Sensitivity to sequencing depth in single-cell cancer genomics

Abstract Background Querying cancer genomes at single-cell resolution is expected to provide a powerful framework to understand in detail the dynamics of cancer evolution. However, given the high costs currently associated with single-cell sequencing, together with the inevitable technical noise arising from single-cell genome amplification, cost-effective strategies that maximize the quality of single-cell data are critically needed. Taking advantage of five published single-cell whole-genome and whole-exome cancer datasets, we studied the impact of sequencing depth and sampling effort towards single-cell variant detection, including structural and driver mutations, genotyping accuracy, clonal inference and phylogenetic reconstruction, using recent tools specifically designed for single-cell data. Results Altogether, our results suggest that, for relatively large sample sizes (25 or more cells), sequencing single tumor cells at depths >5x does not drastically improve somatic variant discovery, the characterization of clonal genotypes or the estimation of phylogenies from single tumor cells. Conclusions We demonstrate that sequencing many individual tumor cells at a modest depth represents an effective alternative to explore the mutational landscape and clonal evolutionary patterns of cancer genomes, without the excessively high costs associated with high-coverage genome sequencing.