variation within 3’ utrs of base excision repair genes and … · 2013-09-13 · 1 variation...

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Variation within 3’ UTRs of base excision repair genes and response to therapy in colorectal

cancer patients: a potential modulation of microRNAs binding

Pardini Barbara1, Rosa Fabio1, Barone Elisa 2, Di Gaetano Cornelia1,3, Slyskova Jana4,5, Novotny

Jan6, Levy Miroslav7, Garritano Sonia8, Vodickova Ludmila4,5, Buchler Tomas9, Gemignani

Federica2, Landi Stefano2, Vodicka Pavel4,5, Naccarati Alessio1,4

1Human Genetics Foundation, Turin, Italy

2Dept. of Biology, University of Pisa, Italy

3Dept of Medical Sciences, University of Turin, Turin, Italy.

4Dept. of Molecular Biology of Cancer, Institute of Experimental Medicine, Prague, Czech Republic

5Inst. Biol. Med. Genet., First Faculty of Medicine, Charles University, Prague, Czech Republic

6Dept. of Oncology, First Faculty of Medicine, Charles University, Prague, Czech Republic

7Dept. of Surgery, First Faculty of Medicine, Charles University and Thomayer University Hospital, Prague,

Czech Republic

8Laboratory of Computational Oncology, Center for Integrated Biology (CIBIO), University of Trento,

Trento, Italy

9Dept. of Oncology, Thomayer Hospital and First Faculty of Medicine, Charles University, Prague, Czech

Republic

Corresponding author:

Barbara Pardini

HuGeF- Human Genetics Foundation Torino

Via Nizza, 52 Torino, Italy.

Tel. +39 0116709543

e-mail: [email protected]

Research. on October 19, 2020. © 2013 American Association for Cancerclincancerres.aacrjournals.org Downloaded from

Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on September 13, 2013; DOI: 10.1158/1078-0432.CCR-13-0314

http://clincancerres.aacrjournals.org/

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Keywords: Base Excision Repair (BER) genes; Colorectal cancer; miRNA binding sites; 3’UTR

polymorphisms; Overall survival; Event-free survival; 5-Fluorouracil-based chemotherapy.

Conflict of interest disclosure. The authors declare that they have no conflicts of interest in the

research.




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TRANSLATIONAL RELEVANCE

Base excision repair (BER) is the main pathway involved in resolving DNA lesions, including

misincorporated uracil (or its analogs), as consequences of 5-fluorouracil (5-FU) chemotherapy. A

fine modulation of BER genes expression, such as the post-transcriptional regulation exerted by

microRNAs, could affect the efficiency of this repair system. The presence of single nucleotide

polymorphisms (SNPs) within 3’UTRs of target genes could alter the binding with specific

microRNAs, modulating gene expression and ultimately affecting cancer susceptibility, prognosis

and therapy outcomes.

This study comprehensively evaluated the associations between SNPs in microRNA target binding

sites of BER genes and clinical outcomes in colorectal cancer (CRC) patients. Variations in the

SMUG1 gene provided biologically plausible predictors of survival, with an additional effect of 5-

FU chemotherapy. The validation of those SNPs in interaction with the clinical variables may allow

the identification of CRC patients with a specific prognosis, which can aid the design of

personalized cancer therapy.




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Abstract

Purpose. Colorectal cancer (CRC) is routinely treated with a 5-Fluorouracil (5-FU)-based

chemotherapy. 5-FU incorporates into DNA, and the Base Excision Repair (BER) pathway

specifically recognizes such damage. We investigated the association of single nucleotide

polymorphisms (SNPs) in the 3`untranslated regions (UTRs) of BER genes, and potentially

affecting the microRNA (miRNA) binding, on the risk of CRC, its progression, and prognosis.

SNPs in miRNA binding sites may modulate the post-transcriptional regulation of gene expression

operated by miRNAs and explain interindividual variability in BER capacity and response to 5-FU.

Experimental Design. We tested 12 SNPs in the 3’UTRs of 5 BER genes for CRC susceptibility in

a case/control study (1098 cases, 1459 healthy controls). Subsequently, we analyzed the role of

these SNPs on clinical outcomes of patients (866 in the Training set, 232 in the Replication set).

Results. SNPs in the SMUG1 and NEIL2 genes were associated with overall survival. In particular,

SMUG1 rs2233921 TT carriers showed increased survival compared to those with GT/GG

genotypes (HR 0.54; 95%CI 0.36-0.89) in the Training set and after pooling results from the

Replication set. The association was more significant following stratification for 5-FU-based

chemotherapy (p=5.6x10-5). A reduced expression of the reporter gene for the T allele of rs2233921

was observed when compared with the common G allele by in vitro assay. None of the genotyped

BER polymorphisms were associated with CRC risk.

Conclusions. We provide the first evidence that variations in miRNA binding sites in BER genes

3’UTR may modulate CRC prognosis and therapy response.




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Introduction

Colorectal cancer (CRC) is the second most common cancer in Western countries (1). Although it is

a preventable and potentially curable malignancy, it still represents the second leading cause of

cancer-related death among men and women (2).

Impaired DNA repair capacity and DNA damage accumulation may lead to cancer development. In

particular, reduced DNA repair activity due to differential genetic background in DNA repair genes

may modulate individual cancer risk (3). Base excision repair (BER) is the main pathway involved

in resolving spontaneous, alkylative, and oxidative DNA lesions (4). Defects in BER have been

linked to several diseases and the disruption of BER coordination or efficacy can potentially foster

tumorigenesis. Subtle variations in BER genes may be related to disease proneness or susceptibility,

and this is particularly important when considering relevant exposures or during the cumulative life

span of an individual (5). In our previous study, subjects homozygous for the variant alleles of 2

exon-localized polymorphisms of BER genes APE1 and hOGG1 were at an increased risk of CRC

(6). Such variant alleles were also associated with significantly lower BER rates in healthy subjects,

suggesting the importance of DNA repair in cancer susceptibility, as well as gene-gene and gene-

environment interactions (7).

BER also removes uracil (or its analogs) misincorporated into the DNA as consequences of

chemotherapy with 5-fluorouracil (5-FU) (8). 5-FU is widely employed in the treatment of a variety

of solid tumors, including CRC (9). Intracellular metabolites of 5-FU inhibit thymidylate synthase

exerting a cytotoxic effect or they may incorporate into RNA and DNA and ultimately activate

apoptosis. In this sense, DNA repair pathways, particularly BER, deserve more attention as they

represent important factors modulating the cell response to 5-FU and its resistance (8).

Notably, a fine regulation of BER genes, such as post-transcriptional and post-translational

regulation could affect the efficiency of this repair system. In the last decade, there has been




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increasing interest in the role of post-transcriptional regulation of gene expression by microRNAs

(miRNAs) and their possible effect on cancer risk and clinical outcomes. MiRNAs are a class of

short non-coding RNAs of approximately 20-25 nucleotides that modulate gene expression through

canonical base pairing between the seed sequence and its complementary seed match sequence,

mainly located in the 3`untranslated region (UTR) of target mRNAs (10). Approximately one third

of the protein-coding genes are controlled by miRNAs, thus almost all cellular pathways are

directly or indirectly influenced by these molecules (11, 12). Aberrant miRNA expression and/or

function is frequently observed in many malignancies, including CRC (13). Furthermore, sequence

variations, such as SNPs, in the seed region or in a target gene site could alter the expression of

miRNA targets with consequences on protein translation (14). When SNPs occur in 3`UTRs, they

may interfere with mRNA stability and translation by altering polyadenylation, protein::mRNA and

miRNA::mRNA regulatory interactions, which may lead to altered cancer susceptibility (15-17).

Polymorphic target sites for miRNAs may play a role in modulating important pathways such as

DNA repair (18), thereby potentially affecting the individual risk of CRC (19, 20).

The aim of the present study was to investigate the role of polymorphisms in potential miRNA

binding sites within the 3’UTR of BER genes in modulating the risk of CRC, its progression and

prognosis. From all identified polymorphisms within the 3’UTRs, using a series of a priori

hypotheses, 12 SNPs in 5 BER genes were selected and analyzed in a case-control study on 1098

CRC cases and 1459 controls from the Czech Republic. In this country, CRC constitutes a serious

health problem as is among the countries with the highest rate of incidence and mortality worldwide

(21). The same set of BER SNPs were additionally investigated for their relationship to the patients’

prognosis and for the effect of 5-FU-based therapy on 1098 patients (866 in the Training set, 232 in

the Replication set) with a detailed follow up. Additionally, the effect of an identified relevant SNP

on the regulation of gene expression was further tested by an in vitro functional assay.




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Material and Methods

Study population and data collection

Blood samples from 1098 subjects were collected among patients with histologically confirmed

CRC, recruited between September 2003 and October 2010 from several oncological departments

in the Czech Republic (Prague (three), Benesov, Brno, Liberec, Ples, Pribram, Usti nad Labem, and

Zlin). Two control groups, whose samples were collected at the same time of cases recruitment,

were included in the study. The first group consisted of 688 hospital-based individuals admitted to

five of the above mentioned gastroenterological departments that had negative colonoscopy results

for malignancy or idiopathic bowel diseases (CFCC, cancer-free colonoscopy inspected controls).

The reasons for undergoing the colonoscopy were: i) positive fecal occult blood test, ii)

hemorrhoids, iii) abdominal pain of unknown origin, and iv) macroscopic bleeding. The second

group of controls consisted of 781 healthy blood donor volunteers (HBDV) collected from a blood

donor centre in Prague. All individuals were subjected to standard examinations to verify the health

status for blood donation and were cancer-free at the time of the sampling. A detailed description of

CRC cases and controls is reported elsewhere (22, 23).

All subjects were informed and provided written consent to participate in the study and to approve

the use of their biological samples for genetic analyses, according to the Helsinki declaration. The

design of the study was approved by the local Ethics Committee. Study subjects provided

information on their lifestyle habits, body mass index (BMI), diabetes, and family/personal history

of cancer, using a structured questionnaire to determine demographic characteristics and potential

risk factors for CRC.

Follow up of the patients

Eight hundred sixty-six of the CRC cases were monitored with follow-ups until August 31, 2011.

They represented the initial Training set recruited in 9 oncological departments in Prague and




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throughout the Czech Republic. For these subjects, clinical data at the time of diagnosis, including

location of the tumor, UICC (International Union Against Cancer) tumor-node-metastasis (TNM)

stage system, grade and adjuvant chemotherapy treatment were collected, as well as information

about distant metastasis, relapse and date of death. Since 223 patients had incomplete clinical

information, they were excluded from the analyses. Three hundred and nineteen CRC cases

received a 5-FU-based adjuvant regimen as first-line postoperative therapy. The therapy consisted

of either a Mayo regimen, delivered as a bolus infusion of 5-FU (425 mg/m2) and leucovorin (10

mg/m2) for 5 days every 4 weeks six times or a simplified DeGramond regimen which consisted of

a 2 h intravenous (i.v.) infusion of leucovorin (200 mg/m2), then a 5-FU i.v. bolus (400 mg/m2)

followed by a 46 h 5-FU continuous i.v. infusion (2400–3000 mg/m2). Three hundred twenty-four

subjects did not receive any adjuvant chemotherapy after surgery. In this study, the outcome

variables measured were 5-FU-based chemotherapy, overall survival (OS, time from diagnosis until

death or censorship), and event-free survival (EFS, time of surgery or end of chemotherapy until

date of relapse, death or censorship).

A second group consisting of 232 CRC patients (Replication set) was recruited between 2008 and

2010 in the surgical department of Thomayer Hospital in Prague, Czech Republic. Similarly to the

Training set, detailed information were available on clinical data at the time of diagnosis, including

location of the tumor, UICC TNM stage system and grade and adjuvant chemotherapy treatment (5-

FU chemoherapy regimes were similar to those employed for the Training set). Follow up for this

second group of patients was performed until 31st March 2013.

Selection of candidate genes

From the complete list of DNA repair genes available online

(http://sciencepark.mdanderson.org/labs/wood/DNA_Repair_Genes.html#BER; March 2012

version), 16 genes were retrieved in the BER pathway.




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Selection of the SNPs in miRNA target binding sites

For each BER gene, SNPs within target binding sites for miRNAs were identified by using the

freely available software MicroSNiper (http://cbdb.nimh.nih.gov/microsniper (24), updated at

March 2012). The 50 detected SNPs were tested for minor allele frequency (MAF,>5% in

Caucasian populations) in the SNP database (dbSNP; http://www.ncbi.nlm.nih.gov/SNP/) to reach

an appropriate representation of all genotypes in our set of cases and controls. The selection of

SNPs was primarily done on the basis of HAPMAP CEU population; whenever this was not

possible, other reference populations were checked (i.e. 1000genomes, phase 1, CEU population).

SNPs with the required MAF were further tested for the possibility to be in linkage disequilibrium

using HaploView (v. 4.2) with the data from HapMap v. 3, release R2 in the CEU population. The

workflow for the selection of the SNPs is depicted in Figure 1.

SNP genotyping

Genomic DNA was isolated from peripheral blood lymphocytes using standard procedures. The

DNA from cases and controls was randomly placed on plates where an equal number of samples

could be run simultaneously. The twelve selected SNPs were genotyped using the KASPTM

genotyping assay, a competitive allele-specific PCR SNP genotyping system (K-bioscience,

Hoddesdon, Herts, UK). For quality control purposes, duplicate samples (5% of the total numbers

of samples) were repeated for each SNP, no template controls were included in each plate (NTCs),

and Hardy-Weinberg equilibrium test was performed.

DNA cloning and in vitro assay

For SMUG1 rs2233921, a Dual-Luciferase reporter assay was used to investigate whether its alleles

were associated with differential gene expression. Initially, a 640 bp fragment of the 3’ UTR region

of SMUG1 containing the G-allele of rs2233921 was PCR-amplified. The PCR primers were both

41bp long. The twenty bases at the primers 3’ ends were specific to the region to be amplified,




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whereas the 15 bases at the 5’ ends were homologous to either side of the XhoI restriction site

within the multiple cloning site of the pmirGLO vector (Promega, Madison, USA). Between the

two sequences, each primer was also designed to include a a XhoI restriction site sequence

(c^tcgag). The complete sequences were: sense primer=

AACGAGCTCGCTAGCCTCGAGGTGCCCTTGGGGCCTTGCAT; and anti-sense primer=

CAGGTCGACTCTAGACTCGAGTACAGATGAGGAGCCTGAGG. The vector was linearized

with XhoI (NEB Inc, Ipswich, USA) and the PCR product was cloned downstream from the firefly

luciferase (Photinus pyralis) reporter gene, using the Clone EZ PCR Cloning Kit (Genscript,

Piscataway, USA). As suggested by manufacturers, competent cells DH5α (NZYTech, Lisboa,

Portugal) were used for transformation after the cloning reaction. In order to obtain a vector with a

SMUG1 3’UTR bearing the T-allele of rs2233921, the construct underwent site-specific

mutagenesis using the Quick Change Lightning Site Direct Mutagenesis kit (Agilent, Milano, Italy).

The sequences of the mutagenic primers were: sense=

TGACAACACATCTCCTGTACTGGAGCAAAAGGTCC; and anti-sense=

GGACCTTTTGCTCCAGTACAGGAGATGTGTTGTCA. Following the digestion of the parental

(methylated) supercoiled dsDNA with Dpn I, XL10-Gold ultra-competent cells (Agilent, Milano,

Italy) were used for transformation.

For the functional assay, HCT-116 cells were plated at a density of approximately 5 x 104 cells per

well in 24-well plates and incubated overnight at 5% CO2, 37°C in a humidified incubator. Cells

were transiently transfected at about 80% confluence using 3µl of Polyfect transfection reagent

(Qiagen, Milano, Italy), and 0.4 µg of the chimeric construct carrying the T or the G allele. The

pmirGLO vector contains the luciferase gene from Renilla reniformis (hRluc-neo), acting as a

control reporter to normalize transfection efficiency. Three independent experiments were carried

out. Six replicates of each experimental point were performed in each experiment. Twenty-four

hours after transfection, cells were washed with a phosphate-buffered saline solution and lysed with

100µl of Passive Lysis Buffer (PLB Promega, Milano, Italy) for optimal stability of the firefly and




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Renilla luciferase reporter enzymes. The culture vessel was shaken for 15 minutes at room

temperature. The lysates were then transferred to 1.5-mL microcentrifuge tubes and centrifuged at

13000 rpm for 1 minute to pellet the debris. Supernatants were transferred to clean tubes and used

for measuring the activity of firefly and Renilla luciferases. The assays were carried out using the

dual-luciferase reporter assay kit (Promega, Milano, Italy). Since the same vector carries both the

luciferase and the Renilla genes, the experimental variability was greatly reduced allowing a more

precise evaluation of small biological differences such as those reflecting the effect of the different

tested alleles.

For each transfection, luminescence intensity was evaluated by a luminometer (Optima FluoStar,

BMG, Ortenberg, Germany), and luciferase activities were averaged from four measurements. The

luminescence intensities of firefly and Renilla luciferase of the non-transfected cells (background)

were subtracted from the values obtained for the transfected cells with the pmiRGLO vector

containing the 3’UTR. The luminescence of the Renilla luciferase was used as the reference value

to calculate the value of firefly luciferase (Luc/Ren ratio of luminescence).

Statistical analyses

Chi-square test (1 degree of freedom), with a type-I error threshold set at α=0.05, was used to verify

whether the genotypes were in Hardy-Weinberg equilibrium in controls. The multivariate logistic

regression (MLR) analysis was used to test the association between genotypes and risk of CRC. The

covariates analysed for inclusion in the multivariate model were: sex, age, smoking habit (non-

smokers vs. smokers and ex-smokers), BMI, familial history of CRC, education level (high,

intermediate and low), and living area (country, suburbs, and town). The associations between

SNPs and CRC risk were calculated by estimating the odds ratios (ORs) and their 95% confidence

intervals (CI), adjusted for both continuous and discrete covariates. For all the genotypes, regression

coefficients for additive model were estimated. For all SNPs with significant P-values per genotype,

the dominant or recessive models were also calculated. The model with the highest likelihood was




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additionally checked for the significance of possible interaction terms in the MLR analysis.

Statistical analyses were performed using R (http://www.rproject.org).

OS in CRC patients was evaluated using the date of death or the date of follow up termination as

the end point. For the EFS, in patients who did not have distant metastasis at the time of diagnosis,

date of relapse, death or end of the study was used as the end point of follow-up. EFS was defined

as the time from surgery/end of therapy to the occurrence of distant metastasis, recurrence or death,

whichever came first. The survival curves for OS and EFS were derived by the Kaplan–Meier

method (R version 2.14-2, Survival package). The relative risk of death was estimated as hazard

ratio (HR) using Cox regression (R version 2.14-2, Survival package). Multivariate survival

analyses were adjusted for age, gender, T, N, M and chemotherapy.

For the in vitro assays, the ratios (Luc/Ren) of the measurements of luminescence, each subtracted

of its respective background, were compared between genotypes using the multifactor analysis of

variance with interactions (MANOVA), where “experiment” and “genotype” were entered as

independent factors in the model. The statistical tests were 2-tailed and carried out using

Statgraphics Centurion software (StatPoint Technologies, Warrenton, Va).




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Results

SNP selection and prediction of their effects on target sites

Out of 16 genes involved in the BER pathway, two did not present any SNPs within their 3`UTRs

(MUTYH and MPG), while 50 SNPs were identified in the remaining 14 genes. A SNP in OGG1

(rs7373786) was not located within a known miRNA binding site. All 49 remaining SNPs were

tested for MAF (>5% in Caucasian populations) in dbSNP. Thirty three SNPs did not reach the

required MAF, and therefore were excluded from the study. An additional four SNPs were found to

be in the same haplotype block (D′ 1, r2 1) of other identified SNPs (NEIL2 rs804292 with

rs1534862; NEIL3 rs1055677 and rs34652756 with rs1055678 and rs2740438 with rs2740439).

Therefore, only one SNP for each group was considered (rs1534862, rs1055678, and rs2740439

respectively). The final set of 12 selected SNPs within putative miRNA binding sites in the 3’UTR

of 5 BER genes is reported in Table I.

Case-control study

The total number of CRC patients included in the study was 1095, among which approximately 2/3

were diagnosed with colon and the rest with rectal cancer (3 of them were excluded due to missing

tumor site information). Out of the 1469 controls, 688 were CFCC and 781 were HBDV. Compared

to subjects of both control groups, CRC cases were more likely to be older and have a slightly

higher BMI. Compared to the HBDV group, they were more likely to have a positive family history

of CRC and lower formal education (Table II).

The genotype and allele frequencies for the investigated SNPs and their association with CRC risk

after adjustment for all listed confounders are reported in Supplementary Table I, together with

the calculation of the Hardy-Weinberg equilibrium in controls. For all analyzed SNPs, the genotype

calls had 99% concordance with duplicate quality controls. Only one SNP, rs2307294, was not in




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Hardy-Weinberg equilibrium in the control group (χ2=9.01 and P-value=0.01), therefore it was

excluded from further analyses.

None of the polymorphisms genotyped in this study were significantly associated with CRC risk.

To overcome issues due to the age effect (mean age in cases = 61.7, mean age in controls = 50.6, t-

test=24.4 p≤0.0001), cases and controls were matched by age quartiles through bootstrap sampling

(10 repetitions). For each subset, the association analyses were repeated and the results averaged.

With this approach, we could ascertain that no changes were observed in the 10 different re-

samplings. Additionally, the analysis was repeated on a subgroup of 800 cases and 800 controls

matched for sex and age (after matching, mean age in cases = 59.0, mean age in controls = 57.7)

and similar results were observed as in the whole group of study: none of the SNPs were associated

with cancer risk (data not shown).

Survival analysis

Training set. The average (median) OS and EFS for patients was 87.8 (72.0) and 83.0 (68.1)

months, respectively. In the preliminary univariate assessment of covariates, gender, age, smoking

habit, T, N, M status and chemotherapy treatment were associated with OS (Table III). Advanced

age, male gender and current smoking status were related to a shortened OS. Men in particular,

showed also a higher risk of relapse or metastasis (OS: HR 1.54; 95% CI 1.22-1.94; p<0.01; EFS:

HR 1.43; 95% CI 1.14-1.80; p<0.01). Four established prognostic factors (T, N, M status and

chemotherapy treatment) were associated with patient survival. Moreover, TNM status was also

associated with an increased risk of recurrence.

The data analysis showed that rs2233921 in SMUG1 gene was significantly associated with OS (HR

0.62; 95% CI 0.39-1.00; p=0.05) but not with EFS (Table IV, Supplementary Table II). In the

dominant model, the association of the T allele with a better survival was even more pronounced

(HR 0.54; 95% CI 0.36-0.89; p=0.003). The univariate Kaplan-Meier plots for OS also reflected




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these results (log-rank test for the dominant model p=0.008; Figure 2A). In contrast, carriers of the

GA and TC genotypes of SMUG1 rs971 and NEIL2 rs6997097 polymorphisms were associated

with a shorter survival (HR 1.38; 95% CI 1.02-1.86; p=0.04; HR 1.77; 95% CI 1.23-2.53; p=0.002,

respectively). However, in the dominant model, only C allele carriers of rs6997097 in NEIL2

showed a shorter survival (HR 1.73; 95% CI 1.21-2.47; p=0.003) (Table IV, Supplementary

Table II).

After stratification of patients according to treatment (treated with 5-FU or not), the previously

observed associations for OS analysis were confirmed only for SMUG1 rs2233921 in both groups

(data not shown). The univariate Kaplan-Meier plot including the stratification for 5-FU-based

chemotherapy provided a strong association with OS (log-rank test p=5.6x10-5; Figure 2B). In

particular, a better survival was observed for patients carrying the TT genotype and undergoing 5-

FU-based chemotherapy. On the other hand, SMUG1 rs971 and NEIL2 rs6997097 associated with a

shorter survival only in those subjects who did not undergo 5-FU chemotherapy.

For EFS analysis, a SNP in NEIL2 (rs1534862) was associated with a lower risk of relapse or

metastasis in both codominant and dominant models (for CT genotype HR 0.73; 95% CI 0.54-0.98;

p=0.04; for T allele HR 0.73; 95% CI 0.55-0.98; p=0.03). Regarding rs1055678 in NEIL3, the TT

genotype was not only associated with an increased risk of recurrence in the codominant model (HR

7.98; 95% CI 1.04-61.34; p=0.05), but also in the dominant model (HR 7.76; 95% CI 1.01-59.52;

p=0.05) (Supplementary Table II and III). Given the low numbers of cases with variant

genotypes, the latter result should be considered with caution. Considering EFS, no association was

observed after stratification according to chemotherapy treatment. The only exception was for

rs1055678 in NEIL3; however, again, in this case only a reduced number of observations were

available (data not shown).




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Replication set and pooled analysis. The average (median) OS and EFS for the Replication set were

82.5 (69.3) and 74.2 (58.7) months, respectively. In the univariate assessment of covariates, gender,

age, smoking habit, T, N, M status and chemotherapy treatment were found to be associated with

either OS or EFS, similarly as in the Training set (see Table III). In this second group, although not

reaching statistical significance, all the SNPs previously found associated with either OS and EFS

(and also after stratification for 5-FU based chemotherapy) showed a similar trend of association

with the exception of NEIL2 rs6997097 and rs1534862 (Table IV, Supplementary Table II and

III). The pooled analysis of the 2 sets confirmed the previously observed associations with OS for

SMUG1 rs2233921 (for TT genotype HR 0.61; 95% CI 0.39-0.95; p=0.03; for T allele HR 0.55;

95% CI 0.37-0.80; p=0.002) and for SMUG1 rs971 (for GA genotype HR 1.41; 95% CI 1.06-1.87;

p=0.02; for A allele HR 1.35; 95% CI 1.03-1.77; p=0.03), as well as for NEIL2 rs6997097 (for TC

genotype HR 1.68; 95% CI 1.19-2.38; p=0.003; for C allele HR 1.66; 95% CI 1.18-2.34; p=0.003).

For rs2233921, these results reflected also the univariate Kaplan-Meier plots for OS (log-rank test

for the dominant model p=0.01; Figure 2C), even after stratification for 5-FU based chemotherapy

(log-rank test p=0.003; Figure 2D).For EFS analysis none of the previously associated SNPs were

confirmed in the pooled analysis.

In vitro assay

For SMUG1 rs2233921, an in vitro assay was used to investigate whether its alleles were associated

with differential gene expression. The results in the HCT-116 cell line derived from CRC are

reported in Figure 3. The G-to-T point mutation of rs2233921 (SMUG1) resulted in a reduction of

the expression of luciferase in all the experiments performed. Combining results from all the

experiments, when considering the luciferase expression with the G allele as the reference (100%),

the average luciferase expression with the T allele of SMUG1 was reduced to 63.1% and this

difference was statistically significant (P-value<10-4).




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Discussion

MiRNAs are increasingly recognized as central players in diverse biological processes, including

DNA repair and DNA damage response (25). An increasing body of evidence has indicated

miRNAs as cancer diagnostic, prognostic and predictive clinical biomarkers (26, 27). The presence

of SNPs within the 3’UTRs of target DNA repair genes which alter the binding with specific

miRNAs and modulate gene expression, may ultimately affect cancer susceptibility (18). Less is

known about the effect of variations in such genes on patient survival and on the effect of therapy

via miRNA modulation (28).

In the present study, we selected 12 SNPs within the 3’UTRs of 5 genes from the BER pathway,

which potentially alter miRNA::mRNA binding affinity, and we investigated their role in CRC

susceptibility in a case-control study. Subsequently, we tested those SNPs as prognostic factors in

CRC patients with detailed follow up information.

The novel finding was that a SNP in the BER gene SMUG1 (rs2233921) was associated with

patient survival in a set of 718 CRC patients. In particular, carriers of the TT genotype showed a

significantly increased OS compared to those with the GG genotype, and the results were even more

pronounced in relation to the 5-FU-based chemotherapy. When another set of 229 patients was

added, the above results were replicated, though reaching a more robust statistical significance only

in the pooled analysis. The covariate analysis indicated that the 2 populations (Training and

Replication sets), although of considerable different size, displayed similar clinical and anamnestic

characteristics to justify a pooled analysis. However, we cannot rule out certain differences, for

example due to a different time interval of follow up and to the collection of clinical information.

Importantly, the observed associations remained significant even if we would apply the stringent

Bonferroni’s correction for multiple comparisons (p threshold = 0.0045, considering α=0.05 and 11

independent tests).




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The association of SMUG1 rs2233921 with increased OS is of particular interest, especially in

relation to the widely employed anti-metabolite 5-FU-based chemotherapy. Variations in the DNA

repair capacity may play a key role in the response to 5-FU exposure (8, 29). In fact, BER

glycosylases are the principal enzymes involved in the cellular response to 5-FU induced damage

(8, 30, 31), with SMUG1 recognized as a main player (32). The overall picture suggests that

SMUG1 excision activity is protective against toxicity caused by 5-FU (8). Other findings also

imply that SMUG1 up-regulation might mediate the development of tumor resistance to 5-FU

treatment (33). Since no genetic variants have been found responsible for a differential functionality

of SMUG1 enzyme (31), variations in post-translational regulation, such as the one exerted by

miRNAs, could be responsible for a different response to 5-FU treatment and survival. An in vitro

assay, carried out to test functional differences between the different alleles of SMUG1 rs2233921,

confirmed that the T allele was associated with a reduced expression of the reporter gene when

compared with the common G allele. This finding supports the hypothesis that the presence of an

“alternative” 3`UTR placed in a “normal” cellular context (and exploiting the “normal” cellular

machineries) may modulate the levels of expression of SMUG1. The assay was performed on

colorectal cell lines, and it is conceivable that these cells express a miRNA set typical of colorectal

cells. At present, we cannot state whether miRNAs, messenger RNA stability or other mechanisms

are involved in the observed genotype-dependent differential expression of SMUG1. However, this

represents an interesting issue for further investigation. In fact, our outcomes support the relevant

role of regulation of BER genes in response to DNA-damaging chemotherapy in the treatment of

colorectal malignancy, which may be affected by inter-individual variability.

Other interesting associations were found for SMUG1 rs971 or NEIL2 rs6997097 polymorphisms

with a shorter survival in both the Training set and the pooled analysis. For an additional 2 SNPs,

one in NEIL2 (rs1534862) and another in NEIL3 (rs1055678), the associations with risk of

recurrence were found only in the Training set. Further investigations on a larger scale are needed




19

to confirm the role of these SNPs. The modulation of NEIL2 and NEIL3 could also be of interest

since both genes belong to a family of glycosylases specialized in the recognition of oxidized

pyrimidines (31). NEIL2, in particular, recognizes lesions on single-stranded DNA and was also

associated with the transcription process (34). Missense polymorphic variants in NEIL2 were

associated with CRC risk, while variants in NEIL3 were associated with colorectal adenoma

(reviewed in (31)). However, to the best of our knowledge, none of their polymorphic variants were

evaluated for their functional effects or for their possible modulation in relationship with 5-FU

therapy.

Unlike our previous studies (18, 19, 35), we did not observe any association of these 3’UTR-

localized SNPs with risk of CRC. With a study group of 1095 cases and 1469 controls, we had

greater than 80% power to detect an association for a recessive/dominant model with OR>1.30 at

alpha=0.05 (with MAF=0.30) (power calculation by NAME software). We are aware of certain

limitations in this case-control study, such as the different age distribution among the cases and

controls. We attempted to control age effect by two different approaches: i) matching cases and

controls by age quartiles through bootstrap sampling (10 times), and ii) repeating the analysis on a

subgroup of 800 cases and 800 controls matched for sex and age. Nevertheless, none of the SNPs

resulted associated to cancer risk.

The rationale for selecting the genes/polymorphisms in this study has been provided by

MicroSNiPer, a web-based tool which predicts the SNPs on putative miRNA targets (24). We did

not rank and test SNPs according to allele-specific Gibbs free binding energy, as did previous

studies (18, 20), but we have analysed systematically all polymorphisms retrieved in the 3’UTR of

BER genes with an adequate MAF in the population. An additional software available online

(Mirnsnpscore; http://www.bigr.medisin.ntnu.no/mirsnpscore/), based on in silico predictions of

SNP effects on microRNA-based gene regulation, was also queried to verify the regulatory effect of

the selected SNPs. Mirnsnpscore sorts SNPs and calculates a DeltaS score which summarizes the




20

predicted effect of a SNP on miRNA regulation. The above described 5 SNPs associated with

clinical outcomes were again reported as having an effect on miRNA binding. In particular,

rs2233921 had the highest DeltaS score among the SNPs predicted in the 3`UTR of SMUG1

(DeltaS=0.95 and 0.94 for miR770-5p and miR665, respectively; Supplementary Table IV).

Several miRNAs predicted to bind to the above mentioned BER genes have been reported in the

colorectal microRNAome (36). Interestingly, new findings suggest that miRNAs also affect drug

resistance in CRC (37). For instance, Mir455-3p, predicted to bind in SMUG1 3’UTR where

rs2233921 resides, may target molecular pathways involved in cell survival after 5-FU or cisplatin

chemotherapy (38). MiRNA pharmacogenomics is a novel and promising field that investigates the

role of miRNAs and polymorphisms affecting miRNAs and their target functions for the prediction

of drug response or the improvement of drug efficiency (39). Recently, polymorphisms in miRNA

related genes were described as predictors of clinical outcomes in stage III CRC patients treated

with 5-FU-based chemotherapy (40). A proposed hypothesis was that the generation of a target

gene expression control modification due to the SNPs in the miRNA precursor and in the mature

molecules could have an effect on the response to 5-FU. We may also extend this hypothesis to the

SNPs in miRNA binding sites of our target BER genes. Accordingly, SNPs residing in the target

sites of AP-2alpha gene were found to alter the miRNA-mRNA interaction resulting in differential

target gene expression which associated with cisplatin chemosensitivity (41). A different post-

translational regulation of an enzyme due to different miRNA sets binding according to specific

alleles has been reported and elucidated by (42). Thus, characterization of the polymorphisms in

miRNA-related genes/target sites and the relative functional impact may provide a solid basis for

miRNA-based therapeutic approaches.

In conclusion, we have identified SNPs potentially affecting miRNA binding to the 3’UTR of BER

genes among which SMUG1 rs2233921 was strongly associated with clinical outcome of CRC

patients treated with 5-FU-based chemotherapy. By in vitro assay, we have demonstrated that this




21

polymorphism shows a differential expression of the reporter gene according to its different alleles,

supporting a possible regulatory role on the expression of SMUG1. Further studies are needed to

replicate these SNPs as predictive biomarkers in independent and larger populations, to more

specifically characterize functional relevance of the identified genetic variants and to find the

biological mechanisms underlying the observed associations.




22

Acknowledgments

This work was supported by the Grant Agency of the Czech Republic (grants CZ GA CR:

GAP304/10/1286, CZ:GACR:GAP304/12/1585).

Authors would like to thank T. O’Hearn II, B. O’Brien and E. Van Emburgh for their excellent

technical support.




23

Legend to figures

Figure 1: Workflow strategy for selection and analyses of SNPs residing in potential miRNA

binding sites in 3’UTR of BER genes.

Figure 2: Kaplan-Meier OS curves of CRC patients stratified for SMUG1 rs2233921 genotypes. A)

and B): dominant model and stratification according to 5-FU-based chemotherapy for the Training

set; C) and D) for the Pooled analysis (Training set + Replication set).

Figure 3: In vitro assay to test the luciferase activity according to SMUG1 rs2233921 different

alleles (G or T). Six independent replicates in 3 independent experiments are summarized as mean

values of relative intensity of luciferase/Renilla luminescence ratio. When considering the luciferase

expression with the G allele as the reference (100%), the luciferase expression with the T allele of

SMUG1 was 71.6%, 52.6%, and 65.2% respectively in the 3 experiments.




24

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Table I. Selected SNPs in the 3’UTR of BER genes, with MAF ≥0.05 (in Caucasians) and within miRNAs target.

Gene ID dbSNP ID Position Variation MAF (Population studied)

SMUG1 rs2233921 80 G/T T=0.482 (HAPMAPCEU)

rs971 422 G/A A=0.320 (HAPMAPCEU)

MBD4

rs2307285 383 A/G G=0.197 (PDR90) rs2307294 447 G/C C=0.100 (NIH-PDR90)

NEIL2 rs1534862 21 C/T T=0.219

(HAPMAPCEU) rs8042921 133 A/G G=0.292

(HAPMAPCEU) rs6997097 190 T/C C=0.055 (PDR90) rs8191670 356 T/C C=0.25 (PDR90)

rs2740439 866 G/C C=0.08 (HAPMAPCEU)

rs4639 969 A/G G=0.401 (HAPMAPCEU)

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1 in linkage disequilibrium (LD) with rs1534862 (Haploview 4.2). Not genotyped. 2 in LD with rs1055678 (Haploview 4.2). Not genotyped.

rs1043180 1039 C/T T=0.111

(HAPMAPCEU) NEIL3

rs10556772 173 C/G G=0.068 (HAPMAPCEU) rs346527562 132 T/C C=0.067 (HAPMAPCEU) rs1055678 342 C/T T=0.068 (HAPMAPCEU)

LIG3 rs1052536 50 C/T T=0.500 (HAPMAPCEU)

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Table II. Characteristics of the study population

CRC cases Control group I (CFCC)

Control group II (HBDV)

All controls

OR (95% CI) p-value

All subjects 1098 688 781 1469 Diagnosis Colon cancer 725 - - -

Rectal cancer 370 - - -

Age (years) 47≤ 94 164 427 591 Ref

48-55 208 145 277 422 3.10 (2.36-4.09) <0.01 56-65 370 209 77 286 8.13 (6.25-10.66) <0.01

>65 423 170 0 170 15.37 (11.66-20.44) <0.01 Sex Females 435 317 343 660 Ref

Males 660 371 438 809 1.23 (1.05-1.45) 0.01 BMI 23.7≤ 184 154 215 369 Ref

23.7-26.2 192 147 213 360 1.07 (0.83-1.37) 0.61 26.3-28.9 226 139 184 323 1.40 (1.10-1.79) 0.01 >28.9 222 172 157 329 1.35 (1.06-1.73) 0.02

Smoking No 536 364 451 815 Ref Yes* 501 254 327 581 1.31 (1.12-1.54) <0.01

Family history No 726 486 718 1204 Ref of CRC Yes 144 90 52 142 1.68 (1.31-2.16) <0.01

Living area City 511 338 614 952 Ref Suburbs 128 118 53 171 1.39 (1.08-1.79) 0.01 Countryside 242 157 112 270 1.67 (1.36-2.05) <0.01

Education Basic 266 171 53 224 Ref Medium 469 327 492 820 0.48 (0.39-0.59) <0.01 High 138 114 231 345 0.34 (0.26-0.44) <0.01

*Ex-smokers are included into this group.

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OR, Odds ratio; 95% CI confidence interval. Significant results in bold. Numbers may not add up to 100% of available subjects because of missing data

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Table III. Clinical and anamnestic characteristics significantly affecting Overall Survival (OS) and Event Free Survival (EFS) of the CRC patients with complete follow up (Cox regression) in the Training set and the Replication set

Training set Replication set Pooled OS EFS OS EFS OS EFS

Na HR (95% CI)

P HR (95% CI)

P Na HR (95% CI)

P HR (95% CI)

P Na HR (95% CI)

P HR (95% CI)

P

Sex Females 357 Ref Ref 78 Ref Ref 435 Ref Ref Males 509 1.54

(1.22-1.94) <0.01 1.43

(1.14-1.80) <0.01 154 2.18

(1.05-4.52)

0.04 1.23 (0.65-2.32)

0.51 663 1.54 (1.24-1.92)

<0.01 1.36 (1.09-1.68)

<0.01

Age (years) 55≤ 235 Ref Ref 61 Ref Ref 296 Ref Ref 56-62 203 1.40

(1.00-1.94) 0.05 1.32

(0.97-1.78) 0.07 59 1.12

(0.47-2.70)

0.80 2.01 (0.88-4.59)

0.10 262 1.42 (1.04-1.93)

0.03 1.41 (1.06-1.87)

0.02

63-69 221 1.33 (0.96-1.84)

0.08 1.07 (0.79-1.46)

0.66 54 1.40 (0.60-3.31)

0.44 2.15 (0.93-4.98)

0.07 537 1.39 (1.03-1.86)

0.03 1.20 (0.91-1.59)

0.20

>69 207 1.87 (1.37-2.57)

<0.01 1.05 (0.76-1.45)

0.77 58 2.43 (1.08-5.50)

0.03 1.01 (0.38-2.73)

0.98 265 2.02 (1.50-2.71)

<0.01 1.05 (0.77-1.43)

0.75

Smoking habit

No 428 Ref Ref 180 Ref Ref 537 Ref Ref

Yes* 116 1.54 (1.13-2.09)

0.01 1.26 (0.91-1.75)

0.16 42 2.60 (1.33-5.08)

0.005 0.97 (0.53-1.78)

0.92 157 1.25 (1.02-1.54)

0.04 1.14 (0.93-1.41)

0.21

pT 1 32 Ref Ref 19 Ref Ref 51 Ref Ref 2 130 2.66

(0.82-8.67) 0.10 2.18

(0.77-6.16) 0.14 39 2.16

(0.25-18.530.48 0.98

(0.09-10.80)0.99 169 2.72

(0.97-7.62) 0.06 2.18

(0.86-5.57) 0.10

3 420 5.84 (1.87-18.28)

<0.01 5.26 (1.95-14.16)

<0.01 122 4.76 (0.65-

0.13 6.06 (0.83-

0.08 542 6.05 (2.25-

<0.001

5.74 (2.36-

<0.001

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35.90) 44.44) 16.26) 13.92) 4 90 12.30

(3.85-39.23) <0.01 8.09

(2.93-22.33) <0.01 46 4.43

(0.57-34.58)

0.16 4.94 (0.64-38.31)

0.13 136 9.45 (3.45-25.89)

<0.0001

7.10 (2.86-17.64)

<0.001

pN 0 359 Ref Ref 149 Ref Ref 508 Ref Ref 1 213 2.04

(1.56-2.67) <0.01 1.76

(1.35-2.30) <0.01 48 2.04

(1.03-4.07)

0.04 1.71 (0.81-3.61)

0.16 261 2.14 (1.67-2.74)

<0.01 1.89 (1.47-2.42)

<0.01

2 51 4.69 (3.17-6.94)

<0.01 3.51 (2.42-5.07)

<0.01 17 1.47 (0.44-4.95)

0.53 3.91 (1.73-8.82)

<0.01 68 3.43 (2.38-4.95)

<0.01 3.50 (2.51-4.90)

<0.01

pM 0 545 Ref Ref 189 Ref Ref 734 Ref Ref 1 150 4.67

(3.68-5.93) <0.01 4.11

(3.28-5.15) <0.01 29 6.07

(3.06-12.04)

<0.01 6.41 (3.32-12.39)

<0.01 179 4.82 (3.86-6.04)

<0.01 4.59 (3.71-5.68)

<0.01

5FU-based chemotherapy

Yes 319 Ref 102 Ref Ref 421 Ref Ref

No 324 1.66 (1.29-2.12)

<0.01 1.14 (0.90-1.45)

0.27 121 0.71 (0.39-1.30)

0.27 0.13 (0.06-0.29)

<0.01 445 1.43 (1.14-1.80)

<0.01 0.85 (0.68-1.06)

0.14

*Ex-smokers excluded HR, hazard ratio; 95% CI, confidence interval. Significant results in bold. a Numbers may not add up to 100% of available subjects because of missing information.

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Table IV. SNPs associated with OS of patients in each set and in the pooled population (Cox regression for adjusted estimates).

Training set Replication set Pooled Gene dbSNP ID

Genotype Na

Events Expected HR (95% CI) b

P Na


P Na


P

SMUG1 rs2233921

GG 205 94 85.3 Ref 76 15 15.3 Ref 281 109 104.0 Ref

GT 392 181 171.9 1.24 (0.90-1.71)

0.19 106 20 20.1 0.99 (0.41-2.41)

0.98 498 201 188.0 1.19 (0.89-1.61)

0.25

TT 121 38 55.8 0.62 (0.39-1.00)

0.05 47 9 8.6 0.35 (0.08-1.49)

0.15 168 47 65.0 0.61 (0.39-0.95)

0.03

GG+GT 597 275 257.2 Ref 182 35 35.42 Ref 779 310 292.0 Ref TT 121 38 55.8 0.54

(0.36-0.81) 0.003 47 9 8.58 0.35

(0.09-1.33) 0.12 168 47 65.0 0.55

(0.37-0.80) 0.002

SMUG1 rs971

GG 262 102 117.2 Ref 87 17 15.6 Ref 349 119 131.6 Ref

GA 368 173 153.5 1.38 (1.02-1.86)

0.04 111 21 23.4 1.82 (0.71-4.68)

0.21 479 194 178.0 1.41 (1.06-1.87)

0.02

AA 92 35 39.3 0.92 (0.57-1.50)

0.75 31 7 6.0 2.13 (0.63-7.27)

0.23 123 42 45.4 1.12 (0.72-1.74)

0.63

GG 262 102 117.2 Ref 87 17 15.6 Ref 349 119 132.0 Ref GA+AA 460 208 192.8 1.27

(0.95-1.70) 0.10 142 28 29.4 1.89

(0.76-4.70) 0.17 602 236 223.0 1.35

(1.03-1.77) 0.03

NEIL2 rs6997097

TT 618 258 266.8 Ref 191 37 36.8 Ref 809 295 304.1 Ref

TC 106 54 43.3 1.77 (1.23-2.53)

<0.01 34 6 6.25 0.68 (0.19-2.40)

0.54 140 60 49.3 1.68 (1.19-2.38)

0.003

CC 5 1 2.9 0.87 (0.12-6.37)

0.89 0 0 0 - - 5 1 2.6 1.11 (0.15-8.06)

0.92

TT 618 258 266.8 Ref 191 37 36.8 Ref 809 295 304.1 Ref TC+CC 111 55 46.2 1.73

(1.21-2.47) 0.003 34 6 6.25 0.68

(0.19-2.40) 0.54 145 61 51.9 1.66

(1.18-2.34) 0.003

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HR, hazard ratio; 95% CI, confidence interval. Significant results in bold. a Numbers may not add up to 100% of available subjects because of missing information. b Adjusted for sex, age, TNM and chemotherapy.

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Published OnlineFirst September 13, 2013.Clin Cancer Res Barbara Pardini, Fabio Rosa, Elisa Barone, et al. modulation of microRNAs bindingresponse to therapy in colorectal cancer patients: a potential Variation within 3' UTRs of base excision repair genes and

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