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Correspondence: Shumpei Ohnami (E-mail: s.onami@scchr.jp)
Whole exome sequencing detects variants of genes that mediate response to anticancer drugs
Sumiko Ohnami1, Takeshi Nagashima1,2, Kenichi Urakami1, Yuji Shimoda1,2, Fukumi Kamada1, Junko Saito3, Akane Naruoka3, Masakuni Serizawa3, Yoko Masuda4,
Shumpei Ohnami1, Masatoshi Kusuhara3,4 and Ken Yamaguchi5,6
1Cancer Diagnostics Research Division, Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan
2SRL Inc., 5-6-50 Shinmachi, Hino-shi, Tokyo 191-0002, Japan 3Drug Discovery and Development Division, Shizuoka Cancer Center Research Institute,
1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan4Regional Resources Division, Shizuoka Cancer Center Research Institute,
1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan5Shizuoka Cancer Center Hospital, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan
6Shizuoka Cancer Center Research Institute, 1007 Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan
(Received November 3, 2016; Accepted December 20, 2016)
ABSTRACT — Certain interindividual differences affecting the efficacy of drug treatment and adverse drug reactions are caused by genetic variants, and their phenotypic effects differ among ethnic groups. In this study, we used whole exome sequencing (WES) systematically to identify germline mutations that influence the activities of drug-metabolizing enzymes, as well as that of a transporter. We analyzed DNA isolated from blood samples from 2,042 Japanese patients with diverse cancers. We identified sequence variants of CYP2B6 (rs3745274), CYP2C9 (rs1057910), CYP2C19 (rs4986893), CYP2C19 (rs4244285), TPMT (rs1142345), NAT2 (rs1799930), NAT2 (rs1799931), UGT1A1 (rs4148323), COMT (rs4680), ABCB1 (rs1045642), and CDA (rs60369023). Wider application of WES will help to determine the effects of mutations on the activities of proteins encoded by drug response genes, and the information gained will accelerate the development of personalized therapies for patients with cancer. Moreover, this knowledge may provide clues for preventing cancer before the onset of symptoms.
Key words: Whole exome sequencing, Drug response genes, Personalized medicine
INTRODUCTION
There are significant differences in patients’ responses to drugs, exhibited through such parameters as drug sensi-tivity and the severity of adverse drug reactions (Deenen et al., 2011; Ma and Lu, 2011). Identification of germline mutations that determine these differences will advance our understanding of the absorption, distribution, metabo-lism, and excretion pathways that influence pharmacody-namics and promise to lead to the development of patient-tailored anticancer therapies (personalized medicine) (Iida et al., 2006).
Polymorphisms within the amino acid sequences of the majority of phase I and phase II drug-metabolizing enzymes (DMEs), as well as transporters, contribute to the
clinical efficacy of drugs (Kurose et al., 2012). Genome-wide association studies focusing on the response to drugs, which include analyses of candidate genes encod-ing DMEs, reveal the endogenous effects of genetic var-iations that affect drug metabolism and drug responses (Low et al., 2014; Sim et al., 2013). However, the appli-cation of pharmacogenomics in the treatment of individ-ual patients is rarely undertaken (Ishiguro et al., 2013). Furthermore, systematizing relevant information about genetic variants is important for optimizing pharmaco-therapy administered to Japanese populations, particularly because of different in allelic and genotypic frequencies and drug dose requirements among ethnic groups (Ota et al., 2015). Next-generation sequencing (NGS) is a criti-cal component of such efforts because it comprehensively
Letter
The Journal of Toxicological Sciences (J. Toxicol. Sci.)Vol.42, No.2, 137-144, 2017
Vol. 42 No. 2
137
identifies common and rare mutations in genes related to drug sensitivity, and recent advances in NGS have stimu-lated pharmacogenetic discovery (Gordon et al., 2014).
We established Project HOPE (High-tech Omics-based Patient Evaluation) to evaluate the biological characteris-tics of cancer and the hereditary predispositions of indi-vidual patients through multiomic analyses that integrate genomics, transcriptomics, proteomics, and metabo-lomics (Yamaguchi et al., 2014). In the present study, we conducted a simultaneous analysis of variants of repre-sentative genes encoding DMEs in patients with diverse cancers. The results reveal a relationship between doc-umented adverse reactions and pharmacogenomics (Kiyotani et al., 2013). These findings will contribute to developing a strategy for tailoring pharmacotherapy to an individual’s genotype.
MATERIALS AND METHODS
SubjectsInvestigators at the Shizuoka Cancer Center launched
Project HOPE for cancer medicine in late January 2014. Blood samples and fresh surgical specimens acquired from matched normal and tumor tissues were subject-ed to whole exome sequencing (WES) and comprehen-sive analyses of gene expression. In the present study, blood samples for germline analysis were obtained from 2,042 patients with cancer at the time of surgery at Shizuoka Cancer Center Hospital, Japan between Janu-ary 2014 and March 2016. The characteristics of the sub-jects are summarized in Table 1. The research plan was designed according to the revised Ethical Guidelines for Human Genome/Gene Analysis Research in Japan (http://www.lifescience.mext.go.jp/files/pdf/n1115_01.pdf) and was approved by the Institutional Review Board of the Shizuoka Cancer Center. Patients who participated in this study provided written informed consent.
DNA extractionGenomic DNA was extracted from whole blood using
the QIAamp DNA Blood Midi kit (Qiagen, Hilden, Germany) and quantified using a Nanodrop (Thermo Fisher Scientific, Waltham, MA, USA). AcroMetrix Oncology Hotspot Control DNA (Thermo Fisher Scientific) served as the standard.
Whole exome sequencingWES was performed using an Ion Proton System with
an AmpliSeq Exome Kit (Thermo Fisher Scientific), as described previously (Urakami et al., 2016). Briefly, 100 ng of DNA was amplified as follows: 99°C, 2 min;
95°C, 15 sec, 10 cycles of 60°C, 16 min, and a final hold at 10°C. Incorporated primers sequences were par-tially digested using FuPa reagent (Thermo Fisher Scientific). Ion Torrent Proton adapters were ligat-ed to the amplicons at 22°C for 30 min followed by 10 min incubation at 72°C, and the library was puri-fied using Agencourt AMPure XT beads (Thermo Fisher Scientific). The library was quantified using quanti-tative polymerase chain reaction (qPCR), and 7 pM library DNA was sequenced using the Ion Torrent Pro-ton Sequencer with a PI chip V2 following the manu-facturer’s protocol (Thermo Fisher Scientific). Sequenc-es were aligned to a human genome reference sequence (hg19 assembly, UCSC) and were quality-trimmed using Ion Torrent Suite version 4.2 (Thermo Fisher Scientific). The definition of heterozygous or homozygous var-iants called using the Integrative Genomics Viewer (http://software.broadinstitute.org/software/igv/) (Robinson et al., 2011) were identified visually and were validated using Sanger sequencing (Fig. 1). Details of analysis workflow of germline mutations from WES data are described by Nagashima et al. (submitted).
Of the genes responsible for the metabolism and transport of anticancer drugs using the WES data, we focused on the genes encoding cytochrome P450 isofor-ms (CYP2A6, CYP2B6, CYP2C9, CYP2C19, CYP2D6) (Bell et al., 2015; Chen and Goldstein, 2009; Crews et al., 2012; Jin et al., 2005; Kim et al., 2004; Kiyotani et al., 2013; Takimoto et al., 2013; Tamaki et al., 2011; Xie et al., 2003), thiopurine methyltransferase (TPMT) (Chouchana et al., 2014), N-acetyltransferase 2 (NAT2) (Sim et al., 2014), UDP glucuronosyl transferase family 1 member A1 (UGT1A1) (Cheng et al., 2014; Sugatani, 2013), catechol-O-methyltransferase (COMT) (Zubieta et al., 2003), ATP-binding cassette subfamily B member 1 (ABCB1) (Bell et al., 2015; Frederiks et al., 2015), and cytidine deaminase (CDA) (Sugiyama et al., 2007, 2009), in the present study (Table 2) because the variants of these genes were previously described to affect drug response in Japanese populations (Kurose et al., 2012).
The allele frequencies of each gene were compared with those obtained from public databases, the Human Genetic Variation Database (HGVD) (Higasa et al., 2016) or the Integrative Japanese Genome Variation Database (iJGVD) (Yamaguchi-Kabata et al., 2015).
The allele frequencies or genotype distributions of each variant were calculated using Fisher’s exact test or Student’s t-test. Each variant was tested to ensure Hardy-Weinberg equilibrium (all p-values > 0.05). All statistical analyses were carried out using Statistical Analysis System software Version 9.2 (SAS Institute, Cary, NC, USA).
Vol. 42 No. 2
138
S. Ohnami et al.
Tabl
e 1
Sel
ecte
d ch
arac
teris
tics o
f pat
ient
s with
can
cer.
Can
cers
nA
geSe
xD
iabe
tes
Taba
cco
use
Alc
ohol
con
sum
ptio
nM
ean
± SD
Mal
eFe
mal
eN
ondi
abet
icD
iabe
ticU
nkno
wn
Non
smok
erSm
oker
**U
nkno
wn
Non
drin
ker
Drin
ker*
**U
nkno
wn
Stom
ach
224
70.8
±9.
916
262
190
340
5916
41
4412
357
Lung
357
68.9
±10
.021
714
030
255
011
124
60
5118
811
8C
olon
605
66.3
±11
.736
124
450
796
225
035
50
105
347
153
Bre
ast
135
57.9
±13
.20
135
124
101
9341
142
4746
Live
r10
669
.5±
9.1
8323
8124
122
840
1477
15H
ead
and
Nec
k17
363
.6±
14.4
125
4815
221
051
122
021
107
45Pa
ncre
as55
68.9
±11
.731
2441
140
1933
37
2919
Kid
ney
2263
.8±
13.3
1012
184
08
140
313
6Es
opha
gus
3967
.0±
10.0
354
345
06
330
328
8U
teru
s34
59.1
±12
.80
3427
70
286
09
1213
Ova
ry31
59.4
±13
.40
3128
30
283
06
1411
Skin
2158
.7±
15.9
1110
156
012
81
29
10Sa
rcom
a25
51.5
±23
.114
1124
10
1213
04
1110
Bra
in30
55.6
±15
.318
1227
30
1217
16
159
GIS
T24
62.3
±14
.612
1221
30
159
02
175
Mel
anom
a16
70.1
±17
.98
814
20
96
14
75
Thym
us13
58.1
±14
.46
711
20
67
02
56
Live
r met
asta
sis
6365
.3±
12.1
5112
576
011
520
1631
16Lu
ng m
etas
tasi
s47
61.7
±16
.129
1844
30
2126
06
2318
Oth
ers*
2263
.9±
11.4
1012
166
010
120
512
5To
tal
2042
65.7
±12
.811
8385
917
3330
54
783
1251
835
211
1557
5*O
ther
s (n)
; bile
duc
t (5)
, gal
lbla
dder
(4),
retro
perit
oneu
m (2
), pa
ncre
as m
etas
tasi
s (3)
, bra
in m
etas
tasi
s (3)
, lym
ph n
ode
met
asta
sis (
1), E
soph
agus
met
asta
sis (
1),
kidn
ey
met
asta
sis (
1), s
plee
n m
etas
tasi
s (1)
, sto
mac
h m
etas
tasi
s (1)
**S
mok
er; p
ast o
r cur
rent
***
Drin
ker;
occa
sion
al o
r reg
ular
Vol. 42 No. 2
139
Genetic variation in drug response genes
Fig.
1.
The
visu
aliz
atio
n of
the
hom
ozyg
ous o
r het
eroz
ygou
s var
iant
s cal
l (U
GT1
A1; r
s 414
8323
) usi
ng In
tegr
ativ
e G
enom
ics V
iew
er (I
GV
) and
val
idat
ion
of th
e va
ri-an
ts b
y Sa
nger
sequ
enci
ng (b
otto
m).
G is
the
refe
renc
e al
lele
and
A is
the
mut
ant a
llele
. Var
iant
s are
hig
hlig
hted
by
an a
rrow
.
Vol. 42 No. 2
140
S. Ohnami et al.
RESULTS AND DISCUSSION
In this study, we conducted WES of DNA isolated from patients with diverse cancers and identified germ-line mutations in nine genes that are associated with ther-apeutic efficacy and adverse drug reactions. The mean depth of coverage of target regions achieved using WES was 114.6-fold, and 99.2% of the amplicons were cov-ered by > 20 reads. The mutation frequencies of cancer types and minor allele frequencies (MAFs) detected are listed in Table 3. The MAFs of the variants did not differ significantly from those of normal subjects listed in the public database (Higasa et al., 2016; Yamaguchi-Kabata et al., 2015), suggesting that WES is useful for the com-prehensive detection of germline mutations. Only the allele frequency of CDA (rs60369023) differed signifi-cantly from that in the public database, which is almost consistent with the results of another study (our calcu-lated MAF was 0.028 compared to 0.022) (Sugiyama et al., 2009). The public database is a repository for genet-ic variations among normal subjects; therefore, the data may not be comparable with those of the present study of patients with cancer. Further studies must be conduct-ed to explain the difference in allele frequencies of CDA mutations. In preliminary experiments, certain genotypes determined using WES were consistent with those of the TaqMan genotyping assay (unpublished data).
Of the 13 variants of 11 genes initially targeted for WES, the assays could not be optimized for CYP2A6 (rs1801272) and CYP2D6 (rs1065852). Similarly, the variants were not detected in the HGVD or iJGVD, like-ly because of the high sequence identities to other CYPs (for example, the sequences of CYP2A6 and CYP2D6 are > 90% identical to those of CYP2A7 and CYP2D7, respec-
tively). If they can be assessed before treatment, these germ-
line mutations may offer important information regarding the severity of adverse drug reactions. In contrast, NGS can be applied to characterize common and rare genom-ic alterations across cancer types (Boland et al., 2013; Lawrence et al., 2014). However, further technical opti-mization is required to confirm variants within genes with high sequence similarities, such as CYPs, because NGS analyses generate a substantial number of false positives (Galindo-González et al., 2015; Quail et al., 2012).
In conclusion, our results suggest the feasibility of research and development focused on providing patients with individualized treatment to improve the efficacy and safety of chemotherapy. Moreover, comprehensive analy-sis using WES may be useful for accelerating the devel-opment of clinical trials to determine the association between germline mutations in genes encoding pharma-cokinetically significant proteins and a patient’s response to chemotherapy. An additional study focused on the asso-ciations between response to anticancer drugs and these germline mutations using prospective pharmacogenetics will be conducted in the future.
ACKNOWLEDGMENTS
We thank Mami Mizuguchi and Tsubura Furuya for their excellent contributions, and the staff of the Shizuoka Cancer Center Hospital for clinical support and sample preparation.
Conflict of interest---- The authors declare that there is no conflict of interest.
Table 2. List of genes with alleles that determine a patient’s response to chemotherapy.Gene name rs number Refernce (major) /
variant (minor) alleleAmino acid residue
changeNucleotide position
(NM) Affected drugs
CYP2B6 rs3745274 G/T Gly172His 523G > T Cyclophosphamide, Propofol, BupropionCYP2C9 rs1057910 A/C Ile359Leu 1100A > C Tolbutamide, Phenytoin, Warfarin CYP2C19 rs4986893 G/A Trp212Stop 661G > A Cyclophosphamide, Omeprazole CYP2C19 rs4244285 G/A Pro227Pro 681G > A Cyclophosphamide, Omeprazole CYP2A6 rs1801272 T/A Leu160His 500T > A TegafurCYP2D6 rs1065852 C/T Pro34Ser 190C > T TamoxifenTPMT rs1142345 A/G Tyr240Cys 896T > C 6-MercaptopurineNAT2 rs1799930 G/A Arg197Gln 697G > A Isoniazid, Sulfapyridine, Procainamide NAT2 rs1799931 G/A Gly286Glu 964G > A Isoniazid, Salazosulfapyridine UGT1A1 rs4148323 G/A Gly71Arg 226G > A IrinotecanCOMT rs4680 G/A Val158Met 721G > A Opioid analgesicABCB1 rs1045642 C/T Ile145Ile 3928C > T Irinotecan, Amrubicin CDA rs60369023 G/A Ala70Thr 387G > A Gemcitabine
Vol. 42 No. 2
141
Genetic variation in drug response genes
Tabl
e 3.
M
inor
alle
le fr
eque
ncie
s of t
he 1
1 va
riant
s of g
enes
invo
lved
in th
e re
spon
se to
ant
ican
cer d
rugs
.C
ance
r Typ
es
Gen
esrs
no.
Ref
eren
ce (m
ajor
) /va
riant
(min
or) a
llele
St
omac
hLu
ngC
olon
Bre
ast
Live
rH
ead&
Nec
kPa
ncre
asK
idne
yEs
opha
gus
Ute
rus
Ova
ryn
= 22
4n
= 35
7n
= 60
5n
= 13
5n
= 10
6n
= 17
3n
= 55
n =
22n
= 39
n =
34n
= 31
CYP
2B6
rs37
4527
4G
/T0.
176
0.17
60.
184
0.17
00.
193
0.18
20.
264
0.22
70.
154
0.05
90.
226
CYP
2C9
rs10
5791
0A
/C0.
025
0.01
80.
023
0.03
70.
009
0.02
30.
018
0.06
80.
077
0.01
50.
065
CYP
2C19
rs49
8689
3G
/A0.
118
0.11
80.
121
0.11
10.
113
0.13
30.
191
0.13
60.
128
0.02
90.
113
CYP
2C19
rs42
4428
5G
/A0.
288
0.31
10.
298
0.32
20.
283
0.25
70.
300
0.15
90.
269
0.33
80.
371
TPM
Trs
1142
345
A/G
0.00
40.
011
0.00
70.
004
0.00
90.
020
0.01
80.
000
0.01
30.
000
0.01
6N
AT2
rs17
9993
0G
/A0.
217
0.20
60.
185
0.16
30.
226
0.17
30.
227
0.20
50.
115
0.27
90.
194
NAT
2rs
1799
931
G/A
0.06
90.
084
0.10
30.
104
0.14
20.
101
0.11
80.
136
0.09
00.
103
0.06
5U
GT1
A1rs
4148
323
G/A
0.17
40.
181
0.18
40.
215
0.18
40.
156
0.20
00.
159
0.21
80.
147
0.14
5C
OM
Trs
4680
G/A
0.29
70.
296
0.30
20.
285
0.34
40.
358
0.45
50.
341
0.32
10.
250
0.40
3AB
CB1
rs10
4564
2C
/T0.
455
0.44
00.
436
0.37
80.
429
0.41
90.
436
0.50
00.
410
0.44
10.
147
CD
Ars
6036
9023
G/A
0.02
00.
035
0.03
60.
033
0.00
50.
017
0.02
70.
023
0.02
60.
029
0.03
2C
YP2
A6
rs18
0127
2T/
An.
d*n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.C
YP2
D6
rs10
6585
2C
/Tn.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.n.
d.C
ance
r Typ
es
Gen
esrs
no.
Ref
eren
ce (m
ajor
) /va
riant
(min
or) a
llele
Sk
inSa
rcom
aB
rain
GIS
TM
elan
oma
Thym
usLi
ver m
eta.
Lung
met
a.O
ther
sA
ll C
ance
rsH
GV
D**
iJG
VD
***
n =
21n
= 25
n =
30n
= 24
n =
16n
= 13
n =
63n
= 47
n =
22n
= 20
42C
YP2
B6
rs37
4527
4G
/T0.
190
0.18
00.
250
0.25
00.
188
0.19
20.
183
0.18
10.
182
0.18
40.
179
0.16
8C
YP2
C9
rs10
5791
0A
/C0.
000
0.00
00.
033
0.00
00.
000
0.00
00.
032
0.03
20.
045
0.02
40.
031
-C
YP2
C19
rs49
8689
3G
/A0.
143
0.22
00.
100
0.12
50.
125
0.07
70.
119
0.19
10.
159
0.12
30.
129
0.13
3C
YP2
C19
rs42
4428
5G
/A0.
262
0.24
00.
333
0.31
30.
438
0.26
90.
278
0.31
90.
250
0.29
60.
282
0.29
7TP
MT
rs11
4234
5A
/G0.
000
0.00
00.
000
0.00
00.
031
0.00
00.
008
0.01
10.
000
0.00
90.
009
-N
AT2
rs17
9993
0G
/A0.
214
0.20
00.
217
0.14
60.
313
0.30
80.
190
0.22
30.
205
0.19
70.
191
0.20
0N
AT2
rs17
9993
1G
/A0.
048
0.14
00.
083
0.18
80.
031
0.11
50.
056
0.10
60.
114
0.09
70.
088
0.08
8U
GT1
A1
rs41
4832
3G
/A0.
167
0.24
00.
133
0.22
90.
188
0.30
80.
151
0.21
30.
114
0.18
20.
186
0.17
6C
OM
Trs
4680
G/A
0.38
10.
360
0.28
30.
333
0.37
50.
308
0.30
20.
340
0.27
30.
314
0.31
20.
321
AB
CB
1rs
1045
642
C/T
0.45
20.
540
0.50
00.
479
0.40
60.
308
0.34
90.
415
0.40
90.
431
0.39
90.
415
CD
Ars
6036
9023
G/A
0.00
00.
020
0.00
00.
021
0.00
00.
077
0.02
40.
021
0.04
50.
028
0.03
8-
CY
P2A
6rs
1801
272
T/A
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
--
CY
P2D
6rs
1065
852
C/T
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
n.d.
--
*n.d
.; no
t det
ecta
ble
**H
GV
D; H
uman
Gen
etic
Var
iatio
n D
atab
ase
***i
JGV
D; I
nteg
rativ
e Ja
pane
se G
enom
e Va
riatio
n D
atab
ase
Vol. 42 No. 2
142
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