Transcriptome Sequencing Reveals a Profile That Corresponds to Genomic Variants in Waldenström Macroglobulinemia

From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only. Regular Article

LYMPHOID NEOPLASIA Transcriptome sequencing reveals a proﬁle that corresponds to genomic variants in Waldenstrom¨ macroglobulinemia

Zachary R. Hunter,1,2 Lian Xu,1 Guang Yang,1,2 Nicholas Tsakmaklis,1 Josephine M. Vos,1 Xia Liu,1 Jie Chen,1 Robert J. Manning,1 Jiaji G. Chen,1 Philip Brodsky,1 Christopher J. Patterson,1 Joshua Gustine,1 Toni Dubeau,1 Jorge J. Castillo,1,2 Kenneth C. Anderson,2,3 Nikhil M. Munshi,2-4 and Steven P. Treon1,2

1Bing Center for Waldenstr¨om’sMacroglobulinemia, Dana-Farber Cancer Institute, Boston, MA; 2Department of Medicine, Harvard Medical School, Boston, MA; 3Jerome Lipper Multiple Myeloma Center, Dana-Farber Cancer Institute, Boston, MA; and 4Department of Medicine, Boston VA Healthcare System, West Roxbury, MA

Key Points Whole-genome sequencing has identified highly prevalent somatic mutations including MYD88, CXCR4,andARID1A in Waldenstr¨om macroglobulinemia (WM). The impact of these • Transcription profiles and other somatic mutations on transcriptional regulation in WM remains to be clarified. We associated with mutated performed next-generation transcriptional profiling in 57 WM patients and compared MYD88, CXCR4, ARID1A, findings to healthy donor B cells. Compared with healthy donors, WM patient samples abnormal cytogenetics showed greatly enhanced expression of the VDJ recombination genes DNTT, RAG1,and RAG2 AICDA including 6q2, and familial , but not . Genes related to CXCR4 signaling were also upregulated and CXCR4 CXCL12 VCAM1 CXCR4 WM are described. included , , and regardless of mutation status, indicating a potential role for CXCR4 signaling in all WM patients. The WM transcriptional profile was • Mutated CXCR4 profiles equally dissimilar to healthy memory B cells and circulating B cells likely due increased show impaired expression differentiation rather than cellular origin. The profile for CXCR4 mutations corresponded of the tumor suppressor to diminished B-cell differentiation and suppression of tumor suppressors upregulated response induced by by MYD88 mutations in a manner associated with the suppression of TLR4 signaling L265P MYD88 and also relative to those mutated for MYD88 alone. Promoter methylation studies of top findings G-protein/MAPK inhibitors. failed to explain this suppressive effect but identified aberrant methylation patterns in MYD88 wild-type patients. CXCR4 and MYD88 transcription were negatively correlated, demonstrated allele-specific transcription bias, and, along with CXCL13, were associated with bone marrow disease involvement. Distinct gene expression profiles for patients with wild-type MYD88, mutated ARID1A, familial predisposition to WM, chr6q deletions, chr3q amplifications, and trisomy 4 are also described. The findings provide novel insights into the molecular pathogenesis and opportunities for targeted therapeutic strategies for WM. (Blood. 2016;128(6):827-838) Introduction

Whole-genome sequencing identified several highly recurring somatic (CXCR4WHIM) are determinants of disease presentation, as well as mutations in patients with Waldenstrom¨ macroglobulinemia (WM).1,2 resistance to ibrutinib.3,6,10 Somatic mutations in ARID1A, a member of In over 90% of WM patients, a single point mutation at NM_002468: the SWItch/sucrose nonfermentable family and epigenetic regula- c.978T.C (rs387907272) in MYD88 is found, resulting in a tor, are also found in 20% of WM patients. Gene losses affecting p.Leu265Pro (L265P) amino acid change.1,3 MYD88 is an adaptor for NF-kB signaling (ie, HIVEP2, TNFAIP3), as well as the ARID1A Toll-like (TLR) and interleukin 1 (IL1) receptors, and the MYD88L265P homolog ARID1B are present in most WM patients.2,11 Deletions in mutation triggers constitutive activation of NF-kB through IRAK and chromosome 6q with and without concurrent 6p amplifications, BTK.1,4,5 Mutated MYD88 WM patients show greater overall survival trisomy 3, and amplifications of 3q, as well as trisomy 4, are also and clinical responses to the BTK inhibitor ibrutinib.6,7 commonly found in WM.12-14 Previous array-based gene expres- Activating CXCR4 frameshift or nonsense mutations in the sion studies of WM were largely conducted prior to these genomic C-terminal tail are found in 30% to 40% of WM patients, are primarily discoveries and therefore the effects of recurrent somatic events on subclonal, and almost always associated with MYD88L265P.2,3,8 These transcriptional regulation remain to be clarified.15-17 We therefore somatic mutations are similar to the causal germ line variants that performed next-generation RNA sequencing in 57 WM patients underlie WHIM (warts, hypogammaglobulinemia, infection, and and compared findings to sorted healthy donor-derived nonmemory myelokathexis) syndrome.2,9 In WM, somatic CXCR4 mutations (CD191CD272) and memory (CD191CD271) B cells. The latter

Submitted March 30, 2016; accepted June 3, 2016. Prepublished online as There is an Inside Blood Commentary on this article in this issue. Blood First Edition paper, June 14, 2016; DOI 10.1182/blood-2016-03- The publication costs of this article were defrayed in part by page charge 708263. payment. Therefore, and solely to indicate this fact, this article is hereby The online version of this article contains a data supplement. marked “advertisement” in accordance with 18 USC section 1734.

BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 827 From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

828 HUNTER et al BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 represent the B-cell population from where most cases of WM are nonsignificant genes are distributed around zero, genes were further filtered for thought to be derived.18,19 those with permutation importance scores greater than the absolute value of the lowest scoring predictor. For reproducibility, the random seed for the analysis was set to 265. Principal component analysis (PCA) was conducted using R’s prcomp, Pearson’s product-moment coefficient was used for correlation studies, and cohort mean values were compared using the Wilcoxon rank sum test. Methods

Sample selection and characterization Gene expression, sequencing, and methylation studies

Bone marrow (BM) aspirates were collected from 57 patients with the WM Gene expression results were validated by using TaqMan gene expression assays consensus diagnosis.20 Participants provided informed consent for sample Hs01027785_m1 (DUSP4), hs00244839_m1 (DUSP5), Hs00243182_m1 (RGS13), collection per the Dana-Farber/Harvard Cancer Center Institutional Review Board. Hs00892674_m1 (RGS16), Hs00698249_m1 (RASSF6), Hs00396602_m1 1 fi WM cells were isolated by CD19 magnetic-activated cell sorting (MACS) (WNK2), Hs00924602_m1 (PRDM5) (Thermo Fisher Scienti c, Waltham MA). fi microbead selection (Miltenyi Biotec, Auburn, CA) from Ficoll-Paque Methylation-speci cPCRassaysforRASSF6, WNK2,andPRDM5 were conducted on bisulfite-converted DNA using previously established (Amersham-Pharmacia Biotech, Piscataway, NJ) separated BM mononuclear 31-33 cells. Peripheral blood mononuclear cells from nine healthy donors (HDs) were protocols. sorted for nonmemory (CD191CD272) and memory B cells (CD191CD271) using a memory B-cell isolation kit (Miltenyi Biotec). RNA and DNA were purified using the AllPrep mini kit (Qiagen, Valencia, CA). Most samples were previously characterized by whole-genome sequencing and all samples were screened for Results MYD88 and CXCR4 gene mutations by Sanger sequencing.2 MYD88L265P and CXCR4 c.1013C.G and c.1013C.A mutations were analyzed by allele-specific The clinical characteristics for the 57 WM patients are presented in 8,21 polymerase chain reaction (PCR) as previously described. Table 1. The distribution of somatic mutations and cytogenetic findings in these patients was similar to those previously described.34 The top Next-generation sequencing and analysis 500 differentially expressed genes for each comparison discussed Transcriptome profiling was conducted by the Center for Cancer Computational below are available as a part of the supplemental Data (available on the Biology at the Dana-Farber Cancer Institute (Boston, MA) using the NEBNext Blood Web site). Ultra RNA library prep kit (New England BioLabs, Ipswich, MA). The paired-end samples were run 2 per lane for 50 cycles on an Illumina HiSeq (Illumina, San Comparing WM to healthy donor B cells Diego, CA). Read-level data are available through dbGAP accession (applied). Reads were aligned to KnownGene HG19/GRCh37 reference using STAR Comparison of WM and HD samples generated a list of 13 571 (Spliced Transcripts Alignment to a Reference).22 Genes with mean raw read differentially expressed genes. To gain further insight into these gene counts of ,10 were not analyzed, leaving 16 888 expressed genes for analysis. expression differences, this gene list was sorted by absolute log2 fold Read counts per gene were obtained using featureCounts from Rsubread, and change, revealing a marked upregulation of DNTT, RAG1, RAG2,and analyzed using voom from the edgeR/limma Bioconductor packages in R IGLL1 in WM samples. These genes have important functional roles in (R Foundation for Statistical Computing, Vienna, Austria).23-27 Differential expression models accounted for sex, prior treatment, as well as MYD88 and VDJ recombination and B-cell receptor (BCR) signaling. Other CXCR4 mutation status. A false discovery rate (FDR) cutoff of 10% was used to upregulated genes relevant to WM biology and pathogenesis included determine significant differentially expressed genes. Functional enrichment analysis CXCL12, VCAM1, CD5L, BMP3,andIGF1. Among the top 40 was conducted using Ingenuity Pathway Analysis (Qiagen). Clustering and differentially expressed genes sorted by absolute expression in WM as correlation analysis was conducted using the variance stabilizing transformation of measured in TpM were CXCR4, the WM prognostic marker B2M,and the count data from the Bioconductor DESeq2 package.28 In all other cases, 2genesrelatedtoBCRsignaling:CD79A and CD79B.Toexamine estimates of gene expression levels are represented in transcripts per million (TpM). transcriptional similarities between the samples in an unbiased manner, Log-fold change (LFC) listed in text and tables are derived from the limma analysis. multidimensional scaling of the 500 genes with the highest variance was used to best represent the data in 2 dimensions (Figure 1A). CXCR4 transduced cell lines and gene expression analysis Although peripheral B cells (PBs) and HD memory B cells (MBs) L265P WHIM Previously described BCWM.1 and MWCL-1 cell lines transduced to clustered together, separation between MYD88 CXCR4 and express CXCR4 with or without activating mutations observed in WM MYD88L265PCXCR4WT genotyped WM patient samples was observed. patients were used to model CXCR4-stimulated gene expression changes in To examine whether the patterns of expression were similar between HD WM.10 Briefly, CXCR4 complementary DNA (cDNA) transcripts were samples and patient mutational groups, a correlation coefficient based subcloned into plenti-internal ribosomal entry site (IRES)–green fluorescent on overall gene expression was generated for each sample-to-sample 4 protein (GFP) vectors, and stably transduced using a lentiviral system. In comparison. These values were averaged together by HD PB/MB cell addition to wild-type (WT) CXCR4, vectors with the mutations c.1013C.G type and WM MYD88/CXCR4 genotype to show the extent of (p.Ser338*), c.932_933insT (p.Thr311fs), and c.1030_1041delinsGT (p.Ser344fs) were generated. Cell lines were stimulated with 50 nM of the correlation between groups (Figure 1B). Strong correlations were CXCR4 ligand CXCL12 (SDF1A) (R&D Systems, Minneapolis MN) for observed across all subsets, though mean correlation values between L265P WHIM 2 hours. RNA from each sample was extracted at baseline and at 2 hours. HD B cells and MYD88 CXCR4 patients were significantly L265P WT Gene expression was assessed using Affymetrix Human Gene ST 2.0 arrays higher vs those between HD B cells and MYD88 CXCR4 (Affymetrix, Santa Clara, CA). Array data are archived in the Gene patients (P 5 .025 and P 5 .036 for PB and MB cells, respectively). Expression Omnibus database (accession number GSE83150). Array data No preferential association was noted between WM samples and were analyzed using the Oligo and limma R bioconductor packages.24,29 HD B-cell type using PCA of the 596 genes differentially expressed between HD PB and MB cells (Figure 1C; supplemental Figure 1). To Statistical analysis better understand the role of B-cell differentiation in WM, we analyzed Random forest analysis was implemented using the cforest function in the party 19 genes linked to the transition from MB to plasmablasts and plasma 35,36 package in R.30 Models were constructed using the cforest_unbiased function cells (Figure 1D). Many of these genes were not only significantly with 20 predictors per tree over 10 000 trees. As variable importance scores of differentially expressed between HD and WM samples, but between From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 TRANSCRIPTOME OF WALDENSTROM¨ MACROGLOBULINEMIA 829

Table 1. Patient characteristics methylation in other malignancies: PRDM5, WNK2,andRASSF6.32,33,37 Median or count Range or percentage Gene expression was validated by PCR inHD B-cell samples as well as Sex, female 21/57 36.8% in MYD88 and CXCR4 genotyped WM patient samples (supplemen- Age at diagnosis, y 60 40-78 tal Figure 4A). Mirroring the transcriptome data, RASSF6 expression L265P WT L265P WHIM b2 microglobulin at diagnosis .3, mg/L 19/46 41.3% was elevated in MYD88 CXCR4 and MYD88 CXCR4 WT Age at biopsy, y 62 41-83 samples but not in HD or MYD88 samples whereas WNK2 and Familial history of WM 3/57 5.3% PRDM5 were elevated only in the MYD88L265PCXCR4WT patients. Splenomegaly 13/57 22.8% To determine relevance of these methylation sites in hematologic Lymphadenopathy 30/57 52.6% malignancies, WM, B-cell lymphoma, and myeloma cell lines were Bone marrow involvement, % 60 5-95 assessed for promoter methylation using methylation-speciﬁcPCR Hemoglobin, g/dL 11.1 8.2-14.5 assays (supplemental Figure 4B). Sixteen MYD88 and CXCR4 Serum IgM, mg/dL 3750 416-8320 Serum IgG, mg/dL 517 82-3890 genotyped primary WM patient samples and 4 healthy donor B-cell Serum IgA, mg/dL 40 6-516 samples were then tested (supplemental Figure 4C). Robust pro- MYD88 mutations 52/57 91.2% moter methylation was observed for WNK2 and PRDM5 in the WT WT CXCR4 mutations 23/57 40.0% MYD88 CXCR4 patients but was absent in HD samples. Partial ARID1A mutations 5/51 9.8% promoter methylation of WNK2 and PRDM5 was also observed in CD79B mutations 4/51 7.8% some MYD88L265P samples. This correlated to a relative reduction in Deletion chromosome 6q 24/53 45.3% gene expression only for WNK2 in the MYD88L265PCXCR4WHIM Amplification chromosome 6p 6/53 11.3% genotyped patient sample with the strongest methylation signal. Amplification chromosome 3q 11/52 19.2% Amplification chromosome 4 10/52 21.1% Cell line models of CXCR4WHIM signaling

To better understand interactions between CXCR4WHIM and WM patients based on MYD88 and CXCR4 mutation status suggesting MYD88L265P in WM, lentiviral transduction was performed to reduced differentiation in both MYD88L265PCXCR4WHIM and, overexpress CXCR4WT, or 1 of 3 documented CXCR4WHIM tran- in particular, MYD88WTCXCR4WT samples. Separation of patient scripts (c.1013C.G, c.932_933insT, or c.1030_1041delinsGT) in samples by genotype can be seen in the PCA of these 19 genes in MYD88L265P expressing BCWM.1 WM cells. Transduced cells were Figure 1E. BCL2 and BCL2L1 were upregulated in all WM samples, stimulated in triplicate with CXCL12 for 2 hours and gene expression however, analysis of the larger BCL2 family revealed stratification profiling performed. The relative change in gene expression from of samples by HD cell type and MYD88/CXCR4 mutation status baseline was then compared between CXCR4WT and CXCR4WHIM (supplemental Figure 2). The proapoptotic BAX gene was down- transduced cells revealing 61 differentially expressed genes. Of these regulated in WM samples and PMAIP1 was uniquely upregulated in genes, 16 of 61 (26.2%) were encompassed in the gene expression MYD88L265PCXCR4WT WM. signature for MYD88L265PCXCR4WHIM patient samples (supplemental Table 1). In 8 of 16 (50%) of these genes, the direction of the change L265P WT Transcriptional effects of CXCR4WHIM relative to MYD88 CXCR4 was same as that observed in the patient samples. However, it was the genes whose direction of change Restricting the analysis to WM patients, PCA of the top 500 high was opposed that revealed the most about the underlying biology. These variance genes accounted for 41% of the overall variability within discordant genes were enriched for suppressors of MAPK and G-protein the first 2 components (Figure 2A). Tumor suppressors including signaling downstream of CXCR4. They were preferentially induced WNK2, TP53I11, PRDM5, and CABLES1 influence the second in CXCR4WHIM cell lines by CXCL12 stimulation and suppressed L265P WT principal component that separates MYD88 CXCR4 from in MYD88L265PCXCR4WHIM patient samples.10,38 Interrogation of L265P WHIM WT WT MYD88 CXCR4 and MYD88 CXCR4 samples (supple- these dual specificity phosphatases (DUSP) and regulator of G-protein mental Figure 1). Supervised clustering of the 3103 genes signaling (RGS) genes revealed RGS1, RGS2, RGS13, DUSP1, DUSP2, L265P WT differentially expressed between MYD88 CXCR4 and DUSP4,DUSP5,DUSP10,DUSP16,andDUSP22 were all significantly L265P WHIM MYD88 CXCR4 revealed that most expression differences suppressed in MYD88L265PCXCR4WHIM vs MYD88L265PCXCR4WT L265P WHIM in MYD88 CXCR4 patients followed a pattern resembling patients. Expression in patients of DUSP4, DUSP5, RGS13,and L265P HD samples, despite carrying the MYD88 mutation (Figure 2B). RGS16, which were selected for validation based on the cell line studies, The top predicted upstream regulator for this gene signature was the can be seen in supplemental Figure 5A. The increased expression of these inhibition of lipopolysaccharide signaling (Figure 2C). To further genes in CXCR4WHIM compared with CXCR4WT transduced lines was explore how CXCR4 mutations modulate MYD88 signaling in WM, validated by real-time PCR (supplemental Figure 5B). Dose-response the gene signature was filtered for genes involved in TLR signaling. curves for these genes following CXCL12 stimulation was conducted in L265P WHIM MYD88 CXCR4 patients showed downregulation of the transduced BCWM.1 and MWCL-1 (supplemental Figure 5C). lipopolysaccharide receptors TLR4 and NOD2, and upregulation of TLR7 and IRAK3 (supplemental Figure 3A). The most significant Gene signatures associated with other clinical and results associated with CXCR4 mutation status were silencing of genes genomic features in patients with MYD88L265PCXCR4WHIM that were elevated in MYD88L265PCXCR4WT patients (supplemental Figure 3B). For the 5 patients with ARID1A mutations, 16 differentially expressed genes were observed. The ARID1A-mutated population fi Promoter methylation studies also demonstrated elevated BM disease in ltration (median, 90%; range, 70%-95%) when compared with non-ARID1A-mutated To investigate the role of methylation in genes differentially expressed patients (median, 58%; range, 5%-95%; P 5 .0259). No statistically based on CXCR4 and MYD88 mutation status, 3 such genes were significant gene expression changes were observed in the 4 patients selected for screening based on their regulation by promoter with CD79B mutations. Analysis based on somatic cytogenetic From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

830 HUNTER et al BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6

MYD88L265PCXCR4WT M L265P WHIM 3 MYD88 CXCR4 MYD88WT M WT MYD88WTCXCR4WT CXCR4 Memory B-Cell 2 M Peripheral Blood B-Cell MM L265P MCMMM MYD88 MC M CXCR4WHIM MCMC M M WT MC value 1 MC 1.0 M M MC MCMCMMC PBMB WT MYD88L265P 0.9 MMC MM CXCR4WT MB MC M 0.8 0 MBPB MC MC M MC MM PB MC M M MB PB MC MB MB M Memory PB WT MCM M –1 PB MB B-Cell Leading logFC dim 2 PBMB M MB MC PB MC M MC M WT WT –2 Pheripheral B-Cell PB M

–4 –2 0 2 4 Pheripheral Memory MYD88L265P MYD88L265P MYD88WT B-Cell B-Cell CXCR4WT CXCR4WHIM CXCR4WT Leading logFC dim 1

C E 0 10 –4 –62 –30 –20 –10 –2 0 2 2 0 4 4 6 20 4 15 L265P WT PC3 10 MYD88 CXCR4 2 MYD88L265PCXCR4WHIM 5 MYD88WTCXCR4WT 0 Memory B-Cell 0 PC3 Peripheral Blood B-Cell –5 –2 –10 PC1 20 10 PC2 0 –10 PC1 PC2 –20 #

D PRDM1 XBP1 IRF4 SPIB CD19 CD38 CXCR5 BCL6 CCR2 IL4R BLK MS4A1 SDC1 CD27 PAX5 BACH2 EBF1 SOX5 IRF8 12 # # ** # ## *# * * * 10 ** * # # #* # * # * # *# # # *# * * # # *# # # # 8 # *# # * * # # # # * *# * * * # * # # # * *# ** 6 * * # * # # *# * * # * # *# 4 * ** * * 2 Transcripts per Million (Log 2) Transcripts * 0 * L265P WT MYD88L265PCXCR4WT MYD88L265PCXCR4WHIM MYD88WTCXCR4WT * P < .05 versus MYD88 CXCR4 HD Memory B-cell HD Peripheral Blood B-cell # P < .05 versus HD Memory B-cells

Figure 1. Comparisons of transcriptional activity for genotyped WM patient samples and healthy donor B cells. (A) Multidimensional scaling of the top 500 genes for all HD and WM samples using Voom transformed expression values in Limma’s plotMDS function.24 (B) Pearson correlation coefficients from the correlation matrix of gene expression were averaged over all the samples for each major group. Correlation values with the HD samples were higher for those WM patients with CXCR4 mutations (P 5 .025 and P 5 .036 for HD PB and HD MB cells, respectively). (C) The 596 genes differentially expressed between the HD PB and HD MB cells were analyzed across all samples using PCA. No preferential relationship between WM samples and HD B-cell type was observed. The first 3 principal components were used to plot the samples representing 54% of the overall variability. (D) Median expression levels for 19 genes known to regulate B-cell to plasma cell transition for HD B-cell and genotyped WM samples. Error bars represent full data range per for each group. (E) PCA of the 19 genes related to B-cell to plasma cell transition shown in panel D depicting the first 3 principal components which covered over 65% of the overall gene expression variability. From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 TRANSCRIPTOME OF WALDENSTROM¨ MACROGLOBULINEMIA 831

AC 15 Predicted Activation Upstream Regulator Activation z-score P-value lipopolysaccharide Inhibited –2.068 1.08E-11 10 IFNG –0.28 1.27E-11 TGFB1 1.622 6.13E-11 5 Interferon alpha 0.076 4.22E-10 TNF 0.558 2.45E-09 TREM1 –1.411 3.34E-08 PC2 0

–5

–10

–20 –10 0 10 20 30 PC1

B MYD88L265P MYD88WT HD B-Cells CXCR4WT CXCR4Nonsense CXCR4Frame Shift CD19+CD27+ CD19+CD27–

Figure 2. Role of CXCR4WHIM mutations in MYD88L265P-mutated WM. (A) PCA of the top 500 high variance genes within the WM patient samples using the first 2 principal components. WM patients with the MYD88L265PCXCR4WT genotype are shown in red; patients mutated for MYD88 with CXCR4 nonsense (NS) or frameshift (FS) mutations are shown in dark and light green, respectively; and patients with MYD88WTCXCR4WT are shown in purple. (B) Heat map of the 3103 genes differentially expressed between the MYD88L265PCXCR4WT and MYD88L265PCXCR4WHIM genotyped samples. Gene order was determined using hierarchical clustering of MYD88L265PCXCR4WT and MYD88L265PCXCR4WHIM expression data whereas the samples where arranged by genotype. Expression data for MYD88WTCXCR4WT WM and healthy donor B-cell samples were added to the heat map for comparison with intact clustered gene order. (C) Top predicted upstream targets of the differentially expressed genes from Ingenuity Pathway Analysis. abnormalities included deletion of chromosome 6q (131 genes), To identify genes associated with relevant WM clinical parameters, amplification of chromosome 6p (65 genes), amplification of chr3q all genes were tested for correlation with serum immunoglobulin including trisomy 3 (11 genes), and trisomy 4 (776 genes). A distinct M (IgM) levels, hemoglobin, and BM disease involvement. After gene expression signature for 263 genes was also observed in the filtering the results by significance (P , .05) and the absolute value of 3 patients who had first-degree relatives with WM. No statistically the correlation estimate .0.3, 312 genes were found to be associated significant differences in gene expression were observed based on with IgM, 965 with hemoglobin, and 3738 with BM involvement. presence of extramedullary disease, elevated B2Mlevels(.3mg/dL), These genes were combined with MYD88/CXCR4 genotype, age, or International Waldenstrom¨ Macroglobulinemia Prognostic Scoring sex, and treatment status in a random forest regression analysis for System score. the first 37 study patients, leaving the remaining 20 patients for From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

832 HUNTER et al BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6

A Serum IgM AJUBA SHF IRS1 Final Model

6000 6000 6000 8000

4000 4000 4000

2000 2000 2000 6000 0 0 0 0.0 0.5 1.0 1.5 2.0 2.5 1.0 1.5 2.0 2.5 1.52.02.53.03.54.04.55.0 BDH2 RSPH3 CEPZ0S 4000 6000 6000 6000

4000 4000 4000 2000 2000 2000 2000 Serum IgM (mg/dl) 0 0 0 2.0 2.5 3.0 3.5 4.0 4.5 1.0 1.5 2.0 2.5 4.4 4.6 4.8 5.0 5.2 5.4 5.6 Serum IgM (mg/dl) TNFRSF17 ITGA3 BMP3 0 6000 6000 6000 0 2000 4000 6000 8000 4000 4000 4000

2000 2000 2000 Predicted Levels (mg/dl)

0 0 0 Adjusted R-squared: 0.4 Training RMSE: 1,270.3 3 4 5 6 7 1.0 1.5 2.0 2.5 3.0 3.5 2 4 6 8 P-value: 3.129e-5 Validation RMSE: 1,340.1 Gene Expression (TpM)

B Bone Marrow Involvement CDC23 AKAP1 KDM1B Final Model

80 80 80 100 60 60 60 40 40 40

20 20 20 80

3.0 3.5 4.0 4.5 5.0 5.5 2.5 3.0 3.5 4.0 4.5 3.5 4.0 4.5 5.0 5.5 6.0 6.5 CXCL13 CXCR4 MYD88 60

80 80 80 60 60 60 40 40 40 40 20 20 20 20 0 1 2 3 4 5 10 11 12 13 5.0 5.5 6.0 6.5 7.0 TP53 TOP3A

BCL7A Bone Marrow Involvement (%) 0

Bone Marrow Involvement (%) 80 80 80

60 60 60 0 4020 60 80 100 40 40 40 Predicted Levels (%) 20 20 20 Adjusted R-squared: 0.5 Training RMSE: 16.1 3.54.04.55.05.56.06.5 2.5 3.0 3.5 4.0 4.5 2 4 6 8 P-value: 3.469e-7 Validation RMSE: 19.3 Gene Expression (TpM)

C Hemoglobin Levels AJUBA SEC62 NKIRAS1 Final Model 14 14 14 15 13 13 13 12 12 12 11 11 11 14 10 10 10 9 9 9 8 8 8 13 0.0 0.5 1.0 1.5 2.0 2.5 5.5 6.0 6.5 7.0 7.5 2.0 2.5 3.0 3.5 OS9 NOTCH1 PIK3AP1 12

14 14 14 13 13 13 11 12 12 12 11 11 11 10 10 10 10 9 9 9 8 8 8 7.5 8.0 8.5 2 3 4 5 6.0 6.5 7.0 7.5 8.0 8.5 9 Hemoglobin Levels (g/dl) Hemoglobin (g/dl) CXCL13 CD46 PIK3CD 8 14 14 14 13 13 13 12 12 12 8 109 11 12 13 1514 11 11 11 10 10 10 Predicted Levels (g/dl) 9 9 9 8 8 8 Adjusted R-squared: 0.3 Training RMSE: 0.95 0 1 2 3 4 5 4.5 5.0 5.5 6.0 6.5 7.0 7.5 4.5 5.0 5.5 6.0 6.5 7.0 P-value: 2.354e-4 Validation RMSE: 1.492 Gene Expression (TpM)

Figure 3. From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

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A 70% MYD88 CXCR4 60%

50%

40%

30%

Percent of Reads Percent 20% Supporting Mutant Allele 10%

0% WM1 WM2 WM3 WM4 WM5 WM6 WM7 WM8 WM9 WM10 WM11 S338 C/G S338 C/A S338 C/G S338 C/A S338 C/A R334 C/T S338 C/A S338 C/G S338 C/A S338 C/G S338 C/G

B MYD88L265P CXCR4WHIM-S338X DNA RNA DNA RNA WM1 WM1 L265P T/C S338X C/G

WM7 WM7 L265P T/C S338X C/A

WM8 WM8 L265P T/C S338X C/G

WM10 WM10 L265P T/C S338X C/G

Figure 4. Mutant allele fraction analysis for MYD88 and CXCR4 in concurrent DNA and RNA WM patient samples. (A) Percentage of reads supporting the mutant allele of MYD88 and CXCR4 for 11 patients with MYD88L265P and nonsense (NS) CXCR4WHIM mutations. Median coverage of the affected nucleotide was 193 (range, 38-456) reads for MYD88 and 11 760 (range, 3318-15 883) reads for CXCR4. The amino acid location and nucleotide change for the CXCR4WHIM-NS mutation is listed for each patient sample. (B) Sanger sequencing of paired DNA and cDNA validated the mutant allele fraction for CXCR4 and MYD88 at the RNA and DNA levels. cross-validation. Filtering genes/predictors by variable importance each other (r 520.46; P 5 .0004). As shown in supplemental Figure 7, score resulted in 40, 236, and 143 associated genes for IgM, BM, this relationship may be influenced by MD88/CXCR4 mutation status. and hemoglobin, respectively (supplemental Figure 6). Functional The gene list associated with hemoglobin included CXCL13,aswellas analysis was conducted on each gene list and top genes from each PIK3AP1, PIK3CD, AJUBA,andOS9 (Figure 3C). Based on these group were selected based on biological significance. The 40 genes findings, CXCL13 was included in an independent serum cytokine associated with IgM were predicted to be downstream of IL6 signaling profiling study of 86 WM patients that validated the CXCL13 (P 5 .0018) and included many genes of WM pathogenic interest correlation between BM (Spearman r 5 0.562; P 5 2.192 3 1028) including TNFRSF17, BMP3, IRS1,andCEPZOS (Figure 3A). Many and hemoglobin (Spearman r 520.567; P 5 1.53 3 1028).39 genes relevant to WM biology, including CXCL13, TP53, CXCR4, MYD88, CDC23,andAKAP1, were associated with BM disease Analysis of mutant allele burden involvement (Figure 3B). Although MYD88 and CXCR4 were both correlated to BM disease involvement (r 520.50; P , .0001 and The percentage of reads supporting the mutant allele relative to WT r 5 0.46; P 5 .0003, respectively), they were negatively correlated with allele was calculated for MYD88, CXCR4, ARID1A,andCD79B in the

Figure 3. Random forest analysis of gene expression identifies genes predictive of important clinical features in WM patients. Gene expression from the first 37 WM samples were analyzed for their utility in predicting serum IgM (A), BM disease involvement (B), and hemoglobin (C) levels using a random forest analysis. Of the statistically significant genes, the top 9 genes from each group deemed the most biologically relevant are shown as single variable correlates and incorporated into a final linear model using the full data set to demonstrate their predictive utility. The final 20 samples withheld for validation are shown in red and the root mean squared error (RMSE) of the final model for both the training and validation subsets is shown. P values have been adjusted for multiple hypotheses testing using the Benjamini-Hochberg FDR. From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

834 HUNTER et al BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6

MYD88L265P MYD88WT HD CXCR4WT CXCR4WHIM MB PB WM (All Samples) Healthy Donor - MB P-value DNTT 71.146 (0.5-2884.9) 0.023 (0-0.1) 0.0005 RAG1 7.0669 (0.2-255.5) 0.038 (0-0.1) 0.0004 RAG2 5.1208 (0-163.3) 0 (0-0) <0.0001 IGF1 3.2971 (0.1-28.5) 0.018 (0-0) <0.0001 BMP3 21.157 (0.5-269.5) 0.127 (0-0.3) <0.0001 CD5L 8.1506 (0.3-372.6) 0.021 (0-0.1) 0.0001 CXCL12 3.44 (0.1-168.5) 0 (0-0) 0.0001 VCAM1 7.2862 (0.1-383.3) 0 (0-0.1) 0.0001 CXCR4 3607.9 (577.9-8578.8) 1160 (913.8-1955) <0.0001

WM Associated Genes B2M 11916 (5501.6-18419.1) 5563 (4920.4-5862.5) <0.0001 BCL2 200.75 (25-392.3) 87.24 (72.4-109.4) <0.0001 BAX 105.47 (56.1-217.5) 205.3 (182.3-244.1) <0.0001 MYD88L265PCXCR4WT MYD88L265PCXCR4WHIM P-value IL17RB 81.364 (13.2-333.4) 19.27 (4.5-151.8) <0.0001 GPER 42.693 (10.2-167.1) 3.995 (0.6-59.3) <0.0001 WT WNT5A 4.2895 (0-28.2) 0.077 (0-7.1) 0.0001 IGF1 5.9519 (1.5-28.5) 1.883 (0.1-19.9) <0.0001 WNK2 11.39 (0.7-65) 0.265 (0-19.5) <0.0001 CXCR4 PRDM5 6.5371 (0.2-31.2) 0.127 (0-39.1) <0.0001

L265P CABLES1 17.641 (5.7-58.8) 2.068 (0.1-37.8) <0.0001 CXXC4 2.5257 (0-21.2) 0.166 (0-16.3) 0.0071 CDKN1C 16.826 (4.2-92) 4.05 (0.5-44.9) 0.0002 MYD88 LGALS3BP 73.226 (8.6-396.9) 6.483 (0.9-139.6) <0.0001 PMAIP1 759.69 (207.3-2711.9) 177.2 (91.6-1114.7) <0.0001 TP53I11 2.7809 (0.6-16.4) 19.68 (0.4-36.9) <0.0001 MYD88L265PCXCR4WT MYD88L265PCXCR4WHIM P-value IL15 10.452 (3.8-18) 1.158 (0.1-13.9) <0.0001 ERRFI1 1.5737 (0.4-3.8) 0.665 (0.3-1.9) 0.0022 WHIM NOD2 3.2243 (0.3-8.8) 0.958 (0.2-6.6) 0.0032 TLR4 11.829 (2.7-53.2) 4.55 (0.6-48.4) 0.0075

CXCR4 TLR7 1.3006 (0.1-28.7) 5.162 (0.2-13.7) 0.0114 IRAK3 1.6527 (0.2-22.7) 6.08 (0.5-40.7) 0.0039 L265P CXCR7 0.755 (0.2-25) 8.855 (0.5-50.6) <0.0001 TSPAN33 40.552 (16.7-101.9) 109.6 (28.4-184.6) <0.0001 PIK3R5 4.9129 (0.3-25.6) 23.61 (1.3-47.7) <0.0001 MYD88 PIK3CG 29.595 (15-55.2) 45.76 (5-69.1) 0.0909

MYD88L265PCXCR4WT MYD88L265PCXCR4WHIM P-value RGS1 395.04 (47.5-1870.9) 72.21 (16.8-646) 0.0001 RGS2 725.64 (37.4-2213.7) 276.8 (63.1-1409.2) 0.0175 RGS13 3.1815 (0.1-163.5) 0.478 (0-5.5) 0.0058 DUSP1 556.48 (158.8-1554.4) 348.6 (154.8-714.1) 0.0184 DUSP2 46.644 (18.2-1234.7) 19.44 (8.3-460) 0.0522 DUSP4 2.2093 (0.1-14.8) 0.669 (0-5.6) 0.0497 DUSP5 122.52 (22.1-963.4) 78.88 (23.5-189) 0.0570 DUSP10 37.776 (5.7-243.1) 13.46 (3.9-105.7) 0.0125 DUSP16 7.1218 (1.1-19.2) 2.489 (0.6-14.2) 0.0001 DUSP22 275.97 (67.8-1730.3) 85.08 (48.4-428.1) <0.0001 MAPK/G-protien Regulators MYD88L265PCXCR4WT MYD88WTCXCR4WT P-value IL6 30.589 (6.6-210.8) 4.618 (0.3-13.9) 0.0150 IRAK2 18.886 (8.1-59.5) 8.222 (1.2-18) 0.0335 TNFAIP3 58.59 (26.7-215.2) 21.74 (10.5-61.3) 0.0231 NFKBIZ 77.897 (26.9-271.8) 24.1 (10.1-88) 0.0314 WT NFKB2 38.611 (18.7-105.5) 25.08 (9.1-41) 0.0024 TIRAP 10.843 (4.2-26.9) 7.856 (3-10.8) 0.1000 PIM1 480.79 (179.9-968.5) 104.9 (50.2-309.1) 0.0002 CXCR4

WT PIM2 555.15 (289.9-981.7) 381.3 (73.9-433.8) 0.0008 CD40 81.62 (23.6-108.1) 44.25 (5.8-98.6) 0.0110 PTBP3 85.417 (46.5-115) 140.9 (106.6-173.8) 0.0095

MYD88 CD86 13.798 (0.7-41.4) 40.67 (15.5-214.6) 0.0179 CXCR3 2.5943 (0.2-15.6) 12.19 (0.6-93.8) 0.0146 IGF1R 0.5259 (0-14.6) 1.911 (1.1-4.1) 0.0349 AKT2 48.196 (36.4-67.8) 53.46 (47-85.7) 0.0438 PIK3AP1 130.5 (51.9-324.2) 210.6 (117.7-454.2) 0.0342

Figure 5. Summary of key gene expression differences. Gene expression levels for all study samples are shown depicting key differences for WM vs normal B-cell samples as well as WM patient intrapatient differences based on MYD88 and CXCR4 genotype. The heat map represents log2 transformed TpM values with median and range TpM values listed for each relevant comparator. P values have been adjusted for multiple hypotheses testing using the Benjamini-Hochberg FDR.

RNA sequencing data. Although the relevant mutations were observed somatic mutations (Figure 4A). The median percentage of mutant reads in the RNA in all cases, many samples with CXCR4WHIM mutations was 37.4% (range, 18.4%-61.4%) and 54.5% (range, 18.2%-60.9%) for had mutant allele burdens in excess of 50%, an unexpected finding MYD88 and CXCR4, respectively (P 5 .0537) with the mutant allele for a typically subclonal variant.8 As there can be differences in burden of CXCR4 being greater than MYD88 in9of11patients mapping efficiencies between indels and single-nucleotide variants, (81.8%). Sanger sequencing of paired DNA and cDNA confirmed MYD88L265P allele burden was then compared with the CXCR4WHIM the mutant allele was underrepresented in the cDNA relative to the allele burden at the RNA level for the 11 patients with nonsense CXCR4 DNA for MYD88L265P in many patients whereas the opposite was From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 TRANSCRIPTOME OF WALDENSTROM¨ MACROGLOBULINEMIA 835

MYD88WT MYD88/CXCR4 Interactions Potential Targets WT • MYD88 and CXCR4 are negatively CXCR4 • All WM Patients correlated and expression levels • Lowest levels of B-cell • CXCR4 (both WT & WHIM) are affected by mutation status. differentiation genes. • CXCL13 • Overall MYD88 expression • Low NFkB Response genes • BCL2 and BCL2L1 negatively correlates with WM Increased expression of genes • IGF1/IGF1R, particularly in bone marrow involvement. CXCR4 associated with PIK3 signaling MYD88L265PCXCR4WT has a positive correlation. • Increased promoter. • Hypomethylating agents in • MYD88 mutant allele expression methylation of PRDM5 and MYD88WT patients WNK2. is often reduced versus the wild • PIK3 delta inhibitors, particularly type allele in the mRNA whereas in MYD88WT WM. Additional the mutant CXCR4 allele is inhibition of PIK3 gamma may be preferentially expressed. All WM necessary for CXCR4WHIM patients

• Up regulated VDJ Genes: DNTT, RAG1, RAG2 • Role for WT CXCR4: Increased CXCL12, CXCR4, VCAM1 • Decreased BAX expression • High levels of BCL2 MYD88L265P • CXCL13 expression MYD88L265P correlates with BM CXCR4WT involvement and CXCR4WHIM Hemoglobin • Highest levels of IGF1. • Silencing of tumor • Highest expresion of B-cell suppressors up regulated by differentiation genes. MYD88 mutations. • Associated with a • High IRAK3 and low TLR4 transcriptional profile that is Expression. the most distinct from HD • Decreased G-protein and samples and other WM MAPK signaling negative genotypes. regulators. • High levels of PMAIP1. • High PIK3R5 and PIK3CG levels.

Figure 6. Summary of key findings by MYD88 and CXCR4 mutation status. Key findings from the transcriptome analysis for all WM samples as well as findings specific to MYD88L265PCXCR4WT, MYD88L265PCXCR4WHIM, and MYD88L265PCXCR4WT WM patients. Additional notes on potential therapeutic targets and findings regarding MYD88 and CXCR4 interactions are also listed.

true for CXCR4 mutations (Figure 4B). This reduced expression of homotypic WM cell clustering observed in the BM of many WM MYD88L265P at the RNA level was also observed in some CXCR4WT patients. Likewise, IGF1, an inducer of AKT1 survival signaling in patients. WM, was a top hit among the most upregulated genes. IGF1 and its receptor IGF1R may therefore be potential therapeutic targets in WM and warrant further investigation.44 Despite previous studies suggesting that WM was derived from Discussion MB, we did not observe increased similarity at the transcription level for MB relative PB as shown in Figure 1B, C, and E. WM represents This study represents the first profile of the transcriptional activated B cells in the processes of differentiating to plasma cells, landscape of WM by next-generation sequencing. Among the evidenced by the high levels of XBP1, PRDM1,andSDC1, which may most striking findings was a .7.8 log-fold upregulation of VDJ obscure signaling indicative of the true cell of origin. However, recombination related genes including RAG1, RAG2,andDNTT in clustering by genes related to B-cell differentiation effectively WM patients samples (Figures 5 and 6). The class switch re- sorted samples from patients with MYD88L265PCXCR4WT vs combination gene AICDA was not observed at meaningful levels MYD88L265PCXCR4WHIM and MYD88WTCXCR4WT suggesting that the consistent with the lack of immunoglobulin class switching in WM. The latter may represent an earlier stage of B-cell differentiation, consistent overexpression of RAG1 and RAG2 is notable because specificsomatic with the previously established lower rate of IgH somatic hyper- mutation patterns such as deletions in BTG1 and ETV6 are associated mutation in MYD88WT WM.45 with their aberrant expression, and are commonly observed in WM The majority of transcriptional differences between patients.2,40,41 The universal expression of CXCR4 and the upregulation MYD88L265PCXCR4WT and MYD88L265PCXCR4WHIM represented of its ligand CXCL12 are suggestive of autocrine/paracrine signaling. normalization of the latter for TLR4 signaling associated gene CXCR4 activation has been reported to increase cell adhesion to expression. This finding may help explain why CXCR4WHIM mutations VCAM1 via VLA4, and VCAM1 was the eighth most upregulated are found almost exclusively in MYD88-mutated patients. Others have gene relative vs HD samples.42,43 Taken together, these findings also reported that CXCR4 activation can affect TLR4 signaling.46-48 suggest that VCAM1, CXCR4, and CXCL12 may facilitate the Regardless of mechanism, the direct downregulation of TLR4,and From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

836 HUNTER et al BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 upregulation of IRAK3 in CXCR4WHIM patients may be the most MYD88L265PCXCR4WT vs MYD88L265PCXCR4WHIM gene signature. proximal explanation for this phenomenon. Although IRAK3 is a MYD88WT patient expression was quite heterogeneous indicat- negative regulator of the IRAK4/1 signaling cascade, it has been shown ing pathogenetic diversity in this population. Large-scale genomic that BTK activation downstream of MYD88 occurs independently exploration will invariably be needed to help clarify the genetic basis of IRAK4/1 and is likely unaffected by IRAK3 upregulation.4,49 In for MYD88WT WM disease. Regardless, a clear downregulation of addition, TLR7 is upregulated in CXCR4WHIM patients and can use the genes associated with NF-kB signaling including IL6, IRAK2, MYD88/IRAK4/IRAK3 complex to initiate regulatory signaling TNFAIP3, NFKBIZ, NFKB2, TIRAP, PIM1,andPIM2 was observed through MAP3K3-dependent activation of NF-kB.50 in these patients. Other genes of interest included the downregulation Compared with the other WM genotypes and healthy donor B cells, of CD40, and upregulation of PTBP3, CD86,andCXCR3. Notably, WM patients with the MYD88L265PCXCR4WT genotype strongly IGF1R, PIK3AP1,andAKT2 were all upregulated, suggesting a upregulate a number of surface receptors and signaling molecules reliance on PIK3 signaling. The antiapoptotic gene BCL2, known to including IL17RB, GPER1, WNT5A,andIGF1. IL17RB signaling may play an important role in WM, was overexpressed expressed in all provide an additional source of NF-kB activation whereas GPER1 and patient samples while the proapoptotic BAX was underexpressed, IGF1 activate AKT1 and MAPK signaling in marginal zone lymphoma suggesting a mechanism independent of the MYD88 and CXCR4 and WM, respectively.44,51,52 However, WNT5A is a suppressor of mutations.65-67 B-cell proliferation.53 Many of the genes upregulated in the Somatic mutations in ARID1A were associated with increased MYD88L265PCXCR4WT patient cohort such as PMAIP1, WNK2, CXCL13, as well as increased BM infiltration. CXCL13 has not PRDM5, CABLES1, CXXC4, and CDKN1C are tumor suppressors been previously described in WM and was also a strong predictor that either promote apoptosis, inhibit cell cycle, or block MAPK of BM disease involvement and hemoglobin levels. CXCL13 may signaling.54-58 Normalization of MYD88L265P-associated tumor sup- also play a role in mast cell recruitment.68 Mast cells can be pressor expression in CXCR4-mutated WM patients may provide the observed admixed with the WM B cells in patient BM histology clearest explanation for the selective inhibition of MYD88 signaling andarethoughttoplayanimportant supportive role in the tumor observed in CXCR4WHIM patients. This yin-and-yang relationship microenvironment of WM.69 Deletions of chromosome 6q were between MYD88 and CXCR4 is further supported by the modulation associated with over 131 differentially expressed genes. Levels of of allele specific transcription, decreasing and increasing the mutation previously identified target genes such as the NF-kBnegative allele burden of each gene, respectively. CXCR4 and MYD88 total regulator HIVEP2,aswellasBCLAF1, FOXO3 and ARID1B were transcription levels were negatively correlated and inversely predictive all suppressed in the presence of 6q deletions.2 Although only of BM disease involvement. Although aberrant promoter methylation 3 patients had strong familial predisposition to WM, these pa- of WNK2 and PRDM5 was prominent inMYD88WTCXCR4WT samples, tients demonstrated significant dysregulation of several cancer- it did not explain the low levels of WNK2 and PRDM5 observed in associated genes including RB1, STAT5B, ZNF300, MAPK9, patients with CXCR4WHIM mutations, thereby strengthening the case and NFKB1. that these findings are related to CXCR4WHIM signaling rather than These studies highlight the pivotal roles of MYD88 and unrelated epigenomic events. CXCR4 signaling in WM. CXCR4 mutations appear to function The strongest gene markers for patients with MYD88L265PCXCR4WHIM primarily as dampeners of negative regulators that are upregu- was the upregulation of CXCR7, which like CXCR4 is activated lated in response to mutant MYD88 signaling. The upregulation by CXCL12, and TSPAN33 which is upregulated in activated of the ligand, adhesion targets, and CXCR4 itself in all WM B cells.59,60 IL15 was uniquely suppressed in patients with patients provides evidence for uniform CXCR4 dysregulation in MYD88L265PCXCR4WHIM and warrants functional studies to assess WM and supports the development of CXCR4 antagonists for its role in WM microenvironment. Preclinical studies have shown WM therapy. The upregulation of PIK3 pathway members and the that CXCR4WHIM transduced cell lines are resistant to PIK3CD increased promoter methylation in MYD88WT patients creates a inhibitors such as idelalisib.10,38 Both PIK3R5 and PIK3CG were strong rationale for preclinical studies of PIK3 inhibitors and significantly higher in CXCR4WHIM patients. PIK3R5 interacts with demethylating agents in this population. The upregulation of the b/g G-protein subunits downstream of G-protein–coupled BCL2 across all WM patient genotypes also supports the receptors such as CXCR4 to activate PIK3CG which may explain development of BCL2 antagonists. Finally, CXCL13 is highly the observed resistance.61 The G-protein and MAPK inhibitors expressed by WM cells, and its expression correlates with RGS1, RGS2, RGS13, DUSP1, DUSP2, DUSP4, DUSP5, DUSP10, important clinical parameters. Further studies to delineate therapeutic DUSP16, and DUSP22 as well as the MAPK-inducible tumor targeting of this cytokine in WM are warranted. suppressor ERRFI1 were significantly downregulated in patients with MYD88L265PCXCR4WHIM vs MYD88L265PCXCR4WT.62 The fact that DUSP2, DUSP4, DUSP5, RGS13,andERRFI1 were all significantly induced by CXCL12 in CXCR4WHIM vs CXCR4WT- transduced lines supports a role for these genes as negative regulators Acknowledgments of CXCR4 signaling and may have implications for MYD88 MAPK signaling as well.63,64 This is similar to a number of secondary events The authors acknowledge the contributions of Yaoyu Wang and John such as MYBBP1A mutations and deletions of TRAF3, TNFAIP3, Quackenbush at the Center for Cancer Computational Biology at the and HIVEP2, which are thought to represent loss of negative regulators Dana-Farber Cancer Institute for RNA Sequencing, and Mathew for NF-kB signaling in WM.2,11,65 The mechanism(s) by which Temple, Leutz Buon, Terry Haley, and the Dana-Farber Research negative regulators of CXCR4 signaling are lost in patients with Computing Center for their invaluable assistance. The authors CXCR4WHIM mutations remain to be clarified warranting further acknowledge the generous support of the WM patients who provided investigation. their samples. Of the 1155 genes that were differentially expressed between This work was supported by Peter S. Bing, a Translational MYD88L265P and MYD88WT patients, 552 were not observed in the Research grant from the Leukemia & Lymphoma Society, a grant From www.bloodjournal.org by Steven Treon on May 23, 2017. For personal use only.

BLOOD, 11 AUGUST 2016 x VOLUME 128, NUMBER 6 TRANSCRIPTOME OF WALDENSTROM¨ MACROGLOBULINEMIA 837 from the International Waldenstrom¨ ’s Macroglobulinemia Founda- studies; G.Y., X.L., and J.C. transduced the cell lines and ran the tion, a National Institutes of Health, National Cancer Institute Spore stimulations; L.X., X.L., J.C., J.G.C., J.M.V., and N.T. prepared the 5P50CA100707-12 Research Development Award, and the Orzag study samples; S.P.T., J.J.C., and T.D. provided patient care and Family Fund for WM Research. obtained consent and samples; R.J.M., P.B., J.G., and C.J.P. selected samples and provided clinical data analysis; and S.P.T., N.M.M., and K.C.A. reviewed the data and provided expert guidance. Conﬂict-of-interest disclosure: The authors declare no competing Authorship ﬁnancial interests. Correspondence: Steven P. Treon, Bing Center for Waldenstrom¨ ’s Contribution: Z.R.H. and S.P.T. designed the study and wrote the Macroglobulinemia, Dana-Farber Cancer Institute, M547, 450 manuscript; Z.R.H. performed the data analysis and informatics Brookline Ave, Boston, MA 02215; e-mail: steven_treon@dfci. studies; X.L. and N.T. performed the validation and methylation harvard.edu.

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2016 128: 827-838 doi:10.1182/blood-2016-03-708263 originally published online June 14, 2016

Transcriptome sequencing reveals a profile that corresponds to genomic variants in Waldenström macroglobulinemia

Zachary R. Hunter, Lian Xu, Guang Yang, Nicholas Tsakmaklis, Josephine M. Vos, Xia Liu, Jie Chen, Robert J. Manning, Jiaji G. Chen, Philip Brodsky, Christopher J. Patterson, Joshua Gustine, Toni Dubeau, Jorge J. Castillo, Kenneth C. Anderson, Nikhil M. Munshi and Steven P. Treon

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