Abstract
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.
Highlights
We applied each of these metrics to 6 patients, 3 clear cell renal cell carcinoma patients and 3 glioma patients, using a 21-mer length in order to assess their applicability to cancer patient cohorts (Figure 1A-D and S2)
Branch Score distance (BSD) suggests that Hellinger distance (HD) produces similar results to JensenShannon divergence (JSD) with distances
We conclude that JSD and HD both produce consistent results in this context suggesting that HD may perform well in other alignment-free applications
Summary
Investigating evolution and heterogeneity of a neoplasm can give insight to the nature and origins of therapeutic resistance as well as assist in predicting response to treatment (Greaves and Maley 2012; Turajlic et al 2018) As a result, and due to the decreasing costs of next-generation sequencing (NGS), there has been a recent increase in longitudinal profiling of patient samples throughout their care leading to a number of high-quality studies (Gerlinger et al 2014; Johnson et al 2014; Mazor et al 2015; Turajlic et al 2018). Alignment-free sequence comparison, defined as any approach calculating similarity/dissimilarity between sequences which does not use or produce alignment, can be used as an alternative approach to address these issues and create holistic patient profiles for assessing evolutionary trajectories and spatiotemporal heterogeneity It is more sensitive in the context of sequence divergences and robust against genome rearrangement compared to alignment approaches(Vinga 2014; Bernard et al 2017). Design: Here, we propose an alignment-free approach for sequence comparison - a wellestablished 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. Conclusion: 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
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