Molecular characterization of breast cancer cell lines by clinical immunohistochemical markers

Abstract

Breast cancer is the leading cause of cancer mortality in females worldwide. Studies based on gene expression profiles have identified different breast cancer molecular subtypes, such as luminal A and B cells, cancer cells that are estrogen receptor (ER) and/or progesterone receptor (PR) positive, human epidermal growth factor 2 (HER2)-enriched cells, cancer cells that exhibit an overexpression of the oncogene HER2, and triple-negative cells, cancer cells that are negative for ER, PR and HER2 expression. Immunohistochemistry is the most common type of method used for the identification of these molecular subtypes, through the identification of specific cell receptors. The present study aimed to evaluate the ER, PR and HER2 receptor expression in human breast cancer cell lines, and to classify the corresponding molecular subtype comparing two alternative methods. In the present study, a panel of human mammary carcinoma cell lines: BT-20; Hs578T; MCF-7; MCF-7/AZ; MDA-MB-231; MDA-MB-468; SKBR3; and T47D were used. Immunohistochemical and immunocytochemistry assays were used to characterize the breast cancer subtypes of these cell lines according to the expression of ER, PR and HER2 receptors. The results revealed the molecular characterization of this panel of breast cancer cell lines, using the differential expression of classical and clinically used markers in concordance with previous studies. In addition, these data are important for additional in vitro studies of these specific receptors.

Establishment and Characterization of a Brca1−/−, p53−/− Mouse Mammary Tumor Cell Line

“…Following expansion and cryopreservation at early passages, CST cells were further characterized in the context of epithelial and mesenchymal breast cancer cell lines ( Figure 2). MCF7 cells (established from human Luminal A, ER+, PR+, HER2-breast cancer [60]) express high levels of the epithelial cell marker E-cadherin (also known as Cadherin-1). 4T1 cells, derived from a murine mammary carcinoma [61], exhibit epithelial morphology, E-cadherin expression, and also some degree of vimentin positivity.…”

Image-based Cell Phenotyping Using Deep Learning

“…However, since cancer is such a heterogeneous condition, the ultimate utility of these cancer cell line models depends on the ability to accurately classify them. Cancer cell lines are primarily classified based on the histopathology of the original tumor but subtyping of the cell lines may require lengthy and significant molecular and genetic profiling [1][2][3][4] . Immunofluorescence phenotyping has contributed significantly to reducing the burden for cell line classification but immunofluorescence relies on the expression of specific cell surface antigens, which is expensive and error-prone, despite significant efforts in standardizing staining, data collection and automation of analysis 5 .…”

“…Immunofluorescence phenotyping has contributed significantly to reducing the burden for cell line classification but immunofluorescence relies on the expression of specific cell surface antigens, which is expensive and error-prone, despite significant efforts in standardizing staining, data collection and automation of analysis 5 . Specifically, immunofluorescence phenotyping may be undesirable because: (1) phenotyping markers may be unavailable or lack specificity, (2) the sample may be too heterogeneous, (3) number of markers required may exceed the number of available fluorescence channels that can be detected, and (4) specific labeling may affect the cell in undesirable ways, such as activation or loss of viability. In many of these situations, an important question is whether individual cells could be phenotyped directly using microscopy images without specific labeling.…”

Expression of Breast Cancer Subtypes Based on the Most Important Biomarkers: Comparison of Clinicopathological Factors and Survival

“…Although histopathological and molecular features are very important, the gold standard for comprehensive classification of treatment depends on gene expression for prediction and prognosis of complications (9,10).…”

“…Molecular profiling distinguishes major BC subtypes, which are often determined via immunohistochemical (IHC) analysis of estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) (10,12,13). These biomarkers are used to classify BC into at least 5 main groups, based on their molecular characteristics: luminal cell-like tumors (subdivided into luminal A and B), basal cell-like (BCL) or triple-negative phenotypic (TNP), Erb-B2, Copyright (3,14).…”