Harvard Researchers Are Using a Breakthrough Tool to Find the Antibodies That Best Knock Out the Coronavirus
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
To find a cure for a deadly infectious disease in the 1995 medical thriller Outbreak, scientists extract the virus's antibodies from its original host—an African monkey.
"When a person is infected, the immune system makes antibodies kind of blindly."
The antibodies prevent the monkeys from getting sick, so doctors use these antibodies to make the therapeutic serum for humans. With SARS-CoV-2, the original hosts might be bats or pangolins, but scientists don't have access to either, so they are turning to the humans who beat the virus.
Patients who recovered from COVID-19 are valuable reservoirs of viral antibodies and may help scientists develop efficient therapeutics, says Stephen J. Elledge, professor of genetics and medicine at Harvard Medical School in Boston. Studying the structure of the antibodies floating in their blood can help understand what their immune systems did right to kill the pathogen.
When viruses invade the body, the immune system builds antibodies against them. The antibodies work like Velcro strips—they use special spots on their surface called paratopes to cling to the specific spots on the viral shell called epitopes. Once the antibodies circulating in the blood find their "match," they cling on to the virus and deactivate it.
But that process is far from simple. The epitopes and paratopes are built of various peptides that have complex shapes, are folded in specific ways, and may carry an electrical charge that repels certain molecules. Only when all of these parameters match, an antibody can get close enough to a viral particle—and shut it out.
So the immune system forges many different antibodies with varied parameters in hopes that some will work. "When a person is infected, the immune system makes antibodies kind of blindly," Elledge says. "It's doing a shotgun approach. It's not sure which ones will work, but it knows once it's made a good one that works."
Elledge and his team want to take the guessing out of the process. They are using their home-built tool VirScan to comb through the blood samples of the recovered COVID-19 patients to see what parameters the efficient antibodies should have. First developed in 2015, the VirScan has a library of epitopes found on the shells of viruses known to afflict humans, akin to a database of criminals' mug shots maintained by the police.
Originally, VirScan was meant to reveal which pathogens a person overcame throughout a lifetime, and could identify over 1,000 different strains of viruses and bacteria. When the team ran blood samples against the VirScan's library, the tool would pick out all the "usual suspects." And unlike traditional blood tests called ELISA, which can only detect one pathogen at a time, VirScan can detect all of them at once. Now, the team has updated VirScan with the SARS-CoV-2 "mug shot" and is beginning to test which antibodies from the recovered patients' blood will bind to them.
Knowing which antibodies bind best can also help fine-tune vaccines.
Obtaining blood samples was a challenge that caused some delays. "So far most of the recovered patients have been in China and those samples are hard to get," Elledge says. It also takes a person five to 10 days to develop antibodies, so the blood must be drawn at the right time during the illness. If a person is asymptomatic, it's hard to pinpoint the right moment. "We just got a couple of blood samples so we are testing now," he said. The team hopes to get some results very soon.
Elucidating the structure of efficient antibodies can help create therapeutics for COVID-19. "VirScan is a powerful technology to study antibody responses," says Harvard Medical School professor Dan Barouch, who also directs the Center for Virology and Vaccine Research. "A detailed understanding of the antibody responses to COVID-19 will help guide the design of next-generation vaccines and therapeutics."
For example, scientists can synthesize antibodies to specs and give them to patients as medicine. Once vaccines are designed, medics can use VirScan to see if those vaccinated again COVID-19 generate the necessary antibodies.
Knowing which antibodies bind best can also help fine-tune vaccines. Sometimes, viruses cause the immune system to generate antibodies that don't deactivate it. "We think the virus is trying to confuse the immune system; it is its business plan," Elledge says—so those unhelpful antibodies shouldn't be included in vaccines.
More importantly, VirScan can also tell which people have developed immunity to SARS-CoV-2 and can return to their workplaces and businesses, which is crucial to restoring the economy. Knowing one's immunity status is especially important for doctors working on the frontlines, Elledge notes. "The resistant ones can intubate the sick."
Lina Zeldovich has written about science, medicine and technology for Popular Science, Smithsonian, National Geographic, Scientific American, Reader’s Digest, the New York Times and other major national and international publications. A Columbia J-School alumna, she has won several awards for her stories, including the ASJA Crisis Coverage Award for Covid reporting, and has been a contributing editor at Nautilus Magazine. In 2021, Zeldovich released her first book, The Other Dark Matter, published by the University of Chicago Press, about the science and business of turning waste into wealth and health. You can find her on http://linazeldovich.com/ and @linazeldovich.
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.