CaClust: linking genotype to transcriptional heterogeneity of follicular lymphoma using BCR and exomic variants (K5B)

Tumours exhibit high genotypic and transcriptional heterogeneity. Both affect cancer progression and treatment, but have been predominantly studied separately in follicular lymphoma. To comprehensively investigate the evolution and genotype-to-phenotype maps in follicular lymphoma, we introduce CaClust, a probabilistic graphical model integrating deep whole exome, single-cell RNA and B-cell receptor sequencing data to infer clone genotypes, cell-to-clone mapping, and single-cell genotyping. CaClust outperforms a state-of-the-art model on simulated and patient data. In-depth analyses of single cells from four samples showcase effects of driver mutations, follicular lymphoma evolution, possible therapeutic targets, and single-cell genotyping that agrees with an independent targeted resequencing experiment. Dataset K5B: 10X Genomics of flowcytometry purified follicular lymphoma cells, 5' chemistry, full transcriptome and BCR enrichment.

肿瘤展现出极高的基因型与转录组异质性。二者均可影响癌症的进展与治疗效果，但在滤泡性淋巴瘤（follicular lymphoma）中，过往研究大多将两者分开单独探究。为全面解析滤泡性淋巴瘤的演化过程及基因型-表型映射关系，我们提出了CaClust——一款整合深度全外显子组测序、单细胞RNA测序与B细胞受体（B-cell receptor, BCR）测序数据的概率图模型，可用于推断克隆基因型、细胞-克隆映射关系以及单细胞基因分型。CaClust在模拟数据集与患者来源数据上的性能均优于当前主流模型。对4份样本的单细胞数据开展的深入分析，揭示了驱动突变、滤泡性淋巴瘤演化的相关特征，同时筛选出潜在治疗靶点，且得到的单细胞基因分型结果与独立靶向重测序实验结果一致。数据集K5B：经流式细胞术（flow cytometry）纯化的滤泡性淋巴瘤细胞的10X Genomics测序数据，采用5'端建库化学技术，涵盖完整转录组与B细胞受体富集测序信息。