Identifying heterogeneity patterns of allelic imbalance on germline variants to infer clonal architecture

It is suggested that the evolution of somatic mutations may be significant impacted by inherited polymorphisms, while the clonal somatic copy-number mutations may contribute to the potential selective advantages of heterozygous germline variants. A fine resolution on clonal architecture of such cooperative germline-somatic dynamics provides insight into tumour heterogeneity and offers clinical implications. Although it is reported that germline allelic imbalance patterns often play important roles, existing approaches for clonal analysis mainly focus on single nucleotide sites. To address this need, we propose a computational method, GLClone that identifies and estimates the clonal patterns of the copy-number alterations on germline variants. The core of GLClone is a hierarchical probabilistic model. The variant allelic frequencies on germline variants are modeled as observed variables, while the cellular prevalence is designed as hidden states and estimated by Bayesian posteriors. A variational approximation algorithm is proposed to train the model and estimate the unknown variables and model parameters. We examine GLClone on several groups of simulation datasets, which are generated by different configurations, and compare to three popular state-of-the-art approaches, and GLClone outperforms on accuracy, especially a complex clonal structure exists.