NUQA: Estimating Cancer Spatial and Temporal Heterogeneity and Evolution through Alignment-Free Methods
| Autor: | Alan Gilmore, Kevin M. Prise, Anna Jurek-Loughrey, Darragh G. McArt, David Gonzalez de Castro, Jose Souza, Manuel Salto-Tellez, Aideen C. Roddy, Paul G. O’Reilly, Alexey Stupnikov |
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| Rok vydání: | 2019 |
| Předmět: |
Sample (statistics)
Computational biology Biology Genetic Heterogeneity 03 medical and health sciences 0302 clinical medicine Phylogenetics Neoplasms Methods Genetics medicine Humans Hellinger distance Divergence (statistics) Molecular Biology Ecology Evolution Behavior and Systematics 030304 developmental biology Sequence (medicine) 0303 health sciences Genetic heterogeneity Cancer medicine.disease Biological Evolution Genetic Techniques Metric (mathematics) Software 030217 neurology & neurosurgery |
| Zdroj: | Molecular Biology and Evolution Roddy, A, Jurek-Loughrey, A, Souza, J, Gilmore, A, O'Reilly, P, Stupnikov, A, Gonzalez de Castro, D, Prise, K, Salto-Tellez, M & McArt, D 2019, ' NUQA: Estimating cancer spatial and temporal heterogeneity and evolution through alignment-free methods ', Molecular Biology and Evolution . https://doi.org/10.1093/molbev/msz182 |
| ISSN: | 1537-1719 0737-4038 |
| Popis: | Longitudinal next-generation sequencing of cancer patient samples has enhanced our understanding of the evolution and progression of various cancers. As a result, and due to our increasing knowledge of heterogeneity, such sampling is becoming increasingly common in research and clinical trial sample collections. Traditionally, the evolutionary analysis of these cohorts involves the use of an aligner followed by subsequent stringent downstream analyses. However, this can lead to large levels of information loss due to the vast mutational landscape that characterizes tumor samples. Here, we propose an alignment-free approach for sequence comparison—a well-established approach in a range of biological applications including typical phylogenetic classification. Such methods could be used to compare information collated in raw sequence files to allow an unsupervised assessment of the evolutionary trajectory of patient genomic profiles. In order to highlight this utility in cancer research we have applied our alignment-free approach using a previously established metric, Jensen–Shannon divergence, and a metric novel to this area, Hellinger distance, to two longitudinal cancer patient cohorts in glioma and clear cell renal cell carcinoma using our software, NUQA. We hypothesize that this approach has the potential to reveal novel information about the heterogeneity and evolutionary trajectory of spatiotemporal tumor samples, potentially revealing early events in tumorigenesis and the origins of metastases and recurrences. Key words: alignment-free, Hellinger distance, exome-seq, evolution, phylogenetics, longitudinal. |
| Databáze: | OpenAIRE |
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