Setting a trap for drug-resistant cancers

Circos plot displaying data from drug-modifier CRISPR screens. The screen conducted in the presence of ABT-199 is shown as a cutout with annotations from the outermost rim: genes ranked from most sensitizing (rank = 1) to most resisting (rank = 2,390); scatter plot of corresponding gene essentiality score; drug-modifier score (shown in replicate), colored to depict sensitizers (red) and resisters (blue). Gene–gene relationships that exhibit AP between drug screens are indicated by purple connections.

By Alissa Kocer

A cancer patient and his family sit in a doctor’s office waiting to see how well he is responding to treatment. As the doctor pulls up his scans, all of the air in the room seems to vanish. When the doctor says, “Your tumor has shrunk and is almost gone!” A collective sigh of relief fills the space. 

Fast forward a couple of months. More scans, more test results. The family feels more confident this time around. The treatment had been working; maybe he’s cured! And then the doctor reveals the news: the tumor is back.

This is an all too common problem. Kris Wood, assistant professor of pharmacology and cancer biology explains: “Patients with advanced cancer often initially respond to most drugs. Their tumors may shrink or even disappear, but over time, the tumors often comes back.” When that happens, it’s known as resistance, and it’s a sign that that the disease is no longer sensitive to the initial drug treatment.

To combat that, Wood and team, including co-first authors Kevin Lin and Justine Rutter and co-senior author Alexandre Puissant, are investigating how to turn a tumor’s strength – its drug resistance – into a weakness. This research has been published in Nature Genetics.

The first step is to figure out what causes cells to become resistant to drug therapy. “If you can figure out the mechanisms that cause resistance,” Wood said, “it opens up other possibilities to prevent, reverse, and/or respond to that resistance.”

But that’s not as easy as it sounds.

If you take a sample of a tumor that has become resistant and look at it at a cellular resolution, you often find that there are many resistance mechanisms. The initial treatment kills off all of the sensitive cells, but that leaves behind a lot of resistant cells that don’t die from treatment, and many of those cells can have different resistance mechanisms. Overcoming this requires creative solutions.

Using CRISPR/Cas9, Wood and team took nine chemotherapy drugs used to treat a blood cancer called acute myeloid leukemia (AML) and conducted genetic screens to identify genes capable of driving resistance to one drug but sensitization to others. They found thousands of examples of where genes are good in some settings and bad in others. With this information in hand, they could begin using knowledge of resistance mechanisms to design evolutionary traps: scenarios in which the mechanisms that make cells resistant to an initial drug therapy will also make them hypersensitive to a subsequent drug therapy. This phenomenon is known as antagonistic pleiotropy.

Wood and team developed a framework to identify sequential drug regimens that may cause cells to become supersensitive. Among them, ABT-199 and JQ-1 showed the highest percentage of antagonistic pleiotropy, so they tested it in a mouse model. Mice were given the human form of AML, and in every model, every time, they found that if they first treated the mice with JQ-1, the resistant cancer cells became hypersensitive to ABT-199 through mechanisms involving the hyperactivation of a transcription factor called MYC.

This research has long-term implications. “We are providing a way to systematically find other genes that way,” Wood said. These principles can be used to identify more examples that cause cancer and tumor cells to fall into evolutionary traps, and therefore, may provide more treatment options for cancer patients.

CITATION: Lin, K.H., Rutter, J.C., Xie, A. et al. Using antagonistic pleiotropy to design a chemotherapy-induced evolutionary trap to target drug resistance in cancer. Nat Genet (2020).