Researchers Are Discovering How to Predict – and Maybe Treat — Pregnancy Complications Early On.
Katie Love wishes there was some way she could have been prepared. But there was no way to know, early in 2020, that her pregnancy would lead to terrifyingly high blood pressure and multiple hospital visits, ending in induced labor and a 56-hour-long, “nightmare” delivery at 37 weeks. Love, a social media strategist in Pittsburgh, had preeclampsia, a poorly understood and potentially deadly pregnancy complication that affects 1 in 25 pregnant women in the United States. But there was no blood test, no easy diagnostic marker to warn Love that this might happen. Even on her first visit to the emergency room, with sky-high blood pressure, doctors could not be certain preeclampsia was the cause.
In fact, the primary but imperfect indicators for preeclampsia — high blood pressure and protein in the urine — haven’t changed in decades. The Preeclampsia Foundation calls a simple, rapid test to predict or diagnose the condition “a key component needed in the fight.”
Another common pregnancy complication is preterm birth, which affects 1 in 10 U.S. pregnancies, but there are few options to predict that might happen, either.
“The best tool that obstetricians have at the moment is still a tape measure and a blood pressure cuff to diagnose whatever’s happening in your pregnancy,” says Fiona Kaper, a vice president at the DNA-sequencing company Illumina in San Diego.
The hunt for such specific biomarkers is now taking off, at Illumina and elsewhere, as scientists probe maternal blood for signs that could herald pregnancy problems. These same molecules offer clues that might lead to more specific treatments. So far, it’s clear that many complications start with the placenta, the temporary organ that transfers nutrients, oxygen and waste between mother and fetus, and that these problems often start well before symptoms arise. Researchers are using the latest stem-cell technology to better understand the causes of complications and test treatments.
Pressing Need
Obstetricians aren’t flying completely blind; medical history can point to high or low risk for pregnancy complications. But ultimately, “everybody who’s pregnant is at risk for preeclampsia,” says Sarosh Rana, chief of maternal-fetal medicine at University of Chicago Medicine and an advisor to the Preeclampsia Foundation. And the symptoms of the condition include problems like headache and swollen feet that overlap with those of pregnancy in general, complicating diagnoses.
The “holy grail" would be early, first-trimester biomarkers. If obstetricians and expecting parents could know, in the first few months of pregnancy, that preeclampsia is a risk, a pregnant woman could monitor her blood pressure at home and take-low dose aspirin that might stave it off.
There are a couple more direct tests physicians can turn to, but these are imperfect. For preterm labor, fetal fibronectin makes up a sort of glue that keeps the amniotic sac, which cushions the unborn baby, attached to the uterus. If it’s not present near a woman’s cervix, that’s a good indicator that she’s not in labor, and can be safely sent home, says Lauren Demosthenes, an obstetrician and senior medical director of the digital health company Babyscripts in Washington, D.C. But if fibronectin appears, it might or might not indicate preterm labor.
“What we want is a test that gives us a positive predictive [signal],” says Demosthenes. “I want to know, if I get it, is it really going to predict preterm birth, or is it just going to make us worry more and order more tests?” In fact, the fetal fibronectin test hasn’t been shown to improve pregnancy outcomes, and Demosthenes says it’s fallen out of favor in many clinics.
Similarly, there’s a blood test, based on the ratio of the amounts of two different proteins, that can rule out preeclampsia but not confirm it’s happening. It’s approved in many countries, though not the U.S.; studies are still ongoing. A positive test, which means “maybe preeclampsia,” still leaves doctors and parents-to-be facing excruciating decisions: If the mother’s life is in danger, delivering the baby can save her, but even a few more days in the uterus can promote the baby’s health. In Ireland, where the test is available, it’s not getting much use, says Patricia Maguire, director of the University College Dublin Institute for Discovery.
Maguire has identified proteins released by platelets that indicate pregnancy — the “most expensive pregnancy test in the world,” she jokes. She is now testing those markers in women with suspected preeclampsia.
The “holy grail,” says Maguire, would be early, first-trimester biomarkers. If obstetricians and expecting parents could know, in the first few months of pregnancy, that preeclampsia is a risk, a pregnant woman could monitor her blood pressure at home and take-low dose aspirin that might stave it off. Similarly, if a quick blood test indicated that preterm labor could happen, doctors could take further steps such as measuring the cervix and prescribing progesterone if it’s on the short side.
Biomarkers in Blood
It was fatherhood that drew Stephen Quake, a biophysicist at Stanford University in California, to the study of pregnancy biomarkers. His wife, pregnant with their first child in 2001, had a test called amniocentesis. That involves extracting a sample from within the uterus, using a 3–8-inch-long needle, for genetic testing. The test can identify genetic differences, such as Down syndrome, but also carries risks including miscarriage or infection. In this case, mom and baby were fine (Quake’s daughter is now a college student), but he found the diagnostic danger unacceptable.
Seeking a less invasive test, Quake in 2008 reported that there’s enough fetal DNA in the maternal bloodstream to diagnose Down syndrome and other genetic conditions. “Use of amniocentesis has plunged,” he says.
Then, recalling that his daughter was born three and a half weeks before her due date — and that Quake’s own mom claims he was a month late, which makes him think the due date must have been off — he started researching markers that could accurately assess a fetus’ age and predict the timing of labor. In this case, Quake was interested in RNA, not DNA, because it’s a signal of which genes the fetus’, placenta’s, and mother’s tissues are using to create proteins. Specifically, these are RNAs that have exited the cells that made them. Tissues can use such free RNAs as messages, wrapping them in membranous envelopes to travel the bloodstream to other body parts. Dying cells also release fragments containing RNAs. “A lot of information is in there,” says Kaper.
In a small study of 31 healthy pregnant women, published in 2018, Quake and collaborators discovered nine RNAs that could predict gestational age, which indicates due date, just as well as ultrasound. With another set of 38 women, including 13 who delivered early, the researchers discovered seven RNAs that predicted preterm labor up to two months in advance.
Quake notes that an RNA-based blood test is cheaper and more portable than ultrasound, so it might be useful in the developing world. A company he cofounded, Mirvie, Inc., is now analyzing RNA’s predictive value further, in thousands of diverse women. CEO and cofounder Maneesh Jain says that since preterm labor is so poorly understood, they’re sequencing RNAs that represent about 20,000 genes — essentially all the genes humans have — to find the very best biomarkers. “We don’t know enough about this field to guess what it might be,” he says. “We feel we’ve got to cast the net wide.”
Quake, and Mirvie, are now working on biomarkers for preeclampsia. In a recent preprint study, not yet reviewed by other experts, Quake’s Stanford team reported 18 RNAs that, measured before 16 weeks, correctly predicted preeclampsia 56–100% of the time.
Other researchers are taking a similar tack. Kaper’s team at Illumina was able to classify preeclampsia from bloodstream RNAs with 85 to 89% accuracy, though they didn’t attempt to predict it. And Louise Laurent, a maternal-fetal medicine specialist and researcher at the University of California, San Diego (UCSD), has defined several pairs of microRNAs — pint-sized RNAs that regulate other ones — in second-trimester blood samples that predict preeclampsia later on.
Placentas in a Dish
The RNAs that show up in these studies often come from genes used by the placenta. But they’re only signals that something’s wrong, not necessarily the root cause. “There still is not much known about what really causes major complications of pregnancy,” says Laurent.
The challenge is that placental problems likely occur early on, as the organ forms in the first trimester. For example, if the placenta did a poor job of building blood vessels through the uterine lining, it might cause preeclampsia later as the growing fetus tries to access more and more blood through insufficient vessels, leading to high blood pressure in the mother. “Everyone has kind of suspected that that is probably what goes wrong,” says Mana Parast, a pathologist and researcher at UCSD.
To see how a placenta first faltered, “you want to go back in time,” says Parast. It’s only recently become possible to do something akin to that: She and Laurent take cells from the umbilical cord (which is a genetic match for the placenta) at the end of pregnancy, and turn them into stem cells, which can become any kind of cell. They then nudge those stem cells to make new placenta cells in lab dishes. But when the researchers start with cells from an umbilical cord after preeclampsia, they find the stem cells struggle to even form proper placenta cells, or they develop abnormally. So yes, something seems to go wrong right at the beginning. Now, the team plans to use these cell cultures to study the microRNAs that indicate preeclampsia risk, and to look for medications that might reverse the problems, Parast says.
Biomarkers could lead to treatments. For example, one of the proteins that commercial preeclampsia diagnostic kits test for is called soluble Flt-1. It’s a sort of anti-growth factor, explains Rana, that can cause problems with blood vessels and thus high blood pressure. Getting rid of the extra Flt-1, then, might alleviate symptoms and keep the mother safe, giving the baby more time to develop. Indeed, a small trial that filtered this protein from the blood did lower blood pressure, allowing participants to keep their babies inside for a couple of weeks longer, researchers reported in 2011.
For pregnant women like Love, even advance warning would have been beneficial. Laurent and others envision a first-trimester blood test that would use different kinds of biomolecules — RNAs, proteins, whatever works best — to indicate whether a pregnancy is at low, medium, or high risk for common complications.
“I prefer to be prepared,” says Love, now the mother of a healthy little girl. “I just wouldn’t have been so thrown off by the whole thing.”
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.