Follicular lymphoma samples in CaClust: a probabilistic model for intraclonal heterogeneity
Clones in the cancer tissue exhibit different genotypes and phenotypes, which can be linked with their evolution, future progression, and possible treatment methods. The development of single-cell RNA sequencing allowed for the measurement of single-cell phenotypes, but without a genotype-phenotype map the phenotypes of the clones cannot be obtained. We introduce CaClust, a probabilistic graphical model that integrates whole exome, ultra-deep single-cell RNA and B-cell receptor sequencing data, to infer clonal genotypes, cell-to-clone mapping, and single-cell genotyping, enabling the combined study of clonal genotypes and phenotypes. CaClust outperforms a state-of-the-art model on simulated and experimental datasets of follicular lymphoma patients. CaClust results on patient data give insights into effects of driver mutations, follicular lymphoma evolution, and possible therapeutic targets. CaClust single-cell genotyping agrees with genotypes observed in an independent targeted resequencing experiment. In short, CaClust enables the first study of genotype-to-phenotype links in follicular lymphoma of such depth and scale.