The Brave New World of Using DNA to Store Data
Netscape co-founder-turned-venture capitalist billionaire investor Marc Andreessen once posited that software was eating the world. He was right, and the takeover of software resulted in many things. One of them is data. Lots and lots and lots of data. In the previous two years, humanity created more data than it did during its entire existence combined, and the amount will only increase. Think about it: The hundreds of 50KB emails you write a day, the dozens of 10MB photos, the minute-long, 350MB 4K video you shoot on your iPhone X add up to vast quantities of information. All that information needs to be stored. And that's becoming an issue as data volume outpaces storage space.
The race is on to find another medium capable of storing massive amounts of information in as small a space as possible.
"There won't be enough silicon to store all the data we need. It's unlikely that we can make flash memory smaller. We have reached the physical limits," Victor Zhirnov, chief scientist at the Semiconductor Research Corporation, says. "We are facing a crisis that's comparable to the oil crisis in the 1970s. By 2050, we're going to need to store 10 to the 30 bits, compared to 10 to the 23 bits in 2016." That amount of storage space is equivalent to each of the world's seven billion people owning almost six trillion -- that's 10 to the 12th power -- iPhone Xs with 256GB storage space.
The race is on to find another medium capable of storing massive amounts of information in as small a space as possible. Zhirnov and other scientists are looking at the human body, looking to DNA. "Nature has nailed it," Luis Ceze, a professor in the Department of Computer Science and Engineering at the University of Washington, says. "DNA is a molecular storage medium that is remarkable. It's incredibly dense, many, many thousands of times denser than the densest technology that we have today. And DNA is remarkably general. Any information you can map in bits you can store in DNA." It's so dense -- able to store a theoretical maximum of 215 petabytes (215 million gigabytes) in a single gram -- that all the data ever produced could be stored in the back of a tractor trailer truck.
Writing DNA can be an energy-efficient process, too. Consider how the human body is constantly writing and rewriting DNA, and does so on a couple thousand calories a day. And all it needs for storage is a cool, dark place, a significant energy savings when compared to server farms that require huge amounts of energy to run and even more energy to cool.
Picture it: tiny specks of inert DNA made from silicon or another material, stored in cool, dark, dry areas, preserved for all time.
Researchers first succeeded in encoding data onto DNA in 2012, when Harvard University geneticists George Church and Sri Kosuri wrote a 52,000-word book on A, C, G, and T base pairs. Their method only produced 1.28 petabytes per gram of DNA, however, a volume exceeded the next year when a group encoded all 154 Shakespeare sonnets and a 26-second clip of Martin Luther King's "I Have A Dream" speech. In 2017, Columbia University researchers Yaniv Erlich and Dina Zielinski made the process 60 percent more efficient.
The limiting factor today is cost. Erlich said the work his team did cost $7,000 to encode and decode two megabytes of data. To become useful in a widespread way, the price per megabyte needs to plummet. Even advocates concede this point. "Of course it is expensive," Zhirnov says. "But look how much magnetic storage cost in the 1980s. What you store today in your iPhone for virtually nothing would cost many millions of dollars in 1982." There's reason to think the price will continue to fall. Genome readers are improving, getting cheaper, faster, and smaller, and genome sequencing becomes cheaper every year, too. Picture it: tiny specks of inert DNA made from silicon or another material, stored in cool, dark, dry areas, preserved for all time.
"It just takes a few minutes to double a sample. A few more minutes, you double it again. Very quickly, you have thousands or millions of new copies."
Plus, DNA has another advantage over more traditional forms of storage: It's very easy to reproduce. "If you want a second copy of a hard disk drive, you need components for a disk drive, hook both drives up to a computer, and copy. That's a pain," Nick Goldman, a researcher at the European Bioinformatics Institute, says. "DNA, once you have that first sample, it's a process that is absolutely routine in thousands of laboratories around the world to multiply that using polymerase chain reaction [which uses temperature changes or other processes]. It just takes a few minutes to double a sample. A few more minutes, you double it again. Very quickly, you have thousands or millions of new copies."
This ability to duplicate quickly and easily is a positive trait. But, of course, there's also the potential for danger. Does encoding on DNA, the very basis for life, present ethical issues? Could it get out of control and fundamentally alter life as we know it?
The chance is there, but it's remote. The first reason is that storage could be done with only two base pairs, which would serve as replacements for the 0 and 1 digits that make up all digital data. While doing so would decrease the possible density of the storage, it would virtually eliminate the risk that the sequences would be compatible with life.
But even if scientists and researchers choose to use four base pairs, other safeguards are in place that will prevent trouble. According to Ceze, the computer science professor, the snippets of DNA that they write are very short, around 150 nucleotides. This includes the title, the information that's being encoded, and tags to help organize where the snippet should fall in the larger sequence. Furthermore, they generally avoid repeated letters, which dramatically reduces the chance that a protein could be synthesized from the snippet.
"In the future, we'll know enough about someone from a sample of their DNA that we could make a specific poison. That's the danger, not those of us who want to encode DNA for storage."
Inevitably, some DNA will get spilt. "But it's so unlikely that anything that gets created for storage would have a biological interpretation that could interfere with the mechanisms going on in a living organism that it doesn't worry me in the slightest," Goldman says. "We're not of concern for the people who are worried about the ethical issues of synthetic DNA. They are much more concerned about people deliberately engineering anthrax. In the future, we'll know enough about someone from a sample of their DNA that we could make a specific poison. That's the danger, not those of us who want to encode DNA for storage."
In the end, the reality of and risks surrounding encoding on DNA are the same as any scientific advancement: It's another system that is vulnerable to people with bad intentions but not one that is inherently unethical.
"Every human action has some ethical implications," Zhirnov says. "I can use a hammer to build a house or I can use it to harm another person. I don't see why DNA is in any way more or less ethical."
If that house can store all the knowledge in human history, it's worth learning how to build it.
Editor's Note: In response to readers' comments that silicon is one of the earth's most abundant materials, we reached back out to our source, Dr. Victor Zhirnov. He stands by his statement about a coming shortage of silicon, citing this research. The silicon oxide found in beach sand is unsuitable for semiconductors, he says, because the cost of purifying it would be prohibitive. For use in circuit-making, silicon must be refined to a purity of 99.9999999 percent. So the process begins by mining for pure quartz, which can only be found in relatively few places around the world.
A new type of cancer therapy is shrinking deadly brain tumors with just one treatment
Few cancers are deadlier than glioblastomas—aggressive and lethal tumors that originate in the brain or spinal cord. Five years after diagnosis, less than five percent of glioblastoma patients are still alive—and more often, glioblastoma patients live just 14 months on average after receiving a diagnosis.
But an ongoing clinical trial at Mass General Cancer Center is giving new hope to glioblastoma patients and their families. The trial, called INCIPIENT, is meant to evaluate the effects of a special type of immune cell, called CAR-T cells, on patients with recurrent glioblastoma.
How CAR-T cell therapy works
CAR-T cell therapy is a type of cancer treatment called immunotherapy, where doctors modify a patient’s own immune system specifically to find and destroy cancer cells. In CAR-T cell therapy, doctors extract the patient’s T-cells, which are immune system cells that help fight off disease—particularly cancer. These T-cells are harvested from the patient and then genetically modified in a lab to produce proteins on their surface called chimeric antigen receptors (thus becoming CAR-T cells), which makes them able to bind to a specific protein on the patient’s cancer cells. Once modified, these CAR-T cells are grown in the lab for several weeks so that they can multiply into an army of millions. When enough cells have been grown, these super-charged T-cells are infused back into the patient where they can then seek out cancer cells, bind to them, and destroy them. CAR-T cell therapies have been approved by the US Food and Drug Administration (FDA) to treat certain types of lymphomas and leukemias, as well as multiple myeloma, but haven’t been approved to treat glioblastomas—yet.
CAR-T cell therapies don’t always work against solid tumors, such as glioblastomas. Because solid tumors contain different kinds of cancer cells, some cells can evade the immune system’s detection even after CAR-T cell therapy, according to a press release from Massachusetts General Hospital. For the INCIPIENT trial, researchers modified the CAR-T cells even further in hopes of making them more effective against solid tumors. These second-generation CAR-T cells (called CARv3-TEAM-E T cells) contain special antibodies that attack EFGR, a protein expressed in the majority of glioblastoma tumors. Unlike other CAR-T cell therapies, these particular CAR-T cells were designed to be directly injected into the patient’s brain.
The INCIPIENT trial results
The INCIPIENT trial involved three patients who were enrolled in the study between March and July 2023. All three patients—a 72-year-old man, a 74-year-old man, and a 57-year-old woman—were treated with chemo and radiation and enrolled in the trial with CAR-T cells after their glioblastoma tumors came back.
The results, which were published earlier this year in the New England Journal of Medicine (NEJM), were called “rapid” and “dramatic” by doctors involved in the trial. After just a single infusion of the CAR-T cells, each patient experienced a significant reduction in their tumor sizes. Just two days after receiving the infusion, the glioblastoma tumor of the 72-year-old man decreased by nearly twenty percent. Just two months later the tumor had shrunk by an astonishing 60 percent, and the change was maintained for more than six months. The most dramatic result was in the 57-year-old female patient, whose tumor shrank nearly completely after just one infusion of the CAR-T cells.
The results of the INCIPIENT trial were unexpected and astonishing—but unfortunately, they were also temporary. For all three patients, the tumors eventually began to grow back regardless of the CAR-T cell infusions. According to the press release from MGH, the medical team is now considering treating each patient with multiple infusions or prefacing each treatment with chemotherapy to prolong the response.
While there is still “more to do,” says co-author of the study neuro-oncologist Dr. Elizabeth Gerstner, the results are still promising. If nothing else, these second-generation CAR-T cell infusions may someday be able to give patients more time than traditional treatments would allow.
“These results are exciting but they are also just the beginning,” says Dr. Marcela Maus, a doctor and professor of medicine at Mass General who was involved in the clinical trial. “They tell us that we are on the right track in pursuing a therapy that has the potential to change the outlook for this intractable disease.”
Since the early 2000s, AI systems have eliminated more than 1.7 million jobs, and that number will only increase as AI improves. Some research estimates that by 2025, AI will eliminate more than 85 million jobs.
But for all the talk about job security, AI is also proving to be a powerful tool in healthcare—specifically, cancer detection. One recently published study has shown that, remarkably, artificial intelligence was able to detect 20 percent more cancers in imaging scans than radiologists alone.
Published in The Lancet Oncology, the study analyzed the scans of 80,000 Swedish women with a moderate hereditary risk of breast cancer who had undergone a mammogram between April 2021 and July 2022. Half of these scans were read by AI and then a radiologist to double-check the findings. The second group of scans was read by two researchers without the help of AI. (Currently, the standard of care across Europe is to have two radiologists analyze a scan before diagnosing a patient with breast cancer.)
The study showed that the AI group detected cancer in 6 out of every 1,000 scans, while the radiologists detected cancer in 5 per 1,000 scans. In other words, AI found 20 percent more cancers than the highly-trained radiologists.
Scientists have been using MRI images (like the ones pictured here) to train artificial intelligence to detect cancers earlier and with more accuracy. Here, MIT's AI system, MIRAI, looks for patterns in a patient's mammograms to detect breast cancer earlier than ever before. news.mit.edu
But even though the AI was better able to pinpoint cancer on an image, it doesn’t mean radiologists will soon be out of a job. Dr. Laura Heacock, a breast radiologist at NYU, said in an interview with CNN that radiologists do much more than simply screening mammograms, and that even well-trained technology can make errors. “These tools work best when paired with highly-trained radiologists who make the final call on your mammogram. Think of it as a tool like a stethoscope for a cardiologist.”
AI is still an emerging technology, but more and more doctors are using them to detect different cancers. For example, researchers at MIT have developed a program called MIRAI, which looks at patterns in patient mammograms across a series of scans and uses an algorithm to model a patient's risk of developing breast cancer over time. The program was "trained" with more than 200,000 breast imaging scans from Massachusetts General Hospital and has been tested on over 100,000 women in different hospitals across the world. According to MIT, MIRAI "has been shown to be more accurate in predicting the risk for developing breast cancer in the short term (over a 3-year period) compared to traditional tools." It has also been able to detect breast cancer up to five years before a patient receives a diagnosis.
The challenges for cancer-detecting AI tools now is not just accuracy. AI tools are also being challenged to perform consistently well across different ages, races, and breast density profiles, particularly given the increased risks that different women face. For example, Black women are 42 percent more likely than white women to die from breast cancer, despite having nearly the same rates of breast cancer as white women. Recently, an FDA-approved AI device for screening breast cancer has come under fire for wrongly detecting cancer in Black patients significantly more often than white patients.
As AI technology improves, radiologists will be able to accurately scan a more diverse set of patients at a larger volume than ever before, potentially saving more lives than ever.