Beyond Henrietta Lacks: How the Law Has Denied Every American Ownership Rights to Their Own Cells
The common perception is that Henrietta Lacks was a victim of poverty and racism when in 1951 doctors took samples of her cervical cancer without her knowledge or permission and turned them into the world's first immortalized cell line, which they called HeLa. The cell line became a workhorse of biomedical research and facilitated the creation of medical treatments and cures worth untold billions of dollars. Neither Lacks nor her family ever received a penny of those riches.
But racism and poverty is not to blame for Lacks' exploitation—the reality is even worse. In fact all patients, then and now, regardless of social or economic status, have absolutely no right to cells that are taken from their bodies. Some have called this biological slavery.
How We Got Here
The case that established this legal precedent is Moore v. Regents of the University of California.
John Moore was diagnosed with hairy-cell leukemia in 1976 and his spleen was removed as part of standard treatment at the UCLA Medical Center. On initial examination his physician, David W. Golde, had discovered some unusual qualities to Moore's cells and made plans prior to the surgery to have the tissue saved for research rather than discarded as waste. That research began almost immediately.
"On both sides of the case, legal experts and cultural observers cautioned that ownership of a human body was the first step on the slippery slope to 'bioslavery.'"
Even after Moore moved to Seattle, Golde kept bringing him back to Los Angeles to collect additional samples of blood and tissue, saying it was part of his treatment. When Moore asked if the work could be done in Seattle, he was told no. Golde's charade even went so far as claiming to find a low-income subsidy to pay for Moore's flights and put him up in a ritzy hotel to get him to return to Los Angeles, while paying for those out of his own pocket.
Moore became suspicious when he was asked to sign new consent forms giving up all rights to his biological samples and he hired an attorney to look into the matter. It turned out that Golde had been lying to his patient all along; he had been collecting samples unnecessary to Moore's treatment and had turned them into a cell line that he and UCLA had patented and already collected millions of dollars in compensation. The market for the cell lines was estimated at $3 billion by 1990.
Moore felt he had been taken advantage of and filed suit to claim a share of the money that had been made off of his body. "On both sides of the case, legal experts and cultural observers cautioned that ownership of a human body was the first step on the slippery slope to 'bioslavery,'" wrote Priscilla Wald, a professor at Duke University whose career has focused on issues of medicine and culture. "Moore could be viewed as asking to commodify his own body part or be seen as the victim of the theft of his most private and inalienable information."
The case bounced around different levels of the court system with conflicting verdicts for nearly six years until the California Supreme Court ruled on July 9, 1990 that Moore had no legal rights to cells and tissue once they were removed from his body.
The court made a utilitarian argument that the cells had no value until scientists manipulated them in the lab. And it would be too burdensome for researchers to track individual donations and subsequent cell lines to assure that they had been ethically gathered and used. It would impinge on the free sharing of materials between scientists, slow research, and harm the public good that arose from such research.
"In effect, what Moore is asking us to do is impose a tort duty on scientists to investigate the consensual pedigree of each human cell sample used in research," the majority wrote. In other words, researchers don't need to ask any questions about the materials they are using.
One member of the court did not see it that way. In his dissent, Stanley Mosk raised the specter of slavery that "arises wherever scientists or industrialists claim, as defendants have here, the right to appropriate and exploit a patient's tissue for their sole economic benefit—the right, in other words, to freely mine or harvest valuable physical properties of the patient's body. … This is particularly true when, as here, the parties are not in equal bargaining positions."
Mosk also cited the appeals court decision that the majority overturned: "If this science has become for profit, then we fail to see any justification for excluding the patient from participation in those profits."
But the majority bought the arguments that Golde, UCLA, and the nascent biotechnology industry in California had made in amici briefs filed throughout the legal proceedings. The road was now cleared for them to develop products worth billions without having to worry about or share with the persons who provided the raw materials upon which their research was based.
Critical Views
Biomedical research requires a continuous and ever-growing supply of human materials for the foundation of its ongoing work. If an increasing number of patients come to feel as John Moore did, that the system is ripping them off, then they become much less likely to consent to use of their materials in future research.
Some legal and ethical scholars say that donors should be able to limit the types of research allowed for their tissues and researchers should be monitored to assure compliance with those agreements. For example, today it is commonplace for companies to certify that their clothing is not made by child labor, their coffee is grown under fair trade conditions, that food labeled kosher is properly handled. Should we ask any less of our pharmaceuticals than that the donors whose cells made such products possible have been treated honestly and fairly, and share in the financial bounty that comes from such drugs?
Protection of individual rights is a hallmark of the American legal system, says Lisa Ikemoto, a law professor at the University of California Davis. "Putting the needs of a generalized public over the interests of a few often rests on devaluation of the humanity of the few," she writes in a reimagined version of the Moore decision that upholds Moore's property claims to his excised cells. The commentary is in a chapter of a forthcoming book in the Feminist Judgment series, where authors may only use legal precedent in effect at the time of the original decision.
"Why is the law willing to confer property rights upon some while denying the same rights to others?" asks Radhika Rao, a professor at the University of California, Hastings College of the Law. "The researchers who invest intellectual capital and the companies and universities that invest financial capital are permitted to reap profits from human research, so why not those who provide the human capital in the form of their own bodies?" It might be seen as a kind of sweat equity where cash strapped patients make a valuable in kind contribution to the enterprise.
The Moore court also made a big deal about inhibiting the free exchange of samples between scientists. That has become much less the situation over the more than three decades since the decision was handed down. Ironically, this decision, as well as other laws and regulations, have since strengthened the power of patents in biomedicine and by doing so have increased secrecy and limited sharing.
"Although the research community theoretically endorses the sharing of research, in reality sharing is commonly compromised by the aggressive pursuit and defense of patents and by the use of licensing fees that hinder collaboration and development," Robert D. Truog, Harvard Medical School ethicist and colleagues wrote in 2012 in the journal Science. "We believe that measures are required to ensure that patients not bear all of the altruistic burden of promoting medical research."
Additionally, the increased complexity of research and the need for exacting standardization of materials has given rise to an industry that supplies certified chemical reagents, cell lines, and whole animals bred to have specific genetic traits to meet research needs. This has been more efficient for research and has helped to ensure that results from one lab can be reproduced in another.
The Court's rationale of fostering collaboration and free exchange of materials between researchers also has been undercut by the changing structure of that research. Big pharma has shrunk the size of its own research labs and over the last decade has worked out cooperative agreements with major research universities where the companies contribute to the research budget and in return have first dibs on any findings (and sometimes a share of patent rights) that come out of those university labs. It has had a chilling effect on the exchange of materials between universities.
Perhaps tracking cell line donors and use restrictions on those donations might have been burdensome to researchers when Moore was being litigated. Some labs probably still kept their cell line records on 3x5 index cards, computers were primarily expensive room-size behemoths with limited capacity, the internet barely existed, and there was no cloud storage.
But that was the dawn of a new technological age and standards have changed. Now cell lines are kept in state-of-the-art sub zero storage units, tagged with the source, type of tissue, date gathered and often other information. Adding a few more data fields and contacting the donor if and when appropriate does not seem likely to disrupt the research process, as the court asserted.
Forging the Future
"U.S. universities are awarded almost 3,000 patents each year. They earn more than $2 billion each year from patent royalties. Sharing a modest portion of these profits is a novel method for creating a greater sense of fairness in research relationships that we think is worth exploring," wrote Mark Yarborough, a bioethicist at the University of California Davis Medical School, and colleagues. That was penned nearly a decade ago and those numbers have only grown.
The Michigan BioTrust for Health might serve as a useful model in tackling some of these issues. Dried blood spots have been collected from all newborns for half a century to be tested for certain genetic diseases, but controversy arose when the huge archive of dried spots was used for other research projects. As a result, the state created a nonprofit organization to in essence become a biobank and manage access to these spots only for specific purposes, and also to share any revenue that might arise from that research.
"If there can be no property in a whole living person, does it stand to reason that there can be no property in any part of a living person? If there were, can it be said that this could equate to some sort of 'biological slavery'?" Irish ethicist Asim A. Sheikh wrote several years ago. "Any amount of effort spent pondering the issue of 'ownership' in human biological materials with existing law leaves more questions than answers."
Perhaps the biggest question will arise when -- not if but when -- it becomes possible to clone a human being. Would a human clone be a legal person or the property of those who created it? Current legal precedent points to it being the latter.
Today, October 4, is the 70th anniversary of Henrietta Lacks' death from cancer. Over those decades her immortalized cells have helped make possible miraculous advances in medicine and have had a role in generating billions of dollars in profits. Surviving family members have spoken many times about seeking a share of those profits in the name of social justice; they intend to file lawsuits today. Such cases will succeed or fail on their own merits. But regardless of their specific outcomes, one can hope that they spark a larger public discussion of the role of patients in the biomedical research enterprise and lead to establishing a legal and financial claim for their contributions toward the next generation of biomedical research.
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.