Research Area: Computational Biology
With the new technique, MIT researchers hope to identify mutations that could be targeted with new cancer therapies.
Anne Trafton | MIT News
March 12, 2024
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, MIT researchers have now come up with a way to screen those mutations much more easily.
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, an MIT assistant professor of biology, a member of the Koch Institute for Integrative Cancer Research, and the senior author of the study.
MIT graduate student Samuel Gould is the lead author of the paper, which appears today in Nature Biotechnology.
Editing cells
The new technique builds on research that Sanchez-Rivera began 10 years ago as an MIT graduate student. At that time, working with Tyler Jacks, the David H. Koch Professor of Biology, and then-postdoc Thales Papagiannakopoulos, Sanchez-Rivera developed a way to use CRISPR genome-editing to introduce into mice genetic mutations linked to lung cancer.
In that study, the researchers showed that they could delete genes that are often lost in lung tumor cells, and the resulting tumors were similar to naturally arising tumors with those mutations. However, this technique did not allow for the creation of point mutations (substitutions of one nucleotide for another) or insertions.
“While some cancer patients have deletions in certain genes, the vast majority of mutations that cancer patients have in their tumors also include point mutations or small insertions,” Sanchez-Rivera says.
Since then, David Liu, a professor in the Harvard University Department of Chemistry and Chemical Biology and a core institute member of the Broad Institute, has developed new CRISPR-based genome editing technologies that can generate additional types of mutations more easily. With base editing, developed in 2016, researchers can engineer point mutations, but not all possible point mutations. In 2019, Liu, who is also an author of the Nature Biotechnology study, developed a technique called prime editing, which enables any kind of point mutation to be introduced, as well as insertions and deletions.
“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.
When they began working on this project, Sanchez-Rivera and Gould calculated that if performed successfully, prime editing could be used to generate more than 99 percent of all small mutations seen in cancer patients.
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, a gene that is mutated in more than half of all cancer patients. 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, 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 — an assembly of four p53 proteins — had been studied before, using a technique that involves inserting artificial copies of a mutated p53 gene into a cell.
Those 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 suffering from tumors that have a defined genetic makeup,” Sanchez-Rivera says. “This approach based on prime editing has the potential to change everything.”
The research was funded, in part, by the National Institute of General Medical Sciences, an MIT School of Science Fellowship in Cancer Research, a Howard Hughes Medical Institute Hanna Gray Fellowship, the V Foundation for Cancer Research, a National Cancer Institute Cancer Center Support Grant, the Ludwig Center at MIT, a Koch Institute Frontier Award, the MIT Research Support Committee, and the Koch Institute Support (core) Grant from the National Cancer Institute.
For a brief period during embryonic development, cells must rely on messenger RNAs provided by the maternal genome. In Developmental Cell, Bartel Lab members detail how cells regulate this limited supply of genetic material.
Greta Friar | Whitehead Institute
March 6, 2024
Whitehead Institute Member Jonathan Weissman and colleagues used large-scale systematic genetic screens to identify the molecules and pathways that populate the mitochondrial surface with important and diverse signaling proteins. They deciphered the logic by which the cell ensures the proper delivery of these proteins. These findings may have important implications for understanding the impact on health and disease when these processes go awry.
Greta Friar | Whitehead Institute
February 26, 2024
A new method from the Weissman Lab provides a detailed look at the family trees of human blood cells and the characteristics of individual cells, providing insights into hematopoietic stem cells (HSCs).
January 19, 2024
Education
- Graduate: PhD, 2017, University of Washington
- Undergraduate: BS, 2010, Micro/Molecular Biology, Portland State University
Research Summary
Sergey Ovchinnikov uses phylogenetic inference, protein structure prediction/determination, protein design, deep learning, energy-based models, and differentiable programming to tackle evolutionary questions at environmental, organismal, genomic, structural, and molecular scales, with the aim of developing a unified model of protein evolution.
Genes expressed from the X and Y chromosomes impact cells throughout the body—not just in the reproductive system—by dialing up or down the expression of thousands of genes found on other chromosomes.
December 13, 2023
New work from the Reddien lab reveals that during tissue regeneration in flatworms, the spatial pattern of stem cell choice is highly heterogenous.
November 27, 2023
Small molecule drugs, such as those used for cancer treatment, tend to concentrate in specific regions of the cell, and can bind to things besides their intended target. These collective, weak interactions may detain a significant percentage of drug molecules. New research from the Young lab trained a machine learning model to predict where a drug will concentrate based on its chemical features, which may be relevant to understanding many cellular processes and to the design of safe and effective drugs.
September 27, 2023
It can be difficult to tell which of the many small genetic differences between us and chimps have been significant to our evolution. New research from Jonathan Weissman and colleagues narrowed in on the key differences in how humans and chimps rely on certain genes, including how humans became able to grow comparatively large brains.
Greta Friar | Whitehead Institute
June 22, 2023
Humans split away from our closest animal relatives, chimpanzees, and formed our own branch on the evolutionary tree about seven million years ago. In the time since—brief, from an evolutionary perspective—our ancestors evolved the traits that make us human, including a much bigger brain than chimpanzees and bodies that are better suited to walking on two feet. These physical differences are underpinned by subtle changes at the level of our DNA. However, it can be hard to tell which of the many small genetic differences between us and chimps have been significant to our evolution.
New research from Whitehead Institute Member Jonathan Weissman; University of California, San Francisco Assistant Professor Alex Pollen; Weissman lab postdoc Richard She; Pollen lab graduate student Tyler Fair; and colleagues uses cutting edge tools developed in the Weissman lab to narrow in on the key differences in how humans and chimps rely on certain genes. Their findings, published in the journal Cell on June 20, may provide unique clues into how humans and chimps have evolved, including how humans became able to grow comparatively large brains.
Studying function rather than genetic code
Only a handful of genes are fundamentally different between humans and chimps; the rest of the two species’ genes are typically nearly identical. Differences between the species often come down to when and how cells use those nearly identical genes. However, only some of the many differences in gene use between the two species underlie big changes in physical traits. The researchers developed an approach to narrow in on these impactful differences.
Their approach, using stem cells derived from human and chimp skin samples, relies on a tool called CRISPR interference (CRISPRi) that Weissman’s lab developed. CRISPRi uses a modified version of the CRISPR/Cas9 gene editing system to effectively turn off individual genes. The researchers used CRISPRi to turn off each gene one at a time in a group of human stem cells and a group of chimp stem cells. Then they looked to see whether or not the cells multiplied at their normal rate. If the cells stopped multiplying as quickly or stopped altogether, then the gene that had been turned off was considered essential: a gene that the cells need to be active–producing a protein product–in order to thrive. The researchers looked for instances in which a gene was essential in one species but not the other as a way of exploring if and how there were fundamental differences in the basic ways that human and chimp cells function.
By looking for differences in how cells function with particular genes disabled, rather than looking at differences in the DNA sequence or expression of genes, the approach ignores differences that do not appear to impact cells. If a difference in gene use between species has a large, measurable effect at the level of the cell, this likely reflects a meaningful difference between the species at a larger physical scale, and so the genes identified in this way are likely to be relevant to the distinguishing features that have emerged over human and chimp evolution.
“The problem with looking at expression changes or changes in DNA sequences is that there are many of them and their functional importance is unclear,” says Weissman, who is also a professor of biology at the Massachusetts Institute of Technology and an Investigator with the Howard Hughes Medical Institute. “This approach looks at changes in how genes interact to perform key biological processes, and what we see by doing that is that, even on the short timescale of human evolution, there has been fundamental rewiring of cells.”
After the CRISPRi experiments were completed, She compiled a list of the genes that appeared to be essential in one species but not the other. Then he looked for patterns. Many of the 75 genes identified by the experiments clustered together in the same pathways, meaning the clusters were involved in the same biological processes. This is what the researchers hoped to see. Individual small changes in gene use may not have much of an effect, but when those changes accumulate in the same biological pathway or process, collectively they can cause a substantive change in the species. When the researchers’ approach identified genes that cluster in the same processes, this suggested to them that their approach had worked and that the genes were likely involved in human and chimp evolution.
“Isolating the genetic changes that made us human has been compared to searching for needles in a haystack because there are millions of genetic differences, and most are likely to have negligible effects on traits,” Pollen says. “However, we know that there are lots of small effect mutations that in aggregate may account for many species differences. This new approach allows us to study these aggregate effects, enabling us to weigh the impact of the haystack on cellular functions.”
Researchers think bigger brains may rely on genes regulating how quickly cells divide
One cluster on the list stood out to the researchers: a group of genes essential to chimps, but not to humans, that help to control the cell cycle, which regulates when and how cells decide to divide. Cell cycle regulation has long been hypothesized to play a role in the evolution of humans’ large brains. The hypothesis goes like this: Neural progenitors are the cells that will become neurons and other brain cells. Before becoming mature brain cells, neural progenitors divide multiple times to make more of themselves. The more divisions that the neural progenitors undergo, the more cells the brain will ultimately contain—and so, the bigger it will be. Researchers think that something changed during human evolution to allow neural progenitors to spend less time in a non-dividing phase of the cell cycle and transition more quickly towards division. This simple difference would lead to additional divisions, each of which could essentially double the final number of brain cells.
Consistent with the popular hypothesis that human neural progenitors may undergo more divisions, resulting in a larger brain, the researchers found that several genes that help cells to transition more quickly through the cell cycle are essential in chimp neural progenitor cells but not in human cells. When chimp neural progenitor cells lose these genes, they linger in a non-dividing phase, but when human cells lose them, they keep cycling and dividing. These findings suggest that human neural progenitors may be better able to withstand stresses—such as the loss of cell cycle genes—that would limit the number of divisions the cells undergo, enabling humans to produce enough cells to build a larger brain.
“This hypothesis has been around for a long time, and I think our study is among the first to show that there is in fact a species difference in how the cell cycle is regulated in neural progenitors,” She says. “We had no idea going in which genes our approach would highlight, and it was really exciting when we saw that one of our strongest findings matched and expanded on this existing hypothesis.”
More subjects lead to more robust results
Research comparing chimps to humans often uses samples from only one or two individuals from each species, but this study used samples from six humans and six chimps. By making sure that the patterns they observed were consistent across multiple individuals of each species, the researchers could avoid mistaking the naturally occurring genetic variation between individuals as representative of the whole species. This allowed them to be confident that the differences they identified were truly differences between species.
The researchers also compared their findings for chimps and humans to orangutans, which split from the other species earlier in our shared evolutionary history. This allowed them to figure out where on the evolutionary tree a change in gene use most likely occurred. If a gene is essential in both chimps and orangutans, then it was likely essential in the shared ancestor of all three species; it’s more likely for a particular difference to have evolved once, in a common ancestor, than to have evolved independently multiple times. If the same gene is no longer essential in humans, then its role most likely shifted after humans split from chimps. Using this system, the researchers showed that the changes in cell cycle regulation occurred during human evolution, consistent with the proposal that they contributed to the expansion of the brain in humans.
The researchers hope that their work not only improves our understanding of human and chimp evolution, but also demonstrates the strength of the CRISPRi approach for studying human evolution and other areas of human biology. Researchers in the Weissman and Pollen labs are now using the approach to better understand human diseases—looking for the subtle differences in gene use that may underlie important traits such as whether someone is at risk of developing a disease, or how they will respond to a medication. The researchers anticipate that their approach will enable them to sort through many small genetic differences between people to narrow in on impactful ones underlying traits in health and disease, just as the approach enabled them to narrow in on the evolutionary changes that helped make us human.