AI 中文总结
本研究开发结合AI复发风险热图与空间蛋白质组学的框架，在TNBC中识别出与复发相关的空间分子特征，构建的13蛋白复合评分可提升预后区分能力，为多尺度生物标志物发现提供新途径。
AI 中文摘要
深度学习模型可从H&E染色切片预测癌症复发，但这些预测背后的局部分子状态仍大多未被揭示。本研究在三阴性乳腺癌（TNBC）中开发了一种基于结局的空间病理学框架，将AI生成的复发风险热图与基于质谱的空间蛋白质组学相结合。在156名患者的队列中，对高分图像块进行基于分布的聚合，在独立测试队列中实现了0.77的AUC和0.77的C指数。批量蛋白质组学分析显示，图像衍生的高风险与细胞周期及基因组维持程序相关，低风险则与免疫激活相关。高低风险图像块共存于同一肿瘤区室，且呈现不同的核和结构特征，揭示了超出组织区室身份的肿瘤内异质性。随后，研究人员将热图作为坐标水平的指导，从2名复发患者中物理分离并分析了46个AI定义的肿瘤区域。空间蛋白质组学分析在两名患者中揭示了一致的分子对比：高风险区域富集有丝分裂程序，低风险区域则富集免疫和抗原呈递程序。从这些空间对比中衍生出的13种蛋白质复合评分在扩大队列中随评分升高呈现无复发生存率降低的趋势，而对应的转录本复合评分在独立的METABRIC TNBC队列中可对无复发生存率进行分层。将蛋白质复合与H&E衍生的风险评分相结合，使袋外C指数从0.679提升至0.739，并增强了3年和5年的时间依赖性区分能力。综上，这些发现明确了经结局训练的AI模型作为空间明确的实验指导的新作用，其可连接预后形态学与局部分子状态，并推进TNBC中基于生物学的多尺度生物标志物发现。
英文摘要
Deep learning models can predict cancer recurrence from H&E stained slides, but the localized molecular states underlying these predictions remain largely obscured. Here, we developed an outcome informed spatial pathology framework in TNBC that integrates AI generated recurrence risk heatmaps with mass spectrometry based spatial proteomics. In a cohort of 156 patients, distribution based aggregation of high scoring patches achieved an AUC of 0.77 and a C-index of 0.77 in an independent test cohort. Bulk proteomics associated high image derived risk with cell cycle and genome maintenance programs and low risk with immune activation. High and low risk patches coexisted within the same tumor compartment and displayed distinct nuclear and architectural features, revealing intratumoral heterogeneity beyond tissue compartment identity. We then used the heatmaps as coordinate level guides to physically isolate and profile 46 AI defined tumor regions from two recurrence patients. Spatial proteomic profiling revealed a concordant molecular contrast across both patients: mitotic programs were enriched in high risk regions and immune and antigen presentation programs in low risk regions. A 13 protein composite derived from these spatial contrasts showed a trend toward poorer recurrence-free survival with increasing scores in an expanded cohort, while the corresponding transcript based composite stratified recurrence free survival in the independent METABRIC TNBC cohort. Integrating the protein composite with the H&E derived risk score improved the out of bag C-index from 0.679 to 0.739 and enhanced time dependent discrimination at 3 and 5 years. Together, these findings define a new role for outcome trained AI models as spatially explicit experimental guides that connect prognostic morphology with localized molecular states and advance biologically grounded, multiscale biomarker discovery in TNBC.
CommentsTriple-negative breast cancer (TNBC), Recurrence, Digital pathology, Artificial intelligence, Spatial proteomics, Tumor microenvironment
