The Lipid Phenotype of Breast Cancer Cells Characterized by Raman Microspectroscopy: Towards a Stratification of Malignancy

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.

尽管分子分类为乳腺癌分类体系带来了诸多富有价值的见解，但由于其成本高昂，且单次样本检测提供的信息可靠性不足，使其在日常临床诊疗中的应用备受质疑。基于对细胞代谢谱进行全局或靶向分析得到的小分子组合的新型研究方法，已被证实与多种肿瘤的从头脂肪生成（de novo lipogenesis）激活、不良预后及更短的无病生存期存在相关性。我们提出假设：乳腺癌细胞的脂质含量或许可作为与乳腺癌进展相关的多种功能的有效间接检测指标。本研究采用拉曼显微光谱法（Raman microspectroscopy）对不同恶性程度的乳腺癌细胞代谢特征进行表征。通过搭载背散射配置的雷尼绍（Renishaw）InVia拉曼显微镜，采集了MDA-MB-435、MDA-MB-468、MDA-MB-231、SKBR3、MCF7及MCF10A细胞的拉曼光谱。我们采用主成分分析（Principal Component Analysis）与偏最小二乘判别分析（Partial Least Squares Discriminant Analyses），对乳腺癌细胞的脂质组成差异进行评估分析。研究发现，与脂质含量相关的特征拉曼峰位于3014、2935、2890及2845 cm⁻¹，而与脂质和蛋白质含量相关的特征峰位于2940 cm⁻¹。构建的分类模型可有效区分转移性细胞与非基底样表型的非转移性细胞，灵敏度达90%，特异性为82.1%。此外，SREBP-1c与ABCA1基因的表达验证了乳腺癌细胞脂质表型的分类结果。值得注意的是，脂肪酸不饱和度的变化与上皮间质转化（epithelial-to-mesenchymal transition）表型密切相关。拉曼显微光谱法是一种极具应用前景的技术，可基于脂质谱对乳腺癌细胞的恶性表型进行表征与分类。本研究中用于区分细胞转移能力的算法，为利用这种快速且无需试剂的工具对乳腺癌细胞进行分层分型迈出了重要的第一步。