Breast Cancer Research and Treatment

, Volume 116, Issue 1, pp 69–77 | Cite as

Effects of infiltrating lymphocytes and estrogen receptor on gene expression and prognosis in breast cancer

  • Alberto Calabrò
  • Tim Beissbarth
  • Ruprecht Kuner
  • Michael Stojanov
  • Axel Benner
  • Martin Asslaber
  • Ferdinand Ploner
  • Kurt Zatloukal
  • Hellmut Samonigg
  • Annemarie Poustka
  • Holger SültmannEmail author
Preclinical Study


The involvement of the immune system for the course of breast cancer, as evidenced by varying degrees of lymphocyte infiltration (LI) into the tumor is still poorly understood. The aim of this study was to evaluate the prognostic value of LI in breast cancer samples using microarray-based screening for LI-associated genes. Starting from the observation that most published ER gene signatures are heavily influenced by the LI effect, we developed and applied a novel approach to dissect molecular signatures. Further, a meta-analysis encompassing 1,044 hybridizations showed that LI alone is not sufficient to highlight breast cancer patients with different prognosis. However, for ER positive patients, high LI was associated with shorter survival times, whereas for ER negative patients, high LI is significantly associated with longer survival. Annotation of LI, in addition to ER status, is important for breast cancer patient prognosis and may have implications for the future treatment of breast cancer.


Breast cancer Computational microdissection Prognosis Lymphocyte infiltration Estrogen receptor 



We thank Sabrina Balaguer-Puig for excellent technical assistance, Andreas Buness for retrieving the external datasets and Dirk Ledwinka for IT support. The study was supported by a grant of the German Federal Ministry for Education and Research (NGFN grant 01GR0418; NGFN grant 01GR0450) and the Austrian Genome Research Program (GEN-AU).

Authors contributions

MS, RK and TB had the initial ideas for the study. AC collected all the data and performed the experiments. TB did the statistical analysis with the help of AC and AB. MA, FP, KZ and HSa collected and reevaluated the patient samples creating the patient samples and annotation for our own dataset. AC, TB, RK, AP and HSü interpreted the results and wrote the manuscript. AC, RK, TB, AP and HSü contributed in discussions. All authors read and approved the final manuscript.

Supplementary material

10549_2008_105_MOESM1_ESM.pdf (163 kb)
(PDF 162 kb)


  1. 1.
    The R core project team (2007) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  2. 2.
    Aaltomaa S, Lipponen P, Eskelinen M, Kosma VM, Marin S, Alhava E et al (1992) Lymphocyte infiltrates as a prognostic variable in female breast cancer. Eur J Cancer 28A:859–864. doi: 10.1016/0959-8049(92)90134-N PubMedCrossRefGoogle Scholar
  3. 3.
    Alexe G, Dalgin GS, Scanfeld D, Tamayo P, Mesirov JP, DeLisi C et al (2007) High expression of lymphocyte-associated genes in node-negative HER2+ breast cancers correlates with lower recurrence rates. Cancer Res 67:10669–10676. doi: 10.1158/0008-5472.CAN-07-0539 PubMedCrossRefGoogle Scholar
  4. 4.
    Aspord C, Pedroza-Gonzalez A, Gallegos M, Tindle S, Burton EC, Su D et al (2007) Breast cancer instructs dendritic cells to prime interleukin 13-secreting CD4+ T cells that facilitate tumor development. J Exp Med 204:1037–1047. doi: 10.1084/jem.20061120 PubMedCrossRefGoogle Scholar
  5. 5.
    Asslaber M, Zatloukal K (2007) Biobanks: transnational, European and global networks. Brief Funct Genomic Proteomic 6:193–201PubMedCrossRefGoogle Scholar
  6. 6.
    Balkwill F, Mantovani A (2001) Inflammation and cancer: back to Virchow? Lancet 357:539–545. doi: 10.1016/S0140-6736(00)04046-0 PubMedCrossRefGoogle Scholar
  7. 7.
    Bates GJ, Fox SB, Han C, Leek RD, Garcia JF, Harris AL et al (2006) Quantification of regulatory T cells enables the identification of high-risk breast cancer patients and those at risk of late relapse. J Clin Oncol 24:5373–5380. doi: 10.1200/JCO.2006.05.9584 PubMedCrossRefGoogle Scholar
  8. 8.
    Beissbarth T, Speed TP (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20:1464–1465. doi: 10.1093/bioinformatics/bth088 PubMedCrossRefGoogle Scholar
  9. 9.
    Bertucci F, Finetti P, Cervera N, Charafe-Jauffret E, Mamessier E, Adelaide J et al (2006) Gene expression profiling shows medullary breast cancer is a subgroup of basal breast cancers. Cancer Res 66:4636–4644. doi: 10.1158/0008-5472.CAN-06-0031 PubMedCrossRefGoogle Scholar
  10. 10.
    Bild AH, Yao G, Chang JT, Wang Q, Potti A, Chasse D et al (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439:353–357. doi: 10.1038/nature04296 PubMedCrossRefGoogle Scholar
  11. 11.
    Biswas DK, Singh S, Shi Q, Pardee AB, Iglehart JD (2005) Crossroads of estrogen receptor and NF-kappaB signaling. Sci STKE 2005:pe27. doi: 10.1126/stke.2882005pe27
  12. 12.
    Buness A, Huber W, Steiner K, Sultmann H, Poustka A (2005) arrayMagic: two-colour cDNA microarray quality control and preprocessing. Bioinformatics 21:554–556. doi: 10.1093/bioinformatics/bti052 PubMedCrossRefGoogle Scholar
  13. 13.
    Cardoso F, Van’t Veer L, Rutgers E, Loi S, Mook S, Piccart-Gebhart MJ (2008) Clinical application of the 70-gene profile: the MINDACT trial. J Clin Oncol 26:729–735. doi: 10.1200/JCO.2007.14.3222 PubMedCrossRefGoogle Scholar
  14. 14.
    Clamp A, Danson S, Clemons M (2002) Hormonal risk factors for breast cancer: identification, chemoprevention, and other intervention strategies. Lancet Oncol 3:611–619. doi: 10.1016/S1470-2045(02)00875-6 PubMedCrossRefGoogle Scholar
  15. 15.
    Coussens LM, Werb Z (2002) Inflammation and cancer. Nature 420(6917):860–867. doi: 10.1038/nature01322 PubMedCrossRefGoogle Scholar
  16. 16.
    Cox DR (1972) Regression models and life tables. J R Stat Soc [Ser A], 187–220Google Scholar
  17. 17.
    de Visser KE, Eichten A, Coussens LM (2006) Paradoxical roles of the immune system during cancer development. Nat Rev Cancer 6:24–37. doi: 10.1038/nrc1782 PubMedCrossRefGoogle Scholar
  18. 18.
    DeNardo DG, Coussens LM (2007) Inflammation and breast cancer. Balancing immune response: crosstalk between adaptive and innate immune cells during breast cancer progression. Breast Cancer Res 9:212. doi: 10.1186/bcr1746 Google Scholar
  19. 19.
    Diehn M, Sherlock G, Binkley G, Jin H, Matese JC, Hernandez-Boussard T et al (2003) SOURCE: a unified genomic resource of functional annotations, ontologies, and gene expression data. Nucleic Acids Res 31:219–223. doi: 10.1093/nar/gkg014 PubMedCrossRefGoogle Scholar
  20. 20.
    Edgar R, Domrachev M, Lash AE (2002) Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30:207–210. doi: 10.1093/nar/30.1.207 PubMedCrossRefGoogle Scholar
  21. 21.
    Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313:1960–1964. doi: 10.1126/science.1129139 PubMedCrossRefGoogle Scholar
  22. 22.
    Griffith CD, Ellis IO, Bell J, Burns K, Blamey RW (1990) Density of lymphocytic infiltration of primary breast cancer does not affect short-term disease-free interval or survival. J R Coll Surg Edinb 35:289–292PubMedGoogle Scholar
  23. 23.
    Hayes DF (2005) Prognostic and predictive factors revisited. Breast 14:493–499. doi: 10.1016/j.breast.2005.08.023 PubMedCrossRefGoogle Scholar
  24. 24.
    Hochberg Y, Benjamini Y (1990) More powerful procedures for multiple significance testing. Stat Med 9:811–818. doi: 10.1002/sim.4780090710 PubMedCrossRefGoogle Scholar
  25. 25.
    Kreike B, van Kouwenhove M, Horlings H, Weigelt B, Peterse H, Bartelink H et al (2007) Gene expression profiling and histopathological characterization of triple-negative/basal-like breast carcinomas. Breast Cancer Res 9:R65. doi: 10.1186/bcr1771
  26. 26.
    Lash AE, Tolstoshev CM, Wagner L, Schuler GD, Strausberg RL, Riggins GJ et al (2000) SAGEmap: a public gene expression resource. Genome Res 10:1051–1060. doi: 10.1101/gr.10.7.1051 PubMedCrossRefGoogle Scholar
  27. 27.
    Marques LA, Franco EL, Torloni H, Brentani MM, da Silva-Neto JB, Brentani RR (1990) Independent prognostic value of laminin receptor expression in breast cancer survival. Cancer Res 50:1479–1483PubMedGoogle Scholar
  28. 28.
    McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM (2005) Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 97:1180–1184PubMedCrossRefGoogle Scholar
  29. 29.
    Menard S, Tomasic G, Casalini P, Balsari A, Pilotti S, Cascinelli N et al (1997) Lymphoid infiltration as a prognostic variable for early-onset breast carcinomas. Clin Cancer Res 3:817–819PubMedGoogle Scholar
  30. 30.
    Miller LD, Smeds J, George J, Vega VB, Vergara L, Ploner A et al (2005) An expression signature for p53 status in human breast cancer predicts mutation status, transcriptional effects, and patient survival. Proc Natl Acad Sci USA 102:13550–13555. doi: 10.1073/pnas.0506230102 PubMedCrossRefGoogle Scholar
  31. 31.
    Nixon AJ, Neuberg D, Hayes DF, Gelman R, Connolly JL, Schnitt S et al (1994) Relationship of patient age to pathologic features of the tumor and prognosis for patients with stage I or II breast cancer. J Clin Oncol 12:888–894PubMedGoogle Scholar
  32. 32.
    Oldford SA, Robb JD, Codner D, Gadag V, Watson PH, Drover S (2006) Tumor cell expression of HLA-DM associates with a Th1 profile and predicts improved survival in breast carcinoma patients. Int Immunol 18:1591–1602. doi: 10.1093/intimm/dxl092 PubMedCrossRefGoogle Scholar
  33. 33.
    Rilke F, Colnaghi MI, Cascinelli N, Andreola S, Baldini MT, Bufalino R et al (1991) Prognostic significance of HER-2/neu expression in breast cancer and its relationship to other prognostic factors. Int J Cancer 49:44–49. doi: 10.1002/ijc.2910490109 PubMedCrossRefGoogle Scholar
  34. 34.
    Schlingemann J, Thuerigen O, Ittrich C, Toedt G, Kramer H, Hahn M et al (2005) Effective transcriptome amplification for expression profiling on sense-oriented oligonucleotide microarrays. Nucleic Acids Res 33:e29. doi: 10.1093/nar/gni029
  35. 35.
    Smyth GK (2004) Linear models and empirical bayes methods for assessing differential expression in microarray experiments Stat Appl Genet Mol Biol 3:Article3Google Scholar
  36. 36.
    Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S, Johnsen H et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98:10869–10874. doi: 10.1073/pnas.191367098 PubMedCrossRefGoogle Scholar
  37. 37.
    Sotiriou C, Neo SY, McShane LM, Korn EL, Long PM, Jazaeri A et al (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100:10393–10398. doi: 10.1073/pnas.1732912100 PubMedCrossRefGoogle Scholar
  38. 38.
    Teschendorff AE, Journee M, Absil PA, Sepulchre R, Caldas C (2007) Elucidating the altered transcriptional programs in breast cancer using independent component analysis. PLOS Comput Biol 3:e161. doi: 10.1371/journal.pcbi.0030161
  39. 39.
    Teschendorff AE, Miremadi A, Pinder SE, Ellis IO, Caldas C (2007) An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 8:R157. doi: 10.1186/gb-2007-8-8-r157
  40. 40.
    van‘t Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA, Mao M et al (2002) Gene expression profiling predicts clinical outcome of breast cancer. Nature 415:530–536. doi: 10.1038/415530aGoogle Scholar
  41. 41.
    van de Vijver MJ, He YD, van’t Veer LJ, Dai H, Hart AA, Voskuil DW et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347:1999–2009. doi: 10.1056/NEJMoa021967 PubMedCrossRefGoogle Scholar
  42. 42.
    West RB, Nuyten DS, Subramanian S, Nielsen TO, Corless CL, Rubin BP et al (2005) Determination of stromal signatures in breast carcinoma. PLoS Biol 3:e187. doi: 10.1371/journal.pbio.0030187
  43. 43.
    Whitford P, Mallon EA, George WD, Campbell AM (1990) Flow cytometric analysis of tumour infiltrating lymphocytes in breast cancer. Br J Cancer 62:971–975PubMedGoogle Scholar
  44. 44.
    Yao C, Lin Y, Ye CS, Bi J, Zhu YF, Wang SM (2007) Role of interleukin-8 in the progression of estrogen receptor-negative breast cancer. Chin Med J (Engl) 120:1766–1772Google Scholar

Copyright information

© Springer Science+Business Media, LLC. 2008

Authors and Affiliations

  • Alberto Calabrò
    • 1
  • Tim Beissbarth
    • 1
  • Ruprecht Kuner
    • 1
  • Michael Stojanov
    • 1
  • Axel Benner
    • 2
  • Martin Asslaber
    • 3
  • Ferdinand Ploner
    • 4
  • Kurt Zatloukal
    • 3
  • Hellmut Samonigg
    • 4
  • Annemarie Poustka
    • 1
  • Holger Sültmann
    • 1
    Email author
  1. 1.Division of Molecular Genome AnalysisGerman Cancer Research CenterHeidelbergGermany
  2. 2.Division of BiostatisticGerman Cancer Research CenterHeidelbergGermany
  3. 3.Institutes of PathologyMedical University of GrazGrazAustria
  4. 4.Clinical OncologyMedical University of GrazGrazAustria

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