Inference of clonal selection in cancer populations using single-cell sequencing data
| Autor: | Viachaslau Tsyvina, Alexander Zelikovsky, Pavel Skums |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Statistics and Probability
Fitness landscape Clone (cell biology) Inference Computational biology Biology Biochemistry 03 medical and health sciences 0302 clinical medicine Phylogenetics Ismb/Eccb 2019 Conference Proceedings Neoplasms medicine Humans Population Genomics and Molecular Evolution Evolutionary dynamics Molecular Biology Phylogeny 030304 developmental biology 0303 health sciences Cancer Sequence Analysis DNA medicine.disease Clone Cells 3. Good health Computer Science Applications Computational Mathematics Computational Theory and Mathematics Single cell sequencing 030220 oncology & carcinogenesis Software 030217 neurology & neurosurgery Clonal selection |
| Zdroj: | Bioinformatics |
| ISSN: | 1460-2059 1367-4803 |
| DOI: | 10.1093/bioinformatics/btz392 |
| Popis: | SummaryIntra-tumor heterogeneity is one of the major factors influencing cancer progression and treatment outcome. However, evolutionary dynamics of cancer clone populations remain poorly understood. Quantification of clonal selection and inference of fitness landscapes of tumors is a key step to understanding evolutionary mechanisms driving cancer. These problems could be addressed using single-cell sequencing (scSeq), which provides an unprecedented insight into intra-tumor heterogeneity allowing to study and quantify selective advantages of individual clones. Here, we present Single Cell Inference of FItness Landscape (SCIFIL), a computational tool for inference of fitness landscapes of heterogeneous cancer clone populations from scSeq data. SCIFIL allows to estimate maximum likelihood fitnesses of clone variants, measure their selective advantages and order of appearance by fitting an evolutionary model into the tumor phylogeny. We demonstrate the accuracy our approach, and show how it could be applied to experimental tumor data to study clonal selection and infer evolutionary history. SCIFIL can be used to provide new insight into the evolutionary dynamics of cancer.Availability and implementationIts source code is available at https://github.com/compbel/SCIFIL. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|