Med Oncol (2014) 31:209 DOI 10.1007/s12032-014-0209-z
ORIGINAL PAPER
The relationship of kinase insert domain receptor gene polymorphisms and clinical outcome in advanced hepatocellular carcinoma patients treated with sorafenib You-Bing Zheng • Mei-Xiao Zhan • Wei Zhao • Bing Liu • Jian-Wen Huang Xu He • Si-Rui Fu • Yan Zhao • Yong Li • Bao-Shan Hu • Li-Gong Lu
•
Received: 20 June 2014 / Accepted: 24 August 2014 Ó Springer Science+Business Media New York 2014
Abstract Kinase insert domain receptor (KDR) is the principal receptor that promotes the pro-angiogenic action of vascular endothelial growth factor and has been the principal target of anti-angiogenic therapies. Our aim was to determine whether single-nucleotide polymorphisms (SNPs) in KDR gene are associated with clinical outcomes after first-line sorafenib therapy in advanced hepatocellular carcinoma (HCC). The SNPs in KDR were tested in 78 advanced HCC patients receiving first-line sorafenib. Correlations with clinicopathological features and survival were analyzed. Patients with AA genotype of rs1870377 and AA genotype of rs2305948 were significantly associated with a better response and longer time to progression (TTP) (5.8 vs 4.0 months, P = 0.001; 5.8 vs 4.5 months, P = 0.016, respectively). Patients harboring AA genotype in rs1870377 and TT/TC genotype in rs2071559 had a longer overall survival (OS) (15.0 vs 9.6 months, P = 0.001; 13.0 vs 9.0 months, P = 0.007, respectively). At multivariate analysis, major vascular invasion and rs1870377 were independent factors in TTP and performance status, rs1870377, and rs2071559 were independent factors in OS. Our results suggest that SNPs in KDR gene can predict clinical outcome in advanced HCC patients receiving first-line sorafenib. You-Bing Zheng, Mei-Xiao Zhan and Wei Zhao contributed equally to this work. Y.-B. Zheng W. Zhao Southern Medical University, Guangzhou, China Y.-B. Zheng M.-X. Zhan W. Zhao B. Liu J.-W. Huang X. He S.-R. Fu Y. Zhao Y. Li B.-S. Hu L.-G. Lu (&) Department of Interventional Radiology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China e-mail: [email protected]
Keywords Sorafenib KDR Hepatocellular carcinoma Single-nucleotide polymorphism
Introduction Hepatocellular carcinoma (HCC) accounts for 70–85 % of primary liver cancer. It is the fifth most prevalent cancer and the third most common cause of cancer-related death in the world [1]. China is one of the areas with a high prevalence of HCC mainly due to chronic hepatitis B carriers accounting for [10 % of Chinese population [2]. HCC develops dependent of accumulation of risk factor, such as HBV, HCV and aflatoxin, and genetic predisposition that lead to a wide range of molecular and cellular abnormalities within heptic cells. It is still a huge challenge to elucidate the pathogenesis and to identify the subjects at risk completely. In two phase III, placebo-controlled, randomized trials, sorafenib treatment significantly improved the time to tumor progression (TTP) and overall survival (OS) of patients with advanced HCC [3, 4]. Sorafenib blocks receptor tyrosine kinase signaling (vascular endothelial growth factor receptor, VEGFR; platelet-derived growth factor receptor, PDGFR; c-Kit and RET) and inhibits downstream Raf serine/threonine kinase activity to prevent tumor growth by anti-angiogenic, anti-proliferative, and/or pro-apoptotic effects [5]. Sorafenib currently sets the new standard for the first-line treatment of advanced HCC (BCLC stage C). The prediction of treatment response and survival may facilitate customized targeted therapy, chemotherapy, or risk-related therapy, resulting in significant increase of survival rate. Recently, several studies have been published on the association between genetic variants of the angiogenetic pathway and global outcome in lung cancer,
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colorectal carcinoma, ovarian cancer, breast cancer, and renal cell carcinoma patients treated with antiangiogenetic agents [6–10]. Angiogenesis is an important step in development of cancer and is necessary for primary tumor growth, invasiveness, and metastasis [11]. It is tightly regulated by different pro-angiogenic and anti-angiogenic factors secreted by both tumor cells and cells of the tumor microenvironment [11]. Anti-angiogenic drugs have changed clinical practice in malignant tumors [12–15], such as sunitinib, bevacizumab, gefitinib, and erlotinib, but plenty of clinical challenges still remain. Several other drugs also targeting the pathways in sorafenib have failed to prove a significant positive impact on the outcome of patients with HCC, such as brivanib and sunitinib [16, 17]. The molecular biological grounds for these discordant results are not well understood. Moreover, only a small number of patients do benefit from antiangiogenic agents, and reliable tools to prospectively identify which patients are more likely to benefit are scarce. In the two sorafenib trials, no statistically significant pre-treatment plasma biomarkers that might predict response in patients receiving sorafenib were identified [18]. Vascular endothelial growth factor (VEGF) and their receptors are believed to be important for regulation of the process of angiogenesis. The VEGF family members are dimeric glycoproteins, which consist of five members, VEGF A, B, C, D, and placental growth factor (PLGF), and bind to specific receptors (VEGFR) [19]. In HCC, high levels of tissue VEGF over-expression and elevated serum levels of VEGF have been shown to be closely associated with poor prognosis [20–22]. VEGFR2, also known as kinase insert domain receptor (KDR), is the principal receptor that promotes the proangiogenic action of VEGFA and has been the principal target of anti-angiogenic therapies. The KDR gene is located on chromosome 4q11-q12. The variation of KDR gene may change the serum concentration and biological function [23]. Several single-nucleotide polymorphisms (SNPs) have been described in the KDR gene, some of which have been shown to affect the expression of the gene. There were also a few reports that showed the predictive value of gene polymorphisms in HCC patients. However, no published data on KDR polymorphisms in association with HCC prognosis are available. Considering that sorafenib is also an inhibitor of KDR, we therefore decided to investigate whether genetic variants in the KDR gene could act as biomarkers for sorafenib treatment outcome. In this study, we hope to define specific patient subgroups more likely to benefit from sorafenib therapy in terms of TTP and OS.
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Materials and methods Patient selection Patients with advanced HCC admitted to our hospital between January 2010 and March 2013 were evaluated retrospectively. All patients satisfied the diagnostic criteria for HCC based on radiologic or histologic grounds according to the American Association for the Study of the Liver guidelines [24]. All including patients in our study had locally advanced or metastatic HCC that was not amenable to surgery or locoregional therapies, including transarterial chemoembolization or local ablation, and received first-line sorafenib treatment. Patients were excluded if they had serious medical comorbidities or previous or current second primary cancers in addition to HCC. The initial sorafenib dose was 400 mg orally twice daily. Patients were monitored every 2–4 weeks, and follow-up consisted of a detailed history, physical examination, blood examination, and abdomen CT/MRI scanning if clinically indicated. Dose reductions of sorafenib and drug interruptions were allowed for severe adverse events, according to the National Cancer Institute’s Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Treatment was continued until radiological or clinical progression, unacceptable toxicity, death, or patient refusal. Tumor response was assessed according to modified response evaluation criteria in solid tumors (mRECIST) [25]. All patients included in this retrospective study were Han Chinese. The study protocol was approved by the Institutional Review Board of Guangdong General Hospital, and written informed consent was obtained from all patients. DNA isolation This was an exploratory retrospective study, and we collected peripheral blood samples from each patient before initial therapy. Peripheral blood samples were drawn into tube containing anticoagulant, centrifuged at 30009g for 5 min, and then stored at -80 °C until measurement. Genomic DNA was isolated from the whole blood cell using Qiagen DNA Isolation Kit (Qiagen GmbH, Hilden, Germany) following the manufacturer’s instructions. We determined DNA concentrations using a UV-1200 spectrophotometer (Mapada Instruments, Shanghai). SNP selection The KDR SNPs selected for genotyping were accessed from the public databases: HapMap (http://www.hapmap. org), and dbSNP (http://www.ncbi.nih.gov). We used
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Table 1 Overview of the SNPs selected in the KDR gene SNP ID
Chr. position
Alleles
Position to transcript/effect
rs2071559
55,992,366
C/T
50 sequence
rs2305948*
55,979,558
C/T
Val297Ile
rs1870377*
55,972,974
T/A
Gln472His
rs34231037*
55,972,946
A/G
Cys482Arg
rs34038364*
55,968,597
G/A
Thr689Met
rs17085262
55,959,141
C/T
Intron
rs6848933 rs6828477
55,969,711 55,966,801
C/G C/T
Intron Intron
rs7671745
55,956,836
A/G
Intron
rs7692791
55,980,239
C/T
Intron
rs4864952
55,947,338
C/T
Intron
rs10020464
55,979,070
C/T
Intron
rs2034965
55,977,800
A/G
Intron
rs1531290
55,986,562
A/G
Intron
rs7667298
55,991,731
C/T
50 UTR
rs12502008
55,991,042
G/T
Intron
rs2305949
55,980,456
C/T
Intron
rs6837735
55,985,815
C/T
Intron
SNP Single-nucleotide polymorphism, KDR kinase insert domain receptor, Chr chromosome, UTR untranslated region
DNA, 500 lM of dNTP, 100 nM primers, and 0.1 units of HotstarTaq polymerase (Qiagen, Valencia, CA). Thermal cycling was performed at 94 °C for 15 min, followed by 45 cycles of 94 °C for 20 s, 50 °C for 30 s, and 72 °C for 60 s, with the final extension at 72 °C for 3 min. After cleaning up the extension reaction products with SpectroCLEAN, the products were transferred to SpectroCHIP using SpectroPOINT, and then scanned through SpectroREADER (MALDI-TOF). Resulting genotype data were collected by Typer v4.0 (Sequenom, CA). To assess the reliability of genotyping, we randomly chose more than 20 % of the samples to sequence twice, and all results showed 100 % concordance. Genotyping was carried out in a blind way in which the status of the patients was unknown to the performers. Measurement of serum KDR level Serum KDR concentrations were measured quantitatively by an enzyme-linked immunoabsorbent assay kit (RAYBIOÒ; Human VEGFR-2 ELISA Kit; RayBiotech, Inc., USA) according to the manufacturer’s instructions. All samples were analyzed twice, and mean values were used for further statistical analysis.
* The four non-synonymous coding SNPs
Statistical analysis genomic sequences to select SNPs from the HapMap database (Phase 2 Public release number 22). Tag SNPs were selected using the Haploview software and SNP tagger approach. Only common SNPs were selected as tagSNPs by analysis of HapMap genotyping data with minor allele frequency of 0.1 or higher and a minimum correlation coefficient of the frequencies (r2) of 0.8. In addition, four SNPs (rs2305948, rs1870377, rs34231037 and rs34038364) were selected because they coded for a nonsynonymous amino acid change in the KDR gene and a functional SNP (rs2071559) in the KDR promoter previously reported to affect transcriptional activity of the KDR promoter, which were included by documentary search. Chromosomal locations, positions, and biological effects of investigated KDR SNPs have been summarized in Table 1. Primer design and genotyping Genotyping was carried out using the iPLEX GoldTM assay on the MassARRAY Platform (Sequenom, CA, USA). Genomic positions for every SNP were downloaded from the Ensembl database (www.ensembl.org), and 100 bp upstream and downstream sequences were used for primer design with the Sequenom MassARRAYÒ Assay Design 3.1 software using default parameters. Multiplex PCR was performed in a 5 lL volume containing C5 ng of genomic
Categorical variables were compared using the v2 test, and continuous variables were compared using the Mann– Whitney test. The genotype frequencies were tested for the Hardy–Weinberg equilibrium using a standard v2. Linkage disequilibrium (LD) pattern among these SNPs was determined using Haploview software. Overall survival was calculated from date of first treatment to death or the end of follow-up, and time to progression (TTP) was calculated from the date of first treatment to radiological progression or the end of follow-up. We used the Kaplan– Meier method and log-rank test to compute the relation of gene polymorphisms and clinical variable to OS rate. A Cox proportional hazards model was used to determine the factors with significance in univariate analysis associated with survival. Data analysis was performed using Statistical Package for Social Sciences (SPSS v.13.0 for Windows; SPSS Inc., Chicago, IL). All tests were two tailed, and P\ 0.05 was considered statistically significant.
Results Patient characteristics and clinical outcomes The characteristics of patients, as well as clinical outcome, are shown in Table 2. 64 males and 14 females with median
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209 Page 4 of 8 Table 2 Patient characteristics and clinical outcome
Med Oncol (2014) 31:209
Characteristics
n
Response (n)
P
13
0.75
B55
52
8
[55
26
5
Age (years)
Gender Male
64
10
Female
14
3
59
7
Negative
19
6
Cirrhosis
0.6 11.8 (10.1–13.4)
0.32 5.3 (3.6–6.1)
0.5 11.8 (10.3–14.1)
4.3 (4.0–5.1)
10.6 (8.7–12.9) 0.25
5.6 (3.8–6.8)
0.3 11.9 (8.4–14.0)
4.2 (2.7–5.0)
10.3 (9.0–12.8) 0.65
0.2
Present
43
7
4.7 (4.1–6.0)
9.8 (7.3–10.9)
Absent
35
6
4.0 (4.2–6.4)
11.5 (9.9–14.7)
58
11
0–1
0.49
2 20 Baseline AFP (ng/ml)
0.22 4.8 (4.0–6.5)
2
0 14.5 (11.2–16.6)
3.6 (3.4–6.1) 0.05
7.3 (5.0–9.1) 0.26
0.2
[400
51
5
5.2 (3.8–7.0)
9.3 (8.2–12.2)
\400
27
8
4.3 (3.4–5.3)
11.9 (9.2–13.7)
Major vascular invasion
0.04
0.01
0
Yes
32
2
3.3 (3.2–4.9)
12.6 (10.5–13.8)
No
48
11
5.8 (4.0–6.3)
8.8 (7.6–11.4)
Extrahepatic metastasis
0.03
0
0
Yes
39
3
3.0 (2.5–5.4)
12.9 (11.8–15.4)
No
39
10
5.8 (4.8–6.8)
9.2 (8.5–11.3)
47
6
\5 31 Child-Pugh class
7
Tumor size (cm) [5
0.26
0.59 4.0 (3.5–6.0)
0.2 10.4 (7.7–12.0)
4.7 (4.1–6.4) 0.75
12.2 (10.2–14.8) 0.35
0.3
A
51
8
5.2 (4.2–6.6)
12.0 (10.4–14.2)
B
27
5
4.3 (3.9–5.5)
10.8 (8.4–11.8)
C
0
0
0
0
age at diagnosis of 48 years (range 37–72) were included. The median duration of sorafenib treatment was 7.8 months (range 2.8–23.8). Dose reduction was applied in 25 patients (32.1 %) because of side effects related to sorafenib. Dose decreased to 400 and 200 mg/day in 20 patients (25.6 %) and 5 patients (6.4 %), respectively. Sorafenib was discontinued permanently because of tumor progression in 32 patients (41.0 %) and side effects in 3 (3.8 %). There were no treatment-related deaths. Table 3 shows the details of the side effects secondary to sorafenib observed based on CTCAE 4.0. The most common adverse effects related to sorafenib were diarrhea (35.9 %), hand-foot skin reaction (25.6 %), and rash (20.5 %), most of which were mild to moderate. The median duration of follow-up was 16.4 months (range 3.8–36.8). Of the 78 patients evaluated, no complete
P
10.8 (9.2–12.2)
4.1 (3.8–4.9)
0.91
ECOG PS
123
OS, month (95 % CI)
0.28
0.07
Positive
P
5.1 (3.8–6.5) 0.69
HBsAg
TTP Time to progression, OS overall survival, ECOG PS eastern cooperative oncology group performance status, AFP a-fetoprotein
TTP, month (95 % CI)
responders, 13 patients (16.7 %) had a partial response, 30 (38.5 %) had stable disease, and 35 (44.8 %) had progressive disease, respectively. The medians TTP and OS were 4.5 months (95 % CI 3.7–5.2 months) and 11.4 months (95 % CI 8.9–13.8 months), respectively. No major vascular invasion and no extrahepatic metastasis were significantly associated with a better response. They were also associated with better clinical benefit (TTP and OS), as well as performance status (Table 2). Genotype analysis The distribution of the tested genotypes did not deviate from Hardy–Weinberg equilibrium (P [ 0.05). The baseline level of serum KDR was 9.2 ± 3.4 lg/L. Serum KDR levels were correlated to no KDR SNPs.
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Discussion
Table 3 Sorafenib-related adverse events Adverse events
No. of patients
Diarrhea Grade 1/2/3
21/5/2
Rash Grade 1/2/3
12/4/0
Hand-foot skin reaction Grade 1/2/3
11/8/1
Fatigue Grade 1/2/3
10/6/0
Anorexia Grade 1/2/3
2/1/0
Nausea Grade 1/2/3
6/3/0
ALT/AST increased Grade 1/2/3
9/3/0
Neutropenia Grade 1/2/3
5/4/0
A total of 45 (57.7 %) patients had a TT or TA genotype for rs1870377, and 33 (42.3 %) patients had an AA genotype. Median TTP was improved for patients showing the AA genotype (5.8 vs 4.0 months; P = 0.001, Fig. 1a), as also was median OS (15.0 vs 9.6 months; P = 0.001, Fig. 1b). A total of 44 (56.4 %) patients had a TT/TC genotype for rs2071559, and 34 (43.6 %) patients had a CC genotype. Median OS was significant longer among patients with rs2071559 TT/TC than CC genotype (13.0 vs 9.0 months, P = 0.007, Fig. 1c). 29 (37.2 %) patients had an AA genotype of rs2305948, and 49 (62.8 %) patients had a GG or GA genotype. TTP proved statistically significant different among these genotypes with 5.8 months for AA genotype and 4.5 months for GG/GA, P = 0.016 (Fig. 1d). No other KDR polymorphisms were related to TTP or OS. Among patients with partial response, the frequencies of rs1870377 AA and rs2305948 AA were 12.8 and 10.3 %, respectively. Patients with genotype AA in rs1870377 and AA in rs2305948 were significantly associated with a better response (P = 0.011 and P = 0.047; Table 4). A multivariate analysis of genotype effects on survival was conducted using Cox proportional hazards models adjusted for clinicopathologic variables. As shown in Table 5, major vascular invasion (HR = 2.51) and rs1870377 (HR = 0.68) remained significant for TTP. Performance status (HR = 2.36), rs1870377 (HR = 0.35), and rs2071559 (HR = 2.25) remained significant independent predictors of OS among all the patients.
The SNPs we genotyped were based on a high-throughput screening method and, therefore, provided a unique opportunity to examine comprehensive KDR SNP taggers’ prognostic impact on HCC. The association between the KDR SNPs and susceptibility or prognosis in several different types of cancers, such as renal cell carcinoma, colorectal cancer, and glioblastoma [10, 26, 27], has been studied. We found that 4 of 18 SNPs in KDR gene were significantly associated with TTP or OS in sorafenib-treated HCC patients, remaining so in multivariate analyses. The multivariate survival analyses also showed that performance status and extrahepatic metastases remained as significant predictors for OS and so was major vascular invasion for TTP in HCC patients. However, we did not found any significant relationship between KDR gene sequence variant and serum KDR levels. Although there are three known members of the VEGFR family, activation of KDR alone is sufficient to have all these effects [28]. The KDR gene is highly polymorphic, and the various polymorphisms can significantly affect the protein expression and biological function [29]. Sequence variants within KDR might affect VEGF–KDR interactions, and it is meaningful to assess the associations between common genetic variants in KDR and outcome of HCC patients. Gene targeting experiments demonstrate that a functional KDR is elementary for the development of vasculogenesis and hematopoiesis [30]. The deficits in KDR function is correlated with vascular dysfunction, including vascular endothelial cell damage, impaired endothelial cell survival, abnormal vascular repair, and decreased antiapoptotic effects of VEGF. Different SNPs in different regions of the KDR gene may influence circulating KDR levels and thus response to anti-KDR therapies. Wang et al. [29] found that rs2071559 C [ T can lead to alter the structure of the binding site in KDR gene promoter region for transcriptional factor E2F, which resulted in decreasing KDR expression. Exonic polymorphisms rs1870377 and rs2305948 (in exon 11 and exon 7, respectively) are located in the fifth and third NH2-terminal Ig-like domains within the extracellular region, which are important for ligand binding, and lead to nonsynonymous amino acid changes at residue 472 Gln [ His and 297 Val [ Ile, respectively. Both rs1870377 T [ A and rs2305948 G [ A resulted in significant decrease in the VEGF binding efficiency to KDR [29]. Glubb et al. [31] found that the nonsynonymous amino acid changes of 472 Gln [ His could increase KDR protein phosphorylation and result in increased microvessel density in lung cancer tumor tissues. It appears that these functional genetic variations in KDR can affect VEGF– KDR interactions and may have some effects on HCC
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Fig. 1 Kaplan–Meier curves for a time to progression of patients with rs1870377 AA and TT/TA (5.8 vs 4.0 months, P = 0.001), b overall survival of patients with rs1870377 AA and TT/TA (15.0 vs 9.6 months; P = 0.001), c overall survival of patients with rs2071559
CC and TT/TC (9.0 vs 13.0 months; P = 0.007), d time to progression of patients with rs2305948 AA and GG/GA (5.8 vs 4.5 months; P = 0.016)
Table 4 Frequency distributions of the investigated gene polymorphisms in patients Polymorphism
Genotype
No. of patients
Response (%)
P
TTP, month (95 % CI)
P
OS, month (95 % CI)
P
rs1870377
AA TT/TA
33 (42.3 %) 45 (57.7 %)
10 (12.8 %) 3 (3.8 %)
0.011
5.8 (4.6–6.9) 4 (3.5–4.4)
0.001
15 (12.9–17.0) 9.6 (8.5–10.6)
0.001
rs2305948
AA
29 (37.2 %)
8 (10.3 %)
0.047
5.8 (3.0–8.6)
0.016
12 (9.8–14.5)
0.231
0.414
4.5 (3.9–5.0)
rs2071559
GG/GA
49 (62.8 %)
5 (6.4 %)
CC
34 (43.6 %)
7 (9.0 %)
TT/TC
44 (56.4 %)
6 (7.7 %)
TTP Time to progression, OS overall survival
123
4.5 (4.0–5.1) 5.5 (4.2–6.8)
10.5 (8.7–11.8) 0.057
9 (7.1–10.9) 13 (9.8–16.1)
0.007
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Table 5 Multivariate analysis of biological parameters for TTP and OS Covariates for TTP
HR (95 % CI)
P
Major vascular invasion
2.51 (1.38–3.65)
0.021
Extrahepatic metastasis
1.31 (0.75–2.69)
0.385
rs1870377 AA
0.68 (0.45–0.89)
0.005
rs2305948 AA
0.55 (0.32–0.95)
0.121
Covariates for OS
HR (95 % CI)
P
ECOG PS
2.36 (1.86–4.58)
0.017
Major vascular invasion
1.21 (0.63–2.23)
0.291
Extrahepatic metastasis
1.79 (1.69–3.96)
0.111
rs1870377 AA
0.35 (0.25–0.86)
0.003
rs2071559 CC
2.25 (1.55–3.39)
0.036
TTP Time to progression, OS overall survival, HR hazard ratio, CI confidence interval
prognosis. No innocent explanation is available for this condition, but it could be due to the complex roles of VEGF signaling in vascular biology. A growing number of studies are trying to identify molecular markers to predict the outcomes and response to tyrosine kinase inhibitor in patients with malignancy, which include gene mutation, gene copy number, gene polymorphisms, or protein expression. Great achievements have been made in identifying molecular predictors of response and survival outcome in non-small cell lung cancer treated with EGFR-TKIs [32]. With respect to HCC, Lee et al. [33] found that differences in the incidence of hand–foot skin reaction may be caused by ethnic differences in SNPs of the genes related to drug metabolism and tumor angiogenesis, but they did not identify any SNP related to sorafenib therapeutic efficacy. Early AFP responses and dynamic plasma VEGF concentration change have also been reported to be useful surrogate markers to predict prognosis in patients with advanced HCC, who receive sorafenib [34, 35]. However, approximately 30 % of patients with advanced HCC in the Sorafenib HCC Assessment Randomized Protocol trial had normal AFP concentrations [36]. It is worth mentioning that there were several limitations in this study. This study was performed in a Han population which might make our findings less applicable to other ethnic groups. The relatively small sample size of our study should also be interpreted with caution. Larger and perspective studies to apply the potential of our findings in other ethnical cohorts in the future should be considered. Although there are limitations, it is still important to determine the relationship of KDR polymorphisms with sorafenib efficacy in HCC.
We conclude that the KDR SNPs represent novel and promising biomarkers of sorafenib treatment outcome. Validation of the role of KDR SNPs in further prospective clinical trials will ultimately offer new method for treatment optimization of sorafenib in selected HCC patients.
Funding Science and Technology Foundation of Guangdong Province, China (Grant No. 2011A030400009); Natural Science Foundation of Guangdong Province, China (Grant No. S2012010010569). Conflict of Interest
None declared.
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209 Page 8 of 8 15. Shepherd FA, Rodrigues PJ, Ciuleanu T, et al. Erlotinib in previously treated non-small-cell lung cancer. N Engl J Med. 2005;353:123–32. 16. Llovet JM, Decaens T, Raoul JL, et al. Brivanib in patients with advanced hepatocellular carcinoma who were intolerant to sorafenib or for whom sorafenib failed: results from the randomized phase III BRISK-PS study. J Clin Oncol. 2013;31:3509–16. 17. Faivre S, Raymond E, Boucher E, et al. Safety and efficacy of sunitinib in patients with advanced hepatocellular carcinoma: an open-label, multicentre, phase II study. Lancet Oncol. 2009;10:794–800. 18. Llovet JM, Pena CE, Lathia CD, et al. Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma. Clin Cancer Res. 2012;18:2290–300. 19. Ferrara N, Gerber HP, LeCouter J. The biology of VEGF and its receptors. Nat Med. 2003;9:669–76. 20. Poon RT, Lau C, Pang R, et al. High serum vascular endothelial growth factor levels predict poor prognosis after radiofrequency ablation of hepatocellular carcinoma: importance of tumor biomarker in ablative therapies. Ann Surg Oncol. 2007;14:1835–45. 21. Pang RW, Joh JW, Johnson PJ, et al. Biology of hepatocellular carcinoma. Ann Surg Oncol. 2008;15:962–71. 22. Zhang Q, Chen X, Zhou J, et al. CD147, MMP-2, MMP-9 and MVD-CD34 are significant predictors of recurrence after liver transplantation in hepatocellular carcinoma patients. Cancer Biol Ther. 2006;5:808–14. 23. Ivy SP, Wick JY, Kaufman BM. An overview of small-molecule inhibitors of VEGFR signaling. Nat Rev Clin Oncol. 2009;6:569–79. 24. Bruix J, Sherman M. Management of hepatocellular carcinoma: an update. Hepatology. 2011;53:1020–2. 25. Lencioni R, Llovet JM. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis. 2010;30:52–60.
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Med Oncol (2014) 31:209 26. Jang MJ, Jeon YJ, Kim JW, et al. Association of VEGF and KDR single nucleotide polymorphisms with colorectal cancer susceptibility in Koreans. Mol Carcinog. 2013;52(Suppl 1):60–9. 27. Sjostrom S, Wibom C, Andersson U, et al. Genetic variations in VEGF and VEGFR2 and glioblastoma outcome. J Neurooncol. 2011;104:523–7. 28. Li X, Claesson-Welsh L, Shibuya M. VEGF receptor signal transduction. Methods Enzymol. 2008;443:261–84. 29. Wang Y, Zheng Y, Zhang W, et al. Polymorphisms of KDR gene are associated with coronary heart disease. J Am Coll Cardiol. 2007;50:760–7. 30. Shalaby F, Ho J, Stanford WL, et al. A requirement for Flk1 in primitive and definitive hematopoiesis and vasculogenesis. Cell. 1997;89:981–90. 31. Glubb DM, Cerri E, Giese A, et al. Novel functional germline variants in the VEGF receptor 2 gene and their effect on gene expression and microvessel density in lung cancer. Clin Cancer Res. 2011;17:5257–67. 32. Heist RS, Christiani D. EGFR-targeted therapies in lung cancer: predictors of response and toxicity. Pharmacogenomics. 2009;10:59–68. 33. Lee JH, Chung YH, Kim JA, et al. Genetic predisposition of hand-foot skin reaction after sorafenib therapy in patients with hepatocellular carcinoma. Cancer. 2013;119:136–42. 34. Personeni N, Bozzarelli S, Pressiani T, et al. Usefulness of alphafetoprotein response in patients treated with sorafenib for advanced hepatocellular carcinoma. J Hepatol. 2012;57:101–7. 35. Tsuchiya K, Asahina Y, Matsuda S, et al. Changes in plasma vascular endothelial growth factor at 8 weeks after sorafenib administration as predictors of survival for advanced hepatocellular carcinoma. Cancer. 2013;120(2):229–37. 36. Raoul JL, Bruix J, Greten TF, et al. Relationship between baseline hepatic status and outcome, and effect of sorafenib on liver function: SHARP trial subanalyses. J Hepatol. 2012;56:1080–8.
ORIGINAL PAPER
The relationship of kinase insert domain receptor gene polymorphisms and clinical outcome in advanced hepatocellular carcinoma patients treated with sorafenib You-Bing Zheng • Mei-Xiao Zhan • Wei Zhao • Bing Liu • Jian-Wen Huang Xu He • Si-Rui Fu • Yan Zhao • Yong Li • Bao-Shan Hu • Li-Gong Lu
•
Received: 20 June 2014 / Accepted: 24 August 2014 Ó Springer Science+Business Media New York 2014
Abstract Kinase insert domain receptor (KDR) is the principal receptor that promotes the pro-angiogenic action of vascular endothelial growth factor and has been the principal target of anti-angiogenic therapies. Our aim was to determine whether single-nucleotide polymorphisms (SNPs) in KDR gene are associated with clinical outcomes after first-line sorafenib therapy in advanced hepatocellular carcinoma (HCC). The SNPs in KDR were tested in 78 advanced HCC patients receiving first-line sorafenib. Correlations with clinicopathological features and survival were analyzed. Patients with AA genotype of rs1870377 and AA genotype of rs2305948 were significantly associated with a better response and longer time to progression (TTP) (5.8 vs 4.0 months, P = 0.001; 5.8 vs 4.5 months, P = 0.016, respectively). Patients harboring AA genotype in rs1870377 and TT/TC genotype in rs2071559 had a longer overall survival (OS) (15.0 vs 9.6 months, P = 0.001; 13.0 vs 9.0 months, P = 0.007, respectively). At multivariate analysis, major vascular invasion and rs1870377 were independent factors in TTP and performance status, rs1870377, and rs2071559 were independent factors in OS. Our results suggest that SNPs in KDR gene can predict clinical outcome in advanced HCC patients receiving first-line sorafenib. You-Bing Zheng, Mei-Xiao Zhan and Wei Zhao contributed equally to this work. Y.-B. Zheng W. Zhao Southern Medical University, Guangzhou, China Y.-B. Zheng M.-X. Zhan W. Zhao B. Liu J.-W. Huang X. He S.-R. Fu Y. Zhao Y. Li B.-S. Hu L.-G. Lu (&) Department of Interventional Radiology, Cancer Center, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China e-mail: [email protected]
Keywords Sorafenib KDR Hepatocellular carcinoma Single-nucleotide polymorphism
Introduction Hepatocellular carcinoma (HCC) accounts for 70–85 % of primary liver cancer. It is the fifth most prevalent cancer and the third most common cause of cancer-related death in the world [1]. China is one of the areas with a high prevalence of HCC mainly due to chronic hepatitis B carriers accounting for [10 % of Chinese population [2]. HCC develops dependent of accumulation of risk factor, such as HBV, HCV and aflatoxin, and genetic predisposition that lead to a wide range of molecular and cellular abnormalities within heptic cells. It is still a huge challenge to elucidate the pathogenesis and to identify the subjects at risk completely. In two phase III, placebo-controlled, randomized trials, sorafenib treatment significantly improved the time to tumor progression (TTP) and overall survival (OS) of patients with advanced HCC [3, 4]. Sorafenib blocks receptor tyrosine kinase signaling (vascular endothelial growth factor receptor, VEGFR; platelet-derived growth factor receptor, PDGFR; c-Kit and RET) and inhibits downstream Raf serine/threonine kinase activity to prevent tumor growth by anti-angiogenic, anti-proliferative, and/or pro-apoptotic effects [5]. Sorafenib currently sets the new standard for the first-line treatment of advanced HCC (BCLC stage C). The prediction of treatment response and survival may facilitate customized targeted therapy, chemotherapy, or risk-related therapy, resulting in significant increase of survival rate. Recently, several studies have been published on the association between genetic variants of the angiogenetic pathway and global outcome in lung cancer,
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colorectal carcinoma, ovarian cancer, breast cancer, and renal cell carcinoma patients treated with antiangiogenetic agents [6–10]. Angiogenesis is an important step in development of cancer and is necessary for primary tumor growth, invasiveness, and metastasis [11]. It is tightly regulated by different pro-angiogenic and anti-angiogenic factors secreted by both tumor cells and cells of the tumor microenvironment [11]. Anti-angiogenic drugs have changed clinical practice in malignant tumors [12–15], such as sunitinib, bevacizumab, gefitinib, and erlotinib, but plenty of clinical challenges still remain. Several other drugs also targeting the pathways in sorafenib have failed to prove a significant positive impact on the outcome of patients with HCC, such as brivanib and sunitinib [16, 17]. The molecular biological grounds for these discordant results are not well understood. Moreover, only a small number of patients do benefit from antiangiogenic agents, and reliable tools to prospectively identify which patients are more likely to benefit are scarce. In the two sorafenib trials, no statistically significant pre-treatment plasma biomarkers that might predict response in patients receiving sorafenib were identified [18]. Vascular endothelial growth factor (VEGF) and their receptors are believed to be important for regulation of the process of angiogenesis. The VEGF family members are dimeric glycoproteins, which consist of five members, VEGF A, B, C, D, and placental growth factor (PLGF), and bind to specific receptors (VEGFR) [19]. In HCC, high levels of tissue VEGF over-expression and elevated serum levels of VEGF have been shown to be closely associated with poor prognosis [20–22]. VEGFR2, also known as kinase insert domain receptor (KDR), is the principal receptor that promotes the proangiogenic action of VEGFA and has been the principal target of anti-angiogenic therapies. The KDR gene is located on chromosome 4q11-q12. The variation of KDR gene may change the serum concentration and biological function [23]. Several single-nucleotide polymorphisms (SNPs) have been described in the KDR gene, some of which have been shown to affect the expression of the gene. There were also a few reports that showed the predictive value of gene polymorphisms in HCC patients. However, no published data on KDR polymorphisms in association with HCC prognosis are available. Considering that sorafenib is also an inhibitor of KDR, we therefore decided to investigate whether genetic variants in the KDR gene could act as biomarkers for sorafenib treatment outcome. In this study, we hope to define specific patient subgroups more likely to benefit from sorafenib therapy in terms of TTP and OS.
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Materials and methods Patient selection Patients with advanced HCC admitted to our hospital between January 2010 and March 2013 were evaluated retrospectively. All patients satisfied the diagnostic criteria for HCC based on radiologic or histologic grounds according to the American Association for the Study of the Liver guidelines [24]. All including patients in our study had locally advanced or metastatic HCC that was not amenable to surgery or locoregional therapies, including transarterial chemoembolization or local ablation, and received first-line sorafenib treatment. Patients were excluded if they had serious medical comorbidities or previous or current second primary cancers in addition to HCC. The initial sorafenib dose was 400 mg orally twice daily. Patients were monitored every 2–4 weeks, and follow-up consisted of a detailed history, physical examination, blood examination, and abdomen CT/MRI scanning if clinically indicated. Dose reductions of sorafenib and drug interruptions were allowed for severe adverse events, according to the National Cancer Institute’s Common Terminology Criteria for Adverse Events (CTCAE) version 4.0. Treatment was continued until radiological or clinical progression, unacceptable toxicity, death, or patient refusal. Tumor response was assessed according to modified response evaluation criteria in solid tumors (mRECIST) [25]. All patients included in this retrospective study were Han Chinese. The study protocol was approved by the Institutional Review Board of Guangdong General Hospital, and written informed consent was obtained from all patients. DNA isolation This was an exploratory retrospective study, and we collected peripheral blood samples from each patient before initial therapy. Peripheral blood samples were drawn into tube containing anticoagulant, centrifuged at 30009g for 5 min, and then stored at -80 °C until measurement. Genomic DNA was isolated from the whole blood cell using Qiagen DNA Isolation Kit (Qiagen GmbH, Hilden, Germany) following the manufacturer’s instructions. We determined DNA concentrations using a UV-1200 spectrophotometer (Mapada Instruments, Shanghai). SNP selection The KDR SNPs selected for genotyping were accessed from the public databases: HapMap (http://www.hapmap. org), and dbSNP (http://www.ncbi.nih.gov). We used
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Table 1 Overview of the SNPs selected in the KDR gene SNP ID
Chr. position
Alleles
Position to transcript/effect
rs2071559
55,992,366
C/T
50 sequence
rs2305948*
55,979,558
C/T
Val297Ile
rs1870377*
55,972,974
T/A
Gln472His
rs34231037*
55,972,946
A/G
Cys482Arg
rs34038364*
55,968,597
G/A
Thr689Met
rs17085262
55,959,141
C/T
Intron
rs6848933 rs6828477
55,969,711 55,966,801
C/G C/T
Intron Intron
rs7671745
55,956,836
A/G
Intron
rs7692791
55,980,239
C/T
Intron
rs4864952
55,947,338
C/T
Intron
rs10020464
55,979,070
C/T
Intron
rs2034965
55,977,800
A/G
Intron
rs1531290
55,986,562
A/G
Intron
rs7667298
55,991,731
C/T
50 UTR
rs12502008
55,991,042
G/T
Intron
rs2305949
55,980,456
C/T
Intron
rs6837735
55,985,815
C/T
Intron
SNP Single-nucleotide polymorphism, KDR kinase insert domain receptor, Chr chromosome, UTR untranslated region
DNA, 500 lM of dNTP, 100 nM primers, and 0.1 units of HotstarTaq polymerase (Qiagen, Valencia, CA). Thermal cycling was performed at 94 °C for 15 min, followed by 45 cycles of 94 °C for 20 s, 50 °C for 30 s, and 72 °C for 60 s, with the final extension at 72 °C for 3 min. After cleaning up the extension reaction products with SpectroCLEAN, the products were transferred to SpectroCHIP using SpectroPOINT, and then scanned through SpectroREADER (MALDI-TOF). Resulting genotype data were collected by Typer v4.0 (Sequenom, CA). To assess the reliability of genotyping, we randomly chose more than 20 % of the samples to sequence twice, and all results showed 100 % concordance. Genotyping was carried out in a blind way in which the status of the patients was unknown to the performers. Measurement of serum KDR level Serum KDR concentrations were measured quantitatively by an enzyme-linked immunoabsorbent assay kit (RAYBIOÒ; Human VEGFR-2 ELISA Kit; RayBiotech, Inc., USA) according to the manufacturer’s instructions. All samples were analyzed twice, and mean values were used for further statistical analysis.
* The four non-synonymous coding SNPs
Statistical analysis genomic sequences to select SNPs from the HapMap database (Phase 2 Public release number 22). Tag SNPs were selected using the Haploview software and SNP tagger approach. Only common SNPs were selected as tagSNPs by analysis of HapMap genotyping data with minor allele frequency of 0.1 or higher and a minimum correlation coefficient of the frequencies (r2) of 0.8. In addition, four SNPs (rs2305948, rs1870377, rs34231037 and rs34038364) were selected because they coded for a nonsynonymous amino acid change in the KDR gene and a functional SNP (rs2071559) in the KDR promoter previously reported to affect transcriptional activity of the KDR promoter, which were included by documentary search. Chromosomal locations, positions, and biological effects of investigated KDR SNPs have been summarized in Table 1. Primer design and genotyping Genotyping was carried out using the iPLEX GoldTM assay on the MassARRAY Platform (Sequenom, CA, USA). Genomic positions for every SNP were downloaded from the Ensembl database (www.ensembl.org), and 100 bp upstream and downstream sequences were used for primer design with the Sequenom MassARRAYÒ Assay Design 3.1 software using default parameters. Multiplex PCR was performed in a 5 lL volume containing C5 ng of genomic
Categorical variables were compared using the v2 test, and continuous variables were compared using the Mann– Whitney test. The genotype frequencies were tested for the Hardy–Weinberg equilibrium using a standard v2. Linkage disequilibrium (LD) pattern among these SNPs was determined using Haploview software. Overall survival was calculated from date of first treatment to death or the end of follow-up, and time to progression (TTP) was calculated from the date of first treatment to radiological progression or the end of follow-up. We used the Kaplan– Meier method and log-rank test to compute the relation of gene polymorphisms and clinical variable to OS rate. A Cox proportional hazards model was used to determine the factors with significance in univariate analysis associated with survival. Data analysis was performed using Statistical Package for Social Sciences (SPSS v.13.0 for Windows; SPSS Inc., Chicago, IL). All tests were two tailed, and P\ 0.05 was considered statistically significant.
Results Patient characteristics and clinical outcomes The characteristics of patients, as well as clinical outcome, are shown in Table 2. 64 males and 14 females with median
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209 Page 4 of 8 Table 2 Patient characteristics and clinical outcome
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Characteristics
n
Response (n)
P
13
0.75
B55
52
8
[55
26
5
Age (years)
Gender Male
64
10
Female
14
3
59
7
Negative
19
6
Cirrhosis
0.6 11.8 (10.1–13.4)
0.32 5.3 (3.6–6.1)
0.5 11.8 (10.3–14.1)
4.3 (4.0–5.1)
10.6 (8.7–12.9) 0.25
5.6 (3.8–6.8)
0.3 11.9 (8.4–14.0)
4.2 (2.7–5.0)
10.3 (9.0–12.8) 0.65
0.2
Present
43
7
4.7 (4.1–6.0)
9.8 (7.3–10.9)
Absent
35
6
4.0 (4.2–6.4)
11.5 (9.9–14.7)
58
11
0–1
0.49
2 20 Baseline AFP (ng/ml)
0.22 4.8 (4.0–6.5)
2
0 14.5 (11.2–16.6)
3.6 (3.4–6.1) 0.05
7.3 (5.0–9.1) 0.26
0.2
[400
51
5
5.2 (3.8–7.0)
9.3 (8.2–12.2)
\400
27
8
4.3 (3.4–5.3)
11.9 (9.2–13.7)
Major vascular invasion
0.04
0.01
0
Yes
32
2
3.3 (3.2–4.9)
12.6 (10.5–13.8)
No
48
11
5.8 (4.0–6.3)
8.8 (7.6–11.4)
Extrahepatic metastasis
0.03
0
0
Yes
39
3
3.0 (2.5–5.4)
12.9 (11.8–15.4)
No
39
10
5.8 (4.8–6.8)
9.2 (8.5–11.3)
47
6
\5 31 Child-Pugh class
7
Tumor size (cm) [5
0.26
0.59 4.0 (3.5–6.0)
0.2 10.4 (7.7–12.0)
4.7 (4.1–6.4) 0.75
12.2 (10.2–14.8) 0.35
0.3
A
51
8
5.2 (4.2–6.6)
12.0 (10.4–14.2)
B
27
5
4.3 (3.9–5.5)
10.8 (8.4–11.8)
C
0
0
0
0
age at diagnosis of 48 years (range 37–72) were included. The median duration of sorafenib treatment was 7.8 months (range 2.8–23.8). Dose reduction was applied in 25 patients (32.1 %) because of side effects related to sorafenib. Dose decreased to 400 and 200 mg/day in 20 patients (25.6 %) and 5 patients (6.4 %), respectively. Sorafenib was discontinued permanently because of tumor progression in 32 patients (41.0 %) and side effects in 3 (3.8 %). There were no treatment-related deaths. Table 3 shows the details of the side effects secondary to sorafenib observed based on CTCAE 4.0. The most common adverse effects related to sorafenib were diarrhea (35.9 %), hand-foot skin reaction (25.6 %), and rash (20.5 %), most of which were mild to moderate. The median duration of follow-up was 16.4 months (range 3.8–36.8). Of the 78 patients evaluated, no complete
P
10.8 (9.2–12.2)
4.1 (3.8–4.9)
0.91
ECOG PS
123
OS, month (95 % CI)
0.28
0.07
Positive
P
5.1 (3.8–6.5) 0.69
HBsAg
TTP Time to progression, OS overall survival, ECOG PS eastern cooperative oncology group performance status, AFP a-fetoprotein
TTP, month (95 % CI)
responders, 13 patients (16.7 %) had a partial response, 30 (38.5 %) had stable disease, and 35 (44.8 %) had progressive disease, respectively. The medians TTP and OS were 4.5 months (95 % CI 3.7–5.2 months) and 11.4 months (95 % CI 8.9–13.8 months), respectively. No major vascular invasion and no extrahepatic metastasis were significantly associated with a better response. They were also associated with better clinical benefit (TTP and OS), as well as performance status (Table 2). Genotype analysis The distribution of the tested genotypes did not deviate from Hardy–Weinberg equilibrium (P [ 0.05). The baseline level of serum KDR was 9.2 ± 3.4 lg/L. Serum KDR levels were correlated to no KDR SNPs.
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Discussion
Table 3 Sorafenib-related adverse events Adverse events
No. of patients
Diarrhea Grade 1/2/3
21/5/2
Rash Grade 1/2/3
12/4/0
Hand-foot skin reaction Grade 1/2/3
11/8/1
Fatigue Grade 1/2/3
10/6/0
Anorexia Grade 1/2/3
2/1/0
Nausea Grade 1/2/3
6/3/0
ALT/AST increased Grade 1/2/3
9/3/0
Neutropenia Grade 1/2/3
5/4/0
A total of 45 (57.7 %) patients had a TT or TA genotype for rs1870377, and 33 (42.3 %) patients had an AA genotype. Median TTP was improved for patients showing the AA genotype (5.8 vs 4.0 months; P = 0.001, Fig. 1a), as also was median OS (15.0 vs 9.6 months; P = 0.001, Fig. 1b). A total of 44 (56.4 %) patients had a TT/TC genotype for rs2071559, and 34 (43.6 %) patients had a CC genotype. Median OS was significant longer among patients with rs2071559 TT/TC than CC genotype (13.0 vs 9.0 months, P = 0.007, Fig. 1c). 29 (37.2 %) patients had an AA genotype of rs2305948, and 49 (62.8 %) patients had a GG or GA genotype. TTP proved statistically significant different among these genotypes with 5.8 months for AA genotype and 4.5 months for GG/GA, P = 0.016 (Fig. 1d). No other KDR polymorphisms were related to TTP or OS. Among patients with partial response, the frequencies of rs1870377 AA and rs2305948 AA were 12.8 and 10.3 %, respectively. Patients with genotype AA in rs1870377 and AA in rs2305948 were significantly associated with a better response (P = 0.011 and P = 0.047; Table 4). A multivariate analysis of genotype effects on survival was conducted using Cox proportional hazards models adjusted for clinicopathologic variables. As shown in Table 5, major vascular invasion (HR = 2.51) and rs1870377 (HR = 0.68) remained significant for TTP. Performance status (HR = 2.36), rs1870377 (HR = 0.35), and rs2071559 (HR = 2.25) remained significant independent predictors of OS among all the patients.
The SNPs we genotyped were based on a high-throughput screening method and, therefore, provided a unique opportunity to examine comprehensive KDR SNP taggers’ prognostic impact on HCC. The association between the KDR SNPs and susceptibility or prognosis in several different types of cancers, such as renal cell carcinoma, colorectal cancer, and glioblastoma [10, 26, 27], has been studied. We found that 4 of 18 SNPs in KDR gene were significantly associated with TTP or OS in sorafenib-treated HCC patients, remaining so in multivariate analyses. The multivariate survival analyses also showed that performance status and extrahepatic metastases remained as significant predictors for OS and so was major vascular invasion for TTP in HCC patients. However, we did not found any significant relationship between KDR gene sequence variant and serum KDR levels. Although there are three known members of the VEGFR family, activation of KDR alone is sufficient to have all these effects [28]. The KDR gene is highly polymorphic, and the various polymorphisms can significantly affect the protein expression and biological function [29]. Sequence variants within KDR might affect VEGF–KDR interactions, and it is meaningful to assess the associations between common genetic variants in KDR and outcome of HCC patients. Gene targeting experiments demonstrate that a functional KDR is elementary for the development of vasculogenesis and hematopoiesis [30]. The deficits in KDR function is correlated with vascular dysfunction, including vascular endothelial cell damage, impaired endothelial cell survival, abnormal vascular repair, and decreased antiapoptotic effects of VEGF. Different SNPs in different regions of the KDR gene may influence circulating KDR levels and thus response to anti-KDR therapies. Wang et al. [29] found that rs2071559 C [ T can lead to alter the structure of the binding site in KDR gene promoter region for transcriptional factor E2F, which resulted in decreasing KDR expression. Exonic polymorphisms rs1870377 and rs2305948 (in exon 11 and exon 7, respectively) are located in the fifth and third NH2-terminal Ig-like domains within the extracellular region, which are important for ligand binding, and lead to nonsynonymous amino acid changes at residue 472 Gln [ His and 297 Val [ Ile, respectively. Both rs1870377 T [ A and rs2305948 G [ A resulted in significant decrease in the VEGF binding efficiency to KDR [29]. Glubb et al. [31] found that the nonsynonymous amino acid changes of 472 Gln [ His could increase KDR protein phosphorylation and result in increased microvessel density in lung cancer tumor tissues. It appears that these functional genetic variations in KDR can affect VEGF– KDR interactions and may have some effects on HCC
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Fig. 1 Kaplan–Meier curves for a time to progression of patients with rs1870377 AA and TT/TA (5.8 vs 4.0 months, P = 0.001), b overall survival of patients with rs1870377 AA and TT/TA (15.0 vs 9.6 months; P = 0.001), c overall survival of patients with rs2071559
CC and TT/TC (9.0 vs 13.0 months; P = 0.007), d time to progression of patients with rs2305948 AA and GG/GA (5.8 vs 4.5 months; P = 0.016)
Table 4 Frequency distributions of the investigated gene polymorphisms in patients Polymorphism
Genotype
No. of patients
Response (%)
P
TTP, month (95 % CI)
P
OS, month (95 % CI)
P
rs1870377
AA TT/TA
33 (42.3 %) 45 (57.7 %)
10 (12.8 %) 3 (3.8 %)
0.011
5.8 (4.6–6.9) 4 (3.5–4.4)
0.001
15 (12.9–17.0) 9.6 (8.5–10.6)
0.001
rs2305948
AA
29 (37.2 %)
8 (10.3 %)
0.047
5.8 (3.0–8.6)
0.016
12 (9.8–14.5)
0.231
0.414
4.5 (3.9–5.0)
rs2071559
GG/GA
49 (62.8 %)
5 (6.4 %)
CC
34 (43.6 %)
7 (9.0 %)
TT/TC
44 (56.4 %)
6 (7.7 %)
TTP Time to progression, OS overall survival
123
4.5 (4.0–5.1) 5.5 (4.2–6.8)
10.5 (8.7–11.8) 0.057
9 (7.1–10.9) 13 (9.8–16.1)
0.007
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Table 5 Multivariate analysis of biological parameters for TTP and OS Covariates for TTP
HR (95 % CI)
P
Major vascular invasion
2.51 (1.38–3.65)
0.021
Extrahepatic metastasis
1.31 (0.75–2.69)
0.385
rs1870377 AA
0.68 (0.45–0.89)
0.005
rs2305948 AA
0.55 (0.32–0.95)
0.121
Covariates for OS
HR (95 % CI)
P
ECOG PS
2.36 (1.86–4.58)
0.017
Major vascular invasion
1.21 (0.63–2.23)
0.291
Extrahepatic metastasis
1.79 (1.69–3.96)
0.111
rs1870377 AA
0.35 (0.25–0.86)
0.003
rs2071559 CC
2.25 (1.55–3.39)
0.036
TTP Time to progression, OS overall survival, HR hazard ratio, CI confidence interval
prognosis. No innocent explanation is available for this condition, but it could be due to the complex roles of VEGF signaling in vascular biology. A growing number of studies are trying to identify molecular markers to predict the outcomes and response to tyrosine kinase inhibitor in patients with malignancy, which include gene mutation, gene copy number, gene polymorphisms, or protein expression. Great achievements have been made in identifying molecular predictors of response and survival outcome in non-small cell lung cancer treated with EGFR-TKIs [32]. With respect to HCC, Lee et al. [33] found that differences in the incidence of hand–foot skin reaction may be caused by ethnic differences in SNPs of the genes related to drug metabolism and tumor angiogenesis, but they did not identify any SNP related to sorafenib therapeutic efficacy. Early AFP responses and dynamic plasma VEGF concentration change have also been reported to be useful surrogate markers to predict prognosis in patients with advanced HCC, who receive sorafenib [34, 35]. However, approximately 30 % of patients with advanced HCC in the Sorafenib HCC Assessment Randomized Protocol trial had normal AFP concentrations [36]. It is worth mentioning that there were several limitations in this study. This study was performed in a Han population which might make our findings less applicable to other ethnic groups. The relatively small sample size of our study should also be interpreted with caution. Larger and perspective studies to apply the potential of our findings in other ethnical cohorts in the future should be considered. Although there are limitations, it is still important to determine the relationship of KDR polymorphisms with sorafenib efficacy in HCC.
We conclude that the KDR SNPs represent novel and promising biomarkers of sorafenib treatment outcome. Validation of the role of KDR SNPs in further prospective clinical trials will ultimately offer new method for treatment optimization of sorafenib in selected HCC patients.
Funding Science and Technology Foundation of Guangdong Province, China (Grant No. 2011A030400009); Natural Science Foundation of Guangdong Province, China (Grant No. S2012010010569). Conflict of Interest
None declared.
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