SECEDO: SNV-based subclone detection using ultra-low coverage single-cell DNA sequencing

Abstract

Abstract Motivation Several recently developed single-cell DNA sequencing technologies enable whole-genome sequencing of thousands of cells. However, the ultra-low coverage of the sequenced data ( < 0.05x per cell) mostly limits their usage to the identification of copy number alterations in multi-megabase segments. Many tumors are not copy number-driven, and thus single-nucleotide variant (SNV)-based subclone detection may contribute to a more comprehensive view on intra-tumor heterogeneity. Due to the low coverage of the data, the identification of SNVs is only possible when superimposing the sequenced genomes of hundreds of genetically similar cells. Thus, we have developed a new approach to efficiently cluster tumor cells based on a Bayesian filtering approach of relevant loci and exploiting read overlap and phasing. Results We developed Single Cell Data Tumor Clusterer (SECEDO, lat. ‘to separate’), a new method to cluster tumor cells based solely on SNVs, inferred on ultra-low coverage single-cell DNA sequencing data. We applied SECEDO to a synthetic dataset simulating 7,250 cells and eight tumor subclones from a single patient, and were able to accurately reconstruct the clonal composition, detecting 92.11% of the somatic SNVs, with the smallest clusters representing only 6.9% of the total population. When applied to four real single-cell sequencing datasets from a breast cancer patient, each consisting of ≈2,000 cells, SECEDO was able to recover the major clonal composition in each dataset at the original coverage of 0.03x, achieving an ARI score of ≈0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈0, even after increasing the coverage in silico by a factor of 10, and was only able to match SECEDO’s performance when pooling data from all four datasets, in addition to artificially increasing the sequencing coverage by a factor of 7. Variant calling on the resulting clusters recovered more than twice as many SNVs as would have been detected if calling on all cells together. Further, the allelic ratio of the called SNVs on each subcluster was more than double relative to the allelic ratio of the SNVs called without clustering, thus demonstrating that calling variants on subclones, in addition to both increasing sensitivity of SNV detection and attaching SNVs to subclones, significantly increases the confidence of the called variants. Availability SECEDO is implemented in C++ and is publicly available at https://github.com/ratschlab/secedo .