These days, cancer is as much a target for researchers with number-crunching skills and their data-mining tools as it is for scientists in biomedical research labs. And one potentially powerful big-data approach to conquering cancer—which involves discovering clever genetic tricks to make cancer cells kill themselves—has moved in a promising new direction this month.
Scottish, Israeli, and American researchers have reported a new discovery that analyzes existing cancer gene databases for clues to combinations of genes that together can kill tumor cells while leaving healthy cells untouched. The idea takes advantage of a phenomenon called synthetic lethality, which in oncology was first explored 17 years ago as a potentially fruitful new line of cancer treatment.
One of the new paper’s coauthors, Eytan Ruppin, director of the University of Maryland’s Center for Bioinformatics and Computational Biology, says cancer cells are like regular cells run amok. They, like regular cells, typically have some 10,000 genes. But in cancer cells, many more of these genes are inactive—meaning that for whatever reasons, those genes don’t produce the proteins that a healthy version of the cell would be producing.
Since the 1920s, it’s been observed that all cells in fact have networks of secret self-destruct switches: When both of a key pair of genes become inactive, the entire cell begins the process of shutdown and cell death.
So, Ruppin says, the hurdle that needs to be overcome in order to make this possible broad-spectrum genetic cancer treatment work is discovering and cataloging as many of these secret “synthetically lethal” (SL) gene pair combinations as nature has provided. Then, when a patient’s cancer is biopsied, and its genome is taken, an oncologist can look to see which genes in the patient’s cancer cells are inactive.
For example, say that the oncologist discovers in an SL database that an inactive gene in a patient’s tumor (call it Gene A) happens to have a corresponding synthetically lethal partner gene (call it Gene B).
In this case, then, a drug that inactivates Gene B will trigger the cell death process in the tumor but not in the person’s healthy cells. (Depending on what Gene B does, there might also be side effects from switching Gene B off. But so long as Gene A remains active throughout the rest of the person’s body, those side-effects should not include cell death.)
Ruppin and his collaborators used a clever data mining technique to discover more than a thousand candidate SL gene combinations. They plumbed the U.S. National Cancer Institute’s Cancer Genome Atlas, which itself contains thousands of genomes of different biopsied tumor samples.
They then ran searches for various inactive genes. So, for the sake of example, say they found some of the cancers in the database with Gene X inactivated. And some of the cancers in the database had Gene Y inactivated. If Genes X and Y don’t form an SL pair, there should then be plenty of examples in the database of cancers where both X and Y were inactive. However, if Genes X and Y do form an SL pair, then there should be almost no examples of tumors in the database where those two genes are both inactive.
“You would have expected them to be inactive together at a certain rate, given their individual inactive frequencies,” Ruppin says. “But when you look at the data, you find that they are never inactive together,” Ruppin adds that this, “is a very strong indication that they are synthetically lethal. Because whenever they were inactive together, they were actually eliminated from the population. Because these cells died.”