New 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.”
Researchers probe extreme gene therapy for severe alcoholism
Story by Freethink
A single shot — a gene therapy injected into the brain — dramatically reduced alcohol consumption in monkeys that previously drank heavily. If the therapy is safe and effective in people, it might one day be a permanent treatment for alcoholism for people with no other options.
The challenge: Alcohol use disorder (AUD) means a person has trouble controlling their alcohol consumption, even when it is negatively affecting their life, job, or health.
In the U.S., more than 10 percent of people over the age of 12 are estimated to have AUD, and while medications, counseling, or sheer willpower can help some stop drinking, staying sober can be a huge struggle — an estimated 40-60 percent of people relapse at least once.
A team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
According to the CDC, more than 140,000 Americans are dying each year from alcohol-related causes, and the rate of deaths has been rising for years, especially during the pandemic.
The idea: For occasional drinkers, alcohol causes the brain to release more dopamine, a chemical that makes you feel good. Chronic alcohol use, however, causes the brain to produce, and process, less dopamine, and this persistent dopamine deficit has been linked to alcohol relapse.
There is currently no way to reverse the changes in the brain brought about by AUD, but a team of U.S. researchers suspected that an in-development gene therapy for Parkinson’s disease might work as a dopamine-replenishing treatment for alcoholism, too.
To find out, they tested it in heavy-drinking monkeys — and the animals’ alcohol consumption dropped by 90% over the course of a year.
How it works: The treatment centers on the protein GDNF (“glial cell line-derived neurotrophic factor”), which supports the survival of certain neurons, including ones linked to dopamine.
For the new study, a harmless virus was used to deliver the gene that codes for GDNF into the brains of four monkeys that, when they had the option, drank heavily — the amount of ethanol-infused water they consumed would be equivalent to a person having nine drinks per day.
“We targeted the cell bodies that produce dopamine with this gene to increase dopamine synthesis, thereby replenishing or restoring what chronic drinking has taken away,” said co-lead researcher Kathleen Grant.
To serve as controls, another four heavy-drinking monkeys underwent the same procedure, but with a saline solution delivered instead of the gene therapy.
The results: All of the monkeys had their access to alcohol removed for two months following the surgery. When it was then reintroduced for four weeks, the heavy drinkers consumed 50 percent less compared to the control group.
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
The researchers then took the alcohol away for another four weeks, before giving it back for four. They repeated this cycle for a year, and by the end of it, the treated monkeys’ consumption had fallen by more than 90 percent compared to the controls.
“Drinking went down to almost zero,” said Grant. “For months on end, these animals would choose to drink water and just avoid drinking alcohol altogether. They decreased their drinking to the point that it was so low we didn’t record a blood-alcohol level.”
When the researchers examined the monkeys’ brains at the end of the study, they were able to confirm that dopamine levels had been replenished in the treated animals, but remained low in the controls.
Looking ahead: Dopamine is involved in a lot more than addiction, so more research is needed to not only see if the results translate to people but whether the gene therapy leads to any unwanted changes to mood or behavior.
Because the therapy requires invasive brain surgery and is likely irreversible, it’s unlikely to ever become a common treatment for alcoholism — but it could one day be the only thing standing between people with severe AUD and death.
“[The treatment] would be most appropriate for people who have already shown that all our normal therapeutic approaches do not work for them,” said Grant. “They are likely to create severe harm or kill themselves or others due to their drinking.”
This article originally appeared on Freethink, home of the brightest minds and biggest ideas of all time.
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.