Got a Virus? Its Name Matters More Than You Think
Dr. Adalja is focused on emerging infectious disease, pandemic preparedness, and biosecurity. He has served on US government panels tasked with developing guidelines for the treatment of plague, botulism, and anthrax in mass casualty settings and the system of care for infectious disease emergencies, and as an external advisor to the New York City Health and Hospital Emergency Management Highly Infectious Disease training program, as well as on a FEMA working group on nuclear disaster recovery. Dr. Adalja is an Associate Editor of the journal Health Security. He was a coeditor of the volume Global Catastrophic Biological Risks, a contributing author for the Handbook of Bioterrorism and Disaster Medicine, the Emergency Medicine CorePendium, Clinical Microbiology Made Ridiculously Simple, UpToDate's section on biological terrorism, and a NATO volume on bioterrorism. He has also published in such journals as the New England Journal of Medicine, the Journal of Infectious Diseases, Clinical Infectious Diseases, Emerging Infectious Diseases, and the Annals of Emergency Medicine. He is a board-certified physician in internal medicine, emergency medicine, infectious diseases, and critical care medicine. Follow him on Twitter: @AmeshAA
It's a familiar scenario: You show up at the doctor feeling miserable—sneezing, coughing, lethargic. We've all been there. And we've all been told the same answer: we're suffering from "a virus."
Failing to establish a specific microbial cause undermines the health of individual patients—and potentially the public at large.
Some patients may be satisfied with that diagnosis, others may be frustrated, and still others may demand antibiotic treatment for a bacterial infection that is usually not even present. As an infectious disease doctor who specializes in pandemic preparedness, I detest using the catch-all "virus" diagnosis for a range of symptoms from common colds to life-threatening pneumonias to unexplained fevers. Failing to establish a specific microbial cause undermines the health of individual patients—and potentially the public at large.
Confirming a specific diagnosis to determine which virus is behind those nasty symptoms is not just an academic exercise. The benefits are plentiful. Patients can forego antibiotic treatment, possibly benefit from antiviral treatment, understand their illness, and be given a prognosis. Additionally, if hospitalized, patients with certain viral infections require specific types of precautions so as not to spread the virus within the hospital.
Another largely undervalued benefit of such an approach is that it allows experts to begin assembling an arsenal of tools that might stave off a global health catastrophe. With severe pandemics, such as the 1918 influenza pandemic that killed 50 to 100 million people, it can be challenging to predict which of the myriad microbial species (bacteria, viruses, fungi, parasites, prions) will be the most likely cause. Many different approaches to prediction exist, but there is a general lack of rigorous analysis about what it takes for any microorganism to reach the pantheon of pandemic pathogens. My colleagues and I at the Johns Hopkins Center for Health Security recently developed a new framework to understand the characteristics of pandemic pathogens.
One of our major conclusions is that the most likely pandemic pathogen will be viral and spread through respiratory means. Viruses rise to the top of the list because, when compared to other types of infectious agents, they have several features that confer pandemic potential: they mutate a lot, the speed of infection is rapid, and there are no broad-spectrum antivirals akin to broad-spectrum antibacterial agents. Contagion through breathing, coughing, and sneezing is likely because it is much more difficult for standard public health measures to extinguish respiratory spread agents compared to other routes of transmission like food, body fluids, or mosquitoes.
With this information, physicians and scientists can begin taking actions to prevent spread of the infection by developing vaccines, testing antiviral compounds, and making diagnostic tests for concerning viruses.
Many of the viral families that could pose a pandemic threat are very common causes of upper respiratory infections like influenza, the common cold, and bronchitis. These viruses cause a wide range of illnesses from mild coughs to serious pneumonias. Indeed, the 2009 H1N1 influenza pandemic virus was discovered in San Diego in a child with very mild illness in whom viral diagnostic testing was pursued. This event highlights the fact that such diseases are not only found in exotic locations in the developing world, but could appear anywhere.
Understanding the patterns of respiratory virus infections -- how frequent they are, which strains are predominating, changes in severity of disease, expanding geographic range -- may provide a glimpse into the first forays of a new human virus or an alert to changing behavior from a well-known virus. With this information, physicians and scientists can begin taking actions to prevent spread of the infection by developing vaccines, testing antiviral compounds, and making diagnostic tests for concerning viruses. Additionally, alerts to healthcare providers will provide greater situational awareness of the patterns of infection.
So, the next time you are given a wastebasket diagnosis of "viral syndrome," push your doctor a little harder. In 2018, we have countless diagnostic tests for viral infections available, many at the point-of-care, that too few physicians use. Not only will you be more satisfied with a real diagnosis, you may be spared an unnecessary course of antibiotics. You can also rest assured that having a name for your virus will help epidemiologists doing a very important job. While we have not yet technologically achieved the famed Tricorder of Star Trek fame that diagnoses everything with a sweep of the hand, using the tools we do have could be one of the keys to detecting the next pandemic virus early enough to intervene.
Dr. Adalja is focused on emerging infectious disease, pandemic preparedness, and biosecurity. He has served on US government panels tasked with developing guidelines for the treatment of plague, botulism, and anthrax in mass casualty settings and the system of care for infectious disease emergencies, and as an external advisor to the New York City Health and Hospital Emergency Management Highly Infectious Disease training program, as well as on a FEMA working group on nuclear disaster recovery. Dr. Adalja is an Associate Editor of the journal Health Security. He was a coeditor of the volume Global Catastrophic Biological Risks, a contributing author for the Handbook of Bioterrorism and Disaster Medicine, the Emergency Medicine CorePendium, Clinical Microbiology Made Ridiculously Simple, UpToDate's section on biological terrorism, and a NATO volume on bioterrorism. He has also published in such journals as the New England Journal of Medicine, the Journal of Infectious Diseases, Clinical Infectious Diseases, Emerging Infectious Diseases, and the Annals of Emergency Medicine. He is a board-certified physician in internal medicine, emergency medicine, infectious diseases, and critical care medicine. Follow him on Twitter: @AmeshAA
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.