A major goal of biomedical research is to reveal genetic dependencies and cellular processes cancer cells rely on in order to exploit them therapeutically. The development of CRISPR-based genome editing technologies have opened the door for efficient perturbation of mammalian genomes and, as such, provide powerful tools for exploring genotype-to-phenotype relationships. Importantly, the combination of CRISPR technology with functional genomics approaches can be harnessed to systematically identify genetic dependencies and cancer vulnerabilities across diverse cell lines and growth conditions. Michael Aregger’s research aims to expand our understanding of how cancer cells dynamically regulate gene expression and cellular metabolism in response to changing environmental conditions, and to use this knowledge to identify genetic liabilities that may present novel targets for combinatorial cancer therapeutic approaches.

1. Studying adaptation to metabolic stress

A common feature of many cancers is the reprogramming of cellular metabolism in order to fuel tumorigenesis. Importantly, this rewiring is highly plastic allowing cancer cells to adapt to various tumor microenvironmental stress conditions. In our lab we apply cutting-edge functional genomics methodologies along with transcriptomics, proteomics and focused biochemical approaches to investigate how metabolic plasticity is regulated and what genetic vulnerabilities arise in cancer cells in response to various environmental stress conditions.

2. Identification of context-dependent fitness genes across genetic and environmental backgrounds

In order to identify genes whose function underlie cell proliferation and survival we have developed various CRISPR-based screening technologies for the systematic knockout of genes in human cells. Our genome-wide CRISPR-Cas9 screens expanded the set of human fitness genes and revealed distinct genetic signatures that can be used to predict differential drug response of cancer cells. In our lab we are using similar CRISPR screening approaches to uncover genetic vulnerabilities in cancer cells and study how different environmental conditions influence dependencies on certain cellular pathways.

Image

3. Mapping of genetic interaction networks

Genetic interactions occur when mutations in two or more genes result in a phenotype that deviates from the predicted combinatorial effect of perturbing those genes. The mapping of such genetic interactions can provide profound insights into the functional wiring and plasticity of cells, predict novel gene functions, and highlight critical genetic dependencies that may be exploited therapeutically to treat diseases such as cancer. We have used co-isogenic cell lines to map genetic interactions of selected metabolic query genes, which revealed novel insights into fatty acid metabolism and the functional role of a previously uncharacterized gene.

To expand our tools to map genetic interactions we developed CHyMErA (Cas Hybrid for Multiplexed Editing and Screening Applications), a CRISPR-based combinatorial screening platform for the perturbation of multiple genes. CHyMErA is based on the co-expression of Cas9 and Cas12a nucleases in conjunction with a hybrid guide RNA (hgRNA) engineered by the fusion of Cas9 and Cas12a guides and expressed from a single U6 promoter. We have applied CHyMErA for the dual-targeting of single genes, the mapping of genetic interactions between paralogs, and for exon-resolution functional genomics in mammalian cells. In our lab we continue exploring CHyMErA as a powerful tool to uncover genetic interactions and dependencies between various cellular pathways.

Image

4. Technology development for systematic functional genomics approaches

We have a strong interest in developing and applying cutting-edge CRISPR-based technologies and continue exploring various Cas modalities and effector domains for our screening applications. Furthermore, we are also expanding the readouts for our screening platforms in order to assess genetic dependencies across more information-rich phenotypes.