With collaborators in the Oxford Ovarian Cancer Lab and Imperial College London, Zhiyuan Hu has identified that OxC-defined EMT-high SOCs carry particularly poor prognosis independent of established clinical parameters. She found these tumours are associated with high frequency of immunosuppressive macrophages, suggesting a potential therapeutic target to improve clinical outcome.
Link to paper
Bulk whole genome sequencing (WGS) enables the analysis of tumor evolution but, because of depth limitations, can only identify old mutational events. The discovery of current mutational processes for predicting the tumor’s evolutionary trajectory requires dense sequencing of individual clones or single cells. Such studies, however, are inherently problematic because of the discovery of excessive false positive mutations when sequencing picogram quantities of DNA. Data pooling to increase the confidence in the discovered mutations, moves the discovery back in the past to a common ancestor.
The PHG Foundation has published a report on Artificial intelligence for genomic medicine. Christopher Yau is very pleased to have been able to share his experience and insight into the subjects covered in this report.
A novel convolutional filter-based approach for learning mutation signature representations.
A novel Bayesian nonparametric regression approach for high-dimensionality multi-modal data integration.
Identification of cancer-promoting genetic alterations using genome sequencing and machine learning.
Unravelling the history of genomic instability through deep reinforcement learning.
Computational genomics to tackle the `silent killer`