title: Reference-free inference of tumor phylogenies from single-cell sequencing data.
abstract: BACKGROUND
  Effective management and treatment of cancer continues to be complicated by the rapid evolution and resulting heterogeneity of tumors. Phylogenetic study of cell populations in single tumors provides a way to delineate intra-tumoral heterogeneity and identify robust features of evolutionary processes. The introduction of single-cell sequencing has shown great promise for advancing single-tumor phylogenetics; however, the volume and high noise in these data present challenges for inference, especially with regard to chromosome abnormalities that typically dominate tumor evolution. Here, we investigate a strategy to use such data to track differences in tumor cell genomic content during progression.
  
  
  RESULTS
  We propose a reference-free approach to mining single-cell genome sequence reads to allow predictive classification of tumors into heterogeneous cell types and reconstruct models of their evolution. The approach extracts k-mer counts from single-cell tumor genomic DNA sequences, and uses differences in normalized k-mer frequencies as a proxy for overall evolutionary distance between distinct cells. The approach computationally simplifies deriving phylogenetic markers, which normally relies on first aligning sequence reads to a reference genome and then processing the data to extract meaningful progression markers for constructing phylogenetic trees. The approach also provides a way to bypass some of the challenges that massive genome rearrangement typical of tumor genomes presents for reference-based methods. We illustrate the method on a publicly available breast tumor single-cell sequencing dataset.
  
  
  CONCLUSIONS
  We have demonstrated a computational approach for learning tumor progression from single cell sequencing data using k-mer counts. k-mer features classify tumor cells by stage of progression with high accuracy. Phylogenies built from these k-mer spectrum distance matrices yield splits that are statistically significant when tested for their ability to partition cells at different stages of cancer.
authors.forename: Ayshwarya
authors.initials: A
authors.lastname: Subramanian
authors.forename: Russell
authors.initials: R
authors.lastname: Schwartz
mesh_terms: D001483:Base Sequence; D002467:Cell Nucleus / Q000378:metabolism; D057225:Data Mining; D018450:Disease Progression; D019143:Evolution, Molecular; D023281:Genomics / Q000379:methods*; D006801:Humans; D000069550:Machine Learning; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology*; D010802:Phylogeny; D017421:Sequence Analysis; D059010:Single-Cell Analysis