Inferring cancer progression from Single-Cell Sequencing while allowing mutation losses

Abstract

Motivation In recent years, the well-known Infinite Sites Assumption (ISA) has been a fundamental feature of computational methods devised for reconstructing tumor phylogenies and inferring cancer progressions. However, recent studies leveraging Single-Cell Sequencing (SCS) techniques have shown evidence of the widespread recurrence and, especially, loss of mutations in several tumor samples. While there exist established computational methods that infer phylogenies with mutation losses, there remain some advancements to be made. Results We present SASC (Simulated Annealing Single-Cell inference): a new and robust approach based on simulated annealing for the inference of cancer progression from SCS data sets. In particular, we introduce an extension of the model of evolution where mutations are only accumulated, by allowing also a limited amount of mutation loss in the evolutionary history of the tumor: the Dollo-k model. We demonstrate that SASC achieves high levels of accuracy when tested on both simulated and real data sets and in comparison with some other available methods. Availability The Simulated Annealing Single-Cell inference (SASC) tool is open source and available at https://github.com/sciccolella/sasc. Supplementary information Supplementary data are available at Bioinformatics online.

The Bourque Distances for Mutation Trees of Cancers

“…Formally, a mutation tree on a finite set of mutations Γ is a rooted tree T with k nodes and a partition of Γ into k disjoint non-empty parts P i so that each P i is assigned as the label of a node of T [2,20]. A large number of computational approaches for reconstructing mutation trees from bulk sequencing data [21,22,23,24,25], singlecell sequencing data [26,27,28,29], or a combination of both [30,31] have been developed over the last years. Unlike phylogenetic trees, mutation trees inferred with these methods will not only differ in their topology but may also be defined on different sets of mutations.…”

Triplet-based similarity score for fully multilabeled trees with poly-occurring labels

“…Given the importance of the task, a multitude of methods for cancer phylogeny reconstruction have been developed over the years. The increasing number of tools created has been encouraged by the diversity of data available; for instance, we are witnessing a shift from bulk sequencing data ( Bonizzoni et al , 2017 , Bonizzoni et al, 2019, ; Hajirasouliha et al , 2014 ; Hajirasouliha and Raphael, 2014 ; Yuan et al , 2015 ) toward single-cell data ( Ciccolella et al , 2018a , b ; El-Kebir, 2018 ; Jahn et al , 2016 ; Zafar et al , 2017 ) and hybrid approaches ( Malikic et al , 2019a , b ).…”

“…they also have labels (corresponding to the mutations) on the internal nodes. While there is a wide range of measures to compare leaf-labeled trees in the literature, ad hoc methods for tumor phylogenies are starting to appear in the past few years ( DiNardo et al , 2020 ; Bernardini et al , 2019 , Bernardini et al 2020 ; Govek et al 2018; Karpov et al , 2019 ); in particular, a detailed study of some notions of distance ( DiNardo et al , 2020 ) has introduced two new measures complementing some more established definitions used in various cancer inference studies ( Ciccolella et al , 2018a , b ). Those new measures are more nuanced, to capture some aspects of the mutation inheritance process, while still being very efficient to compute.…”

“…each label can be assigned to more than one node). The latter feature is needed since recent studies ( Brown et al , 2017 ; Kuipers et al , 2017 ) suggest widespread recurrence and loss of mutations, and more and more methods designed to infer tumor phylogenies considering such a possibility are starting to appear ( Ciccolella et al , 2018a , b ; El-Kebir, 2018 ). In a phylogenetic tree, a mutation loss is represented by a special character in the label, such as a minus sign: the design of our measure allows to handle such evolutionary events effectively, as they uniquely correspond to their label like any other kind of mutation.…”

gpps: an ILP-based approach for inferring cancer progression with mutation losses from single cell data

“…More precisely, the Dollo model requires each mutation to be acquired exactly once in the entire history analyzed, while removing all restrictions on the number of times that a mutation can be lost. The Dollo model as well as the Dollo(k) variants, where each mutation can be lost at most k times, has been introduced recently in the literature on algorithmic approaches for tumor progression inference [12,26]. Since finding a perfect phylogeny on a complete binary matrix can be solved in linear time [21], several tools have incorporated this model to reduce the running time [27] -but single cell data present a large portion of missing data, which makes the problem much harder.…”