Conifer: clonal tree inference for tumor heterogeneity with single-cell and bulk sequencing data

Abstract

Background Genetic heterogeneity of a cancer tumor that develops during clonal evolution is one of the reasons for cancer treatment failure, by increasing the chance of drug resistance. Clones are cell populations with different genotypes, resulting from differences in somatic mutations that occur and accumulate during cancer development. An appropriate approach for identifying clones is determining the variant allele frequency of mutations that occurred in the tumor. Although bulk sequencing data can be used to provide that information, the frequencies are not informative enough for identifying different clones with the same prevalence and their evolutionary relationships. On the other hand, single-cell sequencing data provides valuable information about branching events in the evolution of a cancerous tumor. However, the temporal order of mutations may be determined with ambiguities using only single-cell data, while variant allele frequencies from bulk sequencing data can provide beneficial information for inferring the temporal order of mutations with fewer ambiguities. Result In this study, a new method called Conifer (ClONal tree Inference For hEterogeneity of tumoR) is proposed which combines aggregated variant allele frequency from bulk sequencing data with branching event information from single-cell sequencing data to more accurately identify clones and their evolutionary relationships. It is proven that the accuracy of clone identification and clonal tree inference is increased by using Conifer compared to other existing methods on various sets of simulated data. In addition, it is discussed that the evolutionary tree provided by Conifer on real cancer data sets is highly consistent with information in both bulk and single-cell data. Conclusions In this study, we have provided an accurate and robust method to identify clones of tumor heterogeneity and their evolutionary history by combining single-cell and bulk sequencing data.

Assessing the merits: an opinion on the effectiveness of simulation techniques in tumor subclonal reconstruction

“…The genetics of clonal evolution has been the subject of a large body of research, which has revolutionized our understanding of cancer. A variety of computational algorithms have been developed to reconstruct and model the hierarchical clonal architectures of tumors ( Oesper et al 2013 , Strino et al 2013 , Fischer et al 2014 , Hajirasouliha et al 2014 , Jiao et al 2014 , Roth et al 2014 , Deshwar et al 2015 , El-Kebir et al 2015 , Garvin et al 2015 , Malikic et al 2015 , Niknafs et al 2015 , Popic et al 2015 , El-Kebir et al 2016 , Jahn et al 2016 , Jiang et al 2016 , Ross and Markowetz, 2016 , Roth et al 2016 , Salehi et al 2017 , Zaccaria et al 2017 , Zafar et al 2017 , 2019, Deveau et al 2018 , El-Kebir 2018 , Singer et al 2018 , Malikic et al 2019a , 2019b , Borgsmüller et al 2020 , Cmero et al 2020 , Myers et al 2020 , Ricketts et al 2020 , Xiao et al 2020 , Zaccaria and Raphael 2020 , Andersson et al 2021 , Baghaarabani et al 2021 , Satas et al 2021 , Sundermann et al 2021 , Vavoulis et al 2021 , Fu et al 2022 , Kang et al 2022 , Kozlov et al 2022 , Markowska et al 2022 , Zheng et al 2022 ). Many algorithms are based on the analysis of next-generation (NGS) tumor DNA sequence reads generated from bulk DNA or single cells (scDNAseq).…”