Is There a Blind Spot in the Oversight of Human Subject Research?
Human experimentation has come a long way since congressional hearings in the 1970s exposed patterns of abuse. Where yesterday's patients were protected only by the good conscience of physician-researchers, today's patients are spirited past hazards through an elaborate system of oversight and informed consent. Yet in many ways, the project of grounding human research on ethical foundations remains incomplete.
As human research has become a mainstay of career and commercial advancement among academics, research centers, and industry, new threats to research integrity have emerged.
To be sure, much of the medical research we do meets exceedingly high standards. Progress in cancer immunotherapy, or infectious disease, reflects the best of what can be accomplished when medical scientists and patients collaborate productively. And abuses of the earlier part of the 20th century--like those perpetrated by the U.S. Public Health Service in Guatemala--are for the history books.
Yet as human research has become a mainstay of career and commercial advancement among academics, research centers, and industry, new threats to research integrity have emerged. Many flourish in the blind spot of current oversight systems.
Take, for example, the tendency to publish only "positive" findings ("publication bias"). When patients participate in studies, they are told that their contributions will promote medical discovery. That can't happen if results of experiments never get beyond the hard drives of researchers. While researchers are often eager to publish trials showing a drug works, according to a study my own team conducted, fewer than 4 in 10 trials of drugs that never receive FDA approval get published. This tendency- which occurs in academia as well as industry- deprives other scientists of opportunities to build on these failures and make good on the sacrifice of patients. It also means the trials may be inadvertently repeated by other researchers, subjecting more patients to risks.
On the other hand, many clinical trials test treatments that have already been proven effective beyond a shadow of doubt. Consider the drug aprotinin, used for the management of bleeding during surgery. An analysis in 2005 showed that, not long after the drug was proven effective, researchers launched dozens of additional placebo-controlled trials. These redundant trials are far in excess of what regulators required for drug approval, and deprived patients in placebo arms of a proven effective therapy. Whether because of an oversight or deliberately (does it matter?), researchers conducting these trials often failed in publications to describe previous evidence of efficacy. What's the point of running a trial if no one reads the results?
It is surprisingly easy for companies to hijack research to market their treatments.
At the other extreme are trials that are little more than shots in the dark. In one case, patients with spinal cord injury were enrolled in a safety trial testing a cell-based regenerative medicine treatment. After the trial stopped (results were negative), laboratory scientists revealed that the cells had been shown ineffective in animal experiments. Though this information had been available to the company and FDA, researchers pursued the trial anyway.
It is surprisingly easy for companies to hijack research to market their treatments. One way this happens is through "seeding trials"- studies that are designed not to address a research question, but instead to habituate doctors to using a new drug and to generate publications that serve as advertisements. Such trials flood the medical literature with findings that are unreliable because studies are small and not well designed. They also use the prestige of science to pursue goals that are purely commercial. Yet because they harm science- not patients (many such studies are minimally risky because all patients receive proven effective medications)- ethics committees rarely block them.
Closely related is the phenomenon of small uninformative trials. After drugs get approved by the FDA, companies often launch dozens of small trials in new diseases other than the one the drug was approved to treat. Because these studies are small, they often overestimate efficacy. Indeed, the way trials are often set up, if a company tests an ineffective drug in 40 different studies, one will typically produce a false positive by chance alone. Because companies are free to run as many trials as they like and to circulate "positive" results, they have incentives to run lots of small trials that don't provide a definitive test of their drug's efficacy.
Universities, funding bodies, and companies should be scored by a neutral third-party based on the impact of their trials -- like Moody's for credit ratings.
Don't think public agencies are much better. Funders like the National Institutes of Health secure their appropriations by gratifying Congress. This means that NIH gets more by spreading its funding among small studies in different Congressional districts than by concentrating budgets among a few research institutions pursuing large trials. The result is that some NIH-funded clinical trials are not especially equipped to inform medical practice.
It's tempting to think that FDA, medical journals, ethics committees, and funding agencies can fix these problems. However, these practices continue in part because FDA, ethics committees, and researchers often do not see what is at stake for patients by acquiescing to low scientific standards. This behavior dishonors the patients who volunteer for research, and also threatens the welfare of downstream patients, whose care will be determined by the output of research.
To fix this, deficiencies in study design and reporting need to be rendered visible. Universities, funding bodies, and companies should be scored by a neutral third-party based on the impact of their trials, or the extent to which their trials are published in full -- like Moody's for credit ratings, or the Kelley Blue Book for cars. This system of accountability would allow everyone to see which institutions make the most of the contributions of research subjects. It could also harness the competitive instincts of institutions to improve research quality.
Another step would be for researchers to level with patients when they enroll in studies. Patients who agree to research are usually offered bromides about how their participation may help future patients. However, not all studies are created equal with respect to merit. Patients have a right to know when they are entering studies that are unlikely to have a meaningful impact on medicine.
Ethics committees and drug regulators have done a good job protecting research volunteers from unchecked scientific ambition. However, today's research is plagued by studies that have poor scientific credentials. Such studies free-ride on the well-earned reputation of serious medical science. They also potentially distort the evidence available to physicians and healthcare systems. Regulators, academic medical centers, and others should establish policies that better protect human research volunteers by protecting the quality of the research itself.
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.