Genomic diversity within tumours poses a major challenge for precision medicine; a treatment tailored to one particular cellular subtype may be ineffective against other subclones (Gerlinger et al., 2012), leading to metastases, and the development of drug resistance (Melchor et al., 2014).
While bulk sequencing assays can be used to infer likely clonal structure based on constraints on observed allele frequencies (Jiao et al., 2014; Oesper et al., 2014), such methods are unable to resolve rarer subpopulations, and can provide only limited information regarding the evolutionary dynamics giving rise to the observed genetic diversity (Chowdhury et al., 2015).
With the advent of new methods capable of resolving somatic mutations and copy-number variation for whole genomes at the single-cell level, it is now in princple possible to fully reconstruct the evolutionary history of a cells within a tumour (Hughes et al., 2014; Melchor et al., 2014). However, due to various types of technical and biological noise, there may be significant uncertainty associated with the inferred mutations in each cell, which may lead to biases in the inferred evolutionary relationships.
We are addressing this issue using hierarchical Bayesian models in conjunction with stochastic processes for sequence evolution, allowing for tumour phylogenies to be inferred while accounting for uncertainties in the data.
Gerlinger, M., et al. (2012). Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. New England Journal of Medicine, 366(10), 883-892.
Hughes, A. E. et al. (2014). Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing. PLoS Genetics, 10(7).
Jiao, W. et al. (2014). Inferring clonal evolution of tumors from single nucleotide somatic mutations. BMC Bioinformatics, 15(1), 35.
Melchor, L. et al. (2014). Single-cell genetic analysis reveals the composition of initiating clones and phylogenetic patterns of branching and parallel evolution in myeloma. Leukemia, 28(8), 1705-1715.
Oesper, L., Satas, G., & Raphael, B. J. (2014). Quantifying tumor heterogeneity in whole-genome and whole-exome sequencing data. Bioinformatics, 30(24), 3532-3540.