Skip to main content

Advertisement

Log in

The impact of distinct triple-negative breast cancer subtypes on misdiagnosis and diagnostic delay

  • Preclinical study
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Background

Triple-negative breast cancer (TNBC) includes mostly aggressive types of breast cancer with poor prognosis. Due to its growth pattern, misinterpretation in clinical imaging is more frequent than in non-TNBC. As the group of TNBC contains heterogeneous types of tumors, marker expression-based subtypes have recently been established. We analyzed clinical features and false-negative imaging findings that could potentially lead to diagnostic delay within the subtypes.

Methods

An exploratory analysis compared the imaging features across the a priori defined subtypes and related these findings to molecular subtype, disease stage, potential diagnostic delay, and patient outcome.

Results

TNBC cases were categorized into basal-like (BL; 38.6%), mesenchymal-like (ML; 19.9%), luminal androgen receptor (LAR; 28.3%), and immunomodulatory (IM; 13.3%) subtype. In almost every third patient, malignant classification was missed in at least one imaging method. Misclassification in mammogram was more frequent in ML, while benign ultrasound features were reported more often in the BL subtype. Diagnostic delay due to misclassification in imaging led to tumor growth and/or upgrading of the tumor stage in 8.9% of BL tumors, which had the lowest overall survivals. Despite misclassification rate was higher in the ML subtype it showed better outcomes. Misdiagnosis of axillary lymph node metastasis was higher in LAR; however, this subtype showed a higher percentage of affected axillary lymph nodes.

Conclusion

TNBC subtypes have different clinical features, benign appearances, and diagnostic delay, which can lead to tumor stage upgrade. Future clinical studies on TNBC outcomes might consider the confounder of clinical delay in the subtypes.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Dent R et al (2007) Triple-negative breast cancer: clinical features and patterns of recurrence. Clin Cancer Res 13:4429–4434

    Article  PubMed  Google Scholar 

  2. Kaplan HG, Malmgren JA (2008) Impact of triple negative phenotype on breast cancer prognosis. Breast J 14:456–463

    Article  PubMed  Google Scholar 

  3. Li XB et al (2016) Biomarkers predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer. Am J Clin Pathol 145:871–878

    Article  CAS  PubMed  Google Scholar 

  4. Masuda H et al (2013) Differential response to neoadjuvant chemotherapy among 7 triple-negative breast cancer molecular subtypes. Clin Cancer Res 19:5533–5540

    Article  CAS  PubMed  Google Scholar 

  5. Lehmann BD et al (2011) Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest 121:2750–2767

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Perou CM et al (2000) Molecular portraits of human breast tumours. Nature 406:747–752

    Article  CAS  PubMed  Google Scholar 

  7. Parker JS et al (2009) Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol 27:1160–1167

    Article  PubMed  PubMed Central  Google Scholar 

  8. Turner NC, Reis-Filho JS (2013) Tackling the diversity of triple-negative breast cancer. Clin Cancer Res 19:6380–6388

    Article  CAS  PubMed  Google Scholar 

  9. Le Du F et al (2015) Is the future of personalized therapy in triple-negative breast cancer based on molecular subtype? Oncotarget 6:12890–12908

    PubMed  PubMed Central  Google Scholar 

  10. Chen X (2012) TNBCtype: a subtyping tool for triple-negative breast cancer. Cancer Inform 24:147–156

    CAS  Google Scholar 

  11. Ko ES et al (2010) Triple-negative breast cancer: correlation between imaging and pathological findings. Eur Radiol 20:1111–1117

    Article  PubMed  Google Scholar 

  12. Gao B et al (2014) Mammographic and clinicopathological features of triple-negative breast cancer. Br J Radiol 87:20130496

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Balleyguier C et al (2007) BIRADS classification in mammography. Eur J Radiol 61:192–194

    Article  PubMed  Google Scholar 

  14. Wandtke B, Gallagher S (2017) Reducing delay in diagnosis: multistage recommendation tracking. AJR Am J Roentgenol 209:970–975

    Article  PubMed  Google Scholar 

  15. Resende U, Cabello C, Oliveira Botelho Ramalho S, Zeferino LC (2018) Predictors of pathological complete response in women with clinical complete response to neoadjuvant chemotherapy in breast carcinoma. Oncology 95:229–238

    Article  CAS  PubMed  Google Scholar 

  16. Santonja A et al (2018) Triple negative breast cancer subtypes and pathologic complete response rate to neoadjuvant chemotherapy. Oncotarget 9:26406–26416

    Article  PubMed  PubMed Central  Google Scholar 

  17. Breast Imaging Reporting & Data System. https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/Bi-Rads. Accessed: 20th Sept 2018

  18. Zeng Z et al (2017) Mammography and ultrasound effective features in differentiating basal-like and normal-like subtypes of triple negative breast cancer. Oncotarget 8:79670–79679

    PubMed  PubMed Central  Google Scholar 

  19. Fan C et al (2006) Concordance among gene-expression-based predictors for breast cancer. N Engl J Med 355:560–569

    Article  CAS  PubMed  Google Scholar 

  20. Shaitelman SF et al (2015) Role of ultrasonography of regional nodal basins in staging triple-negative breast cancer and implications for local-regional treatment. Int J Radiat Oncol Biol Phys 93:102–110

    Article  PubMed  Google Scholar 

  21. Diepstraten SCE et al (2014) Value of preoperative ultrasound-guided axillary lymph node biopsy for preventing completion axillary lymph node dissection in breast cancer: a systematic review and meta-analysis. Ann Surg Oncol 21:51–59

    Article  PubMed  Google Scholar 

  22. Richards MA, Westcombe AM, Love SB, Littlejohns P, Ramirez AJ (1999) Influence of delay on survival in patients with breast cancer: a systematic review. Lancet 353:1119–1126

    Article  CAS  PubMed  Google Scholar 

  23. Deniz M et al (2019) Differential prognostic relevance of patho-anatomical factors among different tumor-biological subsets of breast cancer: results from the adjuvant SUCCESS A study. Breast 44:81–89

    Article  PubMed  Google Scholar 

  24. Barber MD, Jack W, Dixon JM (2004) Diagnostic delay in breast cancer. Br J Surg 91:49–53

    Article  CAS  PubMed  Google Scholar 

  25. Gwyn K et al (2004) Racial differences in diagnosis, treatment, and clinical delays in a population-based study of patients with newly diagnosed breast carcinoma. Cancer 100:1595–1604

    Article  PubMed  Google Scholar 

  26. Medeiros GC, Thuler LCS, Bergmann A (2019) Delay in breast cancer diagnosis: a Brazilian cohort study. Pub Health 167:88–95

    Article  CAS  Google Scholar 

  27. Britton P et al (2009) One-stop diagnostic breast clinics: how often are breast cancers missed? Br J Cancer 100:1873–1878

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Jung HK et al (2015) Mammographic and sonographic features of triple-negative invasive carcinoma of no special type. Ultrasound Med Biol 41:375–383

    Article  PubMed  Google Scholar 

  29. Ramirez AJ et al (1999) Factors predicting delayed presentation of symptomatic breast cancer: a systematic review. Lancet 353:1127–1131

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to C. Elfgen.

Ethics declarations

Conflict of interest

All authors declare that they have no conflict of interest.

Ethical approval

The study was approved by the Cantonal Ethics Committee of Zurich, Switzerland (BASEC-No. 2017-00219), and in accordance with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Elfgen, C., Varga, Z., Reeve, K. et al. The impact of distinct triple-negative breast cancer subtypes on misdiagnosis and diagnostic delay. Breast Cancer Res Treat 177, 67–75 (2019). https://doi.org/10.1007/s10549-019-05298-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10549-019-05298-6

Keywords

Navigation