Technologies for doctors to make better decisions faster
Leveraging theory, computation and experiments, my group and I are creating novel measurement and prediction systems for integrated cancer biology. We work in translational projects on three frontiers:
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| Tumour evolution | Tissue context | Interaction networks |
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(1) Revealing the mutational processes acting on cancer genomes, measuring their impact on patient phenotypes, and using them for personalised therapy decisions; |
(2) Improved patient stratification, early detection, and prognosis by predictive modelling of tumour imaging and genomics data; |
(3) Predicting strategies to overcome resistance and reduce toxicity by comparative network analysis of transcriptional responses to combinatorial CRISPR perturbations in single cells. |
Publications
- Macintyre*, Goranova* et al (2018), Copy-number signatures and mutational processes in ovarian carcinoma, Nature Genetics
- Sivakumar*, de Santiago* et al (2017), Master regulators of oncogenic KRAS response in pancreatic cancer: an integrative network biology analysis, PLoS Medicine, 14(1)
- Marass et al (2017), A phylogenetic latent feature model for clonal deconvolution, AOAS 10(4)
- Ross and Markowetz (2016), OncoNEM: Inferring tumour evolution from single-cell sequencing data, Genome Biology, 17:69
Software
- VULCAN - infer cofactors from ChIP-seq differential binding
- OncoNEM - Clonal evolution trees from single cell data
- Bitphylogeny - Bayesian framework for intra-tumour phylogenies
- MEDICC - intra-tumor copy-number comparisons
- nem - inferring Nested Effects Models from downstream perturbation effects
- RedeR - statistical computing and network visualization
