Sloppy Science Happens More Than You Think
The media loves to tout scientific breakthroughs, and few are as toutable – and in turn, have been as touted – as CRISPR. This method of targeted DNA excision was discovered in bacteria, which use it as an adaptive immune system to combat reinfection with a previously encountered virus.
Shouldn't the editors at a Nature journal know better than to have published an incorrect paper in the first place?
It is cool on so many levels: not only is the basic function fascinating, reminding us that we still have more to discover about even simple organisms that we thought we knew so well, but the ability it grants us to remove and replace any DNA of interest has almost limitless applications in both the lab and the clinic. As if that didn't make it sexy enough, add in a bicoastal, male-female, very public and relatively ugly patent battle, and the CRISPR story is irresistible.
And then last summer, a bombshell dropped. The prestigious journal Nature Methods published a paper in which the authors claimed that CRISPR could cause many unintended mutations, rendering it unfit for clinical use. Havoc duly ensued; stocks in CRISPR-based companies plummeted. Thankfully, the authors of the offending paper were responsible, good scientists; they reassessed, then recanted. Their attention- and headline- grabbing results were wrong, and they admitted as much, leading Nature Methods to formally retract the paper this spring.
How did this happen? Shouldn't the editors at a Nature journal know better than to have published this in the first place?
Alas, high-profile scientific journals publish misleading and downright false results fairly regularly. Some errors are unavoidable – that's how the scientific method works. Hypotheses and conclusions will invariably be overturned as new data becomes available and new technologies are developed that allow for deeper and deeper studies. That's supposed to happen. But that's not what we're talking about here. Nor are we talking about obvious offenses like outright plagiarism. We're talking about mistakes that are avoidable, and that still have serious ramifications.
The cultures of both industry and academia promote research that is poorly designed and even more poorly analyzed.
Two parties are responsible for a scientific publication, and thus two parties bear the blame when things go awry: the scientists who perform and submit the work, and the journals who publish it. Unfortunately, both are incentivized for speedy and flashy publications, and not necessarily for correct publications. It is hardly a surprise, then, that we end up with papers that are speedy and flashy – and not necessarily correct.
"Scientists don't lie and submit falsified data," said Andy Koff, a professor of Molecular Biology at Sloan Kettering Institute, the basic research arm of Memorial Sloan Kettering Cancer Center. Richard Harris, who wrote the book on scientific misconduct running the gamut from unconscious bias and ignorance to more malicious fraudulence, largely concurs (full disclosure: I reviewed the book here). "Scientists want to do good science and want to be recognized as such," he said. But even so, the cultures of both industry and academia promote research that is poorly designed and even more poorly analyzed. In Rigor Mortis: How Sloppy Science Creates Worthless Cures, Crushes Hope, and Wastes Millions, Harris describes how scientists must constantly publish in order to maintain their reputations and positions, to get grants and tenure and students. "They are disincentivized from doing that last extra experiment to prove their results," he said; it could prove too risky if it could cost them a publication.
Ivan Oransky and Adam Marcus founded Retraction Watch, a blog that tracks the retraction of scientific papers, in 2010. Oransky pointed out that blinded peer review – the pride and joy of the scientific publishing enterprise – is a large part of the problem. "Pre-publication peer review is still important, but we can't treat it like the only check on the system. Papers are being reviewed by non-experts, and reviewers are asked to review papers only tangentially related to their field. Moreover, most peer reviewers don't look at the underlying or raw data, even when it is available. How then can they tell if the analysis is flawed or the data is accurate?" he wondered.
Mistaken publications also erode the public's opinion of legitimate science, which is problematic since that opinion isn't especially high to begin with.
Koff agreed that anonymous peer review is valuable, but severely flawed. "Blinded review forces a collective view of importance," he said. "If an article disagrees with the reviewer's worldview, the article gets rejected or forced to adhere to that worldview – even if that means pushing the data someplace it shouldn't necessarily go." We have lost the scientific principle behind review, he thinks, which was to critically analyze a paper. But instead of challenging fundamental assumptions within a paper, reviewers now tend to just ask for more and more supplementary data. And don't get him started on editors. "Editors are supposed to arbitrate between reviewers and writers and they have completely abdicated this responsibility, at every journal. They do not judge, and that's a real failing."
Harris laments the wasted time, effort, and resources that result when erroneous ideas take hold in a field, not to mention lives lost when drug discovery is predicated on basic science findings that end up being wrong. "When no one takes the time, care, and money to reproduce things, science isn't stopping – but it is slowing down," he noted. Mistaken publications also erode the public's opinion of legitimate science, which is problematic since that opinion isn't especially high to begin with.
Scientists and publishers don't only cause the problem, though – they may also provide the solution. Both camps are increasingly recognizing and dealing with the crisis. The self-proclaimed "data thugs" Nick Brown and James Heathers use pretty basic arithmetic to reveal statistical errors in papers. The microbiologist Elisabeth Bik scans the scientific literature for problematic images "in her free time." The psychologist Brian Nosek founded the Center for Open Science, a non-profit organization dedicated to promoting openness, integrity, and reproducibility in scientific research. The Nature family of journals – yes, the one responsible for the latest CRISPR fiasco – has its authors complete a checklist to combat irreproducibility, à la Atul Gawande. And Nature Communications, among other journals, uses transparent peer review, in which authors can opt to have the reviews of their manuscript published anonymously alongside the completed paper. This practice "shows people how the paper evolved," said Koff "and keeps the reviewer and editor accountable. Did the reviewer identify the major problems with the paper? Because there are always major problems with a paper."
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.