Genetic Breast Cancer Susceptibility Variants and Prognosis in the Prospectively Randomized SUCCESS A Study

A. Hein; B. Rack; L. Li; A. B. Ekici; A. Reis; M. P. Lux; J. M. Cunningham; M. Rübner; B. L. Fridley; A. Schneeweiss; H. Tesch; W. Lichtenegger; T. Fehm; G. Heinrich; M. Rezai; M W. Beckmann; W. Janni; R. M. Weinshilboum; L. Wang; P. A. Fasching; L. Häberle

doi:10.1055/s-0042-113189

Geburtshilfe und Frauenheilkunde, Table of Contents

Geburtshilfe Frauenheilkd 2017; 77(06): 651-659
DOI: 10.1055/s-0042-113189

GebFra Science

Original Article

Georg Thieme Verlag KG Stuttgart · New York

Genetic Breast Cancer Susceptibility Variants and Prognosis in the Prospectively Randomized SUCCESS A Study

Veranlagung für Brustkrebs: genetische Varianten und Prognose in der prospektiv randomisierten SUCCESS A-Studie

A. Hein

¹Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany

,

B. Rack

²Department of Gynecology and Obstetrics, Ludwig-Maximilians-University Munich, Munich, Germany

,

L. Li

³Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Mayo Medical School-Mayo Foundation, Rochester, MN, USA

⁴Department of Oncology; Institute of Medicinal Biotechnology; Chinese Academy of Medical Sciences & Peking Union Medical College; Tiantan Xili, Beijing, 100050, China

,

A. B. Ekici

⁵Institute of Human Genetics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

,

A. Reis

⁵Institute of Human Genetics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

,

M. P. Lux

¹Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany

,

J. M. Cunningham

⁶Department of Laboratory Medicine & Pathology, Mayo Clinic, Rochester, MN, USA

,

M. Rübner

¹Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany

,

B. L. Fridley

⁷Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA

⁸Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS, USA

,

A. Schneeweiss

⁹Department of Gynecology and Obstetrics, University Hospital Heidelberg, National Center for Tumor Diseases, Heidelberg, Germany

,

H. Tesch

¹⁰Department of Oncology, Onkologie Bethanien, Frankfurt am Main, Germany

,

W. Lichtenegger

¹¹Department of Gynecology and Obstetrics, Charité University Hospital Campus Virchow, Berlin, Germany

,

T. Fehm

¹²Department of Gynecology and Obstetrics, University Hospital Duesseldorf, Heinrich-Heine University, Düsseldorf, Germany

,

G. Heinrich

¹³Department of Gynecologic Oncology, Schwerpunktpraxis für Gynäkologische Onkologie, Fürstenwalde, Germany

,

M. Rezai

¹⁴Department of Breast Diseases, Breast Center of Düsseldorf, Luisenkrankenhaus, Düsseldorf, Germany

,

M W. Beckmann

¹Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany

,

W. Janni

¹⁵Department of Gynecology and Obstetrics, University Hospital Ulm, Ulm, Germany

,

R. M. Weinshilboum

³Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Mayo Medical School-Mayo Foundation, Rochester, MN, USA

,

L. Wang

³Division of Clinical Pharmacology, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic College of Medicine, Mayo Medical School-Mayo Foundation, Rochester, MN, USA

,

P. A. Fasching

¹Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany

¹⁶Department of Medicine, Division of Hematology/Oncology, University of California at Los Angeles, David Geffen School of Medicine, Los Angeles, CA, USA

,

L. Häberle

¹Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Comprehensive Cancer Center Erlangen-EMN, Erlangen, Germany

¹⁷Biostatistics Unit, Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany

› Author Affiliations

Abstract

Large-scale genotyping studies have identified over 70 single nucleotide polymorphisms (SNPs) associated with breast cancer (BC) risk. However, knowledge regarding genetic risk factors associated with the prognosis is limited. The aim of this study was therefore to investigate the prognostic effect of nine known breast cancer risk SNPs. BC patients (n = 1687) randomly sampled in an adjuvant, randomized phase III trial (SUCCESS A study) were genotyped for nine BC risk SNPs: rs17468277(CASP8), rs2981582(FGFR2), rs13281615(8q24), rs3817198(LSP1), rs889312(MAP3K1), rs3803662(TOX3), rs13387042(2q35), rs4973768(SLC4A7), rs6504950(COX11). Cox proportional hazards models were used to test the SNPsʼ association with overall survival (OS) and progression-free survival (PFS). Additional analyses were carried out for molecular subgroups. rs3817198 in LSP1 (lymphocyte-specific protein 1) was the only SNP that significantly influenced OS (p = 0.01) and PFS (p < 0.01) in the likelihood ratio test comparing the genetic survival model with the clinical survival model. In the molecular subgroups, triple-negative patients with two minor alleles in rs3817198 had a much better prognosis relative to OS (adjusted HR 0.03; 95% CI 0.002 – 0.279) and PFS (HR 0.09; 95% CI 0.02 – 0.36) than patients with the common alleles. The same effect on PFS was shown for patients with luminal A tumors (HR 0.19; 95% CI 0.05 – 0.84), whereas patients with luminal B tumors had a poorer PFS with two minor alleles (HR 2.13; 95% CI 1.02 – 4.40). The variant in rs3817198 has a prognostic effect particularly in the subgroup of patients with triple-negative BC, suggesting a possible link with immunomodulation and BC.

Zusammenfassung

In verschiedenen großangelegten Studien, in denen umfangreiche Genotypisierungsstudien vorgenommen wurden, wurden bislang über 70 Einzelnukleotid-Polymorphismen (SNPs) identifiziert, die mit einem erhöhten Brustkrebsrisiko einhergehen. Aber das Wissen über die mit der Prognose assoziierten Risikofaktoren wächst weniger schnell. Ziel dieser Studie war es daher, die Auswirkungen von 9 bekannten Brustkrebsrisiko-SNPs auf die Prognose zu untersuchen. In einer adjuvanten randomisierten Phase-III-Studie (SUCCESS A-Studie) wurden Brustkrebspatientinnen (n = 1687) einer Genotypisierung unterzogen. Die Patientinnen waren zuvor nach dem Zufallsprinzip ausgewählt worden. Bei der Genotypisierung standen 9 Brustkrebsrisiko-SNPs im Mittelpunkt: rs17468277(CASP8), rs2981582(FGFR2), rs13281615(8q24), rs3817198(LSP1), rs889312(MAP3K1), rs3803662(TOX3), rs13387042(2q35), rs4973768(SLC4A7), rs6504950(COX11). Zur Überprüfung des Zusammenhangs zwischen SNP und Gesamtüberleben (OS) bzw. progressionsfreiem Überleben (PFS) wurde eine Cox-Regressionsanalyse durchgeführt. Molekulare Untergruppen wurden einer weiteren Analyse unterzogen. rs3817198 in LSP1 (Lymphozyten-spezifisches Protein 1) war der einzige SNP, für den im Likelihood-Ratio-Test, der das genetische Überlebensmodell mit dem klinischen Überlebensmodell verglich, eine signifikante Auswirkung auf das Gesamtüberleben (p = 0,01) und das progressionsfreie Überleben (p < 0,01) festgestellt wurde. In den molekularen Untergruppen hatten triple-negative Patientinnen mit 2 seltenen Allelen in rs3817198 eine viel bessere Prognose im Hinblick auf ihr Gesamtüberleben (adjustierte HR 0,03; 95%-KI 0,002 – 0,279) und ihr progressionsfreies Überleben (HR 0,09; 95%-KI 0,02 – 0,36), verglichen mit Patientinnen, welche die häufig vorkommenden Allele aufwiesen. Dieselbe Auswirkung auf das progressionsfreie Überleben fand sich auch bei Patientinnen mit Brustkrebs vom Luminal-A-Typ (HR 0,19; 95%-KI 0,05 – 0,84), während Patientinnen mit Brustkrebs vom Typ Luminal-B und 2 seltenen Allelen ein geringeres progressionsfreies Überleben aufwiesen (HR 2,13; 95%-KI 1,02 – 4,40). Die Variante in rs3817198 hatte besonders auf die Untergruppe von Patientinnen mit triple-negativem Brustkrebs eine prognostische Auswirkung, was auf eine mögliche Assoziation zwischen Immunmodulation und Brustkrebs hindeutet.

Key words

breast cancer - SNP - LSP1 - prognosis - TNBC

Schlüsselwörter

Brustkrebs - SNP - LSP1 - Prognose - TNBC

Full Text

References

References
1 Curtis C, Shah SP, Chin SF. et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature 2012; 486: 346-352
2 Sotiriou C, Pusztai L. Gene-expression signatures in breast cancer. N Engl J Med 2009; 360: 790-800
3 Wang L, McLeod HL, Weinshilboum RM. Genomics and drug response. N Engl J Med 2011; 364: 1144-1153
4 Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature 2012; 490: 61-70
5 Weinshilboum RM, Wang L. Pharmacogenetics and pharmacogenomics: development, science, and translation. Annu Rev Genomics Hum Genet 2006; 7: 223-245
6 Garcia-Closas M, Hall P, Nevanlinna H. et al. Heterogeneity of breast cancer associations with five susceptibility loci by clinical and pathological characteristics. PLoS Genet 2008; 4: e1000054
7 Broeks A, Schmidt MK, Sherman ME. et al. Low penetrance breast cancer susceptibility loci are associated with specific breast tumor subtypes: Findings from the Breast Cancer Association Consortium. Hum Mol Genet 2011; 20: 3289-3303
8 Reeves GK, Travis RC, Green J. et al. Incidence of breast cancer and its subtypes in relation to individual and multiple low-penetrance genetic susceptibility loci. JAMA 2010; 304: 426-434
9 Fasching PA, Pharoah PD, Cox A. et al. The role of genetic breast cancer susceptibility variants as prognostic factors. Hum Mol Genet 2012; 21: 3926-3939
10 Garcia-Closas M, Couch FJ, Lindstrom S. et al. Genome-wide association studies identify four ER negative-specific breast cancer risk loci. Nat Genet 2013; 45: 392-398
11 Breast Cancer Association Consortium. Commonly studied single-nucleotide polymorphisms and breast cancer: results from the Breast Cancer Association Consortium. J Natl Cancer Inst 2006; 98: 1382-1396
12 Cox A, Dunning AM, Garcia-Closas M. et al. A common coding variant in CASP8 is associated with breast cancer risk. Nat Genet 2007; 39: 352-358
13 Easton DF, Pooley KA, Dunning AM. et al. Genome-wide association study identifies novel breast cancer susceptibility loci. Nature 2007; 447: 1087-1093
14 Milne RL, Benítez J, Nevanlinna H. et al. Risk of estrogen receptor-positive and -negative breast cancer and single-nucleotide polymorphism 2q35-rs13387042. J Natl Cancer Inst 2009; 101: 1012-1018
15 Ahmed S, Thomas G, Ghoussaini M. et al. Newly discovered breast cancer susceptibility loci on 3p24 and 17q23.2. Nat Genet 2009; 41: 585-590
16 Stacey SN, Manolescu A, Sulem P. et al. Common variants on chromosome 5p12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 2008; 40: 703-706
17 CHEK2 Breast Cancer Case-Control Consortium. CHEK2*1100delC and susceptibility to breast cancer: a collaborative analysis involving 10,860 breast cancer cases and 9,065 controls from 10 studies. Am J Hum Genet 2004; 74: 1175-1182
18 Schmidt MK, Tollenaar RA, de Kemp SR. et al. Breast cancer survival and tumor characteristics in premenopausal women carrying the CHEK2*1100delC germline mutation. J Clin Oncol 2007; 25: 64-69
19 Sautter-Bihl ML, Budach W, Dunst J. et al. DEGRO practical guidelines for radiotherapy of breast cancer I: breast-conserving therapy. Strahlenther Onkol 2007; 183: 661-666
20 Sautter-Bihl ML, Souchon R, Budach W. et al. DEGRO practical guidelines for radiotherapy of breast cancer II. Postmastectomy radiotherapy, irradiation of regional lymphatics, and treatment of locally advanced disease. Strahlenther Onkol 2008; 184: 347-353
21 Stacey SN, Manolescu A, Sulem P. et al. Common variants on chromosomes 2q35 and 16q12 confer susceptibility to estrogen receptor-positive breast cancer. Nat Genet 2007; 39: 865-869
22 Antoniou AC, Wang X, Fredericksen ZS. et al. A locus on 19p13 modifies risk of breast cancer in BRCA1 mutation carriers and is associated with hormone receptor-negative breast cancer in the general population. Nat Genet 2010; 42: 885-892
23 Haiman CA, Chen GK, Vachon CM. et al. A common variant at the TERT-CLPTM1 L locus is associated with estrogen receptor-negative breast cancer. Nat Genet 2011; 43: 1210-1214
24 Ghoussaini M, Fletcher O, Michailidou K. et al. Genome-wide association analysis identifies three new breast cancer susceptibility loci. Nat Genet 2012; 44: 312-318
25 Bojesen SE, Pooley KA, Johnatty SE. et al. Multiple independent variants at the TERT locus are associated with telomere length and risks of breast and ovarian cancer. Nat Genet 2013; 45: 371-384
26 Michailidou K, Hall P, Gonzalez-Neira A. et al. Large-scale genotyping identifies 41 new loci associated with breast cancer risk. Nat Genet 2013; 45: 353-361
27 Oliphant A, Barker DL, Stuelpnagel JR. et al. BeadArray technology: enabling an accurate, cost-effective approach to high-throughput genotyping. Biotechniques 2002; (Suppl.): 56-58 60–61
28 Salmen J, Neugebauer J, Fasching PA. et al. Pooled analysis of the prognostic relevance of progesterone receptor status in five German cohort studies. Breast Cancer Res Treat 2014; 148: 143-151
29 Grambsch PM, Therneau TM. Proportional hazards tests and diagnostics based on weighted residuals. Biometrika 1994; 81: 515-526
30 Chambless LE, Diao G. Estimation of time-dependent area under the ROC curve for long-term risk prediction. Stat Med 2006; 25: 3474-3486
31 Gronnesby JK, Borgan O. A method for checking regression models in survival analysis based on the risk score. Lifetime Data Anal 1996; 2: 315-328
32 May S, Hosmer DW. A simplified method of calculating an overall goodness-of-fit test for the cox proportional hazards model. Lifetime Data Anal 1998; 4: 109-120
33 Riaz M, Berns EM, Sieuwerts AM. et al. Correlation of breast cancer susceptibility loci with patient characteristics, metastasis-free survival, and mRNA expression of the nearest genes. Breast Cancer Res Treat 2012; 133: 843-851
34 Petri B, Kaur J, Long EM. et al. Endothelial LSP1 is involved in endothelial dome formation, minimizing vascular permeability changes during neutrophil transmigration in vivo. Blood 2011; 117: 942-952
35 Liu L, Cara DC, Kaur J. et al. LSP1 is an endothelial gatekeeper of leukocyte transendothelial migration. J Exp Med 2005; 201: 409-418
36 Jongstra-Bilen J, Jongstra J. Leukocyte-specific protein 1 (LSP1): a regulator of leukocyte emigration in inflammation. Immunol Res 2006; 35: 65-74
37 Tamimi RM, Cox D, Kraft P. et al. Breast cancer susceptibility loci and mammographic density. Breast Cancer Res 2008; 10: R66
38 Vachon CM, Scott CG, Fasching PA. et al. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev 2012; 21: 1156-1166
39 Lanigan F, OʼConnor D, Martin F. et al. Molecular links between mammary gland development and breast cancer. Cell Mol Life Sci 2007; 64: 3159-3184

Supplementary Material

Supporting Information