How a Nobel-Prize Winner Fought Her Family, Nazis, and Bombs to Change our Understanding of Cells Forever
When Rita Levi-Montalcini decided to become a scientist, she was determined that nothing would stand in her way. And from the beginning, that determination was put to the test. Before Levi-Montalcini became a Nobel Prize-winning neurobiologist, the first to discover and isolate a crucial chemical called Neural Growth Factor (NGF), she would have to battle both the sexism within her own family as well as the racism and fascism that was slowly engulfing her country
Levi-Montalcini was born to two loving parents in Turin, Italy at the turn of the 20th century. She and her twin sister, Paola, were the youngest of the family's four children, and Levi-Montalcini described her childhood as "filled with love and reciprocal devotion." But while her parents were loving, supportive and "highly cultured," her father refused to let his three daughters engage in any schooling beyond the basics. "He loved us and had a great respect for women," she later explained, "but he believed that a professional career would interfere with the duties of a wife and mother."
At age 20, Levi-Montalcini had finally had enough. "I realized that I could not possibly adjust to a feminine role as conceived by my father," she is quoted as saying, and asked his permission to finish high school and pursue a career in medicine. When her father reluctantly agreed, Levi-Montalcini was ecstatic: In just under a year, she managed to catch up on her mathematics, graduate high school, and enroll in medical school in Turin.
By 1936, Levi-Montalcini had graduated medical school at the top of her class and decided to stay on at the University of Turin as a research assistant for histologist and human anatomy professor Guiseppe Levi. Levi-Montalcini started studying nerve cells and nerve fibers – the tiny, slender tendrils that are threaded throughout our nerves and that determine what information each nerve can transmit. But it wasn't long before another enormous obstacle to her scientific career reared its head.
Science Under a Fascist Regime
Two years into her research assistant position, Levi-Montalcini was fired, along with every other "non-Aryan Italian" who held an academic or professional career, thanks to a series of antisemitic laws passed by Italy's then-leader Benito Mussolini. Forced out of her academic position, Levi-Montalcini went to Belgium for a fellowship at a neurological institute in Brussels – but then was forced back to Turin when the German army invaded.
Levi-Montalcini decided to keep researching. She and Guiseppe Levi built a makeshift lab in Levi-Montalcini's apartment, borrowing chicken eggs from local farmers and using sewing needles to dissect them. By dissecting the chicken embryos from her bedroom laboratory, she was able to see how nerve fibers formed and died. The two continued this research until they were interrupted again – this time, by British air raids. Levi-Montalcini fled to a country cottage to continue her research, and then two years later was forced into hiding when the German army invaded Italy. Levi-Montalcini and her family assumed different identities and lived with non-Jewish friends in Florence to survive the Holocaust. Despite all of this, Levi-Montalcini continued her work, dissecting chicken embryos from her hiding place until the end of the war.
"The discovery of NGF really changed the world in which we live, because now we knew that cells talk to other cells, and that they use soluble factors. It was hugely important."
A Post-War Discovery
Several years after the war, when Levi-Montalcini was once again working at the University of Turin, a German embryologist named Viktor Hamburger invited her to Washington University in St. Louis. Hamburger was impressed by Levi-Montalcini's research with her chicken embryos, and secured an opportunity for her to continue her work in America. The invitation would "change the course of my life," Levi-Montalcini would later recall.
During her fellowship, Montalcini grew tumors in mice and then transferred them to chick embryos in order to see how it would affect the chickens. To her surprise, she noticed that introducing the tumor samples would cause nerve fibers to grow rapidly. From this, Levi-Montalcini discovered and was able to isolate a protein that she determined was able to cause this rapid growth. She later named this Nerve Growth Factor, or NGF.
From there, Levi-Montalcini and her team launched new experiments to test NGF, injecting it and repressing it to see the effect it had in a test subject's body. When the team injected NGF into embryonic mice, they observed nerve growth, as well as the mouse pups developing faster – their eyes opening earlier and their teeth coming in sooner – than the untreated group. When the team purified the NGF extract, however, it had no effect, leading the team to believe that something else in the crude extract of NGF was influencing the growth of the newborn mice. Stanley Cohen, Levi-Montalcini's colleague, identified another growth factor called EGF – epidermal growth factor – that caused the mouse pups' eyes and teeth to grow so quickly.
Levi-Montalcini continued to experiment with NGF for the next several decades at Washington University, illuminating how NGF works in our body. When Levi-Montalcini injected newborn mice with an antiserum for NGF, for example, her team found that it "almost completely deprived the animals of a sympathetic nervous system." Other experiments done by Levi-Montalcini and her colleagues helped show the role that NGF plays in other important biological processes, such as the regulation of our immune system and ovulation.
"The discovery of NGF really changed the world in which we live, because now we knew that cells talk to other cells, and that they use soluble factors. It was hugely important," said Bill Mobley, Chair of the Department of Neurosciences at the University of California, San Diego School of Medicine.
Her Lasting Legacy
After years of setbacks, Levi-Montalcini's groundbreaking work was recognized in 1986, when she was awarded the Nobel Prize in Medicine for her discovery of NGF (Cohen, her colleague who discovered EGF, shared the prize). Researchers continue to study NGF even to this day, and the continued research has been able to increase our understanding of diseases like HIV and Alzheimer's.
Levi-Montalcini never stopped researching either: In January 2012, at the age of 102, Levi-Montalcini published her last research paper in the journal PNAS, making her the oldest member of the National Academy of Science to do so. Before she died in December 2012, she encouraged other scientists who would suffer setbacks in their careers to keep pursuing their passions. "Don't fear the difficult moments," Levi-Montalcini is quoted as saying. "The best comes from them."
New Tech Can Predict Breast Cancer Years in Advance
Every two minutes, a woman is diagnosed with breast cancer. The question is, can those at high risk be identified early enough to survive?
New AI software has predicted risk equally well in both white and black women for the first time.
The current standard practice in medicine is not exactly precise. It relies on age, family history of cancer, and breast density, among other factors, to determine risk. But these factors do not always tell the whole story, leaving many women to slip through the cracks. In addition, a racial gap persists in breast cancer treatment and survival. African-American women are 42 percent more likely to die from the disease despite relatively equal rates of diagnosis.
But now those grim statistics could be changing. A team of researchers from MIT's Computer Science and Artificial Intelligence Laboratory have developed a deep learning model that can more accurately predict a patient's breast cancer risk compared to established clinical guidelines – and it has predicted risk equally well in both white and black women for the first time.
The Lowdown
Study results published in Radiology described how the AI software read mammogram images from more than 60,000 patients at Massachusetts General Hospital to identify subtle differences in breast tissue that pointed to potential risk factors, even in their earliest stages. The team accessed the patients' actual diagnoses and determined that the AI model was able to correctly place 31 percent of all cancer patients in the highest-risk category of developing breast cancer within five years of the examination, compared to just 18 percent for existing models.
"Each image has hundreds of thousands of pixels identifying something that may not necessarily be detected by the human eye," said MIT professor Regina Barzilay, one of the study's lead authors. "We all have limited visual capacities so it seems some machines trained on hundreds of thousands of images with a known outcome can capture correlations the human eye might not notice."
Barzilay, a breast cancer survivor herself, had abnormal tissue patterns on mammograms in 2012 and 2013, but wasn't diagnosed until after a 2014 image reading, illustrating the limitations of human processing alone.
MIT professor Regina Barzilay, a lead author on the new study and a breast cancer survivor herself.
(Courtesy MIT)
Next up: The MIT team is looking at training the model to detect other cancers and health risks. Barzilay recalls how a cardiologist told her during a conference that women with heart diseases had a different pattern of calcification on their mammograms, demonstrating how already existing images can be used to extract other pieces of information about a person's health status.
Integration of the AI model in standard care could help doctors better tailor screening and prevention programs based on actual instead of perceived risk. Patients who might register as higher risk by current guidelines could be identified as lower risk, helping resolve conflicting opinions about how early and how often women should receive mammograms.
Open Questions: While the results were promising, it's unknown how well the model will work on a larger scale, as the study looked at data from just one institution and used mammograms supplied by just one hospital. Some risk factor information was also unavailable for certain patients during the study, leaving researchers unable to fully compare the AI model's performance to that of the traditional standard.
One incentive to wider implementation and study, however, is the bonus that no new hardware is required to use the AI model. With other institutions now showing interest, this software could lead to earlier routine detection and treatment of breast cancer — resulting in more lives saved.
Sarah Mancoll was 22 years old when she noticed a bald spot on the back of her head. A dermatologist confirmed that it was alopecia aerata, an autoimmune disorder that causes hair loss.
Of 213 new drugs approved from 2003 to 2012, only five percent included any data from pregnant women.
She successfully treated the condition with corticosteroid shots for nearly 10 years. Then Mancoll and her husband began thinking about starting a family. Would the shots be safe for her while pregnant? For the fetus? What about breastfeeding?
Mancoll consulted her primary care physician, her dermatologist, even a pediatrician. Without clinical data, no one could give her a definitive answer, so she stopped treatment to be "on the safe side." By the time her son was born, she'd lost at least half her hair. She returned to her Washington, D.C., public policy job two months later entirely bald—and without either eyebrows or eyelashes.
After having two more children in quick succession, Mancoll recently resumed the shots but didn't forget her experience. Today, she is an advocate for including more pregnant and lactating women in clinical studies so they can have more information about therapies than she did.
"I live a very privileged life, and I'll do just fine with or without hair, but it's not just about me," Mancoll said. "It's about a huge population of women who are being disenfranchised…They're invisible."
About 4 million women give birth each year in the United States, and many face medical conditions, from hypertension and diabetes to psychiatric disorders. A 2011 study showed that most women reported taking at least one medication while pregnant between 1976 and 2008. But for decades, pregnant and lactating women have been largely excluded from clinical drug studies that rigorously test medications for safety and effectiveness.
An estimated 98 percent of government-approved drug treatments between 2000 and 2010 had insufficient data to determine risk to the fetus, and close to 75 percent had no human pregnancy data at all. All told, of 213 new pharmaceuticals approved from 2003 to 2012, only five percent included any data from pregnant women.
But recent developments suggest that could be changing. Amid widespread concerns about increased maternal mortality rates, women's health advocates, physicians, and researchers are sensing and encouraging a cultural shift toward protecting women through responsible research instead of from research.
"The question is not whether to do research with pregnant women, but how," Anne Drapkin Lyerly, professor and associate director of the Center for Bioethics at the University of North Carolina at Chapel Hill, wrote last year in an op-ed. "These advances are essential. It is well past time—and it is morally imperative—for research to benefit pregnant women."
"In excluding pregnant women from drug trials to protect them from experimentation, we subject them to uncontrolled experimentation."
To that end, the American College of Obstetricians and Gynecologists' Committee on Ethics acknowledged that research trials need to be better designed so they don't "inappropriately constrain the reproductive choices of study participants or unnecessarily exclude pregnant women." A federal task force also called for significantly expanded research and the removal of regulatory barriers that make it difficult for pregnant and lactating women to participate in research.
Several months ago, a government change to a regulation known as the Common Rule took effect, removing pregnant women as a "vulnerable population" in need of special protections -- a designation that had made it more difficult to enroll them in clinical drug studies. And just last week, the U.S. Food and Drug Administration (FDA) issued new draft guidances for industry on when and how to include pregnant and lactating women in clinical trials.
Inclusion is better than the absence of data on their treatment, said Catherine Spong, former chair of the federal task force.
"It's a paradox," said Spong, professor of obstetrics and gynecology and chief of maternal fetal medicine at University of Texas Southwestern Medical Center. "There is a desire to protect women and fetuses from harm, which is translated to a reluctance to include them in research. By excluding them, the evidence for their care is limited."
Jacqueline Wolf, a professor of the history of medicine at Ohio University, agreed.
"In excluding pregnant women from drug trials to protect them from experimentation, we subject them to uncontrolled experimentation," she said. "We give them the medication without doing any research, and that's dangerous."
Women, of course, don't stop getting sick or having chronic medical conditions just because they are pregnant or breastfeeding, and conditions during pregnancy can affect a baby's health later in life. Evidence-based data is important for other reasons, too.
Pregnancy can dramatically change a woman's physiology, affecting how drugs act on her body and how her body acts or reacts to drugs. For instance, pregnant bodies can more quickly clear out medications such as glyburide, used during diabetes in pregnancy to stabilize high blood-sugar levels, which can be toxic to the fetus and harmful to women. That means a regular dose of the drug may not be enough to control blood sugar and prevent poor outcomes.
Pregnant patients also may be reluctant to take needed drugs for underlying conditions (and doctors may be hesitant to prescribe them), which in turn can cause more harm to the woman and fetus than had they been treated. For example, women who have severe asthma attacks while pregnant are at a higher risk of having low-birthweight babies, and pregnant women with uncontrolled diabetes in early pregnancy have more than four times the risk of birth defects.
Current clinical trials involving pregnant women are assessing treatments for obstructive sleep apnea, postpartum hemorrhage, lupus, and diabetes.
For Kate O'Brien, taking medication during her pregnancy was a matter of life and death. A freelance video producer who lives in New Jersey, O'Brien was diagnosed with tuberculosis in 2015 after she became pregnant with her second child, a boy. Even as she signed hospital consent forms, she had no idea if the treatment would harm him.
"It's a really awful experience," said O'Brien, who now is active with We are TB, an advocacy and support network. "All they had to tell me about the medication was just that women have been taking it for a really long time all over the world. That was the best they could do."
More and more doctors, researchers and women's health organizations and advocates are calling that unacceptable.
By indicating that filling current knowledge gaps is "a critical public health need," the FDA is signaling its support for advancing research with pregnant women, said Lyerly, also co-founder of the Second Wave Initiative, which promotes fair representation of the health interests of pregnant women in biomedical research and policies. "It's a very important shift."
Research with pregnant women can be done ethically, Lyerly said, whether by systematically collecting data from those already taking medications or enrolling pregnant women in studies of drugs or vaccines in development.
Current clinical trials involving pregnant women are assessing treatments for obstructive sleep apnea, postpartum hemorrhage, lupus, and diabetes. Notable trials in development target malaria and HIV prevention in pregnancy.
"It clearly is doable to do this research, and test trials are important to provide evidence for treatment," Spong said. "If we don't have that evidence, we aren't making the best educated decisions for women."