School of Mathematics

Florian Markowetz

Florian Markowetz abstract

University of Cambridge, CRUK Cambridge Institute, Robinson Way, Cambridge, CB20RE, UK  

Inferring cancer evolution 

Cancer has long been understood as a somatic evolutionary process, but many details of tumor progression remain elusive. Here, I will talk about computational methods to infer evolutionary pathways leading to tumour heterogeneity.I will start by discussing how to quantify intra-patient heterogeneity by rigorous analysis of copy-number profiles from multiple patient samples [1]. Using a study in ovarian cancer I will describe how summary statistics of phylogenetic quantification of genetic heterogeneity can predict patient survival [2].Then, I will present a probabilistic framework to reconstruct intra-tumor evolution from data obtained by bulk bisulfite sequencing of mixed tumor samples or single cell genomes. Our approach jointly estimates the number and composition of clones in the sample as well as the most likely tree connecting them [3]. I will finish with an outlook how to put cancer genomics in a tissue context [4,5].   

References: [1] R.F. Schwarz et al, Phylogenetic quantification of intra-tumour heterogeneity, PLoS Comp Bio 10(4) 2014 [2] R.F. Schwarz et al, Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic reconstruction, PLoS Medicine, 2015 Feb 24;12(2):e1001789. [3] Yuan et al, BitPhylogeny: A probabilistic framework for reconstructing intra-tumor phylogenies, Genome Biology, 2015, 16:36 [4] Yuan et al, Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling Science Translational Medicine, 4, 157ra142 (2012) [5] Martins et al, Combined image and genomic analysis of high-grade serous ovarian cancer reveals PTEN loss as a common driver event and prognostic classifier Genome Biology, 2014, 15:526