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ZNF384-Related Fusion Genes Define a Subgroup of Childhood B-Cell

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ZNF384-Related Fusion Genes Define a Subgroup of Childhood B-Cell Acute Lymphoblastic Leukemia SUPPLEMENTARY APPENDIX ZNF384 -related fusion genes define a subgroup of childhood B-cell precursor acute lymphoblastic leukemia with a characteristic immunotype Shinsuke Hirabayashi, 1,2 Kentaro Ohki, 1 Kazuhiko Nakabayashi, 3 Hitoshi Ichikawa, 4 Yukihide Momozawa, 5 Kohji Oka - mura, 6 Akinori Yaguchi, 1,7 Kazuki Terada, 1 Yuya Saito, 1,8 Ai Yoshimi, 1,9 Hiroko Ogata-Kawata, 3 Hiromi Sakamoto, 4 Moto - hiro Kato, 1,10 Junya Fujimura, 7 Moeko Hino, 11 Akitoshi Kinoshita, 12 Harumi Kakuda, 13 Hidemitsu Kurosawa, 14 Keisuke Kato, 9 Ryosuke Kajiwara, 15 Koichi Moriwaki, 16 Tsuyoshi Morimoto, 17 Kozue Nakamura, 18 Yasushi Noguchi, 19 Tomoo Osumi, 1,20 Kazuo Sakashita, 21 Junko Takita, 22 Yuki Yuza, 8 Koich Matsuda, 23 Teruhiko Yoshida, 4 Kenji Matsumoto, 24 Kenichiro Hata, 3 Michiaki Kubo, 5 Yoichi Matsubara, 25 Takashi Fukushima, 26 Katsuyoshi Koh, 27 Atsushi Manabe, 2 Akira Ohara 28 and Nobutaka Kiyokawa 1 for the Tokyo Children’s Cancer Study Group (TCCSG) 1Department of Pediatric Hematology and Oncology Research, National Research Institute for Child Health and Development, Seta - gaya-ku, Tokyo; 2Department of Pediatrics, St. Luke's International Hospital, Chuo-ku, Tokyo; 3Department of Maternal-Fetal Biology, National Research Institute for Child Health and Development, Setagaya-ku, Tokyo; 4Division of Genetics, National Cancer Center Re - search Institute, Chuo-ku, Tokyo; 5Laboratory for Genotyping Development, Center for Integrative Medical Sciences (IMS), RIKEN, Yoko - hama-shi, Kanagawa; 6Department of Systems BioMedicine, National Research Institute for Child Health and Development, Setagaya-ku, Tokyo; 7Department of Pediatrics and Adolescent Medicine, Juntendo University Graduate School of Medicine, Bunkyo-ku, Tokyo; 8Department of Hematology/Oncology, Tokyo Metropolitan Children’s Medical Center, Fuchu-shi, Tokyo; 9Division of Pediatric Hematology and Oncology, Ibaraki Children’s Hospital, Mito-shi, Ibaraki; 10 Division of Stem Cell Transplant and Cellular Therapy, Chil - dren’s Cancer Center, National Center for Child Health and Development, Setagaya-ku, Tokyo; 11 Department of Pediatrics, Chiba Uni - versity Graduate School of Medicine, Chiba-shi, Chiba; 12 Department of Pediatrics, St. Marianna University School of Medicine, Kawasaki-shi, Kanagawa; 13 Department of Haematology/Oncology, Chiba Children’s Hospital, Chiba-shi, Chiba; 14 Department of Pedi - atrics, Dokkyo Medical University, Mibu, Tochigi; 15 Department of Pediatrics, Yokohama City University Hospital, Yokohama-shi, Kana - gawa; 16 Department of Pediatrics, Saitama Medical Center, Saitama Medical University, Kawagoe-shi, Saitama; 17 Department of Pediatrics, Tokai University School of Medicine, Isehara-shi, Kanagawa; 18 Department of Pediatrics, Teikyo University School of Medi - cine, Itabashi-ku, Tokyo; 19 Department of Pediatrics, Japanese Red Cross Narita Hospital, Narita-shi, Chiba; 20 Division of Leukemia and Lymphoma, Children’s Cancer Center, National Center for Child Health and Development, Setagaya-ku, Tokyo; 21 Department of Hema - tology/Oncology, Nagano Children's Hospital, Azumino-shi, Nagano; 22 Department of Pediatrics, Graduate School of Medicine, Univer - sity of Tokyo, Bunkyo-ku, Tokyo; 23 Laboratory of Clinical Sequence,Department of Computational biology and medical Sciences, Graduate school of Frontier Sciences, The University of Tokyo, Minato-ku, Tokyo; 24 Department of Allergy and Clinical Immunology, Na - tional Research Institute for Child Health and Development, Setagaya-ku, Tokyo; 25 National Research Institute for Child Health and De - velopment, Setagaya-ku, Tokyo; 26 Department of Child Health, Faculty of Medicine, University of Tsukuba, Tsukuba-shi, Ibaraki; 27 Department of Hematology/Oncology, Saitama Children’s Medical Center, Saitama-shi, Saitama and 28 Department of Pediatrics, Toho University Omori Medical Center, Ohta-ku, Tokyo, Japan ©2017 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol. 2016.151035 Received: June 14, 2016. Accepted: September 14, 2016. Pre-published: September 15, 2016. Correspondence: [email protected] or [email protected] Supplementary Methods Cases As a result of whole transcriptome sequencing performed after the publication of our previous report1 on the samples obtained from the cases diagnosed initially as "B-others" enrolled in the L0416/0616 study, we identified one of each minor BCR-ABL, ETV6-RUNX1, and TCF3-PBX1-positive cases and, thus, these 3 cases were removed from the B-others group and re-classified to each category of genetic alteration. As a consequence, 333 ALL patients enrolled in the TCCSG L0416/0616 study included 130 B-others patients and 16 BCR-ABL1+, 18 E2A-PBX1+, 54 ETV6-RUNX1+, 3 MLL-AF4+, 2 MLL-AF9+, 65 hyperdiploid, and 3 hypodiploid (291 BCP-ALL patients) as well as 41 T-ALL and 1 unclassified ALL (Supplementary Table 1). In addition to the 15 TCF3-ZNF384-positive patients presented in the manuscript, we identified 1 more patient harboring TCF3-ZNF384 (Case26). Case-26 was female, being 5 years old at presentation, and TCF3-ZNF384 (fusion of TCF3 Ex13 and ZNF384 Ex2) was identified in the BM specimen obtained at the 3rd relapse. As additional genetic mutation, kinase activating point mutation in FLT3 and CDKN2A/B deletion were identified by whole exome sequencing and MLPA, respectively. This patient received SCT as a salvage therapy but died. Since other clinical information of on this patient was not available, she was excluded from our study. Whole transcriptome sequencing and detection of fusion genes Total RNAs were extracted using the miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) according to the manufacturer's instruction. After qualification using the Agilent RNA 6000 Nano Kit (Agilent Technologies, Santa Clara, CA, USA), cDNA libraries were prepared from 1 µg of total RNA using the TruSeq RNA sample preparation kit v2 (Illumina, Inc., San Diego, CA, USA, catalog # RS-122-2001). The resultant libraries were quantified using the KAPA Library Quantification Kit (KAPA Biosystems, Inc., Woburn, MA, USA, catalog # KK4835) and checked for quality and size using the Agilent High Sensitivity DNA Kit (Agilent, catalog # 5067-4626). The samples were loaded onto cBot (Illumina) for clustering on a flow cell, and the flow cell was then sequenced using HiSeq2500 (Illumina) according to the manufacturer’s instructions. A paired-end (2 x 101) run was performed using the SBS Kit v4-HS (Illumina, catalog # FC-401-3001). More than 9 Gb of sequence was obtained for each library by paired-end sequencing (126 bp x2). To avoid multiple counting of each fusion transcript, RNA sequencing data were used after the removal of paired-end reads with the identical nucleotide sequence, which had probably been derived from PCR duplicates during library preparation. For the prediction of fusion genes, the deFuse program2 was used as described previously.3 After applying default filtering of this program, potential alternative splicing and read-through products that the program predicted were eliminated, and candidates that had exon boundary junctions were selected. Microarray and data analysis The data were normalized by the MAS5 algorithm and the baseline was transformed to the median of all samples. The data were filtered with the following steps. (1) Genes with a signal intensity lower than 1.0 in each sample were eliminated (Filtered on expression). (2) Genes that were scored as absent in all samples were eliminated (Filtered on Flags [Present and Marginal]). (3) For the multi-group comparison between ZNF384-related fusion genes positive ALL and TCF3-PBX1-positive or ZNF384 wild-type ALL, one-way analysis of variance (ANOVA) was conducted selecting probe sets with a p-value of <0.01. For the direct comparison between TCF3-ZNF384-positive and TCF3-PBX1-positive or ZNF384 wild-type ALL, a moderated t-test was conducted selecting probe sets with a p-value of <0.01. Performing fold-change analysis using filtered genes, genes were selected that exhibited 2.0-fold or more higher or lower expressions in TCF3-ZNF384-positive ALL in comparison with TCF3-PBX1-positive (Figure 4A, Supplementary Figure 2C, Supplementary Table 6) or ZNF384 wild-type ALL (Supplementary Figure 2F, Supplementary Tables 8). For direct multigroup comparison including conventional genetic subtypes and ZNF384-related fusion gene positive ALL or B-others, hierarchical clustering analysis was performed on the filtered microarray data using the selected classifying gene probe sets reported by Roberts et al. 4 and Den Boer et al.5-6 that can separate the major subtypes of pediatric ALL in distinct clusters. The functional analyses of genes characteristic of TCF3-ZNF384-positive ALLs in comparison with TCF3-PBX1-positive or ZNF384 wild-type ALLs were done using gene set enrichment analysis (GSEA) according to the developers instructions [http://software.broadinstitute.org/gsea/index.jsp] as well as previous reports.7-9 Ranked gene lists of the up- and down-regulated genes in the signatures of TCF3-ZNF384-positive ALL (10 cases) in comparison with TCF3-PBX1-positive (19 cases) or ZNF384 wild-type ALL (83 out of 104 cases that located in distinct cluster from ZNF384-related fusion genes positive cases by hierarchical clustering) were created. The analysis included gene sets for hematopoietic stage6 and human immunologic signatures (C7) from MsigDB (http://software.broadinstitute.org/gsea/index.jsp). GEO accession number, GSE79533. Exome data analyses: Mapping
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