We develop computational approaches to identify drivers of cancer by linking cancer genotypes to molecular and cellular phenotypes. Our work combines two complementary directions by (1) analyzing global portraits of cancer and (2) modeling key mechanisms and their response to perturbations.
1) In large data collections we analyse global portraits of cancer that combine tissue organisation and molecular profiles to infer cancer subtypes and predictive signatures.
- Mello, Wang et al (2013), Poor prog- nosis colon cancer is defined by a dist- inct molecular subtype and develops from serrated precursor lesions, Nature Medicine 19(5)
- Yuan et al (2012), Quantitative image analysis of cellular heterogeneity in breast tumors complements genomic profiling, Science Transl Med 4
- Curtis et al (2012), The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel sub- groups, Nature 486, 7403
2) Focussing on key mechanisms, we model their components, interactions and dynamics in response to perturbations to understand how they are deregulated in cancer and might be targeted by drugs.
- Fletcher et al (2013), Master regulators of FGFR2 signalling and breast cancer risk, Nature Commun 4:2464
- Mulder et al (2012), Diverse epigenetic strategies interact to control epidermal differentiation, Nature Cell Bio 14(7)
- Wang et al (2012), Posterior associat- ion networks and functional modules inferred from rich phenotypes of gene perturbations, PLoS Comp Bio 8(6)
- GoIFISH - quantify single cell heterogeneity from IFISH images
- RedeR - bridging the gap between statistical computing and network visualization
- nem - inferring Nested Effects Models from downstream perturbation effects