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M.C.J. Ma et al.
tering of germline variants was effective. Genes that were significantly mutated in the full cohort or in any one of the four subtypes with more than 100 tumors (BL, DLBCL, FL, and MCL) were included, as well as frequently mutated genes that are targets of activation-induced cytidine deam- inase (AID) (Figure 1, Online Supplementary Table S5). Predictably, the frequency of AID-associated mutations was higher among germinal center-derived lymphomas (BL, DLBCL, FL), but also accounted for 7.6% of all coding and non-coding mutations in MCL (Online Supplementary Table S6). The mutational burden calculated from our tar- geted region correlated significantly with that from the whole exome (Online Supplementary Figure S3A) and was significantly higher in DLBCL and other high-grade tumors than in FL and MCL (Figure 1, Online Supplementary Figure S3B).
The hybrid capture probes utilized in our design also tar- geted recurrent breakpoint regions in the immunoglobulin heavy- and light-chain loci, and recurrent breakpoints in or near the BCL2, MYC and BCL6 genes, and translocations were called using a method that detects discordantly mapped reads19 (Figures 1 and 2A). Our prior validation of this approach in cases with matched FISH data for MYC showed that it is 100% specific, but only ~40% sensitive for translocation detection.13 This limit of sensitivity likely varies for different genes depending on how well the break- points are clustered into hotspots that are targeted by our capture probes. Nonetheless, we observed a significantly higher fraction of BCL6 translocations (57% [27/47]) part- nered to non-immunoglobulin loci (e.g., CIITA, RHOH, EIF4A2, and ST6GAL1) (Online Supplementary Table S7) compared to BCL2 (1% [1/114]) and MYC (5% [2/38]) translocations (Figure 2A; Fisher P-value <0.001). These were more frequent in FL (88% [15/17] of BCL6 transloca- tions) than in DLBCL (39% [9/23] of BCL6 translocations), presumably because the two immunoglobulin loci in FL are either translocated with the BCL2 gene or functioning in immunoglobulin expression.21 We also employed off-target reads to detect DNA CNA in a manner akin to low-pass whole genome sequencing, identified significant peaks of copy gain and losses using GISTIC218 (Figures 1 and 2A, Online Supplementary Figure S4, Online Supplementary Tables S8 and S9), and defined the likely targets of these CNA by integrative analysis of matched gene expression profiling data from 290 tumors (Figure 2B, C, Online Supplementary Figure S4, Online Supplementary Tables S10 and S11). This identified known CNA targets, including but not limited to deletion of TNFAIP3 (6q24.2),22 ATM (11q22.3),23 B2M (15q15.5),24 and PTEN (10q23.21),25 and copy gain of REL and BCL11A (2p15), and TCF4 (18q23).26 In addition, we identified novel targets such as deletion of IBTK (6q14.1), UBE3A (11q22.1) and FBXO25 (8p23.3), and copy gain of ATF7 (12q13.13), UCHL5 (1q31.3), and KMT2A (11q23.3). Importantly, the frequency of DNA CNA in the target genes identified by next-generation sequencing-based analysis significantly correlated with those derived from single nucleotide polymorphism microarray-based meas- urements in independent cohorts of BL, DLBCL, FL and MCL tumors from previously published studies6,20,26-30 (Online Supplementary Figure S5), providing validation for the accuracy of this approach. The CNA peaks, defined as the smallest and most statistically significant region, includ- ed multiple genes that were significantly mutated (Figure 2D) as well as other genes for which we detected mutations at lower frequencies that were not significant by
MutSig2CV (POU2AF1, TP53BP1, FAS, PTEN). Deletions of ATM, B2M, BIRC3 and TNFRSF14 significantly co-associat- ed with mutations of these genes, suggesting that these are complementary mechanisms contributing to biallelic inacti- vation.
Conserved functional hallmarks of B-cell non-Hodgkin lymphoma
To understand key hallmarks that are deregulated by genetic alterations, we performed hypergeometric enrich- ment analysis of genes targeted by recurrent mutations and DNA CNA using DAVID31 (Online Supplementary Table S12). This revealed a significant enrichment of multiple overlap- ping gene sets that could be summarized into hallmark processes associated with epigenetics and transcription (Figure 3A), apoptosis and proliferation (Figure 3B), signal- ing (Figure 3C), and ubiquitination (Figure 3D). One or more genes within these hallmarks was altered in the majority (>50%) of tumors from each of the four major his- tologies included in this study. Genes annotated in epige- netic-associated gene sets were altered in 72%, 70%, 93% and 50% of BL, DLBCL, FL, and MCL, respectively, where- as genes annotated in transcription-associated gene sets were altered in 94%, 91%, 95% and 88% of BL, DLBCL, FL, and MCL, respectively. However, there is an extremely high degree of functional overlap between epigenetics and transcriptional regulation, as well as overlapping gene set annotations for many genes, leading us to consider these categories collectively as a single hallmark. Collectively, genes involved in epigenetics and transcription were mutat- edin94%ofBL,92%ofDLBCL,96%ofFLand89%of MCL, and included those that encode proteins that catalyze post-translational modifications of histones (KMT2D, CREBBP, EZH2, EP300, WHSC1, ASHL1L, KMT2A), com- ponents of the BAF chromatin remodeling complex (ARID1A, SMARCA4, BCL7A, BCL11A), linker histones (HIST1H1E, HIST1H1C, HIST1H1B), and transcription fac- tors (BCL6, IRF4, IRF8, TCF3, TCF4, MYC, REL, PAX5, POU2AF2, FOXO1, CIITA). Genes with a role in signaling included those involved in B-cell receptor signaling (CD79B, ID3, TCF3, TCF4, RFTN1), NFκB (TNFAIP3, CARD11, NFKBIE), NOTCH (NOTCH1, NOTCH2), JAK/STAT (SOCS1, STAT6), PI3K/mTOR (FOXO1, ATP6V1B2, APT6AP1) and G-protein signaling (GNA13, GNAI2). The CD79A and BCL10 genes were also mutated at a lower fre- quency that was not significant by MutSig2CV (Online Supplementary Figure S6A, B). Among these, the RFTN1 gene (Online Supplementary Figure S6C) is a novel recurrently mutated gene that was mutated in 7.4% of DLBCL and encodes a lipid raft protein that is critical for B-cell receptor signaling.32
Deregulation of the ubiquitin proteasome system is important in many cancers,33 but is not a well-defined hall- mark of B-NHL. However, one or more genes with a role in regulating ubiquitination was genetically altered in 61% of BL, 79% of DLBCL, 61% of FL and 82% of MCL (Figure 3D). These included previously described genetic alter- ations such as amplification of MDM2,34 deletions of TNFAIP3,35 CUL4A,36 and RPL5,36 and mutations of KLHL6,37 DTX1,38 UBR5,39 SOCS1,40 and BIRC3.6 In addition, we iden- tified novel targets such as recurrent deletions of IBTK, a negative regulator of Bruton tyrosine kinase,41 and somatic mutation of CDC27 in 14% of MCL, which encodes an E3 ligase for CCND1.42 Therefore, common hallmark process- es are targeted by genetic alterations in the majority of
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