Food Poisoning Outbreaks Are Still A Problem. Powerful Tech Is Fighting Back.
With the pandemic at the forefront of everyone's minds, many people have wondered if food could be a source of coronavirus transmission. Luckily, that "seems unlikely," according to the CDC, but foodborne illnesses do still sicken a whopping 48 million people per year.
Whole genome sequencing is like "going from an eight-bit image—maybe like what you would see in Minecraft—to a high definition image."
In normal times, when there isn't a historic global health crisis infecting millions and affecting the lives of billions, foodborne outbreaks are real and frightening, potentially deadly, and can cause widespread fear of particular foods. Think of Romaine lettuce spreading E. coli last year— an outbreak that infected more than 500 people and killed eight—or peanut butter spreading salmonella in 2008, which infected 167 people.
The technologies available to detect and prevent the next foodborne disease outbreak have improved greatly over the past 30-plus years, particularly during the past decade, and better, more nimble technologies are being developed, according to experts in government, academia, and private industry. The key to advancing detection of harmful foodborne pathogens, they say, is increasing speed and portability of detection, and the precision of that detection.
Getting to Rapid Results
Researchers at Purdue University have recently developed a lateral flow assay that, with the help of a laser, can detect toxins and pathogenic E. coli. Lateral flow assays are cheap and easy to use; a good example is a home pregnancy test. You place a liquid or liquefied sample on a piece of paper designed to detect a single substance and soon after you get the results in the form of a colored line: yes or no.
"They're a great portable tool for us for food contaminant detection," says Carmen Gondhalekar, a fifth-year biomedical engineering graduate student at Purdue. "But one of the areas where paper-based lateral flow assays could use improvement is in multiplexing capability and their sensitivity."
J. Paul Robinson, a professor in Purdue's Colleges of Veterinary Medicine and Engineering, and Gondhalekar's advisor, agrees. "One of the fundamental problems that we have in detection is that it is hard to identify pathogens in complex samples," he says.
When it comes to foodborne disease outbreaks, you don't always know what substance you're looking for, so an assay made to detect only a single substance isn't always effective. The goal of the project at Purdue is to make assays that can detect multiple substances at once.
These assays would be more complex than a pregnancy test. As detailed in Gondhalekar's recent paper, a laser pulse helps create a spectral signal from the sample on the assay paper, and the spectral signal is then used to determine if any unique wavelengths associated with one of several toxins or pathogens are present in the sample. Though the handheld technology has yet to be built, the idea is that the results would be given on the spot. So someone in the field trying to track the source of a Salmonella infection could, for instance, put a suspected lettuce sample on the assay and see if it has the pathogen on it.
"What our technology is designed to do is to give you a rapid assessment of the sample," says Robinson. "The goal here is speed."
Seeing the Pathogen in "High-Def"
"One in six Americans will get a foodborne illness every year," according to Dr. Heather Carleton, a microbiologist at the Centers for Disease Control and Prevention's Enteric Diseases Laboratory Branch. But not every foodborne outbreak makes the news. In 2017 alone, the CDC monitored between 18 and 37 foodborne poison clusters per week and investigated 200 multi-state clusters. Hardboiled eggs, ground beef, chopped salad kits, raw oysters, frozen tuna, and pre-cut melon are just a taste of the foods that were investigated last year for different strains of listeria, salmonella, and E. coli.
At the heart of the CDC investigations is PulseNet, a national network of laboratories that uses DNA fingerprinting to detect outbreaks at local and regional levels. This is how it works: When a patient gets sick—with symptoms like vomiting and fever, for instance—they will go to a hospital or clinic for treatment. Since we're talking about foodborne illnesses, a clinician will likely take a stool sample from the patient and send it off to a laboratory to see if there is a foodborne pathogen, like salmonella, E. Coli, or another one. If it does contain a potentially harmful pathogen, then a bacterial isolate of that identified sample is sent to a regional public health lab so that whole genome sequencing can be performed.
Whole genome sequencing can differentiate "virtually all" strains of foodborne pathogens, no matter the species, according to the FDA.
Whole genome sequencing is a method for reading the entire genome of a bacterial isolate (or from any organism, for that matter). Instead of working with a couple dozen data points, now you're working with millions of base pairs. Carleton likes to describe it as "going from an eight-bit image—maybe like what you would see in Minecraft—to a high definition image," she says. "It's really an evolution of how we detect foodborne illnesses and identify outbreaks."
If the bacterial isolate matches another in the CDC's database, this means there could be a potential outbreak and an investigation may be started, with the goal of tracking the pathogen to its source.
Whole genome sequencing has been a relatively recent shift in foodborne disease detection. For more than 20 years, the standard technique for analyzing pathogens in foodborne disease outbreaks was pulsed-field gel electrophoresis. This method creates a DNA fingerprint for each sample in the form of a pattern of about 15-30 "bands," with each band representing a piece of DNA. Researchers like Carleton can use this fingerprint to see if two samples are from the same bacteria. The problem is that 15-30 bands are not enough to differentiate all isolates. Some isolates whose bands look very similar may actually come from different sources and some whose bands look different may be from the same source. But if you can see the entire DNA fingerprint, then you don't have that issue. That's where whole genome sequencing comes in.
Although the PulseNet team had piloted whole genome sequencing as early as 2013, it wasn't until July of last year that the transition to using whole genome sequencing for all pathogens was complete. Though whole genome sequencing requires far more computing power to generate, analyze, and compare those millions of data points, the payoff is huge.
Stopping Outbreaks Sooner
The U.S. Food and Drug Administration (FDA) acquired their first whole genome sequencers in 2008, according to Dr. Eric Brown, the Director of the Division of Microbiology in the FDA's Office of Regulatory Science. Since then, through their GenomeTrakr program, a network of more than 60 domestic and international labs, the FDA has sequenced and publicly shared more than 400,000 isolates. "The impact of what whole genome sequencing could do to resolve a foodborne outbreak event was no less impactful than when NASA turned on the Hubble Telescope for the first time," says Brown.
Whole genome sequencing has helped identify strains of Salmonella that prior methods were unable to differentiate. In fact, whole genome sequencing can differentiate "virtually all" strains of foodborne pathogens, no matter the species, according to the FDA. This means it takes fewer clinical cases—fewer sick people—to detect and end an outbreak.
And perhaps the largest benefit of whole genome sequencing is that these detailed sequences—the millions of base pairs—can imply geographic location. The genomic information of bacterial strains can be different depending on the area of the country, helping these public health agencies eventually track the source of outbreaks—a restaurant, a farm, a food-processing center.
Coming Soon: "Lab in a Backpack"
Now that whole genome sequencing has become the go-to technology of choice for analyzing foodborne pathogens, the next step is making the process nimbler and more portable. Putting "the lab in a backpack," as Brown says.
The CDC's Carleton agrees. "Right now, the sequencer we use is a fairly big box that weighs about 60 pounds," she says. "We can't take it into the field."
A company called Oxford Nanopore Technologies is developing handheld sequencers. Their devices are meant to "enable the sequencing of anything by anyone anywhere," according to Dan Turner, the VP of Applications at Oxford Nanopore.
"The sooner that we can see linkages…the sooner the FDA gets in action to mitigate the problem and put in some kind of preventative control."
"Right now, sequencing is very much something that is done by people in white coats in laboratories that are set up for that purpose," says Turner. Oxford Nanopore would like to create a new, democratized paradigm.
The FDA is currently testing these types of portable sequencers. "We're very excited about it. We've done some pilots, to be able to do that sequencing in the field. To actually do it at a pond, at a river, at a canal. To do it on site right there," says Brown. "This, of course, is huge because it means we can have real-time sequencing capability to stay in step with an actual laboratory investigation in the field."
"The timeliness of this information is critical," says Marc Allard, a senior biomedical research officer and Brown's colleague at the FDA. "The sooner that we can see linkages…the sooner the FDA gets in action to mitigate the problem and put in some kind of preventative control."
At the moment, the world is rightly focused on COVID-19. But as the danger of one virus subsides, it's only a matter of time before another pathogen strikes. Hopefully, with new and advancing technology like whole genome sequencing, we can stop the next deadly outbreak before it really gets going.
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.