Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, 651 East Dongfeng Road, Guangzhou, 510060, China Breast Disease Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, China School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China Kangyuan Hospital, Guangzhou, 510440, China.
Int J Surg. 2022 Nov;107:106936. doi: 10.1016/j.ijsu.2022.106936. Epub 2022 Sep 20.
BACKGROUND: Postoperative progression and chemotherapy resistance is the major cause of treatment failure in patients with triple-negative breast cancer (TNBC). Currently, there is a lack of an ideal predictive model for the progression and drug sensitivity of postoperative TNBC patients. Diverse programmed cell death (PCD) patterns play an important role in tumor progression, which has the potential to be a prognostic and drug sensitivity indicator for TNBC after surgery. MATERIALS AND METHODS: Twelve PCD patterns (apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis) were analyzed for model construction. Bulk transcriptome, single-cell transcriptome, genomics, and clinical information were collected from TCGA-BRCA, METABRIC, GSE58812, GSE21653, GSE176078, GSE75688, and KM-plotter cohorts to validate the model. RESULTS: The machine learning algorithm established a cell death index (CDI) with a 12-gene signature. Validated in five independent datasets, TNBC patients with high CDI had a worse prognosis after surgery. Two molecular subtypes of TNBC with distinct vital biological processes were identified by an unsupervised clustering model. A nomogram with high predictive performance was constructed by incorporating CDI with clinical features. Furthermore, CDI was associated with immune checkpoint genes and key tumor microenvironment components by integrated analysis of bulk and single-cell transcriptome. TNBC patients with high CDI are resistant to standard adjuvant chemotherapy regimens (docetaxel, oxaliplatin, etc.); however, they might be sensitive to palbociclib (an FDA-approved drug for luminal breast cancer). CONCLUSION: Generally, we established a novel CDI model by comprehensively analyzing diverse cell death patterns, which can accurately predict clinical prognosis and drug sensitivity of TNBC after surgery. A user-friendly website was created to facilitate the application of this prediction model (https://tnbc.shinyapps.io/CDI_Model/).
背景：三阴性乳腺癌（TNBC）患者术后进展和化疗耐药是治疗失败的主要原因。目前，缺乏针对术后 TNBC 患者进展和药物敏感性的理想预测模型。不同的程序性细胞死亡（PCD）模式在肿瘤进展中发挥重要作用，有可能成为术后 TNBC 的预后和药物敏感性指标。
材料与方法：分析了 12 种 PCD 模式（细胞凋亡、坏死性凋亡、细胞焦亡、铁死亡、铜死亡、自噬性细胞死亡、细胞坏死、parthanatos、溶酶体依赖性细胞死亡、自噬依赖性细胞死亡、alkaliptosis 和 oxeiptosis）进行模型构建。从 TCGA-BRCA、METABRIC、GSE58812、GSE21653、GSE75688 和 KM-plotter 队列中收集批量转录组、单细胞转录组、基因组学和临床信息，以验证模型。
结果：机器学习算法建立了一个具有 12 个基因特征的细胞死亡指数（CDI）。在五个独立数据集进行验证后，CDI 高的 TNBC 患者术后预后较差。通过无监督聚类模型鉴定出两种具有不同关键生物学过程的 TNBC 分子亚型。通过将 CDI 与临床特征相结合，构建了一个具有高预测性能的列线图。此外，通过对批量和单细胞转录组的综合分析，CDI 与免疫检查点基因和关键肿瘤微环境成分相关。CDI 高的 TNBC 患者对标准辅助化疗方案（多西他赛、奥沙利铂等）耐药，但对 palbociclib（一种 FDA 批准用于 luminal 乳腺癌的药物）可能敏感。
结论：我们通过综合分析不同的细胞死亡模式建立了一个新的 CDI 模型，该模型可以准确预测 TNBC 患者术后的临床预后和药物敏感性。创建了一个用户友好的网站，以方便该预测模型的应用（https://tnbc.shinyapps.io/CDI_Model/）。
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