Editorial - Imaging in Medicine (2012) Volume 4, Issue 6
Imaging as a potential tool for subtyping breast cancerMassimo Aglietta*, Filippo Montemurro1
- Corresponding Author:
- Massimo Aglietta
University Division of Medical Oncology
Institute for Cancer Research & Treatment at Candiolo
Strada Provinciale 142, Km 3.95, 10060 Candiolo, Turin, Italy
Tel: +39 011 993 3628
E-mail: [email protected]
breast cancer ▪ diffusion-weighted imaging ▪ metabolic imaging ▪ molecular subtypes ▪ MRI ▪ PET
Breast cancer is increasingly being recognized as a collection of different diseases. For many years, medical oncologists have been used to considering hormone receptor (HR) status, HER2 status and markers of the cancer cell cycle, together with tumor size and metastatic spread to the axillary lymph nodes, to infer on prognosis and to assign adjuvant treatments. While these single factors are still looked at in decision making in current clinical practice, a more complex classification, which has been suggested by pivotal multigene expression analysis studies, allows a better recapitulation of breast cancer heterogeneity . Perou et al. initially identified four distinct intrinsic breast cancer subtypes, which they called luminal A, luminal B, HER2-enriched and basallike type, by analyzing gene expression profiles [1–3]. Subsequently, several other investigators have found that molecularly defined subtypes could be identified by the combined use of conventional immunohistochemical markers [4,5]. The luminal A subtype includes good prognosis hormone receptor positive tumors that are low proliferating. The luminal B subtype includes tumors that express HRs, but carry a more adverse prognosis because of higher tumor cell proliferation and/or HER2 positivity. The HER2-enriched (HER2-positive and negative HRs) and triple negative (HER2-negative and negative HRs) subtypes represent the most biologically aggressive subtypes of breast cancer. Science is further investigating the genetics of breast cancer, revealing more complex findings on the heterogeneity of this disease . Meanwhile, the immunohistochemistry-based classification of luminal A, luminal B/HER2- negative, HER2-positive (either luminal or nonluminal) and triple negative subtypes has been proposed as pivotal in estimating prognosis and in assigning adjuvant medical treatments in women with operable breast cancer . However, investigating the biological heterogeneity of breast cancer is revealing other exciting developments, for example, in the field of diagnostic imaging. Radiological techniques have a well-established role in each phase of the management of this disease, from screening to monitoring response to medical treatments. Recent MRI modalities allow the simultaneous study of morphology and quantitative functional parameters that are related to the biology of the tumor. Imaging breast cancer biological heterogeneity, which is known to affect prognosis and therapeutic decisions, constitutes a particularly intriguing field of research. For example, dynamic contrast-enhanced MRI parameters are influenced by perfusion abnormalities due to tumor neovascularization. For this reason, this technique has been proposed as a tool to monitor the activity of newer drugs acting on the tumor vasculature . At the same time, a number of studies have suggested that dynamic contrast-enhanced MRI can capture differences in the histopathological and biological characteristics of breast cancer [9–11]. Most of these studies looked at individual histopathological parameters (i.e., tumor grade, proliferation, hormone receptor expression and HER2 status). However, most intriguingly, a study employing MRI to monitor breast cancer response during neoadjuvant chemotherapy showed that the sensitivity of this imaging modality changes according to the different tumor subtypes defined by immunohistochemistry . In this study, MRI was inaccurate at predicting the pathological response in luminal/HER2 negative tumors. Conversely, it showed a significant accuracy in response prediction in triple negative and HER2-positive tumors. A further technological development of MRI techniques is represented by diffusion weighted imaging (DWI). This technique is based on the detection of the thermal energy-induced motion of water molecules (Brownian motion). The apparent diffusion coefficient (ADC), a quantitative parameter provided by DWI, is closely related to the cellularity and water content of each different tumor . Due to these functional features, together with the provision of morphological information, DWI is being intensively investigated in the management of breast and other cancers. The differential diagnosis of breast nodules (benign vs malignant) and the ability to capture some histopathological features, such as tumor differentiation, are the major potentialities of this technique [14,15]. Furthermore, ADC is sensitive to precocious variations in the cellular content of a tumor mass in response to treatment . For this reason, DWI is promising as a tool to monitor tumor response during treatment. Recently, in a study involving 190 early breast cancer patients undergoing surgery, we examined ADC variations according to both classical biological factors in immunohistochemically defined breast cancer subtypes . A notable finding was that mean ADC values differed between tumor subtypes. Counterintuitively, higher ADC median values, which are usually considered a feature of benign breast nodules, were found in HER2-enriched and triple negative tumors, whereas the median ADC values were significantly lower in luminal subtypes. Another group has produced similar data observations regarding triple negative tumors . Interestingly, these findings correlate with morphological data obtained via mammography and conventional MRI, indicating that aggressive tumors may display characteristics of benign nodules, such as round shape or regular margins . Capturing the tumor metabolism via MR spectroscopy and PET represents other promising methods of imaging the biology underlying different breast cancer subtypes . The list of examples is becoming long, but all of the newest evidence points to the fact that morphofunctional breast imaging parameters are sensitive to the underlying breast cancer subtype and heterogeneity. A first important conclusion from these experiences is that cutoffs to distinguish benign from malignant breast abnormalities, as well as parameter changes in response to therapy, vary and should therefore be redefined according to breast cancer subtype.
The management of breast cancer from early detection and treatment is undergoing a profound change due to the acknowledgment of its molecular heterogeneity. Consequently, newer imaging techniques that are sensitive to tumor biology open the way to exciting developments in this field and and represent a valuable tool in the pursuit of personalized medicine.
Financial & competing interests disclosure
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
No writing assistance was utilized in the production of this manuscript.
- Perou CM, Sorlie T, Eisen MB et al. Molecular portraits of human breast tumours. Nature 406(6797), 747–752 (2000).
- Sorlie T, Perou CM, Tibshirani R et al. Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc. Natl Acad. Sci. USA 98(19), 10869–10874 (2001).
- Parker JS, Mullins M, Cheang MC et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J. Clin. Oncol. 27(8), 1160–1167 (2009).
- Cheang MC, Chia SK, Voduc D et al. Ki67 index, HER2 status, and prognosis of patients with luminal B breast cancer. J. Natl Cancer Inst. 101(10), 736–750 (2009).
- Rakha EA, Elsheikh SE, Aleskandarany MA et al. Triple-negative breast cancer: distinguishing between basal and nonbasal subtypes. Clin. Cancer Res. 15(7), 2302–2310 (2009).
- Stephens PJ, Tarpey PS, Davies H et al. The landscape of cancer genes and mutational processes in breast cancer. Nature 486(7403), 400–404 (2012).
- Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thurlimann B, Senn HJ. 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 (2011).
- O’Connor JP, Jackson A, Parker GJ, Roberts C, Jayson GC. Dynamic contrast-enhanced MRI in clinical trials of antivascular therapies. Nat. Rev. Clin. Oncol. 9(3), 167–177 (2012).
- Montemurro F, Martincich L, Sarotto I et al. Relationship between DCE-MRI morphological and functional features and histopathological characteristics of breast cancer. Eur. Radiol. 17(6), 1490–1497 (2007).
- Tuncbilek N, Karakas HM, Okten OO. Dynamic magnetic resonance imaging in determining histopathological prognostic factors of invasive breast cancers. Eur. J. Radiol. 53(2), 199–205 (2005).
- Chang YW, Kwon KH, Choi DL et al. Magnetic resonance imaging of breast cancer and correlation with prognostic factors. Acta Radiol. 50(9), 990–998 (2009).
- Loo CE, Straver ME, Rodenhuis S et al. Magnetic resonance imaging response monitoring of breast cancer during neoadjuvant chemotherapy: relevance of breast cancer subtype. J. Clin. Oncol. 29(6), 660–666 (2011).
- Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics 26(Suppl. 1), S205–S223 (2006).
- Koh DM, Collins DJ. Diffusion-weighted MRI in the body: applications and challenges in oncology. AJR Am. J. Roentgenol. 188(6), 1622–1635 (2007).
- Schnapauff D, Zeile M, Niederhagen MB et al. Diffusion-weighted echo-planar magnetic resonance imaging for the assessment of tumor cellularity in patients with soft-tissue sarcomas. J. Magn. Reson. Imaging 29(6), 1355–1359 (2009).
- Pickles MD, Gibbs P, Lowry M, Turnbull LW. Diffusion changes precede size reduction in neoadjuvant treatment of breast cancer. Magn. Reson. Imaging 24(7), 843–847 (2006).
- Martincich L, Deantoni V, Bertotto I et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur. Radiol. 22(7), 1519–1528 (2012).
- Youk JH, Son EJ, Chung J, Kim JA, Kim EK. Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusionweighted MR imaging: comparison with other breast cancer subtypes. Eur. Radiol. 22(8), 1724–1734 (2012).
- Schrading S, Kuhl CK. Mammographic, US, and MR imaging phenotypes of familial breast cancer. Radiology 246(1), 58–70 (2008).
- Birdwell RL, Mountford CE, Iglehart JD. Molecular imaging of the breast. AJR Am. J. Roentgenol. 193(2), 367–376 (2009).