AI 引导深度视觉蛋白质组学揭示三阴性乳腺癌复发相关形态分子特征
AI-guided deep visual proteomics reveals recurrence-associated morpho-molecular signatures within triple-negative breast cancers
研究团队开发了 AI 引导的深度视觉蛋白质组学框架，用复发风险热图指导三阴性乳腺癌组织的物理取样与质谱空间蛋白质组分析。在 156 名患者中，高风险区域富集有丝分裂程序，低风险区域则富集免疫与抗原呈递相关程序；基于差异蛋白构建的 13 蛋白复合评分可区分高复发与低复发患者。
Authors:Yesung Cho, Ji Hwan Park, Chan Il Kim, Hyewon Kim, Honglan Li, Yumin Lee, Geongyu Lee, Sujeong Hong, Seong Min Park, Yoonyoung Lee, Hee Sool Rho, Sumin Lee, Amos Chungwon Lee, Changhwan Lee, Hwanyoung Shim, Hyunwook Kim, Hyeji Shin, Sanha Park, Jihoon Yu, Yoon Hee Shin, Sooheon Kim, Hyunjin Park, Seung Min Park, Sangwan Kim, Yujung Kim, Jinwon Jeong, Sung-Im Do, Eun-Young Kim, Dongmyung Shin, Ingu Do, Jongbae Park
Abstract:Outcome informed AI can localize prognostically relevant regions in digital pathology, but directly characterizing the molecular basis of these spatial predictions remains challenging. Here, we developed an outcome informed, AI-guided deep visual proteomics framework that uses recurrence-risk heatmaps to guide physical tissue sampling and mass spectrometry-based spatial proteomics in triple-negative breast cancer (TNBC). In 156 patients, high- and low-risk regions coexisted within the tumor compartment and showed distinct nuclear and architectural features, indicating intratumoral variation beyond broad tissue identity. Guided by these maps, we physically isolated and profiled 46 AI-defined high- and low-risk tumor regions from two recurrent cases. High-risk regions were enriched for mitotic programs, whereas low-risk regions showed immune- and antigen-presentation-associated programs. We used proteins that consistently differed between high- and low-risk regions to construct a 13-protein composite. The composite showed a significant effect to discriminate highly recurrent patients from less recurrent patients in an expanded TNBC cohort, while the corresponding transcript-based score showed a similar trend in METABRIC. Integrating the protein composite with the H&E-derived risk score improved out-of-bag discrimination, increasing the C-index from 0.679 to 0.739 and the 5-year time-dependent AUC from 0.675 to 0.770. These findings establish outcome-informed H&E risk maps as spatial guides for molecular discovery, extending prognostic AI from prediction toward direct biological interrogation of recurrence-associated tissue states.
| Comments: | Triple-negative breast cancer (TNBC), Recurrence, Digital pathology, Artificial intelligence, Spatial proteomics, Tumor microenvironment |
| Subjects: | Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2608.03145 [cs.AI] |
| (or arXiv:2608.03145v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.03145 arXiv-issued DOI via DataCite |
Submission history
From: Geongyu Lee [view email]
[v1]
Tue, 4 Aug 2026 05:19:35 UTC (1,330 KB)
[v2]
Fri, 23 Oct 2026 06:54:09 UTC (239 KB)
来源：arXiv cs.AI · arxiv.org