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Cancer Chemother Pharmacol (2013) 72:825–835
DOI 10.1007/s00280-013-2258-y
ORIGINAL ARTICLE
Pharmacogenetic determinants associated with sunitinib‑induced
toxicity and ethnic difference in Korean metastatic renal cell
carcinoma patients
Hye Ryun Kim · Hyung Soon Park · Woo Sun Kwon · Ji Hyun Lee ·
Yusuke Tanigawara · Sun Min Lim · Hyo Song Kim ·
Sang Jun Shin · Jung Bae Ahn · Sun Young Rha
Received: 16 March 2013 / Accepted: 1 August 2013 / Published online: 8 September 2013
© Springer-Verlag Berlin Heidelberg 2013
association with treatment-related toxicity from sunitinib
using Pearson χ2 test.
Results Common grade 3 or grade 4 treatment-related
toxicities were thrombocytopenia (36.9 %, 24/65), neu-
tropenia (18.4 %, 12/65), anemia (7.7 %, 5/65), and HFS
(12.3 %, 8/65). Patients carrying an ABCG2 421 AA gen-
otype developed significantly more grade 3 or grade 4
thrombocytopenia, neutropenia, and HFS adjusted for age,
sex, and Eastern Cooperative Oncology Group performance
status, and body surface area (odds ratio compared with
AC/CC genotypes [OR] 9.90, P = 0.04, thrombocytopenia;
OR 18.20, P = 0.02, neutropenia; and OR 28.46, P = 0.01,
HFS). In addition, total and surface protein ABCG2 protein
expression was decreased in ABCG2 421 AA mutant cells
compared to wild type.
Conclusion Among 12 genetic polymorphisms, poly-
morphism in the ABCG2 421C>A gene may be mostly
Abstract
Purpose The aim of this study was to investigate the
pharmacogenetic determinants of sunitinib-related toxic-
ity and ethnic difference in metastatic renal cell carcinoma
(mRCC) among Korean patients.
Methods A pharmacogenetic study was performed in 65
patients with mRCC treated with the standard schedule of
sunitinib (50 mg orally once daily for 4 weeks-on/2 weeks-
off). Detailed data regarding the toxicity of sunitinib,
including thrombocytopenia, neutropenia, anemia, and
hand–foot syndrome (HFS), were prospectively collected
in a clinical trial program (n = 38) or standard oncology
practice (n = 27). Total of 12 genetic polymorphisms in
8 candidate genes (CYP1A1, CYP3A5, ABCB1, ABCG2,
PDGFRα, VEGFR2, RET, and FLT3) were analyzed for an
Electronic supplementary material The online version of this
article (doi:10.1007/s00280-013-2258-y) contains supplementary
material, which is available to authorized users.
H. R. Kim · S. M. Lim · H. S. Kim · S. J. Shin · J. B. Ahn ·
S. Y. Rha
Department of Internal Medicine, College of Medicine, Yonsei
University, Seoul, Korea
H. R. Kim · S. M. Lim · H. S. Kim · S. J. Shin · J. B. Ahn
Division of Medical Oncology, Yonsei Cancer Center,
College of Medicine, Yonsei University, 50 Yonsei-ro, 134
Shinchon-dong, Seodaemun-gu, Seoul 120-752, Korea
H. S. Park · J. H. Lee
Department of Pharmacology, Pharmacogenomic Research
Center for Membrane Transporters, College of Medicine, Yonsei
University, Seoul, Korea
W. S. Kwon
Cancer Metastasis Research Center, College of Medicine, Yonsei
University, Seoul, Korea
W. S. Kwon
Brain Korea 21 Project for Medical Sciences, College
of Medicine, Yonsei University, Seoul, Korea
J. H. Lee
Research Center for Human Natural Defense System, College
of Medicine, Yonsei University, Seoul, Korea
Y. Tanigawara
Department of Clinical Pharmacokinetics
and Pharmacodynamics, School of Medicine, Keio University,
Minato, Japan
S. Y. Rha (*)
Yonsei Cancer Research Institute, College of Medicine, Yonsei
University, Seoul, Korea
e-mail: rha7655@yuhs.ac
826 Cancer Chemother Pharmacol (2013) 72:825–835
1 3
associated with the risk of sunitinib-related toxicity in
mRCC patients. Considering the high frequency of 421C>A
SNP in Asian, this may be related to differential toxicities
among ethnic groups.
Keywords Renal cell carcinoma · Sunitinib · Toxicity ·
Polymorphism
Introduction
Sunitinib is a small-molecule receptor tyrosine kinase
inhibitor (TKI) that has been approved as the first-line
treatment for metastatic renal cell carcinoma (mRCC). The
recommended dose and schedule for sunitinib is 50 mg
each day given orally for 4 consecutive weeks followed
by 2 weeks-off per treatment cycle. Adverse events (AEs)
that frequently occurred in patients treated with sunitinib
were hematological toxicities, including thrombocytopenia,
neutropenia, and anemia, and non-hematological toxicities
such as hand–foot syndrome (HFS) and oral mucositis. It
is noteworthy that sunitinib-induced toxicities are often
associated with treatment discontinuation, interruption, and
dose reduction in approximately 30 % of the patients in
studies conducted with Caucasian population [1–3].
Intriguingly, recent data reported that there are differ-
ences regarding severity in toxicity even with similar tox-
icity profiles between Asians and non-Asians and sunitinib-
induced AEs were much more common in Asians [4–6].
Furthermore, 90 % of patients underwent dose modification
related to the AEs in a Japanese study, 58 % of patients did
so in the Asian subpopulation in a global expanded access
program (EAP) study, and 76 % of patients in Korean pop-
ulation [4, 6–8]. Thus, sunitinib-induced toxicity is a seri-
ous problem that should not be ignored, especially in Asian
patients. A discovery of novel predictive factors regard-
ing sunitinib-induced toxicity will help to optimize drug
treatment in individual patients. So far, a few studies have
described clinicopathological factors such as low body sur-
face area (BSA), old age, female sex, or Asian ethnicity as
determinants associated with severe toxicity [6, 9–12]. The
pharmacokinetic results showed that the volume of sunitinib
distribution was decreased in females or in patients with low
body weight [13]. However, these reports are insufficient to
explain the phenomenon and individual patients’ diversity.
Although there is an emerging evidence of variability in
sunitinib-induced toxicity according to individual patients
or ethnic groups, the reason for heterogenous tolerability of
sunitinib is not clear. One possible explanation was suggested
in relation to polymorphisms in genes related to the pharma-
cogenetic or pharmacokinetic pathways of sunitinib [1, 14].
However, these studies were mostly conducted in Caucasian
population and Asian data have not been reported so far.
Genes that are related to the pharmacodynamic path-
ways of sunitinib are based on targeted genes including
vascular endothelial growth factor receptors (VEGFRs) 1,
2, and 3, platelet-derived growth factor receptor (PDGFR)
α and β, Fms-like tyrosine kinase 3 receptor (FLT3), and
the receptor encoded by the ret proto-oncogene (RET).
On the other hand, genes in the pharmacokinetic pathway
are the genes of metabolisms such as cytochrome P450
1A1 (CYP1A1) and cytochrome P450 3A5 (CYP3A5), and
the drug efflux genes of ATP binding cassette member G2
(ABCG2) and ATP binding cassette member B1 (ABCB1)
[14]. Especially, ABCG2, also known as breast cancer
resistance protein (BCRP), is an efflux transporter on the
small intestine and has been known to highly interact with
different TKIs such as gefitinib, erlotinib, imatinib, and
nilotinib [15–24]. The ABCG2 421C>A non-synonymous
polymorphism is known to induce the changes of protein
expression or activity and be frequent polymorphism in
Korean population [23–25].
In this study, we have evaluated whether polymorphisms
of genes regarding pharmacogenetic pathway can be asso-
ciated with sunitinib-induced toxicity and efficacy in
Korean mRCC patients.
Materials and methods
Study population and clinical data collection
We consecutively enrolled 65 histologically confirmed
mRCC patients who were treated with sunitinib and were
available for genetic analysis between March 2006 and
December 2010 at the Yonsei Cancer Center (YCC) in
Seoul, Korea, and who were available for genetic analysis.
Among 65 patients, 38 were treated within as an EAP trial
using sunitinib and the rest 27 patients were treated with
sunitinib as a part of standard oncology practice follow-
ing the similar protocol of the clinical trial. Sunitinib was
administered orally as a single agent at a dosage of 50 mg
daily, consisting of 4 weeks of treatment followed by
2-week rest periods in cycles of 6 weeks until progression
or intolerable toxicity occurred. In our study, we excluded
patients treated for less than one cycle. This study is a ret-
rospective study on blood samples and patients data col-
lected over the past years.
Demographic and clinical data of patients were collected
from the review of electronic medical records. Patient char-
acteristics considered relevant for sunitinib toxicity were
as follows age, sex, BSA, Eastern Cooperative Oncology
Group (ECOG) performance status, histologic type, and
organ function. Risk group stratification was done based
on the Memorial Sloan-Kettering Cancer Center (MSKCC)
risk factor [26]. This study was approved by the Severance
827
Cancer Chemother Pharmacol (2013) 72:825–835
1 3
Hospital Institutional Review Board and all patients signed
written informed consent for the genetic analysis.
Dose reductions of sunitinib were allowed depending on
the type and severity of AEs according to the current guide-
line. Sunitinib toxicity was evaluated using the National
Cancer Institute-Common Toxicity Criteria for Adverse
Effects (NCI-CTCAE) version 3.0. To assess AEs, physical
examination and laboratory assessments were performed at
baseline (before starting sunitinib), after 4 weeks of suni-
tinib therapy, and again after the 2-week rest period (just
before starting the second cycle). We used the AEs after
4 weeks in the first treatment cycle as clinical factor in the
analysis of this study. Among all collected data of AEs, we
focused on thrombocytopenia, neutropenia, anemia, and
HFS, which are more prevalent in Asians than in Caucasian
patients, leading to dose modification or interruption. The
primary outcome measures of this study were thrombocyto-
penia, neutropenia, anemia, and HFS of higher than grade
2 in the first cycle. We grouped the population into patients
with “low” toxicity (grades 0–2) and patients with “high”
toxicity (grades 3–4) for each toxicity type.
We also performed tumor assessments with computed
tomography (CT) or magnetic resonance imaging (MRI)
every 12 weeks until the end of the treatment. Clinical
responses were evaluated using the Response Evaluation
Criteria in Solid Tumor (RECIST version 1.1). PFS was
measured from the first day of sunitinib treatment to tumor
progression or death, whereas OS was measured from the
date of sunitinib treatment until the date of death. Patients
without a known date of death were censored at the time of
the last follow-up.
DNA isolation and analysis of polymorphism
Five milliliter of blood samples was collected in heparinized
tube from each patient and peripheral blood mononuclear
cells (PBMCs) were collected and frozen (−80 °C) until the
assay. Genomic DNA (gDNA) was isolated from PBMCs
before sunitinib treatment. PBMCs were isolated from the
blood using Ficoll-Paque (Pharmacia, Uppsala, Sweden)
and gDNA was extracted with the LaboPass™ Blood kit
(Genotein Biotech, Korea) following the manufacturer’s
instructions. The extracted gDNA was then amplified by
PCR using an Eppendorf Master Cycler Gradient (Brink-
mann Instruments, Inc., USA). PCRs were performed.
For the SNaPshot analysis, exo/SAP-purified PCR prod-
ucts were mixed with AmpliTaq DNA polymerase, four flu-
orescently labeled dideoxynucleoside-5′-triphosphates, and
the reaction buffer contained in an ABI Prism SNaPshot
multiplex kit (Applied Biosystems, Foster City, CA, USA).
Amplicons were then purified with exo/SAP and analyzed
on an ABI Prism 3700 Automated Sequencer (Applied Bio-
systems, Foster City, CA, USA). To analyze melting curves
with Taqman probe, we genotyped solution-phase hybridi-
zation reactions with 5′-nuclease and fluorescence detec-
tion (TaqMan SNP genotyping assay, Applied Biosystems,
Foster City, CA, USA) in a 7300 Real-Time PCR system
(Applera, Norwalk, CT, USA). The PCR contained 20 ng
of genomic DNA, 1 μl of TaqMan Universal Master Mix,
900 nM of each primer, and 200 nM of VIC-labeled and
FAM-labeled probes in 25 μl reactions (Applera, Norwalk,
CT, USA). Amplification conditions were 95 °C for 10 min,
40 cycles of 92 °C for 15 s, and 60 °C for 1 min.
Selection of pharmacogenetic pathway
A total of 12 single-nucleotide polymorphisms (SNPs) in
8 candidate genes, CYP1A1, CYP3A5, ABCG2, ABCB1,
PDGFRα, VEGFR2, RET, and FLT3 were genotyped. Six
polymorphisms in four genes involved in the pharmacoki-
netics and the others involved in the pharmacodynamics of
sunitinib were selected (Table 1). Target polymorphisms
were selected based on the clinical relevance from the
previous reports or the assumption that non-synonymous
amino acid change might lead to the change of protein
functionality.
Assay for total and surface ABCG2 protein expression
To evaluate whether the polymorphism 421C>A might
influence on the expression of ABCG2 transporter, we con-
ducted in vitro experiment using Human Embryonic Kid-
ney (HEK) 293T cell line transfected with ABCG2 wild and
mutant type. First, to compare the total protein expression
level between HEK 293T cell line transfected with ABCG2
wild and mutant type, ABCG2 mutant cells were prepared.
HEK293T cell line was maintained in Dulbecco’s modi-
fied Eagle medium-HG (Invitrogen, Carlsbad, CA) supple-
mented with 10 % fetal bovine serum. Mammalian express-
ible ABCG2 plasmids were constructed using pcDNA 3.1.
Plasmids were transiently transfected into HEK293T cells
using Lipofectamine Plus Reagent (Invitrogen). Second,
total lysates were extracted and Western blotting was con-
ducted with total lysates following routine protocol. Anti-
ABCG2 antibody was purchased from Alexis Corporation
(Lausen, Switzerland). Third, to compare the ABCG2 pro-
tein expression on the surface membrane, we conducted
surface Biotinylation assay. HEK293T cells transfected
with ABCG2 gene wild and mutant type were grown to
80 % confluency in 6-well. The cells were washed with ice-
cold phosphate-buffered saline containing 0.1 Mm CaCl2
and 1 mM MgCl2, and the plasma membrane proteins were
then biotinylated by sulfo-NHS-SS-biotin (Pierce) in phos-
phate-buffered saline for 30 min at 4 °C. After biotinyla-
tion, the cells were washed extensively with 1 % bovine
serum albumin and phosphate-buffered saline to remove
828 Cancer Chemother Pharmacol (2013) 72:825–835
1 3
excess biotin. Then, the cells were lysed, Strepavidin solu-
tion (Streptavidin Agarose Resins, Pierce) was added to the
supernatant, and the mixture was incubated at 4 °C over-
night. Avidin-bound complexes were washed three times
and eluted in SDS sample buffer, resolved by SDS–PAGE,
and immunoblotted with anti-BCRP antibody (Alexis).
Data analysis
Statistical analysis was performed using SPSS 16 (SPSS
Inc., Chicago, IL, USA). Haplotype analysis was performed
using Haploview 3.2 program based on a standard expecta-
tion–maximization algorithm to construct haplotype blocks
[27]. The data were summarized using standard descrip-
tive statistics. For toxicity analysis, we used dichotomous
end points expressed as toxicity higher than grade 2 (yes
or no). All demographic and clinical variables were tested
univariately against the selected primary outcomes using
the χ2 test, Fisher’s exact test, and t tests where appropri-
ate. The statistical evaluation of genotype data and haplo-
types was tested univariately against the selected primary
outcomes using a χ2 test. Genotype frequencies at each
locus were also tested for Hardy–Weinberg equilibrium.
Firstly, association test was conducted with allele or geno-
type level for screening of significant locus. In this study,
twelve loci were evaluated and P value was decided by
Bonferroni’s correction (<0.004). Locus was derived from
screening step, and univariate and multivariate analyses
were performed. All multivariate logistic regression analy-
ses with toxicity were corrected for age, sex, and ECOG
performance status. The Kaplan–Meier method was used to
estimate PFS and OS, and the differences among genetic
polymorphisms were compared using the log-rank test.
All results from univariate and multivariate analyses with
P less than 0.05 were considered significant. All P values
were based on a two-sided hypothesis.
Results
Patients
This study included 51 men and 14 women with a median
age of 59 years (range 36–81). The histology was clear
cell type in 61 patients (93.8 %) and papillary type in
3 patients (4.6 %). Most patients (55/65, 84.6 %) had an
ECOG performance status 0 or 1. Among these 65 patients,
40 (61.5 %) were treatment naïve and 25 (38.5 %) had
received previous medical treatment including 16 patients
treated with cytokine treatment, 6 patients with chemother-
apy, and 3 patients with both immunotherapy and chemo-
therapy. All patients were treated with sunitinib as the
first VEGFR-TKI. Regarding the prognostic stratification,
most patients (90.8 %) were favorable or intermediate risk
group based on the MSKCC risk factor [26]. The detailed
patients’ characteristics are presented in Table 2.
Treatment outcome
All 65 patients received at least 1 week of sunitinib, and
62 (95.3 %) patients received more than 1 cycle of suni-
tinib. With the median 23.8 (range 1.1–57.9) months of
Table 1 Polymorphism genotyped in the pharmacokinetic and pharmacodynamic pathways of sunitinib
Gene rs number Polymorphism Region
CYP1A1 Cytochrome P450 1A1 rs1048943 2,455 A>G Non-synonymous I462V
CYP3A5 Cytochrome P450 3A5 rs776746 219–237 A>G Intron
ABCB1 ATP binding cassette
member B1
rs1128503 1,236 C>T Synonymous G412G
rs2032582 2,677 G>T/A Non-synonymous A893S/T
rs1045642 3,435 C>T Synonymous I1145I
ABCG2 (BCRP) ATP binding
cassette member G2
rs2231142 421 C>A Non-synonymous Q141K
PDGFRαPlatelet-derived
growth factor receptor
rs1800812 −537 G>T Promoter
rs35597368 1,580 C>T Non-synonymous P478S
VEGFR2 (KDR) Vascular endothelial
growth factor receptors
rs2305948 889 G>A Non-synonymous V297I
rs1870377 1,416 T>A Non-synonymous H472Q
RET Ret proto-oncogene rs1799939 2,071 G>A Non-synonymous G691S
FLT3 Fms-like tyrosine
kinase 3 receptor
rs1933437 680 C>T Non-synonymous T227M
829
Cancer Chemother Pharmacol (2013) 72:825–835
1 3
follow-up, the median number of treatment cycle was
7 (range 1–38). Main reasons for drug discontinuation
were disease progression (n = 48, 73.8 %) and treat-
ment-related toxicity (n = 3, 4.7 %; 2 patients with both
grades 3–4 hematological and non-hematological tox-
icities; 1 patient with HFS). In terms of dose intensity,
when we calculated the RDI for 62 patients who received
more than one cycles of treatment, the mean and median
RDI for sunitinib were 80.2 % (SD ± 13.1) and 81.1 %
(range 39.0–100). In terms of the best response to suni-
tinib, 4.6 % (3/65) had a complete response (CR), 36.9 %
(24/65) had a partial response (PR), 49.2 % (32/65) had
Table 2 Patient demographics
and clinical characteristics
Risk factors are ECOG PS≥2,
low hemoglobin, and high
calcium. For patients without
prior cytokine treatment,
additional risk factors were
raised lactate dehydrogenase
and the time of use of interferon
α of <1 year. Patients with
prior cytokine treatment
were classified as favorable,
intermediate, or poor if 0, 1, or
>1 risk factors were present,
respectively. Patients without
prior cytokine treatment
were classified as favorable,
intermediate, or poor if 0, 1–2,
or >2 risk factors were present,
respectively
a All three patients are papillary
type
b ECOG PS = Eastern Coop-
erative Oncology Group perfor-
mance status
c MSKCC = Memorial Sloan-
Kettering Cancer Center
Characteristics Number (%)
Total patient 65 (100)
Sex
Male 51 (78.5)
Female 14 (21.5)
Physiological factor
Age—year (median, range) 59, 36–81
Weight (mean ± SD) 65.3 ± 10.3
BSA (mean ± SD) 1.7 ± 0.1
Histology
Clear 61 (93.8)
Non-clear cell typea3 (4.6)
Unknown 1 (1.5)
ECOG performance statusb
0 18 (27.7)
1 37 (56.9)
2 8 (12.3)
3 1 (1.5)
Previous surgical treatment
Nephrectomy 55 (84.6)
Previous systemic treatment
No 40 (61.5)
Immunotherapy only 16 (24.6)
Chemotherapy only 6 (9.3)
Immunotherapy and chemotherapy 3 (4.6)
Number of previous systemic treatment
0 40 (61.5)
1 22 (33.8)
2 3 (4.7)
Modified MSKCC risk groupc
Favorable 38 (58.5 %)
Intermediate 21(32.3 %)
Poor 6 (9.2 %)
Number of disease site Median 2 (range 1–5)
1 31 (47.6)
2 26 (40.0)
≥3 8 (12.4)
Common site of metastasis
Lung 45 (69.2)
LN 11 (16.9)
Bone 11 (16.9)
Brain 2 (3.0)
Liver 5 (7.6)
830 Cancer Chemother Pharmacol (2013) 72:825–835
1 3
stable disease (SD), and 9.2 % (6/65) had progressive
disease (PD). The median OS for all patients was not
reached in our study (Supplementary Figure 1A), while
the median PFS for all patients was 13.5 months (Sup-
plementary Figure 1B) with similar PFS in patients with
or without prior treatment (median 14.3 vs. 13.8 months,
respectively, P = NS). In terms of MSKCC risk group,
favorable (n = 38, 58.5 %), intermediated (n = 21,
32.3 %), and poor risk groups (n = 6, 9.2 %) showed the
trends toward shorter PFS (16.3, 7.7, and 3.5 months,
respectively).
Toxicities
Dose reduction was reported in 48 (73.8 %) patients and
treatment interruption in 31 (47.6 %) patients. Sunitinib
was reduced to 37.5 mg/day in 38 patients (58.4 %) and
to further 25 mg/day reductions in 10 (15.4 %) patients.
The frequently developed AEs in all grades during the
first treatment cycle were thrombocytopenia (46.2 %,
30/65), neutropenia (46.2 %, 30/65), anemia (32.3 %,
21/65), and HFS (50.8 %, 33/65). Among them, 36.9 %
(24/65) of grade 3 or grade 4 toxicities developed in
thrombocytopenia, 18.4 % (12/65) in neutropenia, 7.6 %
(5/65) in anemia, and 12.3 % (8/65) in HFS. There was
no sunitinib treatment-related mortality in our study
(Table 3).
Pharmacogenetic determinants for sunitinib-induced
toxicity
To evaluate the association between genetic polymor-
phism and sunitinib-related toxicity, an association
analysis was performed in 12 candidate SNPs (Table 4).
Genotype distributions at all loci were consistent with
Hardy–Weinberg equilibrium (each P > 0.05). Among
the 12 candidate SNPs, the ABCG2 421C>A (Q141K)
polymorphism showed a strong association with suni-
tinib-related toxicity, especially HFS, at the allele fre-
quency level. Regarding HFS toxicities, the “high” tox-
icity (grades 3–4) patient group was more likely to have
the “A” allele than the “low” toxicity (grades 0–2) patient
group (68.75 vs. 25.43 %, P = 0.00003; OR of A vs. C,
6.76, CI 2.16–21.13). The P value for ABCG2 421C>A
remained significant at 0.0003 after employing Bonferro-
ni’s correction for multiple comparisons. However, other
gene polymorphisms showed no difference between the
two groups. In haplotype analysis, haplotypes of ABCB1,
VEGFR2, and PDGFR α was evaluated for association
with toxicity. However, there was no significant associa-
tion between four toxicities and haplotypes (Supplemen-
tary figure 2, Supplementary table 2).
We performed a multivariate logistic regression anal-
ysis for the selected ABCG2 421C>A polymorphism
through univariate analysis to evaluate the association
between sunitinib-related toxicity and this polymorphism.
We analyzed the association between selected end points
of thrombocytopenia, neutropenia, anemia, and HFS and
ABCG2 421C>A polymorphism. The results of the mul-
tivariate logistic regression analysis for the thrombocyto-
penia, neutropenia, anemia, HFS, and any toxicity higher
than grade 2 are summarized in Table 5. To evaluate the
role of ABCG2 421C>A polymorphism in predicting the
sunitinib-induced toxicities, we conducted logistic regres-
sion to compensate for compounding factors (age, sex,
ECOG PS, and BSA). ABCG2 421 AA genotype was asso-
ciated with sunitinib-induced toxicity such as thrombocy-
topenia (OR 9.90, CI 1.16–infinity, P = 0.04), neutrope-
nia (OR 18.20, CI 1.49–222.09, P = 0.02), and HFS (OR
28.46, CI 2.22–364.94, P = 0.01). The presence of the AA
genotype at the ABCG2 421C>A locus was related to a
9.9-fold increase in the risk for thrombocytopenia, 18.2-
fold increase in the risk for neutropenia, and 28.46-fold
increase in the risk of HFS.
Table 3 Distribution of toxicity during first cycle
a Toxicities were evaluated by NCI-CTC version 3.0
b “No” represents grade 0,1, or 2 toxicities
c “Yes” represents grade 3 or 4 toxicities
Toxicity gradeaNo %
Thrombocytopenia 30 46.2
1 1 1.5
2 5 7.7
3 23 35.4
4 1 1.5
Neutropenia 30 46.2
1 8 12.3
2 10 15.4
3 11 16.9
4 1 1.5
Anemia 21 32.3
1 11 16.9
2 5 7.7
3 5 7.7
4 0 0
Any hematologic toxicity >grade 2
Nob38 58.4
Ye sc27 41.6
Hand–foot syndrome 33 50.8
1 15 23.1
2 10 15.4
3 8 12.3
4 0 0
831
Cancer Chemother Pharmacol (2013) 72:825–835
1 3
Table 4 Association of each genetic variation with sunitinib-related toxicity in mRCC patients
Gene name; polymorphic site, allele; amino acid; rs number
Polymorphism Minor Thrombocytopenia Neutropenia Anemia Hand–foot syndrome
Allele Odds ratio 95 % CI P value Odds ratio 95 % CI P value Odds ratio 95 % CI P value Odds ratio 95 % CI P value
CYP1A1
c.2,455 A>G
I462V
G 0.6 0.24–1.49 0.27 0.45 0.12–1.64 0.22 0.61 0.14–2.54 0.44 1.22 0.36–4.13 0.75
CYP3A5
c.219–237 A>G
Intron 3
A 0.42 0.17–1.08 0.07 0.60 0.19–1.92 0.39 1.46 0.35–6.03 0.69 0.43 0.09–2.00 0.36
ABCB1
c.1,236 C>T
G412G
C 1.12 0.54–2.31 0.77 0.56 0.21–1.46 0.23 1.0 0.26–3.73 0.63 0.65 0.21–1.99 0.45
ABCB1
c.2,677 G>T/A
A893S/T
T 1.00 0.48–2.11 0.99 1.12 0.45–2.80 0.81 0.29 0.06–1.45 0.18 1.11 0.37–3.28 0.85
ABCB1
c.3,435 C>T
I1145I
T 0.87 0.41–1.83 0.71 0.89 0.35–2.28 0.82 0.33 0.08–1.25 0.16 1.11 0.38–3.28 0.85
ABCG2
c.421 C>A
Q141K
A 1.74 0.81–3.75 0.15 1.89 0.76–4.75 0.17 1.85 0.38–9.14 0.72 6.76 2.16–21.13 0.00003
PDGFRα
–573G>T
Promoter
T 0.508 0.19–1.38 0.18 0.347 0.076–1.590 0.244 2.04 0.24–16.95 0.69 0.264 0.03–2.10 0.302
PDGFRα
c.1,580 C>T
P478S
C 0.638 0.23–1.77 0.386 0.416 0.090–1.923 0.362 1.80 0.22–15.01 0.49 0.313 0.04–2.51 0.467
VEGFR2
c.889 G>A
V297I
A 0.48 0.15–1.58 0.22 0.25 0.03–1.94 0.19 1.09 0.92–1.16 0.35 2.59 0.73–9.23 0.23
VEGFR2
c.1,416 T>A
H472Q
T 0.87 0.42–1.79 0.70 0.89 0.37–2.20 0.81 1.84 0.45–7.48 0.514 1.33 0.47–3.78 0.59
RET
c.2,071 G>A
G691S
A 0.68 0.22–2.06 0.49 2.88 0.94–8.78 0.09 0.57 0.11–2.94 0.61 2.59 0.73–9.22 0.23
FLT3
c.680 C>T
T227M
C 1.24 0.57–2.71 0.59 1.671 0.658–4.246 0.277 0.56 0.15–2.14 0.46 0.818 0.25–2.72 1
832 Cancer Chemother Pharmacol (2013) 72:825–835
1 3
Pharmacogenetic determinants for sunitinib treatment
outcomes
Among the 12 single-nucleotide polymorphisms in 8 can-
didate genes, only a few polymorphisms from pharmaco-
dynamic genes were associated with OS. Prolonged OS
was found in the univariate analysis of patients with the
“A” allele in RET 2251 G/A (GG vs. GA/AA, P = 0.029),
with the “C” allele in PDGFR α 1580T/C (TT vs. TC/CC,
P = 0.037), without the GT in PDGFR α diplotype (GT/
GT vs. others, P = 0.023), and with the GG genotype in
KDR (VEGFR2) (GG vs. GA/AA, P = 0.079) (Supple-
mentary Figure 3). However, these loci were not satisfied
with Bonferroni’s correction cutoff value (<0.004). Also,
there was no association between genetic polymorphism
and PFS in our study.
ABCG2 421 C>A polymorphisms and transporter
expression
To evaluate whether the SNP might influence on the
expression level of the transporter, we conducted in vitro
experiment comparing ABCG2 expression between wild
type and ABCG2 421AA mutant type. Total expression
of ABCG2 protein in whole cell lysate was evaluated by
Western blotting, while ABCG2 protein expression on the
cell surface membrane was done by Biotinylation assay. As
a result, total and surface protein in the mutant transfected
cells was decreased compared with those in the wild-type
ABCG2 cells (Fig. 1). This finding implicates that patients
with Q141K variation of ABCG2 show reduced ABCG2
transporter activity. Decreased transporter expression might
influence in reducing the export into the intestinal lumen
and thus increase the plasma concentration of sunitinib,
which can be associated with increased sunitinib-induced
toxicity risk in patients receiving sunitinib therapy.
Discussion
To the best of our knowledge, this is the study to analyze
the association between sunitinib-induced toxicities and
pharmacogenetic determinants in Asian mRCC patients
treated with sunitinib. The main finding of our study was
that the genetic polymorphism in the ABCG2 421C>A was
associated with the sunitinib-induced toxicities. The risks
of thrombocytopenia, neutropenia, and HFS were signifi-
cantly increased when the AA genotype in ABCG2 421C>A
was present. Consistent with previous reports, our study
showed a higher incidence of thrombocytopenia, neutro-
penia, anemia, and HFS as sunitinib-related toxicities than
those in Caucasian population. Considering the high fre-
quency of ABCG2 421C>A polymorphism in Asian indi-
viduals, this polymorphism might be regard as the determi-
nants for ethnic difference.
According to the International HapMap Project data
(http://www.hapmap.org/), the allelic frequency for this
ABCG2 421C>A differed significantly among ethnic
groups. The minor allele frequency was much lower in
Caucasians than in Asians. The “A” allele of 421C>A
Table 5 Multivariate analysis of ABCG2 421 polymorphism’s
predictive impact on sunitinib-induced toxicities
Bold values represent statistical significance
OR odds ratio, CI confidence interval
a Adjusted for age, sex, ECOG PS, and BSA
ORa95 % CI P value
Thrombocytopenia
ABCG 2 421C/A
CC + CA 1
AA 9.90 1.16–infinity 0.04
Neutropenia
ABCG 2 421C/A
CC + CA 1
AA 18.20 1.49–222.09 0.02
Anemia
ABCG 2 421C/A
CC + CA 1
AA 1.31 0.72–13.21 NS
Hand–foot syndrome
ABCG 2 421C/A
CC + CA 1
AA 28.46 2.22–364.94 0.01
Any hematologic toxicity >grade 2
ABCG 2 421C/A
CC + CA 1
AA 8.24 0.99–infinity 0.05
Fig. 1 ABCG2 protein expression on the cell surface membrane was
done by Biotinylation assay and the total expression of ABCG2 pro-
tein in whole cell lysate was evaluated by Western blotting. Total and
surface protein in the Q141K mutant transfected cells was decreased
compared with those in the wild-type ABCG2 cells. Aldolase was
used as a loading control
833
Cancer Chemother Pharmacol (2013) 72:825–835
1 3
(rs2231142) was found less frequently in Utah residents
with ancestry from northern and western Europe (CEUs)
(11.1 %) than in Asians (Korean, 27.5 %; Japanese, 34.1 %;
Chinese, 29.2 %) [25]. The “A” allele of 421C>A associ-
ated with sunitinib-related toxicity in this study might be
susceptible in other Asian populations, as a case report
from Japan also showed the same results [28]. This ethnic
difference in polymorphism could explain the ethnic differ-
ence in sunitinib toxicity.
Even though RCC is relatively uncommon in the Asian
population compared to the Western population, its inci-
dence is increasing in developed Asian nations such as
Japan or Korea [7]. With increasing experiences with suni-
tinib treatment in Asian mRCC patients, incidences of AEs
are also rising in Asian patients [4, 6, 7]. Recently, Houk
et al. [29] demonstrated that “Asian race” influences the
sunitinib pharmacokinetics by lowering of clearance for
sunitinib and metabolites. These data could explain the
higher toxicities in Asian than those in Caucasian. Suni-
tinib-induced toxicity in Asian population could disturb the
treatment by discontinuation, interruption, and dose reduc-
tion. Although differences in sunitinib-induced toxicity
among ethnicities or individuals are currently well recog-
nized, information regarding the determinants of toxicity is
still scarce.
Recent studies suggested that polymorphisms in specific
genes encoding for metabolizing enzymes, efflux trans-
porters, and targets of sunitinib could be associated with
sunitinib-related toxicities and drug efficacy [1, 14]. In
our study, individual variation of sunitinib-induced toxic-
ity was explained by genetic polymorphisms related to the
pharmacokinetic pathway. However, there were discrepan-
cies among each study because the incidence of genetic
polymorphisms was low and kinds of polymorphisms were
various according to the population.
Our study demonstrated that genetic polymorphism of
ABCG2 421 C>A was associated with sunitinib-induced
toxicity. Differences in sunitinib-induced toxicities accord-
ing to genetic polymorphisms can be explained by under-
standing the function of the ABCG2 transporter. The poten-
tial function of ABCG2 transporters regarding sunitinib
disposition has been explained by various in vitro experi-
ments [30]. These data demonstrated that sunitinib is a
substrate for the ABCG2 transporter and inhibits the efflux
of sunitinib metabolite through ABCG2 transporter via
direct binding [16, 17]. Additionally, polymorphisms in the
ABCG2 gene might have an important impact on ABCG2
protein expression, localization, and function [30]. Our in
vitro data as well as previous data demonstrated that the
non-synonymous SNP of ABCG2 421 C>A, which causes a
glutamine-to-lysine amino acid substitution at position141
(Q141K), has been associated with markedly decreased
levels of ABCG2 protein expression and/or activity [23, 31,
32]. The reduced protein levels and altered ATPase activ-
ity of the AA variant in ABCG2 421 C>A located in api-
cal membrane of small intestinal enterocytes, hepatocytes,
and proximal tubule cells in kidney might affect the oral
absorption and/or elimination of sunitinib [23]. There have
been reports that individuals carrying the ABCG2 421
AA homozygous variant showed higher drug exposure of
ABCG2 substrate such as fluvastatin, pravastatin, simvasta-
tin, and gefitinib [23, 32]. Recently, a Japanese case report
of five mRCC patients demonstrated that the homozygous
AA variant may be associated with severe AEs showing
2.5-fold higher maximum concentration and area under the
concentration of sunitinib [28]. Consistently, our results
showed that sunitinib-induced toxicities were more fre-
quent in the AA genotype of ABCG2 421C>A. Therefore,
based on the previous reports, we could infer that homozy-
gous AA genotype of ABCG2 421C>A reflected in elevated
sunitinib exposure that could explain for more toxicity in
patients with this genotype.
With rapidly evolving therapeutic options for mRCC
[28], predictive biomarkers of mRCC patients treated with
molecular targeted agents are of great clinical benefit.
Although validation of the Motzer’s criteria [26] is impor-
tant, a further implementation of RCC molecular biology
knowledge would be ideal for finding a new predictive
marker in new era of molecular targeted therapies. Serum
VEGF, sVEGFR-2, and sVEGFR-3, which are involved in
the VEGF signaling pathway, might be of value as predic-
tive biomarkers for sunitinib efficacy [33–36]. Recent ret-
rospective analysis showed that hypertension after sunitinib
treatment is associated with improved clinical outcomes
[33]. However, these biomarkers or clinicopathological fac-
tors do not reflect the diversity of each individual patient.
Furthermore, clinical factors such as hypertension could
be known after sunitinib treatment and thus it could not be
predictive marker before treatment. Therefore, efforts to
identify genetic markers such as SNPs continue in order to
develop predictive biomarkers of efficacy with respect to
individual diversity before treatment [9, 37].
We additionally analyzed the association between
genetic polymorphism and survival outcome. Genetic pol-
ymorphism in RET 2251 G>A, PDGFR α 1580T>C, and
KDR (VEGFR2) genes was related to OS, but no genetic
polymorphism from our study showed an association with
PFS. One of the explanations may be that clinical responses
might be related to tumor tissue itself, but germ line poly-
morphisms of DNA isolated from PBMC do not reflect
tumor tissue. Therefore, somatic polymorphism using DNA
extracted from tumor tissue would be needed to predict the
treatment outcome. This study is underpowered because
this was retrospectively conducted to evaluate the associa-
tion between genetic polymorphism and toxicity. There-
fore, more studies will be warranted to validate the role of
834 Cancer Chemother Pharmacol (2013) 72:825–835
1 3
ABCG2 polymorphisms in sunitinib toxicity in the future.
In conclusion, the polymorphism in ABCG2 421 C>A
could be a biomarker in association with sunitinib-induced
toxicity in patients with Asian mRCC. An ABCG2 poly-
morphism may partly explain why individual patients show
different toxicities and Asian patients show more severe
toxicities than Caucasian patients. If the predictive role of
ABCG2 polymorphisms is confirmed in future prospec-
tive study, genotyping for ABCG2 could become a clinical
routine practice to select the appropriate dose and help to
achieve the benefits of personalized medicine. In the next
step, prospective pharmacokinetic and safety study may be
needed to confirm the role of ABCG2 in sunitinib toxicity.
An optimal recommended dose for ABCG2 421AA patients
can be also estimated in future pharmacogenetic and phar-
macokinetic safety study.
Acknowledgments This study was supported by a grant from the
Korea Healthcare Technology R&D Project of the Ministry of Health
and Welfare of Korea (A110641) by grant of Health Fellowship Foun-
dation, and by the Public Welfare & Safety research program through
the National Research Foundation of Korea (NRF), funded by the
Ministry of Science, ICT & Future Planning (2010-0020841).
Conflict of interest The authors have no potential conflicts of inter-
est to disclose.
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