Large-Scale Association Study Identifies ICAM Gene Region As Breast and Prostate Cancer Susceptibility Locus

[CANCER RESEARCH 64, 8906–8910, December 15, 2004] Large-Scale Association Study Identifies ICAM Gene Region as Breast and Prostate Cancer Susceptibility Locus

Stefan Kammerer,1 Richard B. Roth,1 Richard Reneland,1 George Marnellos,1 Carolyn R. Hoyal,1 Nathan J. Markward,1 Florian Ebner,2 Marion Kiechle,3 Ulrike Schwarz-Boeger,3 Lyn R. Griffiths,4 Christian Ulbrich,5 Korbinian Chrobok,6 Gerhard Forster,5 Georg M. Praetorius,5 Peter Meyer,7 Joachim Rehbock,2 Charles R. Cantor,1 Matthew R. Nelson,1 and Andreas Braun1 1Sequenom, Inc., San Diego, California; 2I. Frauenklinik, Klinikum Innenstadt, University of Munich, Munich, Germany; 3Department of Obstetrics & Gynecology, Technical University of Munich, Munich, Germany; 4Genomics Research Centre, School of Health Science, Griffith University Gold Coast, Gold Coast, Queensland, Australia; 5Urology Clinic Munich-Planegg, Munich, Germany; 6Institute of Human Genetics, University Hospital of Tuebingen, Tuebingen, Germany; and 7Genefinder Technologies Ltd., Munich, Germany

ABSTRACT Within different families, the contributions of the different genes will likely vary, thus complicating the ability to detect and replicate We conducted a large-scale association study to identify genes that linkage. influence nonfamilial breast cancer risk using a collection of German For common disease susceptibility genes, direct association ap- cases and matched controls and >25,000 single nucleotide polymorphisms located within 16,000 genes. One of the candidate loci identified was proaches that compare the distribution of genetic marker frequencies The between groups of unrelated individuals are expected to have greater .[0.001 ؍ P ,1.5 ؍ (located on chromosome 19p13.2 [odds ratio (OR effect was substantially stronger in the subset of cases with reported power than traditional linkage studies (5). Recently, there has been The finding was increasing interest in the use of whole-genome association methods to .(0.001 ؍ P ,3.4 ؍ family history of breast cancer (OR subsequently replicated in two independent collections (combined identify genes that are involved in complex trait variation (6–8). To P < 0.001) and was also associated with predisposition to date, however, few large-scale studies have been reported (9–11), and ,1.4 ؍ OR prostate cancer in an independent sample set of prostate cancer cases and the search for common polymorphisms that influence breast cancer ؍ ؍ matched controls (OR 1.4, P 0.002). High-density single nucleotide risk has yet to produce useful markers. In an effort to identify genes polymorphism mapping showed that the extent of association spans 20 kb and variants that influence breast cancer susceptibility, we conducted and includes the intercellular adhesion molecule genes ICAM1, ICAM4, a large-scale case-control study using Ͼ25,000 single nucleotide and ICAM5. Although genetic variants in ICAM5 showed the strongest association with disease status, ICAM1 is expressed at highest levels in polymorphisms (SNPs) located within approximately 16,000 genes. normal and tumor breast tissue. A variant in ICAM5 was also associated We report the identification of intercellular adhesion molecule vari- with disease progression and prognosis. Because ICAMs are suitable ants on chromosome 19p13.2 that confer increased breast and prostate targets for antibodies and small molecules, these findings may not only cancer risk and might predict disease outcome. provide diagnostic and prognostic markers but also new therapeutic opportunities in breast and prostate cancer. MATERIALS AND METHODS INTRODUCTION Study Subjects and Preparation. The discovery sample comprised 254 Breast cancer is the most common form of cancer in women and breast cancer patients attending the Frauenklinik Innenstadt, Munich, Ger- accounts for over 400,000 deaths each year worldwide. Among the many. Lymph node status was positive at time of assessment in 94 cases risk factors identified are family history, diet, reproductive history, (37%), and 18 cases (7%) had known distant metastases. Twenty-four cases (11%) reported a positive family history of breast cancer. The median age at exogenous estrogen exposure, smoking, and alcohol consumption. diagnosis was 56 years (range ϭ 23–87 years). Two hundred and sixty-eight The heritability of breast cancer is estimated at about 25% (1), but controls with a median age of 57 years (range ϭ 17–88 years) were recruited germline mutations in so-called high-penetrance susceptibility genes, from patients with benign disease attending the clinic during the same period. such as BRCA1 and BRCA2 (2), explain Ͻ10% of all cases. Therefore, Controls with a family history of breast or ovarian cancer were excluded. Both it is expected that more common genetic variations with low pen- parents of each study participant were reported to be of German descent. etrance account for a high proportion of breast cancer cases. The German replication sample consisted of 188 cases and 150 controls Family based linkage studies have proven very successful for recruited at the Department of Obstetrics and Gynecology, Technical Univer- identifying genes with highly penetrant alleles that segregate in a sity of Munich. The majority of breast cancer cases were recruited at preop- simple Mendelian fashion within families (3). However, successes in erative visits, and female controls were recruited from healthy individuals or which linkage methods are used have largely been restricted to rare patients with nonmalignant diagnoses. Median age of diagnosis for cases was 59 years (range ϭ 22–87 years), and median age of controls was 50 years diseases and to selected “familial” subsets of common disorders, (range ϭ 19–91 years). All but two participants reported both parents were of accounting for only a small proportion of disease in the population. At German descent. The two exceptions each reported one parent of non-German, least two significant problems are associated with using traditional Eastern European origin. linkage approaches to map complex traits. First, linkage mapping has The Australian replication sample comprised 180 breast cancer cases re- low resolution, and the genetic loci implicated in such studies can cruited by the Pathology Department of Gold Coast Hospital or by the include hundreds of genes. Second, complex diseases are determined Genomics Research Center, Southport. Median age of diagnosis was 50 years by the interaction of multiple genes throughout the genome (4). (range ϭ 24–74 years). Controls consisted of 180 healthy volunteers recruited through the Genomics Research Center. Only controls with no family history of cancer or precancerous conditions were included. Controls were individu- Received 5/20/04; revised 9/1/04; accepted 10/3/04. Ϯ The costs of publication of this article were defrayed in part by the payment of page ally age matched to cases ( 5 years). Median age for controls was 60 years charges. This article must therefore be hereby marked advertisement in accordance with (range ϭ 28–94 years). 18 U.S.C. Section 1734 solely to indicate this fact. The prostate cancer sample consisted of 368 German patients with a median Requests for reprints: Andreas Braun, Sequenom Inc., 3595 John Hopkins Court, San age of diagnosis of 65 years (range ϭ 43–90 years) recruited at the Urology Diego, CA 92121. Phone: 858-202-9018, Fax: 858-202-9020; E-mail: abraun@sequenom. com. Clinic Munich-Planegg, and 368 controls without symptomatic prostate dis- ©2004 American Association for Cancer Research. ease with a median age of 68 years (range ϭ 25–92 years) collected at the 8906

University Hospital of Tuebingen. All subjects involved reported both parents step, the 1,619 SNPs (ϳ5%) with the most statistically significant to be of German ancestry. associations were remeasured in triplicate on each DNA pool. In the All subjects involved in our studies signed a written informed consent and third step, the 74 most significant SNPs (ϳ5%) from step two were the institutional ethics committees of participating institutions approved the individually genotyped in each sample. Fifty-two SNPs were con- experimental protocols. For discovery samples, we extracted DNA from 5 mL firmed to have statistically significant differences between cases and of blood from each subject using a desalting method and quantitated fluoro- controls (P Ͻ 0.05). metrically (Fluoroskan Ascent CF, Labsystems, Helsinki, Finland) using Pico green. DNA pools were generated by combining equimolar amounts of each Genome-wide case-control studies that use tens of thousands of sample as described elsewhere (12, 13). SNPs and liberal statistical selection criteria are expected to yield a SNP Markers and Genotyping. A set of 25,494 SNP markers was se- high proportion of false-positive associations. To distinguish the true lected from a collection of 125,799 experimentally validated polymorphic genetic effects from the false positives, the 52 SNPs were genotyped variations (14). This set was limited to SNPs located within gene coding in two independent collections of breast cancer cases and controls regions, minor allele frequencies Ͼ0.02 (95% have frequencies Ͼ0.1), and a from Germany and Australia. The most consistent and statistically target inter-marker spacing of 40 kb. SNP annotation is based on National significant association was observed with a SNP located on chromo- 8 Center for Biotechnology Information dbSNP database, refSNP, build 118. some 19p13.2. The marker SNP rs1056538, a nonsynonymous vari- Genomic annotation is based on National Center for Biotechnology Informa- ation (V301I) in exon 5 of intercellular adhesion molecule 5 (ICAM5), tion genome build 34. Gene annotation is based on LocusLink genes for which had a P value of 0.001 [odds ratio (OR) ϭ 1.5] in the discovery National Center for Biotechnology Information was providing positions on the ϭ ϭ Mapview FTP site.9 sample and a P value of 0.03 (OR 1.4) and a P of 0.07 (OR 1.3) For pooled DNA assays, 25 ng of case and control DNA pools were used for in the German and Australian replication samples, respectively (Table amplification at each site. We conducted all PCR and MassEXTEND (Seque- 1). The analysis of all three samples resulted in a combined signifi- nom, Inc., San Diego, CA) reactions using standard conditions (13). Relative cance of P Ͻ 0.001 (OR ϭ 1.4) and a significance of P ϭ 0.01 allele frequency estimates were derived from area under the peak calculations (OR ϭ 1.3) within the replication samples only. of mass spectrometry measurements from four analyte aliquots as described We tested an additional 60 SNPs located within 50 kb of the elsewhere (13). For individual genotyping, the same procedure was applied initial marker using the discovery pools to fine map the region of except only 2.5 ng of DNA was used, and only one mass spectrometry association (Fig. 1). The region of highest significance extended measurement was taken. approximately 20 kb, spanning the 3Ј-end of ICAM1 and the Statistical Analysis. We carried out tests of association between disease ICAM4 and ICAM5 genes. We selected 15 SNPs spaced throughout status and each SNP using pooled DNA in a similar fashion as explained elsewhere (15). Sources of measurement variation included pool formation, this region for genotyping in all three samples (Fig. 2). The SNPs PCR/mass extension, and chip measurement. When three or more replicate most strongly associated in the discovery (Fig. 2A) and combined measurements of a SNP were available within a model level, the corresponding samples (Fig. 2B) were two nonsynonymous SNPs in ICAM5, the variance component was estimated from the data. Otherwise, the following ϭ ϫ Ϫ5 historical laboratory averages were used: pool formation 5.0 10 , Table 1 Analysis of association of SNPs in ICAM1 and ICAM5 with breast cancer Ϫ4 Ϫ4 PCR/mass extension ϭ 1.7 ϫ 10 , and chip measurement ϭ 1.0 ϫ 10 .We susceptibility carried out tests of association using individual genotypes that used a ␹2 test of P Genotype P heterogeneity based on allele and genotype frequencies. The DerSimonian- Sample N * MAF † OR ‡ value § frequencies ¶ value ʈ Laird random effects meta-analysis method (16) was used for the analysis of rs1056538 replication samples to test for the consistency of association while permitting ICAM5 Exon 5 AAAAGGG allele frequencies to differ among samples. All tests of allele frequencies (V301I) involving only replication samples are one-sided, confirming the effect ob- German Controls 266 0.44 1.54 0.001 0.21 0.47 0.32 0.006 served in the discovery sample. We derived P values using the log odds of each (Discovery) Cases 231 0.34 0.13 0.42 0.45 German Controls 143 0.43 1.35 0.07 0.18 0.50 0.32 0.15 contrast and their standard errors. Multiple approaches were explored in an (Replication) Cases 181 0.36 0.14 0.43 0.43 effort to identify haplotypes demonstrating a stronger association with disease Australian Controls 175 0.41 1.27 0.03 0.19 0.43 0.38 0.21 status than single sites. These included analyses of 15 SNP haplotypes and (Replication) Cases 175 0.35 0.17 0.36 0.47 subsets thereof using the coalescent theory-based PHASE v2.0 (17) and the Replication 1.30 0.01 Total 1.39 0.0001 score method that relies on the expectation-maximization algorithm (18). No rs5030382 attempt was made to correct P values for multiple testing. Rather, P values are ICAM1 Exon 6 GGGGAAA provided to compare the relative strength of association from multiple depend- (K469E) ent (e.g., SNPs within samples) and independent (e.g., SNPs between samples) German Controls 265 0.46 1.39 0.01 0.22 0.49 0.29 0.04 Ͻ (Discovery) Cases 242 0.38 0.16 0.45 0.39 sources of information. P values 0.05 are referred to as statistically signif- German Controls 142 0.48 1.27 0.07 0.25 0.47 0.28 0.34 icant. (Replication) Cases 178 0.42 0.19 0.48 0.34 Australian Controls 170 0.41 1.14 0.20 0.19 0.44 0.36 0.73 (Replication) Cases 167 0.38 0.17 0.43 0.40 RESULTS Replication 1.20 0.05 Total 1.28 0.004 We carried out a genome-wide association study using 25,494 rs281439 ICAM5 542 bp GGGGCCC SNPs located within 10 kb of 15,995 LocusLink annotated genes and upstream a sample consisting of 254 breast cancer cases and 268 age-matched German Controls 257 0.23 1.09 0.57 0.04 0.37 0.59 0.74 controls of German descent. We used a high-throughput approach (Discovery) Cases 242 0.24 0.06 0.37 0.57 German Controls 143 0.19 1.52 0.02 0.06 0.26 0.69 0.03 using DNA pools, chip-based mass spectrometry (11–13, 19), and a (Replication) Cases 179 0.26 0.06 0.39 0.55 three-step SNP selection strategy. In the first step, we did a single Australian Controls 174 0.21 1.33 0.05 0.05 0.33 0.63 0.25 PCR and primer extension reaction for each SNP on one DNA pool (Replication) Cases 176 0.26 0.09 0.35 0.56 Replication 1.41 0.004 consisting of cases and on one consisting of controls. Relative allele Total 1.27 0.02 frequencies obtained from four mass spectrometry measurements of * Number of subjects with genotypes. the extension products were compared between pools. In the second † Minor relative allele frequency. ‡ Odds ratio with reference to allele that increases frequency in cases. § P value for test comparing allele frequencies between cases and controls. 8 http://www.ncbi.nlm.nih.gov/SNP/ ¶ Relative genotype frequencies. 9 ftp://ftp.ncbi.nih.gov/refseq/H_sapiens/H_sapiens/maps/mapview/BUILD.34/ ʈ P value for tests comparing genotype frequencies between cases and controls. 8907

Fig. 1. Association fine mapping of breast cancer susceptibility region on chromosome 19p13.2. Sixty public domain SNPs in a 100-kb window around the incident SNP (bold “ϩ” Ϫ in center) were compared between pools of cases and controls. The X-axis corresponds to their chromosomal position and the Y-axis to the test P values (shown on the log10 scale). The continuous dark line presents the results of a goodness-of-fit test for an excess of significance (compared with 0.05) in a 10-kb sliding window assessed at 1-kb increments. The continuous light gray line is the result of a nonlinear smoothing function showing a weighted average of the P values across the region. The darkness of each point corresponds to the minor allele frequency of each SNP in the control sample (see legend below graph). The LocusLink gene annotations for National Center for Biotechnology Information genome build 34 are included. original marker rs1056538 (V301I) and rs2228615 (A348T), which samples. Analyses of haplotypes consisting of subsets of the 15 were in near complete linkage disequilibrium (Fig. 2E). A nonsyn- genotyped SNPs did not reveal any haplotype with stronger asso- onymous SNP in ICAM1, rs5030382 (K469E), was significantly ciation than individual SNPs (data not shown). associated in the discovery sample but not in the replication Because there are several common features between breast and

Fig. 2. Genotype analysis of 15 SNPs in a 20-kb window around the ICAM region. A, P values for each SNP in the breast cancer discovery sample. B, meta-analysis P values of the breast cancer replication samples (gray) and all three samples (black). C, P values of the prostate cancer samples. D, Gene map and chromosomal positions as in Fig. 1. SNP locations are indicated as tick marks. E, estimates of linkage disequilibrium in the combined breast cancer sample. Gray-scale ranges from white (linkage disequilibrium ϭ 0) to black (linkage disequilibrium ϭ 1) with increments of 0.1.

8908

Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2004 American Association for Cancer Research. ICAM LOCUS ASSOCIATION WITH BREAST AND PROSTATE CANCER prostate cancer (20), we also tested this locus for association with in tumor progression of several cancers, including breast cancer prostate cancer susceptibility in an independent collection of German (26–28). ICAM1 is also involved in transmembrane signal transduc- cases and controls. Thirteen of the 15 SNPs genotyped in the breast tion after binding to ␤2 integrin ligands and multimer formation (29), cancer sample were genotyped in the prostate cancer sample. The activating the mitogen-activated protein kinase pathway (30) and resulting association pattern was similar to that observed for breast eventually transcription factors like AP-1 that regulate cell prolifera- cancer. The nonsynonymous SNPs, rs1056538 and rs2228615, in tion events (31). The inhibition of AP-1 has been shown to inhibit ICAM5 were most strongly associated (OR ϭ 1.4, P ϭ 0.002; Fig. breast cancer cell growth (32). Relatively little is known about the 2C). roles of ICAM4 and ICAM5 in cell signaling events and tumor The discovery collection of German breast cancer patients included surveillance, but their involvement in similar pathways is likely. information related to family history, age of onset, and severity of ICAM4 has been reported to be exclusively expressed in erythrocytes disease. The genotyped SNPs were tested for association with these and has a suggested role in cell interaction events, including hemo- variables. The SNP rs1056538 and rs2228615 were most strongly stasis and thrombosis (33). ICAM5 is mainly expressed in specific associated with a positive family history of breast cancer (P ϭ 0.0065, areas of the brain (34) and has been implicated in dendritic outgrowth Fisher’s exact test). Fifteen percent of those homozygous for the (35) and rapid cell spreading of microglia (36). Although the reported susceptible allele (C) had a positive family history, compared with 9% expression patterns and described functions of ICAM4 and ICAM5 are of the heterozygotes and none of those homozygous for the protective not indicative of a role in breast and prostate cancer susceptibility, allele. Comparing allele frequencies between cases with a positive their roles in cell adhesion and cell signaling together with their low family history and controls results in a substantially larger estimated level expression in cancer-relevant tissues leave the possibility that effect size than in all cases (OR ϭ 3.4, P ϭ 0.001). There was no their dysregulation or dysfunction may increase cancer risk. association between rs1056538 and age of diagnosis. Also notewor- Our findings are in agreement with previous reports on the involve- thy, the SNP most strongly associated with breast cancer status in the ment of ICAM1 in tumor progression, and the K469E variant is a replication samples, rs281439 located 542 bp 5Ј of ICAM5 (Table 1), potential candidate to influence this process. However, the data also was also associated with indicators of cancer severity (Table 2). suggests an influence of other variants in this region on breast and Patients carrying the allele associated with breast cancer susceptibility prostate cancer susceptibility. Although the genetic evidence favors (G) had a significantly shorter time span between diagnosis and ICAM5, higher relative expression levels of ICAM1 make it a more recruitment, suggesting shorter survival time, and higher rates of favorable candidate for predisposition and potentially tumor progres- metastases to other organs. These results suggest that one or more sion. However, current data cannot exclude any of the three ICAM variants in this region are risk factors for breast and prostate cancer genes as biologically responsible for the observed association. and may influence disease progression and prognosis. The route by which these genetic associations were arrived at and Using semiquantitative RT-PCR, we found expression of ICAM1 in the potential for spurious association must certainly be considered. a variety of normal tissues, including breast and prostate and several Recent published work has brought much needed light to the need for breast tumor samples tested (data not shown). As described previ- proper validation to verify genetic findings for complex traits (37–39). ously, ICAM5 showed strongest expression in brain and, along with In the current study, the initial association found between the ICAM5 ICAM4, was expressed at very low levels in various other tissues, marker and breast cancer status was one result from over 25,000 including breast and prostate, and a number of breast tumor tissues hypothesis tests. A conservative Bonferroni adjustment to yield an and cell lines. experiment-wide type I error rate of 0.05 would demand a test-wise P value on the order of 10Ϫ6. Given the modest sample size, only Ͼ DISCUSSION common variations with relatively large effects (OR 2) would reach such significance levels. Instead, we chose to be more mindful of the In an association study using SNPs in nearly 16,000 genes, we role of type II error rates and apply a more liberal set of criteria in the obtained evidence that a region on chromosome 19p13.2 containing initial phases of the study and verify true genetic effects by independ- the genes ICAM1, ICAM4, and ICAM5 influences breast and prostate ent replication. The analysis of 52 selected markers in the German and cancer risk. The identification of a susceptibility region influencing Australian replication samples resulted in multiple associations of both breast and prostate cancer is particularly interesting because continuing interest, with the ICAM5 providing the most consistent and breast and prostate cancer have many common features, such as statistically significant association (P ϭ 0.01, OR ϭ 1.3). This would hormone-sensitivity, parallel incidence rates in various countries, and not be considered significant on an experiment-wide level after Bon- common genetic alterations (20). The role of intercellular adhesion ferroni adjustment, which would require a test-wise P value on the molecules in cancer has been described (21, 22). ICAM1 is constitu- order of 0.001. Indeed, if the true size of the genetic effect is 1.4, then tively expressed in cells involved in immune response and induced on the total replication sample size of 368 cases and 330 controls has other cells, including endothelial and epithelial cells. It has a well- Ͻ50% power to reject the false null hypothesis at this level. known role in inflammation-related processes and immune surveil- It should be noted that we observed two additional lines of evidence lance, and derangements of its expression have been implicated in the supporting the validity of the association of this region to breast development of a variety of inflammatory diseases (23–25) as well as cancer susceptibility. First, the risk allele for coding SNP rs1056538 is significantly more common in cases with a family history of breast cancer. Second, the independent identification of the same variation Table 2 Association of rs281439 with clinical indicators of breast cancer severity with remarkably similar effects observed in a larger prostate cancer GG GC CC study gives us added reason to believe that one or more variations in Genotype N (N ϭ 14) (N ϭ 90) (N ϭ 139) P value these ICAM genes are influencing breast and prostate cancer suscep- Age of diagnosis (years) 235 48/54/61 * 49/57/65 49/56/63 0.67 tibility in populations of European origin. These are characteristics Years since diagnosis 233 0.6/1.4/4.6 0.3/1.5/5.2 1.1/3.0/6.6 0.02 that have also been observed for the germ line variations in CHEK2 Lymph node metastases (yes) 254 43% (6) 29% (26) 29% (41) 0.56 Organ metastases (yes) 254 29% (4) 8% (7) 4% (6) 0.003 (40, 41). * Quantitative variables are expressed as 1st quartile/median/3rd quartile. Categorical The successful identification of a small gene region associated with variables are expressed as column percentage (count). breast and prostate cancer susceptibility through large-scale associa- 8909

Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2004 American Association for Cancer Research. ICAM LOCUS ASSOCIATION WITH BREAST AND PROSTATE CANCER tion supports the promise of more comprehensive genome-wide stud- 15. Barratt BJ, Payne F, Rance HE, Nutland S, Todd JA, Clayton DG. Identification of ies. The approach presented here using DNA pools and genome-wide the sources of error in allele frequency estimations from pooled DNA indicates an optimal experimental design. Ann Hum Genet 2002;66:393–405. gene-based SNPs is very useful to quickly discover susceptible genes 16. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials 1986; for various cancer types and other complex disorders. However, given 7:177–88. the complexity of the genetic architecture underlying complex trait 17. Stephens M, Donnelly P. A comparison of bayesian methods for haplotype recon- struction from population genotype data. Am J Hum Genet 2003;73:1162–9. variation, genetic analyses, alone, are not likely to unambiguously 18. Schaid DJ, Rowland CM, Tines DE, Jacobson RM, Poland GA. Score tests for identify the genes or genetic variations responsible. Further validation association between traits and haplotypes when linkage phase is ambiguous. Am J in independent samples must be carried out to support genetic evi- Hum Genet 2002;70:425–34. 19. Mohlke KL, Erdos MR, Scott LJ, et al. High-throughput screening for evidence of dence, and functional experiments must be conducted to identify association by using mass spectrometry genotyping on DNA pools. Proc Natl Acad which variations in which genetic and environmental contexts are Sci USA 2002;99:16928–33. 20. Lopez-Otin C, Diamandis EP. Breast and prostate cancer: an analysis of common associated with disease susceptibility and prognosis. The observed epidemiological, genetic, and biochemical features. Endocr Rev 1998;19:365–96. association of genetic variants to disease severity and progression has 21. Frenette PS, Wagner DD. Adhesion molecules–Part 1. N Engl J Med 1996;334: promising implications for patient management. Because ICAMs are 1526–9. 22. O’Hanlon DM, Fitzsimons H, Lynch J, Tormey S, Malone C, Given HF. Soluble cell surface molecules and therefore obvious targets for antibodies and adhesion molecules (E-selectin, ICAM-1 and VCAM-1) in breast carcinoma. Eur J small molecules, these findings also offer new opportunities for ther- Cancer 2002;38:2252–7. apeutic intervention. 23. Kretowski A, Wawrusiewicz N, Mironczuk K, Mysliwiec J, Kretowska M, Kinalska I. Intercellular adhesion molecule 1 gene polymorphisms in Graves’ disease. J Clin Endocrinol Metab 2003;88:4945–9. ACKNOWLEDGMENTS 24. Matsuzawa J, Sugimura K, Matsuda Y, et al. Association between K469E allele of intercellular adhesion molecule 1 gene and inflammatory bowel disease in a Japanese We would like to thank Richard Kolodner for critically reviewing the report population. Gut 2003;52:75–8. 25. Nejentsev S, Laaksonen M, Tienari PJ, et al. Intercellular adhesion molecule-1 K469E and making helpful comments. We would also like to thank all patients polymorphism: study of association with multiple sclerosis. Hum Immunol 2003;64: participating in this study and the members of Sequenom’s high throughput 345–9. center team for generating the data. 26. Ogawa Y, Hirakawa K, Nakata B, et al. Expression of intercellular adhesion molecule-1 in invasive breast cancer reflects low growth potential, negative lymph node involvement, and good prognosis. Clin Cancer Res 1998;4:31–6. REFERENCES 27. Yasuda M, Tanaka Y, Tamura M, et al. Stimulation of beta1 integrin down-regulates ICAM-1 expression and ICAM-1-dependent adhesion of lung cancer cells through 1. Lichtenstein P, Holm NV, Verkasalo PK, et al. Environmental and heritable factors focal adhesion kinase. Cancer Res 2001;61:2022–30. in the causation of cancer-analyses of cohorts of twins from Sweden, Denmark, and 28. Maruo Y, Gochi A, Kaihara A, et al. ICAM-1 expression and the soluble ICAM-1 Finland. N Engl J Med 2000;343:78–85. level for evaluating the metastatic potential of gastric cancer. Int J Cancer 2002;100: 2. Welcsh PL, Owens KN, King MC. Insights into the functions of BRCA1 and BRCA2. 486–90. Trends Genet 2000;16:69–74. 29. Hubbard AK, Rothlein R. Intercellular adhesion molecule-1 (ICAM-1) expression 3. Botstein D, Risch N. Discovering genotypes underlying human phenotypes: past and cell signaling cascades. Free Radic Biol Med 2000;28:1379–86. successes for mendelian disease, future approaches for complex disease. Nat Genet 30. Gardiner EE, D’Souza SE. Sequences within fibrinogen and intercellular adhesion 2003;33(Suppl):228–37. molecule-1 (ICAM-1) modulate signals required for mitogenesis. J Biol Chem 1999; 4. Sing CF, Haviland MB, Reilly SL. Genetic architecture of common multifactorial 274:11930–6. diseases. Ciba Found Symp 1996;197:211–29; discussion 29–32. 31. Shaulian E, Karin M. AP-1 as a regulator of cell life and death. Nat Cell Biol 5. Risch NJ. Searching for genetic determinants in the new millennium. Nature (Lond) 2002;4:E131–6. 2000;405:847–56. 32. Liu Y, Ludes-Meyers J, Zhang Y, et al. Inhibition of AP-1 transcription factor causes 6. Risch N, Merikangas K. The future of genetic studies of complex human diseases. blockade of multiple signal transduction pathways and inhibits breast cancer growth. Science (Wash D C) 1996;273:1516–7. Oncogene 2002;21:7680–9. 7. Collins FS, Guyer MS, Charkravarti A. Variations on a theme: cataloging human 33. Hermand P, Gane P, Huet M, et al. Red cell ICAM-4 is a novel ligand for platelet- DNA sequence variation. Science (Wash D C) 1997;278:1580–1. activated alpha IIbbeta 3 integrin. J Biol Chem 2003;278:4892–8. 8. Goldstein DB, Ahmadi KR, Weale ME, Wood NW. Genome scans and candidate gene approaches in the study of common diseases and variable drug responses. Trends 34. Yoshihara Y, Oka S, Nemoto Y, et al. An ICAM-related neuronal glycoprotein, Genet 2003;19:615–22. telencephalin, with brain segment-specific expression. Neuron 1994;12:541–53. 9. Ozaki K, Ohnishi Y, Iida A, et al. Functional SNPs in the lymphotoxin-alpha gene 35. Tian L, Nyman H, Kilgannon P, et al. Intercellular adhesion molecule-5 induces that are associated with susceptibility to myocardial infarction. Nat Genet 2002;32: dendritic outgrowth by homophilic adhesion. J Cell Biol 2000;150:243–52. 650–4. 36. Mizuno T, Yoshihara Y, Kagamiyama H, et al. Neuronal adhesion molecule telen- 10. Carrasquillo MM, McCallion AS, Puffenberger EG, Kashuk CS, Nouri N, Chakravarti A. cephalin induces rapid cell spreading of microglia. Brain Res 1999;849:58–66. Genome-wide association study and mouse model identify interaction between RET and 37. Lohmueller KE, Pearce CL, Pike M, Lander ES, Hirschhorn JN. Meta-analysis of EDNRB pathways in Hirschsprung disease. Nat Genet 2002;32:237–44. genetic association studies supports a contribution of common variants to suscepti- 11. Kammerer S, Burns-Hamuro LL, Ma Y, et al. Amino acid variant in the kinase bility to common disease. Nat Genet 2003;33:177–82. binding domain of dual-specific A kinase-anchoring protein 2: a disease susceptibility 38. Ioannidis JP, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG. Replication polymorphism. Proc Natl Acad Sci USA 2003;100:4066–71. validity of genetic association studies. Nat Genet 2001;29:306–9. 12. Buetow KH, Edmonson M, MacDonald R, et al. High-throughput development and 39. Trikalinos TA, Ntzani EE, Contopoulos-Ioannidis DG, Ioannidis JP. Establishment of characterization of a genomewide collection of gene-based single nucleotide poly- genetic associations for complex diseases is independent of early study findings. Eur morphism markers by chip-based matrix-assisted laser desorption/ionization time-of- J Hum Genet 2004;12:762–9. flight mass spectrometry. Proc Natl Acad Sci USA 2001;98:581–4. 40. Cybulski C, Huzarski T, Gorski B, et al. A novel founder CHEK2 mutation is 13. Bansal A, van den Boom D, Kammerer S, et al. Association testing by DNA pooling: associated with increased prostate cancer risk. Cancer Res 2004;64:2677–9. an effective initial screen. Proc Natl Acad Sci USA 2002;99:16871–4. 41. The CHEK2 Breast Cancer Case-Control Consortium. CHEK2*1100delC and sus- 14. Nelson MR, Marnellos G, Kammerer S, et al. Large-scale validation of single ceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer nucleotide polymorphisms in gene regions. Genome Res 2004;14:1664–8. cases and 9,065 controls from 10 studies. Am J Hum Genet 2004;74:1175–82.

8910

Downloaded from cancerres.aacrjournals.org on October 2, 2021. © 2004 American Association for Cancer Research. Large-Scale Association Study Identifies ICAM Gene Region as Breast and Prostate Cancer Susceptibility Locus

Stefan Kammerer, Richard B. Roth, Richard Reneland, et al.

Cancer Res 2004;64:8906-8910.

Updated version Access the most recent version of this article at: http://cancerres.aacrjournals.org/content/64/24/8906

Cited articles This article cites 38 articles, 12 of which you can access for free at: http://cancerres.aacrjournals.org/content/64/24/8906.full#ref-list-1

Citing articles This article has been cited by 13 HighWire-hosted articles. Access the articles at: http://cancerres.aacrjournals.org/content/64/24/8906.full#related-urls

E-mail alerts Sign up to receive free email-alerts related to this article or journal.

Reprints and To order reprints of this article or to subscribe to the journal, contact the AACR Publications Subscriptions Department at [email protected].

Permissions To request permission to re-use all or part of this article, use this link http://cancerres.aacrjournals.org/content/64/24/8906. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.