How Emerging Technologies Can Help Us Fight the New Coronavirus
In nature, few species remain dominant for long. Any sizable population of similar individuals offers immense resources to whichever parasite can evade its defenses, spreading rapidly from one member to the next.
Which will prove greater: our defenses or our vulnerabilities?
Humans are one such dominant species. That wasn't always the case: our hunter-gatherer ancestors lived in groups too small and poorly connected to spread pathogens like wildfire. Our collective vulnerability to pandemics began with the dawn of cities and trade networks thousands of years ago. Roman cities were always demographic sinks, but never more so than when a pandemic agent swept through. The plague of Cyprian, the Antonine plague, the plague of Justinian – each is thought to have killed over ten million people, an appallingly high fraction of the total population of the empire.
With the advent of sanitation, hygiene, and quarantines, we developed our first non-immunological defenses to curtail the spread of plagues. With antibiotics, we began to turn the weapons of microbes against our microbial foes. Most potent of all, we use vaccines to train our immune systems to fight pathogens before we are even exposed. Edward Jenner's original vaccine alone is estimated to have saved half a billion lives.
It's been over a century since we suffered from a swift and deadly pandemic. Even the last deadly influenza of 1918 killed only a few percent of humanity – nothing so bad as any of the Roman plagues, let alone the Black Death of medieval times.
How much of our recent winning streak has been due to luck?
Much rides on that question, because the same factors that first made our ancestors vulnerable are now ubiquitous. Our cities are far larger than those of ancient times. They're inhabited by an ever-growing fraction of humanity, and are increasingly closely connected: we now routinely travel around the world in the course of a day. Despite urbanization, global population growth has increased contact with wild animals, creating more opportunities for zoonotic pathogens to jump species. Which will prove greater: our defenses or our vulnerabilities?
The tragic emergence of coronavirus 2019-nCoV in Wuhan may provide a test case. How devastating this virus will become is highly uncertain at the time of writing, but its rapid spread to many countries is deeply worrisome. That it seems to kill only the already infirm and spare the healthy is small comfort, and may counterintuitively assist its spread: it's easy to implement a quarantine when everyone infected becomes extremely ill, but if carriers may not exhibit symptoms as has been reported, it becomes exceedingly difficult to limit transmission. The virus, a distant relative of the more lethal SARS virus that killed 800 people in 2002 to 2003, has evolved to be transmitted between humans and spread to 18 countries in just six weeks.
Humanity's response has been faster than ever, if not fast enough. To its immense credit, China swiftly shared information, organized and built new treatment centers, closed schools, and established quarantines. The Coalition for Epidemic Preparedness Innovations, which was founded in 2017, quickly funded three different companies to develop three different varieties of vaccine: a standard protein vaccine, a DNA vaccine, and an RNA vaccine, with more planned. One of the agreements was signed after just four days of discussion, far faster than has ever been done before.
The new vaccine candidates will likely be ready for clinical trials by early summer, but even if successful, it will be additional months before the vaccine will be widely available. The delay may well be shorter than ever before thanks to advances in manufacturing and logistics, but a delay it will be.
The 1918 influenza virus killed more than half of its victims in the United Kingdom over just three months.
If we faced a truly nasty virus, something that spreads like pandemic influenza – let alone measles – yet with the higher fatality rate of, say, H7N9 avian influenza, the situation would be grim. We are profoundly unprepared, on many different levels.
So what would it take to provide us with a robust defense against pandemics?
Minimize the attack surface: 2019-nCoV jumped from an animal, most probably a bat, to humans. China has now banned the wildlife trade in response to the epidemic. Keeping it banned would be prudent, but won't be possible in all nations. Still, there are other methods of protection. Influenza viruses commonly jump from birds to pigs to humans; the new coronavirus may have similarly passed through a livestock animal. Thanks to CRISPR, we can now edit the genomes of most livestock. If we made them immune to known viruses, and introduced those engineered traits to domesticated animals everywhere, we would create a firewall in those intermediate hosts. We might even consider heritably immunizing the wild organisms most likely to serve as reservoirs of disease.
None of these defenses will be cheap, but they'll be worth every penny.
Rapid diagnostics: We need a reliable method of detection costing just pennies to be available worldwide inside of a week of discovering a new virus. This may eventually be possible thanks to a technology called SHERLOCK, which is based on a CRISPR system more commonly used for precision genome editing. Instead of using CRISPR to find and edit a particular genome sequence in a cell, SHERLOCK programs it to search for a desired target and initiate an easily detected chain reaction upon discovery. The technology is capable of fantastic sensitivity: with an attomolar (10-18) detection limit, it senses single molecules of a unique DNA or RNA fingerprint, and the components can be freeze-dried onto paper strips.
Better preparations: China acted swiftly to curtail the spread of the Wuhan virus with traditional public health measures, but not everything went as smoothly as it might have. Most cities and nations have never conducted a pandemic preparedness drill. Best give people a chance to practice keeping the city barely functional while minimizing potential exposure events before facing the real thing.
Faster vaccines: Three months to clinical trials is too long. We need a robust vaccine discovery and production system that can generate six candidates within a week of the pathogen's identification, manufacture a million doses the week after, and scale up to a hundred million inside of a month. That may be possible for novel DNA and RNA-based vaccines, and indeed anything that can be delivered using a standardized gene therapy vector. For example, instead of teaching each person's immune system to evolve protective antibodies by showing it pieces of the virus, we can program cells to directly produce known antibodies via gene therapy. Those antibodies could be discovered by sifting existing diverse libraries of hundreds of millions of candidates, computationally designed from scratch, evolved using synthetic laboratory ecosystems, or even harvested from the first patients to report symptoms. Such a vaccine might be discovered and produced fast enough at scale to halt almost any natural pandemic.
Robust production and delivery: Our defenses must not be vulnerable to the social and economic disruptions caused by a pandemic. Unfortunately, our economy selects for speed and efficiency at the expense of robustness. Just-in-time supply chains that wing their way around the world require every node to be intact. If workers aren't on the job producing a critical component, the whole chain breaks until a substitute can be found. A truly nasty pandemic would disrupt economies all over the world, so we will need to pay extra to preserve the capacity for independent vertically integrated production chains in multiple nations. Similarly, vaccines are only useful if people receive them, so delivery systems should be as robustly automated as possible.
None of these defenses will be cheap, but they'll be worth every penny. Our nations collectively spend trillions on defense against one another, but only billions to protect humanity from pandemic viruses known to have killed more people than any human weapon. That's foolish – especially since natural animal diseases that jump the species barrier aren't the only pandemic threats.
We will eventually make our society immune to naturally occurring pandemics, but that day has not yet come, and future pandemic viruses may not be natural.
The complete genomes of all historical pandemic viruses ever to have been sequenced are freely available to anyone with an internet connection. True, these are all agents we've faced before, so we have a pre-existing armory of pharmaceuticals and vaccines and experience. There's no guarantee that they would become pandemics again; for example, a large fraction of humanity is almost certainly immune to the 1918 influenza virus due to exposure to the related 2009 pandemic, making it highly unlikely that the virus would take off if released.
Still, making the blueprints publicly available means that a large and growing number of people with the relevant technical skills can single-handedly make deadly biological agents that might be able to spread autonomously -- at least if they can get their hands on the relevant DNA. At present, such people most certainly can, so long as they bother to check the publicly available list of which gene synthesis companies do the right thing and screen orders -- and by implication, which ones don't.
One would hope that at least some of the companies that don't advertise that they screen are "honeypots" paid by intelligence agencies to catch would-be bioterrorists, but even if most of them are, it's still foolish to let individuals access that kind of destructive power. We will eventually make our society immune to naturally occurring pandemics, but that day has not yet come, and future pandemic viruses may not be natural. Hence, we should build a secure and adaptive system capable of screening all DNA synthesis for known and potential future pandemic agents... without disclosing what we think is a credible bioweapon.
Whether or not it becomes a global pandemic, the emergence of Wuhan coronavirus has underscored the need for coordinated action to prevent the spread of pandemic disease. Let's ensure that our reactive response minimally prepares us for future threats, for one day, reacting may not be enough.
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.