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:
|Tumour evolution||Tissue context||Interaction networks|
(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.
- Drews et al (2022), A pan-cancer compendium of chromosomal instability, Nature
- Gehrung et al (2021), Triage-driven diagnosis of Barrett esophagus for early detection of esophageal adenocarcinoma using deep learning, Nature Medicine
- Crispin-Ortuzar et al (2020), Three-Dimensional Printed Molds for image-guided surgical biopsies: an open source computational platform, JCO CCI
- Cmero et al (2020), SVclone: inferring structural variant cancer cell fraction, Nature Comms
- Drews2022_CIN_Compendium - central code hub for "A pan-cancer compendium of chromosomal instability", Drews et al. (2022).
- SliDL - A Python library for deep learning on whole-slide images
- OncoNEM - Clonal evolution trees from single cell data
- MEDICC - intra-tumor copy-number comparisons
- nem - inferring Nested Effects Models from downstream perturbation effects