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Regular Article
LYMPHOID NEOPLASIA
Integrated genomic DNA/RNA profiling of hematologic malignancies in
the clinical setting
Jie He,1,* Omar Abdel-Wahab,2,3,* Michelle K. Nahas,1,* Kai Wang,1,* Raajit K. Rampal,2,3 Andrew M. Intlekofer,4,5
Jay Patel,3 Andrei Krivstov,4,6,7 Garrett M. Frampton,1 Lauren E. Young,1 Shan Zhong,1 Mark Bailey,1 Jared R. White,1
Steven Roels,1 Jason Deffenbaugh,1 Alex Fichtenholtz,1 Timothy Brennan,1 Mark Rosenzweig,1 Kimberly Pelak,1
Kristina M. Knapp,6 Kristina W. Brennan,1 Amy L. Donahue,1 Geneva Young,1 Lazaro Garcia,1 Selmira T. Beckstrom,1
Mandy Zhao,1 Emily White,1 Vera Banning,1 Jamie Buell,1 Kiel Iwanik,1 Jeffrey S. Ross,1 Deborah Morosini,1 Anas Younes,5
Alan M. Hanash,8 Elisabeth Paietta,9 Kathryn Roberts,10 Charles Mullighan,10 Ahmet Dogan,11 Scott A. Armstrong,4,6,7
Tariq Mughal,1,12 Jo-Anne Vergilio,1 Elaine Labrecque,1 Rachel Erlich,1 Christine Vietz,1 Roman Yelensky,1
Philip J. Stephens,1 Vincent A. Miller,1 Marcel R. M. van den Brink,8,13 Geoff A. Otto,1 Doron Lipson,1 and Ross L. Levine2,3,6
1
Foundation Medicine, Cambridge, MA; 2Leukemia Service, Department of Medicine, 3Human Oncology and Pathogenesis Program, 4Cancer Biology and
Genetics Program, 5Lymphoma Service, Department of Medicine, 6Leukemia Center, 7Department of Pediatrics, and 8Bone Marrow Transplant Service,
Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY; 9Department of Medicine (Oncology), Albert Einstein College of Medicine,
Yeshiva University, New York, NY; 10Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN; 11Department of Pathology, Memorial
Sloan Kettering Cancer Center, New York, NY; 12Division of Hematology and Oncology, Tufts University Medical Center, Boston, MA; and 13Division of
Hematologic Oncology, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
Key Points
• Novel clinically available
comprehensive genomic
profiling of both DNA and
RNA in hematologic
malignancies.
• Profiling of 3696 clinical
hematologic tumors identified
somatic alterations that
impact diagnosis, prognosis,
and therapeutic selection.
The spectrum of somatic alterations in hematologic malignancies includes substitutions,
insertions/deletions (indels), copy number alterations (CNAs), and a wide range of gene
fusions; no current clinically available single assay captures the different types of
alterations. We developed a novel next-generation sequencing-based assay to identify all
classes of genomic alterations using archived formalin-fixed paraffin-embedded blood
and bone marrow samples with high accuracy in a clinically relevant time frame, which is
performed in our Clinical Laboratory Improvement Amendments–certified College of
American Pathologists–accredited laboratory. Targeted capture of DNA/RNA and nextgeneration sequencing reliably identifies substitutions, indels, CNAs, and gene fusions,
with similar accuracy to lower-throughput assays that focus on specific genes and types
of genomic alterations. Profiling of 3696 samples identified recurrent somatic alterations
that impact diagnosis, prognosis, and therapy selection. This comprehensive genomic
profiling approach has proved effective in detecting all types of genomic alterations,
including fusion transcripts, which increases the ability to identify clinically relevant
genomic alterations with therapeutic relevance. (Blood. 2016;127(24):3004-3014)
Introduction
In the last decade, our understanding of the somatic cancer genome
has been greatly advanced through gene discovery studies.1-6 These
studies delineated the genomic complexity in different types of
human cancer, in different patients with the same tumor type, and
within an individual’s tumor. These efforts have identified recurrent
somatic alterations that in some cases are specific to a particular tumor
type but in many cases are shared across different malignancies. These
discoveries have had clinical impact in the diagnosis of specific malignancies defined by recurrent somatic alterations3 and in the development of more precise prognostic schema.1 Most importantly, these
studies have identified disease alleles that have guided the use of
molecularly targeted therapy.2 These data underscore the importance
of genomic profiling in clinical oncology and led to the development of
DNA-based genomic tests for cancer patients.7
Cytogenetic studies identified recurrent chromosomal translocations in a spectrum of hematologic malignancies, including acute
myeloid leukemia (AML),8 acute lymphoblastic leukemia (ALL),9
chronic myeloid leukemia (CML),10 and non-Hodgkin lymphomas
(NHLs),11 which impact clinical outcome and can guide therapeutic
decisions.12 More recent studies have identified recurrent mutations and
amplifications/deletions in a spectrum of hematologic malignancies,
including AML,13 ALL, multiple myeloma (MM), and lymphoma, creating a pressing need to develop comprehensive genomic assays to
identify somatic alterations in hematologic malignancies.
Submitted August 14, 2015; accepted February 28, 2016. Prepublished online
as Blood First Edition paper, March 10, 2016; DOI 10.1182/blood-2015-08664649.
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.
*J.H., O.A.-W., M.K.N., and K.W. contributed equally to this work.
The online version of this article contains a data supplement.
3004
© 2016 by The American Society of Hematology
BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
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BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
INTEGRATED DNA AND RNA GENOMIC PROFILING
3005
A
A
TUMOR SAMPLE (FFPE, Blood, BMA)
B
SEQUENCING LIBRARY PREPARATION & HYBRID CAPTURE
Genomic DNA
FFPE
Sequencing Library
C
Total RNA
Biotinylated DNA Baits
Sequencing Library
Biotinylated DNA Baits
BASE SUBSTITUTIONS
Bayesian algorithm
SHORT INSERTIONS/DELETIONS
Local assembly
Hybridization Capture
Blood/BMA (3ml)
PAIRED-END
SEQUENCING
INTERPRETATION
& REPORTING
Analysis methods highlights
• Sensitivity to variants
present at >5% mutant allele
frequency
• Detection of long (40bp+)
indel variants using deBruijn graph-based local
assembly
• CGH-like analysis of readdepth for CNAs assessment
• Genomic rearrangements
and gene fusions are
identified by analyzing
chimeric read pairs from
DNA-seq and RNA-seq
Reporting approach
Interpretation without a
matched normal
• Germline variants from
1000 Genomes project
(dbSNP135) removed
• Known driver
alterations (COSMIC
v62, internal CNA and
fusion list) identified as
biologically significant
• A concise summary of
the biomedical
literature and current
clinical trials is
provided per alteration
>600x
550–600x
500–550x
450–500x
Frequency
Laboratory process highlights
• Requires ≥50ng of dsDNA
(quantified by PicoGreen®)
• Requires ≥300ng of RNA
(quantified by RiboGreen®)
• RNA is converted to cDNA
• Fragmentation by sonication
(Covaris®) and ‘with-bead’ library
construction
• Hybridization capture with
biotinylated DNA
oligonucleotides
• 49 × 49 paired-end sequencing on
the illumina HiSeq platform
• DNA to ~500× average unique
coverage
• RNA to >3M unique pairs
400–450x
350–400x
300–350x
<300x
Sample requirements
• Tumor content: ≥20%
of nucleated
cellularity; ≥30,000
nucleated cells
• FFPE: Fifteen 5
micron sections of a
5 × 5mm2 piece of
tissue
• Peripheral
blood/bone marrow
aspirate: each
collected in an EDTA
tube
GENOMIC REARRANGEMENTS
Chimera pair analysis of DNA-seq
and RNA-seq data
Hybridization Capture
NUCLEIC ACID
EXTRACTION
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
CLINICAL REPORT
COPY NUMBER ALTERATIONS
Comparison with processmatched normal control
OR
B
D
ANALYSIS PIPELINE
Figure 1. Workflow of comprehensive combined DNA and RNA genomic analysis in clinical specimens and coverage distribution of all exons. (A) DNA and RNA
are extracted from fresh blood and bone marrow aspirate (BMA) specimens procured in EDTA or FFPE biopsy/surgical specimens; cDNA from 300 to 500 ng RNA and 50 to
200 ng DNA undergoes whole-genome shotgun library construction. cDNA libraries are hybrid capture selected for 265 genes known to be rearranged in RNA and DNA
libraries are hybrid capture selected for 405 genes known to be altered in DNA. Hybrid-capture selected libraries are sequenced to high depth using the Illumina HiSeq2500
platform; sequence data are processed using a customized analysis pipeline designed to accurately detect multiple classes of genomic alterations: base substitutions, short
insertions/deletions, CNAs, and gene fusions; detected mutations are annotated according to clinical significance and reported. (B) Coverage distribution in DNAseq of all
genes in 108 validation samples including 4 Hapmap controls and 104 hematologic tumor specimens.
Hematologic malignancies have a high frequency of rearrangements that lead to aberrant expression of oncogenes or to the
expression of fusion transcripts that contribute to malignant transformation and maintenance. These include classical chromosomal
translocations, inversions/duplications, interstitial deletions, and
episomal fusions/amplifications that can give rise to disordered
expression of full-length genes or of fusion transcripts. In many
cases, these discoveries have led to the use of targeted therapies in
specific disease subsets. However, current diagnostic assays,
including fluorescence in situ hybridization (FISH) and real-time
polymerase chain reaction (PCR), are designed ad hoc to identify
specific genomic alterations, and in some cases, there are no assays
that can reliably identify specific rearrangements. We sought to
develop an integrated DNA/RNA profiling platform using targeted
next-generation sequencing (NGS) that could reliably identify
single nucleotide substitutions, insertions/deletions, copy number
alterations (CNAs), and rearrangements.
Methods
Description of workflow
DNA and RNA from each patient are extracted and made into barcoded
libraries through separate workflow streams. The DNA and cDNA undergo
library construction and hybrid selection on independent plates. DNA and
RNA samples from the same patient then converge in an analysis pipeline
using the plate names and shared specimen ID. Detailed protocols for DNA
and RNA extraction, cDNA synthesis, library construction, and hybrid
selection are described in supplemental Information, available on the Blood
Web site.
Sequencing
The selected libraries are pooled and sequenced on the Illumina HiSeq2500
to ;5003 unique coverage for DNA and to .3 M unique on-target pairs
for cDNA.
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B
Number of rearrangements
A
BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
HE et al
MAF of short variants called by
previously validated assay
3006
100
R2 = 0.929
80
60
40
20
0
20
40
60
80
90
80
70
60
50
40
30
20
10
0
DNA only
RNA only
DNA+RNA
100% 50% 33% 25% 20% 10%
100
Tumor content
MAF of short variants called by the new assay
C
D
60
1 FLT3 ITD
positive by PCR,
negative by NGS
(<5%)
1
<1%
211 (99%)
100 positive
111 negative
2
<1%
2 IDH2 exon 4
negative by
sanger, positive
by NGS (4%, 26%)
# of samples
50
40
30
ref test–/NGS test+
ref test+/NGS test–
ref test–/NGS test–
ref test+/NGS test+
20
10
FL
T3
N ITD
C PM +D
E 1
BC BP ex 835
R A fu on
-A l 12
BL l g
e
KI 1 fu ne
T si
ID ex on
H on
ID 2 e 17
H x
M JA 1 e on4
PL K x
PM S 2 V on
4
L- 505 61
R + 7F
AR W
5
M A f 15
LL us
fu ion
si
on
s
0
Figure 2. Alteration calling accuracy and concordance with reference platforms. (A) Correlation of short variant MAFs for substitutions and indels called by new vs
previously validated assays. MAFs of 102 substitutions and 59 indels were compared in pairs and showed significant correlation (R2 5 0.929). (B) Summary of sensitivity of
fusion calling in cell line mixes, separated by detection method (DNA, RNA, or both). (C) High concordance (99%) observed between NGS and Sanger sequencing or PCR
validation results. (D) Concordance of alterations between the new assay and other Clinical Laboratory Improvement Amendments (CLIA)–certified reference platforms. The
count of positive call and negative call from reference test and our NGS-base assay from the 11 genes/regions are highlighted by different colors: red (alteration was called
negative by reference test but positive by our NGS test), steel blue (alteration was called positive in reference test and negative in our NGS test), peach (alteration was called
negative in both reference test and negative test), and lime green (alteration was called positive in both reference test and negative test.
Base substitutions, indels, and copy number analysis
Samples with median exon coverage in the range 150 to 2503 are
considered qualified, whereas those with coverage ,1503 are considered
failed. For base substitutions, the mutant allele frequency (MAF) cutoff is
1% for known somatic variants (based on COSMIC v62) and 5% for novel
somatic variants. For indels, the MAF cutoff is 3% for known somatic
variants and 10% for novel somatic variants. Additional details of the
methods were described previously.7
Rearrangement calling methods
A customized alignment workflow was developed for fusion detection from
RNA-seq. Raw sequence reads were aligned to whole transcriptome
(refSeq) first, and reads with suboptimal mapping were aligned to whole
genome references. Alignments to the 2 different references were then
merged and calibrated based on the full genome reference (hg19) for fusion
detection.
Gene rearrangements were detected by identifying clusters of chimeric
read pairs from both DNA (pairs mapping .10 kbp apart or on different
chromosomes) and RNA (pairs mapping to refSeq sequences corresponding to different genes or to genomic loci .10 kbp apart). Chimera clusters
were filtered for repetitive sequence (average mapq .30) and by
distribution of mapped positions (SD .10). Identified rearrangements
were then annotated according to the genomic loci of both clusters and
categorized as gene fusions (eg, BCR-ABL1), gene rearrangements (eg,
IGH-BCL2), or truncating events (eg, TP53 rearrangement). Rearrangement candidates were then filtered based on number of chimera reads
supporting the rearrangement events (for documented fusions, a minimum
10 chimera reads are required; for putative somatic driver rearrangements, 50
chimera reads are required).
In addition to the de novo rearrangement detection method described
above, reads were also separately aligned to a custom reference library
generated based on common fusions and rearrangements. Fusions were
detected based on the observation of reads aligned across the junction of
rearrangement breakpoints. This method includes the detection of 6
common isoforms of MLL-PTD14 (supplemental Table 3a).
Immunoglobulin heavy locus (IGH) rearrangements were detected by
targeting rearrangement hotspots of both common immunoglobulin fusion
partner genes (major and minor translocations involving MYC, BCL2, and
CCND1), as well as IGH breakpoint regions.
Expression measurement
We first calculated read counts per million for each gene, and then relative
expression level was normalized by the median read counts per million of all
449 MM patients. The statistical significance of expression between the
rearrangement positive cohort and rearrangement negative cohort was
determined by a 1-sided Wilcoxon rank-sum test.
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BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
1
AL
EM K
L4
4
XO
R
BC
2
SS
PR ERG 1
TM
NX
RU
Y
X
22
21
MLL bcr
2
20
TC
18
19
F3
3
RAR
17
A
TP5
3
4
2
SLC34A
FIP1L1
PDGFRA
AFF1
1
MLL
(WT)
DNA
2
MLL –
PTD
RNA
15
PML
B X648577
16
CBFB
MYH11
CIITA
3007
B
PB
X1
FO
A
INTEGRATED DNA AND RNA GENOMIC PROFILING
14
5
3
4
5
6
78
910
11 12–16
1 2
3
4
5
6 7 8 2
1 2
3
4
5
6 7 8 9 10 2
1 2
3
4
5
6 7 8 4
3
5
4
3
6 7 8
36
5 6 7 8
4
25%
samples
9–36
5 6 7 8 9 10
11–36
25%
samples
50%
samples
9–36
13
NP
6
M1
12
RO
S1
1O
ET P2
V6
M
PIC
LL
AL
M
CCDC6
RET
MLLT10
R1
X1T1
RUN
FGF
MLLT3
C
8
9
NUP214
ABL1
SET
FG
FR
10
T4
LL
M
7
11
BCL6
MYC
22% samples
9% samples
187,439K
128,753.4M 128,750.9M 128,748.4M 128,745.9M
187,445K
187,451K
187,457K
187,457K
CCND1
22% samples
BCL2
48% samples
69,470K
69,430K
69,390K
69,350K
69,310K
106,042,434
60,760K 60,800K
106,337,406
106,632,378
106,927,350
60,840K
60,880K 60,920K 60,960K
107,222,322
IGH
Figure 3. Combined DNA and RNA sequencing for detection of complex rearrangements. (A) Circos plot showing broad range of fusions in cell line mixes used for
validation of fusion calling. (B) Three MLL-PTD isoforms that were tested (from top to bottom): duplication of MLL exons 2 to 8, exons 2 to 10, and exons 4 to 8. (C) Breakpoints
on the IGH and its partner genes for the 13 clinical concordance samples including 5 IGH-MYC, 2 IGH-BCL6, 5 IGH-CCND1, and 11 IGH-BCL2 rearrangements.
Results
NGS-based test for detecting genomic alterations in DNA
and RNA
The assay we developed uses next-generation DNA and RNA
sequencing and builds on the workflow that has been optimized for
genomic profiling of DNA from patients with solid tumors.7
Genomic profiling is accomplished by integration of data from
targeted DNA and RNA profiling with up-to-date interpretation
of the clinical significance, thereby achieving increased breadth
and improved sensitivity to identify rearrangements that result
in aberrant expression of fusion transcripts. The genes targeted
by DNAseq and RNAseq are listed in supplemental Table 1a-b,
and the intervals of each exons/introns targeted by this assay are
listed in supplemental Table 1c-d. The clinical implications of
targeted genes in ALL, AML, myelodysplastic syndromes (MDS)/
myeloproliferative neoplasms, NHL, and MM are annotated in
supplemental Table 1e. Uniform coverage across all genes are
observed from a total of 108 Hapmap and hematologic tumor
specimens. The distribution of coverage of 405 genes targeted in
DNAseq is shown in Figure 1B, and coverage of each gene is listed
in supplemental Table 1f. The distribution of exonic coverage
(1244 exons) of all clinical relevant genes is shown in supplemental
Figure 1. Eight exons had consistent low coverage ,1003 and
were excluded from further analysis (supplemental Table 1a). The
read depth of each gene targeted in RNAseq from 61 validation
samples is listed in supplemental Table 1g. The current workflow is
shown in Figure 1A and described in the supplemental Methods.
The coverage of each target of genes involved in therapy, clinical
trial, and/or other prognostic and standard of care is summarized in
supplemental Table 1h.
Detection of DNA sequence variants and CNAs
We first established analytic accuracy of detecting substitutions, shorts
insertions and deletions (indels), and CNAs by comparing the
performance of the new assay with a DNA-only assay that had
previously undergone comprehensive validation across a large number
of clinical samples. Compared with the previously validated assay, the
new assay contained an additional 90 genes relevant to hematologic
malignancies. Substitution, indel, and copy number detection were
validated by reanalyzing 47 samples previously profiled with a
validated test7 in which 169 alterations were identified in 55 genes
common to both assays (102 substitutions, 59 indels, and 8 CNAs; 10
low-frequency subclonal variants were excluded from the analysis).
The concordance between the 2 sets of results was 99.4%: 168 of 169
variants were concordant with a single discordant low-frequency
variant (supplemental Table 2a). Additionally, MAF values measured
using the 2 assays were highly correlated (R2 5 0.929; Figure 2A).
These data demonstrate that the current DNA profiling platform can
accurately detect DNA sequence variants and CNAs, as established for
the reference assay.
Detection of genomic rearrangements
To determine our ability to detect genomic rearrangements, we
pooled 21 cell lines (Figure 3A; supplemental Table 2b) with 28
known genomic rearrangements to create 39 mixtures with ratios
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3008
BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
HE et al
A
B
Hodgkin
lymphoma
1%
Histiocytic/dendritic cell
neoplasms/proliferations
1%
Plasma cell
neoplasm
< 1%
Anaplastic large
cell lymphoma
2%
Lymphoblastic
leukemia (B or T)
7%
Waldenstroms
lymphoma 3%
PMBCL
1%
AITL
2%
Skin lymphoma
5%
Myelodysplastic &/or
myeloproliferative
neoplasms
13%
Non-hodgkins
lymphoma (NOS)
4%
Multiple myeloma
39%
Acute myeloid
leukemia/myeloid
sarcoma
17%
Marginal zone
lymphoma
2%
T-cell
lymphoma (NOS)
10%
Diffusion large B cell
lymphoma 38%
Follicular
lymphoma
14%
Non-hodgkin
lymphoma 22%
B-cell
lymphome
(NOS) 10%
C
Mantle
cell
lymphoma
8%
Burkitt lymphoma
1%
D
20
Substitution
Indel
Copy number
Rearrangements
12%
Rearrangement
Percent of patients
15
Copy number
alterations 9%
10
Indels
21%
Base
substitutions
58%
5
TP53
NRAS
KRAS
ASXL1
TET2
DNMT3A
CDKN2A
MLL2
RUNX1
CCND1
CREBBP
BCL2
SRSF2
CDKN2B
RB1
BRAF
MLL
FLT3
WHSC1
ARID1A
IDH2
SF3B1
ATM
EZH2
PTPN11
JAK2
CD36
ETV6
WT1
STAG2
MYD88
U2AF1
TRAF3
TNFAIP3
MYC
CDKN2C
NOTCH1
NF1
BCOR
B2M
NPM1
IGH
ZRSR2
NFRSF14
BCL6
IDH1
LRP1B
FGFR3
EP300
BIRC3
0
Figure 4. Overview of clinical cases profiled by combined DNA/RNA NGS assay. (A) Tumor type distribution of all hematologic tumor cases profiled by the NGS-based
DNA/RNA test. (B) Disease ontology distribution of 297 NHL tumors profiled by the assay. PMBCL, primary mediastinal (thymic) large B-cell lymphoma; AITL,
angioimmunoblastic T-cell lymphoma. (C) Genomic alterations detected from the 1931 clinical specimens and distribution of alteration types. Top 50 most frequent altered
genes are displayed. Red: base substitutions, steel blue: indels, peach: CNAs, lime green: rearrangement. (D) Distribution of alteration types including base substitutions,
indels, CNAs, and rearrangements in the clinical specimens.
of 10%, 20%, 25%, 33%, and 50% representing a range of
clinically relevant tumor fractions. Sensitivity and specificity of
the assay were determined by comparing the genomic rearrangements detected in the pooled samples relative to the constituent
cell lines. The sensitivity for fusion detection at 20% to 100%
tumor fraction was 100% (161 of 161), and 98% at 10% tumor
fraction (84 of 86) (supplemental Table 2b). Very few falsepositive calls were observed, with a positive predictive value of
.98% (245 of 248, with 2 of 3 false calls being at a marginal level
of detection). The combined DNA and RNA sequencing approach
accurately detects a wide variety of genomic rearrangements and gene
fusions. RNA sequencing enables efficient detection of rearrangements
and targets all 28 rearrangements with novel breakpoints in a large
number of genes, whereas DNA sequencing allows high sensitivity for
well-characterized rearrangements affecting specific introns and rearrangements that do not result in expression of a fusion transcript,
including those involving promoter regions. The distribution of
rearrangements called by both DNA and RNA, RNA only, and DNA
only are shown in Figure 2B. RNA sequencing maintains high sensitivity
in high tumor content (100% sensitivity in tumor content 25-100%); at
low tumor fraction (10-20%), DNA sequencing is more sensitive in
selected introns, including, for example, breakpoints involving the MLL
and PDGFRA genes.
Concordance with reference platforms
We performed blinded comparisons with CLIA-certified diagnostic
assays, including Sequenom, reverse transcription-PCR, FISH, and
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BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
A
INTEGRATED DNA AND RNA GENOMIC PROFILING
B
ALL
35
20
15
10
Substitution/Indel
Amplification
Homozygous deletion
Rearrangement
Truncation
20
Percent of patients
Percent of patients
25
AML
25
Substitution/Indel
Amplification
Homozygous deletion
Rearrangement
Truncation
30
3009
15
10
5
5
0
CDKN2A
CDKN2B
TP53
ETV6
NRAS
KRAS
CREBBP
MLL
JAK2
RUNX1
JAK3
NOTCH1
PHF6
ABL1
RB1
SETD2
PTPN11
PAX5
MLL2
IKZF1
FLT3
DNMT3A
PIK3CA
PTEN
ARID1A
MLLT10
BCORL1
FANCA
FBXW7
TET2
ASXL1
STK11
CD36
RHGAP26
WT1
NF1
IDH2
IDH1
CCND3
SRSF2
U2AF1
JAK1
STAG2
ATM
KDM6A
EP300
EZH2
MSH3
BRCA2
SMARCA4
RUNX1
ASXL1
DNMT3A
FLT3
TP53
IDH2
NRAS
MLL
SRSF2
TET2
WT1
NPM1
PTPN11
BCOR
NF1
U2AF1
IDH1
STAG2
CREBBP
CEBPA
KRAS
SF3B1
JAK2
EZH2
CDKN2A
SETBP1
ETV6
PHF6
GATA2
BCORL1
CD36
CDKN2B
KIT
MLL2
MYH11
IKZF1
ZRS2
ARID1A
FOXP1
MLLT10
NSD1
ATM
KDM6A
NUP214
CCND2
GNAS
CBL
ABL1
LRP1B
STK11
0
C
D
MDS
Substitution/Indel
Amplification
Homozygous deletion
Rearrangement
Truncation
20
15
10
20
15
10
0
0
KRAS
NRAS
TP53
WHSC1
CCND1
BRAF
RB1
TRAF3
CDKN2C
FGFR3
DNMT3A
ATM
TET2
CD36
BIRC3
ASXL1
FAF1
CREBBP
CDKN1B
ARID2
ARID1A
PTPN11
IGH
PRDM1
CDKN2A
LRP1B
NF1
ZRSR2
MAP3K14
BRCA2
SETD2
MYC
MLL2
SF3B1
MCL1
ICK
STAG2
NCOR2
AXIN1
MLL
CDH1
CDKN2B
ATR
TRAF2
U2AF1
PIK3CA
IDH2
IDH1
PASK
APC
5
ASXL1
TET2
TP53
STAG2
RUNX1
SRSF2
SF3B1
MLL
DNMT3A
U2AF1
BCOR
NRAS
EZH2
ZRSR2
IDH2
FLT3
CEBPA
JAK2
IDH1
SETBP1
NF1
WT1
GATA2
ETV6
PHF6
STK11
MUTYH
MLL2
NPM1
ATM
CUX1
KRAS
PTPN11
CBL
CSF3R
BRCA2
CXCR4
RB1
SMAD4
CDKN1B
FGFR1
CD36
NTRK1
POLE
BRIP1
TYK2
IKZF1
DAXX
STAT3
MLH1
5
E
Substitution/Indel
Amplification
Homozygous deletion
Rearrangement
Truncation
25
Percent of patients
25
Percent of patients
MM
30
30
NHL
30
Percent of patients
25
Substitution/Indel
Amplification
Homozygous deletion
Rearrangement
Truncation
20
Therapy
Clinical trial
15
Diagnostic
Prognostic
10
5
MLL2
BCL2
TP53
CDKN2A
CREBBP
CDKN2B
TNFAIP3
MYD88
BCL6
B2M
NFRSF14
ARID1A
MYC
TET2
EZH2
CCND1
ATM
CARD11
RB1
CD79B
NOTCH1
IGH
BCL7A
FAS
BCL10
CD36
DDX3X
KRAS
ETV6
CD58
PIM1
SOCS1
CITTA
LRP1B
NOTCH2
CD70
STAT3
SGK1
CXCR4
PTEN
DNMT3A
XPO1
ZRSR2
ASXL1
PAX5
PRDM1
ETS1
MALT1
FOXP1
PIK3R1
0
Figure 5. Genomic landscape in ALL, AML, MDS, NHL, and MM. Observed frequencies of most mutated genes in (A) ALL, (B) AML, (C) MDS, (D) NHL, and (E) MM.
Mutation frequency is grouped by function effect, substitution/indel (red), focal amplification (steel blue), homozygous deletion (peach), rearrangement/fusion (lime green),
and truncation (purple). The numbers of cases included in this analysis are 102 cases in ALL, 274 cases in AML, 130 cases in MDS, 297 cases in NHL, and 753 cases in MM.
In The clinical relevance including therapeutic (green), clinical trial (olive), prognostic (lavender), and diagnostic (blue) are highlighted on top of each gene.
PCR fragment analysis, for 76 clinical specimens (supplemental
Table 2c) previously tested for 214 clinical relevant alterations in 11
genes that are known and routinely tested in clinical practice in AML,
ALL, and MDS (165 short variants and 49 gene fusions; Figures 2C-D).
DNA and RNA were extracted from new unstained sections from the
originally tested formalin-fixed paraffin-embedded (FFPE) block.
Coverage of DNA sequencing across 76 specimens is shown in
supplemental Figure 2 and Table 2d. Of the 101 genomic alterations
identified by Sequenom or gel sizing, 100 were also called by our new
assay (1 positive FLT3 internal tandem duplicate [ITD] was not
detected due to low mutant allele frequency). Of 113 negative genomic
alterations reported previously, our assay confirmed 111 negative calls
From www.bloodjournal.org by guest on August 3, 2017. For personal use only.
3010
BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
HE et al
A
B
SATB1-ALK (Anaplastic large cell lymphoma)
E1–6
GTF2I-BRAF (Multiple myeloma)
E1–4
E1–25
E15–24
E1–12
E1–5
TSPAN3-ROS1 (Multiple myeloma)
ZFP36L1
CRLF2 1%
1%
E10–18
NIN-FLT3 (Hodgkin lymphoma)
KIAA1432-JAK2 (B cell acute lymphoblastic leukemia)
Other (<5 observations)
17%
E20–29
MAFB
2%
E3–25
CCND1/
MYEOV
26%
WWOX
3%
BCL6
4%
E31–43
BCL2
23%
MMSET/
FGFR3
18%
MYC
5%
C
FGFR3 overexpression in
t(4:14)+ MM samples (P = 8.70e–22)
ii)
WHSC1 overexpression in
t(4:14)+ MM samples (P = 5.28e–32)
10
4
5
2
0
0
–5
–2
t(4:14)–MM
iii)
CCND1 overexpression in
t(11:14)+ samples (P = 6.00e–38)
5
0
–5
t(11:14)+
D835H
D835Y
D835V
D835F
D835E
S72*
S451F
t(11:14)–
D
t(4:14)+MM
Y589H
V592D
V592I
V592A
P606L
G613E
N676K
Log ratio of CCND1
t(4:14)–MM
t(4:14)+MM
V491L
Log ratio of FGFR3
i)
15
Copy number gain (6)
2
Exon number
3
Amino acid 0
4
100
5
6
200
7
lg
8
300
9
10
11
12
13
400
500
e10–11–12 Del (1)
Tm
14
Tk
15
600
16
17
700
18 19
20
800
Fusion (1)
21
22
900
23
24
994
FLT3
NM_004119
Protein domains
Types of mutations
Missense SNV
Transmembrane(Tm)
Nonsense SNV
Tyrosine kinase(Tk)
Insertion
Complicated
Deletion (short)
I836del
F612 > Ins
P606 > Ins
F605 > Ins
E604 > Ins
E598 > Ins
Y597 > Ins
E596 > Ins
R595 > Ins
D593 > Ins
V592 > Ins
Y591 > Ins
F590 > Ins
N587 > Ins
D586 > Ins
S585 > Ins
G583 > Ins
V581 > Ins
Q580 > Ins
L576_Q580 > G
L576 > Ins
Y572 > Ins
Immunoglobulin(lg)
Figure 6. Clinical highlights. (A) Novel fusions involving druggable targets such as ALK, BRAF, FLT3, JAK2, and ROS1 (59 partner salmon, 39 partner including kinase
domain of target gene gray; ALCL, anaplastic large cell lymphoma). (B) Distribution of IGH rearrangement partners. (C) Overexpression of (i) FGFR3 and (ii) WHSC1 in
t(4;14)-positive MM cases (n 5 61) compared with t(4;14)-negative MM cases (n 5 388). The violin plot shows the distribution of the log expression ratios of FGFR3 and
From www.bloodjournal.org by guest on August 3, 2017. For personal use only.
BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
and identified 2 IDH2 R140Q mutations that were not detected by the
reference method likely due to the greater sensitivity of this approach
than the orthogonal platform (Figure 2C; supplemental Table 2e).
These 2 IDH2 alterations were later confirmed from an independent
validation using AmpliSeq platform. Overall concordance was 99%
(211 of 214; Figure 2C). The concordance between the NGS-based test
and reference testing, including positive and negative calls, is shown in
Figure 2D. In addition to the concordance analysis, genomic profiling
of the 76 test samples identified 126 additional somatic alterations that
are not covered by available hotspot assays in the given disease type,
including clinically relevant genomic alterations in KRAS, TET2,
EZH2, and DNMT3A (supplemental Table 2e; supplemental Figure 3).
A subset of samples with low-frequency calls (,10%) was selected
for an independent validation using either AmpliSeq assay (supplemental
Methods) or a CLIA-certified non-NGS mutation detection assay.
Overall, 20 of 21 low-frequency variants were confirmed from AmpliSeq
assay and other hotspot clinical assays (supplemental Table 2g).
INTEGRATED DNA AND RNA GENOMIC PROFILING
3011
Human Reference RNA for RNA, a mixture of 10 HapMap samples for
DNA). These samples were sequenced repeatedly over 5 months; 303
single nucleotide polymorphisms (100% 134 of 134) in DNA and
BCR-ABL fusion (100% 132 of 132) in RNA were successfully detected
over the entire time series.
Workflow compatibility with different input materials
We next studied the assays performance using clinical FFPE, blood,
and bone marrow aspirate samples. Blood and bone marrow were
collected in EDTA for both DNA and RNA extractions. DNA and
RNA were obtained from 10 different blood, bone marrow, and FFPE
specimens, and nucleic acids were extracted according to protocols
described in the supplemental Methods. Of these 40 samples, 39 of 40
exceeded performance specifications, with the only failing sample
coming from 1 of 10 FFPE blocks analyzed (supplemental Table 5).
Clinical experience to date
Detection of complex gene rearrangements
In addition to rearrangements that result in expression of a fusion
transcript containing a known or putative oncogene (Figure 3A),
complex gene rearrangements are often identified in patients with
hematologic malignancies. These include large intragenic rearrangements such as MLL partial tandem duplications (PTDs),15 extragenic
rearrangements that involve the immunoglobulin genes, and smaller
internal gene rearrangements, such as FLT3 internal tandem duplications (ITDs)16 that were assessed in the concordance with reference
platforms above (Figure 2D). We profiled 12 samples that were MLLPTD positive by PCR, spanning 3 different isoforms (Figure 3B), and
14 that were negative by PCR. The NGS-based assay identified MLLPTDs in 11 of the 12 PCR positive samples and confirmed the negative
PCR calls in all 14 samples, for an overall accuracy of 96.5% (25 of 26,
the single missed PTD was present below the calling threshold).
Rearrangements involving the IGH locus commonly occur in
hematologic malignancies, including NHL.17 The IGH rearrangementcalling algorithm enables detection of known IGH rearrangements in
addition to novel partners. IGH rearrangement detection was assessed
using 10 cell lines and 38 clinical samples harboring IGH rearrangements
detected by FISH and/or karyotypic data (Figure 3C; supplemental
Table 3a-b). The 10 cell lines were fully concordant, whereas the clinical
samples had 2 discordant positive calls and 1 discordant negative call for
an overall concordance of 94% (45 of 48) (supplemental Table 3b-c).
Assay reproducibility in clinical samples
To demonstrate assay reproducibility, we tested 13 clinical FFPE
specimens in 5 replicates, each in 3 different batches with 1 batch
including 3 intrabatch replicates. In total, the 65 samples contained 82
alterations (42 subs, 13 indels, 15 CNAs, and 12 rearrangements)
providing a comprehensive assessment of intra- and interbatch reproducibility. Concordance between replicates was 97% for both interand intrabatch subsets (supplemental Table 4), with discordant calls being
ascribed to sensitivity limit of the assay. Long-term reproducibility was
assessed through serial assessment of 2 pooled samples (Universal
The new assay was used to perform genomic profiling on 3696
hematologic malignancies submitted to our CLIA-certified, College of
American Pathologists–accredited, New York State–approved laboratory; 3433 of 3696 (93%) specimens submitted for processing were
successfully characterized, including 27% FFPE samples, 21% from
bone marrow aspirates, 18% samples derived from blood, and 34%
samples from pre-extracted nucleic acids from relevant tumor tissues.
This cohort included 39% MM, 22% NHL, 17% AML, and 13% MDS/
myeloproliferative neoplasm specimens (Figure 4A). Different subtypes of NHL in our clinical experiences include DLBCL (41%),
follicular lymphoma (13%), B-cell lymphoma (NOS) (8%), and mantle
cell lymphoma (8%) (Figure 4B). Extracted DNA was sequenced to an
average depth of 5003 and RNA to an average of 6.9 M unique pairs;
263 of 3696 (7%) specimens did not attain quality control specifications
including failures in DNA preparation, or failures in RNA preparation,
or blood sample older than acceptance criteria (1 day old) and no
evidence for tumor, or no somatic driver mutations reported under
qualifying conditions such as low coverage (DNA ,2503), low tumor
purity (,20%), and coverage bias impairing ability to call CNAs
(supplemental Table 6a). Genomic profiling of hematologic malignancies demonstrates that only a relatively small number of genes are
commonly mutated, whereas many specimens harbor a wide range of
rarer events (Figure 4C), including base substitutions, indels, copy
number amplification/losses, and rearrangements (Figure 4D). At least
1 driver alteration was identified in 3246 of 3433 (95%) tumor
specimens, and 2650 (77%) cases harbored $1 alteration linked to a
commercially available targeted therapy or one that is in clinical
development, including NRAS (14% of cases), KRAS (13%), DNMT3A
(7%), CDKN2A (7%), IDH1/2 (5%), BRAF (4%), and FLT3 (4%). In
addition, 61% of cases harbored $1 alteration with known prognostic
relevance in that tumor type, including TP53 (19% of cases), ASXL1
(9%), TET2 (8%), CDKN2B (5%), CREBBP (5%), MLL (4.0%), and
NPM1 (2.0%) (Figure 4C). Disease-specific mutational landscapes of
MM, NHL, ALL, AML, and MDS are further presented in Figure 5
with associated clinical relevance, and a full listing of observed genes
and mutation frequencies are listed in supplemental Table 6b.
Figure 6 (continued) WHSC1 in each group, respectively. For both cases, the difference is statistically significant (FGFR3, P 5 8.7e222; WHSC1, P 5 5.28e232, 1-sided
Wilcoxon rank-sum test). (Ciii) Overexpression of CCND1 in all t(11;14)-positive cases (n 5 67) compared with t(11;14)-negative cases (n 5 3095). The difference is also
statistically significant (P 5 6.00e238, 1-sided Wilcoxon rank-sum test). (D) Overview of short variants (single nucleotide variants [SNVs], ITDs, indels) and large-scale
aberrations including long deletions, copy number gains, and fusions detected in FLT3. The FLT3 protein is drawn with its immunoglobulin-like (Ig, red), transmembrane (Tm,
steel blue), and tyrosine kinase (Tk, peach) domains illustrated. Positions and protein effect of all short variants are indicated. For ITDs (green), samples with ITD at the same
positions are aggregated together regardless of the actual insert sequence. The ranges of large-scale aberrations are indicated with light blue bars. Commonly tested
“hotspot” mutations are shaded in gray.
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BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
HE et al
Table 1. Summary of genomic alterations identified in Ph-like B-ALL cases
Specimen ID
ALL1
Kinase fusion
Kinase CNA/sequence
mutation
IKZF1 deletion or
mutation
PAX5 deletion
or mutation
EBF1
alteration
CDKN2A/B
deletion
PAX5-JAK2
Unknown
Deletion
No
Yes
No
IGH-CRLF2
CRLF2 F232C*
Deletion (single
No
Yes
No
No
No
Yes
RAS pathway
Other lesion
ALL2
ALL3
exon deletion)
ALL4
ALL5
SSBP2-JAK2
Unknown
ALL7
RSCD1-ABL2
Unknown
ALL8
IGH-EPOR
Deletion
ALL6
KRAS G12D
Deletion
No
No
No
Deletion
Deletion and
No
Yes
No
No
Yes*
Deletion
No
Yes
mutation
ALL11
CREBBP R1081fs ETV6
Deletion (single
focal deletion
exon deletion)
ALL12
ALL13
IGH-CRLF2
ALL14
P2RY8-CRLF2
JAK2 T875N
IGH-CRLF2
Unknown
Deletion
ALL15
ALL16
ALL17
PAX5-MEGF9
No
FIP1L1-PDGFRA
ALL18
Summary of genomic alterations including kinase fusions, kinase mutation, and deletions in IKZF1, PAX5, EBF1, and CDKN2A/B in 16 Ph-like ALL patients.
*Mutations that were detected only by the clinical sequencing assay.
In total, we identified 1524 genomic rearrangements in 1256 of 3433
(37%) tumor specimens. These rearrangements involved genes
implicated in chromatin/histone remodeling (20%), transcriptional
regulation (16%), kinase/oncogene activation (15%), apoptosis regulation (13%), cell cycle regulation (13%), and truncation of tumor
suppressors (5%), including novel in-frame fusions in kinase drug targets
in ALK, BRAF, FLT3, JAK2, and ROS1 (Figure 6A). In addition, we
identified a novel translocation involving NOTCH1, as well as several
novel NF1 translocations that might confer sensitivity to MEK inhibitors.
Rearrangements involving the IGH locus were identified in 665 of
3433 tumor specimens, including rearrangements involving CCND1/
MYEOV (28%), BCL2 (25%), MMSET/FGFR3 (17%), MYC (6%),
and BCL6 (4%) (Figure 6B; supplemental Table 6c). Novel IGH
rearrangements were observed in 17% of cases. The locations of
the most common IGH rearrangement partners are summarized in
supplemental Figure 4. In addition, a sizable fraction of IGH
rearrangements involved breakpoints in intergenic regions with
no known oncogene within 7 MB of the breakpoint. Genomic
rearrangements involving IGH are believed to lead to overexpression of the partner oncogene by placing it under the regulatory
control of the IGH locus.18 Expression levels of MMSET/FGFR3
(4p16.3) and CCND1 genes were successfully measured for 449 MM
cases (Figure 6C). As depicted in Figure 6C, we observed overexpression of both CCND1/FGFR3 and CCND1 genes in samples
harboring the respective rearrangements.
Identification of different classes of alterations in a single gene
using an integrated genomic profiling assay
Previous studies have shown that a subset of oncogenes can be
activated by different types of genomic alterations in different
patients.19 Constitutive activation of the FLT3 receptor tyrosine
kinase can occur in a significant proportion of leukemia patients as
a result of somatic ITDs, point mutations (D835/I836), or less
common chromosomal rearrangements.16,20 Somatic alterations
involving the FLT3 locus have been shown to be of prognostic
importance in acute leukemia, and there are potent, specific FLT3
kinase inhibitors in late-stage clinical trials.21 We observed
89 alterations targeting FLT3 in 84 specimens. This included
67 (75%) D835 and ITD mutations detected by existing clinical
tests. We also identified other base substitutions (n 5 14), in-frame
deletion (n 5 1), CNAs (n 5 6), and fusions involving FLT3
(n 5 1) (Figure 6D). For example, 1 AML case that tested negative
for D835Y harbored an I836 del, which has previously been
associated with poor disease-free survival.22 Four cases positive
for FLT3 ITD or D835 were found to also harbor concurrent lowfrequency V592A and S451F mutations, which have been
functionally validated as activating the FLT3 kinase.23 These
results indicate that the sequencing-based assay can robustly
detect a wide range of alterations in driver genes that are not fully
evaluated using conventional methods.
Use of a single unified test in B-cell ALL
Recent genomic studies in B-cell ALL have led to an increased
understanding of the molecular pathogenesis of this B-cell malignancy.
This includes the identification of recurrent substitutions, indels, and
fusion genes that target B-cell differentiation. Most importantly, recent
studies have shown that subsets of patients with high-risk, BCRABL–negative B-cell acute lymphoblastic leukemia (B-ALL) have a
Philadelphia chromosome-like (Ph-like) gene expression signature and
that the majority of cases of Ph-like ALL harbor somatic alterations in
known signaling effectors, including in known and putative therapeutic
targets.24-26 However, current approaches to identify the substitution
mutations, indels, and fusion genes that drive Ph-like ALL require the
use of a series of genomic assays. We performed integrated DNA/RNA
profiling on 16 cases classified as Ph-like ALL by gene expression
profiling. We found deletions in IKZF1 (7 of 16), PAX5 (2 of 16), and
CDKN2A (4 of 16) in a subset of these cases, consistent with previous
reports.27 We also identified known activating mutations in CRLF2,
KRAS, and JAK2 in some cases. Notably, we identified known and
novel gene fusions in 9 of 16 cases. This included 2 JAK2 fusions with
different partner genes (PAX5 and SSBP2), 6 known fusions of CRLF2
and EPOR, and a novel gene fusion involving ABL2, all of which would
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BLOOD, 16 JUNE 2016 x VOLUME 127, NUMBER 24
INTEGRATED DNA AND RNA GENOMIC PROFILING
be expected to activate kinase signaling and to confer sensitivity to
approved drugs. In 1 case of B-ALL, we identified a FIP1L1-PDGRA
fusion, which has been shown to respond to imatinib in patients with
hypereosinophilic syndrome28 (Table 1). These data show that an
integrated DNA/RNA profiling platform can identify a spectrum of
alterations in kinase signaling pathway in B-ALL patients that can guide
the use of molecularly targeted therapies. The result also achieved high
concordance with an integrated research profiling approach which
included genome sequencing, targeted DNA sequencing, RNA
sequencing, and single nucleotide polymorphism array analysis done on
the same samples. Specifically, we observed complete concordance
between our clinical genomic profiling assay and research-grade genomic
analysis, with the exception a CRLF2 mutation and a CDKN2A/B
deletion that were detected only by the clinical sequencing assay.
Discussion
The identification of somatic genomic alterations with clinical
relevance has increased the need for robust genomic profiling assays
that can identify the different types of genomic alterations in clinically
relevant cancer genes. Here we report the development of an integrated
DNA/RNA target capture NGS assay, which we optimized to detect
different classes of genomic events, including base substitutions, indels,
CNAs, and chromosomal rearrangements. This allows the use of a
single test to systematically profile clinical samples from patients
with hematologic malignancies for actionable genomic lesions, in a
clinically relevant time frame and quality standard. As described in
results, 93% specimens were successfully profiled in the clinical setting.
Seven percent of patients received failed report due to failures in DNA
or RNA preparation, process of aged liquid specimen, and no somatic
mutations detected under other qualifying conditions. One of the most
common qualifying conditions is RNA preparation failure (132 cases),
which highlighted the technical challenges of process and sequence
RNA samples (supplemental Table 6a).
Previously described assays largely focused on DNA-based
approaches or used gene-specific RNA tests to detect fusion transcripts
in a small set of genes. We show that targeted RNA capture sequencing
can be used to identify a wide spectrum of fusion genes with known
roles in malignant transformation and therapeutic response and to
identify novel fusions with likely diagnostic and therapeutic relevance.
This approach can identify fusion transcripts present in as few as
10% to 20% of cells and can be used to show that chromosomal
rearrangements result in expression of in-frame, fusion transcripts.
Moreover, combining DNA and RNA capture allows for the
identification of full-length transcripts driven by promoter rearrangements by DNA sequencing, as well as fusion transcripts that are more
easily identified by RNA profiling. This approach has immediate
clinical value in hematologic malignancies, and we believe will show
significant clinical value in epithelial tumors. Of note, this approach
can also be used to reliably measure expression of genes in the target
set, which will inform development and testing of genomic predictors
3013
that combine mutational data and gene expression/epigenetic data to
inform prognosis and to identify novel therapeutic targets.
The clinical relevance of our DNA/RNA profiling5 approach is
underscored by our ability to identify genetic lesions with prognostic
and therapeutic relevance in specific diseases. In the case of B-cell ALL,
recent studies have identified novel genetic markers with prognostic
relevance and identified novel therapeutic targets in patients with high-risk
disease.24-27,29,30 The challenge has been that the critical genes implicated
in B-ALL can be altered by whole gene/intragenic deletions, DNA base
pair substitutions, and larger indels, as well as chromosomal, intergenic,
and cryptic rearrangements that lead to expression of fusion transcripts.
Currently, most centers use an amalgam of DNA, FISH, and gene-specific
RNA approaches to identify a subset of the most critical genetic lesions in
B-ALL. Our assay provides a single profiling platform that can reliably
identify all known actionable disease alleles relevant to B-ALL to improve
diagnosis and risk-adapted therapy for B-ALL patients.
Most importantly, our profiling platform, allows for the increased use
of genomic profiling to improve the diagnosis and treatment of cancer
patients and to more precisely match patients with targeted therapies.
These efforts are critical to more broadly expand precision medicine by
assuring that cancer patients can be offered genomic profiling regardless
of whether they are at tertiary care centers or receive care in other settings.
Authorship
Contribution: J.H., O.A.-W., D.L., G.A.O., and R.L.L. designed
the study, wrote the manuscript, and developed and/or performed
analysis; P.J.S. and V.A.M. designed the study and wrote the
manuscript; O.A.-W., R.K.R., A.M.I., J.P., A.K., A.Y., A.M.H.,
E.P., K.R., C.M., A.D., S.A.A., M.R.M.v.d.B., and R.L.L.
contributed clinical and genomic expertise; J.H., M.K.N., K.W.,
T.B., G.M.F., L.E.Y., S.Z., M.B., M.R., and K.P. performed
analysis; J.H., L.E.Y., J.R.W., S.R., J.D., and A.F. developed
computational tools; M.K.N., K.W.B., A.L.D., G.Y., E.W., and
G.A.O. developed the assay; L.G., S.T.B., M.Z., V.B., J.B., K.I.,
K.M.K., and E.L. performed laboratory experiments; and R.Y.,
T.M., R.E., C.V., J.-A.V., J.S.R., and D.M. evaluated and edited
the manuscript.
Conflict-of-interest disclosure: J.H., M.K.N., K.W., G.M.F., L.E.Y.,
S.Z., M.B., J.R.W., S.R., J.D., T.B., M.R., K.P., K.W.B., A.L.D.,
G.Y., L.G., S.T.B., M.Z., E.W., V.B., J.B., K.I., J.S.S., D.M., T.M.,
J.-A.V., E.L., R.E., C.V., R.Y., P.J.S., V.A.M., G.A.O., and D.L.
are employees and equity holders of Foundation Medicine, Inc.
O.A.-W., A.D., S.A.A., M.R.M.v.d.B., and R.L.L. are consultants for Foundation Medicine, Inc.
Correspondence: Geoff Otto, Foundation Medicine, 150 Second
St, Cambridge, MA 02141; e-mail: [email protected];
Doron Lipson, Foundation Medicine, 150 Second St, Cambridge, MA 02141; e-mail: [email protected];
and Ross L. Levine, Memorial Sloan Kettering Cancer Center,
1275 York Ave, Box 20, New York, NY 10065; e-mail: leviner@
mskcc.org.
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From www.bloodjournal.org by guest on August 3, 2017. For personal use only.
2016 127: 3004-3014
doi:10.1182/blood-2015-08-664649 originally published
online March 10, 2016
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