Abstract
To study the spatial interactions among cancer and non-cancer cells1, we here examined a cohort of 131 tumour sections from 78 cases across 6 cancer types by Visium spatial transcriptomics (ST). This was combined with 48 matched single-nucleus RNA sequencing samples and 22 matched co-detection by indexing (CODEX) samples. To describe tumour structures and habitats, we defined 'tumour microregions' as spatially distinct cancer cell clusters separated by stromal components. They varied in size and density among cancer types, with the largest microregions observed in metastatic samples. We further grouped microregions with shared genetic alterations into 'spatial subclones'. Thirty five tumour sections exhibited subclonal structures. Spatial subclones with distinct copy number variations and mutations displayed differential oncogenic activities. We identified increased metabolic activity at the centre and increased antigen presentation along the leading edges of microregions. We also observed variable T cell infiltrations within microregions and macrophages predominantly residing at tumour boundaries. We reconstructed 3D tumour structures by co-registering 48 serial ST sections from 16 samples, which provided insights into the spatial organization and heterogeneity of tumours. Additionally, using an unsupervised deep-learning algorithm and integrating ST and CODEX data, we identified both immune hot and cold neighbourhoods and enhanced immune exhaustion markers surrounding the 3D subclones. These findings contribute to the understanding of spatial tumour evolution through interactions with the local microenvironment in 2D and 3D space, providing valuable insights into tumour biology.
摘要
为了研究癌细胞与非癌细胞之间的空间相互作用，我们采用Visium空间转录组学（ST）技术分析了来自6种癌症类型、78例患者的131个肿瘤切片。此外，还结合了48个匹配的单核RNA测序样本和22个匹配的CODEX样本。为描述肿瘤结构和生境，我们将被基质成分分隔的空间上独立的癌细胞聚集体定义为“肿瘤微区”。这些微区在不同癌症类型中大小和密度各异，且在转移样本中观察到最大的微区。我们进一步将具有共同基因突变的微区归类为“空间亚克隆”。共有35个肿瘤切片展示了亚克隆结构。具有不同拷贝数变异和突变的空间亚克隆表现出差异化的致癌活性。我们发现微区中心代谢活动增加，而前沿边缘区域的抗原呈递增加。此外，我们观察到在微区内部的T细胞浸润存在差异，而巨噬细胞主要分布在肿瘤边界处。通过共配准16例样本的48个连续ST切片，我们重建了3D肿瘤结构，从而揭示了肿瘤的空间组织和异质性。此外，利用无监督深度学习算法并整合ST和CODEX数据，我们识别出免疫“热”与“冷”邻域，并在3D亚克隆周围发现增强的免疫耗竭标志物。这些发现有助于理解肿瘤在2D和3D空间中通过与局部微环境的相互作用而进行的空间进化，为肿瘤生物学提供了重要见解。
肿瘤空间微区（tumour microregions）：
作者利用空间转录组学技术对6种不同类型的肿瘤进行了深入分析，探讨了肿瘤微区域的空间特征及其与肿瘤类型和转移状态的关系。结果发现，不同肿瘤类型的微区域大小和深度存在显著差异。结直肠癌微区域较大，平均包含2.9层斑点，而乳腺癌和胰腺癌分别为2.1层和2.37层。转移瘤微区域普遍较大且更深，平均深度为3.4层，而原发肿瘤为1.9层。此外，富含导管内皮瘤的乳腺癌样品呈现更多但较小的微区域，可能反映了肿瘤细胞沿分泌管生长的趋势。这种空间分布的差异还可能与不同器官的肿瘤微环境有关。这些发现不仅揭示了不同肿瘤类型在组织学和生物学特征上的差异，也表明肿瘤微区域的空间特征可能与其转移和进展状态密切相关。
