美国普林斯顿大学Benjamin J. Raphael等研究人员合作开发出新方法，可从空间分辨转录组学推断，等位基因特异性拷贝数异常和肿瘤系统发育地理。相关论文于2024年10月30日在线发表在《自然—方法学》杂志上。

研究人员推出了CalicoST，这是一种算法，可以基于空间分辨转录组学（SRT）数据同时推断等位基因特异性拷贝数异常（CNA）并重建空间肿瘤演化或系统发育地理。CalicoST识别出一些重要的CNA类别，包括拷贝中性杂合性丧失和镜像亚克隆CNA，这些在总拷贝数分析中是不可见的。

使用来自人类肿瘤图谱网络的九名患者数据，CalicoST的平均准确率达到86%，比现有方法高出约21%。CalicoST在三个维度空间中重建了两名患者的肿瘤系统发育地理，且均为多个相邻切片。对癌性前列腺器官多个SRT切片的CalicoST分析揭示了前列腺两侧的镜像亚克隆CNA，在遗传和物理空间中形成了分叉的系统发育地理。

研究人员表示，分析肿瘤内的体细胞进化随时间和空间的变化是癌症研究中的一个关键挑战。SRT在肿瘤的数千个空间位置上测量基因表达，但并不能直接揭示基因组异常。

附：英文原文

Title: Inferring allele-specific copy number aberrations and tumor phylogeography from spatially resolved transcriptomics

Author: Ma, Cong, Balaban, Metin, Liu, Jingxian, Chen, Siqi, Wilson, Michael J., Sun, Christopher H., Ding, Li, Raphael, Benjamin J.

Issue&Volume: 2024-10-30

Abstract: Analyzing somatic evolution within a tumor over time and across space is a key challenge in cancer research. Spatially resolved transcriptomics (SRT) measures gene expression at thousands of spatial locations in a tumor, but does not directly reveal genomic aberrations. We introduce CalicoST, an algorithm to simultaneously infer allele-specific copy number aberrations (CNAs) and reconstruct spatial tumor evolution, or phylogeography, from SRT data. CalicoST identifies important classes of CNAs—including copy-neutral loss of heterozygosity and mirrored subclonal CNAs—that are invisible to total copy number analysis. Using nine patients’ data from the Human Tumor Atlas Network, CalicoST achieves an average accuracy of 86%, approximately 21% higher than existing methods. CalicoST reconstructs a tumor phylogeography in three-dimensional space for two patients with multiple adjacent slices. CalicoST analysis of multiple SRT slices from a cancerous prostate organ reveals mirrored subclonal CNAs on the two sides of the prostate, forming a bifurcating phylogeography in both genetic and physical space.

DOI: 10.1038/s41592-024-02438-9

Source: https://www.nature.com/articles/s41592-024-02438-9