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Genotyping analysis and 18FDG uptake in breast cancer patients: a preliminary research



Diagnostic imaging plays a relevant role in the care of patients with breast cancer (BC). Positron Emission Tomography (PET) with 18F-fluoro-2-deoxy-D-glucose (FDG) has been widely proven to be a clinical tool suitable for BC detection and staging in which the glucose analog supplies metabolic information about the tumor. A limited number of studies, sometimes controversial, describe possible associations between FDG uptake and single nucleotide polymorphisms (SNPs). For this reason this field has to be explored and clarified. We investigated the association of SNPs in GLUT1, HIF-1a, EPAS1, APEX1, VEGFA and MTHFR genes with the FDG uptake in BC.


In 26 caucasian individuals with primary BC, whole-body PET-CT scans were obtained and quantitative analysis was performed by calculating the maximum Standardized Uptake Value normalized to body-weight (SUVmax) and the mean SUV normalized to body-weight corrected for partial volume effect (SUVpvc). Human Gene Mutation Database and dbSNP Short Genetic Variations database were used to analyze gene regions containing the selected SNPs. Patient genotypes were obtained using Sanger DNA sequencing analysis performed by Capillary Electrophoresis.


BC patients were genotyped for the following nine SNPs: GLUT1: rs841853 and rs710218; HIF-1a: rs11549465 and rs11549467; EPAS1: rs137853037 and rs137853036; APEX1: rs1130409; VEGFA: rs3025039 and MTHFR: rs1801133. In this work correlations between the nine potentially useful polymorphisms selected and previously suggested with tracer uptake (using both SUVmax and SUVpvc) were not found.


The possible functional influence of specific SNPs on FDG uptake needs further studies in human cancer. In summary, this is the first pilot study, to our knowledge, which investigates the association between a large panel of SNPs and FDG uptake specifically in BC patients. This work represents a multidisciplinary and translational medicine approach to study BC where, the possible correlation between SNPs and tracer uptake, may be considered to improve personalized cancer treatment and care.


Breast cancer (BC) is the leading cause of cancer-related death in women world-wide [1] and presents distinct subtypes associated with different clinical outcomes. Understanding this heterogeneity represents a key factor for the development of targeted preventive and therapeutic interventions [24]. Upon cancer disease occurrence, survival outcomes seem to be dependent not only on the histological type but also on the intensity of lesion measured by 18F-fluoro-2-deoxy-D-glucose Positron Emission Tomography (FDG PET) uptake [5]. FDG PET is a non-invasive diagnostic and prognostic tool that assess tumour metabolism and it is used for treatment planning and the evaluation of therapy response [6]. To improve this technique, it is necessary to discover deregulated tumour-specific markers that may serve as molecular targets for the imaging of cancer [7].

The completion of nine large genome-wide association studies [8, 9] introduced single-nucleotide polymorphisms (SNPs) as risk factors for BC disease [10]. Despite considerable progress, their commercial exploitation in clinical applications remains controversial [11, 12]. In addition, the potential functional influence of specific SNPs on tracer PET uptake needs further investigations in human cancer diseases. Indeed, the first study demonstrating an association between a human SNP (rs3025039 of the Vascular Endothelial Growth Factor A, abbreviated as VEGFA) and FDG uptake in BC, has included a restricted number of 37 ductal BC patients without metastases [13].

Although, the possible correlation between gene polymorphisms and FDG uptake is considered an innovative and interesting example of translational medicine approach, where information from multiple sources are combined aiming to a more personalized care, the number of scientific papers is still limited [1318]. Nowadays, candidate targets used for these studies are polymorphisms in the GLUcose Transporter 1 gene (GLUT1 also known as SLC2A1) and the following three hypoxia-related genes: Hypoxia-Inducible Factor 1alpha (HIF-1a), VEGFA and apurinic/apyrimidinic APEX nuclease 1 (APEX1) [1318].

GLUT family members are often over-expressed in most human malignancies [19] and are involved in tumour initiation and progression. However, they are already present in the respective non-cancerous tissue of origin. The class I transporters (GLUT1), and to a much less extent GLUT3, are the most frequently over-expressed genes in cancer cells. Their over-expression positively correlates with several adverse tumour characteristics and PET uptake in BC [20] and various other malignancies [2123]. Regarding the role of GLUT1 on PET imaging, only two authors have shown that rs841853and rs710218 GLUT1 SNPs influence tracer PET uptake [14, 15]. These two SNPs were considered to be able to determine variations on the behaviour of the glucose transporter in various human diseases, such as diabetic nephropathy and clear-cell renal carcinoma [24, 25], where a high significant allele frequency in the population investigated was found, suggesting their potential clinical application. The rs841853 SNP is located in a non-protein coding region (intron 2 of the GLUT1 gene) and seems to have a role in recruiting glucose over the membrane, accelerating growth cell rate. The rs710218 SNP is positioned in the promoter region of the GLUT1 gene adjacent to a putative HIF-1a binding site [26]. HIF-1a controls oxygen delivery and metabolic adaptation to hypoxia via angiogenesis and glycolysis, respectively and it also regulates, under hypoxic conditions, the expression of genes, like the GLUT1 gene. Numerous clinico-pathological studies have demonstrated that unlike mature normal tissues, 40%-80% of human carcinomas, like melanomas, sarcomas, head and neck, lung, breast and endometrial cancers contain elevated cytoplasmic and nuclear HIF-1a levels [2729]. In addition, Fu XS. et al. and Koukourakis MI. et al. showed that HIF-1a gene polymorphisms, such as rs11549465 and rs11549467, affect its expression [30, 31]. These SNPs seem to be also related with FDG uptake as described by Kim SJ. and co-workers [15].

Hypoxia-inducible factor 2 alpha (HIF-2a), also known as endothelial PAS domain protein 1 (EPAS1), is another member of the hypoxia-inducible factor family and shares many similarities with HIF-1a [32, 33]. However several molecular, biochemical, and physiological studies have established that HIF-1a and HIF-2a are not redundant but have distinct functions [34]. To understand the possible relationship of EPAS1 and the abovementioned HIF-1a SNPs to FDG uptake, we analyzed the only two EPAS1 missense mutations (rs137853037 and rs137853036) with probable pathogenicity as described in the dbSNP Short Genetic Variations database and in the Human Gene Mutation Database where a collection of known gene lesions responsible for human inherited diseases is found.

APEX1, a DNA base excision repair enzyme, has also a role in transcriptional activation of HIF-1 and the hypoxia inducible factor-like factor (HLF). APEX1 polymorphisms have been the object of studies about in several types of cancer including colorectal, breast and non-small cell lung cancer (NSCLC) in order to evaluate their role in cancer susceptibility, development and response to radiotherapy [15, 35]. Interestingly, in NSCLC patients with the APEX1 rs1130409 TT genotype an association, not fully clarified yet, between the abovementioned rs710218 GLUT1 SNP and FDG uptake was shown [15].

Overall, all previous studies have investigated SNPs of a limited number of genes. Furthermore, the type of cancer tissue varies, rendering difficult the evaluation of their real impact on FDG PET uptake in specific cancer types. To our knowledge, no studies have examined the simultaneous presence and role of these specific polymorphisms in BC patients. Therefore, the purpose of this preliminary research was to highlight possible associations between the abovementioned SNPs of the GLUT1, HIF-1a, EPAS1, APEX1 and VEGFA genes and the FDG uptake, in order to identify a large panel of SNPs, for imaging analysis that will allow a more personalized treatment program.



Thirty-three caucasian individuals with primary BC were enrolled for a multidisciplinary project named “Tissue characterization in primary BC: correlation with FDG-PET uptake and with choline peak by proton nuclear MR spectroscopy”. Inclusion criteria for genotyping analysis were: patients candidated for surgery of invasive BC with a tumour size of at least 2 cm, as measured by mammography and breast ultrasonography and not treated with primary chemotherapy. Twenty-six BC patients were finally selected for genotyping analysis using the abovementioned inclusion criteria. The study and the consent procedure were performed according to the Helsinki declaration and were approved by the Ethical Committee (EC) of HSR G. Giglio hospital, Cefalù-Italy.


Before surgical resection of primitive BC, all patients underwent FDG PET-CT studies. The patients were fasted for twelve hours before performing PET-CT scan, and were injected intravenously with FDG (37MBq/10 kg). Patients with a blood glucose level greater than 150 mg/dl were not included in the study. The weight of each patient was measured the day of the PET-CT study. Actual injected and residual radioactivity were measured by the dose measurement system. PET-CT acquisition started 50 min after radiotracer injection and images were acquired from the top of the skull to the middle of the thigh with the arms raised. Whole-body PET-CT scans were obtained using a Discovery STE scanner (General Electric Medical Systems), installed at the Nuclear Medicine Department of LATO-HSR (Cefalù, Italy). The system is a three-dimensional BGO 47 slice PET scanner combined with an helical 8 slice CT scanner. The PET-CT oncological protocol included a low dose CT scan and a 3D PET whole body scan (2.5 min/bed position). Patients breathed normally during the PET and CT exams. PET images were reconstructed by a 3D ordered subset expectation maximization algorithm (OSEM, 28 subsets, 2 iterations, 5.14 mm Gaussian post-smoothing) with corrections for random, scatter and attenuation incorporated into the iterative process.

Quantitative PET measurements

Quantitative analysis was performed calculating, for each breast lesion, the maximum Standardized Uptake Value (SUVmax) and the mean SUV (SUVpvc) normalized to body-weight. Partial volume effect correction (PVC) was performed to compensate spill in (signal from background region that goes inside the lesion) and spill out (signal from the lesion that goes into background region) effects in the SUVpvc [36, 37]. Since the SUVmax is the uptake index least affected by partial volume effect no correction was applied.

Briefly, the PVC method is based on recovery coefficient (RC) curves obtained from NEMA 2001 IQ phantom (equipped with six spheres of different sizes - from 10 mm to 37 mm- to account for size effect) as a function of PET measured metabolic volume and of PET measured sphere-to-background ratio [38]. The metabolic volume was calculated as the 60% isocontour of the maximum pixel intensity automatically drawn on the PET lesion. The radioactivity concentration in the lesion was measured as the average radioactivity concentration within the metabolic volume. The background radioactivity concentration was obtained as the average of four circular ROIs positioned over the background around the lesion. To apply the PVC correction method, PET measured metabolic volumes and lesion-to-background ratios were considered within the following ranges of RC curves: measured diameters (derived from metabolic volume) from 0 to 4 cm and lesion-to-background ratios from 2 to 30.

BC patients genotyping

In this study BC patients were genotyped, by sequencing analysis, for the following polymorphisms: GLUT1 rs841853 (also known as Xba I because is a restriction fragment length polymorphism-RFLP) and rs710218 SNPs (also known as Hpy CH4V); HIF-1a rs11549465 and rs11549467 SNPs; EPAS1 rs137853037 and rs137853036 SNPs; APEX1 rs1130409 SNP; VEGFA rs3025039 and MTHFR rs1801133 SNP.

Human Gene Mutation Database [39] and dbSNP Short Genetic Variations database [40] were used to analyze gene regions containing the selected SNPs. Genomic DNA was extracted from peripheral blood using QIAamp DNA blood mini kit, according to the manufacturer’s specifications (Qiagen). After quality and quantity analysis, genomic DNA was PCR amplified using primers designed by the Primer3 software [41] and listed in Table 1. PCR reactions were performed with 50 ng of genomic DNA in a total volume of 50 μL containing 1X PCR Gold Buffer, 1,5 mM di MgCl2, 200 μM dNTPs, 200 nM of forward and reverse primer mix, 1.25 U of AmpliTaq Gold DNA Polymerase (Applied Biosystems). The thermal cycle profile employed a 5-min denaturing step at 94°C, followed by 35 cycles at 94°C for 45 sec, 59°C for 45 sec, 72°C for 45 sec and a final extension step of 5 min at 72°C.

Table 1 Primers sequence used for genotyping analysis

Quality and quantity of PCR products were assessed on the Bioanalyzer instrument (Agilent Technologies) and were purified using QIAquick PCR purification kit (Qiagen), according to the manufacturer’s specifications. To perform DNA sequencing, purified amplicons were labelled with BigDye Terminator v3.1 Cycle Sequencing Kit following the manufacturer’s standard protocol (Applied Biosystems). The thermal cycle profile employed a 1 min denaturing step at 96°C, followed by 25 cycles at 96°C for 10 sec, 54°C for 5 sec, 60°C for 3 min. Labelled samples were purified with X-terminator purification kit according to manufacturer’s standard protocol and loaded in 3500-Dx Genetic Analyzer (Applied Biosystems) for separation by capillary electrophoresis. Electropherograms and sequence files were analyzed using Sequencing Analysis and SeqScape softwares (Applied Biosystems).

Statistical analysis

The association between the SUVmax and SUVpvc values and genetic factors was analyzed using GraphPad InStat software version 3.05, San Diego California USA. Mann Whitney-U test and Fisher’s exact test were performed. Differences in groups for the medians SUVmax and SUVpvc values were tested. Differences were considered significant when p value was less than or equal to 0.05.



The average age of 26 selected BC patients for genotyping analysis was 56.9 y (age range, 36–88 y; SD, 15.6 y).

FDG PET-CT & quantitative PET measurements

SUVmax and SUVpvc values are shown in Table 2. The average of SUVmax was 7.67 ± 4.01 (range: 1.95-17.65; 95% confidence interval (C.I.) 6.05-9.29). The average of SUVpvc was 7.58 ± 3.88 (range: 2.64-19.15;; 95% C.I. 6.02-9.15), the mean sphere-equivalent diameter of PET measured metabolic volume was 1.39 ± 0.44 cm (range: 0.8-2.55; 95% C.I. 1.21-1.56) and the average PET measured lesion-to-background ratio was 12.12 ± 5.65 (range: 1.92-25.79; 95% C.I. 9.84-14.40). In all cases the lesions had a measured sphere-equivalent diameter and a measured lesion-to-background ratio within the range of the RC curves. PET-TC images will be available in confidence with the radiology reader upon request.

Table 2 SUVmax and SUVpvc values

BC patients mutation analysis of the eight SNPs panel

BC patients, were genotyped for the eight SNPs previously introduced (GLUT1: rs841853 and rs710218; HIF-1a: rs11549465 and rs11549467; EPAS1: rs137853037 and rs137853036; APEX1: rs1130409; VEGFA: rs3025039). Allele frequencies and the percentages of the three possible genotypes for each SNP were calculated. Deviations of Hardy-Weinberg equilibrium were not observed for all SNPs except for the rs3025039 VEGFA polymorphism (Table 3).

Table 3 SNPs analysis results

Association between SNPs and SUV values

In order to evaluate a potential association between the SNPs and the PET tracer uptake, SUVmax and SUVpvc values were used. Patients were divided in subgroups according to SNP genotype and a Mann-Whittney statistical test was performed to evaluate the differences in SUVmax and SUVpvc levels. Unfortunately, the genotype sample size for HIF-1a: rs11549467 and EPAS1: rs137853037 and rs137853036 SNPs was insufficient to apply a statistical analysis (Table 3). No genotype of the selected SNPs showed any significant association with PET tracer uptake (Table 4).

Table 4 Association between genotype and SUVmax and SUVpvc values in BC patients

We also classified the patients into subgroups according to their SUV values (subgroup with high SUV values versus low SUV values one, for both SUVmax and SUVpvc). A Fisher’s exact analysis confirmed that no significant association between PET tracer uptake and specific SNP profiles exists.

Kim SJ. and colleagues have shown that the GLUT1 rs710218 polymorphism is significantly associated with SUVmax in combination with APEX1 rs1130409 SNP in NSCLC disease [15]. To investigate its putative role in FDG uptake in BC, we studied the association between the GLUT1 rs710218 SNP and SUVmax and SUVpvc in patients classified according the APEX1 rs1130409 genotype. The levels of SUVmax and SUVpvc were similar because p value was greater than 0.05 in all GLUT1 rs710218 genotype groups regardless the APEX1 rs1130409 genotype (Table 5).

Table 5 Association between the rs710218 GLUT1 SNP and SUVmax and SUVpvc values in BC patients according to APEX1 rs1130409 genotype

MTHFR SNPs and FDG uptake association analysis

Considering that thromboembolism is a major complication in patients with BC, accompanied also by significant morbidity and mortality [42], we decided to investigate the role of thromboembolism-related SNPs in PET tracer uptake. The rs1801133 and rs181131 SNPs of the 5,10-methylenetetrahydrofolate reductase (MTHFR) gene, encoding for a key enzyme in the folate metabolism pathway, have been associated with reduced enzyme activity and hyperhomocysteinemia related with thromboembolic events [43] and affect chemosensitivity of tumour cells. In addition, Jakubowska A. and co-workers found that the rs1801133 MTHFR SNP is associated with an increased risk for breast and ovarian cancer [44, 45]. MTHFR rs1801133 allele frequencies and the percentages of the three possible genotypes were calculated and deviations of Hardy-Weinberg equilibrium were not observed [46]. No genotype of rs1801133 showed any significant association with PET tracer uptake, as revealed both by Mann-Whittney and Fisher’s exact statistical analysis because p value was greater than 0.05 (Table 4).


Today, a very limited number of reports describe possible associations between FDG uptake and SNPs, rendering this field poorly explored and clarified [1318]. Our study investigated the possible simultaneous association between polymorphisms in GLUT1, HIF-1a, EPAS1, APEX1,VEGFA and MTHFR genes and the FDG-PET uptake. To our knowledge, this is the first work that evaluates the collective impact of the abovementioned SNPs on PET tracer uptake in BC patients. FDG uptake, expressed in terms of SUVmax or SUVpvc, is largely dependent on glucose metabolism. High values are associated with reduced overall survival in cancer patients [41].

GLUT1 is the primary transporter of glucose metabolism and its over-expression has an important role in the survival and rapid growth of cancer cells. The rs841853 polymorphism of GLUT1 is located on the second intron of the gene and as suggested by Kim SJ et al. [15], no change would be expected in the GLUT1 protein sequence and expression. However, the GG genotype, which occurs in about 52% of the European population (data derived by dbSNP Short Genetic Variations database) seems to be related to FDG uptake in BC patients [14]. In our work, although we did not observe deviation from the Hardy–Weinberg equilibrium, we did not find the association between this SNP and the FDG tumour uptake in BC.

The promoter region of the GLUT1 gene harbours another SNP, rs710218 (named also SLC2A1 Hpy CH4V), positioned 400 bp upstream of a putative HIF-1a binding site. Its close proximity to the hypoxia response elements (HRE) may modify the binding affinity of HIF-1 and thus alter the efficiency of the promoter and expression of GLUT1 [24]. In our study, the allele frequencies of rs710218 SNP did not differ significantly from those available in NCBI dbSNP database and no association between this genetic alteration and SUVmax or SUVpvc was found in BC patients, confirming similar data recently obtained in NSCLC [15]. No significant association with FDG uptake exists also when we examined this SNP in combination with the APEX1 rs1130409 genotypes.

APEX1 promotes transcriptional activation of HIF-1 and its reduced levels are related to a decrease in tumour volume and FDG uptake, suggesting that it affects glucose metabolism and cellular proliferation [41]. Homozygosity (TT genotype) for the rs1130409 APEX1 SNP was significantly associated with a poor overall cancer survival [15]. Here, this genotype was not significantly associated with SUV, compared with the GG/TG genotypes, as previously shown by by Kim SJ et al. [15].

HIF1a itself has an SNP (rs11549465) that we studied for possible association with FDG uptake. However, we observed no association in BC disease, in agreement with data previously obtained in NSCLC [15].

VEGFA rs3025039 polymorphism has been related with BC risk and a C > T polymorphism at position 936 in the 3’ untranslated region of the VEGFA gene has been associated with VEGF plasma levels. Specifically, the T-variant is linked to lower VEGF level and associated with increased BC risk [13] and worse outcome [17] compared to the wildtype allele. Wolf G. and co-workers [13] suggested a potential role of this VEGFA polymorphism on the variability of FDG uptake in tumour tissue. However, our study and data reported by Lorenzen S. et al. [17] do not confirm this association.

The MTHFR rs1801133 SNP is highly represented in the Caucasian population [46] and it is related to increased BC risk [3638]. Nevertheless, its role in PET has not been studied yet. Here, we evaluated its importance in FDG uptake, for the first time, finding no associations. Considering its great importance in BC, we still believe that additional studies are needed to clarify its relevance.

Unfortunately, the genotype distributions for the remaining HIF1a: rs11549467, EPAS1: rs137853037 and rs137853036 SNPs did not allow us to evaluate their possible association with SUV.

The possible association between FDG uptake and SNPs is described by a limited number of studies, due to the need for multidisciplinary team and expertise. Moreover, this research field is characterized by controversial reports.

Moreover a strong variability of FDG-PET uptake on BC tissue has been reported [13], but the reason for this variability is not fully understood and may involve various cellular processes and risk factors such as genetic predisposition. Overall, our analysis succeeded to reproduce some previous findings, while we failed to confirm others, which still need to be further investigated. These discrepancies can be explained by the shortage of patients assayed both in our work and previous studies [1315].

In addition these works looked at different groups of people from various European countries. Therefore it is harder to compare findings and to draw conclusions: the geographical and racial/ethnic distribution of an allele and associated genotypes are considered extremely important to fully understand the risk and the development of treatments related to gene polymorphisms (sometimes the allele frequency varies from region to region in the same country) [47].

Concluding, none of the reported analyses included functional evaluation of SNPs in FDG PET uptake. In our work, the potentially useful polymorphisms were not found associated with FDG uptake, using both SUVmax and SUVpvc.

Taking into consideration the clinical impact of a significant association between genetic alterations and PET-CT could have in BC treatment and since current knowledge is limited, additional and larger studies are required to assess the importance of these genotypic variants in the phenotypes or biological functions. Additionally, we cannot exclude the possibility that unknown or known SNPs, not investigated yet, in the same genes could have an important role.


This is the first report to our knowledge investigating the association between a large panel of SNPs genotypes and FDG uptake in BC patients. In this work we shown that none of the nine potentially useful polymorphisms selected and previously suggested by other authors were statistically correlated with FDG PET-CT tracer uptake (using both SUVmax and SUVpvc). The possible functional influence of specific SNPs on FDG uptake needs further studies in human cancer. Concluding, this work represents a multidisciplinary and translational medicine approach to study BC where the possible correlation between gene polymorphisms and tracer uptake may be considered to improve personalized cancer treatment and care.


  1. 1.

    Kamangar F, Dores GM, Anderson WF: Patterns of cancer incidence, mortality, and prevalence across five continents: defining priorities to reduce cancer disparities in different geographic regions of the world. J Clin Oncol. 2006, 24: 2137-2150. 10.1200/JCO.2005.05.2308.

    Article  Google Scholar 

  2. 2.

    Rakha EA, El-Sayed ME, Reis-Filho JS, Ellis IO: Expression profiling technology: its contribution to our understanding of breast cancer. Histopathology. 2008, 52: 67-81.

    CAS  Article  Google Scholar 

  3. 3.

    Bravatà V, Cammarata FP, Forte GI, Minafra L: “Omics” of HER2 Positive Breast Cancer. OMICS. 2013, 17: 119-129. 10.1089/omi.2012.0099.

    Article  Google Scholar 

  4. 4.

    Minafra L, Norata R, Bravatà V, Viola M, Lupo C, Gelfi C, Messa C: Unmasking epithelial-mesenchymal transition in a breast cancer primary culture: a study report. BMC Res Notes. 2012, 5: 343-10.1186/1756-0500-5-343.

    Article  Google Scholar 

  5. 5.

    Bohndiek SE, Brindle KM: Imaging and ‘omic’ methods for the molecular diagnosis of cancer. Expert Rev Mol Diagn. 2010, 10: 417-434. 10.1586/erm.10.20.

    CAS  Article  Google Scholar 

  6. 6.

    Heron DE, Andrade RS, Beriwal S, Smith RP: PET-CT in radiation oncology: the impact on diagnosis, treatment planning, and assessment of treatment response. Am J Clin Oncol. 2008, 31: 352-362. 10.1097/COC.0b013e318162f150.

    Article  Google Scholar 

  7. 7.

    Yang SY, Adelstein J, Kassis AI: Putative molecular signatures for the imaging of prostate cancer. Expert Rev Mol Diagn. 2010, 10: 65-74. 10.1586/erm.09.73.

    Article  Google Scholar 

  8. 8.

    Thomas G, Jacobs KB, Kraft P, Yeager M, Wacholder S, Cox DG, Hankinson SE, Hutchinson A, Wang Z, Yu K, Chatterjee N, Garcia-Closas M, Gonzalez-Bosquet J, Prokunina-Olsson L, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Diver R, Prentice R, Jackson R, Kooperberg C, Chlebowski R, Lissowska J: A multistage genome-wide association study in breast cancer identifi es two new risk alleles at 1p11.2 and 14q24.1 (RAD51L1). Nat Genet. 2009, 41: 579-584. 10.1038/ng.353.

    CAS  Article  Google Scholar 

  9. 9.

    Hunter DJ, Kraft P, Jacobs KB, Cox DG, Yeager M, Hankinson SE, Wacholder S, Wang Z, Welch R, Hutchinson A, Wang J, Yu K, Chatterjee N, Orr N, Willett WC, Colditz GA, Ziegler RG, Berg CD, Buys SS, McCarty CA, Feigelson HS, Calle EE, Thun MJ, Hayes RB, Tucker M, Gerhard DS, Fraumeni JF, Hoover RN, Thomas G, Chanock SJ: A genome-wide association study identifies alleles in FGFR2 associated with risk of sporadic postmenopausal breast cancer. Nat Genet. 2007, 39: 870-874. 10.1038/ng2075.

    CAS  Article  Google Scholar 

  10. 10.

    Offit K: Breast cancer single-nucleotide polymorphisms: statistical significance and clinical utility. J Natl Cancer Inst. 2009, 101: 973-975. 10.1093/jnci/djp184.

    Article  Google Scholar 

  11. 11.

    Goldstein DB: Common genetic variation and human traits. N Engl J Med. 2009, 360: 1696-1698. 10.1056/NEJMp0806284.

    CAS  Article  Google Scholar 

  12. 12.

    Offit K: Genomic profiles for disease risk: predictive or premature?. JAMA. 2008, 299: 1353-1355. 10.1001/jama.299.11.1353.

    CAS  Article  Google Scholar 

  13. 13.

    Wolf G, Aigner RM, Schaffler G, Langsenlehner U, Renner W, Samonigg H, Yazdani-Biuki B, Krippl P: The 936C > T polymorphism of the gene for vascular endothelial growth factor is associated with 18F-fluorodeoxyglucose uptake. Breast Cancer Res Treat. 2004, 88: 205-208. 10.1007/s10549-004-0724-2.

    CAS  Article  Google Scholar 

  14. 14.

    Grabellus F, Sheu SY, Bachmann HS, Lehmann N, Otterbach F, Heusner TA, Antoch G, Bockisch A, Kimmig R, Schmid KW, Stahl AR: The Xba I G > T polymorphism of the glucose transporter 1 gene modulates 18F-FDG uptake and tumor aggressiveness in breast cancer. J Nucl Med. 2010, 51: 1191-1197. 10.2967/jnumed.110.075721.

    CAS  Article  Google Scholar 

  15. 15.

    Kim SJ, Hwang SH, Kim IJ, Lee MK, Lee CH, Lee SY, Lee EY: The association of 18F-deoxyglucose (FDG) uptake of PET with polymorphisms in the glucose transporter gene (SLC2A1) and hypoxia-related genes (HIF1A, VEGFA, APEX1) in non-small cell lung cancer. SLC2A1 polymorphisms and FDG-PET in NSCLC patients. J Exp Clin Cancer Res. 2010, 12: 29-69.

    Google Scholar 

  16. 16.

    Vänttinen M, Nuutila P, Kuulasmaa T, Pihlajamäki J, Hällsten K, Virtanen KA, Lautamäki R, Peltoniemi P, Takala T, Viljanen AP, Knuuti J, Laakso M: Single nucleotide polymorphisms in the peroxisome proliferator-activated receptor delta gene are associated with skeletal muscle glucose uptake. Diabetes. 2005, 54: 3587-3591. 10.2337/diabetes.54.12.3587.

    Article  Google Scholar 

  17. 17.

    Lorenzen S, Panzram B, Keller G, Lordick F, Herrmann K, Becker K, Langer R, Schwaiger M, Siewert JR, Ott K: Association of the VEGF 936C > T polymorphism with FDG uptake, clinical, histopathological, and metabolic response in patients with adenocarcinomas of the esophagogastric junction. Mol Imaging Biol. 2011, 13: 178-186. 10.1007/s11307-010-0330-0.

    Article  Google Scholar 

  18. 18.

    Kalin NH, Shelton SE, Fox AS, Rogers J, Oakes TR, Davidson RJ: The serotonin transporter genotype is associated with intermediate brain phenotypes that depend on the context of eliciting stressor. Mol Psychiatry. 2008, 13: 1021-1027. 10.1038/mp.2008.37.

    CAS  Article  Google Scholar 

  19. 19.

    Macheda ML, Rogers S, Best JD: Molecular and cellular regulation of glucose transporter (GLUT) proteins in cancer. J Cell Physiol. 2005, 202: 654-662. 10.1002/jcp.20166.

    CAS  Article  Google Scholar 

  20. 20.

    Brown RS, Leung JY, Fisher SJ, Frey KA, Ethier SP, Wahl RL: Intratumoral distribution of tritiated-FDG in breast carcinoma: correlation between Glut-1 expression and FDG uptake. J Nucl Med. 1996, 37: 1042-1047.

    CAS  Google Scholar 

  21. 21.

    Grabellus F, Nagarajah J, Bockisch A, Schmid KW, Sheu SY: Glucose transporter 1 expression, tumor proliferation, and iodine/glucose uptake in thyroid cancer with emphasis on poorly differentiated thyroid carcinoma. Clin Nucl Med. 2012, 37: 121-127. 10.1097/RLU.0b013e3182393599.

    Article  Google Scholar 

  22. 22.

    Hamada K, Tomita Y, Qiu Y, Zhang B, Ueda T, Myoui A, Higuchi I, Yoshikawa H, Aozasa K, Hatazawa J: 18F-FDG-PET of musculoskeletal tumors: a correlation with the expression of glucose transporter 1 and hexokinase II. Ann Nucl Med. 2008, 22: 699-705. 10.1007/s12149-008-0173-9.

    Article  Google Scholar 

  23. 23.

    Westerterp M, Sloof GW, Hoekstra OS, Ten Kate FJ, Meijer GA, Reitsma JB, Boellaard R, Van Lanschot JJ, Molthoff CF: 18FDG uptake in oesophageal adenocarcinoma: linking biology and outcome. J Cancer Res Clin Oncol. 2008, 134: 227-236.

    Article  Google Scholar 

  24. 24.

    Hodgkinson AD, Millward BA, Demaine AG: Polymorphisms of the glucose transporter (GLUT1) gene are associated with diabetic nephropathy. Kidney Int. 2001, 59: 985-989. 10.1046/j.1523-1755.2001.059003985.x.

    CAS  Article  Google Scholar 

  25. 25.

    Page T, Hodgkinson AD, Ollerenshaw M, Hammonds JC, Demaine AG: Glucose transporter polymorphisms are associated with clear-cell renal carcinoma. Cancer Genet Cytogenet. 2005, 163: 151-155. 10.1016/j.cancergencyto.2005.07.004.

    CAS  Article  Google Scholar 

  26. 26.

    Amann T, Kirovski G, Bosserhoff AK, Hellerbrand C: Analysis of a promoter polymorphism of the GLUT1 gene in patients with hepatocellular carcinoma. Mol Membr Biol. 2011, 28: 182-186. 10.3109/09687688.2011.554447.

    Article  Google Scholar 

  27. 27.

    Semenza GL: HIF-1 and tumor progression: pathophysiology and therapeutics. Trends Mol Med. 2002, 8: S62-S67. 10.1016/S1471-4914(02)02317-1.

    CAS  Article  Google Scholar 

  28. 28.

    Semenza GL: Hypoxia-inducible factor 1: master regulator of O2 homeostasis. Curr Opin Genet Dev. 1998, 8: 588-594. 10.1016/S0959-437X(98)80016-6.

    CAS  Article  Google Scholar 

  29. 29.

    Talks KL, Turley H, Gatter KC, Maxwell PH, Pugh CW, Ratcliffe PJ, Harris AL: The expression and distribution of the hypoxia-inducible factors HIF1-alpha and HIF2-alpha in normal human tissues, cancers, and tumorassociated macrophages. Am J Pathol. 2000, 57: 411-421.

    Article  Google Scholar 

  30. 30.

    Fu XS, Choi E, Bubley GJ, Balk SP: Identification of hypoxia-inducible factor-1alpha (HIF-1alpha) polymorphism as a mutation in prostate cancer that prevents normoxia-induced degradation. Prostate. 2005, 63: 215-221. 10.1002/pros.20190.

    CAS  Article  Google Scholar 

  31. 31.

    Koukourakis MI, Papazoglou D, Giatromanolaki A, Panagopoulos I, Maltezos E, Harris AL, Gatter KC, Sivridis E: C2028T polymorphism in exon 12 and dinucleotide repeat polymorphism in intron 13 of the HIF1-alpha gene define HIF1-alpha protein expression in non-small cell lung cancer. Lung Cancer. 2006, 53: 257-262. 10.1016/j.lungcan.2006.05.025.

    Article  Google Scholar 

  32. 32.

    Covello KL, Kehler J, Yu H, Gordan JD, Arsham AM, Hu CJ, Labosky PA, Simon MC, Keith B: HIF-2alpha regulates Oct-4: effects of hypoxia on stem cell function, embryonic development, and tumor growth. Genes Dev. 2006, 20: 557-570. 10.1101/gad.1399906.

    CAS  Article  Google Scholar 

  33. 33.

    Scortegagna M, Ding K, Oktay Y, Gaur A, Thurmond F, Yan LJ, Marck BT, Matsumoto AM, Shelton JM, Richardson JA, Bennett MJ, Garcia JA: Multiple organ pathology, metabolic abnormalities and impaired homeostasis of reactive oxygen species in Epas1−/− mice. Nat Genet. 2003, 35: 331-340. 10.1038/ng1266.

    CAS  Article  Google Scholar 

  34. 34.

    Martin CM, Ferdous A, Gallardo T, Humphries C, Sadek H, Caprioli A, Garcia JA, Szweda LI, Garry MG, Garry DJ: Hypoxia-inducible factor-2alpha transactivates Abcg2 and promotes cytoprotection in cardiac side population cells. Circ Res. 2008, 102: 1075-1081. 10.1161/CIRCRESAHA.107.161729.

    CAS  Article  Google Scholar 

  35. 35.

    Chang-Claude J, Ambrosone CB, Lilla C, Kropp S, Helmbold I, Von Fournier D, Haase W, Sautter-Bihl ML, Wenz F, Schmezer P, Popanda O: Genetic polymorphisms in DNA repair and damage response genes and late normal tissue complications of radiotherapy for breast cancer. Br J Cancer. 2009, 100: 1680-1686. 10.1038/sj.bjc.6605036.

    CAS  Article  Google Scholar 

  36. 36.

    Hoetjes NJ, Van Velden FH, Hoekstra OS, Hoekstra CJ, Krak NC, Lammertsma AA, Boellaard R: Partial volume correction strategies for quantitative FDG PET in oncology. Eur J Nucl Med Mol Imaging. 2010, 37: 1679-1687. 10.1007/s00259-010-1472-7.

    Article  Google Scholar 

  37. 37.

    Soret M, Bacharach SL, Buvat I: Partial-volume effect in PET tumor imaging. J Nucl Med. 2007, 48: 932-945. 10.2967/jnumed.106.035774.

    Article  Google Scholar 

  38. 38.

    Gallivanone F, Stefano A, Canevari C, Gianolli L, Messa C, Gilardi MC, Castiglioni I: PVE correction in PET-CT whole-body oncological studies from PVE-affected images. IEEE Trans Nucl Sci. 2011, 58: 736-747.

    Article  Google Scholar 

  39. 39.

    The Human Gene Mutation Database.,

  40. 40.

    The dbSNP Short Genetic Variations database.,

  41. 41.

    Rozen S, Skaletsky HJ: Primer3 on the WWW for general users and for biologist programmers. Bioinformatics Methods and Protocols: Methods in Molecular Biology. Edited by: Krawetz S, Misener S. 2000, Totowa, NJ: Humana Press, 365-386.,

    Google Scholar 

  42. 42.

    Kyriazi V: Breast cancer as an acquired thrombophilic state. J Breast Cancer. 2012, 15: 148-156. 10.4048/jbc.2012.15.2.148.

    Article  Google Scholar 

  43. 43.

    Hosseini M, Houshmand M, Ebrahimi A: MTHFR polymorphisms and breast cancer risk. Arch Med Sci. 2011, 7: 134-137.

    CAS  Article  Google Scholar 

  44. 44.

    Jakubowska A, Gronwald J, Menkiszak J, Górski B, Huzarski T, Byrski T, Edler L, Lubiński J, Scott RJ, Hamann U: Methylenetetrahydrofolate reductase polymorphisms modify BRCA1-associated breast and ovarian cancer risks. Breast Cancer Res Treat. 2007, 104: 299-308. 10.1007/s10549-006-9417-3.

    CAS  Article  Google Scholar 

  45. 45.

    Matakidou A, El Galta R, Webb EL, Rudd MF, Bridle H, GELCAPS Consortium, Eisen T, Houlston RS: Genetic variation in the DNA repair genes is predictive of outcome in lung cancer. Hum Mol Genet. 2007, 16: 2333-2340. 10.1093/hmg/ddm190.

    CAS  Article  Google Scholar 

  46. 46.

    Balding DJ: A tutorial on statistical methods for population association studies. Nat Rev Genet. 2006, 7 (10): 781-791. 10.1038/nrg1916.

    CAS  Article  Google Scholar 

  47. 47.

    Wilcken B, Bamforth F, Li Z, Zhu H, Ritvanen A, Renlund M, Stoll C, Alembik Y, Dott B, Czeizel AE, Gelman-Kohan Z, Scarano G, Bianca S, Ettore G, Tenconi R, Bellato S, Scala I, Mutchinick OM, López MA, De Walle H, Hofstra R, Joutchenko L, Kavteladze L, Bermejo E, Martínez-Frías ML, Gallagher M, Erickson JD, Vollset SE, Mastroiacovo P, Andria G: Geographical and ethnic variation of the 677C > T allele of 5,10 methylenetetrahydrofolate reductase (MTHFR): findings from over 7000 newborns from 16 areas world wide. J Med Genet. 2003, 40: 619-625. 10.1136/jmg.40.8.619.

    CAS  Article  Google Scholar 

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This work was supported by FIRB/MERIT (RBNE089KHH) and “Proteogenomica e Bioimaging in Medicina” project (n. DM45602). The authors wish to thank Dr. Isabella Castiglioni for helpful discussion, Dr Giusi Forte for useful suggestions and Dr Alexandros Xynos for English manuscript editing. Special thanks to “Breast Unit group” for BC patients enrolling in this study.

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Corresponding author

Correspondence to Valentina Bravatà.

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Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

All authors participated to the conception, design, interpretation, elaboration of the findings of the study, drafting and revising the final elaborate. In particular, Dr. VB designed the study, wrote the paper and with Dr. FPC and Dr. LM performed patients genotyping experiments. Dr. SP selected and enrolled the patients and performed FDG PET-CT studies. Dr. AS performed quantitative PET measurements and with Dr. GR and Dr. SN analysed data. Prof. CG, Prof. MCG and Prof. CM participated in the elaboration of the findings of the study, drafting and revising the final elaborate. All authors read and approved the final content of the manuscript.

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Bravatà, V., Stefano, A., Cammarata, F.P. et al. Genotyping analysis and 18FDG uptake in breast cancer patients: a preliminary research. J Exp Clin Cancer Res 32, 23 (2013).

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  • Breast cancer
  • Single nucleotide polymorphisms
  • PET-CT
  • SUVmax
  • SUVpvc