1. Home
  2. IJFCM
  3. Cellular morphology mediated functional characteristics of breast cancer cells
Submit manuscript...
International Journal of
eISSN: 2577-8269

Family & Community Medicine

Opinion Volume 7 Issue 3

Cellular morphology-mediated functional characteristics of breast cancer cells

Masami Okamoto

Advanced Polymeric Nanostructured Materials Engineering, Graduate School of Engineering, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku, Nagoya 468 8511, Japan

Correspondence: Prof. Masami Okamoto, Advanced Polymeric Nanostructured Materials Engineering, Graduate School of Engineering, Toyota Technological Institute, 2-12-1 Hisakata, Tempaku, Nagoya 468 8511, Japan, Tel +81 52 809186, Fax +815280918641

Received: June 15, 2023 | Published: June 28, 2023

Citation: Okamoto M. Cellular morphology-mediated functional characteristics of breast cancer cells. Int J Fam Commun Med. 2023;7(3):115-116. DOI: 10.15406/ijfcm.2023.07.00322

Download PDF

Keywords:

cancer, poor treatment, radiation and chemotherapy, morphology, tumor progression, biophysical cues

Abbreviations

ECM, extracellular matrix; DHM, digital holographic microscopy; Pap, Papanicolaou; ND, maximum nuclear dimension; MHN, maximum nuclear height; AN/AC, nuclear-to-cytoplasmic ratio

Opinion

Breast cancer is one of the most common malignancies in women and has the highest incidence and mortality of all female malignancies.1 The average 5-year survival rate for breast cancer is reported to be approximately 55% due to the poor treatment outcome of metastatic disease, including resistance to radiation and chemotherapy.2 Given the poor prognosis associated with these cancer treatments, there is a compelling need to address new research into effective cancer treatments.

When a traction force is generated within a cell underlying an extracellular matrix (ECM) containing an artificial matrix, the cell inherently senses the stiffness of the surrounding microenvironment and pulls itself into the matrix in response to the applied force. It exerts a force and the traction force can change the morphology and cytoskeleton of the cell (Figure 1).3,4

Figure 1 Cells exert a traction force on the underlying substrate and essentially "feeling" this stiffness.2 In general, cells generate greater traction, establish more stable and well-defined focal adhesions, form more defined actin stress fibers (red), and spread more extensively on stiff substrates than on compliant substrates. A soft substrate is beneficial for neurogenic or adipogenic differentiation, a hard substrate is beneficial for osteogenic differentiation, and an intermediate hardness is indicated because "tissue cells feel and respond to the hardness of their substrate". The substrate favors myogenic lineage engagement.3

Microenvironment-mediated tumor progression in artificial matrices explores design criteria for understanding cancer progression mechanisms and metastatic potential. Cancer cells express multiple biophysical cues and epigenetic programs of phenotypic transition during migration. Quantifying and investigating these biophysical cues as morphological changes in cancer cells have applications not only in hematology but also in cancer treatment and diagnosis.5

Recently, the extracted morphological features can also be used to analyze dynamic changes in cells in neurological diseases and cell stress-related phenomena.6 Moreover, multivariate analysis of morphological data suggests that quantitative cytology is a useful adjunct to conventional testing for the selection of new substances.7 Because biophysical cues exist long before cancer develops, they are most useful in determining whether cells will metastasize. With knowledge of potentially cancerous changes in the body, clinicians may be able to initiate preventative measures in the form of lifestyle changes and chemotherapy.

Morphological change in cancer cells and cancer diagnosis

Using digital holographic microscopy (DHM), contrast agent-free morphological analysis and dynamic behavior of cancer cells have been reported.8 A suitable technique for gynecologic cervical samples has been demonstrated as an alternative to cytological Papanicolaou (Pap) testing.9 Pap test normal cell images showed superficial cells with small nuclei. In contrast, abnormal cell images revealed superficial cells with larger, irregular nuclei. The Benzerdjeb group identified DHM criteria that can distinguish abnormal cells. The best diagnostic criteria were maximum nuclear dimension (ND), maximum nuclear height (MHN), and nuclear-to-cytoplasmic ratio (AN/AC). By using ND and AN/AC cutoff values, an automated algorithm can be built to select abnormal cells.

DHM imaging combined with machine learning for data analysis is a powerful tool for distinguishing SW480 colon cancer cells from leukocytes in suspension.10 Furthermore, DHM analysis using deep learning neural networks identifies isogenic cell lines at different metastatic stages11 and distinguishes between healthy and nutritionally deprived cancer cells.12 Furthermore, cancer cell cycle tracking may identify cancer cell behaviors such as morphologically distinct motility patterns. This leads to targeting mechanisms of cancer growth and chemosensitivity.13

Assessment of cell morphology associated with the contractile and stress-resistant cytoskeleton has implications for phasic signals from cell boundaries and/or nuclei. As reported by the Okamoto group in the literature,14,15 cell morphology of different cancer phenotypes provides a quantitative method to distinguish different effects of cancer biomarkers.16

Acknowledgments

This work was supported by the KAKENHI of the Ministry of education, Sports, science and Technology, Japan.

Conflicts of interest

Author declares that there is no conflicts of interests.

References

  1. Allemani C, Weir HK, Carreira H, et al. Global surveillance of cancer survival 1995–2009: analysis of individual data for 25 676 887 patients from 279 population-based registries in 67 countries (CONCORD-2). Lancet. 2015;385(9972):977–1010.
  2. Hanahan D, Weinberg RA. Hallmarks of Cancer: The Next Generation. Cell. 2011;144(5):646–674.
  3. Chen S, Zhao M, Wu G, et al. Recent advances in morphological cell image analysis. Comput Math Methods Med. 2012:2012101536.
  4. Wang N, Tytell JD, Ingber DE. Mechanotransduction at a distance: mechanically coupling the extracellular matrix with the nucleus. Nat Rev Mol Cell Biol. 2009;10(1):75–82.
  5. Wen JH, Vincent LG, Fuhrmann A, et al. Interplay of matrix stiffness and protein tethering in stem cell differentiation. Nat Mater. 2014;13(10):979–987.
  6. Stacy NI, Isaza R, Wiedner E. First report of changes in leukocyte morphology in response to inflammatory conditions in Asian and African elephants (Elephas maximus and Loxodonta africana). PLoS One. 2017;12(9):e0185277.
  7. Pekny M, Pekna M, Messing A, et al. Astrocytes: a central element in neurological diseases. Acta Neuropathol. 2016;131(3):323–345.
  8. Chen S, Zhao M, Wu G, et al. Recent advances in morphological cell image analysis. Comput Math Methods Med. 2012:2012101536.
  9. Park Y, Depeursinge C, Popescu G. Quantitative phase imaging in biomedicine. Nat Photon. 2018;12:578–589.
  10. Benzerdjeb N, Garbar C, Camparo P, et al. Digital holographic microscopy as screening tool for cervical cancer preliminary study. Cancer Cytopathol. 2016;124(8):573–580.
  11. Chen CL, Mahjoubfar A, Tai LC, et al. Deep learning in label-free cell classification. Sci Rep. 2016;6:21471.
  12. Roitshtain D, Wolbromsky L, Bal E, et al. Quantitative phase microscopy spatial signatures of cancer cells. Cytometry A. 2017;91A(5):482–493.
  13. Strbkova L, Zicha D, Vesely P, et al. Automated classification of cell morphology by coherence-controlled holographic microscopy. J Biomed Opt. 2017;22(8):221–229.
  14. Dickson MA, Schwartz GK. Development of cell-cycle inhibitors for cancer therapy. Curr Oncol. 2009;16(2):36–43.
  15. Domura R, Sasaki R, Okamoto M, et al. Comprehensive study on cellular morphologies, proliferation, motility, and epithelial–mesenchymal transition of breast cancer cells incubated on electrospun polymeric fiber substrates. J Mater Chem B. 2017;5(14):2588–2600.
  16. Sasaki R, Domura R, Okamoto M. Cellular morphologies, motility, and epithelial–mesenchymal transition of breast cancer cells incubated on electrospun polymeric fiber substrates in hypoxia. Mater Today Chem. 2019;11:29–41.
Creative Commons Attribution License

©2023 Okamoto. This is an open access article distributed under the terms of the, which permits unrestricted use, distribution, and build upon your work non-commercially.

Citations

Journal Menu

Useful Links