Breast Cancer Research and Treatment

, Volume 153, Issue 2, pp 445–454 | Cite as

Candidate early detection protein biomarkers for ER+/PR+ invasive ductal breast carcinoma identified using pre-clinical plasma from the WHI observational study



Estrogen receptor (ER)-positive/progesterone receptor (PR)-positive invasive ductal carcinoma accounts for ~45 % of invasive breast cancer (BC) diagnoses in the U.S. Despite reductions in BC mortality attributable to mammography screening and adjuvant hormonal therapy, an important challenge remains the development of clinically useful blood-based biomarkers for risk assessment and early detection. The objective of this study was to identify novel protein markers for ER+/PR+ ductal BC. A nested case–control study was conducted within the Women’s Health Initiative observational study. Pre-clinical plasma specimens, collected up to 12.5 months before diagnosis from 121 cases and 121 matched controls, were equally divided into training and testing sets and interrogated using a customized antibody array targeting >2000 proteins. Statistically significant differences (P < 0.05) in matched case versus control signals were observed for 39 candidates in both training and testing sets, and four markers (CSF2, RYBP, TFRC, ITGB4) remained significant after Bonferroni correction (P < 2.03 × 10−5). A multivariate modeling procedure based on elastic net regression with Monte Carlo cross-validation achieved an estimated AUC of 0.75 (SD 0.06). Most candidates did not overlap with those described previously for triple-negative BC, suggesting sub-type specificity. Gene set enrichment analyses identified two GO gene sets as upregulated in cases—microtubule cytoskeleton and response to hormone stimulus (P < 0.05, q < 0.25). This study has identified a pool of novel candidate plasma protein biomarkers for ER+/PR+ ductal BC using pre-diagnostic biospecimens. Further validation studies are needed to confirm these candidates and assess their potential clinical utility for BC risk assessment/early detection.


ER+ breast cancer Blood biomarkers Proteomics Early detection Antibody array 



This work was principally supported by NIH grant U01CA152637 (C.I.L) and NIH training grant T32CA009168 (M.F.B).

Supplementary material

10549_2015_3554_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1119 kb)


  1. 1.
    Kerlikowske K, Grady D, Rubin SM et al (1995) Efficacy of screening mammography. A meta-analysis. JAMA 273:149–154CrossRefPubMedGoogle Scholar
  2. 2.
    Nyström L, Andersson I, Bjurstam N et al (2002) Long-term effects of mammography screening : updated overview of the Swedish randomised trials. Lancet 359:909–919. doi: 10.1016/S0140-6736(02)08020-0 CrossRefPubMedGoogle Scholar
  3. 3.
    Humphrey LL, Helfand M, Chan BK, Woolf SH (2002) Breast cancer screening: a summary of the evidence for the U.S. Preventive Services Task Force. Ann Intern Med 137:347–360CrossRefPubMedGoogle Scholar
  4. 4.
    USPSTF (2009) Screening for breast cancer: U.S. Preventive Services Task Force recommendation statement. Ann Intern Med 151:716–726. doi: 10.7326/0003-4819-151-10-200911170-00008 W–236 CrossRefGoogle Scholar
  5. 5.
    Miller AB, Wall C, Baines CJ (2014) Twenty five year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: randomised screening trial. BMJ 348:g366–g366. doi: 10.1136/bmj.g366 CrossRefPubMedCentralPubMedGoogle Scholar
  6. 6.
    Brawley OW (2012) Risk-based mammography screening: an effort to maximize the benefits and minimize the harms. Ann Intern Med 156:662–663. doi: 10.7326/0003-4819-156-9-201205010-00012 CrossRefPubMedGoogle Scholar
  7. 7.
    Pace LE, Keating NL (2014) A systematic assessment of benefits and risks to guide breast cancer screening decisions. JAMA 311:1327–1335. doi: 10.1001/jama.2014.1398 CrossRefPubMedGoogle Scholar
  8. 8.
    Onega T, Beaber EF, Sprague BL et al (2014) Breast cancer screening in an era of personalized regimens: a conceptual model and National Cancer Institute initiative for risk-based and preference-based approaches at a population level. Cancer 120:2955–2964. doi: 10.1002/cncr.28771 CrossRefPubMedGoogle Scholar
  9. 9.
    Phillips M, Beatty JD, Cataneo RN et al (2014) Rapid point-of-care breath test for biomarkers of breast cancer and abnormal mammograms. PLoS One 9:e90226. doi: 10.1371/journal.pone.0090226 CrossRefPubMedCentralPubMedGoogle Scholar
  10. 10.
    Lacombe J, Mangé A, Bougnoux A-C et al (2014) A multiparametric serum marker panel as a complementary test to mammography for the diagnosis of node-negative early-stage breast cancer and DCIS in young women. Cancer Epidemiol Biomark Prev 23:1834–1842. doi: 10.1158/1055-9965.EPI-14-0267 CrossRefGoogle Scholar
  11. 11.
    Brooks M (2009) Breast cancer screening and biomarkers. Methods Mol Biol 472:307–321. doi: 10.1007/978-1-60327-492-0_13 CrossRefPubMedGoogle Scholar
  12. 12.
    Li CI (2011) Discovery and validation of breast cancer early detection biomarkers in preclinical samples. Horm Cancer 2:125–131. doi: 10.1007/s12672-010-0061-3 CrossRefPubMedCentralPubMedGoogle Scholar
  13. 13.
    Zaenker P, Ziman MR (2013) Serologic autoantibodies as diagnostic cancer biomarkers–a review. Cancer Epidemiol Biomark Prev 22:2161–2181. doi: 10.1158/1055-9965.EPI-13-0621 CrossRefGoogle Scholar
  14. 14.
    Coronell JAL, Syed P, Sergelen K (2012) The current status of cancer biomarker research using tumour-associated antigens for minimal invasive and early cancer diagnostics. J Proteomics 76:102–115. doi: 10.1016/j.jprot.2012.07.022 CrossRefGoogle Scholar
  15. 15.
    Gong B, Xue J, Yu J et al (2012) Cell-free DNA in blood is a potential diagnostic biomarker of breast cancer. Oncol Lett 3:897–900. doi: 10.3892/ol.2012.576 PubMedCentralPubMedGoogle Scholar
  16. 16.
    Ng EKO, Li R, Shin VY et al (2013) Circulating microRNAs as specific biomarkers for breast cancer detection. PLoS One 8:e53141. doi: 10.1371/journal.pone.0053141 CrossRefPubMedCentralPubMedGoogle Scholar
  17. 17.
    Cuk K, Zucknick M, Heil J et al (2013) Circulating microRNAs in plasma as early detection markers for breast cancer. Int J Cancer 132:1602–1612. doi: 10.1002/ijc.27799 CrossRefPubMedGoogle Scholar
  18. 18.
    Lu H, Ladd J, Feng Z et al (2012) Evaluation of known oncoantibodies, HER2, p53, and cyclin B1, in prediagnostic breast cancer sera. Cancer Prev Res (Phila) 5:1036–1043. doi: 10.1158/1940-6207.CAPR-11-0558 CrossRefGoogle Scholar
  19. 19.
    Pitteri SJ, Amon LM, Busald Buson T et al (2010) Detection of elevated plasma levels of epidermal growth factor receptor before breast cancer diagnosis among hormone therapy users. Cancer Res 70:8598–8606. doi: 10.1158/0008-5472.CAN-10-1676 CrossRefPubMedCentralPubMedGoogle Scholar
  20. 20.
    Fischer JC, Niederacher D, Topp SA et al (2013) Diagnostic leukapheresis enables reliable detection of circulating tumor cells of nonmetastatic cancer patients. Proc Natl Acad Sci USA 110:16580–16585. doi: 10.1073/pnas.1313594110 CrossRefPubMedCentralPubMedGoogle Scholar
  21. 21.
    Reis-Filho JS, Pusztai L (2011) Gene expression profiling in breast cancer: classification, prognostication, and prediction. Lancet 378:1812–1823. doi: 10.1016/S0140-6736(11)61539-0 CrossRefPubMedGoogle Scholar
  22. 22.
    Eroles P, Bosch A, Pérez-Fidalgo JA, Lluch A (2012) Molecular biology in breast cancer: intrinsic subtypes and signaling pathways. Cancer Treat Rev 38:698–707. doi: 10.1016/j.ctrv.2011.11.005 CrossRefPubMedGoogle Scholar
  23. 23.
    Bao P-P, Shu XO, Gao Y-T et al (2011) Association of hormone-related characteristics and breast cancer risk by estrogen receptor/progesterone receptor status in the shanghai breast cancer study. Am J Epidemiol 174:661–671. doi: 10.1093/aje/kwr145 CrossRefPubMedCentralPubMedGoogle Scholar
  24. 24.
    Yang XR, Chang-Claude J, Goode EL et al (2011) Associations of breast cancer risk factors with tumor subtypes: a pooled analysis from the Breast Cancer Association Consortium studies. J Natl Cancer Inst 103:250–263. doi: 10.1093/jnci/djq526 CrossRefPubMedCentralPubMedGoogle Scholar
  25. 25.
    Pestalozzi BC, Zahrieh D, Mallon E et al (2008) Distinct clinical and prognostic features of infiltrating lobular carcinoma of the breast: combined results of 15 International Breast Cancer Study Group clinical trials. J Clin Oncol 26:3006–3014. doi: 10.1200/JCO.2007.14.9336 CrossRefPubMedGoogle Scholar
  26. 26.
    Desantis C, Ma J, Bryan L, Jemal A (2014) Breast cancer statistics, 2013. CA Cancer J Clin 64:52–62. doi: 10.3322/caac.21203 CrossRefPubMedGoogle Scholar
  27. 27.
    Li CI, Mirus JE, Zhang Y et al (2012) Discovery and preliminary confirmation of novel early detection biomarkers for triple-negative breast cancer using preclinical plasma samples from the Women’s Health Initiative observational study. Breast Cancer Res Treat 135:611–618. doi: 10.1007/s10549-012-2204-4 CrossRefPubMedCentralPubMedGoogle Scholar
  28. 28.
    Hays J, Hunt JR, Hubbell FA et al (2003) The Women’s Health Initiative recruitment methods and results. Ann Epidemiol 13:S18–S77CrossRefPubMedGoogle Scholar
  29. 29.
    Study, The Women’S Health Initiative (1998) Design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials 19:61–109CrossRefGoogle Scholar
  30. 30.
    Loch CM, Ramirez AB, Liu Y et al (2007) Use of high density antibody arrays to validate and discover cancer serum biomarkers. Mol Oncol 1:313–320. doi: 10.1016/j.molonc.2007.08.004 CrossRefPubMedCentralPubMedGoogle Scholar
  31. 31.
    Ramirez AB, Loch CM, Zhang Y et al (2010) Use of a single-chain antibody library for ovarian cancer biomarker discovery. Mol Cell Proteomics 9:1449–1460. doi: 10.1074/mcp.M900496-MCP200 CrossRefPubMedCentralPubMedGoogle Scholar
  32. 32.
    Mirus JE, Zhang Y, Li CI et al (2015) Cross-species antibody microarray interrogation identifies a 3-protein panel of plasma biomarkers for early diagnosis of pancreas cancer. Clin Cancer Res 21:1764–1771. doi: 10.1158/1078-0432.CCR-13-3474 CrossRefPubMedGoogle Scholar
  33. 33.
    Ritchie ME, Silver J, Oshlack A et al (2007) A comparison of background correction methods for two-colour microarrays. Bioinformatics 23:2700–2707. doi: 10.1093/bioinformatics/btm412 CrossRefPubMedGoogle Scholar
  34. 34.
    Smyth GK, Speed T (2003) Normalization of cDNA microarray data. Methods 31:265–273CrossRefPubMedGoogle Scholar
  35. 35.
    Friedman J, Hastie T, Tibshirani R (2010) Regularization paths for generalized linear models via coordinate descent. J Stat Softw 33:1–22PubMedCentralPubMedGoogle Scholar
  36. 36.
    Sing T, Sander O, Beerenwinkel N, Lengauer T (2005) ROCR: visualizing classifier performance in R. Bioinformatics 21:3940–3941. doi: 10.1093/bioinformatics/bti623 CrossRefPubMedGoogle Scholar
  37. 37.
    The Cancer Genome Atlas Research Network (2011) Integrated genomic analyses of ovarian carcinoma. Nature 474:609–615. doi: 10.1038/nature10166 CrossRefPubMedCentralGoogle Scholar
  38. 38.
    The Cancer Genome Atlas Research Network (2012) Comprehensive molecular portraits of human breast tumours. Nature 490:61–70. doi: 10.1038/nature11412 CrossRefGoogle Scholar
  39. 39.
    Rho JH, Mead JR, Wright WS et al (2014) Discovery of sialyl Lewis A and Lewis X modified protein cancer biomarkers using high density antibody arrays. J Proteomics 96:291–299. doi: 10.1016/j.jprot.2013.10.030 CrossRefPubMedCentralPubMedGoogle Scholar
  40. 40.
    Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B (Statistical Methodol) 67:301–320. doi: 10.1111/j.1467-9868.2005.00503.x CrossRefGoogle Scholar
  41. 41.
    Habashy HO, Powe DG, Staka CM et al (2010) Transferrin receptor (CD71) is a marker of poor prognosis in breast cancer and can predict response to tamoxifen. Breast Cancer Res Treat 119:283–293. doi: 10.1007/s10549-009-0345-x CrossRefPubMedGoogle Scholar
  42. 42.
    Miller LD, Coffman LG, Chou JW et al (2011) An iron regulatory gene signature predicts outcome in breast cancer. Cancer Res 71:6728–6737. doi: 10.1158/0008-5472.CAN-11-1870 CrossRefPubMedCentralPubMedGoogle Scholar
  43. 43.
    Lu S, Simin K, Khan A, Mercurio AM (2008) Analysis of integrin beta4 expression in human breast cancer: association with basal-like tumors and prognostic significance. Clin Cancer Res 14:1050–1058. doi: 10.1158/1078-0432.CCR-07-4116 CrossRefPubMedGoogle Scholar
  44. 44.
    Diaz LK, Cristofanilli M, Zhou X et al (2005) Beta4 integrin subunit gene expression correlates with tumor size and nuclear grade in early breast cancer. Mod Pathol 18:1165–1175. doi: 10.1038/modpathol.3800411 CrossRefPubMedGoogle Scholar
  45. 45.
    Lipscomb EA, Simpson KJ, Lyle SR et al (2005) The alpha6beta4 integrin maintains the survival of human breast carcinoma cells in vivo. Cancer Res 65:10970–10976. doi: 10.1158/0008-5472.CAN-05-2327 CrossRefPubMedGoogle Scholar
  46. 46.
    Dutta U, Shaw LM (2008) A key tyrosine (Y1494) in the β4 integrin regulates multiple signaling pathways important for tumor development and progression. Cancer Res 68:8779–8787. doi: 10.1158/0008-5472.CAN-08-2125 CrossRefPubMedCentralPubMedGoogle Scholar
  47. 47.
    Beguin Y, Huebers HA, Josephson B, Finch CA (1988) Transferrin receptors in rat plasma. Proc Natl Acad Sci USA 85:637–640CrossRefPubMedCentralPubMedGoogle Scholar
  48. 48.
    Desgrosellier JS, Cheresh DA (2010) Integrins in cancer: biological implications and therapeutic opportunities. Nat Rev Cancer 10:9–22. doi: 10.1038/nrc2748 CrossRefPubMedCentralPubMedGoogle Scholar
  49. 49.
    Bon G, Folgiero V, Di Carlo S et al (2007) Involvement of alpha6beta4 integrin in the mechanisms that regulate breast cancer progression. Breast Cancer Res 9:203. doi: 10.1186/bcr1651 CrossRefPubMedCentralPubMedGoogle Scholar
  50. 50.
    Chen S-T, Pan T-L, Juan H-F et al (2008) Breast tumor microenvironment: proteomics highlights the treatments targeting secretome. J Proteome Res 7:1379–1387. doi: 10.1021/pr700745n CrossRefPubMedCentralPubMedGoogle Scholar
  51. 51.
    Anderson KS, Sibani S, Wallstrom G et al (2011) Protein microarray signature of autoantibody biomarkers for the early detection of breast cancer. J Proteome Res 10:85–96. doi: 10.1021/pr100686b CrossRefPubMedCentralPubMedGoogle Scholar
  52. 52.
    Hanash SM, Taguchi A (2014) Mouse to human blood-based cancer biomarker discovery strategies. Cold Spring Harb Protoc 2014:144–149. doi: 10.1101/pdb.top078808 CrossRefPubMedGoogle Scholar
  53. 53.
    Pascal LE, True LD, Campbell DS et al (2008) Correlation of mRNA and protein levels: cell type-specific gene expression of cluster designation antigens in the prostate. BMC Genom 9:246. doi: 10.1186/1471-2164-9-246 CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Division of Public Health SciencesFred Hutchinson Cancer Research CenterSeattleUSA
  2. 2.Department of Epidemiology, School of Public HealthUniversity of WashingtonSeattleUSA

Personalised recommendations