Clonality Inference from Single Tumor Samples Using Low Coverage Sequence Data
- Cite this paper as:
- 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
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.