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Plenary Paper
LYMPHOID NEOPLASIA
Flow sorting and exome sequencing reveal the oncogenome of primary
Hodgkin and Reed-Sternberg cells
Jonathan Reichel,1,2 Amy Chadburn,3 Paul G. Rubinstein,4 Lisa Giulino-Roth,1,5 Wayne Tam,1 Yifang Liu,1 Rafael Gaiolla,1,6
Kenneth Eng,1 Joshua Brody,7 Giorgio Inghirami,1 Carmelo Carlo-Stella,8,9 Armando Santoro,8 Daoud Rahal,8
Jennifer Totonchy,1 Olivier Elemento,1,10 Ethel Cesarman,1 and Mikhail Roshal1
1
Department of Pathology and Laboratory Medicine, Weill Cornell Medical College, New York, NY; 2Tri-Institutional Training Program in Computational
Biology and Medicine, New York, NY; 3Department of Pathology, Feinberg School of Medicine, Northwestern University, Chicago, IL; 4John H. Stroger
Jr Hospital of Cook County, Rush University Medical Center, Ruth M. Rothstein CORE Center, Chicago, IL; 5Department of Pediatrics, Weill Cornell Medical
College, New York, NY; 6Botucatu School of Medicine, Sao Paulo State University, Botucatu, Brazil; 7Icahn School of Medicine at Mount Sinai, New York,
NY; 8Humanitas Cancer Center, Humanitas Clinical and Research Center, Rozzano (Milan), Italy; 9School of Medicine, University of Milan, Milan, Italy;
and 10Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY
Classical Hodgkin lymphoma (cHL) is characterized by sparsely distributed Hodgkin
and Reed-Sternberg (HRS) cells amid reactive host background, complicating the ac• We show feasibility of whole- quisition of neoplastic DNA without extensive background contamination. We overcame
exome sequencing on purified this limitation by using flow-sorted HRS and intratumor T cells and optimized low-input
primary HRS cells and report exome sequencing of 10 patient samples to reveal alterations in genes involved in antigen
presentation, chromosome integrity, transcriptional regulation, and ubiquitination.
recurrent genetic alterations
b-2-microglobulin (B2M) is the most commonly altered gene in HRS cells, with 7 of 10
characterizing cHL.
cases having inactivating mutations that lead to loss of major histocompatibility complex
• B2M is the most frequently
class I (MHC-I) expression. Enforced wild-type B2M expression in a cHL cell line restored
mutated gene in cHL, strongly MHC-I expression. In an extended cohort of 145 patients, the absence of B2M protein in the
associated with nodular
HRS cells was associated with lower stage of disease, younger age at diagnosis, and
sclerosis subtype, younger
better overall and progression-free survival. B2M-deficient cases encompassed most
age, and better overall
of the nodular sclerosis subtype cases and only a minority of mixed cellularity cases,
survival.
suggesting that B2M deficiency determines the tumor microenvironment and may
define a major subset of cHL that has more uniform clinical and morphologic features.
In addition, we report previously unknown genetic alterations that may render selected patients sensitive to specific targeted
therapies. (Blood. 2015;125(7):1061-1072)
Key Points
Introduction
Despite major progress in genomics of non-Hodgkin lymphomas, the
genome of HRS cells in classical Hodgkin lymphoma (cHL) remains
largely unexplored. Investigations have been hampered by the scarcity
of neoplastic Hodgkin and Reed-Sternberg (HRS) cells within the
tumor, making it difficult to isolate purified HRS cell populations in
sufficient numbers for genome-level pipelines. Targeted analyses have
documented alterations in specific genes in cHL cell lines and HRS
cells obtained by laser capture microdissection (LCM), and have
pointed to the activation of specific pathways, notably nuclear factor
kB (NF-kB).1-3 Genome-level studies have been confined to a few cell
lines derived from end-stage cHL patients and low-resolution copy
number analysis of small numbers of single cells retrieved by LCM.4-6
LCM has also been used to capture HRS cells to evaluate chromosomal imbalances using comparative genomic hybridization7,8 and
to perform transcriptional analysis using whole-genome expression
arrays.9 We have overcome the limitations of LCM by combining flow
cytometric cell sorting (which yields thousands of purified HRS cells
from primary biopsy samples) with a refined exome sequencing library
construction methodology that obviates the need for biased wholegenome amplification techniques. Using these methods, we produced
the first whole-exome deep-sequencing and high-resolution copy
number and single nucleotide polymorphism/small indel analyses
of purified HRS cells from primary cHL samples. We systematically
confirmed mutations identified through exome sequencing by whole
transcriptome sequencing of the purified HRS cells for genes that
were expressed. These data revealed molecular alterations that may
prove relevant for accurate classification and improved prognostication and deserve evaluation as targets for specific therapy.
cHL cases show significant histologic heterogeneity and are
currently subclassified into 4 histologic subtypes. Nodular sclerosis
Submitted November 5, 2014; accepted December 1, 2014. Prepublished
online as Blood First Edition paper, December 8, 2014; DOI 10.1182/blood2014-11-610436.
The publication costs of this article were defrayed in part by page charge
payment. Therefore, and solely to indicate this fact, this article is hereby
marked “advertisement” in accordance with 18 USC section 1734.
The online version of this article contains a data supplement.
There is an Inside Blood Commentary on this article in this issue.
BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
© 2015 by The American Society of Hematology
1061
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BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
REICHEL et al
Figure 1. HRS cell flow sorting and ultralow-input
sequence library validation. (A) Identification of HRS
cells for flow cytometric sorting. HRS cells (red) show
high forward and side scatter, are positive for CD30,
bright for CD40 and CD95, and are typically positive
for CD15. Many cases show various degrees of
rosetting by T cells, resulting in composite CD51/CD451
immunophenotype. CD201 (light blue) B cells and
CD51 (green) T cells with appropriate CD45 and side
scatter parameters were sorted for experiment controls.
(B) Sorted HRS cells could be visualized on a cytospin
using Wright-Giemsa stain to confirm population
identity and purity. Original magnification 3100.
(C) Comparison of depth of sequence coverage per
base between libraries generated with 1 ng (red),
10 ng (blue), and 100 ng (green) of starting genomic
DNA from intratumoral T cells. Depth of coverage was
comparable between 10 ng and 100 ng DNA input,
resulting in 483 vs 523 median coverage, respectively. (D) Each of 2 panels depicts copy number
variation analysis results comparing data between
2 sequenced libraries. Exonic probe segments
(x-axis) vs copy number change on log2 scale (y-axis)
are plotted for a single representative chromosome
(chr 6). Comparing data from a 10-ng low-input library
from intratumoral T cell DNA to a 100-ng normal-input
library from intratumoral T cell DNA from the same case
showed no significant false-positive results; that is, lowinput and normal-input libraries are copy neutral (top).
Numerous segmental copy number alterations could
be seen when data from a 10-ng low-input library from
HRS were compared against intratumoral T cells of the
same case (bottom), indicating that this method reveals
copy number gains and losses. FSC, forward scatter;
SSC, side scatter.
(NS) cHL is the most common histologic subtype (;70% of cases),
followed by mixed cellularity (MC) cHL (15% to 30% of cases). The
other cHL subtypes (lymphocyte-rich and lymphocyte-depleted) are
rare.10 The MC and lymphocyte-depleted subtypes may be part of
a biological continuum, but NS cHL has a distinct epidemiology,
clinical presentation, and histology.11 Studies have demonstrated
that the cHL subtypes differ biologically in terms of the prevalence of
Epstein-Barr virus (EBV) infection, gene-expression patterns, and
cytokine milieu.12,13 Clinically, the MC type of cHL is generally
associated with older age at diagnosis, higher stage, and inferior
prognosis.14-17 It is therefore probable that NS and MC cHL represent distinct tumor entities with different natural histories and
genomic drivers. However, genome-level differences between the
2 subtypes are not yet fully elucidated. Moreover, a significant fraction of cases are difficult to classify due to mixed or ambiguous clinical and histologic features. A retrospective study showed that 10%
to 30% of cases across multiple cohorts received a diagnosis of
cHL “not otherwise specified.”15 To date, classification into different
histologic subtypes has not translated into different treatment approaches at least in part due to lack of fully reproducible objective
criteria for classification. The frontline treatment of all subtypes
consists of combination chemotherapy with or without radiotherapy,
resulting in a 5-year overall survival of ;85%.18 Despite the overall
favorable outcome of treatment, the frequency of relapses in advancedstage cHL can be as high as 30%, and up to 10% of newly diagnosed
cHL patients will not achieve remission.19 Determining which subset
of patients could benefit from more aggressive therapy, improving
survival and relapse rates, has been the goal for many clinical/
radiographic prognostic scoring systems.20 The identification of
specific genomic alterations that are predictive of therapy response
before treatment initiation, or that are in genes that make these
alterations actionable, would provide the rationale for risk-adapted
and targeted therapies in cHL.21
Methods
Tissue specimens
For exome and transcriptome sequencing, we used 10 leftover clinical
samples that had been mechanically dissociated and cryopreserved as viable
cell suspensions following excisional or needle core biopsy. Cases 1 to 9 were
from the Department of Pathology and Laboratory Medicine at Weill Cornell
Medical College, and case 10 was from Mount Sinai Medical Center.
A validation cohort of 176 cases was evaluated for b-2-microglobulin
(B2M) expression by immunohistochemistry on formalin-fixed tissue blocks.
These cases were obtained from Weill Cornell Medical College, Northwestern University, John H. Stroger Jr Hospital of Cook County, and the
Humanitas Cancer Center. An additional 29 cases of HIV-associated cHL
from John H. Stroger Jr Hospital of Cook County were examined by immunohistochemistry for B2M. All cases from the sequencing and expanded validation cohorts were defined as cHL morphologically and immunophenotypically
and classified into histologic subtypes by at least 1 hematopathologist blinded
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BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
EXOME SEQUENCING OF CLASSICAL HODGKIN LYMPHOMA
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Figure 2. Copy number variation analysis of HRS cells. (A)
Representative results for all chromosomes for case 2 (top) and
case 3 (bottom) are shown. HRS cells vs T-cell exon copy number
changes are plotted on log2 scale. Case 2 had a relatively high
frequency of copy number alterations, whereas case 3 had
relatively fewer. Focal losses of the immunoglobulin genes are
seen in chromosomes 14, 2, and 22 (red arrows), and gains in
the TCR genes on chromosomes 7 and 14 (blue arrows). (B)
Circos plot showing the segments containing copy number
variations in the 10 primary cases of cHL plus the 2 cell lines sequenced. The samples correspond to cases 1 through 10 beginning at the outermost ring and followed by cell lines L1236 and
L428 in the inner circle. Important oncogenes, such as REL, can be
seen recurrently amplified (blue), and tumor suppressors (eg,
ATM) can be seen recurrently deleted (red).
to B2M status. EBV status was determined by Epstein-Barr encoding region
in situ hybridization. All cases were collected and used for research with
approval from our respective Institutional Review Boards.
Cell sorting
We adapted the protocol from Fromm et al22 for HRS cell sorting and used a
panel of the following antibodies: CD64-FITC (22; Beckman Coulter [BC],
Miami, FL); CD30-PE (BerH83; Beckton-Dickinson [BD], San Jose,
CA); CD5-ECD (BL1a; BC); CD40-PE-Cy5.5 (custom conjugate, gift of
Jonathan Fromm) or CD40-PerCP-eFluor 710 (1C10; eBiosciences,
San Diego, CA); CD20-PC7 (B9E9; BC); CD15-APC (HI98; BD); CD45
APC-H7 (2D1; BD) or CD45-Krome Orange (J.33; BC); and CD95-Pacific
Blue (DX2; Life Technologies, Grand Island, NY). Briefly, cell suspensions
from cHL tumors containing up to 1 3 108 cells were rapidly defrosted
at 37°C, washed in 50 mL of RPMI 1640/20% fetal bovine serum solution
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BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
REICHEL et al
Table 1. Recurrently mutated genes in cHL with potential pathogenic functions
containing DNase A, stained with the antibody cocktail for 15 minutes
on ice, resuspended in fluorescence-activated cell sorter (FACS) buffer,
and immediately sorted. All sorting experiments were performed on an
FACSAria special-order research sorter using a 130-mm nozzle at 12 psi,
acquiring up to 5 3 107 cells and collecting HRS, B, and T cells from the
tumor using 3-way sort. Sorted cells were captured in N-2-hydroxyethylpiperazine-N9-2-ethanesulfonic acid buffer solution containing 50% fetal
bovine serum.
Library construction and sequencing
We developed a method to produce high-quality data from 10 ng of DNA
by modifying the KAPA Biosystems “with-bead” protocol. We optimized
the shearing and cleanup steps, increased the molar ratio of free adaptors to
sample DNA, and increased time of ligation. DNA was extracted using the
Wizard Genomic DNA Purification Kit (A1120; Promega, Madison, WI)
eluted in 30 mL of 65°C water, followed by 20 mL of 65°C water. DNA was
quantified using Qubit (Life Technologies, Carlsbad, CA) and sheared using
a Covaris S2 at intensity 5, 10% duty cycle, 200 cycles/burst, water fill level
of 12, 50 mL sample volume, and a 210-second treatment time divided into
30-second intervals with centrifugation. Illumina-compatible sequencing
libraries were created using a low-throughput library preparation kit
(KK8221; KAPA Biosystems) by modifying the with-bead protocol (without
size selection) to include a 16-hour adapter ligation step at 20°C with indexed
sequencing adapter oligos (Integrated DNA Technologies), using adapter-toinsert molar ratios ranging between 15:1 and 65:1. We quantified adapterligated molecules using the Library Quantification Kit (KK4824; KAPA
Biosystems) to optimize the number of precapture amplification cycles and
monitored the amplification process in real time using Sybr Green to avoid
overamplification. The solid-phase reversible immobilization ratio for the
postamplification cleanup was 0.83 to exclude carryover adapter dimer. Four
sample libraries (250 ng each) were combined into one exome hybridization
reaction (SeqCap v.3.0, kit 06465684001; Roche NimbleGen, Madison, WI)
and amplified postcapture (8 cycles) using HiFi HotStart ReadyMix
(KK2612; KAPA Biosystems) and primers purchased from Integrated
DNA Technologies. Postcapture libraries were quantified with Qubit, sized
with a 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA), diluted to
10 nM, and sequenced with 1 exome reaction (4 samples) per lane in a HiSeq
sequencer (Illumina, San Diego, CA).
Computational data analysis
Raw FASTQ reads were inspected using FastQC and mapped to the University
of California, Santa Cruz hg19 assembly of the human Genome Reference
Consortium Human Reference 37 using Burrows-Wheeler Aligner.23 Samtools
v.0.1.1824 was used to filter polymerase chain reaction duplicates and reads
with a mapping quality score value below 20. To detect somatic nucleotide
variants and small indels in HRS samples compared with the T-cell somatic
controls, Strelka version 1.0.1025 was used. Somatic variants were annotated using SnpEFf version 3.3.26 Recurrent mutations were systematically inspected for artifacts in the Integrated Genome Viewer.27,28 For
detection of copy number variations, we calculated the log-transformed
ratio (ltr) for every exome target interval (i) of intralibrary normalized read
counts in the tumor sample against those of the normal sample in the
following manner:
ct 1 1
cn 1 1
2 log2
;
ltrðiÞ ¼ log2
lt
ln
where c is the number of reads mapping to a given capture interval, l is the
total library size, t denotes tumor, and n denotes normal. Only intervals with
sufficient coverage (Ct 1 Cn $ 100 reads) were retained for further analysis.
Pan-interval segmentation was then performed using DNAcopy v.1.029
from Bioconductor in R, and segments for which the absolute value of the
mean ltr was ,0.5 were considered copy number neutral. Remaining segments were considered to be copy number gains when the sign of
the mean ltr was positive (ie, when significantly more reads were in the
tumor sample vs the normal sample after normalization), or copy number
losses when the sign of the mean ltr was negative. National Center
for Biotechnology Information, Reference Sequence Database genes
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EXOME SEQUENCING OF CLASSICAL HODGKIN LYMPHOMA
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sequencing libraries were mapped to the human reference genome hg19
using STAR v.2.3.0e. Validation candidates from our exome data were
selected from a set of 363 genes that were reportedly mutated in at least 1
primary case and 1 cell line or at least 2 primary cases, and mutated in
a manner likely to affect the protein. From that set of genes, 274 variants
with coverage in RNA sequencing data were selected using custom scripts
followed by manual inspection using the Integrated Genomics Viewer
v.2.3.32 for visualization.27 Of these, 238 had coverage of at least 2 reads
concordant with the variant (these also comprised a minimum of 10% of the
total number of reads covering the locus) and were considered “validated,”
yielding a positive validation rate of 86.8%.
Immunohistochemistry
Immunohistochemical staining of B2M (rabbit polyclonal, 1:500 dilution;
Leica Microsystems) and of major histocompatibility complex (MHC) class
I (MHC-I; mouse monoclonal, 1:500 dilution, EMR8-5; Abcam, Cambridge, MA) was accomplished using the Bond III Autostainer (Leica
Microsystems, Buffalo Grove, IL). Formalin-fixed, paraffin-embedded tissue
sections were first baked and deparaffinized. Antigen retrieval was
followed by heating the slides at 37°C in Bond EnTizyme solution (Leica
Microsystems) for 10 minutes. Sections were then subjected to sequential
incubations with primary antibody, postprimary (equivalent to secondary
antibody), polymer (equivalent to tertiary antibody), endogenous peroxidase block, diaminobenzidine, and hematoxylin for 15, 8, 8, 5, 10, and
5 minutes (Bond Polymer Refine Detection; Leica Microsystems),
respectively. Lastly, the sections were dehydrated in 100% ethanol and
mounted in Cytoseal XYL (Richard-Allan Scientific, Kalamazoo, MI).
Transfection experiments
The L428 cell line (kindly provided by Anas Younes) was transfected with
either the maxGFP plasmid (Amaxa positive control; Lonza Group, Basel,
Switzerland) alone or in combination with pBJ1-human b2m (plasmid 12099;
Addgene, Cambridge, MA) using Amaxa nucleofection (Lonza Group) protocol in triplicate. The analysis of MHC-I and B2M expression was performed 36 hours later by direct immunofluorescence flow cytometry using
AlexaFluor 647 anti-HLA-A,B,C antibody (W6/32; BioLegend, SanDiego,
CA) and anti-B2M-PE antibody (TU99; BD) on a BD FACSAria sorter gating
only on enhanced green fluorescent protein–positive cells with appropriate
fluorescence spillover compensation.
Statistical analyses
The association of B2M status with clinical parameters, histologic subtype,
and EBV status was determined using Fisher’s exact 2-tailed tests. Unpaired Student t test was used to determine the association of age and B2M status.
Survival analysis was performed using the Kaplan-Meier method and the Cox
proportional hazards model.
Figure 3. SNP and indel analysis reveal recurrent alterations and subsets.
Unsupervised clustering (asymmetric binary distance matrix and complete linkage
hierarchical clustering) based on mutation status of the 104 genes that were
mutated in at least 2 cases divides 10 sequenced cases of cHL into 2 molecular
subgroups—one of which is exclusively wild-type for B2M; the other exclusively
mutated for B2M.
contained within the amplified or lost segments were identified using
custom R scripts.
Confirmatory RNA sequencing
We independently validated a selection of variants discovered in our
exome data by whole transcriptome sequencing on HRS cell populations
from 9 out of 10 primary cases. Using the Arcturus PicoPure RNA
Isolation Kit, 1 to 5 ng of RNA was extracted from flow-sorted HRS cells
and converted to complementary DNA using the Clontech SMARTer Ultra
Low Input RNA Kit, followed by Illumina-compatible sequencing library
construction using a library preparation kit from KAPA Biosystems. RNA
Results
HRS cells can be separated by flow sorting and their
exomes sequenced
We performed flow cytometric isolation of HRS cells in 10 biopsy
samples of primary cHL cases to unambiguously separate HRS cells
from reactive background cells (Figure 1A).22 HRS cell yields ranged
from 1000 to 100 000 cells from 1 3 107 to 5 3 10 7 total analyzed
cells per case. Although some cases demonstrated significant
rosetting of a subset of HRS cells by T cells, nonrosetted HRS
cells were sorted whenever possible. The mean final purity of
HRS cells for all cases was 75% (range 40% to 100%) based on
median variant allele frequency in raw data at somatic variant loci
(see supplemental Table 1 on the Blood Web site). Intratumoral
T cells were also sorted and used as somatic controls in detecting
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REICHEL et al
BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
Figure 4. B2M-inactivating mutations result in lack of MHC-I expression. (A) Diagram showing the localization and type of mutations in B2M in 7 sequenced primary
cases of cHL containing these mutations. (B) Sequence analysis of DNA (top) and RNA (bottom) of the B2M gene in case 8 shows a point mutation in the start site of one
allele and an out-of-frame deletion in another allele. Sequences were visualized using Integrated Genome Viewer. (C) Schematic representation of B2M together with MHC-I
on the cell surface. (D) The L428 cell line was nucleofected with a plasmid encoding the wild-type B2M and a green fluorescent protein (GFP)-expressing plasmid, and flow
cytometry was performed to evaluate MHC-I and B2M expression gating in the GFP1 (red) and GFP2 (blue) populations.
mutations and copy number alterations. The purified HRS cells
displayed typical morphologic features, including multinucleation,
prominent nucleoli, and large size (Figure 1B). We generated whole-
exome sequence data sets with 483 median coverage or greater for
all sorted HRS cell samples (supplemental Table 1). We observed no
significant difference in depth of coverage or copy number profiles
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EXOME SEQUENCING OF CLASSICAL HODGKIN LYMPHOMA
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Figure 5. B2M validation by immunohistochemistry and correlation with subtype and EBV status. (A) Hematoxylin and eosin (H&E) staining and immunohistochemical staining for B2M and for MHC-I are shown for 2 representative cases of cHL. Case 1 has wild-type B2M sequences, whereas case 7 is mutated for
B2M, indicating that this genomic alteration can be determined by lack of B2M expression in HRS cells. Correspondingly, case 1 shows clear Golgi and membrane
localization of MHC-I, whereas staining is diffuse in the cytoplasm in case 7, indicating mislocalization. Original magnifications 320 (H&E) and 360 (B2M and
MHC-I). (B) There was a significant correlation between the lack of B2M expression and the NS subtype of cHL, and between the presence of B2M expression and
the MC subtype of cHL. Cases classified as “Others” include 1 case of lymphocyte-rich cHL and cases with features of both NS and MC, making the distinction
challenging. (C) A cohort of patients with HIV infection and cHL was evaluated for B2M expression; however, the relationship of histologic subtype and B2M
expression did not reach statistical significance in this cohort. (D) The presence of EBV in the HRS cells was assessed by in situ hybridization for Epstein-Barr
encoding region. EBV-negative cases were more frequently also negative for B2M; however, among the EBV-positive cases, both B2M-positive and B2M-negative
cases were identified. IC, immunocompetent; neg, negative; pos, positive.
between our optimized low-input library construction protocol with
down to 10 ng of input DNA and a standard commercial protocol
with 100 ng of DNA (Figure 1C-D).
Large DNA copy number alterations are highly recurrent and
involve critical cancer genes in primary cHL cases
In line with previous reports,30,31 our analyses revealed that HRS
cells from cHL demonstrate a very high number of genomic material
gains and losses mostly due to large segment alterations, with a
median of 75 (range 41-357) genomic segments lost and gained per
case; however, considerable intercase heterogeneity was observed
(representative cases are shown in Fig 2A). Several cases demonstrated
extremely high intrachromosomal copy number variation. Within
this high background of chromosome (chr)-level alterations, recurrent gains and losses in genes highly associated with oncogenesis were evident. We observed recurrent gains of a region in chr 2
containing REL (5/10), BCL11A, XPO1, and variably MYCN (4/10);
focal amplifications involving only NSD1 (chr 5, 4/10); gains
involving CD274 (chr 9, 4/10) and variably JAK2 and MLLT3
(3/10); gains involving UBE2A (chr X, 3/10); gains involving CDK4
(chr 12, 2/10); losses of gene segments involving TNFAIP3 (chr 6,
5/10) and variably MLL, MLLT4, PRDM1 (3/10), and MLL; losses
of ATM and BIRC3 (chr 11, 5/10); and losses of RB1 (chr 13, 4/10),
and BRCA2 (chr 13, 3/10) (Figure 2B; Table 1; supplemental
Tables 2-6).
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REICHEL et al
Table 2. Clinical characteristics of patients by B2M
immunohistochemistry
Total cohort
Median age
32 (11-85)
Β2M positive
Β2M negative
47 (11-85)
30 (13-76)
B2M mutations are biallelic and inactivating and lead to loss of
MHC-I expression
P
,.0001*
(range), y
Males, n (%)
80/145 (55)
35/52 (67)
45/93 (48)
.037*
Histology
NS, n (%)
105/145 (72)
25/52 (48)
80/93 (86)
,.0001*
MC, n (%)
30/145 (21)
23/52 (44)
7/93 (8)
,.0001*
9/145 (6)
3/52 (6)
6/93 (6)
1
I/II, n (%)
84/129 (66)
20/45 (44)
63/85 (74)
III/IV, n (%)
47/129 (36)
25/45 (56)
22/85 (26)
.0011*
B symptoms,
47/113 (41)
19/39 (49)
28/74 (38)
.32
17/127 (13)
2/47 (4)
15/80 (19)
.029
Other or
intermediate,
n (%)
Stage
.0011*
n (%)
Bulk disease,
We chose to explore the role of B2M further because it was the
most frequently mutated gene and showed inactivating bialleic
mutations, including start codon mutations, exon-1 splice-donor
and acceptor-site mutations, and out-of-frame first-exon deletions (Figure 4A). We also observed 100% concordance in B2M
genotype between RNA and DNA sequencing data (Table 1;
Figure 4B), Normally, B2M protein is required for surface expression of MHC-I (Figure 4C). We show that ectopic expression of
wild-type B2M in the L428 cell line lacking B2M induces surface
MHC-I, indicating that this genetic alteration is responsible for this
defect in antigen presentation (Figure 4D).
Confirmation of B2M loss by immunohistochemistry in an
expanded cohort provides a useful diagnostic assay
n (%)
The total cohort included 145 patients; 52 B2M positive and 93 B2M negative.
*Statistically significant difference according to a P value of ,.05.
Point mutations and small indels may define a more
homogenous group of cHL tumors
We found a median of 244 (range 102-505) somatic mutations per case
(supplemental Table 7). We focused on mutations with a probable
impact on protein sequence or expression (ie, nonsense, splice site,
small indels, and missense mutations) that occurred in 2 or more of
our sequenced primary cases of cHL. Using these criteria, we identified
99 recurrently mutated genes, 30 of which were also mutated in one
or both cHL cell lines (supplemental Table 8).
A list of recurrently mutated genes curated on the basis of function
and validation in RNA is shown in Table 1. B2M and TNFAIP3 were
identified as the most commonly mutated genes in cHL cases (7/10
and 6/10, respectively). Of interest, all 7 cases classified as NS had
mutations in B2M, and 6 of these cases had mutations in TNFAIP3.
Case 8 was the only EBV-positive case in the sequenced cohort and the
only case with NS morphology that lacked any TNFAIP3 mutation,
consistent with a previous study reporting frequent mutual exclusivity
of these 2 NF-kB-activating events.32 These data suggest that B2M
mutations and either TNFAIP3 alterations or presence of EBV (with
latent membrane protein 1 and 2 expression) are molecular characteristics of NS cHL. Consistent with the presence of different molecular
features of NS and MC cHL is unsupervised clustering based on
recurrent gene mutations, which revealed 2 distinct groups of cHL
cases (Figure 3). The 7 cases classified as NS cHL clustered together,
whereas the remaining 3 cases belonged to the MC type.
Numerous additional potentially oncogenic mutations that were
recurrent in cHL were identified (Table 1). These included genes
involved in regulation of chromosomal structure, integrity, and stability; nuclear import; protein and histone ubiquitination; and signal transduction. Some alterations, including those in B2M and
TNFAIP3, have been described in diffuse large B-cell lymphoma and
other lymphomas but appear to be more frequent in cHL. Other
alterations can be found in ;820 lymphoid neoplasms in the Catalog
of Somatic Mutations in Cancer but only in 1 to 3 cases, also
suggesting that these are more common in cHL. These alterations
are HRIH2, HELLS, RANBP2, PIM2, SETDB1, SIAH2, WEE1, and
ZNF217. Genes that to the best of our knowledge have not been
previously linked to lymphoid malignancies but have been seen in
solid cancers and myeloid stem cell disorders are CSF2RB, NEK1,
HECW2, SENP7, TBC1D15, TICRR, and ZPF36L1.
To validate and extend these data, we performed immunohistochemistry for B2M in the 10 sequenced cases and found complete
concordance between mutation status and B2M expression in
HRS cells. Therefore, we subsequently used immunohistochemistry to evaluate B2M protein expression in an expanded cohort
from which we purposefully selected an overrepresentation of
MC cases (Figure 5A). Of the 176 cases, 104 (59%) lacked B2M
expression in the HRS cells. We also performed immunohistochemistry in a subset of cases with antibodies to MHC-I. Among
the cases sequenced, those with B2M mutations had mislocalization,
as evidenced by diffuse cytoplasmic staining and no Golgi or membranous positivity (Figure 5A). Overall, we were able to stain 52
cases for both B2M and MHC-I; of these, 10 were positive for these
2 proteins, 41 were negative for both, and 1 was discordant (positive
for B2M and negative for MHC-I). In 9 cases, staining for B2M or
MHC-I was difficult to determine because of high background, poor
tissue preservation, ambiguous staining of HRS cells, or insufficient
HRS cells for accurate assessment. The cases with unclear B2M expression were not assigned to groups and were excluded from further
analysis. We conclude that unlike diffuse large B-cell lymphoma, in
which various mechanisms account for loss of MHC-I expression,33
mutations in B2M are the most common cause of MHC-I loss in cHL.
B2M inactivation, as evidenced by the lack of protein expression
in HRS cells, confirmed a remarkable association with the NS subtype
(86/115 cases, 75%). B2M inactivation was less common in cases of
MC cHL (9/40; 22%; P , .0001), indicating that this immunohistochemical marker is a useful distinguisher of histologic type (Figure 5B).
We also evaluated 29 cases of cHL occurring in individuals with
HIV infection, 18 (62%) of which were found to be B2M negative
(Figure 5C). Among the HIV-associated cHL cohort, the association
with histologic subtype was more tenuous (P 5 .11), consistent with
the notion that B2M inactivation is associated with immunologic
pressure. Correlation of B2M expression with EBV status was evaluated, confirming that EBV-negative cases were more frequently
B2M negative (P 5 .005), albeit with many outliers (Figure 5D),
consistent with previous reports.34
Lack of expression of B2M identifies a type of cHL that presents
in younger patients, at an earlier stage, and with a better
clinical outcome
We assessed the clinical significance of B2M in those cases for
which information was available (n 5 145; 52 B2M-positive cases
and 93 B2M-negative cases). There was a statistically significant
association with older age among the B2M-positive cases (median
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BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
EXOME SEQUENCING OF CLASSICAL HODGKIN LYMPHOMA
1069
Figure 6. Lack of expression of B2M is associated
with a better clinical outcome in advanced disease.
Kaplan-Meier curves of cases with clinical information
show that positivity for B2M by immunohistochemistry
in the HRS cells associates with a poor progressionfree survival (PFS) and overall survival (OS), as
compared with cases that lack B2M expression in the
entire cohort (top row). The middle row shows KaplanMeier curves for patients with stages I and II cHL; the
bottom row shows Kaplan-Meier curves for patients
with stages III and IV cHL. Among patients with advanced stage, but not in patients with early-stage
cHL, positivity for B2M by immunohistochemistry in
the HRS cells showed a trend for poor OS, as
compared with cases that lack B2M expression. NS,
not significant.
age of 47 vs 30 years; P # .0001) and stage III/IV disease (P 5 .001),
and with male predominance (P 5 .037) and bulk disease (P 5 .029)
(Table 2). Cases that lacked B2M expression belonged to a better
clinical outcome category (10-year progression-free survival of 74%
vs 49%; P 5 .026; and overall survival of 87% vs 66%; P 5 .013)
(Figure 6). There was also a trend toward better overall survival and
progression-free survival in the B2M-negative cohort for patients with
stage III/IV disease but not for patients with stage I/II disease (Figure 6).
On multivariate analysis, B2M expression was not found to be an outcome predictor independent of age in our cohort, but larger cohorts are
needed to determine the relevance of B2M as an independent predictor
of clinical outcome. These results indicate that mutations in B2M leading
to lack of protein expression identify a type of cHL that occurs in younger patients with lower-stage disease and that has a better prognosis
than when this specific molecular alteration is not present.
Discussion
We report the first full-exome deep sequencing of purified HRS
cells from cHL tumor specimens and describe consistent alterations
in oncogenic biological processes and considerable heterogeneity
among cHL cases. The genomic study was limited to 10 cases that
were cryopreserved at our institutions, potentially limiting discovery
of the less-frequent genomic alterations in this disorder. The
median depth of sequencing (483) was sufficient for highly
prevalent mutations, although it may be less sensitive to detect
subclonal variants. In addition, the retrospective clinical data we
relied on to elucidate the relationship between B2M and treatment
outcome in cHL were limited to few institutions and could therefore
be underpowered to detect significant clinical associations. Clearly,
larger patient cohorts with increased sequencing depth would further
expand our knowledge of cHL-defining genomic alterations and
the clinical association between the mutations and outcomes with
specific therapies. The approach we developed in this study opens the
opportunity for these larger future investigations to take place.
Because personalization of therapy based on genomic alterations
has become increasingly accepted for oncology patients, the method
reported here could potentially offer the benefits of genomics-driven
therapies to cHL patients. In addition, we anticipate that the methodology we developed in the process of the study has numerous
applications beyond cHL biology. The integration of ultralow input
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1070
BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
REICHEL et al
Table 3. Genes recurrently altered, with targeted therapy approved
or in clinical trials
Name of
altered
gene
ABL1
No. of
altered
samples
3
Potential therapeutic treatment
Reference
Dasatinib, Nilotinib, Ponatinib, AS703569,
AT9283, Bafetinib, Bosutinib,
Imatinib, XL228, Saracatinib
ATM
5
Niraparib, Olaparib, BMN673, Rucaparib,
35
Veliparib
BIRC3
5
(cIAP2)
BRCA2#
AEG40826, TL 32711, AT-406, GDC0917,
36
LCL161
3
Rucaparib, Olaparib, BMN673, E7449,
37
Niraparib, Veliparib,
CD274
4
(PDL1)
CDK4
MDX-1105, MPDL3280A, MDX-1105,
38
RG7446
2
PD0332991, Alvocidib, Palbociclib,
39
LY2835219, BAY1000394, and others
JAK2
3
Ruxolitinib, AZD1480, LY2784544, AT9283,
40
SAR30250, INCB280503, TG101348,
XL019, ITF2357, INCB018424
PIM2
2
SGI-1776
41,42
UBE2A*
6*
DKCI-73, Seliciclib (Roscovitine), CR8,
39,43
P276-00, Dinaciclib (SCH727965),
Alvocidib (Flavopiridol), SNS-032,
BAY1000394
WEE1
2
MK-1775
XPO1
5
Selinexor (KPT-330)
44
#Mutations in the BRCA2 gene render cells sensitive to poly(ADP-ribose)
polymerase (PARP) inhibition; therefore, PARP inhibitors are listed.
*UBE2A is amplified in 3 cases and there are splice-site alterations in 3 cases
(1 case also has a nonsynonymous mutation). UBE2A is phosphorylated and activated
by CDK9, for which inhibitors are available (note that SNS-032 and CDKI-73 are
selective CDK9 inhibitors, whereas others inhibit multiple cyclin-dependent kinases).
with standard DNA-sequencing pipelines allows streamlined genomic studies of very small samples such as fine-needle aspirate specimens
from multiple tumor types and sorted samples in the context of minimal
residual disease detection.
The overall oncogenome of cHL contains alterations in genes
responsible for interactions with the immune system, preservation
of genomic stability, and transcriptional regulation. Some of these
alterations have previously been described in hematologic and
nonhematologic malignancies and are potentially therapeutically targetable (reviewed in Table 3).35-44 Among the genes recurrently
altered in cHL, some encode proteins that have been proposed to play
a role in lymphomagenesis, although mutations in these genes have
not been previously described in lymphoid malignancies. PIM
kinases are overexpressed in chronic lymphocytic leukemia, mantle
cell lymphoma, and multiple myeloma; and in vitro inhibition results
in cellular toxicities.41,42,45 Inhibitors of WEE1 enhance killing of
Burkitt lymphoma cells.46 NEK11/2 mice develop lymphomas late
in life with a much higher incidence than wild-type littermates.47
The functional and pathological roles of these newly discovered
genetic alterations in the context of cHL are yet to be determined;
this is challenging because of the lack of cHL animal models or cell
culture systems that include the tumor cell microenvironment.
The role of B2M in cHL pathogenesis deserves particular mention.
There have been numerous important studies aimed at predicting the
treatment outcome in cHL.20 Many of these studies have focused on
the immune response and tumor microenvironment. For example,
increased numbers of cytotoxic T cells correlate with poor outcomes,48,49
whereas the presence of intratumoral FOXP31 regulatory T cells and
a FOXP3-to-granzyme B ratio of .1 are associated with better
survival.48,50 A gene expression profiling study of whole cHL
biopsies showed that CD68 RNA levels and intratumoral macrophage infiltration also predict disease-specific survival.51 The tumor
inflammatory cells may be affected by antigen presentation and the
production of inflammatory mediators by the HRS cells. Accordingly, several reports have shown a lack of expression of MHC
classes I and II by HRS cells.34,52-54 MHC-I is expressed by virtually
all nucleated cells and is essential for recognition of antigen by CD8
cytotoxic T cells. It consists of an a chain encoded in the MHC
genetic locus together with a b chain (B2M). Oudejans et al first
documented a lack of expression of MHC-I and B2M in the HRS
cells of a significant proportion of cHL cases, and reported that EBVpositive cases expressed significantly higher levels of MHC-I and
B2M molecules than cases lacking EBV, although the association
was not absolute.34 This observation was confirmed by others52-54
and led to the proposal that EBV provides alternative molecular
mechanisms for avoiding tumor immunity. A lack of MHC-I expression in HRS cells was also reported as an independent adverse prognostic factor in cHL.54 We show here that the molecular mechanism
leading to MHC-I downregulation in HRS cells is through inactivating mutations of B2M, and these mutations likely explain
specific clinical and histologic characteristics. Our observations are
also in line with a recent report demonstrating B2M mutations in 2
cell lines and showed concordant MHC-I downregulation.6
We show that unsupervised clustering of recurrent mutations reveals
a close association between inflammatory background-based (NS vs
MC) and molecular categorizations of the disease. In particular, all
the cases of NS that we sequenced had B2M-inactivating mutations
and NF-kB pathway–activating alterations (via TNFAIP3 alterations
or EBV infection). In contrast, the cases classified as MC were
more heterogeneous molecularly, and no single defining alteration or
pathway was found in any of the 3 cases. Remarkably, expression of
a single protein (B2M) can serve as a useful proxy for this molecular
characterization of cHL. Our data indicate that B2M mutations result
in a lack of protein expression that can be used to identify a specific
molecular category of cHL, characterized in most cases by the NS
histology. These results are consistent with studies showing that the
NS type of cHL has a better prognosis and occurs in younger patients
with lower-stage disease.14-17 In contrast, cases with B2M protein
expression did not reveal definable molecular characteristics and
appear to correspond to a more molecularly heterogeneous group,
presenting at an older age and with worse clinical outcome. Because
our genomic sampling of B2M wild-type cases was limited, we anticipate that the sequencing of larger panels of such cases will reveal
additional molecular subtypes or unifying characteristics. It will be
interesting to determine in larger cohorts whether the subset of B2Mpositive NS cases have distinguishing clinical and molecular features.
Our methodology provides an opportunity for further prospective
comprehensive genomic exploration of the less common subtypes of
cHL and of treatment-resistant and recurrent disease.
Acknowledgments
The authors thank Maryke Appel of KAPA Biosystems, Daniel
Burgess of Roche NimbleGen, and Chad Locklear of Integrated
DNA Technologies for thoughtful discussions regarding library
preparation, exome capture, and adapter oligos.
This work was supported by the Department of Pathology and
Laboratory Medicine of Weill Cornell Medical College; the TriInstitutional Training Program in Computational Biology and
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BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
Medicine (J.R.); the Schwartz Family Research Fund and the
Robert H. Lurie Comprehensive Cancer Center (providing support
for tissue microarrays from the Chicago cohorts); the Brazilian
Ministry of Higher Education Foundation CAPES (Coordination
for the Improvement of Higher Education Personnel) (R.G.);
the Ministry of Health (RF 2010-2313979) (C.C.-S. and G.I.);
and the Italian Association for Cancer Research grant 15835
(C.C.-S.).
Authorship
Contribution: E.C. and M.R. conceived of the experiment, advised
on every aspect, and conceived of the manuscript; M.R. and J.R.
sorted primary cases; J.R., R.G., and M.R. optimized and constructed libraries; J.R. analyzed data computationally with O.E.,
who advised on the computational analyses; M.R. performed the
EXOME SEQUENCING OF CLASSICAL HODGKIN LYMPHOMA
1071
transfection experiment; E.C., A.C., P.G.R., L.G.-R., G.I., C.C.-S.,
A.S., and D.R. compiled clinical cohort data; P.G.R. and L.G.-R.
ran the survival analysis; Y.L., W.T., A.C., and E.C. performed
immunohistochemistry and evaluated the staining; K.E. examined
samples for loss-of-heterozygosity (not reported); J.B. provided
a primary sample; and J.T. performed validation of B2M mutations
using Sanger sequencing (not shown).
Conflict-of-interest disclosure: The authors declare no competing
financial interests.
The current affiliation for M.R. is Departments of Laboratory
Medicine and Pathology, Memorial Sloan Kettering Cancer Center,
New York, NY.
Correspondence: Mikhail Roshal, Departments of Laboratory
Medicine and Pathology, Memorial Sloan Kettering Cancer
Center, 1275 York Ave, New York, NY 10065; e-mail: roshalm@
mskcc.org; and Ethel Cesarman, Department of Pathology and
Laboratory Medicine, Weill Cornell Medical College, 1300 York
Ave, New York, NY 10065; e-mail: [email protected].
References
1. Bargou RC, Leng C, Krappmann D, et al. Highlevel nuclear NF-kappa B and Oct-2 is a common
feature of cultured Hodgkin/Reed-Sternberg cells.
Blood. 1996;87(10):4340-4347.
2. Lake A, Shield LA, Cordano P, et al. Mutations of
NFKBIA, encoding IkappaB alpha, are a recurrent
finding in classical Hodgkin lymphoma but are not
a unifying feature of non-EBV-associated cases.
Int J Cancer. 2009;125(6):1334-1342.
3. Schmitz R, Hansmann ML, Bohle V, et al.
TNFAIP3 (A20) is a tumor suppressor gene in
Hodgkin lymphoma and primary mediastinal B cell
lymphoma. J Exp Med. 2009;206(5):981-989.
4. Joos S, Menz CK, Wrobel G, et al. Classical
Hodgkin lymphoma is characterized by recurrent
copy number gains of the short arm of
chromosome 2. Blood. 2002;99(4):1381-1387.
5. Steidl C, Shah SP, Woolcock BW, et al. MHC
class II transactivator CIITA is a recurrent gene
fusion partner in lymphoid cancers. Nature. 2011;
471(7338):377-381.
6. Liu Y, Abdul Razak FR, Terpstra M, et al. The
mutational landscape of Hodgkin lymphoma cell
lines determined by whole-exome sequencing.
Leukemia. 2014;28(11):2248-2251.
7. Steidl C, Telenius A, Shah SP, et al. Genomewide copy number analysis of Hodgkin ReedSternberg cells identifies recurrent imbalances
with correlations to treatment outcome. Blood.
2010;116(3):418-427.
8. Hartmann S, Martin-Subero JI, Gesk S, et al.
Detection of genomic imbalances in
microdissected Hodgkin and Reed-Sternberg
cells of classical Hodgkin’s lymphoma by arraybased comparative genomic hybridization.
Haematologica. 2008;93(9):1318-1326.
9. Tiacci E, Döring C, Brune V, et al. Analyzing
primary Hodgkin and Reed-Sternberg cells to
capture the molecular and cellular pathogenesis
of classical Hodgkin lymphoma. Blood. 2012;
120(23):4609-4620.
10. Swerdlow SH, Campo E, Harris NL, et al, eds.
WHO Classification of Tumours of Haematopoietic
and Lymphoid Tissues. 4th ed. Lyon, France:
International Agency for Research on Cancer;
2008.
11. Mani H, Jaffe ES. Hodgkin lymphoma: an update
on its biology with new insights into classification.
Clin Lymphoma Myeloma. 2009;9(3):206-216.
12. Devilard E, Bertucci F, Trempat P, et al. Gene
expression profiling defines molecular subtypes of
classical Hodgkin’s disease. Oncogene. 2002;
21(19):3095-3102.
13. Levy A, Armon Y, Gopas J, et al. Is classical
Hodgkin’s disease indeed a single entity? Leuk
Lymphoma. 2002;43(9):1813-1818.
14. Axtell LM, Myers MH, Thomas LH, Berard CW,
Kagan AR, Newell GR. Prognostic indicators in
Hodgkin’s disease. Cancer. 1972;29(6):
1481-1488.
15. Allemani C, Sant M, De Angelis R, MarcosGragera R, Coebergh JW; EUROCARE Working
Group. Hodgkin disease survival in Europe and
the U.S.: prognostic significance of morphologic
groups. Cancer. 2006;107(2):352-360.
16. Ansell SM. Hodgkin lymphoma: 2012 update on
diagnosis, risk-stratification, and management.
Am J Hematol. 2012;87(12):1096-1103.
17. Gobbi PG. Tumor burden in Hodgkin’s lymphoma:
much more than the best prognostic factor.
Crit Rev Oncol Hematol. 2014;90(1):17-23.
18. Chang Y, Cesarman E, Pessin MS, et al.
Identification of herpesvirus-like DNA sequences
in AIDS-associated Kaposi’s sarcoma. Science.
1994;266(5192):1865-1869.
19. Kuruvilla J. Standard therapy of advanced
Hodgkin lymphoma. Hematology (Am Soc
Hematol Educ Program). 2009;2009(1):497-506.
20. Derenzini E, Younes A. Predicting treatment
outcome in classical Hodgkin lymphoma: genomic
advances. Genome Med. 2011;3(4):26.
21. DeVita VT Jr, Costa J. Toward a personalized
treatment of Hodgkin’s disease. N Engl J Med.
2010;362(10):942-943.
22. Fromm JR, Kussick SJ, Wood BL. Identification
and purification of classical Hodgkin cells from
lymph nodes by flow cytometry and flow
cytometric cell sorting. Am J Clin Pathol. 2006;
126(5):764-780.
26. Cingolani P, Platts A, Wang LL, et al. A program
for annotating and predicting the effects of single
nucleotide polymorphisms, SnpEff: SNPs in the
genome of Drosophila melanogaster strain
w1118; iso-2; iso-3. Fly (Austin). 2012;6(2):80-92.
27. Dobin A, Davis CA, Schlesinger F, et al. STAR:
ultrafast universal RNA-seq aligner.
Bioinformatics. 2013;29(1):15-21.
28. Thorvaldsdóttir H, Robinson JT, Mesirov JP.
Integrative Genomics Viewer (IGV): highperformance genomics data visualization and
exploration. Brief Bioinform. 2013;14(2):178-192.
29. Venkatraman ES, Olshen A. DNA copy number
data analysis. Package “DNAcopy” v.1.40.0.
Bioconductor; 2014.
30. Weber-Matthiesen K, Deerberg J, Poetsch M,
Grote W, Schlegelberger B. Numerical
chromosome aberrations are present within the
CD301 Hodgkin and Reed-Sternberg cells in
100% of analyzed cases of Hodgkin’s disease.
Blood. 1995;86(4):1464-1468.
31. Martin-Subero JI, Knippschild U, Harder L, et al.
Segmental chromosomal aberrations and
centrosome amplifications: pathogenetic
mechanisms in Hodgkin and Reed-Sternberg
cells of classical Hodgkin’s lymphoma? Leukemia.
2003;17(11):2214-2219.
32. Kato M, Sanada M, Kato I, et al. Frequent
inactivation of A20 in B-cell lymphomas. Nature.
2009;459(7247):712-716.
33. Challa-Malladi M, Lieu YK, Califano O, et al.
Combined genetic inactivation of b2-Microglobulin
and CD58 reveals frequent escape from immune
recognition in diffuse large B cell lymphoma.
Cancer Cell. 2011;20(6):728-740.
34. Oudejans JJ, Jiwa NM, Kummer JA, et al.
Analysis of major histocompatibility complex class I
expression on Reed-Sternberg cells in relation to
the cytotoxic T-cell response in Epstein-Barr
virus-positive and -negative Hodgkin’s disease.
Blood. 1996;87(9):3844-3851.
23. Li H, Durbin R. Fast and accurate short read
alignment with Burrows-Wheeler transform.
Bioinformatics. 2009;25(14):1754-1760.
35. Cremona CA, Behrens A. ATM signalling and
cancer. Oncogene. 2014;33(26):3351-3360.
24. Li H, Handsaker B, Wysoker A, et al; 1000
Genome Project Data Processing Subgroup. The
Sequence Alignment/Map format and SAMtools.
Bioinformatics. 2009;25(16):2078-2079.
36. Miura K, Karasawa H, Sasaki I. cIAP2 as
a therapeutic target in colorectal cancer and other
malignancies. Expert Opin Ther Targets. 2009;
13(11):1333-1345.
25. Saunders CT, Wong WS, Swamy S, Becq J,
Murray LJ, Cheetham RK. Strelka: accurate
somatic small-variant calling from sequenced
tumor-normal sample pairs. Bioinformatics. 2012;
28(14):1811-1817.
37. Rosen EM, Pishvaian MJ. Targeting the BRCA1/2
tumor suppressors. Curr Drug Targets. 2014;
15(1):17-31.
38. Topalian SL, Drake CG, Pardoll DM. Targeting
the PD-1/B7-H1(PD-L1) pathway to activate
From www.bloodjournal.org by guest on June 16, 2017. For personal use only.
1072
REICHEL et al
anti-tumor immunity. Curr Opin Immunol. 2012;
24(2):207-212.
39. Bose P, Simmons GL, Grant S. Cyclin-dependent
kinase inhibitor therapy for hematologic
malignancies. Expert Opin Investig Drugs. 2013;
22(6):723-738.
40. Bhagwat N, Koppikar P, Keller M, et al. Improved
targeting of JAK2 leads to increased therapeutic
efficacy in myeloproliferative neoplasms. Blood.
2014;123(13):2075-2083.
41. Chen LS, Redkar S, Bearss D, Wierda WG,
Gandhi V. Pim kinase inhibitor, SGI-1776, induces
apoptosis in chronic lymphocytic leukemia cells.
Blood. 2009;114(19):4150-4157.
42. Yang Q, Chen LS, Neelapu SS, Miranda RN,
Medeiros LJ, Gandhi V. Transcription and
translation are primary targets of Pim kinase
inhibitor SGI-1776 in mantle cell lymphoma.
Blood. 2012;120(17):3491-3500.
43. Shchebet A, Karpiuk O, Kremmer E, Eick D,
Johnsen SA. Phosphorylation by cyclindependent kinase-9 controls ubiquitin-conjugating
enzyme-2A function. Cell Cycle. 2012;11(11):
2122-2127.
44. Mahajan K, Mahajan NP. WEE1 tyrosine kinase,
a novel epigenetic modifier. Trends Genet. 2013;
29(7):394-402.
BLOOD, 12 FEBRUARY 2015 x VOLUME 125, NUMBER 7
45. Cervantes-Gomez F, Chen LS, Orlowski RZ,
Gandhi V. Biological effects of the Pim kinase
inhibitor, SGI-1776, in multiple myeloma. Clin
Lymphoma Myeloma Leuk. 2013;13(Suppl 2):
S317-S329.
intratumoral FOXP31 regulatory T cells with
improved survival in germinal center-like diffuse
large B-cell lymphoma, follicular lymphoma and
classical Hodgkin’s lymphoma. Haematologica.
2008;93(2):193-200.
46. Cozzi M, Giorgi F, Marcelli E, et al. Antitumor
activity of new pyrazolo[3,4-d]pyrimidine SRC
kinase inhibitors in Burkitt lymphoma cell lines and
its enhancement by WEE1 inhibition. Cell Cycle.
2012;11(5):1029-1039.
51. Steidl C, Lee T, Shah SP, et al. Tumorassociated macrophages and survival in classic
Hodgkin’s lymphoma. N Engl J Med. 2010;
362(10):875-885.
47. Chen Y, Chen CF, Chiang HC, et al. Mutation
of NIMA-related kinase 1 (NEK1) leads to
chromosome instability. Mol Cancer. 2011;
10(1):5.
52. Murray PG, Constandinou CM, Crocker J,
Young LS, Ambinder RF. Analysis of major
histocompatibility complex class I, TAP
expression, and LMP2 epitope sequence in
Epstein-Barr virus-positive Hodgkin’s disease.
Blood. 1998;92(7):2477-2483.
48. Kelley TW, Pohlman B, Elson P, Hsi ED. The ratio
of FOXP31 regulatory T cells to granzyme B1
cytotoxic T/NK cells predicts prognosis in classical
Hodgkin lymphoma and is independent of bcl-2
and MAL expression. Am J Clin Pathol. 2007;
128(6):958-965.
49. Alvaro-Naranjo T, Lejeune M, Salvadó-Usach MT,
et al. Tumor-infiltrating cells as a prognostic factor
in Hodgkin’s lymphoma: a quantitative tissue
microarray study in a large retrospective cohort of
267 patients. Leuk Lymphoma. 2005;46(11):
1581-1591.
50. Tzankov A, Meier C, Hirschmann P, Went P, Pileri
SA, Dirnhofer S. Correlation of high numbers of
53. Lee SP, Constandinou CM, Thomas WA, et al.
Antigen presenting phenotype of Hodgkin ReedSternberg cells: analysis of the HLA class I
processing pathway and the effects of
interleukin-10 on epstein-barr virus-specific
cytotoxic T-cell recognition. Blood. 1998;92(3):
1020-1030.
54. Diepstra A, Niens M, Vellenga E, et al.
Association with HLA class I in Epstein-Barr-viruspositive and with HLA class III in Epstein-Barrvirus-negative Hodgkin’s lymphoma. Lancet.
2005;365(9478):2216-2224.
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2015 125: 1061-1072
doi:10.1182/blood-2014-11-610436 originally published
online December 8, 2014
Flow sorting and exome sequencing reveal the oncogenome of primary
Hodgkin and Reed-Sternberg cells
Jonathan Reichel, Amy Chadburn, Paul G. Rubinstein, Lisa Giulino-Roth, Wayne Tam, Yifang Liu,
Rafael Gaiolla, Kenneth Eng, Joshua Brody, Giorgio Inghirami, Carmelo Carlo-Stella, Armando
Santoro, Daoud Rahal, Jennifer Totonchy, Olivier Elemento, Ethel Cesarman and Mikhail Roshal
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