Christina Curtis

Our laboratory couples innovative experimental approaches, high-throughput omic technologies, statistical inference and computational modeling to interrogate the evolutionary dynamics of tumor progression and therapeutic resistance. To this end, I and my team have developed an integrated experimental and computational framework to measure clinically relevant patient-specific parameters and to measure clonal dynamics. My research also aims to develop a systematic interpretation of genotype/phenotype associations in cancer by leveraging state-of-the-art technologies and robust data integration techniques. For example, using integrative statistical approaches to mine multiple data types I lead a seminal study that redefined the molecular map of breast cancer, revealing novel subgroups with distinct clinical outcomes and subtype-specific drivers.

Research Areas:

• Tumor evolutionary dynamics
• Novel therapeutic targets
• The genotype to phenotype map in cancer