Breast Cancer Research and Treatment

, Volume 143, Issue 1, pp 189–201 | Cite as

Genomic copy number imbalances associated with bone and non-bone metastasis of early-stage breast cancer

  • Yanhong Liu
  • Renke Zhou
  • Lars O. Baumbusch
  • Spyros Tsavachidis
  • Abenaa M. Brewster
  • Kim-Anh Do
  • Aysegul Sahin
  • Gabriel N. Hortobagyi
  • Joseph H. Taube
  • Sendurai A. Mani
  • Jørgen Aarøe
  • Fredrik Wärnberg
  • Anne-Lise Børresen-Dale
  • Gordon B. Mills
  • Patricia A. ThompsonEmail author
  • Melissa L. Bondy


The aim of this study is to identify and validate copy number aberrations in early-stage primary breast tumors associated with bone or non-bone metastasis. Whole-genome molecular inversion probe arrays were used to evaluate copy number imbalances (CNIs) in breast tumors from 960 early-stage patients with information about site of metastasis. The CoxBoost algorithm was used to select metastasis site-related CNIs and to fit a Cox proportional hazards model. Gains at 1q41 and 1q42.12 and losses at 1p13.3, 8p22, and Xp11.3 were significantly associated with bone metastasis. Gains at 2p11.2, 3q21.3–22.2, 3q27.1, 10q23.1, and 14q13.2–3 and loss at 7q21.11 were associated with non-bone metastasis. To examine the joint effect of CNIs and clinical predictors, patients were stratified into three risk groups (low, intermediate, and high) based on the sum of predicted linear hazard ratios. For bone metastasis, the hazard (95 % confidence interval) for the low-risk group was 0.32 (0.11–0.92) compared to the intermediate-risk group and 2.99 (1.74–5.11) for the high-risk group. For non-bone metastasis, the hazard for the low-risk group was 0.34 (0.17–0.66) and 2.33 (1.59–3.43) for the high-risk group. The prognostic value of loss at 8p22 for bone metastasis and gains at 10q23.1 for non-bone metastasis, and gain at 11q13.5 for both bone and non-bone metastases were externally validated in 335 breast tumors pooled from four independent cohorts. Distinct CNIs are independently associated with bone and non-bone metastasis for early-stage breast cancer patients across cohorts. These data warrant consideration for tailoring surveillance and management of metastasis risk.


Breast cancer Bone metastasis Non-bone metastasis Copy number imbalances Molecular inversion probe array 



We thank Melissa May, Dr. Jeong Yun Shim, and Dr. Agbe Samuel for retrieving and processing all the tumor specimens used in the study; Wanda Williams, who supervised the medical record abstraction; Phyllis Adatto, who supervised the study staff; and Betsy C. Wertheim for careful review and editing of the manuscript. For providing tumor specimens and clinical data, the authors gratefully acknowledge for the MicMa cohort, Dr. Bjørn Naume, and Dr. Marit Synnestvedt; for the ULL cohort, Dr. Anita Langerød; for the MDG cohort, Dr. Vilde D. Haakensen and Dr. Åslaug Helland, all Oslo University Hospital, Norway. For the Uppsala cohort we wish to thank Dr. Johan Botling, Department of Surgery, Uppsala University, Sweden. For technical support performing the aCGH experiments we wish to thank Dr. Hans Kristian M. Vollan, Ida J. Schneider, and Eldri U. Due, Oslo University Hospital, Norway. This study was supported by the National Institutes of Health (NIH) through grant R01CA089608. Additional support was provided by Susan G. Komen for the Cure, by the National Breast Cancer Foundation, and by NIH through SPORE P50CA116199, MD Anderson’s Cancer Center Support Grant (CCSG) CA016672, and the Arizona Cancer Center’s CCSG CA023074. Grants to ALBD lab supporting this study: Norwegian Research Council grants 175240/S10, 159188/S10, and 193387/V50, Norwegian Cancer Society grant 138296-PR-2008-0108.

Conflict of interest

No potential conflicts of interest were disclosed.

Supplementary material

10549_2013_2796_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1160 kb)


  1. 1.
    American Cancer Society (2010). Breast cancer facts and figures 2009–2010.
  2. 2.
    Coleman RE (2006) Clinical features of metastatic bone disease and risk of skeletal morbidity. Clin Cancer Res 12(20 Pt 2):6243s–6249s. doi: 10.1158/1078-0432.CCR-06-0931 PubMedCrossRefGoogle Scholar
  3. 3.
    Paget S (1989) The distribution of secondary growths in cancer of the breast (1889). Cancer Metastasis Rev 8(2):98–101PubMedGoogle Scholar
  4. 4.
    Kang Y, Siegel PM, Shu W, Drobnjak M, Kakonen SM, Cordon-Cardo C, Guise TA, Massague J (2003) A multigenic program mediating breast cancer metastasis to bone. Cancer Cell 3(6):537–549. doi: S1535610803001326 PubMedCrossRefGoogle Scholar
  5. 5.
    Ramaswamy S, Ross KN, Lander ES, Golub TR (2003) A molecular signature of metastasis in primary solid tumors. Nat Genet 33(1):49–54. doi: 10.1038/ng1060ng1060 PubMedCrossRefGoogle Scholar
  6. 6.
    Evans AJ, James JJ, Cornford EJ, Chan SY, Burrell HC, Pinder SE, Gutteridge E, Robertson JF, Hornbuckle J, Cheung KL (2004) Brain metastases from breast cancer: identification of a high-risk group. Clin Oncol (R Coll Radiol) 16(5):345–349CrossRefGoogle Scholar
  7. 7.
    Duchnowska R, Szczylik C (2005) Central nervous system metastases in breast cancer patients administered trastuzumab. Cancer Treat Rev 31(4):312–318. doi: 10.1016/j.ctrv.2005.04.008 PubMedCrossRefGoogle Scholar
  8. 8.
    Hicks DG, Short SM, Prescott NL, Tarr SM, Coleman KA, Yoder BJ, Crowe JP, Choueiri TK, Dawson AE, Budd GT, Tubbs RR, Casey G, Weil RJ (2006) Breast cancers with brain metastases are more likely to be estrogen receptor negative, express the basal cytokeratin CK5/6, and overexpress HER2 or EGFR. Am J Surg Pathol 30(9):1097–1104. doi: 10.1097/01.pas.0000213306.05811.b900000478-200609000-00005 PubMedCrossRefGoogle Scholar
  9. 9.
    Nam BH, Kim SY, Han HS, Kwon Y, Lee KS, Kim TH, Ro J (2008) Breast cancer subtypes and survival in patients with brain metastases. Breast Cancer Res 10(1):R20. doi: 10.1186/bcr1870 PubMedCentralPubMedCrossRefGoogle Scholar
  10. 10.
    Bos PD, Zhang XH, Nadal C, Shu W, Gomis RR, Nguyen DX, Minn AJ, van de Vijver MJ, Gerald WL, Foekens JA, Massague J (2009) Genes that mediate breast cancer metastasis to the brain. Nature 459(7249):1005–1009. doi: 10.1038/nature08021 PubMedCentralPubMedCrossRefGoogle Scholar
  11. 11.
    Minn AJ, Gupta GP, Siegel PM, Bos PD, Shu W, Giri DD, Viale A, Olshen AB, Gerald WL, Massague J (2005) Genes that mediate breast cancer metastasis to lung. Nature 436(7050):518–524. doi: 10.1038/nature03799 PubMedCentralPubMedCrossRefGoogle Scholar
  12. 12.
    Singletary SE, Walsh G, Vauthey JN, Curley S, Sawaya R, Weber KL, Meric F, Hortobagyi GN (2003) A role for curative surgery in the treatment of selected patients with metastatic breast cancer. Oncologist 8(3):241–251PubMedCrossRefGoogle Scholar
  13. 13.
    Brewster AM, Do KA, Thompson PA, Hahn KM, Sahin AA, Cao Y, Stewart MM, Murray JL, Hortobagyi GN, Bondy ML (2007) Relationship between epidemiologic risk factors and breast cancer recurrence. J Clin Oncol 25(28):4438–4444. doi: 10.1200/JCO.2007.10.6815 PubMedCrossRefGoogle Scholar
  14. 14.
    Thompson PA, Brewster AM, Kim-Anh D, Baladandayuthapani V, Broom BM, Edgerton ME, Hahn KM, Murray JL, Sahin A, Tsavachidis S, Wang Y, Zhang L, Hortobagyi GN, Mills GB, Bondy ML (2011) Selective genomic copy number imbalances and probability of recurrence in early-stage breast cancer. PLoS One 6(8):e23543. doi: 10.1371/journal.pone.0023543 PubMedCentralPubMedCrossRefGoogle Scholar
  15. 15.
    Binder H, Schumacher M (2008) Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinformatics 9:14. doi: 10.1186/1471-2105-9-14 PubMedCentralPubMedCrossRefGoogle Scholar
  16. 16.
    Hoeting J, Madigan D, Raftery AE, Volinsky C (1999) Bayesian model averaging: a tutorial. Stat Sci 14:382–417CrossRefGoogle Scholar
  17. 17.
    Volinsky C, Madigan D, Raftery A, Kronmal R (1997) Bayesian model averaging in proportional hazard models: assessing the risk of a stroke. Appl Stat 46:433–448Google Scholar
  18. 18.
    Rodriguez C, Hughes-Davies L, Valles H, Orsetti B, Cuny M, Ursule L, Kouzarides T, Theillet C (2004) Amplification of the BRCA2 pathway gene EMSY in sporadic breast cancer is related to negative outcome. Clin Cancer Res 10(17):5785–5791. doi: 10.1158/1078-0432.CCR-03-04100/17/5785 PubMedCrossRefGoogle Scholar
  19. 19.
    Szepetowski P, Ollendorff V, Grosgeorge J, Courseaux A, Birnbaum D, Theillet C, Gaudray P (1992) DNA amplification at 11q13.5–q14 in human breast cancer. Oncogene 7(12):2513–2517PubMedGoogle Scholar
  20. 20.
    Bekri S, Adelaide J, Merscher S, Grosgeorge J, Caroli-Bosc F, Perucca-Lostanlen D, Kelley PM, Pebusque MJ, Theillet C, Birnbaum D, Gaudray P (1997) Detailed map of a region commonly amplified at 11q13– >q14 in human breast carcinoma. Cytogenet Cell Genet 79(1–2):125–131PubMedCrossRefGoogle Scholar
  21. 21.
    Hirano A, Emi M, Tsuneizumi M, Utada Y, Yoshimoto M, Kasumi F, Akiyama F, Sakamoto G, Haga S, Kajiwara T, Nakamura Y (2001) Allelic losses of loci at 3p25.1, 8p22, 13q12, 17p13.3, and 22q13 correlate with postoperative recurrence in breast cancer. Clin Cancer Res 7(4):876–882PubMedGoogle Scholar
  22. 22.
    Utada Y, Haga S, Kajiwara T, Kasumi F, Sakamoto G, Nakamura Y, Emi M (2000) Allelic loss at the 8p22 region as a prognostic factor in large and estrogen receptor negative breast carcinomas. Cancer 88(6):1410–1416. doi: 10.1002/(SICI)1097-0142(20000315) PubMedCrossRefGoogle Scholar
  23. 23.
    Dahmane N, Sanchez P, Gitton Y, Palma V, Sun T, Beyna M, Weiner H, Ruiz i, Altaba A (2001) The sonic Hedgehog-Gli pathway regulates dorsal brain growth and tumorigenesis. Development 128(24):5201–5212PubMedGoogle Scholar
  24. 24.
    Liu CJ, Lin SC, Chen YJ, Chang KM, Chang KW (2006) Array-comparative genomic hybridization to detect genomewide changes in microdissected primary and metastatic oral squamous cell carcinomas. Mol Carcinog 45(10):721–731. doi: 10.1002/mc.20213 PubMedCrossRefGoogle Scholar
  25. 25.
    Martinez-Cardus A, Martinez-Balibrea E, Bandres E, Malumbres R, Gines A, Manzano JL, Taron M, Garcia-Foncillas J, Abad A (2009) Pharmacogenomic approach for the identification of novel determinants of acquired resistance to oxaliplatin in colorectal cancer. Mol Cancer Ther 8(1):194–202. doi: 10.1158/1535-7163.MCT-08-0659 PubMedCrossRefGoogle Scholar
  26. 26.
    Lassmann S, Weis R, Makowiec F, Roth J, Danciu M, Hopt U, Werner M (2007) Array CGH identifies distinct DNA copy number profiles of oncogenes and tumor suppressor genes in chromosomal- and microsatellite-unstable sporadic colorectal carcinomas. J Mol Med (Berl) 85(3):293–304. doi: 10.1007/s00109-006-0126-5 CrossRefGoogle Scholar
  27. 27.
    Maire G, Forus A, Foa C, Bjerkehagen B, Mainguene C, Kresse SH, Myklebost O, Pedeutour F (2003) 11q13 alterations in two cases of hibernoma: large heterozygous deletions and rearrangement breakpoints near GARP in 11q13.5. Genes Chromosomes Cancer 37(4):389–395. doi: 10.1002/gcc.10223 PubMedCrossRefGoogle Scholar
  28. 28.
    Hughes-Davies L, Huntsman D, Ruas M, Fuks F, Bye J, Chin SF, Milner J, Brown LA, Hsu F, Gilks B, Nielsen T, Schulzer M, Chia S, Ragaz J, Cahn A, Linger L, Ozdag H, Cattaneo E, Jordanova ES, Schuuring E, Yu DS, Venkitaraman A, Ponder B, Doherty A, Aparicio S, Bentley D, Theillet C, Ponting CP, Caldas C, Kouzarides T (2003) EMSY links the BRCA2 pathway to sporadic breast and ovarian cancer. Cell 115(5):523–535. doi: S0092867403009309 PubMedCrossRefGoogle Scholar
  29. 29.
    Surawska H, Ma PC, Salgia R (2004) The role of Ephrins and Eph receptors in cancer. Cytokine Growth Factor Rev 15(6):419–433. doi: 10.1016/j.cytogfr.2004.09.002 PubMedCrossRefGoogle Scholar
  30. 30.
    Liu J, Ghanim M, Xue L, Brown CD, Iossifov I, Angeletti C, Hua S, Negre N, Ludwig M, Stricker T, Al-Ahmadie HA, Tretiakova M, Camp RL, Perera-Alberto M, Rimm DL, Xu T, Rzhetsky A, White KP (2009) Analysis of Drosophila segmentation network identifies a JNK pathway factor overexpressed in kidney cancer. Science 323(5918):1218–1222. doi: 10.1126/science.1157669 PubMedCentralPubMedCrossRefGoogle Scholar
  31. 31.
    Lee J, Sayegh J, Daniel J, Clarke S, Bedford MT (2005) PRMT8, a new membrane-bound tissue-specific member of the protein arginine methyltransferase family. J Biol Chem 280(38):32890–32896. doi: 10.1074/jbc.M506944200 PubMedCrossRefGoogle Scholar
  32. 32.
    Goodison S, Yuan J, Sloan D, Kim R, Li C, Popescu NC, Urquidi V (2005) The RhoGAP protein DLC-1 functions as a metastasis suppressor in breast cancer cells. Cancer Res 65(14):6042–6053. doi: 10.1158/0008-5472.CAN-04-3043 PubMedCentralPubMedCrossRefGoogle Scholar
  33. 33.
    Wang Z, Shen D, Parsons DW, Bardelli A, Sager J, Szabo S, Ptak J, Silliman N, Peters BA, van der Heijden MS, Parmigiani G, Yan H, Wang TL, Riggins G, Powell SM, Willson JK, Markowitz S, Kinzler KW, Vogelstein B, Velculescu VE (2004) Mutational analysis of the tyrosine phosphatome in colorectal cancers. Science 304(5674):1164–1166. doi: 10.1126/science.1096096 PubMedCrossRefGoogle Scholar
  34. 34.
    Niedergethmann M, Alves F, Neff JK, Heidrich B, Aramin N, Li L, Pilarsky C, Grutzmann R, Allgayer H, Post S, Gretz N (2007) Gene expression profiling of liver metastases and tumour invasion in pancreatic cancer using an orthotopic SCID mouse model. Br J Cancer 97(10):1432–1440. doi: 10.1038/sj.bjc.6604031 PubMedCentralPubMedCrossRefGoogle Scholar
  35. 35.
    Batlle E, Bacani J, Begthel H, Jonkheer S, Gregorieff A, van de Born M, Malats N, Sancho E, Boon E, Pawson T, Gallinger S, Pals S, Clevers H (2005) EphB receptor activity suppresses colorectal cancer progression. Nature 435(7045):1126–1130. doi: 10.1038/nature03626 PubMedCrossRefGoogle Scholar
  36. 36.
    Sheng Z, Wang J, Dong Y, Ma H, Zhou H, Sugimura H, Lu G, Zhou X (2008) EphB1 is underexpressed in poorly differentiated colorectal cancers. Pathobiology 75(5):274–280. doi: 10.1159/000151707 PubMedCrossRefGoogle Scholar
  37. 37.
    Nakamoto M, Bergemann AD (2002) Diverse roles for the Eph family of receptor tyrosine kinases in carcinogenesis. Microsc Res Tech 59(1):58–67. doi: 10.1002/jemt.10177 PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yanhong Liu
    • 1
  • Renke Zhou
    • 1
  • Lars O. Baumbusch
    • 2
  • Spyros Tsavachidis
    • 1
  • Abenaa M. Brewster
    • 3
  • Kim-Anh Do
    • 4
  • Aysegul Sahin
    • 5
  • Gabriel N. Hortobagyi
    • 6
  • Joseph H. Taube
    • 7
  • Sendurai A. Mani
    • 7
  • Jørgen Aarøe
    • 2
    • 8
  • Fredrik Wärnberg
    • 9
  • Anne-Lise Børresen-Dale
    • 2
    • 8
  • Gordon B. Mills
    • 10
  • Patricia A. Thompson
    • 11
    Email author
  • Melissa L. Bondy
    • 1
  1. 1.Department of Pediatrics, Dan L. Duncan Cancer CenterBaylor College of MedicineHoustonUSA
  2. 2.Department of Genetics, Institute for Cancer ResearchOslo University Hospital and The Norwegian Radium HospitalOsloNorway
  3. 3.Departments of Clinical Cancer PreventionThe University of Texas MD Anderson Cancer CenterHoustonUSA
  4. 4.Departments of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonUSA
  5. 5.Departments of PathologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  6. 6.Departments of Breast Medical OncologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  7. 7.Departments of Molecular PathologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  8. 8.Faculty of Medicine, K.G. Jebsen Center for Breast Cancer Research, Institute for Clinical MedicineUniversity of OsloOsloNorway
  9. 9.Department of SurgeryUppsala UniversityUppsalaSweden
  10. 10.Departments of Systems BiologyThe University of Texas MD Anderson Cancer CenterHoustonUSA
  11. 11.Arizona Cancer CenterUniversity of ArizonaTucsonUSA

Personalised recommendations