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

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 five 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 Adjusted Rand Index (ARI) score of ≈ 0.6. The current state-of-the-art SNV-based clustering method achieved an ARI score of ≈ 0, even after merging cells to create higher coverage data (factor 10 increase), and was only able to match SECEDO’s performance when pooling data from all five 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. Instructions to download the data and the evaluation code to reproduce the findings in this paper are available at: https://github.com/ratschlab/secedo-evaluation. The code and data of the submitted version is archived at: https://doi.org/10.5281/zenodo.6516955.

High-resolution detection of copy number alterations in single cells with HiScanner

“…We also sequenced 179 cells at low coverage (~0.5X on average) from biopsies of an initial (n=88) and recurrent (n=91) meningioma tumor pair. HiScanner revealed phases and timing of tumor progression by detecting subclonedefining CNAs, which were subsequently corroborated by an orthogonal clustering based on somatic point mutations 24 . Lastly, we built a web-based tool, ViScanner, to facilitate visualization of HiScanner output.…”

“…Next, we investigated whether cluster assignments derived from CNAs were consistent with patterns of somatic SNVs (sSNVs). To this end, we employed a recently developed somatic SNV-based cell clustering method, SECEDO 24 . Initially, applying SECEDO with its default settings failed to differentiate between aneuploid and diploid cells, which are a hallmark of meningiomas.…”

DICE: Fast and Accurate Distance-Based Reconstruction of Single-Cell Copy Number Phylogenies

“…Finally, some recent approaches have combined CNAs and single-nucleotide variants (SNVs) under a single model (Satas et al 2020, Chen et al 2022, Sollier et al 2023, Zhang et al 2023). SNVs, while more challenging to infer accurately from low-coverage single-cell sequencing data than CNAs (Rozhonova et al 2022), could provide additional information and lead to more accurate tumor cell lineage trees.…”

Phertilizer: Growing a clonal tree from ultra-low coverage single-cell DNA sequencing of tumors

“…Moreover, it is non-trivial to convert the inferred parameters of SBMClone ’s stochastic block model to clonal genotypes, which may impact downstream analyses. Similarly, given a set of candidate SNV loci, SECEDO [ 29 ] first calls SNVs using a Bayesian filtering approach and then subsequently clusters cells using the called SNVs. While both of these clustering methods capitalize on the ever-increasing throughput of ultra-low coverage scDNA-seq methods, neither method constrains the output by a tree and CNA features are used only in an ad hoc manner or for orthogonal validation.…”