Abstract:
|
Intra-tumor heterogeneity arises because tumors evolve over time and different descendants of the original cell acquire new mutations, which they then pass on to their progeny. However, to date there are limited methods for inferring clonal evolution of tumor from RNA-seq data. In this poster, we present a new statistical method to reconstruct the evolutionary history and population frequency of the subclonal lineages of tumor cells from RNA-seq measurements. Our method uses a Bayesian nonparametric prior and nested stick-breaking process to allow for evolutional trees of infinite nodes, and to identify cell population frequencies which have the highest likelihood of generating the observed RNA-seq data. Markov Chain Monte Carlo method based on slice sampling is incorporated to perform Bayesian inference. Simulations demonstrate that the proposed method reliably recover the phylogenetic chain and population frequency of the subclonal lineages of tumor cells.
|