Frequent CTLA4-CD28 gene fusion in diverse types ...

Published Ahead of Print on January 27, 2016, as doi:10.3324/haematol.2015.139253. Copyright 2016 Ferrata Storti Foundation.

Frequent CTLA4-CD28 gene fusion in diverse types of T cell lymphoma by Hae Yong Yoo, Pora Kim, Won Seog Kim, Seung Ho Lee, Sangok Kim, So Young Kang, Hye Yoon Jang, Jong-Eun Lee, Jaesang Kim, Seok Jin Kim, Young Hyeh Ko, and Sanghyuk Lee Haematologica 2016 [Epub ahead of print] Citation: Yoo HY, Kim P, Kim WS, Lee SH, Kim S, Kang SY, Jang HY, Lee JE, Kim J, Kim SJ, Ko YH, and Lee S. Frequent CTLA4-CD28 gene fusion in diverse types of T cell lymphoma. Haematologica. 2016; 101:xxx doi:10.3324/haematol.2015.139253 Publisher's Disclaimer. E-publishing ahead of print is increasingly important for the rapid dissemination of science. Haematologica is, therefore, E-publishing PDF files of an early version of manuscripts that have completed a regular peer review and have been accepted for publication. E-publishing of this PDF file has been approved by the authors. After having E-published Ahead of Print, manuscripts will then undergo technical and English editing, typesetting, proof correction and be presented for the authors' final approval; the final version of the manuscript will then appear in print on a regular issue of the journal. All legal disclaimers that apply to the journal also pertain to this production process.

Frequent CTLA4-CD28 gene fusion in diverse types of T cell lymphoma Hae Yong Yoo1,2*, Pora Kim3*, Won Seog Kim2,4*, Seung Ho Lee1*, Sangok Kim3,5*, So Young Kang6, Hye Yoon Jang7, Jong-Eun Lee7, Jaesang Kim8, Seok Jin Kim2,4, Young Hyeh Ko2,6†, and Sanghyuk Lee3,5,8† 1

Department of Health Sciences and Technology, Samsung Advanced Institute for Health

Sciences and Technology, Sungkyunkwan University, Seoul 06351, Korea 2

Samsung Biomedical Research Institute, Research Institute for Future Medicine, Samsung

Medical Center, Seoul 06351, Korea 3

Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul

03760, Korea 4

Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center,

Sungkyunkwan University School of Medicine, Seoul 06351, Korea 5

Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Korea

6

Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of

Medicine, Seoul 06351, Korea 7

DNA Link Inc., Seoul 03759, Korea

8

Department of Life Science, Ewha Womans University, Seoul 06351, Korea

*These authors contributed equally to this work. †

These authors are co-corresponding authors of this article.

Running title: CTLA4-CD28 gene fusion in TCL

Corresponding Authors: Young Hyeh Ko, Department of pathology, Samsung medical Center, Sungkyunkwan University School of Medicine, and Samsung Biomedical Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea, Phone: +82-23140-2762 Fax: +82-2-3410-0025 E-mail: [email protected] Sanghyuk Lee, Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea, Phone: +82-2-3277-2888 [email protected]

Text word count: 2911 Abstract word count: 201 Number of figures: 4 Number of tables: 0 Number of references: 27 Supplementary file: 1

Fax: +82-2-3277-6809

E-mail:

ABSTRACT CTLA4 and CD28 are co-regulatory receptors of opposite roles in T cell signaling. We identified a fusion between the two genes from partial gene duplication in a case of angioimmunoblastic T cell lymphoma by RNA sequencing. The fusion gene consists of the extracellular domain of CTLA4 and the cytoplasmic region of CD28, likely capable of transforming inhibitory signals into stimulatory signals for T cell activation. Ectopic expression of fusion transcript in Jurkat and H9 cells resulted in enhanced proliferation and AKT and ERK phosphorylation, indicating activation of downstream oncogenic pathways. To estimate the frequency of this gene fusion in mature T cell lymphomas, we examined 115 T cell lymphoma samples of diverse subtypes using RT-PCR and Sanger sequencing. We identified the fusion in 26 of 45 angioimmunoblastic T-cell lymphomas (58%), 9 of 39 peripheral T-cell lymphomas, not otherwise specified (23%), and 9 of 31 extranodal NK/T cell lymphomas (29%). We further investigated the mutation status of 70 lymphomaassociated genes using ultra-deep targeted resequencing for 74 mature T cell lymphoma samples. The mutational landscape we obtained suggests that T cell lymphoma results from diverse combinations of multiple-gene mutations. The CTLA4-CD28 gene fusion is likely a major contributor to T cell lymphoma pathogenesis and represents a potential target for antiCTLA4 cancer immunotherapy.

INTRODUCTION Peripheral T cell lymphoma is a malignant neoplasm of mature T cells. Recent genomic studies have identified highly recurrent somatic mutations in TET2, DNMT3A, IDH2, and RHOA in diverse subtypes of mature T cell lymphoma (TCL)

1-5

. However, their roles in the

regulation of T cell signaling and oncogenesis have yet to be elucidated. Furthermore, none of the mutant genes has been clearly demonstrated as a dominant oncogenic driver. Therefore, identifying additional driver mutations and dissecting the interplay with T cell signaling components is still necessary. CTLA4 and CD28, members of the immunoglobulin superfamily that are expressed on the surfaces of T cells, are regulatory co-receptors of T cell signaling. They play critical and opposite roles in maintaining balanced T cell signaling and, thus, the proper level of immune activation

6,7

. Perturbing this balance can result in a number of undesirable consequences

such as autoimmunity, transplant rejection, or even malignant TCL 6. Accordingly, controlling T cell signaling through these two co-receptors has been a key strategy for recent cancer immunotherapies including anti-CTLA4 antibody therapy

8, 9

or chimeric antigen receptor T

cell therapy utilizing the intracellular signaling domain of CD28

10

. In this study, we

identified a fusion between CTLA4 and CD28 in a case of angioimmunoblastic TCL by whole transcriptome sequencing and analyzed the frequency of gene fusion in 117 cases of TCLs. Functional study of fusion gene indicates fusion between CTLA4 and CD28 results in activation of downstream oncogenic pathways. Mutational status of 70 lymphoma-associated genes using ultra-deep targeted resequencing for 74 mature TCL tumor samples were analyzed.

METHODS

Sample description Clinical information for the patients is summarized in Supplementary Table S2. For Sanger sequencing using 115 TCL tumor samples and targeted deep sequencing using 74 TCL tumor samples, formalin-fixed, paraffin-embedded (FFPE) tumor tissues were utilized. QIAamp DNA Mini Kit (Qiagen) and the RNeasy Mini Kit (Qiagen) were respectively used for DNA and RNA extractions. All patient samples were obtained with informed consent in Samsung Medical Center, Seoul, Korea, and the study was approved by the Institutional Review Board in accordance with the Declaration of Helsinki. Detection of CTLA4-CD28 mutation For detection of CTLA4-CD28 fusion transcript, we carried out reverse transcription (RT) with an random hexamers and total RNA isolated from FFPE samples followed by PCR amplification with the following oligonucleotide primers; RT_F1, RT_R1, RT_F2, RT_R2 and RT_F3. For characterization of genomic rearrangement, the CTLA4-CD28 fusion gene was amplified by PCR using genomic DNA of the clinical specimen as the template. Amplification was performed with Herculase 2 Fusion DNA Polymerase (Agilent Technologies) and the primers; Fusion_F1, Fusion_R1, Fusion_F2 and Fusion_R2. The resulting PCR products were analyzed by agarose gel electrophoresis and sequenced using two independent primer pairs. Cell proliferation and cytokine assays Jurkat (human T cell acute lymphoblastic leukemia) and H9 (human cutaneous T lymphocyte lymphoma) cells transfected with a construct expressing the CTLA4-CD28 fusion protein. Cells expressing CTLA4 and CTLA4-CD28 fusion were seeded in 96-well plates in triplicates at a density of 5×103 cells/well in 100

μl

of RPMI-1640 medium

containing 10% FBS and antibiotics. For stimulation, a 96-well plate was coated with 5 μg/ml

goat anti-mouse IgG (Ab frontier) or with 2 μg/ml anti-CD3 (HIT3a, BD pharmingen) or with the combination of 2 μg/ml anti-CD3 (HIT3a, BD pharmingen), 2 μg/ml anti-CD28 (CD28.2 BD pharmingen) or 5 μg/ml anti-CTLA4 (BNI3, BD phamingen) for overnight or 2 hrs in 37°C. After 48 hrs, cell proliferation was evaluated using Cell Counting Kit-8 (Dojindo), according to the manufacturer's instructions, and the absorbance value for each well was measured at 450 nm using a microplate reader (Spectra Max 180, Molecular Devices). Each experiment was repeated three times. For cytokine assay, cells were stimulated with 15 ng/ml PMA and 290 ng/ml Ionomycin (eBioscience) for 3 hrs. Cells were plated on 24-well plates coated with 5 μg/ml goat anti-mouse IgG (Ab frontier) or with 2

μg/ml anti-CD3

μg/ml anti-CD3 (HIT3a, BD pharmingen), 2 μg/ml anti-CD28 (CD28.2 BD pharmingen) or 5 μg/ml anti(HIT3a, BD pharmingen) or with the combination of 2

CTLA4 (BNI3, BD phamingen). After 24 hrs, the supernatants were examined using Human IL-2 ELISA kits (Thermo scientific) according to the manufacturer's instructions. Each experiment was repeated three times. Selection of 70 frequently mutated genes in lymphoma and bioinformatic analysis for targeted sequencing We have identified 62 frequently mutated genes from 10 genomics studies on the B and T cell lymphoma

1, 3, 5, 11-18

. Then we added three p53-related genes and five JAK-STAT

signaling genes for targeted deep sequencing. The list of 70 target genes is provided in Supplementary Table S1. We further compiled somatic mutations in the target genes from the original references which collectively yielded 832 mutations in 62 genes (Supplementary File S1). Targeted sequencing was performed using 74 TCLs. Bioinformatics analysis of targeted deep sequencing data is described in supplementary method.

RESULTS Identification and validation of the CTLA4-CD28 fusion gene The fusion transcript was initially predicted from analyses of RNA-Seq data from previously described TCL patients

5

using the FusionScan program. Multiple reads that

mapped to exon 3 of the CTLA4 gene in tandem with exon 4 of the CD28 gene were identified (Figure 1). The depth profile of the RNA-Seq data, reflecting the expression level of each exon, shows abrupt changes at the break points in both genes, which is consistent with the presence of fusion transcripts (Figure 1B). We verified the fusion via RT-PCR using two different primer sets in fusion-positive patient samples and seven TCL cell lines (Figure 2A ; Supplementary Figure S1). CTLA4-CD28 fusion cDNA was successfully amplified from the patient samples and two T lymphoblastoid cell lines, CEMC1-15 and CEMC7-14. Subsequent Sanger sequencing confirmed that the PCR products contained the fusion transcripts between exon 3 of the CTLA4 and exon 4 of the CD28 genes. To estimate the frequency of this gene fusion in TCL patients, we examined 115 TCL patient samples of diverse subtypes using RT-PCR and Sanger sequencing (Supplementary Table S2). The results indicated that CTLA4-CD28 fusion occurs in the all tested subtypes of TCL with an overall frequency of 38%. The fusion was observed more frequently in TCL of follicular helper T cell phenotype (TFH) than non-TFH TCL. The fusion was identified in 26 of 45 angioimmunoblastic TCL (AITL) patients (58%), 9 of 39 peripheral TCL not otherwise specified (PTCL-NOS) patients (23%), and 9 of 31 NK/T cell lymphoma patients (29%) (Supplementary Table S2; Supplementary Figure S2). Among PTCL-NOS, 5 of 9 PTCL-NOS with TFH phenotype (56%) and 5 of 16 non-TFH PTCL-NOS (31%) showed the CTLA4CD28 fusion. The CTLA4-CD28 fusion was not observed in blood samples of 50 healthy individuals.

Functional analyses of the CTLA4-CD28 fusion gene Notably, the predicted protein generated from this fusion gene features the extracellular and transmembrane domains of CTLA4 and the cytosolic signaling domain of CD28 (Figure 1A). A possible outcome is inappropriate activation of T cell signaling. Therefore, we proceeded to analyze the effect of the CTLA4-CD28 fusion on cell proliferation and cytokine production in T cells. Jurkat (human T cell acute lymphoblastic leukemia) and H9 (human cutaneous T lymphocyte lymphoma) cells transfected with a construct expressing the CTLA4-CD28 fusion protein showed a proliferation rate approximately 30% higher than cells transfected with only the vector or the wild-type CTLA4 expression construct after stimulation with anti-CTLA4 antibody (Figure 2B; Supplementary Figure S3A). The surface expression levels of CTLA4 and the CTLA4-CD28 fusion were comparable in Jurkat cell line (Supplementary Figure S4). Also, interleukin 2 (IL-2), the definitive marker of T cell activation, was produced at a six fold higher level (Figure 2C; Supplementary Figure S3B). These results indicate that the CTLA4-CD28 fusion protein likely mediates activating signals upon T cell stimulation. Next, we examined the phosphorylation of AKT and ERK1/2, which represents the activation of two critical pathways downstream of T cell receptor signaling. As expected, CD28-mediated co-stimulation of T cells led to increased AKT and ERK phosphorylation (Figure 2D). Importantly, cells expressing the CTLA4-CD28 fusion also showed increased AKT and ERK phosphorylation relative to cells expressing wild-type CTLA4 upon stimulation with an anti-CTLA4 antibody. These results strongly suggest that the CTLA4CD28 fusion could lead to constitutive T cell activation by converting inhibitory signals into activating signals. Of note, Shin et al. developed the CTLA4-CD28 chimera for adoptive T

cell therapy of cancer, and studied its biological roles and therapeutic efficacy using mouse T cells

19

. Importantly, they also demonstrated that the fusion gene delivered activating rather

than inhibitory T cell signal.

Genomic structure of the CTLA4-CD28 fusion gene Two genes are located in tandem 129 kbp apart on chromosome 2, with CD28 preceding CTLA4 on the same strand. The gene order is reversed in the CTLA4-CD28 fusion case, raising a strong possibility that partial duplications have occurred (Figure 3A). Extrachromosomal amplification by episome formation, which was observed for the NUP214-ABL1 fusion in T-cell acute lymphoblastic leukemia20, could not be ruled out in all cases, but was shown not to have occurred in multiple cases examined by fluorescence in situ hybridization (FISH) experiment (Supplementary Figure S5). Consistent with our hypothesis, quantitative PCR analyses of genomic DNA demonstrated that the copy number gains for portions of the CD28 and CTLA4 genes represented in the fusion were significantly higher in the fusion-positive patients than those in the fusion-negative lymphoma patients (Figure 3B; Supplementary Figure S6). Next, we mapped the exact positions of the break points in the genomic DNA of fusionpositive patients. For the fusion-positive patient shown in Figs. 1 and 2, we amplified a 2.5kb genomic DNA fragment (Figure 3D). Subsequent Sanger sequencing revealed that the fragment contained 696 bp of CTLA4 intron 3, 1457 bp of CD28 intron 3, and 47 bp of intervening sequence from a LINE retrotransposon element that is normally located 104 kb downstream of the CTLA4 gene on chromosome 2 (Figure 3E; Supplementary Figure S7). We characterized another patient in whose genome 426 bp of CTLA4 intron 3 was joined to 3185 bp of CD28 intron 3. Surprisingly, the most frequent arrangement was direct fusion of two

exons with no intron sequences included. We observed six other patients and two cell lines with such fusions. Despite the diversity in their genomic structures, all the genome arrangements we observed in patient samples would generate identical fusion transcripts and proteins. We also analyzed the mRNA expression levels of the CD28, CTLA4, and CTLA4-CD28 fusion genes in AITL patient samples using quantitative RT-PCR (Figure 3C). Fusion mRNA was expressed only in fusion-positive patients, and the expression level was comparable to that of CTLA4. CD28 and CTLA4 expression levels were approximately 3 and 7 times higher in fusion-positive patients, respectively, which suggests the presence of a feed-forward circuit that amplifies the signaling from the CTLA4-CD28 fusion receptor. Stronger binding affinity to CD80 and CD86 ligands of CTLA4 compared to CD28 may have roles in amplifying signals in the CTLA4-CD28 fusion receptor 6. In sum, patients with the CTLA4-CD28 fusion have not only copy number gain at the genomic level but also show elevated CD28 and CTLA4 expression that is consistent with abnormal activation of the T cell population.

Mutational landscape of lymphoma-related genes from targeted deep sequencing Several somatic mutations have already been implicated as driver mutations in TCL, including the point mutations IDH2 R172K, DNMT3A R882H, RHOA G17V, and CD28 T195P and loss-of-function (stop-gain or frameshift) mutations in the TET2 gene

3-5

.

Determining the functional relationship between these mutations and the CTLA4-CD28 fusion would be of the utmost importance to understanding the molecular mechanisms underlying TCL. We selected 70 genes that had been reported to be frequently mutated in various types of T and B cell lymphoma (Supplementary Table S1)

1, 3-5, 11-17

. Targeted deep

sequencing data for all exons of the 70 genes were produced with an average sequencing

depth of 1,204X and 98.9% of target coverage using 2,965 primer pairs. The ultra-high depth of the sequencing is expected to reveal variants of low frequency but with important functional roles. In total, 74 tumor tissues from 29 AITL, 15 PTCL-NOS, 26 extranodal NK/T cell lymphoma (ENKL), 2 enteropathy-associated T cell lymphoma (EATL), and 2 anaplastic large cell lymphoma (ALCL) patients were examined. The sequencing data were analyzed using our own computational pipeline, which was designed to identify somatic mutations in tumor cells without the normal control (Supplementary Figure S8). We identified 11 missense mutations in 10 different genes and 24 TET2 loss-of-function mutations, including 8 novel mutations (Figure 4; Supplementary Figure S8). Loss-of-function mutations in the TET2 gene occurred most frequently in all subtypes (47 patients, 64%). Excluding TET2 mutations with low allele frequency (< 5%), which were observed mostly for R544X and R1404X (Supplementary Figure S9), the TET2 mutation rate was only 30% (22 patients), with significant concentration in the AITL subtype (15 patients, P = 0.008). Apparently, many mutations were observed at low frequencies below 20%, demonstrating the power of ultra-deep sequencing. These low frequency mutations are likely from the heterogeneity of the tumor cells as well as presence of normal or stromal cells. The RHOA mutation was mostly observed in the AITL subtype (21 of 29 AITL patients; 72%), with only two mutations observed in the 15 PTCL-NOS patients (13%). We identified 8 low frequency mutations (allele frequency < 5%) in addition to 12 (41% of AITL patients) high frequency mutations, which agrees well with previous reports on AITL based on Sanger sequencing

3-5

. Among other recurrent mutations, IDH2 R172K and DNMT3A R882H

mutations were found in 7 and 5 patients, respectively. These latter two mutations have been described as ageing-related initiating mutations that are associated with the clonal expansion

of prelymphoma stem cells and can be detected in the blood of elderly individuals without apparent hematologic malignancies 21-23. The frequency of the CTLA4-CD28 fusion was 30% in this targeted deep-sequencing analysis (22 out of 74 patients), appearing in all subtypes tested. Sixteen out of 22 fusionpositive patients contained additional mutations. In 11 AITL patients with the CTLA4-CD28 fusion, 9 (82%) patients and 10 (91%) patients also harbored TET2 mutation and RHOA mutation respectively. CTLA4-CD28 fusion was not found in 4 patients with CD28 T195P mutation which also led to up-regulation of TCR signaling

24

, indicating the relevance of

CD28 in the process of lymphomagenesis. In ENKL, most of the fusion cases were devoid of additional mutation. In fact, a substantially high proportion of cases were devoid of any mutations among tested genes, and even TET2 mutation-positive cases mostly contained no other mutations. It is in this regard that a novel recurrent TCF3 G302S is notable, especially for the ENKL subtype. The transcription factor TCF3 (E2A) is required for B and T lymphocyte development, and the significance of mutations in TCF3 and its negative regulator ID3 has recently been highlighted in Burkitt lymphoma

14, 15, 17

. Genomic examination with additional ENKL

patients should be carried out to substantiate TCF3 G302S mutation as a marker and a potential driver of ENKL subtype.

DISCUSSION Here, we report that the CTLA4-CD28 fusion gene is a novel, high-frequency mutation for diverse types of TCL. Two other groups have recently reported the identification of the CTLA4-CD28 fusion gene in Sézary syndrome, an aggressive rare variant of cutaneous T cell

lymphoma 25, 26. That this mutation is not limited to Sézary syndrome but is found in a broad range of TCL types with the overall frequency of 30% and typically in combination with other mutations should be of significance. We also provide the overall mutational landscape for TCL which indicates that the CTLA4-CD28 fusion represents one of recurrent genetic events for full blown neoplastic transformation. Targeted deep sequencing analyses for 70 genes implicated in TCL showed that a large majority of patients contained more than one mutated gene. Although not all of the mutations have been mechanistically demonstrated to be oncogenic, we suggest that multiple mutational events, including those described in this study, are required for the full development of TCL. It has been proposed and partly demonstrated that TCLs and myeloid leukemia feature the age-related accumulation of premalignant mutations in DNMT3A, TET2, JAK2, and GNAS which are associated with subsequent clonal hematopoietic expansion

21-23

.

Similarly in B cell lymphoma, it has been shown that circulating B-cells bearing Bcl-2 translocations do not cause follicular lymphoma per se but can evolve into overt follicular lymphoma with additional mutations 27. Our data indicate that CTLA4-CD28 fusion disrupts cellular homeostasis via inappropriate activation of T cell signaling. Given that such dysregulation likely lies at the core of oncogenesis, it is possible that the CTLA4-CD28 fusion provides a target for potential immunotherapy. In fact, Sekulic et al. reported an n-of-1 trial for a female patient who had suffered from Sézary syndrome for 8 years26. Administration of the CTLA4-blocking antibody ipilimumab 8 showed dramatic initial responses for first two months, but the disease subsequently progressed rapidly resulting in death 3 months after the last dose. The tragic outcome notwithstanding, the result showed that an immunotherapy targeting the fusion gene is viable in principle, and with an improvement in dosage and timing, the response may

become more durable. Targeted therapy and immunotherapy as parts of combination therapy regimen is an emerging paradigm of cancer treatment. Thus, the identification of frequent CTLA4-CD28 fusion gene will provide a new therapeutic opportunity for relevant TCL patients, and elucidation of the exact mechanism by which the CTLA4-CD28 fusion interacts with other mutations should provide further insights into the molecular nature of TCL development, as well as new strategies for curbing this disease.

Acknowledgments This work was supported by grants from the Technology Innovation Program of the Ministry of Trade, Industry and Energy, Republic of Korea (10050154 to S.L.), the National Research Foundation of Korea (NRF-2014M3C9A3065221, NRF-2012M3A9D1054744 to S.L., NRF2015K1A4A3047851 to S.L. and J.K.), the Samsung grant (SM01132671 and SMO1150911 to Y.H.K.), and a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (HI14C3414 to Y.H.K. and H.Y.Y., HI14C2331 to H.Y.Y.). This study was also supported by the Ministry of Health & Welfare, Republic of Korea (A120262 to JK).

Authorship Contributions Conception and design: S. Lee, Y.H. Ko, Development of methodology: H.Y. Yoo, S. Lee

Acquisition of data (performed experiment, acquired samples and clinical data, provided facilities, etc.): W.S. Kim, Y.H. Ko, S.H. Lee, S.Y. Kang, H.Y. Jang, J.E. Lee Analysis and interpretation of data (e.g., statistical analysis, biostatistics, computational analysis): P. Kim, S. Kim Writing, review, and/or revision of the manuscript: H.Y. Yoo, J. Kim, S. Lee, Y.H. Ko Administrative, technical, or material support (i.e., reporting or organizing data, constructing databases): W.S. Kim, S.J. Kim Study supervision: S. Lee, Y.H. Ko

Disclosure of Potential Conflicts of Interest None.

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FIGURE LEGENDS

Figure 1. Identification of the CTLA4-CD28 gene fusion. A, top, schematic diagram of the gene fusion; bottom, sequencing chromatogram. Numbers on the transcript indicate the nucleotide position of exons. B, alignment of RNA-Seq data from a fusion-positive patient. Read-depth plots indicate the depth coverage of aligned RNA-Seq reads. SP = signal peptide, TM = transmembrane region.

Figure 2. Validation and functional analyses of the CTLA4-CD28 fusion gene. A, validation of the CTLA4-CD28 fusion transcript by RT-PCR using samples

from patients and cell lines.

Arrows indicate the approximate positions of oligonucleotide primers on the CTLA4-CD28 fusion transcript. PCR products were amplified from patient 1, CEMC1-15 cells and CEMC7-14 cells and validated by Sanger sequencing. -actin was used as an internal control, and NTC indicates the no template control. Normal tonsil tissue and 293T cells were used as negative controls. B, Jurkat cells expressing the CTLA4-CD28 fusion show enhanced cell proliferation (*P < 0.05 and **P < 0.01 compared with cells expressing wild-type CTLA4) after co-stimulation with anti-CD3/CTLA4 antibodies. Each experiment was repeated three times with five replicates, and the data are expressed as the mean ± standard deviation. C, expression of the CTLA4-CD28 fusion enhanced interleukin 2 (IL-2) production after CTLA4 activation (**P < 0.01 compared with cells expressing wild-type CTLA4). Jurkat cells were stimulated with PMA/Ionomycin (P/I) without or with anti-CD3/CTLA4 antibodies to activate CTLA4. IL-2 measurement was carried out three independent times, and the data are expressed as the mean ± standard deviation. D, expression of the CTLA4-CD28 fusion enhanced phosphorylation of AKT and ERK1/2 after CTLA4 activation with an anti-CTLA4 antibody.

Figure 3. Genomic structure of the CTLA4-CD28 fusion gene. A, schematic diagram of the gene duplication producing the fusion gene. B, copy-number analysis of CD28 and CTLA4 genes in AITL patient samples using Q-PCR (*P = 0.009, **P = 0.001). Copy number changes, estimated relative to that in the peripheral blood cells from a normal individual, were shown in the box plot. The values are from two independent experiments. C, expression levels of CD28, CTLA4, and CTLA4-CD28 fusion transcripts in AITL patient samples using Q-PCR (*P = 0.001, **P = 0.0001, ***P = 0.002). The values are from three independent experiments. D, structural analysis of genomic loci for CTLA4-CD28 fusion patients. The PCR (2.5 kb) product amplified from genomic DNA of patient 1 was subsequently validated by Sanger sequencing. No product was amplified from the control normal tonsil cells and the no template control (NTC). E, schematic diagrams of the genomic structure of the CTLA4CD28 fusion from 8 patients and two cell lines. The arrows indicate the position of primers (Fusion F1 and Fusion R1).

Figure 4. Mutational landscape of driver genes from targeted resequencing. Each column represents an independent patient. Novel mutations are indicated with asterisks. TCL subtypes are color-coded above the main window, and bars on the right side show the relative proportions of the indicated mutation among TCL subtypes. The CTLA4-CD28 row indicates the presence of fusion gene based on RT-PCR and Sanger sequencing. TET2 mutations of all locations are shown in top rows, with the color of the highest allele frequency for patients with multiple TET2 mutations (position-wise mutation plot provided in Supplementary Fig. S9). The cumulative numbers of patients positive for each mutation are indicated on the right.

Supplementary Information for Frequent CTLA4-CD28 gene fusion in diverse types of T cell lymphoma Hae Yong Yoo1,2*, Pora Kim3*, Won Seog Kim2,4*, Seung Ho Lee1*, Sangok Kim3,5*, So Young Kang6, Hye Yoon Jang7, Jong-Eun Lee7, Jaesang Kim8, Seok Jin Kim2,4, Young Hyeh Ko2,6†, and Sanghyuk Lee3,5,8† 1

Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul 06351, Korea 2

Samsung Biomedical Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea 3

Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul 03760, Korea 4

Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea 5

Department of Bio-Information Science, Ewha Womans University, Seoul 03760, Korea

6

Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul 06351, Korea 7

DNA Link Inc., Seoul 03759, Korea

8

Department of Life Science, Ewha Womans University, Seoul 06351, Korea

*These authors contributed equally to this work. †

These authors are co-corresponding authors of this article.

Corresponding Authors: Young Hyeh Ko, Department of pathology, Samsung medical Center, Sungkyunkwan University School of Medicine, and Samsung Biomedical Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul 06351, Korea, Phone: +82-2-3140-2762 Fax: +82-2-3410-0025 E-mail: [email protected] Sanghyuk Lee, Ewha Research Center for Systems Biology (ERCSB), Ewha Womans 1

University, Seoul 03760, Korea, Phone: +82-2-3277-2888 Fax: +82-2-3277-6809 E-mail: [email protected]

2

Table of Contents Supplementary Methods Quantitative real time PCR ............................................................................................... 4 Cell culture and transfection

........................................................................................... 4

Antibodies and immunoblotting

..................................................................................... 4

Library preparation and targeted deep sequencing

......................................................... 5

Bioinformatics analysis of targeted deep sequencing data. .............................................. 6

Supplementary Tables Supplementary Table S1. List of genes for targeted deep sequencing .............................. 8 Supplementary Table S2. Study subjects for sequencing and their clinical information.. 10 Supplementary Table S3. List of gene-specific PCR primers........................................... 13 Supplementary Table S4. Summary statistics of targeted deep sequencing ..................... 14

Supplementary Figures Supplementary Fig. S1. Validation of the CTLA4-CD28 fusion transcript ...................... 16 Supplementary Fig. S2. Validation of the CTLA4-CD28 fusion gene in patients ............ 17 Supplementary Fig. S3. Functional analyses of the CTLA4-CD28 fusion gene in H9 cell line ............................................................................................................................ 18 Supplementary Fig. S4. Surface expression levels of CTLA4 and the CTLA4-CD28 fusion ........................................................................................................................ 19 Supplementary Fig. S5. Fluorescence in situ hybridization analysis for the fusion ......... 20 Supplementary Fig. S6. Copy number analysis of CD28 and CTLA4 genes ................... 21 Supplementary Fig. S7. Sanger sequencing of genomic DNA for mapping exact fusion points ........................................................................................................................ 22 Supplementary Fig. S8. Computational pipeline for analyzing targeted deep sequencing data ........................................................................................................................... 23 Supplementary Fig. S9. Mutation profile of TET2 gene from targeted sequencing ......... 24

Supplementary Files Supplementary File S1. List of known mutations in lymphoma

3

Supplementary Methods Quantitative real-time PCR. For quantitative analysis of genomic DNA and mRNA in patient samples, 30 ng genomic DNA and 2 g mRNA were used, respectively. mRNA was reversetranscribed, using Super Script 2 (Invitrogen) with random primer. qRT-PCR was carried out with SYBR Green PCR master mix (Applied Biosystems) and gene specific primers listed (Supplementary Table 3) on ABI PRISM 7900HT. We compared the normalized the CT values using the housekeeping gene -actin.

Cell culture and transfection. The Jurkat E6.1 (human T cell acute lymphoblastic leukemia), H9 (human cutaneous T lymphocyte lymphoma) and HUT78 (human cutaneous T lymphocyte lymphoma) cell lines obtained from the American Type Culture Collection (ATCC) were maintained in RPMI-1640 medium containing 10% FBS, 100 U/ml penicillin, 100 μg/ml streptomycin and 250 ng/ml amphotericin B at 37 °C and 5% CO2. Cultured cells were regularly tested for mycoplasma infection using the MycoAlert mycoplasma detection kit (Lonza). To generate expression vector plasmids for CTLA4 and CTLA4-CD28 fusion, cDNA encoding CTLA4 and CD28 were amplified by PCR from appropriate sources and inserted into LZRSpMBN linker IRES EGFP vector. Plasmids expressing CTLA4 and CTLA4-CD28 fusion were transfected into cells using the Nucleofector I device (Amaxa) with Nucleofector solution V and program X-001 (Jurkat) and G-014 (H9). Typically, one million cells were transfected with 2 μg of plasmid. A GFP-expressing plasmid was used to measure transfection efficiency. Expression of CTLA4 and CTLA4-CD28 fusion proteins was analyzed by flow cytometry. Transfected cells were stained with PE-Cy7 CD28 (CD28.2, BD pharmingen) and PE-Cy5 CTLA4 (BNI3, BD pharmingen) for 30 min and analyzed using FACS Verse (BD bioscience).

Antibodies and immunoblotting. Antibodies used for protein blot were phospho-AKT(S473) (Cell Signaling Technology), AKT antibody (Cell Signaling Technology), phosphoERK(T202/Y204) antibody (Cell Signaling Technology), ERK antibody (Cell Signaling Technology), α-tubulin antibody (Santa Cruz Biotechnology). Horseradish peroxidase (HRP) conjugated secondary antibodies (Bio-Rad) were used to detect primary antibodies. Equal protein loading was assessed by immunoblotting for α-tubulin. For immunoblotting analysis of CTLA4 and CTLA4-CD28 fusion signaling, cells transfected with indicated plasmids were treated with 2 μg/ml mouse anti-human CD28 (BD Pharmingen), 2 μg/ml mouse anti-human CTLA4 (BD 4

Pharmingen) or normal 2 μg/ml rabbit IgG for 10 min on ice, followed by crosslinking with 5 μg/ml goat anti-mouse IgG or goat anti-rabbit IgG (Ab frontier) for 10 min on ice. Then cells were incubated at a 37°C for 1 hour and lysed. Equal amounts of cell lysates were subjected to SDS-PAGE, transferred and probed with antibody. Equal protein loading was assessed by immunoblotting for α-tubulin.

Library preparation and targeted deep sequencing. We produced the deep sequencing data for 70 target genes on Ion PI v2 chip following the Ion AmpliSeq protocol of Life Technologies. A set of primer pairs were designed by the Ion AmpliSeq Designer in order to cover the coding DNA sequences and adjacent intronic regions (5 bp) of target genes. We achieved the design coverage of 98.9% for the target region of 227 kbp with 2,965 amplicons. Genomic DNA was pre-treated with uracil-DNA glycosylase (UDG) that had been reported to reduce high number of artifactual single nucleotide changes in FFPE samples (Do H et al. Clinical Chemistry 2013). Sequencing libraries were prepared according to the manufacturer’s protocol. Briefly, 20 ng of UDG-treated FFPE DNAs were amplified with the Ion AmpliSeq Library kit 2.0 for each of 74 lymphoma patient cases. Primers were partially digested, and each sample was barcoded with the Ion Xpress Barcode Adapters kit. Adapter-ligated and barcoded libraries were purified with AMPure XP reagent (Beckman Coulter) and PCR-amplified for 5 cycles. Resulting products were quantified by Agilent 2100 BioAnalyzer and Agilent Bioanalyzer DNA High-Sensitivity LabChip (Agilent Technologies). We pooled 20 uniquely barcoded libraries at the equimolar concentration for multiplexed sequencing on a single Ion PI v2 chip. Emulsion PCR was performed for the pooled sample on Ion OneTouch system using Ion PI Template OT2 200 Kit v3. Template-positive ISPs were sequenced on Ion PI v2 chip using Ion PI Sequencing 200 Kit v3 in the Ion Proton System. Mapping of sequencing data to the human genome (see below) indicated that the depths of reading ranged from 728X to 2578X with the average depth of 1204X. Summary of amplicon sequencing is available in Supplementary Table 4. Library preparation and sequencing procedures were performed by DNA Link Inc. in Korea.

5

Bioinformatics analysis of targeted deep sequencing data. The overview of the computational pipeline for analyzing deep sequencing data is shown in Supplementary Fig. S8. Human genome assembly (GRCh37, hg19) of repeat-masked version was downloaded from the UCSC genome browser. The sequencing data were mapped to the human genome using the Torrent Mapping Alignment Program (TMAP ver. 3.4.1) with the option of ‘mapall -g 2 –a 0 –y stage1 map1’. Genome Analysis Toolkit (GATK ver. 1.6.7)19 was used for local realignment and base quality score recalibration. Next, we called SNPs and indels using the GATK HaplotypeCaller with the option of ‘-dontUseSoftClippedBases -stand_emit_cof 20 -stand_call_cof 20’. Finally, ANNOVAR (ver. 2013-05-20) was used for functional annotation of genetic variants with ljb23_all for hg19_refGene release 66 transcriptome model20. Mutation analysis by HaplotypeCaller yielded 64,062 variations including 9,183 exonic ones. Filtering out nonfunctional candidates (synonymous mutations and in-frame indels), we obtained 9,104 mutations of functional significance comprising 745 nonsynonymous point mutations, 155 stop gain mutations, 2 stop loss mutations, 8,202 frame-shift indels. The large number of frame-shift indels is presumably due to the well-known sequencing error for homopolymer repeats in Ion Proton sequencing. For known point mutations from the literature survey (Supplementary File S1), we simply checked their presence in the list of point mutations, and confirmed the presence of 8 missense mutations (RHOA_G17V, IDH2_R172K, DNMT3A_R882H, CD28_T195P, FYN_R176C, VAV1_E524D, RHOA_T19I, PLCG1_S345F) and 2 nonsense mutations in TET2 gene (TET2_R544X, TET2_R1404X). For novel mutations, we applied several stringent filters to reduce false positives. Firstly, SNPs with population diversity in the dbSNP database (ver. 143) were removed. We further filtered out mutations of germline origin using exome sequencing data for paired samples of tumor and matched normal samples from 5 AITL patients, 100 lung cancer patients, 50 gastric cancer patients in Korean population (in-house data). Finally, we confirmed the mutation by Sanger sequencing for cases when patient samples were available. We have thus identified 3 novel cases of point mutations (CD28_F51I, TCF3_G302S, and KMT2D_S4251F). Loss-of-function mutations in TET2 gene are prevalent in myeloid cancers and lymphoma. Thus, a careful analysis of indels leading to loss-of-function is essential, especially for the sequencing data with homopolymer problem. Through a careful evaluation, we determined reliable indels as (i) no.

with patient recurrency over 10% turned out to be false positives in most cases. Among 182 indels from the GATK HaplotypeCaller predictions on TET2 gene, we have obtained 7 indels satisfying 6

both conditions. In addition, 24 missense mutations were predicted in TET2 gene, 6 of which were filtered out as SNPs with population diversity and additional 11 candidates were nondamaging according to ANNOVAR annotation, leaving 7 missense mutations of functional consequences. Including 10 nonsense mutations, the number of loss-of-function mutations in TET2 gene was 24 (7 misssense mutations, 10 nonsense mutations, 7 indels), 19 of which had been previously reported in hematological tumors (Supplementary Fig. S9). Five mutations (E1151X, 1715-1717del, 1747-1751del, Y1148D, G1861V) were novel from our study, and Y1148D mutation was confirmed by Sanger sequencing. Deep sequencing data have been deposited in the NCBI Sequence Read Archive (SRA) database under accession ID SRP056397 and BioProject ID PRJNA278986.

7

Supplementary Table S1. List of genes for targeted deep sequencing Gene Symbol

Lymphoma Type*

Publication

First Author

Ref. No.

ACTB

DLBCL

PNAS 2012

Lohr JG

14

ANKRD11

TP53-related

ARID1A

DLBCL

PNAS 2013

Zhang J

18

ATM

PTCL

Nat. Genet. 2014

Palomero T

3

B2M

PTCL

Nat. Genet. 2014

Palomero T

3

BCL10

FL

Nat. Genet. 2014

Okosun J

15

BCL2

BL

Nature 2012

Schmitz R

17

BRAF

DLBCL

PNAS 2012

Lohr JG

14

BTG1

DLBCL

PNAS 2012

Lohr JG

14

CARD11

FL

Nat. Genet. 2014

Okosun J

15

CCND3

BL

Nature 2012

Schmitz R

17

CD28

AITL

Nat. Genet. 2014

Yoo HY

5

CD58

PTCL

Nat. Genet. 2014

Palomero T

3

CD79B

FL

Nat. Genet. 2014

Okosun J

15

CDKN2A

PTCL

Nat. Genet. 2014

Palomero T

3

CREBBP

FL

Nat. Genet. 2014

Okosun J

15

DNMT3A

TCL

NEJM 2012

Couronné L

26

EBF1

FL

Nat. Genet. 2014

Okosun J

15

EZH2

AITL

Nat. Genet. 2014

Yoo HY

5

FYN

PTCL

Nat. Genet. 2014

Palomero T

3

GNA13

BL

Nature 2012

Schmitz R

17

HIST1H1C

FL

Nat. Genet. 2014

Okosun J

15

HIST1H1E

FL

Nat. Genet. 2014

Okosun J

15

ID3

BL

Nature 2012

Schmitz R

17

IDH1

DLBCL

PNAS 2013

Zhang J

18

IDH2

AITL

BLOOD 2012

Cairns RA

1

IRF8

DLBCL

PNAS 2013

Zhang J

18

JAK2

JAK-STAT pathway

JAK3

JAK-STAT pathway

KLHL6

FL

Nat. Genet. 2014

Okosun J

15

KMT2C

AITL

Nat. Genet. 2014

Yoo HY

5

KMT2D

AITL

Nat. Genet. 2014

Yoo HY

5

KRAS

DLBCL

PNAS 2012

Lohr JG

14

LILRB1

AITL

Nat. Genet. 2014

Yoo HY

5

MEF2B

FL

Nat. Genet. 2014

Okosun J

15

MKI67

BL

Nature 2012

Schmitz R

17

8

MTOR

DLBCL

PNAS 2013

Zhang J

18

MUC2

AITL

Nat. Genet. 2014

Yoo HY

5

MYC

BL

Nature 2012

Schmitz R

17

MYD88

FL

Nat. Genet. 2014

Okosun J

15

NOTCH1

DLBCL

PNAS 2012

Lohr JG

14

P2RY8

DLBCL

PNAS 2012

Lohr JG

14

PCLO

DLBCL

PNAS 2012

Lohr JG

14

PIK3CD

DLBCL

PNAS 2013

Zhang J

18

PIK3R1

DLBCL

PNAS 2013

Zhang J

18

PIM1

DLBCL

PNAS 2012

Lohr JG

14

PLCG1

AITL

Nat. Genet. 2014

Yoo HY

5

POU2F2

DLBCL

PNAS 2013

Zhang J

18

PRKD2

PTCL

Nat. Genet. 2014

Palomero T

3

PTPN1

PMBCL

Nat. Genet. 2014

Gunawardana J

13

RHOA

AITL

Nat. Genet. 2014

Yoo HY

5

RHOT2

PTCL

Nat. Genet. 2014

Palomero T

3

SGK1

BL

Nature 2012

Schmitz R

17

SMARCAL1

PTCL

Nat. Genet. 2014

Palomero T

3

SMARCD1

AITL

Nat. Genet. 2014

Yoo HY

5

SOCS1

FL

Nat. Genet. 2014

Okosun J

15

STAT1

JAK-STAT pathway

STAT2

JAK-STAT pathway

STAT3

JAK-STAT pathway

STAT6

FL

Nat. Genet. 2014

Okosun J

15

TCF3

BL

Nature 2012

Schmitz R

17

TET2

TCL

NEJM 2012

Couronné L

26

TET3

PTCL

Nat. Genet. 2014

Palomero T

3

TNFAIP3

FL

Nat. Genet. 2014

Okosun J

15

TNFRSF14

FL

Nat. Genet. 2014

Okosun J

15

TP53

TP53-related

TP63

TP53-related

VAV1

AITL

Nat. Genet. 2014

Yoo HY

5

WIF1

DLBCL

PNAS 2013

Zhang J

18

WWOX

TP53-related

*AITL = angioimmunoblastic T cell lymphoma, BL = Burkitt lymphoma, DLBCL = diffuse large B cell lymphoma, FL = Follicular lymphoma, PMBCL = primary mediastinal large B cell lymphoma, PTCL = peripheral T cell lymphoma, TCL = T cell lymphoma

9

Supplementary Table S2. Study subjects for sequencing and their clinical information case no.

Diagnosis

Age

Gender

Tissue of origin

CD28-CTLA4 fusion

Targeted resequencing

1

AITL

66

M

Lymph node

N

no

2

AITL

64

M

Lymph node

N

yes

3

AITL

28

F

Lymph node

P

yes

4

AITL

59

M

Lymph node

P

yes

5

AITL

76

F

Lymph node

P

yes

6

AITL

72

M

Lymph node

P

yes

7

AITL

67

M

Lymph node

P

no

8

AITL

59

M

Lymph node

P

yes

9

AITL

52

M

Lymph node

P

yes

10

AITL

42

M

Lymph node

P

yes

11

AITL

60

F

Lymph node

N

yes

12

AITL

53

F

Lymph node

P

yes

13

AITL

67

M

Lymph node

N

yes

14

AITL

57

F

Lymph node

P

yes

15

AITL

58

M

Lymph node

N

no

16

AITL

62

M

Lymph node

P

no

17

AITL

51

M

Lymph node

P

yes

18

AITL

60

F

Lymph node

P

yes

19

AITL

57

F

Lymph node

P

yes

20

AITL

47

M

Lymph node

P

yes

21

AITL

50

F

Lymph node

N

yes

22

AITL

58

M

Lymph node

N

no

23

AITL

67

M

Lymph node

N

no

24

AITL

75

M

Lymph node

P

no

25

AITL

64

M

Lymph node

N

yes

26

AITL

74

M

Lymph node

P

no

27

AITL

72

M

Lymph node

N

no

28

AITL

75

M

Lymph node

N

yes

29

AITL

62

M

Lymph node

P

yes

30

AITL

38

M

Lymph node

N

yes

31

AITL

54

M

Lymph node

P

yes

32

AITL

57

M

Lymph node

P

yes

33

AITL

65

M

Lymph node

P

yes

34

AITL

41

F

Lymph node

N

no

35

AITL

77

M

Lymph node

N

no

36

AITL

78

M

Lymph node

N

yes

37

AITL

79

M

Lymph node

N

no

38

AITL

57

M

Lymph node

P

no

39

AITL

73

F

Lymph node

N

yes

10

40

AITL

70

M

Lymph node

P

yes

41

AITL

63

M

Lymph node

P

no

42

AITL

60

F

Lymph node

P

yes

43

AITL

77

M

Lymph node

N

no

44

AITL

56

M

Lymph node

N

no

45

AITL

48

M

Lymph node

N

yes

46

ALCL. ALK-

62

M

Lymph node

N

yes

47

ALCL. ALK+

9

M

Mediastinum

N

yes

48

EATL

52

F

Small intestine

N

no

49

EATL

53

F

Jejunum

N

yes

50

EATL

50

M

Ileum

N

yes

51

ENKL

45

M

Nasal cavity

P

yes

52

ENKL

58

F

Lymph node

P

yes

53

ENKL

31

M

Cecum

N

yes

54

ENKL

64

M

Nasal cavity

P

yes

55

ENKL

54

M

Soft palate

N

yes

56

ENKL

50

M

Nasal cavity

N

yes

57

ENKL

46

M

Nasal cavity

N

yes

58

ENKL

66

F

Nasal cavity

N

yes

59

ENKL

57

M

Testis

N

yes

60

ENKL

34

M

Nasal cavity

N

yes

61

ENKL

51

F

Nasal cavity

N

no

62

ENKL

41

F

Lymph node

N

no

63

ENKL

48

M

Nasal cavity

P

yes

64

ENKL

48

M

Paranasal sinus

N

no

65

ENKL

47

M

Testis

N

yes

66

ENKL

43

M

Ileum

P

yes

67

ENKL

25

M

Jejunum

N

yes

68

ENKL

45

M

Testis

N

yes

69

ENKL

83

F

Nasal cavity

N

yes

70

ENKL

54

F

Nasal cavity

N

yes

71

ENKL

40

M

Colon

P

yes

72

ENKL

75

M

Nasal cavity

N

yes

73

ENKL

55

F

Nasal cavity

N

no

74

ENKL

60

M

Nasal cavity

P

yes

75

ENKL

44

F

Nasal cavity

N

yes

76

ENKL

32

F

Nasal cavity

N

yes

77

ENKL

73

M

Nasal cavity

ND

yes

78

ENKL

34

M

Nasal cavity

ND

yes

79

ENKL

76

F

Soft tissue

N

no

80

ENKL

43

F

Nasal cavity

N

no

81

ENKL

62

M

Nasal cavity

P

no

82

ENKL

44

M

Nasopharynx

P

yes

11

83

ENKL

24

M

Skin

N

no

84

ENKL*

55

M

Lymph node

ND

yes

85

PTCL

74

M

Lymph node

P

no

86

PTCL

29

F

Mediastinum

N

yes

87

PTCL

51

M

Lymph node

N

yes

88

PTCL

62

M

Stomach

N

no

89

PTCL?

64

F

Tonsil

P

no

90

PTCL

54

F

Lymph node

N

no

91

PTCL

67

M

Lymph node

N

no

92

PTCL

46

F

Lymph node

P

yes

93

PTCL

48

F

Lymph node

P

yes

94

PTCL

15

M

Lymph node

N

no

95

PTCL

70

M

Lymph node

N

no

96

PTCL

78

M

Larynx

N

no

97

PTCL

69

F

Jejunum

N

no

98

PTCL

49

M

Soft tissue

N

yes

99

PTCL

57

M

Lymph node

N

yes

100

PTCL

66

M

Lymph node

P

yes

101

PTCL

60

F

Lymph node

N

no

102

PTCL

73

F

Lymph node

P

yes

103

PTCL

60

F

Lymph node

N

yes

104

PTCL

49

F

Lymph node

N

no

105

PTCL

47

F

Lymph node

N

no

106

PTCL

51

F

Lymph node

P

no

107

PTCL

77

M

Lymph node

N

no

108

PTCL

24

M

Lymph node

N

no

109

PTCL

38

M

Skin

N

yes

110

PTCL

42

F

Colon

N

no

111

PTCL

71

F

Lymph node

N

no

112

PTCL

55

F

Colon

P

no

113

PTCL

75

F

Nasopharynx

ND

yes

114

PTCL

65

F

Tonsil

P

no

115

PTCL

68

M

Lymph node

N

yes

116

PTCL

53

M

Lymph node

N

yes

117

PTCL

69

M

Lymph node

N

no

118

PTCL

76

M

Lymph node

P

yes

119

PTCL

39

F

Lymph node

N

yes

AITL: Angioimmunoblastric T cell lymphoma, ENKL: Extranodal NK/T cell lymphoma, * ENKL, nodal, ALCL; Anaplastic large cell lymphoma, EATL: Enteropathy-associated T cell lymphoma, PTCL: Peripheral T cell lymphoma, NOS (not otherwise specified) N: negative, P:positive, ND: not done

12

Supplementary Table S3. List of gene-specific PCR primers PCR Type

cDNA RT-PCR primer

cDNA qPCR primer

gDNA PCR primer

gDNA qPCR primer

Figure Reference Fig. 2A, Fig. S2

Primer Name

Sequence

Fusion_cRT1 F Fusion_cRT1 R

5’- GATCCTTGCAGCAGTTAGTTCGGGG-3’ 5’- GGGCTGGTAATGCTTGCGGGTGGGC-3’

Fig. S1A


5’- GGACTGAGGGCCATGGACACGGGAC-3’ 5’- GGAGCGATAGGCTGCGAAGTCGC-3’

Fig. S1B


5’-AGCTGAACCTGGCTACCAGG-3’ 5’-GGAGCGATAGGCTGCGAAGTCGC-3’

Fig. 2A, Figs. S1,S2

-actin_cRT F -actin_cRT R

5’-CCAACCGCGAGAAGATGACC-3’ 5’-GGTCCAGACGCAGGATGGC-3’

Fig. 3C

CD28_cQ F CD28_cQ R

5’-ACAATGCGGTCAACCTTAGC-3’ 5’-ACCTGAAGCTGCTGGGAGTA-3’

Fig. 3C

CTLA4_cQ F CTLA4_cQ R

5’-GTGCCCAGATTCTGACTTCC-3’ 5’-CTGGCTCTGTTGGGGGCATTTTC-3’

Fig. 3C

Fusion_cQ F Fusion_cQ R

5’-CAGCAGTTAGTTCGGGGTTG-3’ 5’-GCGGGGAGTCATGTTCATGT-3’

Fig. 3C

 -actin_cQ F  -actin_cQ R

5’-CCAACCGCGAGAAGATGACC-3’ 5’-GGTCCAGACGCAGGATGGC-3’

Fig. 3E

Fusion_G1 F Fusion_G1 R

5’- CTGTCTCAGGGAGGCTCTGC-3’ 5’- CGGCTGGCTTCTGGATAGG-3’

Fig. S8A

Fusion_G1 F Fusion_G2 R

5’-CTGTCTCAGGGAGGCTCTGC-3’ 5’-GGCGGTCATTTCCTATCCAG-3’

Fig. S6

CD28_gQ1 F CD28_gQ1 R

5’-AGGCATTGATGAGGATACGC-3’ 5’-TTCTATCCCTTGCCATGACC-3’

Fig. S6


5’-AGGGATGGGTTACAGCACAG-3’ 5’-GAGTTCGAGGAAGCCAGTTG-3’

Fig. S6


5’-CGGTGAGCAAGCAGAATACA-3’ 5’-GGAAGAGCAACCAACTCCAG-3’

Fig. S6


5’-GGCCCACATTCCAACTTACC-3’ 5’-GGGAAGAGGCTCCCAGAATC-3’

Fig. S6


5’-TCCAATCAGACCAGGTAGGAGC-3’ 5’-CCACAACCCACTTTGGATCTCC-3’

Fig. 3B, Fig. S6


5’-CACAGGCATGTTCCTACCTCAGG-3’ 5’-GGACCTGAAGGGTGACGAGG-3’

Fig. 3B, Fig. S6

CTLA4_gQ1 F CTLA4_gQ1 R

5’-CTCACTATCCAAGGACTGAGGGC-3’ 5’-CTGGGTTCCGTTGCCTATGC-3’

Fig. S6


5’-TGCAATTTAGGGGTGGACCT-3’ 5’-AGAATCTGGGCACGGTTCTGGAT-3’

Fig. S6


5’-CCATCACCTGGAAGTCACCT-3’ 5’-CCCAGTCAAGCAAACTGGAT-3’

Fig. S6


5’-CAGGGAAGTTTTGTGGAGGA-3’ 5’-CACAATTCCACGCAATCAAG-3’

Fig. 3B, Fig. S6

 -actin_gQ F  -actin_gQ R

5’-TGAGCCTCATCTCCCACGTA-3’ 5’-TCTCAGCCAGCACCATGACT-3’

13

Supplementary Table S4. Summary statistics of targeted deep sequencing No. Exons

No. Amplicons

CDS (bp)

ACTB

5

14

1128

ANKRD11

11

80

ARID1A

20

ATM

Gene

Target (bp)

design coverage

experimental coverage

bp

%

bp

%

1178

1178

100.0

1178

100.0

7992

8102

7650

94.4

7679

94.8

82

6858

7058

6987

99.0

6898

97.7

62

152

9171

9791

9747

99.6

9771

99.8

B2M

3

7

360

390

390

100.0

390

100.0

BCL10

3

10

702

732

732

100.0

732

100.0

BCL2

2

8

753

773

773

100.0

715

92.5

BRAF

18

39

2301

2481

2446

98.6

2449

98.7

BTG1

2

8

516

536

536

100.0

533

99.4

CARD11

24

54

3465

3705

3705

100.0

3705

100.0

CCND3

6

13

927

987

987

100.0

959

97.2

CD28

4

11

663

703

703

100.0

703

100.0

CD58

6

13

757

817

817

100.0

817

100.0

CD79B

6

11

693

753

753

100.0

753

100.0

CDKN2A

5

11

912

962

962

100.0

962

100.0

CREBBP

31

96

7329

7639

7638

100.0

7497

98.1

DNMT3A

24

46

2864

3104

3104

100.0

3104

100.0

EBF1

16

31

1776

1936

1936

100.0

1936

100.0

EZH2

19

38

2256

2446

2446

100.0

2421

99.0

FYN

12

27

1770

1890

1890

100.0

1890

100.0

GNA13

4

15

1134

1174

1174

100.0

1166

99.3

HIST1H1C

1

7

642

652

652

100.0

652

100.0

HIST1H1E

1

7

660

670

640

95.5

670

100.0

ID3

2

5

360

380

380

100.0

380

100.0

IDH1

8

19

1245

1325

1325

100.0

1325

100.0

IDH2

11

20

1359

1469

1464

99.7

1469

100.0

IRF8

8

18

1281

1361

1353

99.4

1361

100.0

JAK2

23

56

3399

3629

3629

100.0

3629

100.0

JAK3

23

51

3375

3605

3601

99.9

3605

100.0

KLHL6

7

24

1866

1936

1936

100.0

1841

95.1

KMT2C

59

199

14736

15326

15103

98.5

9903

99.9

KMT2D

54

199

16614

17154

17148

100.0

1154

97.2

KRAS

5

11

687

737

737

100.0

15212

99.3

LILRB1

14

22

1967

2107

1713

81.3

16726

97.5

MEF2B

8

16

1107

1187

1171

98.7

737

100.0

14

MKI67

14

103

9771

9911

9819

99.1

1770

84.0

MTOR

57

116

7650

8220

8215

99.9

8159

99.3

MUC2

50

108

8442

8942

8191

91.6

7769

86.9

MYC

3

16

1365

1395

1395

100.0

1395

100.0

MYD88

5

14

954

1004

1004

100.0

1004

100.0

NOTCH1

34

99

7668

8008

7901

98.7

7930

99.0

P2RY8

1

10

1080

1090

1090

100.0

0

0

PCLO

25

181

15446

15696

15571

99.2

15601

99.4

PIK3CD

22

44

3135

3355

3355

100.0

3355

100.0

PIK3R1

17

33

2297

2467

2462

99.8

2467

100.0

PIM1

6

16

1215

1275

1275

100.0

1219

95.6

PLCG1

32

64

3876

4196

4196

100.0

4196

100.0

POU2F2

14

22

1444

1584

1584

100.0

1558

98.4

PRKD2

18

39

2637

2817

2817

100.0

2794

99.2

PTPN1

10

20

1308

1408

1408

100.0

1308

92.9

RHOA

4

9

582

622

622

100.0

622

100.0

RHOT2

19

32

1857

2047

1986

97.0

2028

99.1

SGK1

17

32

1935

2105

2105

100.0

2105

100.0

SMARCAL1

16

42

2865

3025

3025

100.0

3025

100.0

SMARCD1

13

27

1548

1678

1678

100.0

1678

100.0

SOCS1

1

6

636

646

646

100.0

588

91.0

STAT1

23

44

2257

2487

2487

100.0

2487

100.0

STAT2

23

40

2556

2786

2782

99.9

2657

95.4

STAT3

23

40

2313

2543

2521

99.1

2543

100.0

STAT6

21

42

2544

2754

2714

98.6

2754

100.0

TCF3

19

34

2192

2382

2382

100.0

2382

100.0

TET2

9

70

6098

6188

6188

100.0

6111

98.8

TET3

11

61

5388

5498

5498

100.0

5498

100.0

TNFAIP3

8

28

2373

2453

2453

100.0

2453

100.0

TNFRSF14

8

15

852

932

932

100.0

932

100.0

TP53

12

21

1263

1383

1383

100.0

1328

96.0

TP63

16

36

2200

2360

2360

100.0

2360

100.0

VAV1

27

44

2538

2808

2808

100.0

2808

100.0

WIF1

10

17

1140

1240

1240

100.0

1240

100.0

WWOX

10

20

1303

1403

1403

100.0

1403

100.0

Total

1105

2965

216,353

227,403

224,902

98.9

222,449

97.82

15

Supplementary Fig. S1. Validation of the CTLA4-CD28 fusion transcript. (a) Validation of the CTLA4-CD28 fusion transcript by RT-PCR using samples from patients and cell lines. Arrows indicate the approximate positions of oligonucleotide primers on the CTLA4-CD28 fusion transcript. PCR products were amplified from patient 1, CEMC1-15 cells and CEMC7-14 cells and validated by Sanger sequencing. -actin was used as an internal control, and NTC indicates the no template control. Normal tonsil tissue and 293T cells were used as negative controls. (b) The fusion products from CEMC1-15 and CEMC7-14 cells were shorter due to the partial deletion in the exon 2 of CTLA4 gene (391-457 bp region). This is a frame-shift deletion that would lead to loss-of-function of the fusion transcript. Thus, the fusion transcript is expected to play no active functional role in these cell lines.

16

Supplementary Fig. S2. Validation of the CTLA4-CD28 fusion gene in patients. Fusion transcripts were validated by RT-PCR using FFPE samples from TCL patients. Arrows indicate the approximate positions of oligonucleotide primers on the CTLA4-CD28 fusion transcript. PCR products were validated by Sanger sequencing. -actin was used as an internal control, and NTC indicates the no template control.

17

Supplementary Fig. S3. Functional analyses of the CTLA4-CD28 fusion gene in H9 cell line. (a) In vitro proliferation assay in H9 cells. H9 cells expressing the CTLA4-CD28 fusion show enhanced cell proliferation (*P < 0.05 compared with cells expressing wild-type CTLA4) after co-stimulation with anti-CD3/CTLA4 antibodies. Each experiment was repeated three times with five replicates, and the data are expressed as the mean ± standard deviation. (b) Expression of the CTLA4-CD28 fusion enhanced interleukin 2 (IL-2) production after CTLA4 activation (**P < 0.01 compared with cells expressing wild-type CTLA4). H9 cells were stimulated with PMA/Ionomycin (P/I) without or with anti-CD3/CTLA4 antibodies to activate CTLA4. IL-2 measurement was carried out three independent times, and the data are expressed as the mean ± standard deviation.

18

Supplementary Fig. S4. Surface expression levels of CTLA4 and the CTLA4-CD28 fusion. Transfected Jurkat cells were stained with PE-Cy5 labeled anti-CTLA4 and PE-Cy7 labeled antiCD28. GFP-positive cells were gated and analyzed. Only the cells expressing CTLA4 and CTLA4-CD28 fusion transcripts have positive signals on PE-Cy5 labeled anti-CTLA4 staining.

19

Supplementary Fig. S5. Fluorescence in situ hybridization analysis for the fusion. Metaphase chromosome of normal cells without fusion showed two orange signals which indicated merged green and red signal derived from closely located CD28 and CTLA genes, respectively. The cells with fusion showed two orange signals as well. There was no separate signal indicating the presence of episomal fusion gene. This does not rule out the possibility of episome formation in all fusion-positive cases.

20

Supplementary Fig. S6. Copy number analysis of CD28 and CTLA4 genes in fusion negative and positive TCL patients. Copy number change was estimated relative to that in the peripheral blood cells from a normal individual. Quantitative real time PCR was carried out using multiple primer pairs covering genomic loci of CD28 and CTLA4 genes. -actin was used as a normalizing control. Four out of five primer pairs within the fusion region showed significant copy number gains in fusion-positive patients. Results from two representative primer pairs (CD28_gQ5 and CTLA4_gQ1) were shown in Fig. 3b. The fusion regions are indicated by the bracket at the top, and the primer positions are indicated by the bars at the bottom. Cases of statistically significant copy number gain are indicated by the asterisks (P-value < 0.05).

21

Supplementary Fig. S7. Sanger sequencing of genomic DNA for mapping exact fusion points. In Case 1, a LINE element of 47 bp long was identified between the fusion introns. Cases 3-6 and two cell lines are examples of direct joining of two exons. Vertical bars indicate the break points.

22

Supplementary Fig. S8. Computational pipeline for analyzing targeted deep sequencing data

23

Supplementary Fig. S9. Mutation profile of TET2 gene from targeted sequencing

24