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SUMMARY:Multi-state evolutionary model quantifies tumour cellular plasticity
DESCRIPTION:Statistical Bioinformatics SeminarDr Gladys Poon\, HKU\n\n\n\n\n\n\n\n\n\n\n\n\n\nCell-state transition dynamics are important in many diseases in cancer\, transitions among distinct cell states can affect treatment effectiveness and metastasis. Cancer evolutionary studies based on phylogenetics often assume weak or no selection – especially in the recent past – to estimate effective population sizes and evolutionary forces across a relatively long time span. However\, this assumption is violated during tumour growth when cells rapidly proliferate and outcompete one another under strong selection pressures.We adopt a stochastic modelling approach where cells divide according to a birth-death branching process and couple their fates with a Markov model for phenotypic transitions. Both genotype and phenotype are simultaneously inherited by the next generation. Phylogenies are constructed for a subsampled population of cells and coupled cell state information at a single time-point is used to estimate transition rates. We show that it is possible to determine phenotypic transition dynamics for specific population trajectories by ‘fine-graining’ node depth levels. This feature is crucial for addressing the proliferation of tumours which are mixtures of clones with different selection advantages.We pair our analysis with cell state annotations derived specifically for single-cell RNAsequencing (scRNAseq) data to define evolutionary relatedness between cell phenotypes. We then apply our computational framework to published metastatic pancreatic cancer phylogenies reconstructed using CRISPR-based lineage tracing in mice\, where scRNAseq information is available for each leaf. By comparing across metastatic locations in a single mouse\, we are able to reveal changes in selection pressures and cell phenotypic transition rates during metastatic progression. Inferred cell state transitions are supported by inferred RNA velocities. \n\n\n\n\n\nThis is an online event held via Zoom.\n\n\n\n\nSubscribe to our seminar mailing list\n\n\n\n\n→\n\n\n\n\n\n\n\nFind out more about the Statistical Bioinformatics seminar series\n\n\n\n\n\n→\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nDr Gladys Poon\n\n\n\nDr Gladys Poon completed both her undergraduate (Physics) and postgraduate (PhD in Oncology) studies at the University of Cambridge. During her time in the Blundell lab\, Gladys used population genetics to quantify the expected mutation burden and unknown drivers in human cancers\, particularly in acute myeloid leukemia. She also combined newly gained experimental expertise with her quantitative background to interrogate clonal evolutionary patterns in leukemic bone marrow\, revealing levels of positive selection during the human lifespan. Her research bridged between mathematics\, cancer genomics and computational modelling. \n\n\n\nGladys is now with the SMA lab at HKU – working on cellular plasticity in hepatocellular carcinoma. This lab uses lineage tracing experiments in mice and modelling to understand the role of cellular plasticity during tumor evolution.
URL:https://spds.sydney.edu.au/event/multi-state-evolutionary-model-quantifies-tumour-cellular-plasticity/
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