The Moffat Lab has broad interests in the regulation of cell growth and proliferation in cancer cell lines. We utilize genome-scale lentiviral-based RNA interference technology and computational techniques to develop essential gene profiles in cultured mammalian cell lines and identify pairwise genetic interactions in order to define genetic interaction networks that are critical for cancer cell proliferation. Along with our collaborators, we are among a small group of labs in the world that is attempting to identify essential genes in human cancer cell lines. We are working on genes that encode cell surface proteins that are critical for cell proliferation and whose proper expression is dependent on metabolic pathways and cellular state. Our research is concentrated in three areas:

1. Identification of genetic interactions and differential gene essentiality in mammalian cancer cell lines

Genomic analyses are yielding a host of new information on the multiple genetic abnormalities associated with specific types of cancer. A comprehensive description of cancer-associated genetic abnormalities can improve our ability to classify tumors into clinically relevant subgroups and sometimes identify drivers. Unfortunately, the functional significance of cancer-associated mutations is often difficult to discern. Our work has focused largely on utilizing lentivirus-based RNA interference (RNAi) genetic screening technologies to identify and study essential genes and synthetic lethality in cancer cells. We develop and use short-hairpin RNA libraries and apply them, along with novel computational approaches, for discovering genes required for cellular growth and proliferation. Essential genes are defined as genes that are indispensable for cellular life, and they constitute a minimal gene set required for a living cell. However, this definition implies that essentiality is a binary feature of cells and is far too strict; for example, some non-essential genes become essential under certain growth conditions or in certain genetic backgrounds. As a result, we liken the term essentiality to fitness. In the context of cancer cells, gene essentiality may be reflected in the tissue type or cancer subtype from which the cancer cells arose or may be generally essential across many cancer cell types. Understanding where and when a gene is essential will pave the way for a genetic interaction map that will contribute to our understanding of normal and diseased cellular states.

For more information, check out:
Marcotte et al (2012),  Koh et al (2012)

2. Cancer cell surface genes and cellular fitness

Cell surface proteins include receptors and non-receptors. Cell surface receptors (also known as membrane receptors or transmembrane receptors) are integral membrane proteins that take part in communication between the cell and the extracellular environment. Extracellular signaling molecules attach to the receptor, triggering phenotypic changes, a process typically referred to as signal transduction. Our lab studies the stem and cancer stem cell marker CD133 in order to gain functional insight into this molecule. In addition, we are interested in novel or uncharacterized cell surface proteins that are essential for cellular fitness. In conjunction with the Sidhu Lab and other collaborators, we are developing synthetic antibody reagents against essential cell surface molecules that could be used as research tools for exploring protein structure and function.

For more information, check out:
Mak et al (2012a),  Mak et al (2012b),  Mak et al (2011),  Marcotte et al (2012)

3. Synthetic genetic technologies for exploring gene/protein function and protein-protein interactions in mammalian cells

The ability to perturb gene/protein expression and measure protein-protein interactions is of central importance to biological research and facilitates our understanding of how molecular events drive phenotypic outcomes. Moreover, large-scale genetic and protein interaction data can be used to generate interaction networks that can be used to predict disease genes and model the biology in any organism. We are focused on making improvements to existing RNAi and protein-protein interaction technologies to further build capacity to explore genetic interactions and protein-protein interactions in cancer cells.

For more information, check out:
Blakely et al (2011),  Ketela et al (2011),  Mak et al (2010) 

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