Dondi Arthur, Borgsmüller Nico, Ferreira Pedro F, Haas Brian J, Jacob Francis, Heinzelmann-Schwarz Viola, Beerenwinkel Niko
Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland.
SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland.
Genome Res. 2025 Apr 14;35(4):900-913. doi: 10.1101/gr.279281.124.
In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variant calling, LongSom reannotates marker gene-based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.
在癌症中，基因和转录组变异会产生克隆异质性，导致治疗耐药性。长读长单细胞RNA测序（LR scRNA-seq）有潜力同时检测基因和转录组变异。在此，我们展示了LongSom，这是一种计算流程，利用高质量的LR scRNA-seq数据来识别新生体细胞单核苷酸变异（SNV），包括线粒体中的变异（mtSNV）、拷贝数改变（CNA）和基因融合，以重建肿瘤克隆异质性。在进行体细胞变异识别之前，LongSom使用细胞突变谱对基于标记基因的细胞类型进行重新注释。LongSom通过应用一系列严格的筛选条件和统计测试，将体细胞SNV与噪声和种系多态性区分开来。将LongSom应用于人类卵巢癌样本，我们检测到了与临床相关的体细胞SNV，并通过匹配的DNA样本进行了验证。利用体细胞SNV和融合，LongSom发现了具有不同预测治疗结果的亚克隆。总之，LongSom无需正常样本即可进行新生变异检测，有助于癌症进化、克隆异质性和治疗耐药性的研究。
