Open Access

Functional nsSNPs from carcinogenesis-related genes expressed in breast tissue: Potential breast cancer risk alleles and their distribution across human populations

Human Genomics20062:287

DOI: 10.1186/1479-7364-2-5-287

Received: 9 December 2005

Accepted: 9 December 2005

Published: 1 March 2006

Abstract

Although highly penetrant alleles of BRCA1 and BRCA2 have been shown to predispose to breast cancer, the majority of breast cancer cases are assumed to result from the presence of low-moderate penetrant alleles and environmental carcinogens. Non-synonymous single nucleotide polymorphisms (nsSNPs) are hypothesised to contribute to disease susceptibility and approximately 30 per cent of them are predicted to have a biological significance. In this study, we have applied a bioinformatics-based strategy to identify breast cancer-related nsSNPs from 981 carcinogenesis-related genes expressed in breast tissue. Our results revealed a total of 367 validated nsSNPs, 109 (29.7 per cent) of which are predicted to affect the protein function (functional nsSNPs), suggesting that these nsSNPs are likely to influence the development and homeostasis of breast tissue and hence contribute to breast cancer susceptibility. Sixty-seven of the functional nsSNPs presented as commonly occurring nsSNPs (minor allele frequencies ≥ 5 per cent), representing excellent candidates for breast cancer susceptibility. Additionally, a non-uniform distribution of the common functional nsSNPs among different human populations was observed: 15 nsSNPs were reported to be present in all populations analysed, whereas another set of 15 nsSNPs was specific to particular population(s). We propose that the nsSNPs analysed in this study constitute a unique resource of potential genetic factors for breast cancer susceptibility. Furthermore, the variations in functional nsSNP allele frequencies across major population backgrounds may point to the potential variability of the molecular basis of breast cancer predisposition and treatment response among different human populations.

Keywords

breast cancer predisposition nsSNPs breast tissue expression carcinogenesis-related genes PolyPhen

Introduction

Mutations of BRCA1[1] and BRCA2[2] confer high breast cancer risk to the carriers. Such highly penetrant mutations are only responsible for a small fraction (~5-10 per cent) of all breast cancer cases,[3, 4] however, suggesting the presence of other, yet to be identified, mutations in other breast cancer predisposition genes [57]. Mutations in a number of genes, such as p53,[8]ATM[6] and Chek2,[9] have also been shown to contribute to breast cancer risk in a very small fraction of breast cancer cases. So far, no other high-penetrant breast cancer susceptibility gene has been identified; however, genetic variations including single nucleotide polymorphisms (SNPs) have been hypothesised to act as low-moderate penetrant alleles and contribute to breast cancer, as well as other complex diseases [7, 1012].

Variations in protein sequence and function are mainly due to the non-synonymous form of SNPs (nsSNPs). The fraction of nsSNPs in the genome is relatively low (~10 per cent of all coding SNPs)[13] compared with other types, but they are more likely to alter the structure, function and interaction of the proteins, and thus constitute a set of candidate genetic factors associated with disease predisposition [14, 15]. Approximately 30 per cent of the nsSNPs are predicted to have biological consequences [1618]. Several nsSNPs from the proteins acting in a variety of cellular pathways--such as apoptosis,[19] oxidative stress[20] and signal transduction[21]--have already been reported to be associated with an increased/decreased risk of breast cancer.

Several studies have described cancer-relevant nsSNPs;[2225] however, to our knowledge they have not been studied in the context of expression of genes in a particular tissue. Clearly, in order for genes to be linked to a disease of a tissue, their protein products should somehow influence that particular tissue, either as exogenous proteins (such as hormones) or endogenous proteins (such as the proteins expressed in that tissue) [26, 27]. In this study, we have applied a bioinformatics-based strategy and identified potentially functional nsSNPs from endogenous carcinogenesis-related proteins expressed in breast tissue.

Methods

Genes

The Ensembl transcript identifiers (http://​www.​ensembl.​org/​)[28] of the genes expressed in breast tissue were retrieved from the TissueInfo database (db) (http://​icb.​med.​cornell.​edu/​services/​tissueinfo/​query) [29]. The list of carcinogenesis-related genes from 18 different categories ('DNA adduct', 'DNA damage', 'DNA replication', 'angiogenesis', 'apoptosis', 'behavior', 'cell cycle', 'cell signaling', 'development', 'gene regulation', 'transcription', 'immunology', 'metabolism', 'metastasis', 'pharmacology', 'signal transduction', 'tumor suppressors/oncogenes' and 'miscellaneous') was retrieved from the National Cancer Institute's Cancer Genome Anatomy Project Genetic Annotation Initiative ([CGAP-GAI] website [http://​lpgws.​nci.​nih.​gov/​html-cgap/​cgl/​]) [30]. The genes retrieved from the TissueInfo and the CGAP-GAI resources were then cross-referenced with each other to identify the group of carcinogenesis-related genes that are expressed in breast tissue.

nsSNPs

The nsSNPs from the group of carcinogenesis-related genes expressed in breast tissue were retrieved from dbSNP build 120 (http://​www.​ncbi.​nlm.​nih.​gov/​SNP/​) [31]. Only the nsSNPs detected in ≥ 2 chromosomes in a sample panel of ≥ 40 chromosomes were included in this study (validated nsSNPs). Seventeen nsSNPs were found in both less and more than 5 per cent of the chromosomes analysed in different sample sets; for simplicity, we have classified such nsSNPs within the nsSNP set with ≥ 5 per cent minor allele frequencies throughout this paper.

PolyPhen analysis

The PolyPhen predictions[18] were retrieved from a pre-computed dbSNP-PolyPhen resource. All PolyPhen predictions were based on either alignment of at least five similar proteins (for a more reliable prediction) or structural parameters.

Results

The results obtained in this study are summarised in Table 1 and constitute only the validated nsSNPs with a reliable prediction made by the PolyPhen prediction tool (see Methods). A total of 367 nsSNPs from 189 carcinogenesis-related genes expressed in breast tissue are presented. A total of 109 nsSNPs (28.4 per cent) from 75 genes were predicted potentially to affect the protein function (functional nsSNPs). Additionally, 61.5 per cent (n = 67) of the potentially functional nsSNPs represented commonly occurring nsSNPs in the population (≥ 5 per cent minor allele frequency; Table 2). In this paper, we mainly discuss the commonly occurring functional nsSNPs; however, the list of rarely occurring functional nsSNPs can also be found under the supplementary table (http://​www.​ozceliklab.​com/​Breast_​rare_​nsSNPs/​).
Table 1

Summary of the results.

 

n

Genes

 

   Carcinogenesis-related genes

2,832

Expressed in breast tissue

981

With validated nsSNPs

189

With functional nsSNPs

75

nsSNPs

 

   Validated nsSNPs

367

Benign by PolyPhen

258

Functional by PolyPhen

109

With ≥ 5% minor allele frequency

67

With < 5% minor allele frequency

42

Abbreviation: n = number; nsSNP = non-synonymous form of single nucleotide polymorphisms. Please note that only the genes and the nsSNPs for which a reliable PolyPhen prediction (based on ≥ 5 proteins in the alignment) was available are shown in this table.

Table 2

Functional and common non-synonymous form of single nucleotide polymorphisms (nsSNPs) from the breast tissue-expressed carcinogenesis-related genes.

Genea

Accession

number

SNP IDb

Amino acid

changec

Codond

Damaging

allele

Damaging

amino acide

PolyPhen

prediction

Pathwayf

ACY1

NM_000666.1

rs2229152

R386C

cgt/tgt

t

C

Probably damaging

IM

ADD1

NM_014189.2

rs4961

G460W

ggg/tgg

t

W

Probably damaging

IM

ADD1

NM_014189.2

rs4962

N541I

aat/att

t

I

Probably damaging

IM

ADD1

NM_014189.2

rs4971

Y270N

tat/aat

a

N

Probably damaging

IM

ADM

NM_001124.1

rs5005

S50R

agc/agg

g

R

Possibly damaging

AN

ADRB2

NM_000024.3

rs1042713

G16R

gga/aga

a

R

Possibly damaging

BE, IM

ALDH2

NM_000690.2

rs671

E504K

gaa/aaa

a

K

Possibly damaging

IM, PH

APOE

NM_000041.1

rs429358

C130R

tgc/cgc

c

R

Probably damaging

IM

AXIN2

NM_004655.1

rs2240308

P50S

cct/tct

t

S

Probably damaging

DE

C2

NM_000063.3

rs4151648

R734C

cgc/tgc

t

C

Possibly damaging

IM

CD2

NM_001767.2

rs699738

H266Q

cac/caa

a

Q

Probably damaging

AN, IM, MET

CDH12

NM_004061.2

rs4371716

V68M

gtg/atg

g

V

Probably damaging

IM

CHGA

NM_001275.2

rs729940

R399W

cgg/tgg

t

W

Probably damaging

IM

CHGA

NM_001275.2

rs9658667

G382S

ggc/agc

a

S

Possibly damaging

IM

CLU

NM_001831.1

rs9331936

N317H

aac/cac

c

H

Possibly damaging

IM

CSF1

NM_000757.3

rs2229165

G438R

ggg/agg

a

R

Probably damaging

IM

CSF3R

NM_000760.2

rs3917973

M231T

atg/acg

c

T

Probably damaging

IM

CSF3R

NM_000760.2

rs3917974

Q346R

cag/cgg

g

R

Possibly damaging

IM

CSF3R

NM_000760.2

rs3917991

D510H

gac/cac

c

H

Possibly damaging

IM

CYBA

NM_000101.1

rs4673

Y72H

tac/cac

c

H

Possibly damaging

IM

CYP11B1

NM_000497.2

rs4541

A386V

gcg/gtg

c

A

Possibly damaging

PH

CYP11B1

NM_000497.2

rs5287

M160I

atg/atc

c

I

Possibly damaging

PH

CYP11B1

NM_000497.2

rs5294

Y439H

tac/cac

t

Y

Probably damaging

PH

CYP11B1

NM_000497.2

rs5312

E383V

gag/gtg

t

V

Probably damaging

PH

CYP1B1

NM_000104.2

rs1800440

N453S

aac/agc

g

S

Possibly damaging

IM, PH

CYP2A6

NM_000762.4

rs1801272

L160H

ctc/cac

a

H

Probably damaging

IM, PH

CYP2B6

NM_000767.3

rs2279343

K262R

aag/agg

a

K

Possibly damaging

PH

CYP2C9

NM_000771.2

rs1799853

R144C

cgt/tgt

t

C

Probably damaging

IM, PH

DAG1

NM_004393.1

rs2131107

S14W

tcg/tgg

c

S

Probably damaging

IM

ENG

NM_000118.1

rs1800956

D366H

gac/cac

c

H

Possibly damaging

AN, DE, IM, MET

EPHX1

NM_000120.2

rs1051740

Y113H

tac/cac

c

H

Possibly damaging

IM, ME, PH

ERBB2

NM_004448.1

rs1058808

P1170A

ccc/gcc

g

A

Possibly damaging

IM, ST, TS/ON

F2R

NM_001992.2

rs2230849

Y187N

tac/aac

a

N

Probably damaging

IM

FPR1

NM_002029.3

rs867228

E346A

gag/gcg

c

A

Possibly damaging

IM

FUCA2

NM_032020.3

rs3762001

H371Y

cat/tat

t

Y

Possibly damaging

IM

GAA

NM_000152.2

rs1800307

G576S

ggc/agc

a

S

Possibly damaging

IM

GBP1

NM_002053.1

rs1048425

T349S

acc/agc

g

S

Possibly damaging

CS

GYS1

NM_002103.3

rs5453

P691A

cca/gca

g

A

Probably damaging

IM

GYS1

NM_002103.3

rs5456

K130E

aag/gag

g

E

Possibly damaging

IM

GYS1

NM_002103.3

rs5461

N283S

aat/agt

g

S

Possibly damaging

IM

HK2

NM_000189.4

rs2229629

R844K

agg/aag

g

R

Possibly damaging

IM, MIS

LIG4

NM_002312.2

rs1805388

T9I

act/att

t

I

Possibly damaging

DA, DD

MC1R

NM_002386.2

rs1805005

V60L

gtg/ttg

t

L

Possibly damaging

IM

MC1R

NM_002386.2

rs1805007

R151C

cgc/tgc

t

C

Probably damaging

IM

MC1R

NM_002386.2

rs3212366

F196L

ttc/ctc

c

L

Probably damaging

IM

MMP9

NM_004994.1

rs2250889

R574P

cgg/ccg

g

R

Possibly damaging

AN, IM

MMP9

NM_004994.1

rs3918252

N127K

aac/aag

g

K

Probably damaging

AN, IM

MNDA

NM_002432.1

rs2276403

H357Y

cac/tac

t

Y

Possibly damaging

GR, TR

MUC4

NM_004532.2

rs2259292

G88D

ggc/gac

g

G

Possibly damaging

IM

NFATC1

NM_006162.3

rs754093

C751G

tgt/ggt

g

G

Probably damaging

IM

NOTCH4

NM_004557.2

rs2071282

P203L

ccc/ctc

t

L

Probably damaging

IM, TS/ON

PGM3

NM_015599.1

rs473267

D466N

gat/aat

a

N

Possibly damaging

IM

PLAU

NM_002658.1

rs2227564

L141P

ctg/ccg

t

L

Possibly damaging

AN

PLAUR

NM_002659.1

rs4760

L317P

ctc/ccc

c

P

Possibly damaging

AN

PTGS2

NM_000963.1

rs5272

E488G

gag/ggg

g

G

Probably damaging

IM, MIS

PTPN3

NM_002829.2

rs3793524

A90P

gcc/ccc

g

A

Probably damaging

CC, CS

SLC1A5

NM_005628.1

rs3027956

P17A

ccc/gcc

g

A

Possibly damaging

IM

STAT2

NM_005419.2

rs2066816

Q66H

cag/cat

t

H

Possibly damaging

IM, ST

TBXAS1

NM_001061.2

rs5760

G390V

ggc/gtc

t

V

Probably damaging

IM

TBXAS1

NM_001061.2

rs5762

R425C

cgc/tgc

t

C

Probably damaging

IM

TBXAS1

NM_001061.2

rs5770

R261G

agg/ggg

g

G

Probably damaging

IM

TDG

NM_003211.2

rs4135113

G199S

ggc/agc

a

S

Possibly damaging

DD

TUBA1

NM_006000.1

rs3731891

R243C

cgc/tgc

t

C

Probably damaging

CS, MET

TYR

NM_000372.2

rs1042602

S192Y

tct/tat

a

Y

Possibly damaging

ME

VCAM1

NM_001078.2

rs3783613

G413A

ggt/gct

c

A

Possibly damaging

AN, CS, IM, MET

XRCC1

NM_006297.1

rs25489

R280H

cgt/cat

a

H

Possibly damaging

DD, DR, IM

XRCC1

NM_006297.1

rs1799782

R194W

cgg/tgg

t

W

Probably damaging

DD, DR, IM

Abbreviations: AN = angiogenesis; BE = behaviour, CC = cell cycle; CS = cell signalling; DA = DNA adduct; DD = DNA damage; DE = development; GR = gene regulation; IM = immunology; ME = metabolism;

MET = metastasis; MIS = miscellaneous; PH = pharmacology; ST = signal transduction; TS/ON = tumour suppressor/oncogene; TR = transcription.

All nsSNPs are with ≥ 5 per cent minor allele frequency.

a The gene symbols are as approved by the HUGO Gene Nomenclature Committee [67].

b SNP identifiers (IDs) correspond to the dbSNP IDs (http://​www.​ncbi.​nlm.​nih.​gov/​SNP/​) [31].

c The position of the amino acid substitution and the amino acids specified by the major and minor SNP alleles are indicated.

d The codons specified by the major and the minor SNP alleles are shown. The nucleotide change is underlined.

e One-letter codes for the amino acids that are predicted to affect the protein function by PolyPhen.

f The pathway(s) that the proteins are implicated in are as shown by the Cancer Genome Anatomy Project Genetic Annotation Initiative website (http://​lpgws.​nci.​nih.​gov/​html-cgap/​cgl/​) [30].

A fraction of protein products of genes bearing commonly occurring functional nsSNPs were found to be involved in one or more carcinogenesis-related biological pathways compiled by the CGAP-GAI[30] (Table 2). Such nsSNPs were mostly found in the proteins from DNA repair (three genes, four nsSNPs); metastasis (four genes, four nsSNPs); angiogenesis (seven genes, eight nsSNPs); pharmacology (seven genes, ten nsSNPs); and immunology (38 genes, 51 nsSNPs).

We have also analysed the distribution of the commonly occurring functional nsSNPs across human populations. For simplicity, we have categorised the frequency information obtained from different dbSNP entries into three major groups: African (African and African-American), Caucasian (Caucasian and European) and Asian (Chinese and East Asian) populations. Minor allele frequencies for nsSNPs were available for at least three different human populations for 30 out of 67 commonly occurring functional nsSNPs (Table 3). Fifteen nsSNPs were found in all populations analysed (n ≥ 3). In the case of the remaining 15 nsSNPs, five were found exclusively in one population (ADM-S50R and MMP9-N127K in African; ALDH2-E504K and MNDA-H357Y in Asian; MC1R-R151C in Caucasian). Additionally, three nsSNPs were found in Caucasian, Asian or Hispanic samples, but not in the African samples (CHGA-G382S, CYP1B1-N453S and CYP2C9-R144C). Moreover, in the case of five nsSNPs, the major and the minor alleles were different among the populations analysed (ADBR2-G16R, CDH12-V68M, ERBB2-P1170A, PGM3-D466N and SLC1A5-P17A).
Table 3

Functional and common non-synonymous form of single nucleotide polymorphisms (nsSNPs) with frequency information available from different human populations.

Genea

SNP IDb

Amino acid change c

African

Asian

Caucasian

Hispanic

ADD1

rs4961

G460W

46 chr. G = 0.891 T = 0.109

48 chr. G = 0.521 T = 0.479

48 chr. G = 0.833 T = 0.167

n/a

ADM

rs5005

S50R

46 chr. C = 0.957 G = 0.043

48 chr. C = 1.000

48 chr. C = 1.000

n/a

ADRB2

rs1042713

G16R

46 chr. G = 0.609 A = 0.391

48 chr. A = 0.583 G = 0.417

46 chr. G = 0.674 A = 0.326

n/a

ALDH2

rs671

E504K

48 chr. G = 1.000

48 0 G = 0.771 A = 0.229

58 chr. G = 1.000

44 chr. G = 1.000

CDH12

rs4371716

V68M

46 chr. T = 0.674 C = 0.326

48 chr. C = 0.812 T = 0.188

48 chr. C = 0.729 T = 0.271

n/a

CHGA

rs729940

R399W

114 chr. C = 0.954 T = 0.046

88 chr. C = 0.715 T = 0.285

104 chr. C = 0.893 T = 0.107

56 chr. C = 0.769 T = 0.231

CHGA

rs9658667

G382S

114 chr. G = 1.000

88 chr. G = 0.982 A = 0.018

104 chr. G = 0.951 A = 0.049

56 chr. G = 0.941 A = 0.059

CSF3R

rs3917973

M231T

48 chr. T = 0.938 C = 0.062

48 chr. T = 1.000

58 chr. T = 0.983 C = 0.017

46 chr. T = 1.000

CSF3R

rs3917991

D510H

48 chr. G = 0.750 C = 0.250

48 chr. G = 1.000

58 chr. G = 1.000

46 chr. G = 0.935 C = 0.065

CYBA

rs4673

Y72H

48 chr. C = 0.542 T = 0.458

1480 chr. G = 0.907 A = 0.093

60 chr. C = 0.683 T = 0.317

46 chr. C = 0.783 T = 0.217

CYP1B1

rs1800440

N453S

48 chr. A = 1.000

48 chr. A = 0.958 G = 0.042

62 chr. A = 0.806 G = 0.194

46 chr. A = 0.761 G = 0.239

CYP2A6

rs1801272

L160H

46 chr. T = 1.000

46 chr. T = 1.000

60 chr. T = 0.900 A = 0.100

46 chr. T = 0.978 A = 0.022

CYP2C9

rs1799853

R144C

48 chr. C = 1.000

48 chr. C = 0.979 T = 0.021

62 chr. C = 0.871 T = 0.129

46 chr. C = 0.935 T = 0.065

ENG

rs1800956

D366H

46 chr. C = 0.978 G = 0.022

1480 chr. C = 0.942 G = 0.058

46 chr. C = 1.000

n/a

EPHX1

rs1051740

Y113H

48 chr. T = 0.917

C = 0.083

84 chr. T = 0.620

C = 0.380

62 chr. T = 0.613

C = 0.387

46 chr. T = 0.587

C = 0.413

ERBB2

rs1058808

P1170A

40 chr. C = 0.775 G = 0.225

1502 chr. G = 0.514 C = 0.486

48 chr. G = 0.646 C = 0.354

n/a

FPR1

rs867228

E346A

44 chr. G = 0.818 T = 0.182

46 chr. G = 0.761 T = 0.239

48 chr. G = 0.771 T = 0.229

n/a

FUCA2

rs3762001

H371Y

44 chr. G = 0.818 A = 0.182

1282 chr. G = 0.789 A = 0.211

44 chr. G = 0.795 A = 0.205

n/a

LIG4

rs1805388

T9I

48 chr. C = 0.979

T = 0.021

48 chr. G = 0.792

A = 0.208

62 chr. C = 0.871

T = 0.129

46 chr.

C = 0.848

T = 0.152

MC1R

rs1805007

R151C

42 chr. C = 1.000

40 chr. C = 1.000

46 chr. C = 0.891 T = 0.109

n/a

MMP9

rs2250889

R574P

46 chr. C = 0.870 G = 0.130

1488 chr. C = 0.688 G = 0.312

48 chr. C = 0.896 G = 0.104

n/a

MMP9

rs3918252

N127K

48 chr. C = 0.938 G = 0.062

48 chr. C = 1.000

48 chr. C = 1.000

n/a

MNDA

rs2276403

H357Y

46 chr. C = 1.000

1484 chr. C = 0.944 T = 0.056

48 chr. C = 1.000

n/a

PGM3

rs473267

D466N

46 chr. T = 0.565 C = 0.435

84 chr. C = 0.750 T = 0.250

48 chr. C = 0.688 T = 0.312

n/a

PLAU

rs2227564

L141P

48 chr. C = 0.979 T = 0.021

1492 chr. G = 0.783 A = 0.217

44 chr. C = 0.659 T = 0.341

n/a

PTPN3

rs3793524

A90P

46 chr. G = 0.522 C = 0.478

1498 chr. G = 0.628 C = 0.372

46 chr. C = 0.717 G = 0.283

n/a

SLC1A5

rs3027956

P17A

46 chr. G = 0.957 C = 0.043

42 chr. G = 0.524 C = 0.476

146 chr. C = 0.710 G = 0.290

n/a

TYR

rs1042602

S192Y

46 chr. C = 0.957 A = 0.043

48 chr. C = 1.000

48 chr. C = 0.750 A = 0.250

n/a

VCAM1

rs3783613

G413A

48 chr. G = 0.938 C = 0.062

44 chr. G = 0.977 C = 0.023

48 chr. G = 1.000

n/a

XRCC1

rs25489

R280H

48 chr. G = 0.937

A = 0.063

84 chr. C = 1.000

62 chr. G = 0.968

A = 0.032

46 chr.

G = 0.957

A = 0.043

Abbreviations: chr: chromosomes; n/a: not available.

a The gene symbols are as approved by the HUGO Gene Nomenclature Committee [67].

b SNP identifiers (IDs) correspond to the dbSNP IDs (http://​www.​ncbi.​nlm.​nih.​gov/​SNP/​) [31].

c The position of the amino acid substitution and the amino acids specified by the major and minor SNP alleles are indicated. The frequency information is as in dbSNP build 123 and is based on ≥ 40 chromosomes. Please note that the samples annotated as African and African-American; Caucasian and European; Chinese and East Asian are combined together here and are referred to as African, Caucasian and Asian, respectively. Whenever more than one entry was available for a group, only the information from the entries with the highest number of chromosomes is included here.

Discussion

A portion of SNPs is considered to contribute to complex disease development [7, 1012]. SNPs in or around the candidate genes might be directly linked to a disease; however, not all SNPs are supposed to affect gene expression and function, so selection of those with potential effects is keenly debated [32]. Several studies have developed tools and/or systematically analysed nsSNPs to identify those that affect gene function based on evolutionary conservation or structural parameters [1618, 33]. PolyPhen[18] is one such web-based tool utilised to select the nsSNPs that are likely to affect protein function. In short, the PolyPhen predictions are based on protein alignments, structural parameters or sequence annotations. The sensitivity of PolyPhen has been reported to be approximately 82 per cent [18].

In this study, we hypothesised that the systematic analysis of candidate genes that are expressed in the affected tissue is likely to improve and enrich the identification of disease-susceptibility alleles. Accordingly, using a bioinformatics-based strategy, we identified the functional nsSNPs from a large number of genes related to the carcinogenesis-related pathways (DNA repair, cell cycle, signal transduction, etc), which are expressed in breast tissue. We propose that these potentially functional nsSNPs can result in abnormalities at the protein level, which are likely to affect the development, metabolism and homeostasis of the breast tissue, and thus can contribute to breast cancer susceptibility.

The genes with functional nsSNPs identified in this study were from a variety of carcinogenesis-related cellular pathways. According to this information, possible biological roles for these nsSNPs may be suggested. For example, nsSNPs from angiogenesis- and metastasis-related proteins may have roles in tumour growth and the development of metastatic tumours [34, 35]. Additionally, DNA repair nsSNPs may lead to the accumulation of somatic mutations and thus can participate in cancer initiation and promotion [3436]. Furthermore, together with the DNA repair nsSNPs, the nsSNPs from the pharmacology genes may also be good candidates for the studies targeting the efficacy, differential response and adverse effect of chemo-/radiotherapy in breast cancer [3739]. The majority of the nsSNPs were from the genes related to immunological responses (74.6 per cent), which can both suppress and promote tumorigenesis [34]. It is likely that the larger number of the functional nsSNPs in immune system-related genes is a reflection of the large number of immunology genes in the breast tissue-expressed gene set (60 per cent).

A considerable number of genes with functional nsSNPs have been previously linked to breast cancer aetiology: ADM,[40]ADRB2,[41]APOE,[42]CHGA,[43]CSF1,[44]CYP1B1,[45]DAG1,[46]ENG,[47]EPHX1,[48]ERBB2,[49]F2R,[50]MMP9,[51]MUC4,[52]NFATC1,[53]NOTCH4,[54]PLAU,[55]PLAUR,[55]PTGS2[56] and VCAM1 [57]. Therefore, we propose that the nsSNPs in Table 2 are excellent candidates as genetic factors involved in breast cancer initiation, promotion or progression. Additionally, some of these nsSNPs may be critical for breast cancer treatment outcome.

When the distribution of the commonly occurring functional nsSNPs was analysed, differences in the major alleles and the allele frequencies across human populations were observed. For example, 15 commonly occurring nsSNPs were found in all populations, whereas another set of 15 nsSNPs was specific to particular population(s). These differences might be reflections of either the age of the allele, founder effects or the dissimilar selective pressures acting on different populations [58, 59]. Most importantly, the data also indicate that a common nsSNP with a potential biological consequence in our set was equally likely to be either prevalent across different human populations or limited to some populations. Clearly, the latter prompted us to conclude that the population-specific functional nsSNPs may contribute to the genetic predisposition in individuals with a specific background. In this regard, this conclusion is consistent with previous studies in which genetic variations with significantly different allelic frequencies among populations were found to be associated with specific disease or differential drug responses [6065]. This information may be particularly helpful to researchers in determining which nsSNPs may be relevant to utilise in specific population-based studies. In addition, although further analyses are required, it is tempting to speculate that these nsSNPs may be a part of the potential variability of the molecular basis of breast cancer predisposition and drug response among different human populations.

Data integration from several databases forms the basis of our strategy to determine functional SNPs of breast tissue-expressed genes. The quality and the quantity of the genomic data within individual databases influence the comprehensiveness of the combined data. The functional SNP list presented in this study is a result of data integration from three databases -- namely, TissueInfo,[29] Ensembl,[28] and dbSNP [31]. The non-matching data fields (eg transcript identifiers) between TissueInfo, Ensembl and dbSNP have been the main source of missing data. For example, although BRCA1 was known to have a potentially functional SNP (predicted previously), this information has not been captured because of non-matching transcript identifier information for BRCA1 in the databases. Thus, incompatibility of data in different databases has been a rate-limiting factor for the bioinformatics-based strategies presented here. The improvement of the quality and the quantity of genomic data in the databases will prove beneficial for researching complex questions. Also, the genes presented in this paper are based on the expressed sequence tag information, which may lead to an under-representation of rarely expressed genes [29, 66]. Data integration using other tissue expression databases is likely to enrich the quality of the data produced. Nevertheless, although it is possible that the SNPs presented here may not represent the most comprehensive list, the SNPs identified using the proposed strategy represent a valuable resource for studying the genetic predisposition to breast cancer.

Conclusion

In conclusion, we have designed a novel strategy to identify potentially functional variants of cancer-related genes expressed in breast tissue. Our results demonstrated the presence of 109 nsSNPs with a potential biological consequence, 67 of which were frequent in human populations. We propose that, together with other genetic and environmental factors, these nsSNPs may be involved in breast cancer initiation and progression; thus, these nsSNPs represent the premium candidates as genetic variations of breast cancer predisposition. We also suggest that a considerable fraction of the nsSNPs may, in fact, be population-specific genetic variations.

Declarations

Acknowledgements

The authors thank Baris Tuncertan and Mehjabeen Shariff for retrieving the data from the dbSNP and the pre-computed PolyPhen resource and Dr Michelle Cotterchio for critically reading the manuscript. This work was supported by grants (BCTR0100627) from the Susan Komen Breast Cancer Foundation, USA, and the Canadian Breast Cancer Foundation. Sevtap Savas is supported, in part, by a 'CIHR Strategic Training Program Grant -- The Samuel Lunenfeld Research Institute Training Program: Applying Genomics to Human Health' fellowship.

Authors’ Affiliations

(1)
Fred A. Litwin Centre for Cancer Genetics, Samuel Lunenfeld Research Institute, Mount Sinai Hospital
(2)
Department of Pathology and Laboratory Medicine, Mount Sinai Hospital
(3)
Department of Laboratory Medicine and Pathobiology, University of Toronto
(4)
Department of Medicine, Brigham and Women's Hospital and Harvard Medical School

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