SUPPLEMENT
Materials and Methods
Case selection
From the Tokyo Children’s Cancer Study Group (TCCSG) bioresources, we selected RNA
samples obtained from 401 pediatric acute lymphoblastic leukemia (ALL) patients consisted of
three subcohorts.
The first subcohort is an uncontinuous series of randomly selected 42
BCP-ALL patients without conventional cytogenetic abnormalities. Most of the patients were
newly diagnosed with ALL while some relapsed patients were also included. The second one
is a continuous series of newly diagnosed 333 pediatric ALL patients enrolled in the TCCSG
L0416/0616 study from December 2004 to July 2007.
This group include 41 T-ALL, 1
unclassified ALL, and 291 BCP-ALL patients consisted of 15 BCR-ABL1+, 17 E2A-PBX1+, 53
ETV6-RUNX1+, 3 MLL-AF4+, 2 MLL-AF9+, 65 hyperdiploid, 3 hypodiploid, and 133
BCP-ALL patients without conventional cytogenetic abnormalities (unpublished data). The
third subcohort include 26 BCP-ALL without conventional cytogenetic abnormalities selected
from outside the L0416/0616 cohort by means of the dull or negative expression of CD10.
To investigate unknown fusion genes, whole transcriptome sequencing was performed
on RNA samples obtained from 55 pediatric BCP-ALL patients without conventional
cytogenetic abnormalities including entire patients of the first subcohort and 13 patients
randomly selected from the second subcohort. To estimate the frequency of fusion gene being
newly identified, RNA samples obtained from remaining patients of the second subcohort were
used for screening with RT-PCR.
Since all four EP300-ZNF384+ cases identified in the first
and second subcohorts revealed dull or negative expression of CD10 based on
immunophenotypic examination, we screened a further 26 of CD10-dull or -negative BCP-ALL
cases without conventional cytogenetic abnormalities (the third subcohort) by RT-PCR to find
out another cases with fusion gene being newly identified.
Diagnoses were made on the basis of the morphology and routine examinations,
including immunophenotyping, cytogenetic analysis, DNA contents analysis, and the real-time
PCR detection of 8 fusion transcripts: MLL-AF4, MLL-AF9, MLL-ENL, major BCR-ABL1,
minor BCR-ABL1, ETV6-RUNX1, E2A-PBX, and SIL-TAL1, as described previously.1 The
investigations were approved by the institutional review boards of all participating institutions.
Informed consent was obtained from parents or guardians, and informed assent was obtained
from the patients when appropriate based on their age and level of understanding.1
Whole transcriptome sequencing and detection of fusion genes
Total RNAs were extracted from bone marrow-derived leukemic cells of the patient using the
miRNeasy Mini Kit (Qiagen, Valencia, CA, USA) and whole transcriptome sequencing was
performed, as described previously.2,3
Briefly, after qualification using Agilent RNA 6000
Nano Kit (Agilent Technologies, Santa Clara, CA, USA), cDNA libraries were prepared from 1
g of total RNA by using the TruSeq RNA sample preparation kit v2 (Illumina, Inc., San Diego,
CA, USA, catalog # RS-122-2001).
The resultant libraries were quantified using KAPA
Library Quantification Kit (KAPA Biosystems, Inc., Woburn, MA, USA, catalog # KK4835)
and checked for quality and size using Agilent High Sensitivity DNA Kit (Agilent, catalog #
5067-4626). The samples were loaded on to the cBot (Illumina) for clustering on a flow cell,
the flow cell was then sequenced using a HiSeq1500 (Illumina) according to manufacturer’s
instructions. A paired-end (2 x 101) run was performed using the SBS Kit v3-HS (Illumina,
catalog # FC-401-3001). At least 30,000,000 reads were obtained for each sample.
To avoid multiple counting of each fusion transcript, RNA sequencing data were used
after removal of paired-end reads with the identical nucleotide sequence, which had probably
been derived from PCR duplicates during library preparation. For prediction of fusion genes,
the deFuse program5 was used as described previously.6 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.
EP300-ZNF384 fusion was supported by 39 spanning reads and 146 split reads in case-1 and by
52 spanning reads and 68 split reads in case-2.
RT-PCR
For detection of the EP300-ZNF384 fusion transcript, PCR was carried out as described
previously4 and a 372-bp fragment was amplified using the following primers:
EP300-forward-S3,
5’-tctaggggtgggtcaacagt-3’
5’-ctgtcagcaaggtggggtag-3’.
and
ZNF384-reverse-AS3
Sanger sequencing of the PCR products was performed as
described previously.3
Fluorescence in situ hybridization (FISH) analysis
Dual color FISH analysis was performed on bone marrow specimens obtained from Case 1
using the bacterial artificial chromosome (BAC) clones RP11-958C6 (labeled with Green-dUTP,
Abbott Laboratories, Abbott Park, IL, USA) and RP11-1137P19 (labeled with Orange-dUTP,
Abbott),
including
EP300
and
ZNF384
genes,
respectively
(http://genome.ucsc.edu/cgi-bin/hgGateway). By employing standard techniques, 1,000 cells
were examined.
Japan).
Actual examinations were performed by LSI Medience Corporation (Tokyo,
References for Materials and Methods
1
Kiyokawa N, Iijima K, Tomita O, Miharu M, Hasegawa D, Kobayashi K, et al.
Significance of CD66c expression in childhood acute lymphoblastic leukemia. Leuk Res
2014; 38: 42-48.
2
Kobayashi K, Mitsui K, Ichikawa H, Nakabayashi K, Matsuoka M, Kojima Y, et al.
ATF7IP as a novel PDGFRB fusion partner in acute lymphoblastic leukaemia in children.
Br J Haematol 2014; 165: 836-841.
3
Masuzawa A, Kiyotani C, Osumi T, Shioda Y, Iijima K, Tomita O, et al. Poor responses to
tyrosine kinase inhibitors in a child with precursor B-cell acute lymphoblastic leukemia
with SNX2-ABL1 chimeric transcript. Eur J Haematol 2014; 92: 263-267.
4
Tomita O, Iijima K, Ishibashi T, Osumi T, Kobayashi K, Okita H, et al. Sensitivity of
SNX2-ABL1 toward tyrosine kinase inhibitors distinct from that of BCR-ABL1. Leuk Res
2014; 38: 361-370.
5
McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MG, et al. deFuse: an
algorithm for gene fusion discovery in tumor RNA-Seq data. PLoS Comput Biol 2011; 7:
e1001138.
6
Gotoh M, Ichikawa H, Arai E, Chiku S, Sakamoto H, Fujimoto H, et al. Comprehensive
exploration of novel chimeric transcripts in clear cell renal cell carcinomas using whole
transcriptome analysis. Genes Chromosomes Cancer 2014; 53: 1018-1032.
Legends to supplementary Figures and Table
Supplementary Figure 1.
(A) The EP300-ZNF384 fusion transcripts were amplified by RT-PCR.
In Case 1, the
presence of EP300-ZNF384 fusion gene was only confirmed in the samples obtained at 2nd and
3rd relapse, because of unavailability of the samples at initial diagnosis and 1st relapse.
As an
internal control, the human GAPDH gene was also detected.
Lane 1, molecular weight
marker; Lanes 2-8, patient RT–PCR products as indicated.
(B) The result of Sanger
sequencing of the PCR product obtained from Case 1 (at 2nd relapse) is indicated. The PCR
products obtained from the other patients showed identical sequences.
Supplementary Figure 2.
Histograms of CD19, CD10, and aberrant myeloid antigens (CD13 and CD33) of
EP300-ZNF384 fusion gene-positive cases (Cases 1-6) are indicated with a positive rate (%).
As a control, histograms of a typical CD10-positive B-cell precursor acute lymphoblastic
leukemia case (CD10+Cnt) are also shown. X-axis, fluorescence intensity; Y-axis, relative cell
number.
Supplementary Table.
Immunophenotypes of patients.
Immunophenotypic features of patients with the
EP300-ZNF384 fusion gene are summarized.