Cancer genomics is dependent on computational biology and bioinformatics to produce new knowledge. My research is devoted to the development and application of statistical models for inferring genomic abnormalities from high dimensional genomic measurements in tumour samples. We are interested in understanding cancer genomes from the perspective of identifying pathogenic driver alterations and how tumours evolve over time. Our work involves the development of statistical models, algorithms and computational approaches to analyze large, high dimensional genomics and transcriptomic data sets derived from tumours in order to describe mutational landscapes of cancer subtypes and quantify clonal evolution and intratumoural heterogeneity. Much of our work is devoted to analysis and interpretation of next generation sequencing data applied in cancer-focused experimental designs.
Computational research interests:
- inference of somatic variants (mutations, copy number alterations, genome rearrangements) from next generation sequencing data
- interpreting the impact of somatic mutations on transcriptional profiles
- inference of clonal population structures from deep mutational profiles
Cancer genomics research interests:
- Characterization of clonal diversity and tumour evolution across spatial and temporal dimensions in ovarian cancer
- Defining the mutational landscapes of triple negative breast cancer and ovarian clear cell carcinoma
- Modeling drug selection in breast cancer through serial analysis of xeno-engrafted breast cancers
- Dr. Sam Aparicio
- Dr. David Huntsman
More information is available http://compbio.bccrc.ca.