CACTUS: integrating clonal architecture with genomic clustering and transcriptome profiling of single tumor cells
Genome Medicine, vol. 13, pp. 45
Abstract
BACKGROUND: Drawing genotype-to-phenotype maps in tumors is of paramount importance for understanding tumor heterogeneity. Assignment of single cells to their tumor clones of origin can be approached by matching the genotypes of the clones to the mutations found in RNA sequencing of the cells. The confidence of the cell-to-clone mapping can be increased by accounting for additional measurements. Follicular lymphoma, a malignancy of mature B cells that continuously acquire mutations in parallel in the exome and in B cell receptor loci, presents a unique opportunity to join exome-derived mutations with B cell receptor sequences as independent sources of evidence for clonal evolution. METHODS: Here, we propose CACTUS, a probabilistic model that leverages the information from an independent genomic clustering of cells and exploits the scarce single cell RNA sequencing data to map single cells to given imperfect genotypes of tumor clones. RESULTS: We apply CACTUS to two follicular lymphoma patient samples, integrating three measurements: whole exome, single-cell RNA, and B cell receptor sequencing. CACTUS outperforms a predecessor model by confidently assigning cells and B cell receptor-based clusters to the tumor clones. CONCLUSIONS: The integration of independent measurements increases model certainty and is the key to improving model performance in the challenging task of charting the genotype-to-phenotype maps in tumors. CACTUS opens the avenue to study the functional implications of tumor heterogeneity, and origins of resistance to targeted therapies. CACTUS is written in R and source code, along with all supporting files, are available on GitHub ( https://github.com/LUMC/CACTUS ).
Authors 11
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Affiliation as printed
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, Warsaw, 02-097, Poland
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Biomedical Data Sciences, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Leiden Genome Technology Center, Leiden University Medical Center, Einthovenweg 20, Leiden, 2333 ZC, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
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Leiden University Medical Center
Affiliation as printed
Department of Hematology, Leiden University Medical Center, Albinusdreef 2, Leiden, 2333 ZA, The Netherlands
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Affiliation as printed
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Stefana Banacha 2, Warsaw, 02-097, Poland
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