Do New Tools Need New Ethics?
Scarcely a week goes by without the announcement of another breakthrough owing to advancing biotechnology. Recent examples include the use of gene editing tools to successfully alter human embryos or clone monkeys; new immunotherapy-based treatments offering longer lives or even potential cures for previously deadly cancers; and the creation of genetically altered mosquitos using "gene drives" to quickly introduce changes into the population in an ecosystem and alter the capacity to carry disease.
The environment for conducting science is dramatically different today than it was in the 1970s, 80s, or even the early 2000s.
Each of these examples puts pressure on current policy guidelines and approaches, some existing since the late 1970s, which were created to help guide the introduction of controversial new life sciences technologies. But do the policies that made sense decades ago continue to make sense today, or do the tools created during different eras in science demand new ethics guidelines and policies?
Advances in biotechnology aren't new of course, and in fact have been the hallmark of science since the creation of the modern U.S. National Institutes of Health in the 1940s and similar government agencies elsewhere. Funding agencies focused on health sciences research with the hope of creating breakthroughs in human health, and along the way, basic science discoveries led to the creation of new scientific tools that offered the ability to approach life, death, and disease in fundamentally new ways.
For example, take the discovery in the 1970s of the "chemical scissors" in living cells called restriction enzymes, which could be controlled and used to introduce cuts at predictable locations in a strand of DNA. This led to the creation of tools that for the first time allowed for genetic modification of any organism with DNA, which meant bacteria, plants, animals, and even humans could in theory have harmful mutations repaired, but also that changes could be made to alter or even add genetic traits, with potentially ominous implications.
The scientists involved in that early research convened a small conference to discuss not only the science, but how to responsibly control its potential uses and their implications. The meeting became known as the Asilomar Conference for the meeting center where it was held, and is often noted as the prime example of the scientific community policing itself. While the Asilomar recommendations were not sufficient from a policy standpoint, they offered a blueprint on which policies could be based and presented a model of the scientific community setting responsible controls for itself.
But the environment for conducting science changed over the succeeding decades and it is dramatically different today than it was in the 1970s, 80s, or even the early 2000s. The regime for oversight and regulation that has provided controls for the introduction of so-called "gene therapy" in humans starting in the mid-1970s is beginning to show signs of fraying. The vast majority of such research was performed in the U.S., U.K., and Europe, where policies were largely harmonized. But as the tools for manipulating humans at the molecular level advanced, they also became more reliable and more precise, as well as cheaper and easier to use—think CRISPR—and therefore more accessible to more people in many more countries, many without clear oversight or policies laying out responsible controls.
There is no precedent for global-scale science policy, though that is exactly what this moment seems to demand.
As if to make the point through news headlines, scientists in China announced in 2017 that they had attempted to perform gene editing on in vitro human embryos to repair an inherited mutation for beta thalassemia--research that would not be permitted in the U.S. and most European countries and at the time was also banned in the U.K. Similarly, specialists from a reproductive medicine clinic in the U.S. announced in 2016 that they had performed a highly controversial reproductive technology by which DNA from two women is combined (so-called "three parent babies"), in a satellite clinic they had opened in Mexico to avoid existing prohibitions on the technique passed by the U.S. Congress in 2015.
In both cases, genetic changes were introduced into human embryos that if successful would lead to the birth of a child with genetically modified germline cells—the sperm in boys or eggs in girls—with those genetic changes passed on to all future generations of related offspring. Those are just two very recent examples, and it doesn't require much imagination to predict the list of controversial possible applications of advancing biotechnologies: attempts at genetic augmentation or even cloning in humans, and alterations of the natural environment with genetically engineered mosquitoes or other insects in areas with endemic disease. In fact, as soon as this month, scientists in Africa may release genetically modified mosquitoes for the first time.
The technical barriers are falling at a dramatic pace, but policy hasn't kept up, both in terms of what controls make sense and how to address what is an increasingly global challenge. There is no precedent for global-scale science policy, though that is exactly what this moment seems to demand. Mechanisms for policy at global scale are limited–-think UN declarations, signatory countries, and sometimes international treaties, but all are slow, cumbersome and have limited track records of success.
But not all the news is bad. There are ongoing efforts at international discussion, such as an international summit on human genome editing convened in 2015 by the National Academies of Sciences and Medicine (U.S.), Royal Academy (U.K.), and Chinese Academy of Sciences (China), a follow-on international consensus committee whose report was issued in 2017, and an upcoming 2nd international summit in Hong Kong in November this year.
These efforts need to continue to focus less on common regulatory policies, which will be elusive if not impossible to create and implement, but on common ground for the principles that ought to guide country-level rules. Such principles might include those from the list proposed by the international consensus committee, including transparency, due care, responsible science adhering to professional norms, promoting wellbeing of those affected, and transnational cooperation. Work to create a set of shared norms is ongoing and worth continued effort as the relevant stakeholders attempt to navigate what can only be called a brave new world.
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.