Zhang Jingyuan, Sun Xuejun
Department of Breast Surgery, Shannxi Provincial Cancer Hospital, Xi'an, Shaanxi, China.
Department of General Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
J Cell Mol Med. 2025 Jun;29(11):e70572. doi: 10.1111/jcmm.70572.
Triple-negative breast cancer (TNBC) is associated with a poor prognosis due to insufficient molecular subtyping precision and limited actionable targets. Although metabolic reprogramming underlies TNBC chemotherapy resistance, establishing metabolic subtyping systems and investigating drug sensitivity across distinct metabolic subgroups could provide novel therapeutic avenues for breast cancer management. GSVA (Gene Set Variation Analysis) analysis of metabolic pathways reveals significant differences in TNBC (Triple-Negative Breast Cancer) patients. TNBC patients are classified into four metabolic subtypes through consensus clustering, based on their GSVA values of metabolic pathways. These subtypes are: MS_1, characterised by increased lipogenic activity; MS_2, characterised by increased carbohydrate and nucleotide metabolism; MS_3, a metabolism-active subtype with activation of all types of metabolism; and MS_4, characterised by suppressed metabolic activity across all types of metabolism. We next propose a novel method called MODIN (Multiomics-Driven Drug-Cell Interaction Network), which embeds multi-omics gene information (mRNA expression, copy number variation and DNA methylation) and drug SMILES data into a latent space, and then employs a multi-head attention-based interaction module to accurately predict the LN_IC50 values of 621 drugs in TNBC. Based on MODIN, noteworthy disparities in drug sensitivity emerge between the patient cohorts categorised as MS_2 and MS_3. MS_3 patients show a significantly higher sensitivity to chemotherapy regimens, especially for doxorubicin and docetaxel, while the MS_2 cohort displays marked resistance to these drugs. Our study reveals the metabolic heterogeneity of TNBC, and TNBC patients with increased carbohydrate and nucleotide metabolism exhibit the poorest prognoses and greater resistance to doxorubicin and docetaxel.
三阴性乳腺癌（TNBC）由于分子亚型分类精度不足和可操作靶点有限，预后较差。尽管代谢重编程是TNBC化疗耐药的基础，但建立代谢亚型系统并研究不同代谢亚组之间的药物敏感性，可为乳腺癌治疗提供新的途径。对代谢途径进行基因集变异分析（GSVA）发现，TNBC（三阴性乳腺癌）患者存在显著差异。基于代谢途径的GSVA值，通过一致性聚类将TNBC患者分为四种代谢亚型。这些亚型分别是：MS_1，其特征是脂肪生成活性增加；MS_2，其特征是碳水化合物和核苷酸代谢增加；MS_3，一种所有类型代谢均被激活的代谢活跃亚型；以及MS_4，其特征是所有类型的代谢活性均受到抑制。接下来，我们提出了一种名为MODIN（多组学驱动的药物-细胞相互作用网络）的新方法，该方法将多组学基因信息（mRNA表达、拷贝数变异和DNA甲基化）和药物SMILES数据嵌入到一个潜在空间中，然后采用基于多头注意力的相互作用模块来准确预测TNBC中621种药物的LN_IC50值。基于MODIN，在分类为MS_2和MS_3的患者队列之间出现了值得注意的药物敏感性差异。MS_3患者对化疗方案表现出显著更高的敏感性，尤其是对阿霉素和多西他赛，而MS_2队列对这些药物表现出明显的耐药性。我们的研究揭示了TNBC中的代谢异质性，碳水化合物和核苷酸代谢增加的TNBC患者预后最差，对阿霉素和多西他赛的耐药性更强。
