title: SubClonal Hierarchy Inference from Somatic Mutations: Automatic Reconstruction of Cancer Evolutionary Trees from Multi-region Next Generation Sequencing.
abstract: Recent improvements in next-generation sequencing of tumor samples and the ability to identify somatic mutations at low allelic fractions have opened the way for new approaches to model the evolution of individual cancers. The power and utility of these models is increased when tumor samples from multiple sites are sequenced. Temporal ordering of the samples may provide insight into the etiology of both primary and metastatic lesions and rationalizations for tumor recurrence and therapeutic failures. Additional insights may be provided by temporal ordering of evolving subclones--cellular subpopulations with unique mutational profiles. Current methods for subclone hierarchy inference tightly couple the problem of temporal ordering with that of estimating the fraction of cancer cells harboring each mutation. We present a new framework that includes a rigorous statistical hypothesis test and a collection of tools that make it possible to decouple these problems, which we believe will enable substantial progress in the field of subclone hierarchy inference. The methods presented here can be flexibly combined with methods developed by others addressing either of these problems. We provide tools to interpret hypothesis test results, which inform phylogenetic tree construction, and we introduce the first genetic algorithm designed for this purpose. The utility of our framework is systematically demonstrated in simulations. For most tested combinations of tumor purity, sequencing coverage, and tree complexity, good power (≥ 0.8) can be achieved and Type 1 error is well controlled when at least three tumor samples are available from a patient. Using data from three published multi-region tumor sequencing studies of (murine) small cell lung cancer, acute myeloid leukemia, and chronic lymphocytic leukemia, in which the authors reconstructed subclonal phylogenetic trees by manual expert curation, we show how different configurations of our tools can identify either a single tree in agreement with the authors, or a small set of trees, which include the authors' preferred tree. Our results have implications for improved modeling of tumor evolution and the importance of multi-region tumor sequencing.
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Noushin
authors.initials: N
authors.lastname: Niknafs
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Violeta
authors.initials: V
authors.lastname: Beleva-Guthrie
authors.affiliation: Department of Applied Math and Statistics, Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Daniel Q
authors.initials: DQ
authors.lastname: Naiman
authors.affiliation: Department of Biomedical Engineering and Institute for Computational Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America; Department of Oncology, Johns Hopkins Medical Institutions, Baltimore, Maryland, United States of America.
authors.forename: Rachel
authors.initials: R
authors.lastname: Karchin
mesh_terms: D000465:Algorithms; D000818:Animals; D001483:Base Sequence; D060965:Clonal Evolution / Q000235:genetics*; D004252:DNA Mutational Analysis / Q000379:methods*; D004273:DNA, Neoplasm / Q000235:genetics*; D019143:Evolution, Molecular; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D051379:Mice; D008969:Molecular Sequence Data; D009154:Mutation / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics*; D010363:Pattern Recognition, Automated / Q000379:methods; D059010:Single-Cell Analysis / Q000379:methods
chemical_list: D004273:DNA, Neoplasm