Advanced solid cancers are complex assemblies of tumor, immune, and stromal cells characterized by high intratumoral variation. We use highly multiplexed tissue imaging, 3D reconstruction, spatial statistics, and machine learning to identify cell types and states underlying morphological features of known diagnostic and prognostic significance in colorectal cancer. Quantitation of these features in high-plex marker space reveals recurrent transitions from one tumor morphology to the next, some of which are coincident with long-range gradients in the expression of oncogenes and epigenetic regulators. At the tumor invasive margin, where tumor, normal, and immune cells compete, T-cell suppression involves multiple cell types and 3D imaging shows that seemingly localized 2D features such as tertiary lymphoid structures are commonly interconnected and have graded molecular properties. Thus, while cancer genetics emphasizes the importance of discrete changes in tumor state, whole-specimen imaging reveals large-scale morphological and molecular gradients analogous to those in developing tissues.

中文翻译：

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结直肠癌状态转换和免疫相互作用的多路复用 3D 图谱

晚期实体癌是肿瘤、免疫和基质细胞的复杂组合，其特征在于肿瘤内高度变异。我们使用高度多路复用的组织成像、3D 重建、空间统计和机器学习来识别在结直肠癌中具有已知诊断和预后意义的形态特征的细胞类型和状态。在高复杂标记空间中对这些特征的定量揭示了从一种肿瘤形态到另一种肿瘤形态的反复转变，其中一些与癌基因和表观遗传调节因子表达的长程梯度一致。在肿瘤浸润边缘，肿瘤细胞、正常细胞和免疫细胞在此竞争，T 细胞抑制涉及多种细胞类型，3D 成像显示看似局部的 2D 特征（如三级淋巴结构）通常相互关联并具有分级的分子特性。因此，虽然癌症遗传学强调了肿瘤状态离散变化的重要性，但全样本成像揭示了类似于发育组织中的大规模形态和分子梯度。