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Pathognomonic and epistatic genetic alterations in B-cell non- Hodgkin lymphoma [preprint]
Man Chun John Ma The University of Texas MD Anderson Cancer Center
Et al.
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Repository Citation John Ma M, Chen BJ, Green MR. (2019). Pathognomonic and epistatic genetic alterations in B-cell non- Hodgkin lymphoma [preprint]. University of Massachusetts Medical School Faculty Publications. https://doi.org/10.1101/674259. Retrieved from https://escholarship.umassmed.edu/faculty_pubs/1615
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1 Pathognomonic and epistatic genetic alterations in 2 B-cell non-Hodgkin lymphoma 3 4 Man Chun John Ma1¥, Saber Tadros1¥, Alyssa Bouska2, Tayla B. Heavican2, Haopeng Yang1, 5 Qing Deng1, Dalia Moore3, Ariz Akhter4, Keenan Hartert3, Neeraj Jain1, Jordan Showell1, 6 Sreejoyee Ghosh1, Lesley Street5, Marta Davidson5, Christopher Carey6, Joshua Tobin7, 7 Deepak Perumal8, Julie M. Vose9, Matthew A. Lunning9, Aliyah R. Sohani10, Benjamin J. 8 Chen11, Shannon Buckley12, Loretta J. Nastoupil1, R. Eric Davis1, Jason R. Westin1, Nathan H. 9 Fowler1, Samir Parekh8, Maher K. Gandhi7, Sattva S. Neelapu1, Douglas Stewart5, Javeed 10 Iqbal2, Timothy Greiner2, Scott J. Rodig13, Adnan Mansoor5, Michael R. Green1,14,15* 11 1Department of Lymphoma and Myeloma, Division of Cancer Medicine, The University of Texas MD 12 Anderson Cancer Center, Houston, TX, USA; 2Department of Pathology and Microbiology, University of 13 Nebraska Medical Center, Omaha, NE, USA; 3Eppley Institute for Research in Cancer and Allied 14 Diseases, University of Nebraska Medical Center, Omaha, NE, USA; 4Department of Pathology and 15 Laboratory Medicine, University of Calgary, Calgary, AB, Canada; 5Section of Hematology, Department of 16 Medicine, University of Calgary, Calgary, AB, Canada; 6Northern Institute for Research, Newcastle 17 University, Newcastle upon Tyne, England; 7Mater Research, University of Queensland, QLD, Australia; 18 8Division of Hematology and Medical Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, 19 USA; 9Department of Internal Medicine, Division of Hematology-Oncology, University of Nebraska 20 Medical Center, Omaha, NE, USA; 10Department of Pathology, Massachusetts General Hospital and 21 Harvard Medical School, Boston, MA, USA; 11Department of Pathology, University of Massachusetts 22 Medical School, UMass Memorial Medical Center, Worcester, MA, USA; 12Department of Genetics, Cell 23 Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, USA; 13Department of 24 Pathology, Brigham and Womens Hospital, Boston, MA, USA; 14Department of Genomic Medicine, 25 University of Texas MD Anderson Cancer Center, Houston, TX, USA; 15Center for Cancer Epigenetics, 26 University of Texas MD Anderson Cancer Center, Houston, TX, USA. 27 ¥Equally contributed 28 *Corresponding Author 29 Michael R. Green, Ph.D. 30 Departments of Lymphoma/Myeloma and Genomic Medicine, 31 University of Texas MD Anderson Cancer Center, 32 1515 Holcombe Blvd, Unit 903, 33 Houston, TX 77030, USA 34 Ph.: +1-713-745-4244 35 Email: [email protected] 36
37
38 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
39 KEY POINTS
40 1. Genetic perturbation of the ubiquitin proteasome system is an emerging hallmark of B-
41 cell non-Hodgkin lymphoma (B-NHL).
42 2. Co-occurring sets of genetic alterations define B-NHL subtypes and likely represent
43 epistatic interactions.
44
45 ABSTRACT
46 B-cell non-Hodgkin lymphoma (B-NHL) encompasses multiple clinically and phenotypically
47 distinct subtypes of malignancy with unique molecular etiologies. Common subtypes of B-NHL
48 such as diffuse large B-cell lymphoma (DLBCL) have been comprehensively interrogated at the
49 genomic level, but other less common subtypes such as mantle cell lymphoma (MCL) remain
50 sparsely characterized. Furthermore, multiple B-NHL subtypes have thus far not been
51 comprehensively compared to identify conserved or subtype-specific patterns of genomic
52 alterations. Here, we employed a large targeted hybrid-capture sequencing approach
53 encompassing 380 genes to interrogate the genomic landscapes of 755 B-NHL tumors at high
54 depth; primarily including DLBCL, MCL, follicular lymphoma (FL), and Burkitt lymphoma (BL).
55 We identified conserved hallmarks of B-NHL that were deregulated across major subtypes, such
56 as the frequent genetic deregulation of the ubiquitin proteasome system (UPS). In addition, we
57 identified subtype-specific patterns of genetic alterations, including clusters of co-occurring
58 mutations that are pathognomonic. The cumulative burden of mutations within a single cluster
59 were more significantly discriminatory of B-NHL subtypes than individual mutations, implicating
60 likely patterns of genetic epistasis that contribute to disease etiology. We therefore provide a
61 framework of co-occurring mutations that deregulate genetic hallmarks and likely cooperate in
62 lymphomagenesis of B-NHL subtypes. bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
63 INTRODUCTION
64 Non-Hodgkin lymphomas (NHL) are a heterogeneous group of lymphoid malignancies that
65 predominantly arise from mature B-cells (B-NHL). Although mature B-cell neoplasms
66 encompass 38 unique diagnostic subtypes, over 85% of cases fall within only 7 subtypes1,2.
67 Recent next generation sequencing (NGS) studies have shed light onto the key driver mutations
68 in many of these NHL subtypes; for example, large studies of diffuse large B-cell lymphoma
69 (DLBCL) have led to proposed genomic subtypes that have unique etiologies3-5. However, many
70 less-common NHL subtypes such as mantle cell lymphoma (MCL) have not been as extensively
71 characterized6,7. Furthermore, until recently3,4 genetic alterations have been considered in a
72 binary fashion as either driver events, which directly promote disease genesis or progression, or
73 passenger events, which have little or no impact on disease biology. In contrast to this principle,
74 most B-NHLs do not result from a single dominant driver but instead result from the serial
75 acquisition of genetic alterations that cooperate in lymphomagenesis. The genetic context of
76 each mutation therefore likely determines its oncogenic potential, and groups of mutations
77 should therefore be considered collectively rather than as singular events. For example, the ‘C5’
78 and ‘MCD’ clusters identified in DLBCL by Chapuy et al. and Schmitz et al., respectively, are
79 characterized by the co-occurrence of CD79B and MYD88 mutations3,4. In animal models, the
80 Myd88 L265P mutation was found to promote down-regulation of surface IgM and a phenotype
81 resembling B-cell anergy8. However, this effect could be rescued by Cd79b mutation, showing
82 that these co-occurring mutations are epistatic8. The characterization of other significantly co-
83 occurring genetic alterations are therefore likely to reveal additional important epistatic
84 relationships.
85
86 We approached this challenge by performing genomic profiling of 755 B-NHLs across different
87 subtypes. Through this cross-sectional analysis we characterized genomic hallmarks of B-NHL bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
88 and identified pathognomonic genetic alterations, including disease-specific mechanisms for
89 deregulating hallmark processes and protein complexes, and sets of significantly co-associated
90 events that represent subtype-specific epistatic genetic alterations. This study therefore
91 provides new insight into how cooperating genetic alterations may contribute to molecularly and
92 phenotypically distinct subtypes of B-NHL.
93
94 METHODS
95 An overview of our approach is shown in Figure S1. For detailed methods, please refer to
96 supplementary information.
97
98 Tumor DNA samples
99 We collected DNA for 755 B-NHL tumors, including 199 follicular lymphoma (FL), 196 mantle
100 cell lymphoma (MCL), 148 diffuse large B-cell lymphoma (DLBCL), 108 Burkitt’s lymphoma
101 (BL), 45 chronic lymphocytic leukemia / small lymphocytic lymphoma (CLL/SLL), 24 marginal
102 zone lymphoma (MZL), 21 high-grade B-cell lymphoma not otherwise specified (HGBL-NOS),
103 and 14 high-grade B-cell lymphoma with MYC, BCL2 and/or BCL6 rearrangement (DHL) (Table
104 S1). A total of 502 samples were obtained from the University of Nebraska Medical Center, and
105 were prioritized for inclusion in this study if they had fresh/frozen tissue available (n=577) and
106 been previously interrogated by Affymetrix U133 Plus 2.0 gene expression microarrays
107 (n=290)9-11. An additional series of 178 formalin-fixed paraffin-embedded (FFPE) tumors were
108 collected from other centers. Samples were de-identified but accompanied by their diagnosis
109 from medical records, plus overall survival time and status (alive, dead) when available. Medical
110 record diagnosis was used in all cases except for those with fluorescence in situ hybridization
111 showing translocations in MYC and BCL2 and/or BCL6, which were amended to DHL. bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
112 Sequencing results for a subset of 52 BL tumors was described previously12. All MCL samples
113 were either positive for CCND1 translocation by FISH or positive for CCND1 protein expression
114 by IHC, depending on the diagnostic practices of the contributing institution.
115
116 Next generation sequencing
117 A total of 100-1000ng of gDNA was sonicated using a Covaris S2 Ultrasonicator, and libraries
118 prepared using KAPA Hyper Prep Kits (Roche) with TruSeq Adapters (Bioo Scientific) and a
119 maximum of 8 cycles of PCR (average of 6 cycles). Libraries were qualified by TapeStation
120 4200, quantified by Qubit and 10- to 12-plexed for hybrid capture. Each multiplexed library was
121 enriched using our custom LymphoSeq panel encompassing the full coding sequences of 380
122 genes that have previously been reported to be somatically mutated in B-cell lymphoma6,7,13-32
123 (Table S2, Supplementary Methods), as well as tiling recurrent translocation breakpoints.
124 Enrichments were amplified with 4-8 cycles of PCR and sequenced on a single lane of an
125 Illumina HiSeq 4000 with 100PE reads in high-output mode at the Hudson Alpha Institute for
126 Biotechnology or the MD Anderson Sequencing and Microarray Facility. Variants were called
127 using our previously validated ensemble approach12,21, copy number alterations identified using
128 off-target reads with the CopyWriteR tool33, and translocation called using FACTERA34.
129 Germline polymorphisms were filtered using dbSNP annotation and the EXAC dataset
130 containing 60,706 healthy individuals35. Significantly mutated genes were defined by
131 MutSig2CV36, significant DNA copy number alterations by GISTIC237, and the clonal
132 representation of mutations by ABSOLUTE38. Mutation and CNA data are publicly viewable
133 through cBioPortal: https://www.cbioportal.org/study/summary?id=mbn_mdacc_2013. For
134 further details, refer to supplementary methods.
135 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
136 RESULTS
137 Recurrently mutated genes highlight conserved functional hallmarks of B-NHL
138 We used a 380 gene custom targeted sequencing approach to interrogate the genomes of 755
139 mature B-NHLs, sequencing to an average depth of 599X (Min, 101X; Max, 1785X; Table S1)
140 for a total yield of 2.06 Tbp. Somatic nucleotide variants (SNVs) and small insertions/deletions
141 (InDels) were identified using an ensemble approach that we have previously validated21 (Table
142 S3) and significantly mutated genes were identified using MutSig2CV (Table S4). Genes that
143 were significantly mutated in the full cohort or in any one of the 4 subtypes with >100 tumors
144 (BL, DLBCL, FL, MCL), as well as frequently mutated genes that are targets of AID (Table S5,
145 Figure 1), were included in downstream analyses. Mutation distributions were classified using a
146 novel metric that classifies them into hotspot, clustered or diffuse (Figure S2). The residue of
147 mutational hotspots and conserved domains targeted by clustered mutations are shown on
148 figure 1. As a proof of principle, genes such as EZH2 and MYD88 with known mutational
149 hotspots were classified as ‘hotspot’, and genes such as CREBBP and TCF3 that accumulate
150 mutations within a single domain were classified as ‘clustered’. The mutational burden
151 calculated from our targeted region significantly correlated with that from the whole exome
152 (Figure S3A) and was significantly higher in DLBCL and other high-grade tumors compared to
153 FL and MCL (Figure 1; Figure S3B). The mutational signatures were also different between
154 malignancies, but were predominated by mutations attributable to failure of double-strand break
155 repair by homologous recombination39 (Signature 3; Table S6).
156
157 To identify key hallmarks that are deregulated by somatic mutations, we grouped genes using
158 DAVID functional annotation clustering40. BCL6 function is not annotated in gene sets within
159 public databases, but was an obvious feature of the recurrently mutated genes, so this hallmark bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
160 was assigned according to literature support. The most frequently perturbed processes were
161 chromatin modification and cell signaling, with most of the genes in these categories having
162 been well described in prior reports. Chromatin modifying genes were mutated in 68% of BL,
163 66% of DLBCL, 91% of FL and 45% of MCL, and included those that encode proteins that
164 catalyze post-translational modifications of histones (KMT2D, CREBBP, EZH2, EP300,
165 WHSC1, ASHL1L and KMT2A), components of the SWI/SNF chromatin remodeling complex
166 (ARID1A, SMARCA4, BCL7A), linker histones (HIST1H1E, HIST1H1C, HIST1H1B), and the
167 TET2 gene. Genes with a role in signaling included those involved in B-cell receptor (CD79B,
168 ID3, TCF3, RFTN1), toll-like receptor (MYD88), NFĸB (TNFAIP3, CARD11, NFKBIE), Notch
169 (NOTCH1, NOTCH2), JAK/STAT (SOCS1, STAT6), PI3K/mTOR (FOXO1, ATP6V1B2,
170 APT6AP1) and G-protein (GNA13, GNAI2) signaling. The CD79A and BCL10 genes were also
171 mutated at a lower frequency that was not significant by MutSig (Figure S4A-B). Among these,
172 the RFTN1 gene (Figure S4C) is a novel recurrently mutated gene that was mutated in 7.4% of
173 DLBCL and encodes a lipid raft protein that is critical for B-cell receptor signaling41.
174
175 Other processes that are not as well described as genetic hallmarks of B-NHL included BCL6
176 function and the ubiquitin proteasome system (UPS). Genes associated with BCL6 function
177 encompassed BCL6 itself and its interacting proteins (BCL6, TBL1XR1, BCOR, SPEN),
178 regulators of BCL6 activity or expression (MEF2B, IRF8, IRF4), and a critical BCL6 target gene
179 (PRDM1). Recurrent mutations of the NCOR1 and NCOR2 genes that encode BCL6 co-
180 repressor proteins were also observed (Figure S4D-E), but were not significant by MutSig2CV.
181 Multiple recurrently mutated chromatin modifying genes have also been implicated in BCL6
182 function and therefore also contribute to this hallmark, including CREBBP and EZH242,43. The
183 recurrently mutated UPS genes included the CDC27 gene, which encodes an E3 ligase for
184 CCND144 that is mutated in 14% of MCL and has not been previously described in the literature. bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
185 The TMEM51 gene was also identified as a novel recurrently mutated gene in 9% of BL, though
186 the function of this gene is poorly defined. The cross-sectional analysis of multiple lymphoma
187 subtypes therefore identified conserved hallmarks that are recurrently targeted by somatic
188 mutations in B-NHL.
189
190 Enrichment of functional hallmarks by structural alterations
191 The hybrid capture probes utilized in our design also targeted recurrent breakpoint regions in
192 the immunoglobulin heavy- and light-chain loci, as well as the regions of recurrent breakpoints
193 in or near the BCL2, MYC and BCL6 genes. Translocations were called using a method that
194 detects discordantly mapped reads34 and our prior validation of this approach in cases with
195 matched fluorescence in situ hybridization (FISH) data for MYC showed that it is 100% specific,
196 but only ~40% sensitive for translocation detection12. In addition, we failed to detect CCND1
197 translocations using this approach. However, we did observe a significantly higher fraction of
198 BCL6 translocations (57% [27/47]) partnered to non-immunoglobulin loci (eg. CIITA, RHOH,
199 EIF4A2, ST6GAL1; Table S7) compared to BCL2 (1% [1/114]) and MYC (5% [2/38])
200 translocations (Figure 2A; Fisher P-value <0.001). These were more frequent in FL (88%
201 [15/17] of BCL6 translocations) as compared to DLBCL (39% [9/23] of BCL6 translocations),
202 presumably because the two immunoglobulin loci in FL are either translocated with the BCL2
203 gene or functioning in immunoglobulin expression45. We also employed off-target reads to
204 detect DNA copy number alterations (CNAs) in a manner akin to low-pass whole genome
205 sequencing, identified significant peaks of copy gain and losses using GISTIC237 (Figure 2A;
206 Figure S5; Table S8-9), and defined the likely targets of these CNAs by integrative analysis of
207 matched gene expression profiling (GEP) data from 290 tumors (Figure 2B-C, Figure S5, Table
208 S10-11). This identified known CNA targets (Figure 2D), including but not limited to deletion of
209 TNFAIP3 (6q24.2)46, ATM (11q22.3)47, B2M (15q15.5)48 and PTEN (10q23.21)49, and copy gain bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
210 of REL and BCL11A (2p15), and TCF4 (18q23)50. In addition, we identified novel targets such
211 as deletion of IBTK (6q14.1), UBE3A (11q22.1) and FBXO25 (8p23.3), and copy gain of ATF7
212 (12q13.13), UCHL5 (1q31.3), and KMT2A (11q23.3). Several CNA peaks, defined as the
213 smallest and most statistically significant region, included genes that were significantly mutated
214 (Figure 2E) as well as other genes for which we detected mutations at lower frequencies that
215 were not significant by MutSig (POU2AF1, TP53BP1, FAS, PTEN). Furthermore, for several of
216 these genes (ATM, B2M, BIRC3 and TNFRSF14), deletions were significantly co-associated
217 with mutations, suggesting that deletion and mutation are complementary mechanisms
218 contributing to biallelic inactivation.
219
220 Analyzing targets of translocations and CNAs added to enrichment of functional categories that
221 we observed within recurrently mutated genes, including chromatin modification (BCL11A) and
222 signaling (TCF4, PTEN). This was most prominently observed for genes with a role in apoptosis
223 and the cell cycle (CDKN2A, CDKN2B, FAS, RPL5, DFFB, MDM2, CDK6) and the UPS
224 (CUL4A, FBXO25, IBTK, RNF38, UBAP1, UBE3A, UBQLN1, UCHL5). Recurrently altered UPS
225 genes collectively promote or suppress the abundance or activity of proteins with roles in other
226 hallmark characteristics (Figure 3A). For example; SOCS151, BIRC352, DTX153,54, IBTK55,
227 TRIM1356, TNFAIP353 and UBE3A53 all inhibit the function of proteins with a role in important B-
228 cell signaling pathways (Figure 3B), thereby establishing a functional link between genetic
229 alteration of the UPS and deregulation of signaling and adding an additional layer to the
230 mechanisms by which B-cell receptor signaling is perturbed in B-NHL. The combined analysis of
231 mutations and structural alterations therefore identified large sets of genes that are targeted by
232 genetic alterations and collectively contribute to hallmark features that are commonly
233 deregulated across multiple B-NHL subtypes.
234 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
235
236 Disease-specific patterns of genetic alterations
237 Some interesting patterns of disease-specificity were obvious for both mutations and structural
238 alterations. We therefore formally tested the over- or under-representation of these events in
239 each of the 4 subtypes with >100 tumors (BL, DLBCL, FL, MCL), compared to all other tumors
240 in the study (Figure 4; Table S12). Using matched GEP data for a subset of cases (Table S1),
241 we also tested the association between genetic alterations and molecularly-defined Burkitt’s
242 lymphoma subtypes (n=154; Figure S6-7, Table S13), as well as DLBCL cell of origin subtypes
243 (n=98; Figure S7, Table S14). These analyses showed that the genetic alterations associated
244 with clinically-defined BL were also associated with molecularly-defined BL. Moreover, genetic
245 alterations that we and others have characterized as being over-represented in ABC-like DLBCL
246 such as CD79B mutation57, MYD88 mutation58 and TCF4 copy gain50 were significantly over-
247 represented in this analysis also.
248
249 We observed some interesting patterns within hallmark characteristics that differ between
250 subtypes. For example, linker histone mutations were present in germinal center B (GCB)-cell
251 derived malignancies (BL, DLBCL, FL) at variable frequencies, but were largely absent from
252 MCL (Figure S8). In contrast, mutations of genes encoding the H3K36 methyltransferases,
253 WHSC1 (aka MMSET/NSD2) and ASH1L, were frequent in MCL and largely absent from GCB-
254 derived malignancies. We also noted that the SWI/SNF complex was perturbed in different ways
255 in different diseases (Figure 5). Specifically, mutations of the SMARCA4 (aka. BRG1)
256 component of the ATPase module were significantly enriched in BL (24%) compared to other
257 subtypes (4%, Q-value<0.001), while mutations of the BCL7A component of the ATPase
258 module were significantly enriched in FL (11%) compared to other subtypes (4%, Q- bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
259 value=0.007). In contrast, mutations of ARID1A were frequent in both BL (19%) and FL (15%),
260 and DNA copy number gains of BCL11A were frequent in both DLBCL (28%) and FL (22%).
261 The SWI/SNF complex is therefore a target of recurrent genetic alterations, as previously
262 suggested59, but the manner in which this complex is perturbed varies between B-NHL subtypes
263 (Figure 5). Similar disease-specific patterns were also observed for signaling genes; for
264 example, TCF3 and ID3 have important functions in normal germinal center B-cells60, but
265 mutations of these genes are specifically enriched within BL and are rarely found in the other
266 GCB-derived malignancies, DLBCL and FL. Similarly, the ATP6AP1 and ATP6V1B2 genes that
267 function in mTOR signaling61,62 are specifically mutated in FL, and the DUSP2 gene which
268 inactivates ERK1/263 and STAT364 is specifically mutated in DLBCL. The disease-specific
269 patterns of genetic alterations therefore reveal subtle but important differences in how each
270 subtype of B-NHL perturbs hallmark features.
271
272 Pathognomonic sets of co-associated genomic alterations in B-NHL subtypes
273 We next defined how each genetic alteration co-associated with or mutually-excluded other
274 genetic alterations by pairwise assessments using false-discovery rate (FDR)-corrected Fisher’s
275 tests (Table S15). A matrix of the transformed FDR Q-values (-logQ) was used for unsupervised
276 hierarchical clustering to identify clusters of co-associated genetic alterations. Together with
277 patterns of disease-specificity, unsupervised clustering revealed clear groupings of co-
278 associated events for BL, DLBCL, FL and MCL (Figure 4). We identified a single cluster that
279 was specifically enriched in DLBCL, included co-associated genetic alterations that were over-
280 represented in the ABC-like, and overlapped with the previously described C5/MCD clusters3,4.
281 We also identified a cluster consisting of TP53 mutations and multiple CNAs similar to the C2
282 subtype reported in DLBCL, but which was enriched in both FL and DLBCL. A cluster with
283 features similar to the C3/EZB DLBCL clusters was also observed, but was specifically enriched bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
284 in FL. The remaining two previously reported DLBCL clusters (C1/N2 and C4) were not
285 observed in this analysis, which may be a result of the different clustering strategies used and/or
286 the lower number of DLBCL tumors in this study compared to other prior DLBCL-focused
287 efforts3,4. We therefore focused our subsequent description on BL, FL and MCL.
288
289 The core set of co-associated genetic alterations in BL were MYC translocation and mutation,
290 and mutations of SMARCA4, CCND3 and ID3. Mutations of GNAI2, GNA13, FOXO1, TCF3,
291 ARID1A and TMEM51 were also clustered with this core set, but were present at lower
292 frequencies. Mutations of TP53 and copy gain of 1q were frequent in BL, but did not cluster
293 together with other BL-associated genetic alterations. The core set of genetic alterations in FL
294 were BCL2 translocations and mutations, and mutations of KMT2D, CREBBP and EZH2, in line
295 with prior observations16,21. Copy loss of 10q23.31 encompassing the FAS and PTEN genes,
296 and mutations EEF1A1, TNFRSF14, IRF8, BCL7A, ATP6V1B2 and ATP6AP1 were present at
297 lower frequencies and clustered with this core set. The MCL cluster included mutations and
298 deletions of ATM and BIRC3, as well as frequent deletions of 9p21.32 (CDKN2A and CDKN2B),
299 9q21.3 (UBQLN1), 13q14.2 (miR-15a/16 and TRIM13), 13q34 (CUL4A and ING1), and 1p21
300 (RPL5). Mutation of BCOR, UBR5, SP140, CCND1 and WHSC1 were also significantly over-
301 represented in MCL compared to other subtypes, and were associated with this cluster. These
302 data show that different subtypes of B-NHL are defined by characteristic sets of co-associated
303 genetic alterations that affect multiple hallmarks.
304
305 Combinations of clonal genetic alterations define B-NHL subtypes
306 Our data have revealed statistical enrichment of individual genetic alterations in subtypes of B-
307 NHL, and pairwise relationships between different genetic alterations that define clusters of bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
308 subtype-specific events. However, individual genetic alterations from disease-specific clusters
309 are also observed at variable frequencies in other B-NHL subtypes (Figure 6A). We therefore
310 investigated whether the combination of multiple genetic alterations from each cluster, rather
311 than the presence or absence of a single alteration, were pathognomonic. By enumerating the
312 number of genetic alterations from each disease-specific cluster in each tumor, we found that
313 this was indeed the case. Specifically over half of all BL, FL and MCL tumors possessed ≥3
314 genetic alterations defined by their respective subtype-specific clusters (Figure 6). These rates
315 were significantly higher than those observed in other diseases (Fisher P <0.001 for all
316 comparisons). For example, DLBCL and FL share many of the same recurrently mutated genes,
317 particularly when considering the C3/EZB subtype of DLBCL3,4. Thus, one or more FL cluster
318 alterations are found in 93% of FL tumors, but also in 54% of DLBCL tumors. However, FL
319 tumors have a significantly higher rate of accumulation of these mutations, with 68% of FL
320 tumors bearing ≥3 of the FL cluster alterations but only 18% of DLBCL tumors bearing ≥3 of
321 these genetic alterations (Fisher P < 0.001). This pattern is also conserved for the BL and MCL
322 clusters, for which the rate of acquiring ≥3 of the cluster alterations was significantly higher in BL
323 or MCL compared to other subtypes of B-NHL, respectively. Thus, B-NHL subtypes are defined
324 by the acquisition of multiple genetic alterations from subtype-associated clusters.
325
326 The co-association of multiple mutations may be suggestive of a clonal structure in which early
327 driver mutations are clonal and later driver mutations are present in sub-clones. We therefore
328 utilized ABSOLUTE (Table S16-18) to determine whether co-associated genetic alterations are
329 present at different clonal fractions, and could therefore be phased to create clonal hierarchies.
330 This analysis failed for a small subset of cases and could not be performed for X-linked genes
331 or translocations. However, it clearly revealed the majority of co-associated genetic alterations
332 were present at cancer cell fractions (CCF) >0.9, which is indicative that each mutation is bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
333 clonally represented in every cancer cell at the time of sampling. These data suggest that
334 combinations of mutations may therefore be required for effective expansion of lymphoma cells,
335 such that common precursor cells (CPCs) are not detectable within the clonal structure of the
336 clinically-detected tumor. These data therefore support the premise of epistatic interactions
337 between co-occurring genetic alterations in B-NHL.
338
339 DISCUSSION
340 By performing cross-sectional genomic profiling of a large cohort of tumors, we have defined
341 genes and functional hallmarks that are recurrently targeted by genetic alterations and showed
342 that combinations of genetic alterations are disease-defining features of B-NHL subtypes. Some
343 of the functional hallmarks that we identified have been previously appreciated in other
344 diseases, with a few exceptions. For example, the mutation of genes with roles in chromatin
345 modification are known to be a hallmark of FL65 and we observed that over 90% of FLs
346 possessed mutations in one or more of the genes in this category. However, these mutations
347 were also observed in two thirds of BL and DLBCL tumors and nearly half of MCLs. There are
348 subtype-specific patterns of chromatin modifying gene alterations, such as those that we
349 described for H3K36 methylation in MCL and the unique patterns of SWI/SNF mutations across
350 GCB-derived B-NHLs. But we suggest that the genetic deregulation of chromatin modification
351 should be considered a general hallmark of B-NHL. In addition, we suggest that the
352 deregulation of BCL6 function and perturbation of the ubiquitin proteasome system are
353 hallmarks of B-NHL that require further investigation. We have highlighted some of the known
354 substrates of recurrently altered UPS genes from the literature, which shows the potential for
355 these genetic alterations to contribute to aberrant B-cell receptor signaling and proliferation.
356 However, many of the genes within these categories have not been functionally studied in the bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
357 context of B-cell lymphoma, which represents a significant gap in our understanding of disease
358 etiology.
359
360 The role of epistatic interactions between co-occurring genetic alterations is also an emerging
361 field that requires further investigation. These interactions are not uncommon in cancer66, but
362 our data show that they are pervasive and disease-defining features of the B-NHL genetic
363 landscape. Epistasis between co-associated genetic alterations identified in this study requires
364 formal validation in cell line and/or animal models. However, there are many instances in which
365 co-occurring genetic alterations that we observed have already been shown to cooperate in
366 lymphomagenesis. In addition to the aforementioned example of MYD88 and CD79B mutations,
367 transgenic mouse models of Ezh2 activating mutations or conditional deletion of Crebbp or
368 Kmt2d have shown that these events are not alone sufficient for lymphomagenesis42,67-71. We
369 and others have observed a co-association between the mutation of these genes and BCL2
370 translocations16,21, and the addition of a Bcl2 transgene to these murine models indeed
371 promoted lymphoma at a significantly higher rate than that observed with the Bcl2 transgene
372 alone42,67-71. These genetic alterations are therefore significantly more lymphomagenic in
373 combination than they are alone, which provides proof of principal that an epistatic relationship
374 exists between these co-occurring genetic alterations. Future studies focusing on other co-
375 occurring mutations, such as MYC translocation and SMARCA4 mutation in BL, CREBBP and
376 KMT2D mutation in FL, TCF4 copy gain and MYD88 mutation in DLBCL, and ATM mutation and
377 RPL5 deletion in MCL, should therefore be performed to further explore these concepts and
378 define their underlying functional relationship. We suggest that combinations of genetic
379 alterations are likely to more accurately recapitulate the biology of B-NHL than single gene
380 models, and may reveal contextually different functional roles of genetic alterations depending
381 on the co-occurring events. bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
382
383 In conclusion, we have provided a framework of functional hallmarks and co-occurring genetic
384 alterations that are enriched within B-NHL subtypes. These genetic alterations likely represent
385 epistatic interactions that underpin the biology of these tumors, and represent an opportunity for
386 better understanding lymphoma etiology so that we can identify novel rational approaches for
387 therapeutic targeting of the underlying biology.
388
389 ACKNOWLEDGEMENTS
390 This research was supported by NCI R01CA201380 (MRG), the Nebraska Department of
391 Health and Human Services (LB506 2016-17; MRG), the Jaime Erin Follicular Lymphoma
392 Research Consortium (MRG and SSN), the Futcher Foundation (LN and MRG), the Schweitzer
393 Family Fund (JRW, RED, MRG), a Leukemia and Lymphoma Society Fellow Award (HY), and
394 NCI cancer center support grants to the University of Texas MD Anderson Cancer Center (P30
395 CA016672) and the Fred & Pamela Buffet Cancer Center (P30 CA036727).
396
397 AUTHOR CONTRIBUTIONS
398 MCJM and ST performed experiments, analyzed data and wrote the manuscript. AB analyzed
399 data. AB, TBH, HY, QD, DM, KH, NJ, JS, and SG performed experiments. AA, LS, MD, CC, JT,
400 DP, KMV, MAL, ARS, BJC, RB, SSN, LN, RED, JW, SP, MKG, DS, JI, TG, SR, and AM
401 provided samples and/or clinical data. MRG conceived and supervised the study, performed
402 experiments, analyzed the data and wrote the manuscript. All authors reviewed and approved
403 the manuscript.
404 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
405 DISCLOSURES OF CONFLICTS OF INTEREST
406 The authors have no conflicts of interest related to this work.
407
408
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602
603
604
605 FIGURE LEGENDS
606 Figure 1: Recurrently mutated genes in B-NHL subtypes. An oncoplot shows significantly
607 mutated genes across our cohort of 755 B-NHL tumors, arranged according to functional
608 category and frequency. The mutational burden and distribution of mutation types for each case
609 are shown at the top. Amino acid residues for mutational hotspots and/or domains targeted by
610 clustered mutations are shown on the right.
611
612 Figure 2: Structural alterations in B-NHL subtypes. A) A circos plot shows translocations of
613 the MYC (purple), BCL2 (orange) and BCL6 (green) genes, and GISTIC tracks of DNA copy
614 number gains (red) and losses (blue). B-C) Volcano plots of integrative analysis results showing
615 the changes in gene expression of genes within peaks of DNA copy number gain (B) or loss (C).
616 The shaded region in B marks genes with significantly increased expression in tumors with
617 increased copy number compared to those without; the converse is true for the shaded region in
618 C. D) An oncoplot with DNA copy number losses and gains ranked according to their frequency
619 shows the distribution of structural alterations across tumors. E) Oncoplots show the overlap of
620 structural alterations and mutations that target the same genes. P-values are derived from a
621 Fisher’s exact test (ns, not significant). bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
622
623
624
625
626
627 Figure 3: Genetic alterations of the ubiquitin proteasome system (UPS) and potential
628 interplay with BCR signaling. A) A graphical illustration of UPS gene genetic alterations
629 (mutations, teal; deletions, blue; copy gain, red) and literature supported targets of the UPS
630 genes. The function of UPS gene targets is categorized according to the hallmarks shown in
631 figure 1. B) A schematic of the different layers of genetic alterations (mutations, teal; deletions,
632 blue; copy gain, red) affecting signaling pathways in B-NHL. UPS genes are highlighted with a
633 yellow halo.
634
635 Figure 4: Subtype-specific clusters of co-occurring genetic alterations. The frequency (bar
636 graph) and over/under-representation (blue to yellow scale) of mutations and structural
637 alterations is shown on the left for BL, DLBCL, FL and MCL. Over/under-representation in
638 molecular Burkitt and cell of origin molecular subtypes is shown for each alteration at the
639 bottom. The correlation matrix of co-associated (green) and mutually-exclusive (purple)
640 relationships was clustered and identified groups of co-occurring genetic alterations that were
641 predominantly over-represented in a single B-NHL subtype.
642
643 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
644
645
646
647
648
649 Figure 5: Subtype-specific patterns of SWI/SNF complex mutations. A) An oncoplot shows
650 the frequency of genetic alterations in genes that encode components of the SWI/SNF complex.
651 B) A schematic of the SWI/SNF complex shows recurrently mutated genes, ARID1A,
652 SMARCA4 and BCL7A, and the BCL11A gene that is targeted by 2p15 DNA copy number
653 gains. C-E) Lollipop plots show the distribution of mutations in the SWI/SNF components
654 ARID1A (C), SMARCA4 (D), and BCL7A (E). F) A heatplot shows the location of chromosome
655 2p DNA copy number gains (red) in all tumors (rows) with 2p15 copy gain (n=92, copy number >
656 2.2) ordered from highest DNA copy number (top) to lowest (bottom). The BCL11A gene is in
657 the peak focal copy gain.
658
659 Figure 6: Cumulative burden of clonal co-occurring mutations. A) A bar plot shows the
660 frequency of tumors with genetic alterations from each subtype-defining cluster, grouped by the
661 number of co-occurring alterations observed per tumor. The frequency of tumors with 3 or more
662 genetic alterations per tumor is significantly higher in the subtype for which each cluster is
663 named (***Fisher P-value < 0.001). B-D) An oncoplot, colored by cancer cell fraction (CCF) from
664 ABSOLUTE, shows the representation and clonality of BL cluster (B), FL cluster (C) and MCL
665 cluster (D) mutations across the dataset. The rate of co-occurrence of multiple genetic
666 alterations in a single tumor (columns) is observably higher in the subtypes for which the bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
667 clusters are named, and the co-occurring genetics are most often clonal (CCF > 0.9). BL, Burkitt
668 Lymphoma. HG, other high-grade lymphoma (DHL/THL, HGBL-NOS). DLBCL, diffuse large B-
669 cell lymphoma. FL, follicular lymphoma. MCL, mantle cell lymphoma. LG, other low-grade
670 lymphoma (CLL/SLL, MZL). bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Figure 1 HGBL-NOS CLL/SLL DHL MZL Mutation Type: BL DLBCL FL MCL Multiple 30 Nonsense/Frameshift Mutational 20 Missense
Burden 10 Nucleotide Change: (mutations / Mb) C:G > A:T T:A > A:T 1.00 C:G > T:A T:A > C:G 0.75 C:G > G:C T:A > G:C Nucleotide InDel 0.50 Change 0.25 Freq. Residue / Domain 10% 20% 30% KMT2D CREBBP KAT ARID1A EZH2 SET (Y646N/F/S) SMARCA4 Helicase HIST1H1E Linker_Histone EP300 KAT BCL7A BCL_N TET2
Chromatin WHSC1 SET
Modification ASH1L HIST1H1C HIST1H1B Linker_Histone KMT2A IGLL5 CARD11 PIM1 GNA13 ID3 HLH MYD88 TIR (L265P) NOTCH1 NOTCH2 STAT6 DNA_binding FOXO1 GNAI2 Gα SGK1 Protein_Kinase TCF3 HLH TNFAIP3 ATP6V1B2 (R400Q/G) CD79B ITAM (Y197S/H/N)
Signaling Pathways RFTN1 Raftlin NFKBIE DUSP2 Rhodanese ATP6AP1 BCL2 ATM MYC TP53 DNA_binding
Apop. / CCND3
Cell Cyc. CCND1 Cyclin_N MEF2B IRF8 TBL1XR1 SPEN PRDM1
BCL6 BCL6
Function BCOR IRF4 IRF CDC27 TPR_11 (Y641N) SOCS1 UBR5 KLHL6 BTB
UPS DTX1 WWE BIRC6 BIRC3 TNFRSF14 B2M CIITA Imm. CD58 DDX3X BTG1 BTG BTG2 BTG EEF1A1
Other SP140 TMEM51 TMEM51 (E169*) bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. Figure 2 It is made available under a CC-BY-NC-ND 4.0 International license. A B C 0.0001 0.0001
0.001 0.001
0.01 0.01
0.1 0.1 Q-value Q-value 1.0 1.0 -1-2 0 1 2 -1-2 0 1 2 T-test Score T-test Score
D HGBL-NOS CLL/SLL Gene(s) BLDHL DLBCL FL MCL MZL Freq. 10% 20% MYC BCL6
t(-;-) BCL2 6q24.2 TNFAIP3 6q14.1 EEF1A1, IBTK 1p21.3 RPL5 13q14.2 miR-15a*, miR-16*, TRIM13 13q34 ING1, CUL4A 9p21.3 CDKN2A, CDKN2B, MTAP 11q22.3 ATM, POU2AF1, DDX10 9q21.32 UBQLN1, FGD3 1p36.32 TNFRSF14, DFFB 15q15.1 B2M, TP53BP1 15q11.2 UBE3A 11q22.1 BIRC3* 8p23.3 FBXO25 1p36.11 RCC1 10q23.31 PTEN, FAS
Copy Loss 2q22.3 ZEB2 10p13 RBM17 14q31.3 1q43 CEP170 1q23.3 8q24.23 7q32.2 ING3 5q23.2 8q23.3 12q13.13 ATF7 18q21.31 TCF4 12q15 MDM2* 1q23.1 MEF2D, SETDB1 1q42.3 ARID4B 1q31.3 UCHL5 7p22.3 7q21.1 CDK6 18q23 BCL2 7q36.3 WDR60 17q23.1 miR-21 2p15 REL, BCL11A, XPO1
Copy Gain 11q23.3 KMT2A, CXCR5 9p13.3 RNF38, UBAP1 13q31.3 miR-17 8p23.3 11q13.4 10q26.3 PPP2R2D 4p16.3 Translocations: Copy Number Alterations (CNA): IgH partner non-Ig partner Bi-allelic loss Multiple copy gain IgL partner Single copy loss Single copy gain E ATM B2M t(-;18) p<0.0001 POU2AF1* p<0.0001 TP53BP1* p<0.0001 BCL2 -11q22.3 -15q15.1 +18q23
TNFRSF14 p=0.0102 EEF1A1 ns -1p36.32 -6q14.1 Mutation Type: Multiple Nonsense/Frameshift Missense BIRC3 p<0.0001 TNFAIP3 ns -11q22.1 -6q24.2 Figure 3 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license. A Signa lin BCL10 g LYN NEMO
BIRC3 BTK NOTCH1 SOCS1 TNFAIP3
DTX1 IBTK
RC3H2 IKBKG r KLHL6 TRIM13 e
h
t AKT UBR5 UPS UBE3A
O PAIP2 MDM2
BIRC6 FOXB25
CCNA1 ELK1 CDC27 CUL4A
RNF38 UCHL5 e CCND1 DDB2 l c TP53 E2F1 y l C A el poptosis & C
B TLR BCR NOTCH1/2 CD19
MYD88 α DTX1 SOCS1 LYN β IRAK4 SYK IRAK1 CARD11 PKCβ PI3K PIP3 BCL10 BLNK PTEN BTK MALT1 BIRC3 PLCG2 TRAF6 AKT UBE3A KLHL6 RC3H2 IBTK MEK ATP6V1B2 NFκB TNFAIP3 mTOR Signaling TRIM13 DUSP2 ERK ATP6AP1
TCF4TCF3 Ig expression TCF4ID3 TCF4TCF4 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Figure 4 BL DLBCL FL MCL SMARCA4 CCND3 ID3 MYC_tx Q-Value MYC DDX3X GNAI2 BL cluster Mutually Exclusive Co-Associated GNA13 0.001 1 0.001 FOXO1 TCF3 TMEM51 ARID1A BCL6 CARD11 TET2 BIRC6 Q-value CIITA NFKBIE PRDM1 Under-Represented Over-Represented CD58 0.001 1 0.001 DUSP2 BTG2 EP300 TNFAIP3 TBL1XR1 IRF4 AMP_10q26.3 AMP_18q21.31 Q-value AMP_18q23 MYD88 CD79B BCL6_tx 0.001 1 0.001 PIM1 IGLL5 DLBCL cluster BTG1 non-mBL mBL DTX1 HIST1H1B B2M SOCS1 GCB-like ABC-like SGK1 HIST1H1E RFTN1 KMT2A MEF2B KLHL6 HIST1H1C STAT6 EEF1A1 TNFRSF14 IRF8 BCL7A KMT2D BCL2_tx CREBBP FL cluster BCL2 EZH2 ATP6V1B2 ATP6AP1 DEL_10q23.31 AMP_1q23.1 AMP_1q31.3 AMP_12q13.13 AMP_12q15 AMP_7p22.3 AMP_7q21.2 AMP_7q36.3 AMP_1q42.3 AMP_2p15 AMP_8p23.3 TP53 DEL_8q24.23 AMP_11q13.4 CNA cluster AMP_11q23.3 AMP_13q31.3 AMP_9p13.3 AMP_4p16.3 DEL_2q22.3 DEL_1p36.11 DEL_1p36.32 DEL_6q24.2 DEL_6q14.1 DEL_15q11.2 DEL_15q15.1 AMP_8q23.3 AMP_17q23.1 DEL_1q23.3 DEL_1q43 BIRC3 ATM DEL_11q22.1 DEL_11q22.3 DEL_8p23.3 MCL cluster DEL_1p21.3 DEL_9q21.32 DEL_9p21.3 DEL_13q14.2 DEL_13q34 DEL_10p13 DEL_7q32.2 DEL_14q31.3 BCOR UBR5 SP140 CCND1 DEL_5q23.2 WHSC1 ASH1L CDC27 NOTCH1 SPEN NOTCH2 20 40 60 20 40 60 20 40 60 20 40 60 Freq. Freq. Freq. Freq. mBL COO Enrichment Enrichment Enrichment Enrichment ID3 B2M ATM IRF4 IRF8 MYC PIM1 TP53 TET2 TCF3 BCL2 BCL6 CIITA DTX1 EZH2 CD58 BTG1 BTG2 SGK1 UBR5 IGLL5 SPEN BCOR BIRC3 BIRC6 GNAI2 SP140 EP300 KLHL6 STAT6 ASH1L BCL7A RFTN1 CD79B GNA13 CDC27 KMT2A MEF2B DUSP2 MYD88 FOXO1 KMT2D SOCS1 DDX3X CCND1 CCND3 PRDM1 MYC_tx WHSC1 ARID1A NFKBIE EEF1A1 BCL6_tx BCL2_tx CARD11 TMEM51 NOTCH1 TNFAIP3 NOTCH2 CREBBP TBL1XR1 ATP6AP1 DEL_1q43 HIST1H1B HIST1H1E SMARCA4 HIST1H1C AMP_2p15 ATP6V1B2 TNFRSF14 DEL_13q34 DEL_10p13 AMP_18q23 AMP_12q15 DEL_8p23.3 DEL_1p21.3 DEL_9p21.3 DEL_7q32.2 DEL_5q23.2 DEL_2q22.3 DEL_6q24.2 DEL_6q14.1 DEL_1q23.3 AMP_9p13.3 AMP_4p16.3 AMP_1q23.1 AMP_1q31.3 AMP_7p22.3 AMP_7q21.2 AMP_7q36.3 AMP_1q42.3 AMP_8p23.3 AMP_8q23.3 DEL_11q22.1 DEL_11q22.3 DEL_9q21.32 DEL_13q14.2 DEL_14q31.3 DEL_8q24.23 DEL_1p36.11 DEL_1p36.32 DEL_15q11.2 DEL_15q15.1 AMP_10q26.3 AMP_11q13.4 AMP_11q23.3 AMP_13q31.3 AMP_17q23.1 DEL_10q23.31 AMP_18q21.31 AMP_12q13.13 bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Figure 5
A BL DLBCL FL MCL B BCL11A ARID1A 19% 7% 15% 6% BCL7A ARID1A SMARCA4 24% 3% 8% 6% BCL7A 7% 7% 11% 0% BCL11A 11% 21% 18% 2% Alteration Type: Multiple Nonsense/Frameshift Missense Copy gain SMARCA4
P158 C 8 ARID1A
Q200
0 F BCL11A ARID BAF250_C
0 400 800 1200 1600 2000 2285aa
D 5 SMARCA4 T910 R1192
0 HSA BRK SNF2_N Helica.. SnAC Brom..
0 400 800 1200 1647aa
W31 E 19 BCL7A
R11 M1
0 BCL_N
0 100 210aa p25.2 p24.1 p22.1 p16.1 p14 p12 # Mutations Amino acid position Chromosome 2, p-arm bioRxiv preprint first posted online Jun. 19, 2019; doi: http://dx.doi.org/10.1101/674259. The copyright holder for this preprint (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
Figure 6
BL Cluster Alterations FL Cluster Alterations MCL Cluster Alterations A *** *** *** *** *** *** *** *** *** 100% 100% 100% # of Alterations 80% 80% 80% 0 per tumor 60% 60% 60% 1 per tumor 40% 40% 40% 2 per tumor
% of tumors 20% 20% 20% ≥3 per tumor
0% 0% 0%
BL FL BL FL BL FL MCL MCL MCL DLBCL DLBCL DLBCL
BL HG DLBCL FL MCL LG CCF B MYC 1.0 ID3 TCF3 CCND3 SMARCA4 ARID1A 0.5 GNA13 GNAI2 FOXO1 TMEM51 0
BL HG DLBCL FL MCL LG C KMT2D CREBBP BCL2 TNFRSF14 EZH2 -10q23.31 IRF8 BCL7A ATP6V1B2 EEF1A1 STAT6
BL HG DLBCL FL MCL LG D -1p21 ATM -13q34 -13q14.2 -11q22.3 -11q22.1 -9q21.32 -9p21.3 -1q23.3 -1q43 -10p13 -8p23.3 BIRC3