De novo detection of somatic variants in high-quality long-read single-cell RNA sequencing data
- Arthur Dondi1,2,
- Nico Borgsmüller1,2,
- Pedro F. Ferreira1,2,
- Brian J. Haas3,
- Francis Jacob4,
- Viola Heinzelmann-Schwarz4,
- Tumor Profiler Consortium and
- Niko Beerenwinkel1,2
- 1Department of Biosystems Science and Engineering, ETH Zurich, 4056 Basel, Switzerland;
- 2SIB Swiss Institute of Bioinformatics, 4056 Basel, Switzerland;
- 3Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts 02142, USA;
- 4Ovarian Cancer Research, Department of Biomedicine, University Hospital Basel and University of Basel, 4031 Basel, Switzerland
Abstract
In cancer, genetic and transcriptomic variations generate clonal heterogeneity, leading to treatment resistance. Long-read single-cell RNA sequencing (LR scRNA-seq) has the potential to detect genetic and transcriptomic variations simultaneously. Here, we present LongSom, a computational workflow leveraging high-quality LR scRNA-seq data to call de novo somatic single-nucleotide variants (SNVs), including in mitochondria (mtSNVs), copy number alterations (CNAs), and gene fusions, to reconstruct the tumor clonal heterogeneity. Before somatic variant calling, LongSom reannotates marker gene-based cell types using cell mutational profiles. LongSom distinguishes somatic SNVs from noise and germline polymorphisms by applying an extensive set of hard filters and statistical tests. Applying LongSom to human ovarian cancer samples, we detected clinically relevant somatic SNVs that were validated against matched DNA samples. Leveraging somatic SNVs and fusions, LongSom found subclones with different predicted treatment outcomes. In summary, LongSom enables de novo variant detection without the need for normal samples, facilitating the study of cancer evolution, clonal heterogeneity, and treatment resistance.
Footnotes
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[Supplemental material is available for this article.]
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Article published online before print. Article, supplemental material, and publication date are at https://www.genome.org/cgi/doi/10.1101/gr.279281.124.
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Freely available online through the Genome Research Open Access option.
- Received March 6, 2024.
- Accepted February 28, 2025.
This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/.
