Mutationsin Cancer (COSMIC) database was searched to look for previ-ously identified variants at the same amino acid residue inother tumors (20). Multiple sequence alignment of histone 1genes was performed by inputting sequences from FASTA filesobtained from uniprot.org into ClustalW2. Mutations wereannotated in GeneDoc. The likely effect of mutations wasassessed using SIFT analysis (21).

Deletions in KDM6A were identified using an algorithm todetect deletion of whole exons (windows defined as the regionsused in the Agilent exome capture). This was done by comparingthe read depth between the tumor and normal samples. Themean depth across the window was required to be > 0.2 of themedian depth in the normal sample and < 0.06 in the tumorsample, with the normal value being at least 8� greater than thetumor value. Positive findings using this method were con-firmed or excluded following visualization in IntegratedGenome Viewer (IGV).

Survival curves were plotted (Kaplan–Meier) and the statis-tical significance of the difference between curves tested usingthe log-rank test, with P < 0.05 taken as the level of significance.Multivariate analysis was performed using the Cox regressionmodel inputting the epigenetic mutations with a significantimpact on survival by univariate log-rank statistics and otherstandard clinical factors known to influence survival in patientswith myeloma.

Targeted sequencing in previously treated patients—UAMSOne hundred fifty-six patients who had previously undergone

treatment had bone marrow samples taken and genomic pro-filing (FoundationOne Heme; Foundation Medicine) performedas part of their disease reassessment work-up. Review of this datawas approved by the UAMS institutional review board. Thedemographics of the patients included in this analysis are shownin Supplementary Table S3. CD138þ cells were isolated frombone marrow aspirates as described previously (22) with DNAand RNA extracted using the Puregene and RNeasy kits (Qiagen),respectively. Samples were submitted to a Clinical LaboratoryImprovement Amendments–certified, New York State and theCollege of American Pathologists–accredited laboratory (Foun-dation Medicine) for next-generation sequencing–based geno-mic profiling. Hybridization capture was applied to �50 ng ofextracted DNA or RNA for 405 cancer-related genes and selectintronic regions from 31 genes (FoundationOne Heme DNAonly; Supplementary Table S405); targeted RNA-seq for rear-rangement analysis was performed for 265 genes frequentlyrearranged in cancer. Sequencing of captured libraries was per-formed (Illumina HiSeq 2500) to a median exon coverage depthof >250�, and resultant sequences were analyzed for basesubstitutions, insertions, deletions, copy number alterations(focal amplifications and homozygous deletions), and selectgene fusions, as described previously (23, 24). To maximize

mutation-detection accuracy (sensitivity and specificity) inimpure clinical specimens, the test was previously optimizedand validated to detect base substitutions at a �5% mutantallele frequency (MAF) and indels with a �10% MAF with�99% accuracy (23, 24). The mutations reported on the F1 testwere all manually, individually reviewed. Mutations wereretained and included in the subsequent analysis only if eitherthey were classified by Foundation Medicine as definitely"known," were frameshift, nonsense, or splice-site mutationsor if a mutation affecting the same amino acid residue had beenpreviously recognized in another tumor [determined by anal-ysis of COSMIC datasets directly (20) and via visualizationusing the St Jude's PeCan data portal (25)].

Because of the greater depthof the FoundationOneHeme test, acutoff for mutations being present in �5% reads was applied tothis dataset (mutations that could have been detected using thedepth achieved in the MyXI study). Epigenetic genes from the listin Supplementary Table S2 (and analyzed in the MyXI data),which were also sequenced in the UAMS dataset are shownin Supplementary Table S4. This comprises: core/linker histones(n ¼ 12), histone demethylases (n ¼ 5), histone methyltrans-ferases (n¼ 11), HDAC (n¼ 3), HATs (n¼ 4), DNAmethylationmodifiers (n ¼ 4), readers (n ¼ 2), and chromatin assembly/remodeling (n ¼ 9).

The percentage of patients with mutations in each class ofepigenetic modifier were compared between the MyXI datasetand the UAMS dataset using the z test, multiple testing wascorrected for using the Bonferroni method. As CCF could not becalculated for thepatients in theUAMSdataset (due to the absenceof copy number data), the variant allele frequencies were com-pared for those genes with at least two mutated samples in bothdatasets.

ResultsWhole-exome sequencing was performed on samples from

463 patients in the Myeloma XI trial prior to treatment. Weidentified mutations in genes encoding epigenetic modifiers in

Table 1. Epigenetic genesmutated in�5 of 463 (>1%) newly diagnosed patientsin Myeloma XI dataset

GeneNo. of patients(N ¼ 463) Percentage

ARID1A 6 1.30%ARID2 6 1.30%ARID4A 5 1.08%ARID5B 5 1.08%CHD2 7 1.51%CHD4 9 1.94%EHMT2 5 1.08%EP300 6 1.30%HIST1H1C 12 2.59%HIST1H1E 13 2.81%KDM3B 7 1.51%KDM6A 6 1.30%MLL 8 1.73%MLL2 6 1.30%MLL3 7 1.51%MLL4 7 1.51%MLL5 5 1.08%SETD2 6 1.30%SETDB1 5 1.08%TET2 5 1.08%

Epigenetic Modifier Mutations in Myeloma

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over half (53%) of patients. Twenty epigenetic modifiergenes were mutated in at least 5 of 463 (>1%) individualswith frequencies shown in Table 1, CCF shown in Fig. 1, andmutation location annotated in Fig. 2; Supplementary Fig. S1.The distribution of the main translocation subgroups, knownto drive myeloma pathogenesis, did not differ significantlybetween patients with and without a mutation in any epige-netic modifier (Supplementary Fig. S2).

Below we report a detailed analysis of the frequency andpotential clinical impact of mutations in each class of epigeneticmodifier.

Core and linker histone mutations: HIST1H1E is significantlymutated at myeloma diagnosis

The gene HIST1H1E, which encodes a linker histone H1protein, was found to be significantly mutated in the MyXIcohort (14) at diagnosis with mutations in 2.8% of patients(13/463, P < 1 � 10�10, q < 1 � 10�10). Further analysisrevealed recurrent nonsynonymous mutations in other histone1 family genes including HIST1H1B (0.2%, 1/463), HIST1H1C

(2.6%, 12/463), and HIST1H1D (0.6%, 3/463) with mutationsin at least one of these genes (HIST1H1B-E) occurring in 6%(28/463) of patients. The mutations (Fig. 2A) in these geneswere predominantly missense SNVs affecting the globulardomain of histone H1. HIST1H1B-E were aligned (Fig. 2B)with sites affected by mutation highlighted. There were threesites of recurrent mutations at residues equivalent to alanine 61and 65 and lysine 81 of HIST1H1E. There were also severaladditional mutations within the globular domain across var-iants located between residues 100 and 110. The globulardomain of these genes is a region frequently mutated in othercancers including follicular lymphoma and diffuse large B-celllymphoma (DLBCL; refs. 26, 27). The observations that themutations occurred in a conserved region and there were nomutations in one known gene, HIST1H1A, supports thehypothesis that these are not passenger mutations and maycarry some significance to myeloma pathogenesis.

Mutations in HIST1H1B-E were tested for their impact onprotein function using SIFT analysis. This analysis was possiblein 29 of 31 mutations and 69% (20/29) were found to be

Figure 1.

Cancer clonal fraction (CCF) in genes encoding epigenetic modifiers. The proportion of tumor cells containing an SNV was calculated in the 370 sampleswith accurate copy number assessments and plotted as the CCF and 95% confidence interval for each sample with the indicated mutation, corrected for coverage.Mutations were considered clonal and shown in red if the upper bound of the CCF confidence interval was � 95% and subclonal if the upper bound ofthe CCF confidence interval was <95%. The proportion of samples with each genemutation that were clonal and subclonal are shown in the bar chart below the genename. Epigenetic modifiers were analyzed for CCF if they were mutated in �5/463 (>1%) patients.

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damaging (Supplementary Table S5).HIST1H1B-Emutations didnot have a prognostic impact (Supplementary Fig. S3).

CCF analysis showed HIST1H1E and HIST1H1C mutations tobe highly clonal (Fig. 1) suggesting that they are either acquiredearly in clonal development or selected for at progression tosymptomatic disease, but nevertheless play an important role inmyeloma pathogenesis in these patients.

Mutationswere also seen in the core histone proteins 2A (4.5%,21/463 patients, one frameshift mutation, remainder missenseSNVs), 2B (3.7%, 17/463, one frameshift mutation, remaindermissense SNVs), 3 (2.8%, 13/463, one frameshift, one nonsense,remainder missense SNVs), and 4 (1.7%, 8/463, all missenseSNVs) but with a low frequency of mutation in each familymember, with no individual gene being mutated in more thanfour patients. The total number of patients harboring a mutationin any histone protein (linker or core) was 18% (83/463) withthe presence or absence of any mutation having no effect onprogression or survival.

Histone modifier mutations: Mutations/deletions in KDM6A/UTX may shorten OS from diagnosisMethylation modifiers. Potentially deleterious mutations in his-tonemethyltransferase/demethylase enzymes were found in 24%(112/463) of patients, though the percentage with each genemutated was low (Fig. 3). The most frequently mutated genefamily in themethyltransferases wasMLL/2/3/4/5 (7%of patients,31/463. By gene: MLL 1.7%, 8/463, MLL2 1.3%, 6/463, MLL31.5%, 7/463,MLL4 1.5%, 7/463, andMLL5 1.1%, 5/463), but norecurrent mutations were seen across variants. Of 36 mutations(across the 31patients), 29weremissense SNVs, 4nonsense SNVs,2 splice-site SNVs, and 1 frameshiftmutation. As in other diseases,mutations in MLL family genes are widely distributed across thegenes with no conserved sites or regions of mutation (Supple-mentary Fig. S1). The majority of mutations identified in ourpatients were novel, but those previously identified in otherdiseases included one in MLL3, p.R190Q (endometrial; ref. 20)and in MLL4, p.R297� (large intestine; ref. 28). The presence of

Figure 2.

Site of mutations in the genes encoding histone 1 variants. A, The common protein variants are shown with the globular domain of each highlighted in purple.The sites of mutation are indicated by the markers with red dots indicating missense mutations and purple dots frameshift mutations. There was 1 mutation inHIST1H1B in 1 patient, 12 mutations in HIST1H1C in 12 patients, 3 mutations in HIST1H1D in 3 patients, and 15 mutations in HIST1H1E in 13 patients. There were nomutations in HIST1H1A. B, The common proteins of histone 1 are aligned and labeled such that 1H1A/H1.1 indicates the protein histone 1.1, encoded by geneHIST1H1A, 1H1B/H1.5 indicates the protein histone 1.5 encoded by gene HIST1H1B, etc. The numbers indicate the amino acid number. The protein domains areindicated by the colored bars overlying the protein sequence, green denotes the N-terminal domain, purple the globular domain, and red the C-terminal domain.Mutations found in the patients sequenced are indicated by colored square, pink ¼ missense SNV, yellow ¼ frame shift insertion, and green ¼ frame shiftdeletion. At some sites, there was more than one patient with a mutation. The overlying arrows indicate amino acids where the mutations in different proteinvariants occur at the same equivalent amino acid residues.






MLL family mutations in patients with myeloma did not have aneffect on PFS or OS.

No mutations in the H3K27 methyltransferase EZH2 weredetected, in contrast to the finding of recurrent mutations in

other B-cell malignancies. There were only two patients withmutations affecting the H3K36 methyltransferase MMSET,with none of the MMSET-activating mutations at p.E1099K(previously identified in MM1.S myeloma cell line; ref. 29)seen. EHMT2, the gene encoding the H3K9 methyltransferaseG9a was mutated in 5 of 463 patients (1%, all missensemutations) with one in the SET domain (p.Y1097F) and onein the ankyrin repeat "reader" domain (p.E699K; Supplemen-tary Fig. S1).

The most frequently mutated demethylase gene was KDM3B,1.5% (7/463) of patients with two splice-site mutations, onenonsense mutation, and four missense mutations, none of whichhave been previously identified in other cancers in the COSMICdatabase. The primary site of action of KDM3B is H3K9, anddeletions of this gene have been implicated in myelodysplasticsyndrome associated with 5q- (30, 31).

KDM6A/UTXmutations were seen in 1.3% (6/463) of patients,were all missense mutations, and were highly clonal (Fig. 1).p.R118K, had been previously identified in lung cancer (32),p.Q398H in gastric adenocarcinoma (20), whereas anotherp.G66A occurred at the same residue as the p.G66D mutationpreviously identified in the OPM-2 myeloma cell line (10).Further analysis of this gene for whole exonic deletions increasedthenumber of patients affected by apotentially inactivating lesionto 3% (15/463). Patients with a KDM6A mutation or deletionhad a reducedOS comparedwith wild-type on univariate analysis[Fig. 4A; medians not reached, log-rank P¼ 0.0498, percent aliveat 2 years 51% (95% CI, 30–85) vs. 80% (95% CI, 77–84) with asimilar trend for PFS].

Acetylationmodifiers. Themost frequentlymutated gene encodinga HAT or HDAC was the HAT EP300 mutated in 1.3% (6/463)of patients. Mutations in this gene have been previously iden-tified in a number of other malignancies including DLBCL,where they are most commonly found in the HAT enzymaticdomain (33–35). One frameshift deletion (p.S90fs) andone nonsense mutation (p.Q1077�) are likely to result in anabsence of functional protein. Of the four missense mutations,one occurs in the active HAT domain (p.P1388S) and so maydirectly affect the catalytic function of the protein, whereas onein the bromodomain (p.V1079L) and two in the zinc fingerbinding domain (p.T1775P, p.G1778P) may still have signif-icant effects on the protein function by affecting target binding.Mutations in CREBBP, a closely related member of the KAT3family of histone acetyltransferases were also found in threepatients. One mutation, p.R1360Q occurs at the same aminoacid as the p.R1360� mutation seen in several diseases includ-ing non-Hodgkin B-cell lymphoma (33). EP300/CREBBP havea wide range of targets and are able to acetylate all four histonesas well as being involved in many cellular processes linked tocancer such as cell-cycle progression, p53 activity, DNA repair,and apoptosis (36).

There were no genes encoding histone reader proteinsthat were mutated in more than 1% (5/463) of patients atdiagnosis.

DNA methylation modifier mutations: Mutations in DNAmethylation modifiers are associated with a shorter OS fromdiagnosis.

DNA methylation modifiers were mutated in 4% (17/463) ofpatients at diagnosis. This included specific mutations previously

Figure 3.

Mutations in histone methyltransferases and demethylases. Themethyltransferases and demethylases located at their most commonlyrecognized site of activity on histone 3 are shown with the percentage ofpatients harboring a mutation. Those in bold are mutated in five or morepatients in the dataset.

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reported in numerous tumor types such as p.R132C in IDH1previously reported in glioma, chondrosarcoma, and AML (37–41), and p.R140W in IDH2 and p.C1378Y and p.Y1661� in TET2previously reported in AML/MDS (42–44) and p.E784K inDNMT3A previously reported in biliary tract tumors (45).A mutation was also seen at p.E477K in DNMT3A, the site ofrecurrent mutation in AML and MPNs (p.E477� and p.E477fs;refs. 46–48). Collectively, mutations in any DNA methylation

modifier (TET1/2/3 n ¼ 11, IDH1/2 n ¼ 2, or DNMT1/3A/Bn¼ 6) were associated with a shorter OS on univariate analysis, Fig.4B [medians not reached, P ¼ 0.045, percent alive at 2 years 58%(95% CI, 39–88) vs. 80% (95% CI, 76–84)]. There was no signif-icant effect on PFS. This effect on OS with no effect on PFS isexplained by a significantly shortened postprogression survival forpatients with mutations in DNA methylation modifiers comparedwith those without (P ¼ 0.002, log-rank test, data not shown).

Figure 4.

Overall survival (OS) is shorter in patientswith a KDM6Amutation or deletion (mut/del) and thosewith amutation in anyDNAmethylationmodifier.A,Kaplan–Meiercurves showing patientswith aKDM6Amutation or deletion (n¼ 15, dashed) and thosewithout (n¼ 448, solid). Progression-free survival (PFS)medianmut/del 16.8months versus wild-type 26.6 months (log-rank P ¼ 0.695). Overall survival medians not reached, (log-rank P ¼ 0.0498). Percent alive at 2 years: mut/del51% (95% CI, 30–85), wild-type 80% (95% CI, 77–84). Survival on x-axis is plotted as the time since randomization. B, Kaplan–Meier curves showing patients witha DNA methylation modifier mutation (n ¼ 17: TET1/2/3 n ¼ 11, IDH1/2 n ¼ 2 or DNMT1/3A/B n ¼ 6, dashed) and those without (n ¼ 446, solid). Progression-freesurvival medianmutation not reached versuswild-type 26.6months (log-rank P¼0.852). Overall survival medians not reached, (log-rank P¼0.0455). Percent aliveat 2 years: mutation 58% (95% CI, 39–88), wild-type 80% (95% CI, 76–84). Survival on x-axis is plotted as the time since randomization.






Chromatin remodeling complex mutationsThe most frequently mutated genes involved in chromatin

remodeling were CHD4 in 1.9% (9/463, all missense) ofpatients and CHD2 in 1.5% (7/463, one frameshift, six mis-sense). Both are members of the nucleosome remodeling anddeacetylase complex (NuRD). This highlights the potential roleof the NuRD complex in myeloma pathogenesis; however, itsaction may not be entirely epigenetic as it has also been recentlynoted that CHD4 may also have roles in DNA damage repairand cell-cycle progression independent of the NuRD complexand epigenetic activity (49).

Other remodeling genes mutated in more than 1% of patientsinclude ARID1A (encoding BAF 250a) in 1.3% (6/463, twononsense and four missense) and ARID2 (encoding BAF200) in1.3% (6/463, three nonsense, one splice site, twomissense). Theirgene products are part of in the switch nonfermenting/sucrosenonfermenting (SWI/SNF)/ remodeling complex and are respon-sible for its interaction with DNA. Mutations in genes encodingmembers of this complex have been previously demonstrated tobe recurrently mutated in both solid tumors and hematologicmalignancies, in up to 20%of cancer patients overall (50, 51). Themechanisms behind this have yet to be fully elucidated and indifferent diseases has been suggested tobe related to the role of theSWI/SNF complex in DNA damage repair (52), nucleosomepositioning, DNAse hypersensitivity sites, the regulation of devel-opmental gene expression and/or the interaction of the complexwith both histone and DNAmodifiers (53). We found previouslyidentified mutations at p.M918I in ARID1A (seen in renal cellcarcinoma; ref. 20) at p.Q937� in ARID2 (seen in biliary tractcarcinomaandmelanoma; refs. 20, 45), p.Q1611� inARID2 (seenin squamous cell carcinoma; ref. 54) and p.A1555S in ARID2(seen recurrently in head and neck carcinoma; ref. 55).

Survival analysisFor each class of epigenetic modifier, the PFS and OS for

patients with a mutation in any gene within the class werecompared with patients without. In addition, patients withmutations of interest were compared with those without in thefollowing cases, KDM6A mutations or deletions, MLL familygene mutations and histone 1 family gene mutations. An effecton OS was found on univariate analysis for KDM6Amutations/deletions and DNAmodifier mutations as described previously.Multivariate analysis using a Cox regressionmodelwas thereforecarried out considering other factors known to have an adverseeffect on OS; presence of an adverse translocation t(4;14),t(14;16) or t(14;20), del17p, gain or amplification of 1q, inter-national staging score and age >70 years. In this model, DNAmodifier mutations, in addition to del17p, ISS and gain oramplification of 1q remained significant but not KDM6A muta-tions/deletions (Supplementary Table S6).

The frequency of mutations in epigenetic modifiers increasesfollowing treatment

Longitudinal investigations are planned for the patients in theUK Myeloma XI trial, but at present, the majority remain inremission. Therefore, to study the likely differences in the fre-quency ofmutations in epigeneticmodifiers as disease progresses,we utilized available data for a series of 156 previously treatedpatients with myeloma from the UAMS who underwent targetedsequencing. Because of the different methods used, we restrictedour comparison to only those epigenetic modifier genes that were

sequenced in both studies (Supplementary Table S4). Results aresummarized in Fig. 5A and Supplementary Table S7 and show anincrease in the number of patients with a mutation in any epige-netic modifier in samples taken at later stages of disease. There wasa statistically significant increase in the number of patients witha mutation in any histone methyltransferase gene (6.9%,32/463 MyXI vs. 17%, 26/156 UAMS) or any DNA methylationmodifier (1.9%, 9/463 MyXI vs. 8.3%, 13/156 UAMS) and anotable increase in histone acetyl-transferase gene mutations(2.4%, 11/463 MyXI vs. 7.1%, 11/156 UAMS). These changesappear to be the result of increases in mutations in MLL2, MLL3,SETD2, CREBBP, DNMT3A, and TET2 (Fig. 5B).

The variant allele frequency was compared between the MYXIand UAMS samples (Supplementary Fig. S4) with no statisticallysignificant differences seen. An analysis of the distribution ofmutations across risk groups, as definedby gene expressionprofilerisk score (GEP70; Supplementary Fig. S5) and UAMS molecularsubgroups (Supplementary Fig. S6) found a slight overrepresen-tation of the PR subgroup in those patients with an epigeneticmodifier mutation compared with those without.

Of note in the UAMS dataset, two of the three mutations inHIST1H1E had been previously identified, p.A65P in the MyXIdataset and p.P161S in a lymphoid neoplasm (COSMIC), where-as the third, p.A47V, occurred at the same residue as p.A47P seenin a MyXI patient. This supports the evidence of likely impact ofHIST1H1Emutations inmyeloma suggestedby theMyXI analysis.Recurrent mutations were also seen in the UAMS dataset at thesame location in IDH1, p.R132C and p.R132H, with p.R132Chaving been also identified in an MyXI patient.

DiscussionWe report mutations within genes encoding epigenetic modi-

fiers in myeloma with an impact on pathology and survival atdiagnosis and an increased frequency after treatment. The spec-trum of mutations in myeloma is broad with no single epigeneticmodifier being mutated in a large proportion of patients. Severalof themutations identified have been previously related to cancerpathogenesis and/ormay openpossibilities for targeted treatmentstrategies for subgroups of patients. This work changes our under-standingof the epigenetic landscape ofmyeloma exposing awiderspectrum of epigenetic processes than previously recognized, thatmay be altered in large numbers of patients, affecting diseasebiology and outcome.

One of our key findings is the significant mutation ofHIST1H1E and similar mutations across histone 1 family genesin diagnostic samples. HistoneH1 acts to control the higher orderstructure of chromatin by spacing nucleosomes and holdingDNAin place as it winds around each nucleosome octamer. It has,therefore, been suggested to affect gene transcription via themodulation of the accessibility of DNA to transcription factors.Mutations in the histone H1 family genes have been found inother hematologic malignancies including follicular lymphomaand DLBCL (27, 56). In follicular lymphoma, these were alsonoted to be predominantly in the globular domain and one suchmutation (Ser102Phe) was functionally demonstrated to affectimpaired ability of histone H1 to associate with chromatin (56)This is close to several sites of mutation identified in our studybetween residues 100 and 110.More recently, histoneH1has alsobeen shown to play a role in regulating DNA methylation viaDNMT1andDNMT3Bbinding andalteringH3K4methylationby

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affecting binding of the methyltransferases SET7/9 (26, 57). Itmight, therefore, be postulated that it is via thesemechanisms thatmutations inhistoneH1mayhave anoncogenic effect. Analysis ofpaired sample mutation and DNA methylation analysis willfurther inform this hypothesis.

HIST1H1E andHIST1H1Cmutations (along with KDM6A andARID2) were highly clonal, suggesting they may be acquiredearly in pathogenesis or selected for at progression to symp-tomatic disease. Our analysis estimates CCF using a methodthat correlates well with single-cell analysis results; however, incertain situations copy number alterations or polyclonality mayyield anomalous CCF estimates for individual mutations.Future improvements in techniques for calculating CCF mayfurther inform these results.

Our analysis identifies 3% of patients at diagnosis with apotentially inactivating lesion in KDM6A/UTX. This is lower thanthe frequency of mutations previously identified (10) and oftenreported in six of 58 patients (10%). This previous analysis,however, included cell lines, and when these are removed andonly patient samples from the analysis considered the percentagewith a lesion falls to 4% (2/49), in keeping with our study.

The Myeloma XI study pairs mutation and outcome datagiving us the first opportunity to explore any possible associ-ation between epigenetic modifier mutations and outcome.Individual epigenetic modifier genes are mutated in smallnumbers of patients, and so the size of our dataset limits thepower to detect any specific gene effects. We therefore groupedmutations (11 groups as defined previously) and on univariate

analysis identified a detrimental effect on OS of KDM6Amutation/deletion (log-rank P ¼ 0.0498) and DNA methyla-tion modifiers (log-rank P ¼ 0.045). If a Bonferroni correctionfor multiple testing were applied to this data, the P valuesobtained would fall above the level considered significant;however, this arbitrary cutoff may miss a clinically meaningfuleffect. Further investigation in future studies will help to clarifythis. Notably, the effect of DNA methylation modifier muta-tions on OS withstood multivariate analysis, and in largercohorts, it would be of interest to investigate the independentassociation of mutations within this, and other groups, onoutcomes.

Myeloma is part of a spectrum of malignancies arising from B-cell populations at various stages of B-cell ontogeny.Mutations inepigeneticmodifiers are seen across this spectrum but we can nowshow that different patterns are seen dependent upon the biologyof the population examined. Recurrent mutations in HIST1H1,MLL, and EZH gene families have been identified in DLBCL andfollicular lymphoma (26, 34). We show that HIST1H1 and MLLmutations are seen in myeloma, although at a much lowerfrequency, whereas EZH2 mutations are not seen, suggesting adifferent pathogenic mechanism. The different spectrum of epi-geneticmutations ismoremarkedwhenwe compare lymphoid tomyeloid diseases such as acute myeloid leukemia, myelodysplas-tic syndromes, and myeloproliferative neoplasms, where muta-tions in DNA-modifying enzymes such as DNMT3A, IDH, andTET2predominate (58, 59).We foundmutations in these genes inmyeloma, but at a much lower frequency.

Figure 5.

The frequency of mutations in genes encoding epigenetic modifiers at presentation and following treatment by class of epigenetic modifier. A, A comparisonbetween the percentage of patients with a mutation in each class of epigenetic modifier at presentation (Myeloma XI data, shown in black bars) and after treatment(UAMSdata, shown in graybars). There is a significant increase in the percentage of previously treated patientswith amutation for those groups indicated by � . (z testof difference in proportions P < 0.05). Those comparisons remaining significant after multiple test correction (Boferroni method) are indicated by �� . Full data areshown in Supplementary Table S7. Only mutations in genes sequenced in both studies (i.e., the genes sequenced in the UAMS F1 dataset, SupplementaryTable S4) were included for this comparison. B, The frequency (%) of mutations in genes of interest within each class.






Our results highlight possible targeted treatment approachesfor patients either at diagnosis or at relapse. For example,patients with a KDM6A/UTX mutation or deletion might beamenable to the use of EZH2 inhibitors, currently in the earlystages of clinical development for lymphoma patients. Inhibit-ing EZH2, the H3K27 methyltransferase, may counteract theincreased H3K27methylation resulting from inactivation of thedemethylase. A recent study also reports that ARID1A-mutatedcancers may be sensitive to EZH2 inhibition, demonstrating asynthetic lethal effect via the PI3–AKT pathway (60). Patientswith IDH mutations might be amenable to IDH inhibitorscurrently in early stages of development and a more globalstrategy might be possible for patients with mutations in DNAmethylation modifiers with demethylating agents.

On comparison with results from focused sequencing of 156previously treated patients, we show an increase in the numberof patients with a mutation in genes encoding a histonemethyltransferases and DNA methylation modifiers. This sug-gests that these events may either play a role in disease pro-gression or occur more frequently following exposure to induc-tion chemotherapy in resistant subclones. The change in fre-quency of mutations in DNA methylation modifiers as mye-loma progresses is supported by data showing a change in themethylation pattern at different disease stages (3). There areseveral limitations to our comparison, however, including thedifferent sequencing methods and depth. These results will,therefore, need to be validated in matched patient populationsfollowing relapse within a clinical trial setting using the sameanalysis method.

This whole-exome analysis ofMyeloma XI patients at diagnosisis the first extensive analysis of the spectrum of mutations inepigenetic modifiers in a uniformly treated population in mye-loma. An association with clinical outcome for KDM6A mutatedor deleted patients, andmutations inDNAmethylationmodifiersis suggested in our dataset but will need validation in largerstudies or meta-analysis due to the low overall frequency of themutations. These data further emphasize the importance ofepigenetics in myeloma and provide potential new targets forpersonalized therapeutic strategies for patients. Our findingssupport the use of sequencing-based diagnostics in myelomaboth at diagnosis and relapse to identify potentially prognosticand/or targetable lesions.

Disclosure of Potential Conflicts of InterestC. Pawlyn is a consultant/advisory board member for Celgene and Takeda.

M.F. Kaiser is a consultant/advisory board member for Amgen and Celgene.C. Heuck is an employee of Janssen R&D, reports receiving speakers bureauhonoraria from Foundation Medicine, and is a consultant/advisory boardmember for Celgene and Millennium/Takeda. G. Cook reports receiving acommercial research grant from Celgene, speakers bureau honoraria from andis a consultant/advisory board member for Celgene and Janssen. R.G. Owenreports receiving a commercial research grant from Celgene, speakers bureauhonoraria from Celgene and Takeda, support for attending academic meetingsfrom Janssen and Takeda, and is a consultant/advisory board member for

Celgene. W.M. Gregory reports receiving speakers bureau honoraria fromJanssen and is a consultant/advisory board member for Celgene. G.H. Jacksonreports receiving speakers bureau honoraria fromAmgen, Celgene, Janssen, andTakeda and is a consultant/advisory board member for Amgen, Celgene,Janssen, Merck Sharp & Dohme, and Takeda. G.J. Morgan is a consultant/advisory board member for Bristol-Meyers Squibb, Celgene, Janssen, Kesios,Novartis, and Takeda. No potential conflicts of interest were disclosed by theother authors.

Authors' ContributionsConception and design: C. Pawlyn, B. Barlogie, F.E. Davies, G.J. MorganDevelopment of methodology: C. Pawlyn, G.J. MorganAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): C. Pawlyn, C. Heuck, L. Melchor, D.B. Begum,G. Cook, M.T. Drayson, R.G. Owen, G.H. Jackson, B. Barlogie, F.E. Davies,B.A. Walker, G.J. MorganAnalysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis): C. Pawlyn, C. Heuck, L. Melchor, C.P. Wardell,A. Murison, S.S. Chavan, D.A. Cairns, E.M. Boyle, W.M. Gregory, F.E. Davies,B.A. Walker, G.J. MorganWriting, review, and/or revision of the manuscript: C. Pawlyn, M.F. Kaiser,L. Melchor, C.P. Wardell, D.C. Johnson, D.B. Begum, D.A. Cairns, G. Cook,M.T.Drayson, R.G.Owen,W.M.Gregory, G.H. Jackson, F.E.Davies, B.A.Walker,G.J. MorganAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases):C.Heuck,D.B. Begum,N.M.Dahir, P.Z. Proszek,D.A. Cairns, J.R. Jones, G.H. Jackson, B. Barlogie, G.J. MorganStudy supervision: G.J. Morgan

AcknowledgmentsThe authors thank all the patients and staff at centers throughout the

United Kingdom whose participation made this study possible, the NCRIHaemato-oncology subgroup, and to all principle investigators for theirdedication and commitment to recruiting patients to the study. The principalinvestigators at the four top recruiting centers were Dr. Don Milligan (Heartof England NHS Foundation Trust), Dr. Jindriska Lindsay (Kent andCanterbury Hospital), Dr. Nigel Russell (Nottingham University Hospital),and Dr. Clare Chapman (Leicester Royal Infirmary). The support of theClinical Trials Research Unit at The University of Leeds was essential tothe successful running of the study and the authors thank all the staffincluding Helen Howard, Corrine Collett, Jacqueline Ouzman and AlexSzubert. The authors also acknowledge The Institute of Cancer ResearchTumour Profiling Unit for their support and technical expertise in this study.and thank Tim Vojt for help with Figure 3.

Grant SupportThis work was supported by a Myeloma UK program grant, Cancer Research

UK CTAAC sample collection grants (C2470/A12136 and C2470/A17761), aCancer ResearchUKBiomarkers and ImagingDiscovery andDevelopment grant(C2470/A14261), and funds from the NIH Biomedical Research Centre at theRoyal Marsden Hospital and Institute of Cancer Research. Work carried out atthe Myeloma Institute, University of Arkansas for Medical Sciences was sup-ported by a grant from theNIH. C. Pawlyn is aWellcome Trust Clinical ResearchFellow, F.E. Davies is a Cancer Research UK Senior Cancer Fellow.

The costs of publication of this articlewere defrayed inpart by the payment ofpage charges. This article must therefore be hereby marked advertisement inaccordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Received July 28, 2015; revised March 24, 2016; accepted April 27, 2016;published OnlineFirst April 27, 2016.

References1. Morgan GJ, Walker BA, Davies FE. The genetic architecture of multiple

myeloma. Nat Rev Cancer 2012;12:335–48.2. Pawlyn C, Kaiser MF, Davies FE, Morgan GJ. Current and poten-

tial epigenetic targets in multiple myeloma. Epigenomics 2014;6:215–28.

3. Walker BA, Wardell CP, Chiecchio L, Smith EM, Boyd KD, Neri A, et al.Aberrant global methylation patterns affect the molecular pathogenesisand prognosis of multiple myeloma. Blood 2011;117:553–62.

4. Ezponda T, Popovic R, ShahMY,Martinez-Garcia E, Zheng Y, MinDJ, et al.The histone methyltransferase MMSET/WHSC1 activates TWIST1 to

Pawlyn et al.





promote an epithelial-mesenchymal transition and invasive properties ofprostate cancer. Oncogene 2013;32:2882–90.

5. Hudlebusch HR, Theilgaard-Monch K, Lodahl M, Johnsen HE, RasmussenT. Identification of ID-1 as a potential target gene of MMSET in multiplemyeloma. Br J Haematol 2005;130:700–8.

6. Kuo AJ, Cheung P, Chen K, Zee BM, Kioi M, Lauring J, et al. NSD2 linksdimethylation of histone H3 at lysine 36 to oncogenic programming. MolCell 2011;44:609–20.

7. Martinez-Garcia E, Popovic R,MinDJ, Sweet SM, Thomas PM, Zamdborg L,et al. The MMSET histone methyl transferase switches global histonemethylation and alters gene expression in t(4;14) multiple myeloma cells.Blood 2011;117:211–20.

8. Keats JJ, Reiman T, Maxwell CA, Taylor BJ, Larratt LM, Mant MJ, et al. Inmultiple myeloma, t(4;14)(p16;q32) is an adverse prognostic factor irre-spective of FGFR3 expression. Blood 2003;101:1520–9.

9. Walker BA, Leone PE, Chiecchio L, Dickens NJ, Jenner MW, Boyd KD,et al. A compendium of myeloma-associated chromosomal copynumber abnormalities and their prognostic value. Blood 2010;116:e56–65.

10. van Haaften G, Dalgliesh GL, Davies H, Chen L, Bignell G, Greenman C,et al. Somatic mutations of the histone H3K27 demethylase gene UTX inhuman cancer. Nat Genet 2009;41:521–3.

11. ChapmanMA, LawrenceMS, Keats JJ, Cibulskis K, Sougnez C, Schinzel AC,et al. Initial genome sequencing and analysis ofmultiple myeloma. Nature2011;471:467–72.

12. Oyer JA, Huang X, Zheng Y, Shim J, Ezponda T, Carpenter Z, et al. Pointmutation E1099K in MMSET/NSD2 enhances its methyltranferase activityand leads to altered global chromatin methylation in lymphoid malig-nancies. Leukemia 2014;28:198–201

13. Walker BA, Wardell CP, Murison A, Boyle EM, BegumDB, Dahir NM, et al.APOBEC family mutational signatures are associated with poor prognosistranslocations in multiple myeloma. Nat Commun 2015;6:6997.

14. Walker BA, Boyle EM,Wardell CP, Murison A, BegumDB, Dahir NM, et al.Mutational spectrum, copy number changes, and outcome: results of asequencing study of patients with newly diagnosedmyeloma. J ClinOncol2015;33:3911–20.

15. Kozarewa I, Rosa-Rosa JM, Wardell CP, Walker BA, Fenwick K, Assiotis I,et al. A modified method for whole exome resequencing from minimalamounts of starting DNA. PLoS One 2012;7:e32617.

16. Boeva V, Popova T, Bleakley K, Chiche P, Cappo J, Schleiermacher G, et al.Control-FREEC: a tool for assessing copy number and allelic content usingnext-generation sequencing data. Bioinformatics 2012;28:423–5.

17. Boyle EM, Proszek PZ, Kaiser MF, Begum D, Dahir N, Savola S, et al.A molecular diagnostic approach able to detect the recurrent geneticprognostic factors typical of presenting myeloma. Genes ChromosomesCancer 2015;54:91–8

18. Melchor L, Brioli A, Wardell CP, Murison A, Potter NE, Kaiser MF, et al.Single-cell genetic analysis reveals the composition of initiating clones andphylogenetic patterns of branching and parallel evolution in myeloma.Leukemia 2014;28:1705–15

19. St. Jude Children's Research Hospital. St. Jude – Washington UniversityPediatric Cancer Genome Project. Available from: https://www.stjude.org/research/pediatric-cancer-genome-project.html

20. Catalogue of Somatic Mutations in Cancer (COSMIC). Catalogue ofSomatic Mutations in Cancer (COSMIC) [database on the internet].Available from: http://cancer.sanger.ac.uk/cosmic.

21. Kumar P, Henikoff S, Ng PC. Predicting the effects of coding non-synon-ymous variants on protein function using the SIFT algorithm. Nat Protoc2009;4:1073–81.

22. HeuckCJ,QuP, vanRhee F,Waheed S,Usmani SZ, Epstein J, et al. Five geneprobes carrymost of the discriminatory power of the 70-gene riskmodel inmultiple myeloma. Leukemia 2014;28:2410–3.

23. Frampton GM, Fichtenholtz A, Otto GA, Wang K, Downing SR, He J, et al.Development and validation of a clinical cancer genomic profiling testbased on massively parallel DNA sequencing. Nat Biotechnol 2013;31:1023–31.

24. Lipson D. Identification of actionable genomic alterations in hematologicmalignancies by a clinical next generation sequencing-based assay(abstract). In: Proceedings of the 55th ASH Annual Meeting and Exposi-tion; 2013 Dec 7–10; New Orleans, LA: Washington, DC: ASH; 2013.Abstract nr 230.

25. Zhou X, Edmonson MN, Wilkinson MR, Patel A, Wu G, Liu Y, et al.Exploring genomic alteration in pediatric cancer using ProteinPaint. NatGenet 2016;48:4–6.

26. Li H, Kaminski MS, Li Y, Yildiz M, Ouillette P, Jones S, et al. Mutations inlinker histone genes HIST1H1 B, C, D, and E; OCT2 (POU2F2); IRF8; andARID1A underlying the pathogenesis of follicular lymphoma. Blood2014;123:1487–98.

27. Morin RD, Mungall K, Pleasance E, Mungall AJ, Goya R, Huff RD,et al. Mutational and structural analysis of diffuse large B-celllymphoma using whole-genome sequencing. Blood 2013;122:1256–65.

28. Giannakis M, Hodis E, JasmineMu X, Yamauchi M, Rosenbluh J, CibulskisK, et al. RNF43 is frequentlymutated in colorectal and endometrial cancers.Nat Genet 2014;46:1264–6.

29. Oyer JA, Huang X, Zheng Y, Shim J, Ezponda T, Carpenter Z, et al. Pointmutation E1099K in MMSET/NSD2 enhances its methyltranferase activityand leads to altered global chromatin methylation in lymphoid malig-nancies. Leukemia 2014;28:198–201.

30. Graubert TA, Payton MA, Shao J, Walgren RA, Monahan RS, Frater JL, et al.Integrated genomic analysis implicates haploinsufficiency of multiplechromosome 5q31.2 genes in de novo myelodysplastic syndromes patho-genesis. PLoS One 2009;4:e4583.

31. Hu Z, Gomes I, Horrigan SK, Kravarusic J, Mar B, Arbieva Z, et al. A novelnuclear protein, 5qNCA (LOC51780) is a candidate for the myeloidleukemia tumor suppressor gene on chromosome 5 band q31. Oncogene2001;20:6946–54.

32. Imielinski M, Berger AH, Hammerman PS, Hernandez B, Pugh TJ, Hodis E,et al. Mapping the hallmarks of lung adenocarcinoma with massivelyparallel sequencing. Cell 2012;150:1107–20.

33. Pasqualucci L, Dominguez-Sola D, Chiarenza A, Fabbri G, Grunn A,Trifonov V, et al. Inactivating mutations of acetyltransferase genes inB-cell lymphoma. Nature 2011;471:189–95.

34. Pasqualucci L, Trifonov V, Fabbri G, Ma J, Rossi D, Chiarenza A, et al.Analysis of the coding genome of diffuse large B-cell lymphoma.Nat Genet2011;43:830–7.

35. Morin RD, Mendez-Lago M, Mungall AJ, Goya R, Mungall KL, Corbett RD,et al. Frequent mutation of histone-modifying genes in non-Hodgkinlymphoma. Nature 2011;476:298–303.

36. Iyer NG, Ozdag H, Caldas C. p300/CBP and cancer. Oncogene 2004;23:4225–31.

37. Bleeker FE, Lamba S, Leenstra S, Troost D, Hulsebos T, VandertopWP, et al.IDH1 mutations at residue p.R132 (IDH1(R132)) occur frequently inhigh-grade gliomas but not in other solid tumors. Human Mutat 2009;30:7–11.

38. Schnittger S,HaferlachC,UlkeM,AlpermannT, KernW,Haferlach T. IDH1mutations are detected in 6.6% of 1414 AML patients and are associatedwith intermediate risk karyotype and unfavorable prognosis in adultsyounger than 60 years and unmutated NPM1 status. Blood 2010;116:5486–96.

39. Mardis ER, Ding L, Dooling DJ, Larson DE, McLellan MD, Chen K, et al.Recurring mutations found by sequencing an acute myeloid leukemiagenome. N Engl J Med 2009;361:1058–66.

40. Marcucci G, Maharry K, Wu YZ, Radmacher MD, Mrozek K, MargesonD, et al. IDH1 and IDH2 gene mutations identify novel molecularsubsets within de novo cytogenetically normal acute myeloid leuke-mia: a Cancer and Leukemia Group B study. J Clin Oncol 2010;28:2348–55.

41. Amary MF, Bacsi K, Maggiani F, Damato S, Halai D, Berisha F, et al. IDH1and IDH2 mutations are frequent events in central chondrosarcoma andcentral and periosteal chondromas but not in other mesenchymaltumours. J Pathol 2011;224:334–43.

42. Jeziskova I, Razga F, Bajerova M, Racil Z, Mayer J, Dvorakova D. IDH2mutations in patients with acute myeloid leukemia: missense p.R140mutations are linked to disease status. Leukemia Lymphoma 2010;51:2285–7.

43. Andersson AK, Miller DW, Lynch JA, Lemoff AS, Cai Z, Pounds SB, et al.IDH1 and IDH2 mutations in pediatric acute leukemia. Leukemia2011;25:1570–7.

44. Papaemmanuil E, Gerstung M, Malcovati L, Tauro S, Gundem G, VanLoo P, et al. Clinical and biological implications of driver mutations inmyelodysplastic syndromes. Blood 2013;122:3616–27.






45. LiM, Zhang Z, Li X, Ye J,WuX, Tan Z, et al.Whole-exome and targeted genesequencing of gallbladder carcinoma identifies recurrent mutations in theErbB pathway. Nat Genet 2014;46:872–6.

46. Gaidzik VI, Schlenk RF, Paschka P, Stolzle A, Spath D, Kuendgen A, et al.Clinical impact of DNMT3A mutations in younger adult patients withacute myeloid leukemia: results of the AML Study Group (AMLSG). Blood2013;121:4769–77.

47. Ley TJ, Ding L, Walter MJ, McLellan MD, Lamprecht T, Larson DE, et al.DNMT3A mutations in acute myeloid leukemia. N Engl J Med 2010;363:2424–33.

48. Stegelmann F, Bullinger L, Schlenk RF, Paschka P, Griesshammer M,Blersch C, et al. DNMT3A mutations in myeloproliferative neoplasms.Leukemia 2011;25:1217–9.

49. O'Shaughnessy A, Hendrich B. CHD4 in the DNA-damage response andcell cycle progression: not so NuRDy now. Biochem Soc Transact 2013;41:777–82.

50. Kadoch C, Hargreaves DC, Hodges C, Elias L, Ho L, Ranish J, et al.Proteomic and bioinformatic analysis of mammalian SWI/SNF com-plexes identifies extensive roles in human malignancy. Nat Genet2013;45:592–601.

51. Shain AH, Pollack JR. The spectrum of SWI/SNF mutations, ubiquitous inhuman cancers. PLoS One 2013;8:e55119.

52. Watanabe R, Ui A, Kanno S, Ogiwara H, Nagase T, Kohno T, et al. SWI/SNF factors required for cellular resistance to DNA damage includeARID1A and ARID1B and show interdependent protein stability. CancerRes 2014;74:2465–75.

53. Skulte KA, Phan L, Clark SJ, Taberlay PC. Chromatin remodeler muta-tions in human cancers: epigenetic implications. Epigenomics 2014;6:397–414.

54. Pickering CR, Zhou JH, Lee JJ, Drummond JA, Peng SA, Saade RE, et al.Mutational landscape of aggressive cutaneous squamous cell carcinoma.Clin Cancer Res 2014;20:6582–92.

55. Martin D, Abba MC, Molinolo AA, Vitale-Cross L, Wang Z, Zaida M, et al.The head and neck cancer cell oncogenome: a platform for the develop-ment of precision molecular therapies. Oncotarget 2014;5:8906–23.

56. Okosun J, Bodor C, Wang J, Araf S, Yang CY, Pan C, et al. Integratedgenomic analysis identifies recurrent mutations and evolution patternsdriving the initiation and progression of follicular lymphoma. Nat Genet2014;46:176–81.

57. Yang SM, Kim BJ, Norwood Toro L, Skoultchi AI. H1 linker histonepromotes epigenetic silencing by regulating both DNA methylation andhistone H3 methylation. Proc Natl Acad Sci USA 2013;110:1708–13.

58. Haferlach T, Nagata Y, Grossmann V, Okuno Y, Bacher U, Nagae G, et al.Landscape of genetic lesions in 944 patients with myelodysplastic syn-dromes. Leukemia 2014;28:241–7.

59. Im AP, Sehgal AR, Carroll MP, Smith BD, Tefferi A, Johnson DE, et al.DNMT3A and IDH mutations in acute myeloid leukemia and othermyeloid malignancies: associations with prognosis and potential treat-ment strategies. Leukemia 2014;28:1774–83.

60. Bitler BG, Aird KM, Garipov A, Li H, Amatangelo M, Kossenkov AV, et al.Synthetic lethality by targeting EZH2methyltransferase activity inARID1A-mutated cancers. Nat Med 2015;21:231–8.


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