Study Shows “Living Drug” Can Provide a Lasting Cure for Cancer
Doug Olson was 49 when he was diagnosed with chronic lymphocytic leukemia, a blood cancer that strikes 21,000 Americans annually. Although the disease kills most patients within a decade, Olson’s case progressed more slowly, and courses of mild chemotherapy kept him healthy for 13 years. Then, when he was 62, the medication stopped working. The cancer had mutated, his doctor explained, becoming resistant to standard remedies. Harsher forms of chemo might buy him a few months, but their side effects would be debilitating. It was time to consider the treatment of last resort: a bone-marrow transplant.
Olson, a scientist who developed blood-testing instruments, knew the odds. There was only a 50 percent chance that a transplant would cure him. There was a 20 percent chance that the agonizing procedure—which involves destroying the patient’s marrow with chemo and radiation, then infusing his blood with donated stem cells—would kill him. If he survived, he would face the danger of graft-versus-host disease, in which the donor’s cells attack the recipient’s tissues. To prevent it, he would have to take immunosuppressant drugs, increasing the risk of infections. He could end up with pneumonia if one of his three grandchildren caught a sniffle. “I was being pushed into a corner,” Olson recalls, “with very little room to move.”
Soon afterward, however, his doctor revealed a possible escape route. He and some colleagues at the University of Pennsylvania’s Abramson Cancer Center were starting a clinical trial, he said, and Olson—still mostly symptom-free—might be a good candidate. The experimental treatment, known as CAR-T therapy, would use genetic engineering to turn his T lymphocytes (immune cells that guard against viruses and other pathogens) into a weapon against cancer.
In September 2010, technicians took some of Olson’s T cells to a laboratory, where they were programmed with new molecular marching orders and coaxed to multiply into an army of millions. When they were ready, a nurse inserted a catheter into his neck. At the turn of a valve, his soldiers returned home, ready to do battle.
“I felt like I’d won the lottery,” Olson says. But he was only the second person in the world to receive this “living drug,” as the University of Pennsylvania investigators called it. No one knew how long his remission would last.
Three weeks later, Olson was slammed with a 102-degree fever, nausea, and chills. The treatment had triggered two dangerous complications: cytokine release syndrome, in which immune chemicals inflame the patient’s tissues, and tumor lysis syndrome, in which toxins from dying cancer cells overwhelm the kidneys. But the crisis passed quickly, and the CAR-T cells fought on. A month after the infusion, the doctor delivered astounding news: “We can’t find any cancer in your body.”
“I felt like I’d won the lottery,” Olson says. But he was only the second person in the world to receive this “living drug,” as the University of Pennsylvania investigators called it. No one knew how long his remission would last.
An Unexpected Cure
In February 2022, the same cancer researchers reported a remarkable milestone: the trial’s first two patients had survived for more than a decade. Although Olson’s predecessor—a retired corrections officer named Bill Ludwig—died of COVID-19 complications in early 2021, both men had remained cancer-free. And the modified immune cells continued to patrol their territory, ready to kill suspected tumor cells the moment they arose.
“We can now conclude that CAR-T cells can actually cure patients with leukemia,” University of Pennsylvania immunologist Carl June, who spearheaded the development of the technique, told reporters. “We thought the cells would be gone in a month or two. The fact that they’ve survived 10 years is a major surprise.”
Even before the announcement, it was clear that CAR-T therapy could win a lasting reprieve for many patients with cancers that were once a death sentence. Since the Food and Drug Administration approved June’s version (marketed as Kymriah) in 2017, the agency has greenlighted five more such treatments for various types of leukemia, lymphoma, and myeloma. “Every single day, I take care of patients who would previously have been told they had no options,” says Rayne Rouce, a pediatric hematologist/oncologist at Texas Children’s Cancer Center. “Now we not only have a treatment option for those patients, but one that could potentially be the last therapy for their cancer that they’ll ever have to receive.”
Immunologist Carl June, middle, spearheaded development of the CAR-T therapy that gave patients Bill Ludwig, left, and Doug Olson, right, a lengthy reprieve on their terminal cancer diagnoses.
Penn Medicine
Yet the CAR-T approach doesn’t help everyone. So far, it has only shown success for blood cancers—and for those, the overall remission rate is 30 to 40 percent. “When it works, it works extraordinarily well,” says Olson’s former doctor, David Porter, director of Penn’s blood and bone marrow transplant program. “It’s important to know why it works, but it’s equally important to know why it doesn’t—and how we can fix that.”
The team’s study, published in the journal Nature, offers a wealth of data on what worked for these two patients. It may also hold clues for how to make the therapy effective for more people.
Building a Better T Cell
Carl June didn’t set out to cure cancer, but his serendipitous career path—and a personal tragedy—helped him achieve insights that had eluded other researchers. In 1971, hoping to avoid combat in Vietnam, he applied to the U.S. Naval Academy in Annapolis, Maryland. June showed a knack for biology, so the Navy sent him on to Baylor College of Medicine. He fell in love with immunology during a fellowship researching malaria vaccines in Switzerland. Later, the Navy deployed him to the Fred Hutchinson Cancer Research Center in Seattle to study bone marrow transplantation.
There, June became part of the first research team to learn how to culture T cells efficiently in a lab. After moving on to the National Naval Medical Center in the ’80s, he used that knowledge to combat the newly emerging AIDS epidemic. HIV, the virus that causes the disease, invades T cells and eventually destroys them. June and his post-doc Bruce Levine developed a method to restore patients’ depleted cell populations, using tiny magnetic beads to deliver growth-stimulating proteins. Infused into the body, the new T cells effectively boosted immune function.
In 1999, after leaving the Navy, June joined the University of Pennsylvania. His wife, who’d been diagnosed with ovarian cancer, died two years later, leaving three young children. “I had not known what it was like to be on the other side of the bed,” he recalls. Watching her suffer through grueling but futile chemotherapy, followed by an unsuccessful bone-marrow transplant, he resolved to focus on finding better cancer treatments. He started with leukemia—a family of diseases in which mutant white blood cells proliferate in the marrow.
Cancer is highly skilled at slipping through the immune system’s defenses. T cells, for example, detect pathogens by latching onto them with receptors designed to recognize foreign proteins. Leukemia cells evade detection, in part, by masquerading as normal white blood cells—that is, as part of the immune system itself.
June planned to use a viral vector no one had tried before: HIV.
To June, chimeric antigen receptor (CAR) T cells looked like a promising tool for unmasking and destroying the impostors. Developed in the early ’90s, these cells could be programmed to identify a target protein, and to kill any pathogen that displayed it. To do the programming, you spliced together snippets of DNA and inserted them into a disabled virus. Next, you removed some of the patient’s T cells and infected them with the virus, which genetically hijacked its new hosts—instructing them to find and slay the patient’s particular type of cancer cells. When the T cells multiplied, their descendants carried the new genetic code. You then infused those modified cells into the patient, where they went to war against their designated enemy.
Or that’s what happened in theory. Many scientists had tried to develop therapies using CAR-T cells, but none had succeeded. Although the technique worked in lab animals, the cells either died out or lost their potency in humans.
But June had the advantage of his years nurturing T cells for AIDS patients, as well as the technology he’d developed with Levine (who’d followed him to Penn with other team members). He also planned to use a viral vector no one had tried before: HIV, which had evolved to thrive in human T cells and could be altered to avoid causing disease. By the summer of 2010, he was ready to test CAR-T therapy against chronic lymphocytic leukemia (CLL), the most common form of the disease in adults.
Three patients signed up for the trial, including Doug Olson and Bill Ludwig. A portion of each man’s T cells were reprogrammed to detect a protein found only on B lymphocytes, the type of white blood cells affected by CLL. Their genetic instructions ordered them to destroy any cell carrying the protein, known as CD19, and to multiply whenever they encountered one. This meant the patients would forfeit all their B cells, not just cancerous ones—but regular injections of gamma globulins (a cocktail of antibodies) would make up for the loss.
After being infused with the CAR-T cells, all three men suffered high fevers and potentially life-threatening inflammation, but all pulled through without lasting damage. The third patient experienced a partial remission and survived for eight months. Olson and Ludwig were cured.
Learning What Works
Since those first infusions, researchers have developed reliable ways to prevent or treat the side effects of CAR-T therapy, greatly reducing its risks. They’ve also been experimenting with combination therapies—pairing CAR-T with chemo, cancer vaccines, and immunotherapy drugs called checkpoint inhibitors—to improve its success rate. But CAR-T cells are still ineffective for at least 60 percent of blood cancer patients. And they remain in the experimental stage for solid tumors (including pancreatic cancer, mesothelioma, and glioblastoma), whose greater complexity make them harder to attack.
The new Nature study offers clues that could fuel further advances. The Penn team “profiled these cells at a level where we can almost say, ‘These are the characteristics that a T cell would need to survive 10 years,’” says Rouce, the physician at Texas Children’s Cancer Center.
One surprising finding involves how CAR-T cells change in the body over time. At first, those that Olson and Ludwig received showed the hallmarks of “killer” T-cells (also known as CD8 cells)—highly active lymphocytes bent on exterminating every tumor cell in sight. After several months, however, the population shifted toward “helper” T-cells (or CD4s), which aid in forming long-term immune memory but are normally incapable of direct aggression. Over the years, the numbers swung back and forth, until only helper cells remained. Those cells showed markers suggesting they were too exhausted to function—but in the lab, they were able not only to recognize but to destroy cancer cells.
June and his team suspect that those tired-looking helper cells had enough oomph to kill off any B cells Olson and Ludwig made, keeping the pair’s cancers permanently at bay. If so, that could prompt new approaches to selecting cells for CAR-T therapy. Maybe starting with a mix of cell types—not only CD8s, but CD4s and other varieties—would work better than using CD8s alone. Or perhaps inducing changes in cell populations at different times would help.
Another potential avenue for improvement is starting with healthier cells. Evidence from this and other trials hints that patients whose T cells are more robust to begin with respond better when their cells are used in CAR-T therapy. The Penn team recently completed a clinical trial in which CLL patients were treated with ibrutinib—a drug that enhances T-cell function—before their CAR-T cells were manufactured. The response rate, says David Porter, was “very high,” with most patients remaining cancer-free a year after being infused with the souped-up cells.
Such approaches, he adds, are essential to achieving the next phase in CAR-T therapy: “Getting it to work not just in more people, but in everybody.”
Doug Olson enjoys nature - and having a future.
Penn Medicine
To grasp what that could mean, it helps to talk with Doug Olson, who’s now 75. In the years since his infusion, he has watched his four children forge careers, and his grandkids reach their teens. He has built a business and enjoyed the rewards of semi-retirement. He’s done volunteer and advocacy work for cancer patients, run half-marathons, sailed the Caribbean, and ridden his bike along the sun-dappled roads of Silicon Valley, his current home.
And in his spare moments, he has just sat there feeling grateful. “You don’t really appreciate the effect of having a lethal disease until it’s not there anymore,” he says. “The world looks different when you have a future.”
This article was first published on Leaps.org on March 24, 2022.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers