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Prime Editing Helps Scientists Screen the Effects of Cancer Mutations

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Cancer is a genetic disease caused by mutations in the DNA code. Screening cancer mutations can shed light on their role in the development, progression and treatment response of a tumor. 

“While we have a good idea about the organization and content of our genome, we still do not understand why and how certain mutations can lead to the development of cancer and other human diseases with strong genetic links and how this information could be leveraged for clinical applications,” Dr. Francisco Sánchez-Rivera, assistant professor of biology at the Massachusetts Institute of Technology (MIT), told Technology Networks.

Sánchez-Rivera’s research aim is to understand how genetic variation shapes human physiology and disease, with a specific focus on cancer. “Knowing which mutations cause disease and why remains one of the most important problems we need to solve in order to understand and treat cancer and other diseases,” he said.

Mutations can arise in hundreds of different genes, and each gene can become mutated in varying ways. There is a lot of ground to cover, and screening for genetic mutations is not an easy task. Sánchez-Rivera and colleagues’ goal is to use a combination of precision genome engineering and functional genetic approaches to tackle this challenge.  

In Nature Biotechnology, the research team demonstrate the utility of prime editing, the newest kid-on-the-block in genome editing, for rapidly screening tumor mutations. “Prime editing is a transformative genome editing technology developed in 2019 by Dr. Andrew Anzalone, Professor David Liu and colleagues at the Broad Institute of MIT and Harvard, which can be used to rewrite DNA with remarkable precision,” explained Sánchez-Rivera, who is the study’s senior author.

Prime editing vs base editing

Scientists have been working to refine CRISPR gene-editing technology for a variety of basic research and clinical applications. In 2016, Liu and colleagues developed base editing, a type of genome editing that enables scientist to engineer some point mutations without inducing double-strand DNA breaks, but not all possible point mutations.

“There are two main types of base editors that can be used to engineer transition mutations (C↔T, G↔A, A↔G, T↔C),” explained Sánchez-Rivera. These are cytosine base editors (CBEs), which enable C–G and T–A base conversion, and adenosine base editors (ABEs), which enable A–T and G–C base conversion. “Recent work combining rational protein engineering and directed evolution has shown that ‘canonical’ base editors can be engineered to install transversion mutations (e.g. A•T to C•G),” Sánchez-Rivera added. He described base editing as an “incredibly powerful and efficient technology” that has “matured at an unprecedented pace”, but that is not without its limitations.

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The types of mutations that can be engineered using base editing – nucleotide transitions and some transversions – do not necessarily reflect the breadth of variants that can occur across human diseases such as cancer.

“While a significant fraction of disease-associated mutations are transitions and transversions, many variants can be compound mutants (i.e., affecting ≥two nucleotides, including both transitions and transversions, and nucleotides are not always next to each other) or indels, which are not amenable to base editing,” Sánchez-Rivera said.

Base editors have also been known to install undesired mutations next to the intended SNV site.

“Base editing guide RNA designs are also limited to the targeted protospacer sequence and to the appropriate positioning of target nucleotides within an optimal editing window that varies among different CBEs and ABEs,” Sánchez-Rivera added. “Lastly, it can be challenging to design on-target control base editing guide RNAs to engineer silent mutations in protein coding genes, which can be quite useful for gene and variant functional studies.”

Prime editing circumvents some of base editing’s shortfalls. “It can be used to engineer virtually any type of genetic alteration, including all types of SNVs and indels,” Sánchez-Rivera said.

Prime editors are molecular machines consisting of a Cas9 nickase fused to a reverse transcriptase enzyme. The Cas9 component provides sequence-specific DNA binding and nickase activity, whereas the reverse transcriptase generates a DNA molecule encoding a desired mutation that can be used to rewrite a targeted segment of the genome.

“Prime editing is often referred to as a ‘search-and-replace’ method because prime editors are directed to engineer a mutation of interest at a specific site in the genome by virtue of the instructions encoded in a prime editing guide RNA (pegRNA),” Sánchez-Rivera explained. “These pegRNA molecules contain both a protospacer (the ‘search’ sequence) and a 3′ extension sequence (the ‘replace’ sequence that dictates the mutation to be installed at the site). Because the mutation is encoded in a pegRNA, prime editing can be used to engineer any type of SNV and small indel.”

Prime editing reveals pathogenic TP53 mutations

The MIT team hypothesized prime editing could be utilized to create over 99% of all small mutations that are seen in cancer patients, but there was one obstacle: pegRNAs that direct CRISPR enzymes to “cut” in specific areas of the genome in prime editing can vary in their levels of efficiency and precision. “To circumvent this, we developed a prime editing ‘sensor’ assay that couples individual pegRNAs to their cognate target sites, which are carefully designed to recapitulate the native sequence and genomic context of genes and sequences we intend to target,” Sánchez-Rivera said.

This enabled the team to simultaneously deploy and quantify prime editing across thousands of sensor sites and endogenous genes, by amplifying and then sequencing the sensor target site from a population of cells that had been engineered to express individual prime editing sensors. In a proof-of-concept study, they created and screened a library of ~30,000 prime editing sensors in A549 lung adenocarcinoma cells.

The pegRNAs encoded in the sensors had been designed to engineer over 1,000 variants of the TP53 tumor suppressor gene, the most frequently mutated gene in cancer patients. “This allowed us to test the hypothesis that some TP53 mutations may not act in a dominant negative fashion, and that certain variants previously considered to be non-pathogenic may have been ignored or misclassified due to lack of appropriate technologies that allow physiological expression of endogenous genetic variants,” Sánchez-Rivera said.

Specific types of variants – notably those that affect the p53 protein oligomerization domain – were found to be pathogenic, contradicting findings from previous studies that relied on overexpressing artificial copies of mutated TP53 in cells. This is a cautionary tale, Samuel Gould, the study’s first author and a student in Sánchez-Rivera’s lab, said: “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.”

“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,” he continued.

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

Investigating diverse types of genetic variants

Though Sánchez-Rivera and colleagues interrogated over 1,000 mutations in TP53, they did not engineer and investigate every single possible mutation that could possibly affect the p53 protein.

It’s also possible that the genetic variants studied might elicit different effects in different cell types; this work focused only on lung adenocarcinoma cells. These limitations, acknowledged by the researchers, present possible avenues for future research studies.

Hundreds of genetic mutations in other genes beyond TP53 are associated with cancer and other genetic disorders. The Sánchez-Rivera lab is expanding its approach to investigate diverse types of genetic variants, combining the methods adopted here with chromosome engineering to explore how large chromosomal rearrangements interact with diverse types of mutations in cancer progression and therapy.

“We encourage readers to take a look at the supplementary material accompanying our manuscript, where we include detailed experimental protocols designed for researchers to seamlessly adopt our approaches. We also developed of a computational pipeline called Prime Editing Guide Generator (PEGG), which is publicly available and allows scientists to design and create prime editing sensor libraries,” Sánchez-Rivera said. “Lastly, to encourage data transparency, all analysis scripts and Jupyter notebooks for generating each figure that appears in our study are available in a GitHub repository.”

About the interviewee

Dr. Francisco J. Sánchez-Rivera is an assistant professor of biology at the Kock Institute for Integrative Cancer Research at MIT. His research aims to understand how genetic variation shapes normal physiology and disease, with a focus on cancer. His laboratory develop and apply genome engineering technologies, genetically-engineered mouse models (GEMMs  and single-cell lineage tracing and omics approaches to obtain comprehensive biological pictures of disease evolution at single cell resolution. By doing so, they hope to produce actionable discoveries that could pave the way for better therapeutic strategies to treat cancer and other diseases.

Reference: Gould SI, Wuest AN, Dong K, et al. High-throughput evaluation of genetic variants with prime editing sensor libraries. Nat Biotech. 2024. doi: 10.1038/s41587-024-02172-9