Deriving accurate phylogenetic tumour trees, the equivalent of a genealogic tree for tumour cells, is an important prerequisite to the study of intra-tumour genetic heterogeneity (ITH) and tumour growth, the hallmark signature of on going cancer evolution. First, I set up to reconstruct high-quality phylogenetic trees from whole genome bulk sequencing and further refine the trees using single-cell sequencing data, where the co-occurrence of somatic mutational events help restrain the possible tree structures. To identify the best strategy to derive phylogenetic trees, I participated in two international efforts to benchmark existing phylogeny reconstruction or subclonal reconstruction methods. Copy number aberrations are a common type of genomic changes occurring during cancer evolution and a good handle to reconstruct ITH, especially at the single-cell resolution. Therefore, to further annotate the tree with single-cell copy number events, I developed my own package to derive copy number profiles from single-cell sequencing data. My method is generic and can be applied to other types of DNA-profiling technologies, such as methylation data, off-target reads from targeted sequencing data, or shallow coverage whole genome sequencing.
Second, to model the relationship between genetic and epigenetic/transcriptomic subclones, I first looked at integrating genome, methylation, and transcriptomic profiles of undifferentiated sarcomas at the bulk level. Then, from the observation that there is a strong gene-dosage effect, i.e. on average a monotonic relationship between expression of the genes and their number of DNA copies, both at the bulk and single-cell levels, I proposed a strategy to infer copy-number profiles from the RNA profiles of single cells and supervised a student to use G&T data to train a machine learning algorithm. Then, I developed a method to call chromothripsis, a typical catastrophic event in sarcomas, and studied its impact on the driver landscape and their expression across cancer types, including sarcomas.
Finally, I planned to study the evolution of cancers, especially in response to treatment. In a clinical setting, multiple strategies can be used to derive time series and study cancer evolution in response to treatment. Here, I first contributed to the literature on the interpretation of variant allele frequencies in terms of tumour evolution and tumour growth parameters. Second, from G&T pre- and post- treatment in cancer xenografts, I helped ask where selective pressure through treatment is active. Finally, I optimised the design to profile single cells pre- and post- treatment using G&T of the primary and recurrences of one sarcoma patient.
