The majority of drugs developed to treat cancers end up failing, usually because of toxicity or lack of efficacy. The underlying mechanisms of those failures are often poorly understood, but a new study on cancer cell lines indicates that knowledge of why some drugs do work isn’t entirely on the mark, either.
The study, conducted by researchers at Cold Spring Harbor Laboratory on New York’s Long Island and published Wednesday in the journal Science Translational Medicine, found that the 10 drugs it examined appeared to work through targets that differed from the ones they putatively use to kill cancer cells.
The researchers, led by cancer biologist Jason Sheltzer, specifically looked at 10 drugs known to not be susceptible to resistance mutations. They then used CRISPR-Cas9 gene editing to remove the drugs’ putative targets from cancer cells, finding that they still worked anyway. The study stemmed from work Sheltzer had done whereby he found that when a protein believed to be essential to cancer cells’ survival, MELK, was removed from cancer cells, the cells stayed alive anyway. Moreover, a MELK inhibitor, OncoTherapy Science’s OTS167, was still able to kill them. In other words, the results indicate that the developers of OTS167 were wrong about the drug’s target. The study was also able to show that another OncoTherapy Science drug, OTS964, was not a TOPK inhibitor, but a CDK11 inhibitor.
OncoTherapy Science, which is based in Japan, did not immediately respond to a request for comment.
“We show that – contrary to previous reports obtained predominantly with RNA interference and small-molecule inhibitors – the proteins ostensibly targeted by these drugs are nonessential for cancer cell proliferation,” the researchers wrote, referring to the more standard method currently used to determine drug targets.
The study does come with a few limitations. For example, the researchers used decades-old cell lines that do not fully monitor the biology of tumors. However, the overall implication is that CRISPR-Cas9 technology could be used to complement the traditional RNA-interference technology and vice versa, thus improving confidence in results. With the 97 percent failure rate of cancer drugs, improving the ability to predict patient responses could improve their chances of success in the clinic.
Another caveat is that none of the drugs included in the study are currently on the market. It could also be complicated to use the genetic methods applied to look at some drugs that are available, such as monoclonal antibodies. On a call with reporters to discuss the results, Sheltzer said that the basic methodology the researchers applied would be suitable for studying drugs like monoclonal antibodies. Still, looking at drugs like immunotherapies would be complicated because that would need to happen in an organism with a functioning immune system, meaning the techniques from the study would have to be adapted to work in an animal like a mouse.
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