发表机构
Faculty of Technology, Bielefeld University（比勒费尔德大学技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CancerZigZag提出种子锚定扩散框架，仅用肿瘤细胞训练模型，从正常细胞种子生成候选云，无需配对数据，在多种癌症中验证了其探索肿瘤相关状态的潜力。
AI 中文摘要
单细胞癌症数据集主要是横断面数据，很少提供将类似健康的细胞与肿瘤相关状态联系起来的配对或纵向观察。我们引入了CancerZigZag，这是一种基于种子初始化的扩散框架，用于从未配对的正常上皮细胞群体中探索性生成肿瘤相关的单细胞候选云。对于每种癌症背景，扩散模型仅使用肿瘤来源的上皮细胞进行训练，并通过重复的部分潜空间扰动和反向扩散应用于留出的类似健康的种子，从而生成随机候选云，无需配对测量或分类器引导。我们将CancerZigZag应用于结直肠癌、乳腺癌、肺癌和肾细胞癌背景，并探索了由扰动深度和ZigZag循环次数定义的参数空间。在所报告的操作配置中，候选云包含针对每个评估种子被分类为朝向留出的肿瘤来源参考群体的输出。残余的种子依赖性组织在不同背景下有所差异，其中结直肠癌的结构最清晰，肺癌的组织较为适中，而乳腺癌和肾细胞癌的云级结构有限。代表性候选还显示出与留出的类似健康和肿瘤来源参考群体之间观察到的转录变化的方向一致性。CancerZigZag不被解释为确定性健康到肿瘤转变或细胞进展的模型。相反，它提供了一个参考信息丰富的框架，用于从未配对的类似健康种子中探索肿瘤相关的候选分布，并量化肿瘤相关位移与残余种子依赖性之间的背景依赖性关系。
英文摘要
Single-cell cancer datasets are predominantly cross-sectional and rarely provide paired or longitudinal observations linking individual healthy-like cells to tumor-associated states. We introduce CancerZigZag, a seed-initialized diffusion-based framework for exploratory generation of tumor-associated single-cell candidate clouds from unpaired epithelial cell populations. For each cancer context, a diffusion model is trained exclusively on tumor-derived epithelial cells and applied through repeated partial latent-space perturbation and reverse diffusion to held-out healthy-like seeds, generating stochastic candidate clouds without paired measurements or classifier guidance. We applied CancerZigZag to colorectal, breast, lung, and renal cell carcinoma contexts and explored parameter landscapes defined by perturbation depth and the number of ZigZag cycles. Across the reported operating configurations, candidate clouds contained outputs classified toward held-out tumor-derived reference populations for each evaluated seed. Residual seed-dependent organization varied across contexts, with the clearest structure in colorectal cancer, more modest organization in lung cancer, and limited cloud-level structure in breast and renal cell carcinoma. Representative candidates also showed directional concordance with transcriptional shifts observed between held-out healthy-like and tumor-derived reference populations. CancerZigZag is not interpreted as a model of deterministic healthy-to-tumor transformation or cellular progression. Instead, it provides a reference-informed framework for exploring tumor-associated candidate distributions from unpaired healthy-like seeds and quantifying the context-dependent relationship between tumor-associated displacement and residual seed dependence.
Comments26 pages, 4 figures, 4 tables. Accepted for oral presentation at ISCB-AI 2026
