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

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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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