Constance Li

Dr. Constance Li

PhD

Positions

Assistant Professor

Cumming School of Medicine, Department of Biochemistry and Molecular Biology

Member

Arnie Charbonneau Cancer Institute

Contact information

Phone number

Office: 403.220.8163

Location

Office: HMRB309

Preferred method of communication

Administrative Assistant

Monica Flohr Mauchline
Email: monica.mauchline@ucalgary.ca
Office: 403.220.6553

Background

Educational Background

PhD University of Toronto,

Biography

Dr. Constance Li is a computational biologist with a focus in bioinformatics training as an Assistant Professor (Teaching) at the University of Calgary. She obtained her BMath at the University of Waterloo and her PhD studying cancer genomics at the University of Toronto under the supervision of Dr. Paul Boutros. Following this, she pursued a postdoctoral fellowship at National Cancer Centre Singapore with Dr. Gopal Iyer and Dr. Melvin Chua. Dr. Li's expertise lies in the computational analysis of biological data, and is particularly interested in the molecular associations of cancer health disparities.

Research

Areas of Research

Area of Focus
  • Cancer genomics
Summary of Research

Cancer is a heterogenous disease with differential characteristics in different groups of people. For example, cancer varies profoundly by sex. The differential cancer burden between male and female patients has been described in incidence, prevalence, and mortality rates in myriad non-sex-specific cancers throughout multiple regions of the world. Studies motivated by these epidemiological sex disparities in cancer describe corresponding differences in its clinical characteristics, treatment response, and widespread molecular differences including epigenetic changes, altered transcriptomic programs, and genomic mutations. Similar differences exist in other group characteristics such as age. I am interested in exploring the genomic associations of cancer disparities, their relationships with factors such as the tumour microenvironment, and in leveraging this understanding to improve biomarker design.