J Pathol Transl Med.  2021 Jan;55(1):16-25. 10.4132/jptm.2020.09.03.

Imaging features of breast cancer molecular subtypes: state of the art

Affiliations
  • 1Department of Radiology, Seoul National University Hospital, Seoul, Korea
  • 2Department of Radiology, Seoul National University College of Medicine, Seoul, Korea
  • 3Institute of Radiation Medicine, Seoul National University Medical Research Center, Seoul, Korea

Abstract

Characterization of breast cancer molecular subtypes has been the standard of care for breast cancer management. We aimed to provide a review of imaging features of breast cancer molecular subtypes for the field of precision medicine. We also provide an update on the recent progress in precision medicine for breast cancer, implications for imaging, and recent observations in longitudinal functional imaging with radiomics.

Keyword

Breast neoplasms; Magnetic resonance imaging; Gene expression profiles

Figure

  • Fig. 1 A 56-year-old woman with a luminal A-like breast cancer. (A) Mammography shows a spiculated mass with calcifications (arrow). (B) Enhanced T1-weighted magnetic resonance imaging shows an irregular, spiculated mass (arrow). Histopathology revealed a 1.5-cm invasive ductal carcinoma with low histologic grade. American Joint Committee on Cancer (AJCC) anatomic stage was T1N0M0. Immunohistochemistry analysis revealed that estrogen receptor 90% positive, progesterone receptor 1% positive, human epidermal growth factor receptor 2–negative, and Ki-67, 1% positive. Multigene assay recurrence score was 10 and low risk. The 9-year distant recurrence risk was estimated as 3%. She did not receive adjuvant chemotherapy, but received aromatase inhibitor.

  • Fig. 2 A 44-year-old woman with a luminal A-like breast cancer. (A) Mammography shows an oval non-calcified mass (arrow). (B) Enhanced T1-weighted magnetic resonance imaging shows an irregular mass with internal rim-enhancement (arrow). Histopathology revealed a 1.7-cm invasive ductal carcinoma with intermediate histologic grade. American Joint Committee on Cancer (AJCC) anatomic stage was T1N0M0. Immunohistochemistry analysis revealed that estrogen receptor 90% positive, progesterone receptor 5% positive, human epidermal growth factor receptor 2 negative, and Ki-67, 4% positive. Multigene assay recurrence score was 23. The 10-year distant recurrence risk was estimated as 12% and high risk. She received adjuvant chemotherapy and tamoxifen.

  • Fig. 3 A 67-year-old woman with a human epidermal growth factor receptor 2 (HER2)–positive breast cancer. (A) Mammography shows ill-defined asymmetry with pleomorphic microcalcifications (arrows). (B) Enhanced T1-weighted magnetic resonance imaging (MRI) shows an 8.2-cm ill-defined, diffuse irregular mass with internal heterogeneous enhancement. Needle biopsy revealed an invasive ductal carcinoma with high histologic grade. Immunohistochemistry analysis revealed that estrogen receptor and progesterone receptor negative, and HER2 positive. (C) Following combined docetaxel, carboplatin and dual HER2 blockade, there is no residual mass and but subtle enhancements in the breast on MRI (arrows). (D) Mammography shows two hookwires around the residual calcifications (arrows). Surgical histopathology revealed pathological complete response in the breast and axilla.

  • Fig. 4 A 35-year-old woman with a triple-negative breast cancer. (A) Enhanced T1-weighted magnetic resonance imaging (MRI) shows a 3.3 cm round mass with an internal rim enhancement and peritumoral heterogeneous enhancement (arrow) (B) T2-weighted MRI shows a central cystic necrosis and peritumoral edema (arrow). Needle biopsy revealed an invasive ductal carcinoma with high histologic grade. Immunohistochemistry analysis revealed that estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 negative. (C) Following chemotherapy, enhanced T1-weighted MRI shows a 3.4 cm round mass without response to chemotherapy.


Reference

References

1. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012; 490:61–70.
2. Sorlie T, Perou CM, Tibshirani R, et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci U S A. 2001; 98:10869–74.
Article
3. Prat A, Pineda E, Adamo B, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015; 24(Suppl 2):S26–35.
Article
4. Coates AS, Winer EP, Goldhirsch A, et al. Tailoring therapies--improving the management of early breast cancer: St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2015. Ann Oncol. 2015; 26:1533–46.
5. Curigliano G, Burstein HJ, Winer EP, et al. De-escalating and escalating treatments for early-stage breast cancer: the St. Gallen International Expert Consensus Conference on the Primary Therapy of Early Breast Cancer 2017. Ann Oncol. 2017; 28:1700–12.
6. Burstein HJ, Curigliano G, Loibl S, et al. Estimating the benefits of therapy for early-stage breast cancer: the St. Gallen International Consensus Guidelines for the primary therapy of early breast cancer 2019. Ann Oncol. 2019; 30:1541–57.
Article
7. Cho N. Molecular subtypes and imaging phenotypes of breast cancer. Ultrasonography. 2016; 35:281–8.
Article
8. Lee SH, Park H, Ko ES. Radiomics in breast imaging from techniques to clinical applications: a review. Korean J Radiol. 2020; 21:779–92.
Article
9. Leithner D, Horvat JV, Marino MA, et al. Radiomic signatures with contrast-enhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: initial results. Breast Cancer Res. 2019; 21:106.
Article
10. Kim S, Kim MJ, Kim EK, Yoon JH, Park VY. MRI radiomic features: association with disease-free survival in patients with triple-negative breast cancer. Sci Rep. 2020; 10:3750.
Article
11. Song L, Lu H, Yin J. Preliminary study on discriminating HER2 2+ amplification status of breast cancers based on texture features semi-automatically derived from pre-, post-contrast, and subtraction images of DCE-MRI. PLoS One. 2020; 15:e0234800.
Article
12. Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology. 2014; 273:365–72.
Article
13. Grimm LJ, Zhang J, Mazurowski MA. Computational approach to radiogenomics of breast cancer: Luminal A and luminal B molecular subtypes are associated with imaging features on routine breast MRI extracted using computer vision algorithms. J Magn Reson Imaging. 2015; 42:902–7.
Article
14. Bae MS, Seo M, Kim KG, Park IA, Moon WK. Quantitative MRI morphology of invasive breast cancer: correlation with immunohistochemical biomarkers and subtypes. Acta Radiol. 2015; 56:269–75.
Article
15. Sutton EJ, Oh JH, Dashevsky BZ, et al. Breast cancer subtype inter-tumor heterogeneity: MRI-based features predict results of a genomic assay. J Magn Reson Imaging. 2015; 42:1398–406.
Article
16. Waugh SA, Purdie CA, Jordan LB, et al. Magnetic resonance imaging texture analysis classification of primary breast cancer. Eur Radiol. 2016; 26:322–30.
Article
17. Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. NPJ Breast Cancer. 2016; 2:16012.
Article
18. Agner SC, Rosen MA, Englander S, et al. Computerized image analysis for identifying triple-negative breast cancers and differentiating them from other molecular subtypes of breast cancer on dynamic contrast-enhanced MR images: a feasibility study. Radiology. 2014; 272:91–9.
Article
19. Chamming’s F, Ueno Y, Ferre R, et al. Features from computerized texture analysis of breast cancers at pretreatment MR imaging are associated with response to neoadjuvant chemotherapy. Radiology. 2018; 286:412–20.
Article
20. Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res. 2017; 19:57.
Article
21. Chia SK, Bramwell VH, Tu D, et al. A 50-gene intrinsic subtype classifier for prognosis and prediction of benefit from adjuvant tamoxifen. Clin Cancer Res. 2012; 18:4465–72.
Article
22. Prat A, Parker JS, Fan C, et al. Concordance among gene expression-based predictors for ER-positive breast cancer treated with adjuvant tamoxifen. Ann Oncol. 2012; 23:2866–73.
Article
23. Iwamoto T, Booser D, Valero V, et al. Estrogen receptor (ER) mRNA and ER-related gene expression in breast cancers that are 1% to 10% ER-positive by immunohistochemistry. J Clin Oncol. 2012; 30:729–34.
Article
24. Ng CK, Schultheis AM, Bidard FC, Weigelt B, Reis-Filho JS. Breast cancer genomics from microarrays to massively parallel sequencing: paradigms and new insights. J Natl Cancer Inst. 2015; 107:djv015.
Article
25. Liedtke C, Mazouni C, Hess KR, et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J Clin Oncol. 2008; 26:1275–81.
Article
26. Paik S, Tang G, Shak S, et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J Clin Oncol. 2006; 24:3726–34.
Article
27. Shin HJ, Kim HH, Huh MO, et al. Correlation between mammographic and sonographic findings and prognostic factors in patients with node-negative invasive breast cancer. Br J Radiol. 2011; 84:19–30.
Article
28. Chae EY, Moon WK, Kim HH, et al. Association between ultrasound features and the 21-gene recurrence score cssays in patients with oestrogen receptor-positive, HER2-negative, invasive breast cancer. PLoS One. 2016; 11:e0158461.
29. Yepes MM, Romilly AP, Collado-Mesa F, et al. Can mammographic and sonographic imaging features predict the Oncotype DX recurrence score in T1 and T2, hormone receptor positive, HER2 negative and axillary lymph node negative breast cancers? Breast Cancer Res Treat. 2014; 148:117–23.
Article
30. Grimm LJ, Johnson KS, Marcom PK, Baker JA, Soo MS. Can breast cancer molecular subtype help to select patients for preoperative MR imaging? Radiology. 2015; 274:352–8.
Article
31. Arteaga CL, Sliwkowski MX, Osborne CK, Perez EA, Puglisi F, Gianni L. Treatment of HER2-positive breast cancer: current status and future perspectives. Nat Rev Clin Oncol. 2011; 9:16–32.
Article
32. Zhou BP, Hung MC. Dysregulation of cellular signaling by HER2/neu in breast cancer. Semin Oncol. 2003; 30:38–48.
33. Perez EA, Romond EH, Suman VJ, et al. Four-year follow-up of trastuzumab plus adjuvant chemotherapy for operable human epidermal growth factor receptor 2-positive breast cancer: joint analysis of data from NCCTG N9831 and NSABP B-31. J Clin Oncol. 2011; 29:3366–73.
Article
34. Vaz-Luis I, Ottesen RA, Hughes ME, et al. Impact of hormone receptor status on patterns of recurrence and clinical outcomes among patients with human epidermal growth factor-2-positive breast cancer in the National Comprehensive Cancer Network: a prospective cohort study. Breast Cancer Res. 2012; 14:R129.
Article
35. Gianni L, Eiermann W, Semiglazov V, et al. Neoadjuvant and adjuvant trastuzumab in patients with HER2-positive locally advanced breast cancer (NOAH): follow-up of a randomised controlled superiority trial with a parallel HER2-negative cohort. Lancet Oncol. 2014; 15:640–7.
Article
36. Elias SG, Adams A, Wisner DJ, et al. Imaging features of HER2 overexpression in breast cancer: a systematic review and meta-analysis. Cancer Epidemiol Biomarkers Prev. 2014; 23:1464–83.
Article
37. Yoon GY, Chae EY, Cha JH, et al. Imaging and clinicopathologic features associated with pathologic complete response in HER2-positive breast cancer receiving neoadjuvant chemotherapy with dual HER2 blockade. Clin Breast Cancer. 2020; 20:25–32.
Article
38. Choi WJ, Kim Y, Cha JH, et al. Correlation between magnetic resonance imaging and the level of tumor-infiltrating lymphocytes in patients with estrogen receptor-negative HER2-positive breast cancer. Acta Radiol. 2020; 61:3–10.
Article
39. Nuciforo P, Pascual T, Cortes J, et al. A predictive model of pathologic response based on tumor cellularity and tumor-infiltrating lymphocytes (CelTIL) in HER2-positive breast cancer treated with chemo-free dual HER2 blockade. Ann Oncol. 2018; 29:170–7.
Article
40. Prat A, Adamo B, Cheang MC, Anders CK, Carey LA, Perou CM. Molecular characterization of basal-like and non-basal-like triple-negative breast cancer. Oncologist. 2013; 18:123–33.
Article
41. Lehmann BD, Bauer JA, Chen X, et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J Clin Invest. 2011; 121:2750–67.
Article
42. Masuda N, Lee SJ, Ohtani S, et al. Adjuvant capecitabine for breast cancer after preoperative chemotherapy. N Engl J Med. 2017; 376:2147–59.
Article
43. Dogan BE, Turnbull LW. Imaging of triple-negative breast cancer. Ann Oncol. 2012; 23:Suppl 6. vi23–9.
Article
44. Lamb PM, Perry NM, Vinnicombe SJ, Wells CA. Correlation between ultrasound characteristics, mammographic findings and histological grade in patients with invasive ductal carcinoma of the breast. Clin Radiol. 2000; 55:40–4.
Article
45. Uematsu T, Kasami M, Yuen S. Triple-negative breast cancer: correlation between MR imaging and pathologic findings. Radiology. 2009; 250:638–47.
Article
46. Youk JH, Son EJ, Chung J, Kim JA, Kim EK. Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes. Eur Radiol. 2012; 22:1724–34.
Article
47. Kim GR, Ku YJ, Cho SG, Kim SJ, Min BS. Associations between gene expression profiles of invasive breast cancer and Breast Imaging Reporting and Data System MRI lexicon. Ann Surg Treat Res. 2017; 93:18–26.
Article
48. Kawashima H, Inokuchi M, Furukawa H, Kitamura S. Triple-negative breast cancer: are the imaging findings different between responders and nonresponders to neoadjuvant chemotherapy? Acad Radiol. 2011; 18:963–9.
49. Bae MS, Shin SU, Ryu HS, et al. Pretreatment MR imaging features of triple-negative breast cancer: association with response to neoadjuvant chemotherapy and recurrence-free survival. Radiology. 2016; 281:392–400.
Article
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