Phylogenetic analysis of multiprobe fluorescence in situ hybridization data from tumor cell populations

Abstract

Motivation: Development and progression of solid tumors can be attributed to a process of mutations, which typically includes changes in the number of copies of genes or genomic regions. Although comparisons of cells within single tumors show extensive heterogeneity, recurring features of their evolutionary process may be discerned by comparing multiple regions or cells of a tumor. A useful source of data for studying likely progression of individual tumors is fluorescence in situ hybridization (FISH), which allows one to count copy numbers of several genes in hundreds of single cells. Novel algorithms for interpreting such data phylogenetically are needed, however, to reconstruct likely evolutionary trajectories from states of single cells and facilitate analysis of tumor evolution.Results: In this article, we develop phylogenetic methods to infer likely models of tumor progression using FISH copy number data and apply them to a study of FISH data from two cancer types. Statistical analyses of topological characteristics of the tree-based model provide insights into likely tumor progression pathways consistent with the prior literature. Furthermore, tree statistics from the resulting phylogenies can be used as features for prediction methods. This results in improved accuracy, relative to unstructured gene copy number data, at predicting tumor state and future metastasis.Availability: Source code for software that does FISH tree building (FISHtrees) and the data on cervical and breast cancer examined here are available at ftp://ftp.ncbi.nlm.nih.gov/pub/FISHtrees.Contact: sachowdh@andrew.cmu.eduSupplementary information: Supplementary data are available at Bioinformatics online.

The evolution of tumour phylogenetics: principles and practice

“…Only recently have new phylogeny algorithms emerged to deal with the peculiarities of tumour versus species evolution 84–88 . In the next section, we survey the diversity of methods available, with particular focus on those suited to modern sequencing technologies.…”

“…1c). Single-cell tumour phylogenetics predates single-cell sequencing (scSeq), as it was applied through various older methods offering more limited profiling of single cells via microsatellite 76 or FISH 69 markers; such approaches remain valuable owing to their ability to examine much larger numbers of cells than scSeq 39,84,149 (TABLE 3). Nevertheless, the introduction of scSeq to tumour phylogenetics by Navin et al 71 deserves much of the credit for bringing tumour phylogenetics into the mainstream of cancer research.…”

“…The majority of published tools for single-cell phylogenetics are still based on pre-scSeq technologies 84,148,151–153 , with just a handful having been developed specifically for scSeq. Kim and Simon 89 introduced the muttree program, which uses a custom combinatorial inference to find trees optimized for a specialized probabilistic model that differs from the models used by other tools which accept the same input.…”

“…There are models for representing the more complex nature of evolution by CNVs versus evolution by SNVs 51 , and there are some custom-designed phylogeny tools for specific variants of CNV evolution 43,69,84,85,151,152 . A Bayesian probabilistic model, as has been used in many tumour phylogeny approaches 47,79,86,135 , can handle arbitrarily complex evolutionary scenarios and is well suited to learning the complicated lineage-specific rate parameters one would need for such a model 78,101,152 , given the diversity of CNV hypermutability mechanisms any given tumour might exhibit 20,25,94,161 .…”

Reference-free inference of tumor phylogenies from single-cell sequencing data

“…The trees allow us to learn markers of progression driving key steps in tumor evolution, identify and classify tumor subtypes with possibly different underlying mechanisms of action, and enable predictive modeling of future stages of progression. Much prior work has shown the utility of tumor phylogenies using data from singlecell Fluorescent In-Situ Hybridization (FISH) [12][13][14][15] and microarray technologies [16,17]. Advances in deep sequencing technology, particularly single-cell sequencing [18,19], have extended this interest by promising a granular view of heterogeneity and progression within a single tumor.…”