OncoNEM: inferring tumor evolution from single-cell sequencing data

Abstract

Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM’s robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-0929-9) contains supplementary material, which is available to authorized users.

Single-cell sequencing data reveal widespread recurrence and loss of mutational hits in the life histories of tumors

“…As we have shown in this work, modeling doublets is straightforward for a mutation-centric approach like SCITE (Jahn et al 2016). For sample-centric approaches such as BitPhylogeny (Yuan et al 2015) and OncoNEM (Ross and Markowetz 2016), the integration of doublets may be a bit more involved, as the topology underlying the evolutionary history is no longer tree-like in the presence of admixed samples.…”

“…With the emergence of next-generation sequencing techniques, it is possible to systematically analyze individual tumors at a genetic level from admixed cell samples and, more recently, from sequencing the DNA of individual tumor cells (Navin 2014;Van Loo and Voet 2014). These technical advances, together with a prospect of high-precision cancer therapies, have spurred the development of a variety of computational approaches to reconstruct not only the clonal structure but also the entire mutation history of individual tumors (Strino et al 2013;Hajirasouliha et al 2014;Jiao et al 2014;Kim and Simon 2014;Qiao et al 2014;Deshwar et al 2015;El-Kebir et al 2015;Malikic et al 2015;Niknafs et al 2015;Popic et al 2015;Yuan et al 2015;Jahn et al 2016;Jiang et al 2016;Ross and Markowetz 2016;Donmez et al 2017). A common feature of all these approaches is the infinite sites assumption (ISA) (Kimura 1969) to exclude the possibility of the same genomic site being hit by multiple mutations throughout the lifetime of a tumor.…”

Single-cell DNA sequencing reveals a late-dissemination model in metastatic colorectal cancer

“…Subsequent work from our group and others has led to the development of high-coverage single-cell sequencing methods to detect genome-wide mutations at base-pair resolution (Xu et al 2012;Zong et al 2012;Wang et al 2014;Leung et al 2015Leung et al , 2016Wang and Navin 2015;Gawad et al 2016). Computational methods can be used to infer phylogenetic trees from single-cell sequencing data (Davis and Navin 2016;Jahn et al 2016;Ross and Markowetz 2016). However, a major challenge is that current single-cell DNA sequencing methods are lowthroughput and expensive.…”

Inferring Cancer Progression from Single-cell Sequencing while Allowing Mutation Losses

“…We decided to compare SASC against SCITE [13]: while B-SCITE [19] is a clear improvement over SCITE, it combines single cell data with bulk sequencing data -since we do not manage the latter kind of data, a fair comparison is not feasible. OncoNEM [27] and SiFit [34] were excluded because they infer cell lineage progressions instead of mutational progression, therefore it is not possible to compare our predictions with theirs. Figures 5 and 7 show the comparison of accuracy between SASC and SCITE; in average both the methods scores relatively close to each other obtaining good results in both the experiments.…”

“…Various methods have been developed for this purpose [13,27,34], some of them introducing a hybrid approach of combining both SCS and VAF data [25,19,29]. Most of these methods, however, rely on the Infinite Sites Assumption (ISA), which states that a mutation is acquired at most once in the tree and is never lost.…”