CONIPHER: a computational framework for scalable phylogenetic reconstruction with error correction

Abstract

Abstract Intra-tumour heterogeneity provides the fuel for the evolution and selection of subclonal tumour cell populations. However, accurate inference of tumour subclonal architecture and reconstruction of tumour evolutionary history from bulk DNA sequencing data remains challenging. Sequencing and alignment artefacts cannot be distinguished from real cancer somatic mutations and errors in the identification of copy number alterations or complex evolutionary events (e.g. mutation losses) affect the estimated cellular prevalence of mutations, leading to errors in mutation clustering and phylogenetic reconstructions. In this paper we present a new computational framework, CONIPHER (COrrecting Noise In PHylogenetic Evaluation and Reconstruction), that accurately infers subclonal structure and phylogenetic relationships from multi-sample tumour sequencing, accounting for both copy number alterations and mutation errors. CONIPHER outperforms similar methods on simulated datasets, and in particular scales to a large number of tumour samples and clones. As such, CONIPHER enables automated phylogenetic analysis which can be effectively applied to large sequencing datasets generated with different technologies.