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Cancer Communications

, 39:57 | Cite as

Polymorphisms of MTHFR and TYMS predict capecitabine-induced hand-foot syndrome in patients with metastatic breast cancer

  • Shaoyan Lin
  • Jian Yue
  • Xiuwen Guan
  • Peng Yuan
  • Jiayu Wang
  • Yang Luo
  • Ying Fan
  • Ruigang Cai
  • Qiao Li
  • Shanshan Chen
  • Pin Zhang
  • Qing Li
  • Fei MaEmail author
  • Binghe XuEmail author
Open Access
Original article

Abstract

Background

Breast cancer is a global problem, and a large number of new cases are diagnosed every year. Capecitabine is effective in patients with metastatic breast cancer (MBC). Hand-foot syndrome (HFS) is a common adverse effect of capecitabine. In this study, we investigated the association between single nucleotide polymorphisms (SNPs) in genes involved in capecitabine metabolism pathways and capecitabine-induced HFS in Chinese patients with MBC to identify some predictive genetic biomarkers.

Methods

We selected 3 genes involved in capecitabine metabolism and screened genetic variants in these target genes. We genotyped a total of 22 SNPs in the thymidylate synthase gene (TYMS), the methylene tetrahydrofolate reductase gene (MTHFR), and the ribonucleotide reductase M1 gene (RRM1) in 342 MBC patients treated with capecitabine-based chemotherapy. The genotype distributions of each SNP in patients with and without HFS were assessed using Pearson’s χ2 test, and the relationship between HFS and genotypes of SNPs was determined using logistic regression analysis. The association between SNPs and their corresponding gene expression was analyzed using the Blood expression quantitative trait loci (eQTL) browser online tools.

Results

We found 4 positive sites for HFS in the TYMS and MTHFR genes: TYMS rs2606241 (P = 0.022), TYMS rs2853741 (P = 0.019), MTHFR rs3737964 (P = 0.029), and MTHFR rs4846048 (P = 0.030). Logistic regression analyses showed that the genotype AG of MTHFR rs3737964 [odds ratio (OR) = 0.54, 95% confidence interval (CI) 0.31–0.97, P = 0.038] and MTHFR rs4846048 (OR = 0.54, 95% CI 0.30–0.98, P = 0.042) were protective factors of HFS, whereas the genotype CT of TYMS rs2853741 (OR = 2.25, 95% CI 1.31–3.87, P = 0.012) increased the risk of HFS. The association between the genotype GT of TYMS rs2606241 (OR = 1.27, 95% CI 0.73–2.23, P = 0.012) and HFS was uncertain. Further eQTL analyses confirmed that the alleles of rs3737964 and rs4846048 affected the gene expression levels of MTHFR in cis.

Conclusions

We have identified four potentially useful pharmacogenetic markers, TYMS rs2606241, TYMS rs2853741, MTHFR rs3737964, and MTHFR rs4846048 to predict capecitabine-induced HFS in MBC patients.

Keywords

Capecitabine Polymorphism Metastatic breast cancer Hand-foot syndrome TYMS MTHFR 

Abbreviations

MBC

metastatic breast cancer

HFS

hand-foot syndrome

5-FU

5-fluorouracil

TYMS

thymidylate synthase

MTHFR

methylene tetrahydrofolate reductase

RRM1

ribonucleotide reductase M1

dUMP

deoxyuridine monophosphate

dTMP

deoxythymidine monophosphate

5-10 MTHF

5-10 methylenetetrahydrofolate

5-MeTHF

5-methylenetetrahydrofolate

FUDP

fluorouridine diphosphate

FdUDP

fluorodeosyuridine diphosphate

SNP

single nucleotide polymorphism

DPYD

dehydrogenase gene

CDD

cytidine deaminase gene

ER

estrogen receptor

PR

progesterone receptor

HER2

human epidermal growth factor receptor 2

PCR

polymerase chain reaction

hME

homogeneous Mass EXTEND

CI

confidence interval

OR

odds ratios

eQTL

expression quantitative trait loci

CR

complete response

PR

partial response

SD

stable disease

PD

progressive disease

3′-UTR

3′-untranslated region

miRNA

microRNA

ENOSF1

enolase superfamily member 1

5′-VNTR

5′-variable number of tandem repeat

DPD

dihydropyrimidine dehydrogenase

TP

thymidine phosphorylase

FdUrd

5-fluorodeoxyuridine

TK

thymidine kinase

FdUMP

fluorodeoxyuridine 5′-monophosphate

5-10 CH2 FH4

5-10 methylene-tetrahydrofolate

FH2

dihydrofolate

5-10 CH=FH4

5-10 methenyltetrahydrofolate

DHFR

dihydrofolate reductase

5-CHO FH4

5-formyltetrahydrofolate

5-CH3 FH4

5-methyltetrahydrofolate

MS

methionine synthase

FH4

tetrahydrofolate

Background

Breast cancer is a global problem, and 1.7 million new cases are diagnosed per year [1]. Approximately 6%–10% of breast cancer patients present metastatic disease when initially diagnosed, and over 30% of patients with non-metastatic disease will relapse [2]. Capecitabine (N4-pentyloxycarbonyl-5′-deoxy-5-fluorocytidine) has been widely used for the treatments of breast [3, 4], colon [5], and gastric cancers [6], and has also been considered as an option for hepatocellular carcinoma [7] and rectal cancer [8]. Capecitabine is often administrated as second-line monotherapy for metastatic breast cancer (MBC) patients whose disease is resistant to anthracycline, taxane, or both [9]. The common capecitabine-induced adverse events include hand-foot syndrome (HFS), increased bilirubin, diarrhea, stomatitis, nausea, neutropenia, and cardiotoxicity [10, 11]. Despite not life-threatening, HFS, characterized by tenderness, redness, and swelling of palms and soles, can be very debilitating and impair the quality of life. Although HFS is manageable, if it is not handled well, it can deteriorate rapidly and lead to treatment interruptions which may affect the treatment efficacy [12].

Capecitabine, a novel oral fluoropyrimidine carbamate, may be converted to 5-fluorouracil (5-FU) selectively in tumors through a cascade of different enzymes [13]. Thymidylate synthase (TYMS), methylene tetrahydrofolate reductase (MTHFR), and ribonucleotide reductase M1 (RRM1) are involved in capecitabine metabolism pathways. The metabolic pathways by which 5-FU and the prodrug capecitabine are converted to active nucleotide analogues are described in details [14]. TYMS catalyzes the conversion of deoxyuridine monophosphate (dUMP) to deoxythymidine monophosphate (dTMP), MTHFR converts 5-10 methylenetetrahydrofolate (5-10 MTHF) into 5-methylenetetrahydrofolate (5-MeTHF) [14], and RRM1 is involved in ribonucleotide reductase reaction converting fluorouridine diphosphate (FUDP) to fluorodeosyuridine diphosphate (FdUDP) [15]. Single nucleotide polymorphisms (SNPs) are used in researches comparing genotype frequencies and copy number variations between cases and controls to identify new cancer-susceptible genes and potential markers predicting therapy response and drug resistance. Any failure in the metabolism system, with a special distinction of SNP genotypes present in the drug-metabolic genes, might result in drug resistance or therapy toxicity.

The availability of tools for predicting toxicity would allow physicians to choose proper treatment regimens to elicit a positive response while keeping adverse effects under acceptable levels. There have been several studies about the biomarkers predicting toxicity of capecitabine, which have been mainly focused on a limited number of known candidates, such as dihydropyrimidine dehydrogenase gene (DPYD) [16, 17] and cytidine deaminase gene (CDD) [18], based on white patient population. However, until recently, only a limited number of SNPs in MTHFR [19], TYMS [17], and no SNPs in RRM1 genes associated with HFS have been identified. Accordingly, we genotyped 22 SNPs in MTHFR, TYMS, and RRM1 genes involved in capecitabine metabolism pathways and combined the medical data with the experimental results in hope of seeking potential genetic markers, which may help identify candidate patients suitable for capecitabine treatment, thus promoting the benefit from capecitabine and avoiding severe adverse effects.

Patients and methods

Patient selection

The study was conducted on female MBC patients admitted to the National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (Beijing, China) between January 2010 and September 2012. All patients received capecitabine-based therapy (capecitabine: 1000 mg/m2 orally twice daily, on days 1–14), mostly in combination with docetaxel (75 mg/m2, 1-h intravenous infusion on day 1) and vinorelbine (25 mg/m2 for 20-min intravenous infusion or 60 mg/m2 orally on days 1 and 8) every 3 weeks as one cycle. The inclusion criteria were as follows: female patients with MBC confirmed by pathological or cytological techniques; patients without other malignant cancers; patients eligible for capecitabine-based therapy; complete medical records including age at diagnosis, tumor size, lymph node status, stage, estrogen receptor (ER) status, progesterone receptor (PR) status, human epidermal growth factor receptor 2 (HER2) status, menstrual situation, and previous locoregional or systemic therapy; and complete follow-up data. Regular outpatient or telephone follow-ups were carried out, and the last follow-up was March 1, 2014. Follow-up examinations included computed tomography (CT) of metastases, breast ultrasounds, and tumor marker examination. Treatment responses were evaluated every two cycles of treatment (21 days per cycle) according to Response Evaluation Criteria In Solid Tumors (RECIST) 1.0.

SNP selection

We selected genes possibly related to toxicity of capecitabine-based chemotherapy in MBC patients, according to the metabolism pathway of capecitabine. Three candidate genes were selected: TYMS, MTHFR, and RRM1. A total of 22 SNPs from the public database (http://hapmap.ncbi.nlm.nih.gov/) and the 1000 Genomes Project database (http://www.1000genomes.org) [20] in 3 key genes in the Chinese Han population were genotyped. All loci were in the balance of Hardy–Weinberg (P > 0.05), with minor allele frequency greater than 0.05.

DNA extraction

We collected 2 mL heparin-anticoagulated blood samples from all participants, and extracted DNA from peripheral blood leucocytes by the phenol–chloroform method using a blood DNA kit (BioTeke Corporation, Beijing, China). The blood samples were stored at − 80 °C, then added to a centrifuge tube after melting at room temperature. The tube was tightly capped and centrifuged for 15 min at 5000×g. The supernatant was discarded, and the pellet was suspended with lysis buffer containing 10 mmol/L Tris–HCl (pH 8.0), 0.1 mol/L ethylene diamine tetraacetic acid, 20 μg/mL RNA enzyme, and 0.5% sodium-dodecyl sulphate for 1 h at 37 °C. The cellular lysates were digested overnight at 37 °C with proteinase K (100 μg/mL). After digestion, samples were subsequently blended with a same volume of Tris–HCl-satured phenol (pH 7.4) and centrifuged for 15 min at 8000×g.

The aqueous phase was added with a same volume of phenol–chloroform (1:1) in a new tube and centrifuged for 15 min at 8000×g. The aqueous phase was collected again in a new tube and precipitated with a 10% volume of ammonium acetate (10 mol/L) and a two times volume of absolute ethanol at − 20 °C. The pellet was rinsed with 75% ethanol twice and resuspended with appropriate TE buffer. The concentration of extracted DNA was assessed using a spectrophotometer. According to Sequenom, the concentration should be higher than 10 ng/μL, and the A260/280 ratio between 1.8 and 2.0. The extracted DNA samples were stored in 1.5-mL EP tubes at − 80 °C.

SNP genotyping

Polymerase chain reaction (PCR) and extension primers were designed according to Assay Design 3.1 software (Sequenom Inc., San Diego, CA, USA) and synthesized by the Beijing Genomics Institute (Beijing, China). High-throughput MassARRAY spectrometry platform (Sequenom Inc.) was used for SNP genotyping. The homogeneous Mass EXTEND (hME) reaction is shown in Fig. 1. Following PCR amplification of a locus of interest, a primer extension was performed using an hME primer that was designed to anneal next to the SNP. The key feature of the scheme was the use of a terminator mixture that yielded allele-specific extension products differing in length and mass by at least one nucleotide. The genotyping was completed by Bomiao Biological Technology (Beijing, China). The polymorphisms genotyped are shown in Table 1. For quality control, we designed parallel group and blank group (DNA-free), and the reappear rate of the parallel group was 100%. Besides, all trial and analysis personnel were blinded to disease condition of all samples.
Fig. 1

The homogeneous Mass EXTEND (hME) reaction. A deoxyguanosine triphosphate (dGTP) was used along with terminators dideoxyadenosine 5′-triphosphate (ddATP), dideoxycytidine 5′-triphosphate (ddCTP), and dideoxythymidine 5′-triphosphate (ddTTP). For the T allele (T), ddATP was incorporated, extending the primer to a 24-mer. For the C allele (C), dGTP was incorporated prior to the termination of the extension by incorporation of a ddATP, yielding a 25-mer. U primer marked unextended primer

Table 1

The polymerase chain reaction (PCR) primers and homogeneous Mass EXTEND (hME) primers of 22 single nucleotide polymorphisms (SNPs) in three candidate genes

Gene

Localization

SNP

PCR primer (5′ → 3′)

hME primer (5′ → 3′)

TYMS

18p11.32

2790

First: ACGTTGGATGGGATGCCGAGGTAAAAGTTC

GATTTTTGACCTAGTTCCTT

Second: ACGTTGGATGAACTGATAGGTCACGGACAG

 

2,853,741

First: ACGTTGGATGGGAAACAGATCTCAAACAGC

GGTACCACGTTTTCCTGCGGTCTTGTC

Second: ACGTTGGATGAGCACAGTTCCCACGTTTTC

 

2,606,241

First: ACGTTGGATGCCACAGCTGAGAGTCTTAGG

GGGCGCAGTCCTTCCC

Second: ACGTTGGATGACCAGACGGTTCCCAAAGG

 

3,786,362

First: ACGTTGGATGTTGGACAGCCTGGGATTCTC

GCCCAAGTCCCCTTC

Second: ACGTTGGATGCAAAATGCCTCCACTGGAAG

 

1,004,474

First: ACGTTGGATGTAAAACTGTGACTCTCCCCC

GACCTCAGATGGTGATGTTCGTCTA

Second: ACGTTGGATGGGGAAAGGCTGACATACATC

 

9,947,507

First: ACGTTGGATGAATTCTTCTGCCTCAGCCTC

GCCCCCGTCTCTACTAAAA

Second: ACGTTGGATGTGGACAACATGGTGAAACCC

 

699,517

First: ACGTTGGATGCCACTGAAGAACCCTAAAAG

GGAGAAAGACTGACAATATCCTTC

Second: ACGTTGGATGACTTTTACCTCGGCATCCAG

 

9,967,368

First: ACGTTGGATGTGGGTGACAGAGCCGTATG

AACCCAGATATTCCTTTCTATT

Second: ACGTTGGATGGAATCCATGGTCTCCACAAC

 

15,872

First: ACGTTGGATGACAGAACTACACTACCAAGG

CCCCCCTCTCATGGTCACTGTTCC

Second: ACGTTGGATGGAAAGTCCTCTCATGGTCAC

 

MTHFR

1p36.3

1,801,133

First: ACGTTGGATGCTTGAAGGAGAAGGTGTCTG

AACGCGTGATGATGAAATCG

Second: ACGTTGGATGTGCATGCCTTCACAAAGCGG

 

1,801,131

First: ACGTTGGATGTCTCCCGAGAGGTAAAGAAC

CATGAGCTGACCAGTGAAG

Second: ACGTTGGATGAGGAGCTGCTGAAGATGTGG

 

3,737,964

First: ACGTTGGATGTGATGGCTGTAGATCCTCAC

GCAGCCCTCAAAAAAAACCTTTC

Second: ACGTTGGATGTCAAATAGGAACCAGCCCTC

 

4,846,049

First: ACGTTGGATGAACTAAGCCCTCGAACCAAG

TGCACGGGCTCCAAG

Second: ACGTTGGATGTGTTTTGCCTGTACTGCACG

 

2,274,976

First: ACGTTGGATGTATGTGTGTGTAGGACGAGG

CATACAGCTTTCCCCAC

Second: ACGTTGGATGATGTACTGGATGATGGTGCG

 

3,753,582

First: ACGTTGGATGACGCAGTGGGCGCCAGGGA

CTCATTTTAAACCTGCCTCCCCGGCGA

Second: ACGTTGGATGTGCCTTTTAAACCTGCCTCC

 

4,846,048

First: ACGTTGGATGTTTGGTTTGGTGGTGGCTTC

GGAATCAGTTAGTTCTGACACCAACAA

Second: ACGTTGGATGTCCAGACCAGAAGCAGTTAG

 

RRM1

11p15.5

725,519

First: ACGTTGGATGCACTTTAACTCTAGAAGATTG

TGGTGAAGAAATATGTAATGCCTCA

Second: ACGTTGGATGGCCTAGCATATAAAGTGCTC

 

720,106

First: ACGTTGGATGGCAGTAATAAGAGCAGTTATC

AAACATTTATAACAAACTTAACATAC

Second: ACGTTGGATGAAGGGTCAAATGAGTACCTC

 

1,042,858

First: ACGTTGGATGAGGGTTTGAAGACTGGGATG

GAACTGGATTGGATTAGC

Second: ACGTTGGATGGCTTCTCCTTATTTAGAGTG

 

1,042,927

First: ACGTTGGATGCCACCAGTCAAAGCAGTAAA

GGTAAGTAAGGTTTCATCACCC

Second: ACGTTGGATGCAGGGAGTGGTTAAGTAAGG

 

11,030,918

First: ACGTTGGATGTCCTGACGCAAATCAGAGCC

GGCTTACCCTGCCCTGCTTAAAAT

Second: ACGTTGGATGCCACTGAAGAACCCTAAAAG

 

1,980,412

First: ACGTTGGATGGGTCTTCAGAACTATGAGAG

GGGCCAAAGGTATTTAAGTTTCCTATG

Second: ACGTTGGATGCCAGAGGACAAAGGTATTTA

 

TYMS thymidylate synthase, MTHFR methylene tetrahydrofolate reductase, RRM1 ribonucleotide reductase M1

Statistical analysis

Statistical analysis was performed using SPSS 19.0 and SNPStats softwares (IBM Inc., Chicago, IL, USA). The PLINK software was used to calculate genotype frequency, P value, and 95% confidence interval (CI). Hardy–Weinberg Equilibrium (HWE) was checked using the χ2 test or Fisher’s test. The loci deviated from HWE were excluded. The associations between polymorphisms and toxicity were estimated by unconditional logistic regression, and odds ratios (OR) and 95% CI were calculated. The ORs were adjusted for potential confounders, such as age and menstruation. All statistical tests were two-sided and considered significant when P was less than 0.05. Furthermore, we also examined the potential expression quantitative trait loci (eQTL) effects of the significant SNPs by extracting data from the Blood eQTL browser (https://www.genenetwork.nl/bloodeqtlbrowser/) [21].

Results

Patient characteristics

A total of 342 female patients with MBC were enrolled in our study, aging from 26 to 81 years (median, 51 years). All patients received capecitabine-based treatment, mostly in combination with docetaxel (59.9%) and vinorelbine (28.7%). The patient characteristics are summarized in Table 2. Of the 342 patients, 7 (2.0%) had complete response (CR), 194 (56.7%) had partial response (PR), 90 (26.3%) had stable disease (SD), 39 (11.4%) had progressive disease (PD), and 12 (3.5%) had unknown disease status; 141 (41.2%) experienced leukopenia, 109 (31.9%) had neutropenia, 101 (29.5%) had increased aminotransferase, 193 (56.4%) had HFS, 204 (59.6%) had nausea and vomiting, and 72 (21.1%) had increased bilirubin. HFS and increased bilirubin were relatively specific capecitabine-induced adverse events.
Table 2

Clinicopathological characteristics of 342 metastatic breast cancer patients (MBC) with or without hand-foot syndrome (HFS)

Characteristic

Total [cases (%)]

Patients with HFS [cases (%)]

Patients without HFS [cases (%)]

Age (years)

 ≤ 40

57 (16.7)

30 (8.8)

27 (7.9)

 > 40

285 (83.3)

163 (47.7)

122 (35.7)

Family history of cancera

 None

275 (80.4)

152 (44.4)

123 (36.0)

 Breast cancer or ovarian cancer

19 (5.6)

8 (2.3)

11 (3.2)

 Other malignancies

48 (14.0)

33 (9.6)

15 (4.4)

Menstrual station

 Premenopause

218 (63.7)

120 (35.1)

98 (28.7)

 Postmenopause

118 (34.5)

71 (20.8)

47 (13.7)

 Unclear

6 (1.8)

2 (0.6)

4 (1.2)

Clinical stage

 I + II

154 (45.0)

89 (26.0)

65 (19.0)

 III

143 (41.8)

85 (24.9)

58 (17.0)

 IV

14 (4.1)

5 (1.5)

9 (2.6)

 Unclear

31 (9.1)

14 (4.1)

17 (5.0)

Pathological type

 Intraductal carcinoma

4 (1.2)

2 (0.6)

2 (0.6)

 Infiltrating ductal carcinoma

306 (89.5)

172 (50.3)

134 (39.2)

 Invasive lobular carcinoma

17 (5.0)

11 (3.2)

6 (1.8)

 Others

11 (3.2)

7 (2.0)

4 (1.2)

 Unclear

4 (1.2)

1 (0.3)

3 (0.9)

Pathological grade

 Grade 1

10 (2.9)

8 (2.3)

2 (0.6)

 Grade 2–3

170 (49.7)

96 (28.1)

74 (21.6)

 Unclear

162 (47.4)

89 (26.0)

73 (21.3)

Vascular invasion

 No

308 (90.1)

174 (50.9)

134 (39.2)

 Yes

34 (9.9)

19 (5.6)

15 (4.4)

Axillary lymph node metastasis at initial diagnosis

 No

106 (31.0)

54 (15.8)

52 (15.2)

 Yes

225 (65.8)

136 (39.8)

89 (26.0)

 Unclear

11 (3.2)

3 (0.9)

8 (2.3)

Distant metastasis at initial diagnosis

 No

324 (94.7)

186 (54.4)

138 (40.4)

 Yes

14 (4.1)

5 (1.5)

9 (2.6)

 Unclear

4 (1.2)

2 (0.6)

2 (0.6)

ER status

 Positive

215 (62.9)

123 (36.0)

92 (26.9)

 Negative

117 (34.2)

65 (19.0)

52 (15.2)

 Unclear

10 (2.9)

5 (1.5)

5 (1.5)

PR status

 Positive

213 (62.3)

117 (34.2)

96 (28.1)

 Negative

118 (34.5)

70 (20.5)

48 (14.0)

 Unclear

11 (3.2)

6 (1.8)

5 (1.5)

HER2 status

 Positive

83 (24.3)

47 (13.7)

36 (10.5)

 Negative

230 (67.3)

130 (38.0)

100 (29.2)

 Unclearb

29 (8.5)

16 (4.7)

13 (3.8)

Therapy

 Capecitabine

20 (5.8)

10 (2.9)

10 (2.9)

 Docetaxel plus capecitabine

205 (59.9)

123 (36.0)

82 (24.0)

 Vinorelbine plus capecitabine

98 (28.7)

48 (14.0)

50 (14.6)

 Othersc

19 (5.6)

12 (3.5)

7 (2.0)

Therapy line

 First-line

211 (61.7)

121 (35.4)

90 (26.3)

 Multi-line

131 (38.3)

72 (21.1)

59 (17.3)

Metastatic site

 No visceral metastasis (including local recurrence)

104 (30.4)

60 (17.5)

44 (12.9)

 Visceral metastasis

238 (69.6)

133 (38.9)

105 (30.7)

Maintenance therapy

 Yes

137 (40.1)

94 (27.5)

43 (12.6)

 No

205 (59.9)

99 (28.9)

106 (31.0)

Response evaluation

 CR

7 (2.0)

5 (1.5)

2 (0.6)

 PR

194 (56.7)

107 (31.3)

87 (25.4)

 SD

90 (26.3)

56 (16.4)

34 (9.9)

 PD

39 (11.4)

20 (5.8)

19 (5.6)

 Unclear

12 (3.5)

5 (1.5)

7 (2.0)

Survival conditiond

 Alive

218 (63.7)

124 (36.3)

94 (27.5)

 Dead

124 (36.3)

69 (20.2)

55 (16.1)

ER estrogen receptor, PR progesterone receptor, HER2 human epidermal growth factor receptor 2, CR complete response, PR partial response, SD stable disease, PD progressive disease

aBreast cancer or ovarian cancer or other malignances of first- or second-degree relatives

bEquivocal results (HER2++) without fluorescence in situ hybridization testing

cOther capecitabine-based therapies

dPatients’ survival conditions by last follow-up

Associations between gene polymorphisms and risks of increased bilirubin and HFS

No association between polymorphisms of the 3 candidate genes and the risk of increased bilirubin was found (data not shown). There was no significant association between polymorphisms of RRM1 and the risk of HFS (Table 3). The most investigated polymorphisms of MTHFR, rs1801133 and rs1801131, were not related with 5-FU toxicities in our study. We found that TYMS rs2606241 (P = 0.022), TYMS rs2853741 (P = 0.019), MTHFR rs3737964 (P = 0.029), and MTHFR rs4846048 (P = 0.030) were associated with the risk of HFS (Table 3).
Table 3

Frequencies of 22 SNPs of 3 genes in metastatic breast cancer patients with and without capecitabine-induced HFS

Gene

SNP 

Genotype

Patients with HFS [cases (%)]

Patients without HFS [cases (%)]

P value

Total

  

193

149

 

TYMS

rs2790

AA

78 (40.4)

48 (32.2) 

0.316

 

GA

87 (45.1)

74 (49.7)

 
 

GG

26 (13.5)

24 (16.1)

 
 

NA

2 (1.0)

3 (2.0)

 

rs15872

TT

73 (37.8)

68 (45.6)

0.265

 

TC

96 (49.7)

61 (40.9)

 
 

CC

22 (11.4)

17 (11.4)

 
 

NA

2 (1.0)

3 (2.0)

 

rs699517

TT

73 (37.8)

70 (47.0)

0.195

 

TC

97 (50.3)

61 (40.9)

 
 

CC

20 (10.4)

15 (10.1)

 
 

NA

3 (1.6)

3 (2.0)

 

rs1004474

AA

61 (31.6)

45 (30.2)

0.366

 

GA

97 (50.3)

69 (46.3)

 
 

GG

32 (16.6)

34 (22.8)

 
 

NA

3 (1.6)

1 (0.7)

 

rs2606241

GG

44 (22.8)

34 (22.8)

0.022

 

GT

113 (58.5)

69 (46.3)

 
 

TT

33 (17.1)

44 (29.5)

 
 

NA

3 (1.6)

2 (1.3)

 

rs2853741

TT

38 (19.7)

48 (32.2)

0.019

 

TC

113 (58.5)

68 (45.6)

 
 

CC

39 (20.2)

31 (20.8)

 
 

NA

3 (1.6)

2 (1.3)

 

rs3786362

TT

135 (69.9)

86 (57.7)

0.055

 

CT

48 (24.9)

53 (35.6)

 
 

CC

7 (3.6)

8 (5.4)

 
 

NA

3 (1.6)

2 (1.3)

 

rs9947507

TT

193 (100.0)

149 (100.0)

NA

rs9967368

CC

55 (28.5)

52 (34.9)

0.231

 

CG

97 (50.3)

61 (40.9)

 
 

GG

38 (19.7)

33 (22.1)

 
 

NA

3 (1.6)

3 (2.0)

 

MTHFR

rs1801131

AA

143 (74.1)

101 (67.8)

0.291

 

CA

47 (24.4)

45 (30.2)

 
 

CC

1 (0.5)

1 (0.7)

 
 

NA

2 (1.0)

2 (1.3)

 

rs1801133

TT

60 (31.1)

52 (34.9)

0.662

 

TC

99 (51.3)

69 (46.3)

 
 

CC

31 (16.1)

25 (16.8)

 
 

NA

3 (1.6)

3 (2.0)

 

rs2274976

GG

163 (84.5)

131 (87.9)

0.558

 

GA

26 (13.5)

17 (11.4)

 
 

AA

1 (0.5)

0 (0.0)

 
 

NA

3 (1.6)

1 (0.7)

 

rs3737964

GG

165 (85.5)

113 (75.8)

0.029

 

AG

25 (13.0)

33 (22.1)

 
 

NA

3 (1.6)

3 (2.0)

 

rs3753582

TT

160 (82.9)

131 (87.9)

0.389

 

GT

29 (15.0)

15 (10.1)

 
 

GG

1 (0.5)

1 (0.7)

 
 

NA

3 (1.6)

2 (1.3)

 

rs4846048

AA

165 (85.5)

115 (77.2)

0.030

 

AG

25 (13.0)

33 (22.1)

 
 

NA

3 (1.6)

1 (0.7)

 

rs4846049

GG

138 (71.5)

97 (65.1)

0.338

 

GT

49 (25.4)

49 (32.9)

 
 

TT

3 (1.6)

2 (1.3)

 
 

NA

3 (1.6)

1 (0.7)

 

RRM1

rs720106

AA

142 (73.6)

98 (65.8)

0.054

 

AG

40 (20.7)

46 (30.9)

 
 

GG

7 (3.6)

2 (1.3)

 
 

NA

4 (2.1)

3 (2.0)

 

rs725519

AA

142 (73.6)

98 (65.8)

0.532

 

AG

40 (20.7)

46 (30.9)

 
 

GG

7 (3.6)

2 (1.3)

 
 

NA

4 (2.1)

3 (2.0)

 

rs1042858

AA

116 (60.1)

78 (52.3)

0.491

 

CA

64 (33.2)

56 (37.6)

 
 

CC

10 (5.2)

9 (6.0)

 
 

NA

3 (1.6)

6 (4.0)

 

rs1042927

AA

118 (61.1)

81 (54.4)

0.415

 

CA

61 (31.6)

57 (38.3)

 
 

CC

11 (5.6)

9 (6.0)

 
 

NA

3 (1.6)

2 (1.3)

 

rs1980412

CC

61 (31.6)

58 (38.9)

0.359

 

TC

102 (52.8)

69 (46.3)

 
 

TT

26 (13.5)

19 (12.8)

 
 

NA

4 (2.1)

3 (2.0)

 

rs11030918

TT

103 (53.4)

74 (49.7)

0.378

 

CT

66 (34.2)

61 (40.9)

 
 

CC

21 (10.9)

12 (8.1)

 
 

NA

3 (1.6)

2 (1.3)

 

P value < 0.05 was considered statistically significant (in italics)

SNP, single nucleotide polymorphism; HFS, hand-foot syndrome; TYMS, thymidylate synthase; MTHFR, methylene tetrahydrofolate reductase; RRM1, ribonucleotide reductase M1; NA, not applicable

We performed unconditional logistic regression analysis using the SNPStats software. The results indicated that the genotype AG of MTHFR rs3737964 (OR = 0.54, 95% CI 0.31–0.97, P = 0.038) and MTHFR rs4846048 (OR = 0.54, 95% CI 0.30–0.98, P = 0.042) were protective factors for HFS, whereas the genotype CT of TYMS rs2853741 (OR = 2.25, 95% CI 1.31–3.87, P = 0.012) was a risk factor for HFS (Table 4). However, the association between the genotype GT of TYMS rs2606241 (OR = 1.27, 95% CI 0.73–2.23, P = 0.012) and HFS was still uncertain.
Table 4

Unconditional logistic regression analyses assessing associations of gene polymorphisms with HFS

Gene

SNP

Genetic model

Genotype

Patients without HFS [cases (%)]

Patients with HFS [cases (%)]

OR (95% CI)

P value

AIC

BIC

TYMS

rs2606241

Codominant

GG

34 (22.8)

44 (22.8)

1.00

0.012

449.7

476.4

GT

69 (46.3)

113 (58.5)

1.27 (0.73–2.23)

TT

44 (29.5)

33 (17.1)

0.55 (0.28–1.06)

Dominant

GG

34 (22.8)

44 (22.8)

1.00

0.960

456.5

479.4

GT + TT

113 (75.8)

146 (75.6)

0.99 (0.58–1.68)

Recessive

GG + GT

103 (69.1)

157 (81.3)

1.00

0.004

448.4

471.3

TT

44 (29.5)

33 (17.1)

0.46 (0.27–0.79)

Overdominant

GG + TT

78 (52.3)

77 (39.9)

1.00

0.018

450.9

473.8

GT

69 (46.3)

113 (58.5)

1.72 (1.10–2.70)

Log-additive

NA

NA

NA

0.74 (0.53–1.03)

0.071

453.3

476.2

rs2853741

Codominant

TT

48 (32.2)

38 (19.9)

1.00

0.012

449.6

476.4

CT

68 (45.6)

113 (58.5)

2.25 (1.31–3.87)

CC

31 (20.8)

39 (20.2)

1.71 (0.81–3.31)

Dominant

TT

48 (32.2)

38 (19.9)

1.00

0.005

448.5

471.4

TC + CC

99 (66.4)

152 (78.8)

2.09 (1.25–3.49)

Recessive

TT + TC

116 (77.9)

151 (78.2)

1.00

0.950

456.4

479.4

CC

31 (20.8)

39 (20.2)

0.98 (0.57–1.70)

Overdominant

TT + CC

79 (53.0)

77 (38.9)

1.00

0.012

450.1

473.1

TC

68 (45.6)

113 (58.5)

1.77 (1.13–2.77)

Log-additive

NA

NA

NA

1.35 (0.97–1.88)

0.075

453.3

476.2

MTHFR

rs4846048

NA

AA

115 (77.2)

165 (85.5)

1.00

0.042

453.7

476.7

AG

33 (22.1)

25 (13.0)

0.54 (0.30–0.98)

rs3737964

NA

GG

113 (75.8)

165 (85.5)

1.00

0.038

458.8

470.3

AG

33 (22.1)

25 (13.0)

0.54 (0.31–0.97)

The lower the AIC and BIC values, the more accurate the model

HFS hand-food syndrome, OR odds ratio, CI confidence interval, AIC Akaike’s information criterion, BIC Bayes information criterion, NA not applicable

Associations between the SNPs and corresponding gene expression

TYMS rs2606241 and rs2853741 were not associated with TYMS expression, whereas MTHFR rs3737964 and rs4846048 were significantly associated with MTHFR expression (both P < 0.001).

Discussion

The present study demonstrated that genetic polymorphisms in MTHFR and TYMS were associated with capecitabine-induced HFS in MBC patients. We identified 4 SNPs significantly associated with HFS. Further analysis showed that the genotype AG of MTHFR rs3737964 and the genotype AG of MTHFR rs4846048 were protective factors for HFS, whereas the genotype CT of TYMS rs2853741 was a risk factor.

Until recently, few researches have explored the biomarkers for toxicity of capecitabine in patients with MBC. Two retrospective studies [22, 23] and an exploratory analysis [24] have shown that HFS occurrence in capecitabine-treated patients might be associated with improved efficacy and suggested that early dose adjustment according to the severity of HFS might improve its efficacy. However, there is still little information available about predictive biomarkers for HFS. rs9936750, a polymorphism in an intergenic region of different genes, was shown to be associated with an increased risk of capecitabine-induced HFS [25], but the sample size is too small to be convincing and the result needs further confirmation. Another variant rs3215400 in the cytidine deaminase promoter has been demonstrated to be plausibly associated with severe capecitabine-induced HFS [18], but such association was not observed in another study [26]. In the present study, we identified 4 potential predictive biomarkers for HFS in Chinese female MBC patients treated with capecitabine, so that capecitabine therapy might be further tailored to patient response.

Optimal efficacy of 5-FU requires elevated intratumoral concentration of 5-10 MTHF, which is mainly controlled by MTHFR [27]. As 5-10 MTHF inhibits TYMS activity in conjunction with 5-fluorodeoxyuridine 5′-monophosphate (5-FdUMP), reduced MTHFR activity, which is associated with increased levels of 5-10 MTHF, theoretically leads to more effective TYMS inhibition. The metabolic pathway of capecitabine is shown in Fig. 2. Previous clinical studies have suggested that MTHFR gene polymorphisms might have impact on fluoropyrimidine responsiveness [28, 29]. Two of the most investigated polymorphisms of MTHFR, rs1801133 (Ala222Val, 677C>T) and rs1801131 (Glu429Ala, 1298A>C), have been proved to have close association with the onset of some cancers, including colorectal cancer [30] and breast cancer [31]. Several meta-analyses have demonstrated that MTHFR C677T polymorphism may be a risk factor for thyroid [32], breast and ovarian cancers [33]. Accordingly, we investigated influence of MTHFR rs1801133 and rs1801131 on toxicities of capecitabine therapy in patients with MBC, but found no such associations. This result was in agreement with those of some [34, 35, 36], whereas other studies have demonstrated significant associations between MTHFR rs1801133 and 5-FU-induced toxicity [37, 38]. The reason for this discrepancy is not clear. It may be due to different sample sizes or patient selection. Large-scale studies are needed to determine whether testing for these two variants is clinically useful. Our results showed that the frequencies of the genotype AG of MTHFR rs4846048 (P = 0.030) and MTHFR rs3737964 (P = 0.029) were significantly lower in patients with capecitabine-induced HFS than in those without HFS. Further multivariate unconditional logistic regression analysis indicated that the genotype AG of MTHFR rs3737964 (OR = 0.54, 95% CI 0.31–0.97, P = 0.038) and MTHFR rs4846048 (OR = 0.54, 95% CI 0.30–0.98, P = 0.042) were protective factors for HFS. Due to the catalytic deficit of MTHFR, subsequent to its polymorphic variants, the increased 5-10 MTHF concentration enhanced the formation and stability of the inhibitory complex composing of 5-10 MTHF, TYMS, and 5-FdUMP, thereby increasing the potential toxicity of fluoropyrimidines [39]. Lying in the promoter region of MTHFR, rs3737964 did not result in coding amino acid polymorphisms, but possibly led to transcription factor binding difference. rs4846048, located on the 3′-untranslated region (3′-UTR) of MTHFR, might influence the microRNA (miRNA) binding regulation, thus enhancing the message RNA (mRNA) expression. Further eQTL analyses based on the Blood eQTL browser confirmed that the alleles of rs3737964 and rs4846048 could affect the gene expression levels of MTHFR in cis. Therefore, it was inferred that the MTHFR enzymatic activity in subjects with the genotype AG of rs3737964 and rs4846048 might be improved through increasing mRNA transcription, and hence to protect patients from HFS.
Fig. 2

Metabolic pathways of capecitabine and pharmacologically related targets. 5-FU 5-fluorouracil, DPD dihydropyrimidine dehydrogenase, TP thymidine phosphorylase, FdUrd 5-fluorodeoxyuridine, TK thymidine kinase, dUMP deoxyuridine monophosphate, FdUMP fluorodeoxyuridine 5′-monophosphate, dTMP deoxythymidine monophosphate, DNA deoxyribonucleic acid, TYMS thymidylate synthase, 5-10 CH2FH4 5-10 methylene-tetrahydrofolate, FH2 dihydrofolate, 5-10 CH=FH4 5-10 methenyltetrahydrofolate, MTHFR methylenetetrahydrofolate reductase, DHFR dihydrofolate reductase, 5-CHOFH4 5-formyltetrahydrofolate, 5-CH3FH4 5-methyltetrahydrofolate, MS methionine synthase, FH4 tetrahydrofolate

TYMS is an enzyme that catalyzes the conversion of dUMP to dTMP, and is the main intracellular target of the active 5-FU metabolite, 5-FdUMP, which forms a ternary complex with TYMS and 5-10 MTHF [40]. Elevated TYMS expression or activity is a well-known mechanism of resistance to 5-FU [41]. Ooyama et al. [42] found that the copy number of TYMS (18p11.32) showed a strong association with drug resistance, which may lead to the use of TYMS copy number as a predictive marker for drug sensitivity of fluoropymidines. The rs2612091 and rs2741171 variants, lying downstream of TYMS within an intron of enolase superfamily member 1 (ENOSF1), were significant associated with HFS, irrespective of the two TYMS polymorphisms {5′-variable number of tandem repeat (5′-VNTR) 2R/3R [36] and 3′-UTR 6 bp ins-del [43]} that have previously been reported to affect 5-FU toxicity [16]. Therefore, TYMS rs2612091 and rs2741171 were not included in the present study. We included 9 less-investigated polymorphisms of TYMS and found differences in frequencies of TYMS rs2606241 and rs2853741 between patients with and without HFS. Our results showed that the frequencies of the genotype GT of TYMS rs2606241 (P = 0.022) and the genotype CT of TYMS rs2853741 (P = 0.019) were significantly higher in patients with capecitabine-induced HFS than in those without HFS. Logistic regression reflected that the genotype CT of TYMS rs2853741 (OR = 2.25, 95% CI 1.31–3.87, P = 0.012) seemed to raise the susceptibility to HFS. Although the association was uncertain, the genotype GT of TYMS rs2606241 (OR = 1.27, 95% CI 0.73–2.23, P = 0.012) tended to increase the risk of HFS. Lecomte et al. [43] suggested that the low TYMS mRNA expression level in patients with 2R/2R genotype was associated with a higher risk of 5-FU-induced adverse events. Falling both in the promoter region of TYMS, the genotype GT of rs2606241 and the genotype CT of rs2853741 did not change the coding amino acid sequence. However, the mRNA expression might be reduced due to the impact on the transcription binding sites. Therefore, we hypothesized that the decreasing TYMS mRNA expression in patients with the genotype GT of rs2606241 and the genotype CT of rs2853741 might increase the risk of 5-FU-induced HFS because of the high efficacy of TYMS inhibition. However, further eQTL data did not afford much information about the association between rs2606241 and rs2853741 and gene expression level of TYMS. Whether these two polymorphisms are implicated in gene regulation at a post-transcriptional level through decreased mRNA stability needs further validation.

The specific mechanisms underlying the effect of the above mentioned genotypes on HFS are still not quite valid. We also cannot neglect that these results must be view as preliminary and need additional confirmation. Further prospective studies on a larger number of patients are desirable to confirm and quantitate these associations in additional datasets and to understand the mechanistic origins of capecitabine toxicity. Besides, further efforts to identify additional polymorphisms and rare variants associated with capecitabine toxicity remain valid.

Conclusions

In summary, we have identified a panel of potentially useful pharmacogenetic markers predicting capecitabine-induced HFS in MBC patients. Our findings may help clinicians identify patients who have a low risk of capecitabine-induced HFS and improve treatment decision for MBC patients.

Notes

Acknowledgements

The authors wish to thank all the study participants, research staff, and students who participated in this work.

The abstract has been presented in the poster session in the 2018 American Society of Clinical Oncology Annual Meeting, Chicago, Illinois, USA, June 1–5, 2018.

Authors’ contributions

All authors have contributed significantly, and all authors were in agreement with the content of the manuscript. BX and FM directed the study, obtained financial support and were responsible for study design. SL drafted the initial manuscript. XG carried out genotyping and statistical analyses. JY performed overall project management and sample processing. PY, JW, YL, YF, RC, QL, SC, PZ and QL collected clinical data. All authors read and approved the final manuscript.

Funding

This work was supported by the Chinese Academic of Medical Sciences Initiative for Innovative Medicine (CAMS-12M-1-010) and Beijing Municipal Science & Technology Commission (Z151100004015024).

Ethics approval and consent to participate

The study was approved by the ethics committee of National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College. All patients voluntarily signed an informed consent form.

Consent for publication

We have obtained consent from the participants to publish and report individual patient data.

Competing interests

The authors declare that they have no competing interests.

References

  1. 1.
    da Costa Vieira RA, Biller G, Uemura G, Ruiz CA, Curado MP. Breast cancer screening in developing countries. Clinics. 2017;72(4):244–53.CrossRefGoogle Scholar
  2. 2.
    Dawood S, Broglio K, Ensor J, Hortobagyi GN, Giordano SH. Survival differences among women with de novo stage IV and relapsed breast cancer. Ann Oncol. 2010;21(11):2169–74.CrossRefGoogle Scholar
  3. 3.
    Cetin B, Benekli M, Turker I, Koral L, Ulas A, Dane F, et al. Lapatinib plus capecitabine for HER2-positive advanced breast cancer: a multicentre study of Anatolian Society of Medical Oncology (ASMO). J Chemother. 2014;26(5):300–5.CrossRefGoogle Scholar
  4. 4.
    Saura C, Garcia-Saenz JA, Xu B, Harb W, Moroose R, Pluard T, et al. Safety and efficacy of neratinib in combination with capecitabine in patients with metastatic human epidermal growth factor receptor 2-positive breast cancer. J Clin Oncol. 2014;32(32):3626–33.CrossRefGoogle Scholar
  5. 5.
    Schmoll HJ, Twelves C, Sun W, O’Connell MJ, Cartwright T, McKenna E, et al. Effect of adjuvant capecitabine or fluorouracil, with or without oxaliplatin, on survival outcomes in stage III colon cancer and the effect of oxaliplatin on post-relapse survival: a pooled analysis of individual patient data from four randomised controlled trials. Lancet Oncol. 2014;15(13):1481–92.CrossRefGoogle Scholar
  6. 6.
    Bang YJ, Kim YW, Yang HK, Chung HC, Park YK, Lee KH, et al. Adjuvant capecitabine and oxaliplatin for gastric cancer after D2 gastrectomy (CLASSIC): a phase 3 open-label, randomised controlled trial. Lancet. 2012;379(9813):315–21.CrossRefGoogle Scholar
  7. 7.
    Brandi G, Venturi M, De Lorenzo S, Garuti F, Frega G, Palloni A, et al. Sustained complete response of advanced hepatocellular carcinoma with metronomic capecitabine: a report of three cases. Cancer Commun. 2018;38(1):41.CrossRefGoogle Scholar
  8. 8.
    Yu X, Wang QX, Xiao WW, Chang H, Zeng ZF, Lu ZH, et al. Neoadjuvant oxaliplatin and capecitabine combined with bevacizumab plus radiotherapy for locally advanced rectal cancer: results of a single-institute phase II study. Cancer Commun. 2018;38(1):24.CrossRefGoogle Scholar
  9. 9.
    Pallis AG, Boukovinas I, Ardavanis A, Varthalitis I, Malamos N, Georgoulias V, et al. A multicenter randomized phase III trial of vinorelbine/gemcitabine doublet versus capecitabine monotherapy in anthracycline- and taxane-pretreated women with metastatic breast cancer. Ann Oncol. 2012;23(5):1164–9.CrossRefGoogle Scholar
  10. 10.
    Baratelli C, Zichi C, Di Maio M, Brizzi MP, Sonetto C, Scagliotti GV, et al. A systematic review of the safety profile of the different combinations of fluoropyrimidines and oxaliplatin in the treatment of colorectal cancer patients. Crit Rev Oncol Hematol. 2018;122:21–9.CrossRefGoogle Scholar
  11. 11.
    Peng J, Dong C, Wang C, Li W, Yu H, Zhang M, et al. Cardiotoxicity of 5-fluorouracil and capecitabine in Chinese patients: a prospective study. Cancer Commun. 2018;38(1):22.CrossRefGoogle Scholar
  12. 12.
    Webster-Gandy JD, How C, Harrold K. Palmar-plantar erythrodysesthesia (PPE): a literature review with commentary on experience in a cancer centre. Eur J Oncol Nurs. 2007;11(3):238–46.CrossRefGoogle Scholar
  13. 13.
    Miwa M, Ura M, Nishida M, Sawada N, Ishikawa T, Mori K, et al. Design of a novel oral fluoropyrimidine carbamate, capecitabine, which generates 5-fluorouracil selectively in tumours by enzymes concentrated in human liver and cancer tissue. Eur J Cancer. 1998;34(8):1274–81.CrossRefGoogle Scholar
  14. 14.
    Thorn CF, Marsh S, Carrillo MW, McLeod HL, Klein TE, Altman RB. PharmGKB summary: fluoropyrimidine pathways. Pharmacogenet Genomics. 2011;21(4):237–42.PubMedGoogle Scholar
  15. 15.
    Xu XL, Zheng J, Mao WM, Ling ZQ. RRM1 *151A>T, RRM1 −756T>C, and RRM1 −585T>Gis associated with increased susceptibility of lung cancer in Chinese patients. Cancer Med. 2016;5(8):2084–90.CrossRefGoogle Scholar
  16. 16.
    Rosmarin D, Palles C, Pagnamenta A, Kaur K, Pita G, Martin M, et al. A candidate gene study of capecitabine-related toxicity in colorectal cancer identifies new toxicity variants at DPYD and a putative role for ENOSF1 rather than TYMS. Gut. 2015;64(1):111–20.CrossRefGoogle Scholar
  17. 17.
    Rosmarin D, Palles C, Church D, Domingo E, Jones A, Johnstone E, et al. Genetic markers of toxicity from capecitabine and other fluorouracil-based regimens: investigation in the QUASAR2 study, systematic review, and meta-analysis. J Clin Oncol. 2014;32(10):1031–9.CrossRefGoogle Scholar
  18. 18.
    Caronia D, Martin M, Sastre J, de la Torre J, Garcia-Saenz JA, Alonso MR, et al. A polymorphism in the cytidine deaminase promoter predicts severe capecitabine-induced hand-foot syndrome. Clin Cancer Res. 2011;17(7):2006–13.CrossRefGoogle Scholar
  19. 19.
    Loganayagam A, Arenas Hernandez M, Corrigan A, Fairbanks L, Lewis CM, Harper P, et al. Pharmacogenetic variants in the DPYD, TYMS, CDA and MTHFR genes are clinically significant predictors of fluoropyrimidine toxicity. Br J Cancer. 2013;108(12):2505–15.CrossRefGoogle Scholar
  20. 20.
    Abecasis GR, Altshuler D, Auton A, Brooks LD, Durbin RM, Gibbs RA, et al. A map of human genome variation from population-scale sequencing. Nature. 2010;467(7319):1061–73.CrossRefGoogle Scholar
  21. 21.
    Westra H-J, Peters MJ, Esko T, Yaghootkar H, Schurmann C, Kettunen J, et al. Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet. 2013;45:1238.CrossRefGoogle Scholar
  22. 22.
    Azuma Y, Hata K, Sai K, Udagawa R, Hirakawa A, Tohkin M, et al. Significant association between hand-foot syndrome and efficacy of capecitabine in patients with metastatic breast cancer. Biol Pharm Bull. 2012;35(5):717–24.CrossRefGoogle Scholar
  23. 23.
    Kurt M, Aksoy S, Guler N. Could the hand-foot syndrome after capecitabine treatment be associated with better outcome in metastatic breast cancer patients? Acta Oncol. 2006;45(5):625–6.CrossRefGoogle Scholar
  24. 24.
    Zielinski C, Lang I, Beslija S, Kahan Z, Inbar MJ, Stemmer SM, et al. Predictive role of hand-foot syndrome in patients receiving first-line capecitabine plus bevacizumab for HER2-negative metastatic breast cancer. Br J Cancer. 2016;114(2):163–70.CrossRefGoogle Scholar
  25. 25.
    Wheeler HE, Gonzalez-Neira A, Pita G, de la Torre-Montero JC, Alonso R, Lopez-Fernandez LA, et al. Identification of genetic variants associated with capecitabine-induced hand-foot syndrome through integration of patient and cell line genomic analyses. Pharmacogenet Genomics. 2014;24(5):231–7.PubMedPubMedCentralGoogle Scholar
  26. 26.
    Ribelles N, Lopez-Siles J, Sanchez A, Gonzalez E, Sanchez MJ, Carabantes F, et al. A carboxylesterase 2 gene polymorphism as predictor of capecitabine on response and time to progression. Curr Drug Metab. 2008;9(4):336–43.CrossRefGoogle Scholar
  27. 27.
    Largillier R, Etienne-Grimaldi MC, Formento JL, Ciccolini J, Nebbia JF, Ginot A, et al. Pharmacogenetics of capecitabine in advanced breast cancer patients. Clin Cancer Res. 2006;12(18):5496–502.CrossRefGoogle Scholar
  28. 28.
    Cohen V, Panet-Raymond V, Sabbaghian N, Morin I, Batist G, Rozen R. Methylenetetrahydrofolate reductase polymorphism in advanced colorectal cancer: a novel genomic predictor of clinical response to fluoropyrimidine-based chemotherapy. Clin Cancer Res. 2003;9(5):1611–5.PubMedGoogle Scholar
  29. 29.
    Etienne MC, Formento JL, Chazal M, Francoual M, Magne N, Formento P, et al. Methylenetetrahydrofolate reductase gene polymorphisms and response to fluorouracil-based treatment in advanced colorectal cancer patients. Pharmacogenetics. 2004;14(12):785–92.CrossRefGoogle Scholar
  30. 30.
    Xie SZ, Liu ZZ, Yu JH, Liu L, Wang W, Xie DL, et al. Association between the MTHFR C677T polymorphism and risk of cancer: evidence from 446 case-control studies. Tumour Biol. 2015;36(11):8953–72.CrossRefGoogle Scholar
  31. 31.
    Li K, Li W, Dong X. Association of 677 C>T (rs1801133) and 1298 A>C (rs1801131) polymorphisms in the MTHFR gene and breast cancer susceptibility: a meta-analysis based on 57 individual studies. PLoS ONE. 2014;9(6):e71290.CrossRefGoogle Scholar
  32. 32.
    Yan Y, Han F, Fu H, Xia W, Qin X. Association between MTHFR C677T polymorphism and thyroid cancer risk: a meta-analysis. Tumour Biol. 2014;35(8):7707–12.CrossRefGoogle Scholar
  33. 33.
    He L, Shen Y. MTHFR C677T polymorphism and breast, ovarian cancer risk: a meta-analysis of 19,260 patients and 26,364 controls. Onco Targets Ther. 2017;10:227–38.CrossRefGoogle Scholar
  34. 34.
    Ruzzo A, Graziano F, Loupakis F, Rulli E, Canestrari E, Santini D, et al. Pharmacogenetic profiling in patients with advanced colorectal cancer treated with first-line FOLFOX-4 chemotherapy. J Clin Oncol. 2007;25(10):1247–54.CrossRefGoogle Scholar
  35. 35.
    Ruzzo A, Graziano F, Loupakis F, Santini D, Catalano V, Bisonni R, et al. Pharmacogenetic profiling in patients with advanced colorectal cancer treated with first-line FOLFIRI chemotherapy. Pharmacogenomics J. 2008;8(4):278–88.CrossRefGoogle Scholar
  36. 36.
    Schwab M, Zanger UM, Marx C, Schaeffeler E, Klein K, Dippon J, et al. Role of genetic and nongenetic factors for fluorouracil treatment-related severe toxicity: a prospective clinical trial by the German 5-FU Toxicity Study Group. J Clin Oncol. 2008;26(13):2131–8.CrossRefGoogle Scholar
  37. 37.
    Gusella M, Frigo AC, Bolzonella C, Marinelli R, Barile C, Bononi A, et al. Predictors of survival and toxicity in patients on adjuvant therapy with 5-fluorouracil for colorectal cancer. Br J Cancer. 2009;100(10):1549–57.CrossRefGoogle Scholar
  38. 38.
    Afzal S, Jensen SA, Vainer B, Vogel U, Matsen JP, Sorensen JB, et al. MTHFR polymorphisms and 5-FU-based adjuvant chemotherapy in colorectal cancer. Ann Oncol. 2009;20(10):1660–6.CrossRefGoogle Scholar
  39. 39.
    Longley DB, Harkin DP, Johnston PG. 5-fluorouracil: mechanisms of action and clinical strategies. Nat Rev Cancer. 2003;3(5):330–8.CrossRefGoogle Scholar
  40. 40.
    Pinedo HM, Peters GF. Fluorouracil: biochemistry and pharmacology. J Clin Oncol. 1988;6(10):1653–64.CrossRefGoogle Scholar
  41. 41.
    Etienne MC, Chazal M, Laurent-Puig P, Magne N, Rosty C, Formento JL, et al. Prognostic value of tumoral thymidylate synthase and p53 in metastatic colorectal cancer patients receiving fluorouracil-based chemotherapy: phenotypic and genotypic analyses. J Clin Oncol. 2002;20(12):2832–43.CrossRefGoogle Scholar
  42. 42.
    Ooyama A, Okayama Y, Takechi T, Sugimoto Y, Oka T, Fukushima M. Genome-wide screening of loci associated with drug resistance to 5-fluorouracil-based drugs. Cancer Sci. 2007;98(4):577–83.CrossRefGoogle Scholar
  43. 43.
    Lecomte T, Ferraz JM, Zinzindohoue F, Loriot MA, Tregouet DA, Landi B, et al. Thymidylate synthase gene polymorphism predicts toxicity in colorectal cancer patients receiving 5-fluorouracil-based chemotherapy. Clin Cancer Res. 2004;10(17):5880–8.CrossRefGoogle Scholar

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Shaoyan Lin
    • 1
  • Jian Yue
    • 2
  • Xiuwen Guan
    • 1
  • Peng Yuan
    • 2
  • Jiayu Wang
    • 1
  • Yang Luo
    • 1
  • Ying Fan
    • 1
  • Ruigang Cai
    • 1
  • Qiao Li
    • 1
  • Shanshan Chen
    • 1
  • Pin Zhang
    • 1
  • Qing Li
    • 1
  • Fei Ma
    • 1
    Email author
  • Binghe Xu
    • 1
    Email author
  1. 1.Department of Medical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingP. R. China
  2. 2.Department of VIP Medical Services, National Cancer Center/National Clinical Research Center for Cancer/Cancer HospitalChinese Academy of Medical Sciences & Peking Union Medical CollegeBeijingP. R. China

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