Skip to main content

Advertisement

SpringerLink
Log in

Breast carcinoma subtypes show different patterns of metastatic behavior

  • Original Article
  • Published:
Virchows Archiv Aims and scope Submit manuscript

Abstract

The aim of our retrospective study was to analyze patterns of subtype specific metastatic spread and to identify the time course of distant metastases. A consecutive series of 490 patients with breast cancer who underwent surgery and postoperative treatment at Semmelweis University, Hungary, and diagnosed between the years 2000 and 2007 was identified from the archives of the 2nd Department of Pathology, Hungary. Molecular subtypes were defined based on the 2011 St. Gallen recommendations. Statistical analysis was performed with SPSS Statistics for Windows, Version 22.0. Distant metastasis free survival (DMFS) was defined as the time elapsed between the first pathological diagnosis of the tumor and the first distant metastasis detection. Distant metastases were detected in 124 patients. Mean time to develop metastasis was 29 months (range 0–127 months). The longest DMFS was observed in the Luminal A (LUMA) subtype (mean 39 months) whereas the shortest was seen in the HER2-positive (HER2+) subtype (mean 21 months; p = 0.012). We confirmed that HER2+ tumors carry a higher risk for distant metastases (42.1%). LUMA-associated metastases were found to be solitary in 59% of cases, whereas HER2+ tumors showed multiple metastases in 79.2% of cases. LUMA tumors showed a preference for bone-only metastasis as compared with HER2+ and triple negative breast cancer (TNBC) cases, which exhibited a higher rate of brain metastasis. The most frequent second metastatic sites of hormone receptor (HR) positive tumors were the lung and liver, whereas the brain was the most affected organ in HR-negative (HR−) cases. Tumor subtypes differ in DMFS and in pattern of distant metastases. HER2+ tumors featured the most aggressive clinical course. Further identification of subtype-specific factors influencing prognosis might have an impact on clinical care and decision-making.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Gerratana L et al (2015) Pattern of metastasis and outcome in patients with breast cancer. Clin Exp Metastasis 32(2):125–133

    Article  CAS  PubMed  Google Scholar 

  2. Joensuu K et al (2013) ER, PR, HER2, Ki-67 and CK5 in early and late relapsing breast cancer-reduced CK5 expression in metastases. Breast Cancer (Auckl) 7:23–34

    Google Scholar 

  3. Kast K et al (2015) Impact of breast cancer subtypes and patterns of metastasis on outcome. Breast Cancer Res Treat 150(3):621–629

    Article  CAS  PubMed  Google Scholar 

  4. Kennecke H et al (2010) Metastatic behavior of breast cancer subtypes. J Clin Oncol 28(20):3271–3277

    Article  PubMed  Google Scholar 

  5. Savci-Heijink CD et al (2015) Retrospective analysis of metastatic behaviour of breast cancer subtypes. Breast Cancer Res Treat 150(3):547–557

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Aurilio G et al (2013) Discordant hormone receptor and human epidermal growth factor receptor 2 status in bone metastases compared to primary breast cancer. Acta Oncol 52(8):1649–1656

    Article  CAS  PubMed  Google Scholar 

  7. Cancello G et al (2013) Progesterone receptor loss identifies Luminal B breast cancer subgroups at higher risk of relapse. Ann Oncol 24(3):661–668

    Article  CAS  PubMed  Google Scholar 

  8. Sihto H et al (2011) Breast cancer biological subtypes and protein expression predict for the preferential distant metastasis sites: a nationwide cohort study. Breast Cancer Res 13(5):R87

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Blighe K et al (2014) Whole genome sequence analysis suggests intratumoral heterogeneity in dissemination of breast cancer to lymph nodes. PLoS One 9(12):e115346

    Article  PubMed  PubMed Central  Google Scholar 

  10. Sant M et al (2004) Breast carcinoma survival in Europe and the United States. Cancer 100(4):715–722

    Article  PubMed  Google Scholar 

  11. Zardavas D et al (2014) The AURORA initiative for metastatic breast cancer. Br J Cancer 111(10):1881–1887

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Goldhirsch A et al (2011) Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St. Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 22(8):1736–1747

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Goldhirsch A (2013) Personalized adjuvant therapies: lessons from the past: the opening address by the St. Gallen 2013 award recipient. Breast 22(Suppl 2):S3–S7

    Article  PubMed  Google Scholar 

  14. Cserni G et al (2014) Distribution pattern of the Ki67 labelling index in breast cancer and its implications for choosing cut-off values. Breast 23(3):259–263

    Article  PubMed  Google Scholar 

  15. Selmeci TTA, Róna Á, Molnár BÁ, Kenessey I, Székely B, Szász AM, Kulka J (2014) Prognostic role of progesteron receptor expression in HER2 negative Luminal B breast cancers. Nőgyógyászati Onkológia 19:47–49

    Google Scholar 

  16. Wolff AC et al (2007) American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. J Clin Oncol 25(1):118–145

    Article  CAS  PubMed  Google Scholar 

  17. Wolff AC et al (2014) Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. Arch Pathol Lab Med 138(2):241–256

    Article  PubMed  Google Scholar 

  18. Ostrovnaya I et al (2010) A metastasis or a second independent cancer? Evaluating the clonal origin of tumors using array copy number data. Stat Med 29(15):1608–1621

    PubMed  PubMed Central  Google Scholar 

  19. Bueno-de-Mesquita JM et al (2009) Validation of 70-gene prognosis signature in node-negative breast cancer. Breast Cancer Res Treat 117(3):483–495

    Article  CAS  PubMed  Google Scholar 

  20. van de Vijver MJ et al (2002) A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med 347(25):1999–2009

    Article  PubMed  Google Scholar 

  21. Gyorffy B et al (2015) Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer. Int J Cancer 136(9):2091–2098

    Article  CAS  PubMed  Google Scholar 

  22. Sotiriou C et al (2006) Gene expression profiling in breast cancer: understanding the molecular basis of histologic grade to improve prognosis. J Natl Cancer Inst 98(4):262–272

    Article  CAS  PubMed  Google Scholar 

  23. Brockton NT et al (2015) The Breast Cancer to Bone (B2B) Metastases Research Program: a multi-disciplinary investigation of bone metastases from breast cancer. BMC Cancer 15:512

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kim HJ et al (2015) Metastasis-free interval is closely related to tumor characteristics and has prognostic value in breast cancer patients with distant relapse. J Breast Cancer 18(4):371–377

    Article  PubMed  PubMed Central  Google Scholar 

  25. Rugo HS (2008) The importance of distant metastases in hormone-sensitive breast cancer. Breast 17(Suppl 1):S3–S8

    Article  PubMed  Google Scholar 

  26. Soni A et al (2015) Breast cancer subtypes predispose the site of distant metastases. Am J Clin Pathol 143(4):471–478

    Article  PubMed  Google Scholar 

  27. Aleskandarany MA et al (2015) Markers of progression in early-stage invasive breast cancer: a predictive immunohistochemical panel algorithm for distant recurrence risk stratification. Breast Cancer Res Treat 151(2):325–333

    Article  CAS  PubMed  Google Scholar 

  28. Sato K et al (2016) Prognostic significance of the progesterone receptor status in Ki67-high and -low Luminal B-like HER2-negative breast cancers. Breast Cancer 23(2):310–317

    Article  PubMed  Google Scholar 

  29. Desmedt C, Yates L, Kulka J (2016) Catalog of genetic progression of human cancers: breast cancer. Cancer Metastasis Rev 35(1):49–62

    Article  CAS  PubMed  Google Scholar 

  30. Kulka J et al (2016) Comparison of predictive immunohistochemical marker expression of primary breast cancer and paired distant metastasis using surgical material: a practice-based study. J Histochem Cytochem 64(4):256–267

    Article  CAS  PubMed  Google Scholar 

  31. Kimbung S, Loman N, Hedenfalk I (2015) Clinical and molecular complexity of breast cancer metastases. Semin Cancer Biol 35:85–95

    Article  CAS  PubMed  Google Scholar 

  32. Bahrami T et al (2016) The molecular signature of breast cancer metastasis to bone. Anti-Cancer Drugs 27(9):824–831

    Article  CAS  PubMed  Google Scholar 

  33. Lee JY et al (2016) Gene expression profiling of breast cancer brain metastasis. Sci Rep 6:28623

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Weidle UH et al (2016) Dissection of the process of brain metastasis reveals targets and mechanisms for molecular-based intervention. Cancer Genomics Proteomics 13(4):245–258

    CAS  PubMed  Google Scholar 

  35. Kimbung S et al (2016) Transcriptional profiling of breast cancer metastases identifies liver metastasis-selective genes associated with adverse outcome in Luminal A primary breast cancer. Clin Cancer Res 22(1):146–157

    Article  CAS  PubMed  Google Scholar 

  36. Kimbung S et al (2015) Contrasting breast cancer molecular subtypes across serial tumor progression stages: biological and prognostic implications. Oncotarget 6(32):33306–33318

    PubMed  PubMed Central  Google Scholar 

  37. Smid M et al (2008) Subtypes of breast cancer show preferential site of relapse. Cancer Res 68(9):3108–3114

    Article  CAS  PubMed  Google Scholar 

  38. Savci-Heijink CD et al (2016) A novel gene expression signature for bone metastasis in breast carcinomas. Breast Cancer Res Treat 156(2):249–259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Harrell JC et al (2012) Genomic analysis identifies unique signatures predictive of brain, lung, and liver relapse. Breast Cancer Res Treat 132(2):523–535

    Article  CAS  PubMed  Google Scholar 

  40. Lin NU et al (2012) Clinicopathologic features, patterns of recurrence, and survival among women with triple-negative breast cancer in the National Comprehensive Cancer Network. Cancer 118(22):5463–5472

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pestalozzi BC et al (2006) Identifying breast cancer patients at risk for central nervous system (CNS) metastases in trials of the International Breast Cancer Study Group (IBCSG). Ann Oncol 17(6):935–944

    Article  CAS  PubMed  Google Scholar 

  42. Saunus JM et al (2015) Integrated genomic and transcriptomic analysis of human brain metastases identifies alterations of potential clinical significance. J Pathol 237(3):363–378

    Article  CAS  PubMed  Google Scholar 

  43. Rostami R et al (2016) Brain metastasis in breast cancer: a comprehensive literature review. J Neuro-Oncol 127(3):407–414

    Article  CAS  Google Scholar 

  44. Olson EM et al (2013) Clinical outcomes and treatment practice patterns of patients with HER2-positive metastatic breast cancer in the post-trastuzumab era. Breast 22(4):525–531

    Article  PubMed  PubMed Central  Google Scholar 

  45. Dawood S et al (2010) Prognosis of women with metastatic breast cancer by HER2 status and trastuzumab treatment: an institutional-based review. J Clin Oncol 28(1):92–98

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank Rigóné Káli Elvira for the careful reading of the manuscript and the valuable comments.

Authors’ contributions

AMT, LV, BAM, IAM, JK, and AMSz conceived the study. BAM, IAM, JM, KF, JT, CSz, BSz, and PD carefully selected the patients’ clinical/oncological and pathological data and provided the patients’ follow-up data. LV, AB, and SVK performed data/statistical analysis. All authors were involved in data interpretation and in writing the paper and had final approval of the submitted and published manuscript.

Author information

Authors and Affiliations

  1. 1st Department of Surgery, Semmelweis University, Üllői út 78, Budapest, 1082, Hungary

    István Artúr Molnár, Béla Ákos Molnár, Krisztina Fekete & Judit Tamás

  2. 2nd Department of Pathology, Semmelweis University, Üllői út 93, Budapest, 1091, Hungary

    Laura Vízkeleti, Péter Deák, Csilla Szundi, Borbála Székely, Marcell A. Szász, Balázs Ács, Janina Kulka & Anna-Mária Tőkés

  3. MTA-SE-NAP, Brain Metastasis Research Group, Semmelweis University, Budapest, Hungary

    Laura Vízkeleti

  4. Department of Tumor Biology, National Korányi Institute of Pulmonology, Semmelweis University, Pihenő út 1, Budapest, 1121, Hungary

    Judit Moldvay

  5. Faculty of Electrical Engineering and Information Technology, University of Oradea, Str. Universitatii nr. 1, Oradea, Romania

    Stefan Vári-Kakas

  6. MTA-SE Tumor Progression Research Group, 2nd Department of Pathology, Semmelweis University, Üllői út 93, Budapest, 1091, Hungary

    Anna-Mária Tőkés

Authors
  1. István Artúr Molnár
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Béla Ákos Molnár
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Vízkeleti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Krisztina Fekete
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Judit Tamás
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Péter Deák
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Csilla Szundi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Borbála Székely
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Judit Moldvay
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Stefan Vári-Kakas
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Marcell A. Szász
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Balázs Ács
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Janina Kulka
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Anna-Mária Tőkés
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anna-Mária Tőkés.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Funding

This work was supported by MKOT 2014-2016, SE-OTKA 2014-2015, KTIA_NAP_13-2014-0021, and OTKA grant no. K116151. AMSz was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

Additional information

István Artúr Molnár and Béla Ákos Molnár equally contributed.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Molnár, I.A., Molnár, B.Á., Vízkeleti, L. et al. Breast carcinoma subtypes show different patterns of metastatic behavior. Virchows Arch 470, 275–283 (2017). https://doi.org/10.1007/s00428-017-2065-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00428-017-2065-7

Keywords

Search

Navigation