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New Technologies Reduce Variability in Breast Cancer Subtyping

New Technologies Reduce Variability in Breast Cancer Subtyping

Emerging technologies in breast cancer diagnostics aim to improve the accuracy of breast cancer subtyping

Breast cancer is the leading cause of cancer for women worldwide.1 While advances in treatments have improved patient outcomes, breast cancer treatments need to be applied early and appropriately— which relies on effective diagnostics to classify the breast cancer subtype.2 However, breast cancer subtype classification is not always straightforward due to the heterogeneity of the cancer. Fortunately, new and emerging technologies in breast cancer diagnostics can get around this issue and help improve the accuracy of breast cancer subtyping. 

Conventional immunohistochemical diagnostic pitfalls 

Laboratories often use imaging techniques to assess breast cancer receptor status, with immunohistochemical analysis being one of the most common strategies. Biopsies are taken from the tumor tissue and stained for their receptor status. But imaging techniques like immunohistochemistry face variability problems arising from the heterogeneity of breast cancer and a lack of standardization. Without standardization, laboratories with different equipment and methods can yield variable results as well as interpretations, leading to false positives and negatives.3 And, as the biopsy is typically taken at one time point and from one section of the tumor, the validity of the results relies on the sample to represent the entire cancer cell population. Because breast cancer cells are heterogeneous within the tumor, at metastasized sites, and at different time points, the diagnosed subtype may be suboptimal for different regions of the tumor, or for later points in time.4 As treatment plans are governed by the cancer subtype, this variability can have severe consequences for patient outcomes. As a result, researchers are developing new imaging strategies for breast cancer diagnostics using robust techniques. 

Breast Cancer Subtypes

Breast cancer cells can be classified by their receptor status, which is a measure of expression levels of cell surface estrogen and progesterone receptors and of transmembrane human epidermal growth factor receptor 2 (HER2).5 Identifying receptor expression levels classifies the breast cancer as hormone receptor-positive or -negative and HER2-positive or -negative, resulting in four subtypes:


  • Luminal A = hormone receptor-positive, HER2-negative
  • Luminal B = hormone receptor-positive, HER2-positive
  • HER2 enriched = hormone receptor-negative, HER2-positive
  • Triple negative = hormone receptor-negative, HER2-negative

Identifying the breast cancer subtype guides treatment and provides insight into a patient’s prognosis.6 Treatments have been developed to target the different receptors, such as endocrine (hormone receptor-positive breast cancer) or
HER2-targeted (HER2-positive breast cancer) therapy, relying heavily on the extent of receptor expression. Consequently, accurate detection of breast cancer receptor levels impacts the treatment course for a patient, and the outcomes.7

Imaging diagnostic solutions

Standardizing the imaging process 

As immunohistochemistry is a well-established technique among laboratories, Boston Cell Standards has looked at ways to reduce immunohistochemistry variability by standardizing the imaging process. The company developed a set of molecular standards with standardized units (molecules per cell), offering quantitative and precise results, which could reduce error and the rate of false positives/negatives. This approach can reduce variability from experimental method but not from disease heterogeneity, which still needs to be addressed. For the latter, scientists have looked to alternative imaging techniques, such as molecular imaging. 

Radiolabeled probes 

Molecular imaging relies on probes that detect target structures, such as hormone receptors or HER2 on breast cancer cells. Probes are administered to the patient where they circulate around the body until they reach their target structures (as opposed to immunohistochemical analysis, which probes a biopsy sample). Molecular imaging techniques are non-invasive and allow whole-body imaging, revealing receptor status across the primary and metastasized tumor sites.8 While different labels have been used to detect probes binding to breast cancer receptors, radioactive labels (or radiolabel) have attracted much attention. These probes generate signals that are captured by different detectors, such as positron emission tomography (PET) and single photon emission computed tomography (SPECT), depending on the radiolabel used.9 

To measure estrogen receptor status, 18F-fluorinated forms of estradiol have been used.10 Zionexa developed a form of an estradiol-based probe (18F-fluoroestradiol) that has been approved for clinical use in France (Estrotep) and the US (Cerianna). While in earlier stages of development, radiolabeled probes (such as 18F-fluoro furanyl norprogesterone) have also been developed to target the progesterone receptor. Recently, Dehdashti et al. demonstrated that 18F-fluoro furanyl norprogesterone can safely determine progesterone-receptor status in breast cancer patients.11 

High-affinity radiolabeled proteins 

To determine HER2-receptor status, radiolabeled, high-affinity proteins, such as affibodies, have been used in molecular imaging. A radiolabeled form of the affibody ABY-025 (developed by Affibody) was able to detect and classify the HER2 status of metastasized breast cancer sites in patients.12 The radiolabeled affibody system is currently in Phase 2/3 clinical trials.13 

Emerging technologies

Radiomics

While immunohistochemistry is a common imaging strategy for detecting breast cancer subtype, other imaging systems are used to screen for breast cancer, such as 10 Clinical Lab Manager March 2021 ClinicalLabManager.com magnetic resonance imaging (MRI) and mammography. Studies have looked at pulling out features from these images to generate high-dimensional data—an approach known as “radiomics.” Son et al. recently developed a radiomics approach to determine receptor status from images acquired through digital breast tomosynthesis, where the system performed best at detecting triple-negative breast cancers compared to other subtypes.14 Conversely, Leithner et al. focused on MRI-based radiomic approaches. These systems were capable of detecting breast cancer receptor status both with15 and without16 image-contrasting substances, achieving better than 79 percent and 90 percent accuracy, respectively, in classifying subtypes. 

Point-of-care diagnostics 

Alongside imaging techniques, biomarker tests can also aid detection of breast cancer receptor status. Researchers have explored point-of-care diagnostics to classify breast cancer subtype from blood samples and biomarkers. For example, examining circulating tumor cells in the blood to determine their receptor status.17 While in early stages of development, this point-of-care diagnostic used a microfluidic/μHall chip system that detected breast cancer receptors using antibody-labeled magnetic nanoparticles. More recently, Min et al. designed another promising pointof-care diagnostic system, called “CytoPAN,” that can analyze biopsy (fine needle aspiration) samples in one hour to determine the breast cancer subtype.18 The CytoPAN device is an “automated image cytometry platform” that uses fluorescent probes, offering high accuracy for cancer detection (100 percent) and receptor status (>93 percent). 

As breast cancer receptor status guides treatment options and prognosis, subtyping diagnostics need to be accurate and reliable. With recent improvements to subtyping techniques and the wide array of diagnostics in development, these more effective breast cancer diagnostics aim to provide reliable results to guide better treatment options and improve patient outcomes. 

References

1. Heer, E. et al. “Global burden and trends in premenopausal and postmenopausal breast cancer: a population-based study.” The Lancet Global Health 8 (2020): e1027–e1037. 

2. DeSantis, C. E., et al. “Breast cancer statistics, 2017, racial disparity in mortality by state.” CA: A Cancer Journal for Clinicians (2017): 439–448. 

3. Dunstan, R. W., et al. “The use of immunohistochemistry for biomarker assessment-Can it compete with other technologies?” Toxicologic Pathology (2011): 988–1002. 

4. Turashvili, G., et al. “Tumor heterogeneity in breast cancer.” Frontiers in Medicine (2017). 

5. Waks, A. G., et al. “Breast cancer treatment: a review.” Journal of the American Medical Association (2019): 288–300. 

6. Yersal, O., et al. “Biological subtypes of breast cancer: Prognostic and therapeutic implications.” World Journal of Clinical Oncology (2014): 412–424. 

7. Mosly, D., et al. “Predictive markers of endocrine response in breast cancer.” World Journal of Experimental Medicine (2018): 1–7. 

8. Alcantara, D., et al. “Molecular imaging of breast cancer: Present and future directions.” Frontiers in Chemistry (2014). 

9. Altunay, B., et al. “HER2-directed antibodies, affibodies and nanobodies as drug-delivery vehicles in breast cancer with a specific focus on radioimmunotherapy and radioimmunoimaging.” European Journal of Nuclear Medicine and Molecular Imaging (2020): 1–19. 

10. Linden, H. M., et al. “Clinical potential of estrogen and progesterone receptor imaging.” PET Clinics (2018): 415–422. 

11. Dehdashti, F., et al. “Assessment of progesterone receptors in breast carcinoma by PET with 21- 18F-fluoro-16α,17α-[(R)- (1′-α- furylmethylidene) dioxy]-19-norpregn-4-ene-3,20- dione.” Journal of Nuclear Medicine (2012): 363–370. 

12. Sörensen, J., et al. “Measuring HER2-receptor expression in metastatic breast cancer using [68Ga]ABY-025 Affibody PET/ CT.” Theranostics (2016): 262–271. 

13. Gebhart, G., et al. “Imaging diagnostic and therapeutic targets: Human epidermal growth factor receptor 2.” Journal of Nuclear Medicine (2016): 81S-88S. 

14. Son, J., et al. “Prediction of breast cancer molecular subtypes using radiomics signatures of synthetic mammography from digital breast tomosynthesis.” Scientific Reports (2020): 1–11. 

15. Leithner, D., et al. “Radiomic signatures with contrastenhanced magnetic resonance imaging for the assessment of breast cancer receptor status and molecular subtypes: Initial results.” Breast Cancer Research (2019): 106. 

16. Leithner, D., et al. “Radiomic signatures derived from diffusion-weighted imaging for the assessment of breast cancer receptor status and molecular subtypes.” Molecular Imaging and Biology (2020): 453–461. 

17. Issadore, D. “Point-of-care rare cell cancer diagnostics.” Methods in Molecular Biology (2015): 123–137. 18. Min, J., et al. “CytoPAN—Portable cellular analyses for rapid point-of-care cancer diagnosis.” Science Translational Medicine (2020): 9746.