从单细胞水平对乳腺癌代谢分类而治
细胞疯狂生长是肿瘤的共同特征之一，为了有效提供细胞疯狂生长所需的大量能量和大分子，肿瘤需要对细胞代谢和微环境进行重新编程。不过，肿瘤对葡萄糖、脂肪酸和谷氨酰胺等主要能量来源的依赖和利用存在差异，这与基因变化、营养素和氧气的获取等一系列因素密切相关。近年来的研究不断发现，肿瘤之间和肿瘤内部的癌细胞代谢存在可塑性和灵活性，从而揭示了可用靶向药物治疗的代谢靶点。对单细胞基因转录核糖核酸（RNA）进行测序，与多细胞或细胞系测序相比，可精准捕获肿瘤细胞的内在和外在特征、发现不同的细胞亚组、分析克隆多样性，更重要的是可以确定肿瘤异质性的关键因素。确定癌细胞代谢亚型，可能改善患者结局并预测治疗效果。既往研究已将卵巢癌、肝癌、胰腺癌等若干肿瘤类型分为不同的代谢亚型。不过，乳腺癌细胞能量代谢失调机制仍然未知，并且缺乏用于乳腺癌组织的详细代谢分类法。
2021年3月5日，美国基因与细胞治疗学会和《细胞》旗下《分子治疗》在线发表复旦大学附属肿瘤医院余天剑、马丁、刘莹莹、肖毅、龚悦、江一舟、邵志敏、胡欣、狄根红等学者的研究报告，通过多细胞和单细胞RNA测序分析，揭示了人类乳腺癌的代谢异质性及其分类法，为个体化代谢靶向治疗奠定了基础，尤其对常用靶向药物无效的乳腺癌具有重要临床意义。
该研究首先对多个乳腺癌患者数据集进行基因组学、转录组学、代谢组学、单细胞转录组学特征进行综合分析，从而确定乳腺癌的能量代谢特征。
随后，根据能量代谢特征，可将乳腺肿瘤分为两个独特的预后类型：
第一类表现为糖酵解活性高和患者生存率低
第二类特征为脂肪酸氧化和谷氨酰胺分解
利用该能量代谢分类法，对三个独立的大样本患者队列进行归类，可反映出不同肿瘤之间的代谢异质性，并且进一步证实其代谢物的复杂性。
该研究还发现，恶性细胞与非恶性细胞相比，对肿瘤代谢起到了决定性作用。此外，单细胞测序表明，肿瘤细胞的代谢状态与肿瘤微环境衍生因子的相互作用显著不同。
值得注意的是，代谢特征不同的各种免疫细胞及其类群相比，具有免疫抑制功能的免疫细胞代谢活性显著较高。
因此，该研究结果表明，利用具有预后和靶向治疗价值的能量代谢分类法，可揭示人类乳腺癌整体和单细胞水平的能量代谢异质性及其根本原因。单细胞转录组测序可提供新的能量代谢特征分类，从而根据患者或细胞类型特定癌症代谢制定个体化治疗策略。
Mol Ther. 2021 Mar 5. Online ahead of print.
Bulk and Single-cell Transcriptome Profiling Reveal the Metabolic Heterogeneity in Human Breast Cancers.
Yu TJ, Ma D, Liu YY, Xiao Y, Gong Y, Jiang YZ, Shao ZM, Hu X, Di GH.
Fudan University Shanghai Cancer Center, Shanghai, China; Fudan University Shanghai Cancer Center, Fudan University, Shanghai, China; Shanghai Medical College, Fudan University, Shanghai, China.
An emerging view regarding cancer metabolism is its heterogeneous and context specific, but it remains to be elucidated in breast cancers. Here, we characterized the energy-related metabolic features of breast cancers through integrative analyses of multiple datasets with genomics, transcriptomics, metabolomics and single-cell transcriptome profiling. Energy-related metabolic signatures were used to stratify breast tumors into two prognostic clusters: cluster 1 exhibits high glycolytic activity and decreased survival rate; and the signatures of cluster 2 are enriched in fatty acid oxidation and glutaminolysis. The intertumoral metabolic heterogeneity was reflected by the clustering among three independent large cohorts, and the complexity was further verified at the metabolite level. In addition, we found that the metabolic status of malignant cells rather than that of nonmalignant cells is the major contributor at the single-cell resolution, and its interactions with factors derived from the tumor microenvironment are unanticipated. Notably, among various immune cells and their clusters with distinguishable metabolic features, those with immunosuppressive function presented higher metabolic activities. Collectively, we uncovered the heterogeneity in energy metabolism using a classifier with prognostic and therapeutic value. Single-cell transcriptome profiling provided novel metabolic insights that could ultimately tailor therapeutic strategies based on patient- or cell type-specific cancer metabolism.
KEYWORDS: breast cancer; metabolism; single-cell RNA sequencing
PMID: 33677091
DOI: 10.1016/j.ymthe.2021.03.003