Abstract
Inference of intra-tumor heterogeneity can provide valuable insight into cancer evolution. Somatic mutations detected by sequencing can help estimate the purity of a tumor sample and reconstruct its subclonal composition. While several methods have been developed to infer intra-tumor heterogeneity, the majority of these tools rely on variant allele frequencies as estimated via ultra-deep sequencing from multiple samples of the same tumor. In practice, obtaining sequencing data from a large number of samples per patient is only feasible in a few cancer types such as liquid tumors, or in rare cases involving solid tumors selected for research. We introduce CTPsingle, which aims to infer the subclonal composition using low-coverage sequencing data from a single tumor sample. We show that CTPsingle is able to infer the purity and the clonality of single-sample tumors with high accuracy even restricted to a coverage depth of \(\sim \)30x.
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Acknowledgements
This project was funded by a Prostate Cancer Canada Movember Team grant and the Terry Fox Research Institute New Frontiers Program to CCC; NSERC Discovery Frontiers Grant on the Cancer Genome Collaboratory, Genome Canada Bioinformatics and Computational Biology Program Grant and NSERC Discovery Grant to SCS; NSERC CREATE (139277) fellowship and Vanier Canada Graduate Scholarship to SM.
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The supplementary material including additional figures are located at https://github.com/nlgndnmz/CTPsingle.
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Donmez, N., Malikic, S., Wyatt, A.W., Gleave, M.E., Collins, C.C., Sahinalp, S.C. (2016). Clonality Inference from Single Tumor Samples Using Low Coverage Sequence Data. In: Singh, M. (eds) Research in Computational Molecular Biology. RECOMB 2016. Lecture Notes in Computer Science(), vol 9649. Springer, Cham. https://doi.org/10.1007/978-3-319-31957-5_6
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DOI: https://doi.org/10.1007/978-3-319-31957-5_6
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