Abstract
Although molecular classification brings interesting insights into breast cancer taxonomy, its implementation in daily clinical care is questionable because of its expense and the information supplied in a single sample allocation is not sufficiently reliable. New approaches, based on a panel of small molecules derived from the global or targeted analysis of metabolic profiles of cells, have found a correlation between activation of de novo lipogenesis and poorer prognosis and shorter disease-free survival for many tumors. We hypothesized that the lipid content of breast cancer cells might be a useful indirect measure of a variety of functions coupled to breast cancer progression. Raman microspectroscopy was used to characterize metabolism of breast cancer cells with different degrees of malignancy. Raman spectra from MDA-MB-435, MDA-MB-468, MDA-MB-231, SKBR3, MCF7 and MCF10A cells were acquired with an InVia Raman microscope (Renishaw) with a backscattered configuration. We used Principal Component Analysis and Partial Least Squares Discriminant Analyses to assess the different profiling of the lipid composition of breast cancer cells. Characteristic bands related to lipid content were found at 3014, 2935, 2890 and 2845 cm−1, and related to lipid and protein content at 2940 cm−1. A classificatory model was generated which segregated metastatic cells and non-metastatic cells without basal-like phenotype with a sensitivity of 90% and a specificity of 82.1%. Moreover, expression of SREBP-1c and ABCA1 genes validated the assignation of the lipid phenotype of breast cancer cells. Indeed, changes in fatty acid unsaturation were related with the epithelial-to-mesenchymal transition phenotype. Raman microspectroscopy is a promising technique for characterizing and classifying the malignant phenotype of breast cancer cells on the basis of their lipid profiling. The algorithm for the discrimination of metastatic ability is a first step towards stratifying breast cancer cells using this rapid and reagent-free tool.
Highlights
Despite the reduction in mortality in breast cancer patients due to earlier diagnosis and implementation of adjuvant chemo- and hormone therapies, breast cancer is still the commonest cause of cancer death in women worldwide [1]
The transcription factors SREBP-1c and LXR maintain cholesterol homeostasis through complementary pathways of feedback inhibition and feed-forward activation [15], [31], [32]. To assess their coordinated action in the lipid phenotype of breast cancer cells, we explored the LXR pathways in a set of breast cancer cells according to their malignant phenotype including both non-metastatic and metastatic cells: MCF7, which expressed hormone receptors like luminal A tumors; SKBR3, a phenotype with amplifications of the ErbB2 oncogene; MDA-MB-468, p53 mutated cells with basal-like phenotype; and two different metastatic models: MDA-MB-435, with lung metastasis tropism, and MDA-MB-231 with bone metastasis tropism, both belonging to the basal-like phenotype [33]
These results showed the differences in regulation of lipid metabolism pathways in breast cancer cells
Summary
Despite the reduction in mortality in breast cancer patients due to earlier diagnosis and implementation of adjuvant chemo- and hormone therapies, breast cancer is still the commonest cause of cancer death in women worldwide [1]. Many factors and genes are involved in the initiation of breast cancer, but mortality is due to metastatic disease [2]. The different biological behaviors and metastatic patterns observed among the distinct breast cancer phenotypes may suggest different mechanisms of invasion and metastasis, the biological features of breast tumors have proven insufficient for a comprehensive description of progression at first diagnosis, due to the heterogeneity of the disease [5]. Molecular classification provides interesting insights into breast cancer taxonomy, its implementation in clinical care is questionable because it is too expensive to be introduced in daily pathological diagnosis, and because the information supplied is of insufficient reliability in single sample allocation [8]
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