NUQA: Estimating cancer spatial and temporal heterogeneity and evolution through alignment-free methods

Aideen Roddy, Anna Jurek-Loughrey, J Souza, Alan Gilmore, Paul O'Reilly, Alexey Stupnikov, David Gonzalez de Castro, Kevin Prise, Manuel Salto-Tellez, Darragh McArt

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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 characterises tumour 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 hypothesise that this approach has the potential to reveal novel information about the heterogeneity and evolutionary trajectory of spatiotemporal tumour samples, potentially revealing early events in tumorigenesis and the origins of metastases and recurrences.
Original languageEnglish
JournalMolecular Biology and Evolution
Early online date19 Aug 2019
Publication statusEarly online date - 19 Aug 2019


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