The gut microbiota has been demonstrated to be correlated with the clinical phenotypes of diseases, including cancers. However, there are few studies on clinical subtyping based on the gut microbiota, especially in breast cancer (BC) patients. Here, using machine learning methods, we analysed the gut microbiota of BC, colorectal cancer (CRC), and gastric cancer (GC) patients to identify their shared metabolic pathways and the importance of these pathways in cancer development. Based on the gut microbiota-related metabolic pathways, human gene expression profile and patient prognosis, we established a novel BC subtyping system and identified a subtype called “challenging BC”. Tumours with this subtype have more genetic mutations and a more complex immune environment than those of other subtypes. A score index was proposed for in-depth analysis and showed a significant negative correlation with patient prognosis. Notably, activation of the TPK1-FOXP3-mediated Hedgehog signalling pathway and TPK1-ITGAE-mediated mTOR signalling pathway was linked to poor prognosis in “challenging BC” patients with high scores, as validated in a patient-derived xenograft (PDX) model. Furthermore, our subtyping system and score index are effective predictors of the response to current neoadjuvant therapy regimens, with the score index significantly negatively correlated with both treatment efficacy and the number of immune cells. Therefore, our findings provide valuable insights into predicting molecular characteristics and treatment responses in “challenging BC” patients.

中文翻译：

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基于共生微生物组的多组学分子亚型预测乳腺癌的分子特征和治疗反应

肠道微生物群已被证明与疾病（包括癌症）的临床表型相关。然而，基于肠道微生物群的临床亚型研究很少，尤其是乳腺癌（BC）患者。在这里，我们使用机器学习方法分析了 BC、结直肠癌 (CRC) 和胃癌 (GC) 患者的肠道微生物群，以确定他们共有的代谢途径以及这些途径在癌症发展中的重要性。基于肠道菌群相关代谢途径、人类基因表达谱和患者预后，我们建立了一个新的 BC 亚型系统，并确定了一种称为“挑战性 BC”的亚型。与其他亚型相比，该亚型的肿瘤具有更多的基因突变和更复杂的免疫环境。提出评分指标进行深入分析，结果显示与患者预后呈显着负相关。值得注意的是，TPK1-FOXP3 介导的 Hedgehog 信号通路和 TPK1-ITGAE 介导的 mTOR 信号通路的激活与高分“挑战性 BC”患者的不良预后有关，这一点在患者来源的异种移植 (PDX) 模型中得到了验证。此外，我们的分型系统和评分指数是当前新辅助治疗方案反应的有效预测因子，评分指数与治疗效果和免疫细胞数量显着负相关。因此，我们的研究结果为预测“具有挑战性的 BC”患者的分子特征和治疗反应提供了宝贵的见解。