Research Statement
- Develop a suite of software implementing a novel statistical models for evolutionary inference from whole genome sequencing data from tumours.
- Utilize an unprecedented evolutionary profiling of thousands of tumour samples to investigate the relationship between clonal evolution and clinical outcomes across tumour types.

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Management Team
- Sohrab Shah, Principal Investigator
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Development Team
- Sohrab Salehi, PhD Cand
- Fatemeh Dorri, PhD Cand
- Andrew McPherson, PDF
- Jafar Taghiyar, Software Developer
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Collaborators
- Alexandre Bouchard-Cote, Collaborator
Outcomes
The evolution of tumours is thought to be a major determinant of the tumour aggressiveness and response to therapy. By developing tools that can reconstruct the evolution of individual tumours and by using these tools to relate tumour heterogeneity to clinical behavior, we set the stage for tumour evolution-aware design of customized therapies for cancer patients.
Software
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All software available at the Shah Lab for Computational Cancer Biology
Latest Publications & Presentations
Clonal genotype and population structure inference from single-cell tumor sequencing.
Nature methods, 2016;13(7):573-6
Divergent modes of clonal spread and intraperitoneal mixing in high-grade serous ovarian cancer.
Nature genetics, 2016;48(7):758-67
Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution.
Nature, 2015;518(7539):422-6