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A vector illustration of a pair of hands using forceps to add and remove bits of DNA from a large DNA double helix, with icons of lungs, heart, eye, and brain in the background.
Until now, it wasn't possible to quickly and easily screen mutations in their natural setting to see what role they may play in cancer development.

New Gene Editing Tool Rapidly Screens Cancer Mutations

With the new technique, researchers hope to identify mutations that could be targeted with new cancer therapies

Massachusetts Institute of Technology
Published:Mar 12, 2024
|3 min read
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CAMBRIDGE, MA — Tumors can carry mutations in hundreds of different genes, and each of those genes may be mutated in different ways—some mutations simply replace one DNA nucleotide with another, while others insert or delete larger sections of DNA.

Until now, there has been no way to quickly and easily screen each of those mutations in their natural setting to see what role they may play in the development, progression, and treatment response of a tumor. Using a variant of CRISPR genome editing known as prime editing, Massachusetts Institute of Technology (MIT) researchers have now come up with a way to screen those mutations much more easily. Findings have been published recently in Nature Biotechnology

The researchers demonstrated their technique by screening cells with more than 1,000 different mutations of the tumor suppressor gene p53, all of which have been seen in cancer patients. This method, which is easier and faster than any existing approach, and edits the genome rather than introducing an artificial version of the mutant gene, revealed that some p53 mutations are more harmful than previously thought.

This technique could also be applied to many other cancer genes, the researchers say, and could eventually be used for precision medicine, to determine how an individual patient’s tumor will respond to a particular treatment.

“In one experiment, you can generate thousands of genotypes that are seen in cancer patients, and immediately test whether one or more of those genotypes are sensitive or resistant to any type of therapy that you’re interested in using,” says Francisco Sanchez-Rivera, PhD, an MIT assistant professor of biology, a member of the Koch Institute for Integrative Cancer Research, and the senior author of the study.

Reducing noise

When they began working on this project, Sanchez-Rivera and Samuel Gould, BA, a PhD student at MIT and lead author of the paper, calculated that if performed successfully, prime editing could be used to generate more than 99 percent of all small mutations seen in cancer patients. “Prime editing in theory solves one of the major challenges with earlier forms of CRISPR-based editing, which is that it allows you to engineer virtually any type of mutation,” Sanchez-Rivera says. 

However, to achieve that, they needed to find a way to optimize the editing efficiency of the CRISPR-based system. The prime editing guide RNAs (pegRNAs) used to direct CRISPR enzymes to cut the genome in certain spots have varying levels of efficiency, which leads to “noise” in the data from pegRNAs that simply aren’t generating the correct target mutation. 

The MIT team devised a way to reduce that noise by using synthetic target sites to help them calculate how efficiently each guide RNA that they tested was working. “We can design multiple prime-editing guide RNAs with different design properties, and then we get an empirical measurement of how efficient each of those pegRNAs is. It tells us what percentage of the time each pegRNA is actually introducing the correct edit,” Gould says.

Analyzing mutations

The researchers demonstrated their technique using p53 gene. From a dataset that includes sequencing information from more than 40,000 patients, the researchers identified more than 1,000 different mutations that can occur in p53. “We wanted to focus on p53 because it’s the most commonly mutated gene in human cancers, but only the most frequent variants in p53 have really been deeply studied. There are many variants in p53 that remain understudied,” Gould says. 

Using their new method, the researchers introduced p53 mutations in human lung adenocarcinoma cells and then measured the survival rates of these cells, allowing them to determine each mutation’s effect on cell fitness.

Among their findings, they showed that some p53 mutations promoted cell growth more than had been previously thought. These mutations, which prevent the p53 protein from forming a tetramer, had been studied before, using a technique that involves inserting artificial copies of a mutated p53 gene into a cell. Those earlier studies found that these mutations did not confer any survival advantage to cancer cells. 

However, when the MIT team introduced those same mutations using the new prime editing technique, they found that the mutation prevented the tetramer from forming, allowing the cells to survive. Based on the studies done using overexpression of artificial p53 DNA, those mutations would have been classified as benign, while the new work shows that under more natural circumstances, they are not.

“This is a case where you could only observe these variant-induced phenotypes if you're engineering the variants in their natural context and not with these more artificial systems,” Gould says. “This is just one example, but it speaks to a broader principle that we’re going to be able to access novel biology using these new genome-editing technologies.”

Because it is difficult to reactivate tumor suppressor genes, there are few drugs that target p53, but the researchers now plan to investigate mutations found in other cancer-linked genes, in hopes of discovering potential cancer therapies that could target those mutations. They also hope that the technique could one day enable personalized approaches to treating tumors.

“With the advent of sequencing technologies in the clinic, we'll be able to use this genetic information to tailor therapies for patients with tumors that have a defined genetic makeup,” Sanchez-Rivera says. “This approach based on prime editing has the potential to change everything.”

- This press release was originally published on the Massachusetts Institute of Technology website