New & Noteworthy
March 21, 2013
At first our favorite small eukaryote, S. cerevisiae, might not seem like a great model for cancer studies. After all, budding yeast can’t tell us anything about some of the pathways that go wrong in cancer, like growth factor signaling. And it clearly can’t help explain what happens in specific tissues of the human body. But in other ways, it actually turns out to be a great model.
For example, all the details of cell cycle control were originally worked out in yeast. And now a whole new batch of genes has been found that influence a phenomenon, chromosome instability (CIN), that is important in both yeast and cancer cells.
As the name implies, chromosomes are unstable in cells suffering from CIN. Big chunks of DNA are lost, or break off and fuse to different chromosomes, turning the genome into an aneuploid mess. And this mess has consequences.
CIN can cause new mutations or make old ones have a stronger effect. Eventually these mutations can affect genes that are important for keeping a cell’s growth in line. Once these are compromised, a tumor cell is born.
Since CIN is pretty common in yeast, we might be able to better understand it in cancer cells by studying it in yeast. The Hieter lab at the University of British Columbia has come up with a powerful screen to get yeast to confess why it CINs.
A previous study from the group set the stage by finding a large group of mutants that have CIN phenotypes, implying that those genes are involved in keeping chromosome structure stable. In a new paper in G3: Genes, Genomes, Genetics, van Pel et al. uncovered the network of interactions among the genes in this set, using synthetic genetic array (SGA) technology. And they confirmed that the human homologs of some of these genes interact in the same way as in yeast, making them potential targets for cancer therapies.
The idea behind SGA studies is that if two proteins are involved in the same process, then a strain carrying mutations in both of their genes will be much worse off than a strain carrying either single mutation. In the worst case, the double mutant will be dead. This is known as a synthetic lethal interaction.
Yeast is a great model for doing these sorts of studies on a very large scale. We can construct networks showing how lots of different genes interact, and most importantly, find the genes that are central to many interactions. These “hubs” are likely to be the key players in those processes.
The researchers looked specifically for interactions between genes that are involved with CIN in yeast and are also similar to human cancer-related genes. They came up with various interaction hubs that will be interesting research subjects for a long time to come. In this study, they focused on one of these: genes involved with the DNA replication fork.
One of these in particular, CTF4, is a hub for both physical and genetic interactions. Unfortunately, Ctf4p doesn’t look like a good target for chemotherapy. It’s thought to act as a scaffold, and lacks any known activity that could potentially be inhibited by a drug. However, the interaction network around CTF4 that van Pel et al. uncovered suggests another way to target this hub. If a gene that interacts with CTF4 itself has a synthetic lethal interaction with another gene, and we could re-create the synthetic lethal phenotype in a cancer cell, we might be able to knock out the whole process. And that is just what they found in human cells.
First the authors identified a couple of human genes that were predicted from the yeast screen to be close to human CTF4 in the interaction network and to have a synthetic lethal interaction with each other. They then lowered the expression of one using small interfering RNA (siRNA), and reduced the activity of the other with a known inhibitor. Neither treatment alone had much effect, but combining them significantly reduced cell viability.
Since cancer cells frequently carry mutations in CIN genes, it should be possible to create a synthetic lethal interaction, guided by the yeast interaction network, where one partner is mutated in cancer cells (equivalent to using siRNA in this study) and the other partner is inhibited with a drug. Since it relies on a cancer-specific mutation, this approach has the potential to selectively target cancer cells while not disturbing normal cells, the ultimate goal for chemotherapy.