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Wednesday, June 26th, 2019
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| 12:00a |
For Catherine Drennan, teaching and research are complementary passions Catherine Drennan says nothing in her job thrills her more than the process of discovery. But Drennan, a professor of biology and chemistry, is not referring to her landmark research on protein structures that could play a major role in reducing the world’s waste carbons.
“Really the most exciting thing for me is watching my students ask good questions, problem-solve, and then do something spectacular with what they’ve learned,” she says.
For Drennan, research and teaching are complementary passions, both flowing from a deep sense of “moral responsibility.” Everyone, she says, “should do something, based on their skill set, to make some kind of contribution.”
Drennan’s own research portfolio attests to this sense of mission. Since her arrival at MIT 20 years ago, she has focused on characterizing and harnessing metal-containing enzymes that catalyze complex chemical reactions, including those that break down carbon compounds.
She got her start in the field as a graduate student at the University of Michigan, where she became captivated by vitamin B12. This very large vitamin contains cobalt and is vital for amino acid metabolism, the proper formation of the spinal cord, and prevention of certain kinds of anemia. Bound to proteins in food, B12 is released during digestion.
“Back then, people were suggesting how B12-dependent enzymatic reactions worked, and I wondered how they could be right if they didn’t know what B12-dependent enzymes looked like,” she recalls. “I realized I needed to figure out how B12 is bound to protein to really understand what was going on.”
Drennan seized on X-ray crystallography as a way to visualize molecular structures. Using this technique, which involves bouncing X-ray beams off a crystallized sample of a protein of interest, she figured out how vitamin B12 is bound to a protein molecule.
“No one had previously been successful using this method to obtain a B12-bound protein structure, which turned out to be gorgeous, with a protein fold surrounding a novel configuration of the cofactor,” says Drennan.
Carbon-loving microbes show the way
These studies of B12 led directly to Drennan’s one-carbon work. “Metallocofactors such as B12 are important not just medically, but in environmental processes,” she says. “Many microbes that live on carbon monoxide, carbon dioxide, or methane — eating carbon waste or transforming carbon — use metal-containing enzymes in their metabolic pathways, and it seemed like a natural extension to investigate them.”
Some of Drennan’s earliest work in this area, dating from the early 2000s, revealed a cluster of iron, nickel, and sulfur atoms at the center of the enzyme carbon monoxide dehydrogenase (CODH). This so-called C-cluster serves hungry microbes, allowing them to “eat” carbon monoxide and carbon dioxide.
Recent experiments by Drennan analyzing the structure of the C-cluster-containing enzyme CODH showed that in response to oxygen, it can change configurations, with sulfur, iron, and nickel atoms cartwheeling into different positions. Scientists looking for new avenues to reduce greenhouse gases took note of this discovery. CODH, suggested Drennan, might prove an effective tool for converting waste carbon dioxide into a less environmentally destructive compound, such as acetate, which might also be used for industrial purposes.
Drennan has also been investigating the biochemical pathways by which microbes break down hydrocarbon byproducts of crude oil production, such as toluene, an environmental pollutant.
“It’s really hard chemistry, but we’d like to put together a family of enzymes to work on all kinds of hydrocarbons, which would give us a lot of potential for cleaning up a range of oil spills,” she says.
The threat of climate change has increasingly galvanized Drennan’s research, propelling her toward new targets. A 2017 study she co-authored in Science detailed a previously unknown enzyme pathway in ocean microbes that leads to the production of methane, a formidable greenhouse gas: “I’m worried the ocean will make a lot more methane as the world warms,” she says.
Drennan hopes her work may soon help to reduce the planet’s greenhouse gas burden. Commercial firms have begun using the enzyme pathways that she studies, in one instance employing a proprietary microbe to capture carbon dioxide produced during steel production — before it is released into the atmosphere — and convert it into ethanol.
“Reengineering microbes so that enzymes take not just a little, but a lot of carbon dioxide out of the environment — this is an area I’m very excited about,” says Drennan.
Creating a meaningful life in the sciences
At MIT, she has found an increasingly warm welcome for her efforts to address the climate challenge.
“There’s been a shift in the past decade or so, with more students focused on research that allows us to fuel the planet without destroying it,” she says.
In Drennan’s lab, a postdoc, Mary Andorfer, and a rising junior, Phoebe Li, are currently working to inhibit an enzyme present in an oil-consuming microbe whose unfortunate residence in refinery pipes leads to erosion and spills. “They are really excited about this research from the environmental perspective and even made a video about their microorganism,” says Drennan.
Drennan delights in this kind of enthusiasm for science. In high school, she thought chemistry was dry and dull, with no relevance to real-world problems. It wasn’t until college that she “saw chemistry as cool.”
The deeper she delved into the properties and processes of biological organisms, the more possibilities she found. X-ray crystallography offered a perfect platform for exploration. “Oh, what fun to tell the story about a three-dimensional structure — why it is interesting, what it does based on its form,” says Drennan.
The elements that excite Drennan about research in structural biology — capturing stunning images, discerning connections among biological systems, and telling stories — come into play in her teaching. In 2006, she received a $1 million grant from the Howard Hughes Medical Institute (HHMI) for her educational initiatives that use inventive visual tools to engage undergraduates in chemistry and biology. She is both an HHMI investigator and an HHMI professor, recognition of her parallel accomplishments in research and teaching, as well as a 2015 MacVicar Faculty Fellow for her sustained contribution to the education of undergraduates at MIT.
Drennan attempts to reach MIT students early. She taught introductory chemistry classes from 1999 to 2014, and in fall 2018 taught her first introductory biology class.
“I see a lot of undergraduates majoring in computer science, and I want to convince them of the value of these disciplines,” she says. “I tell them they will need chemistry and biology fundamentals to solve important problems someday.”
Drennan happily migrates among many disciplines, learning as she goes. It’s a lesson she hopes her students will absorb. “I want them to visualize the world of science and show what they can do,” she says. “Research takes you in different directions, and we need to bring the way we teach more in line with our research.”
She has high expectations for her students. “They’ll go out in the world as great teachers and researchers,” Drennan says. “But it’s most important that they be good human beings, taking care of other people, asking what they can do to make the world a better place.”
This article appears in the Spring 2019 issue of Energy Futures, the magazine of the MIT Energy Initiative. | | 12:01a |
Study: Social robots can benefit hospitalized children A new study demonstrates, for the first time, that “social robots” used in support sessions held in pediatric units at hospitals can lead to more positive emotions in sick children.
Many hospitals host interventions in pediatric units, where child life specialists will provide clinical interventions to hospitalized children for developmental and coping support. This involves play, preparation, education, and behavioral distraction for both routine medical care, as well as before, during, and after difficult procedures. Traditional interventions include therapeutic medical play and normalizing the environment through activities such as arts and crafts, games, and celebrations.
For the study, published today in the journal Pediatrics, researchers from the MIT Media Lab, Boston Children’s Hospital, and Northeastern University deployed a robotic teddy bear, “Huggable,” across several pediatric units at Boston Children’s Hospital. More than 50 hospitalized children were randomly split into three groups of interventions that involved Huggable, a tablet-based virtual Huggable, or a traditional plush teddy bear. In general, Huggable improved various patient outcomes over those other two options.
The study primarily demonstrated the feasibility of integrating Huggable into the interventions. But results also indicated that children playing with Huggable experienced more positive emotions overall. They also got out of bed and moved around more, and emotionally connected with the robot, asking it personal questions and inviting it to come back later to meet their families. “Such improved emotional, physical, and verbal outcomes are all positive factors that could contribute to better and faster recovery in hospitalized children,” the researchers write in their study.
Although it is a small study, it is the first to explore social robotics in a real-world inpatient pediatric setting with ill children, the researchers say. Other studies have been conducted in labs, have studied very few children, or were conducted in public settings without any patient identification.
But Huggable is designed only to assist health care specialists — not replace them, the researchers stress. “It’s a companion,” says co-author Cynthia Breazeal, an associate professor of media arts and sciences and founding director of the Personal Robots group. “Our group designs technologies with the mindset that they’re teammates. We don’t just look at the child-robot interaction. It’s about [helping] specialists and parents, because we want technology to support everyone who’s invested in the quality care of a child.”
“Child life staff provide a lot of human interaction to help normalize the hospital experience, but they can’t be with every kid, all the time. Social robots create a more consistent presence throughout the day,” adds first author Deirdre Logan, a pediatric psychologist at Boston Children’s Hospital. “There may also be kids who don’t always want to talk to people, and respond better to having a robotic stuffed animal with them. It’s exciting knowing what types of support we can provide kids who may feel isolated or scared about what they’re going through.”
Joining Breazeal and Logan on the paper are: Sooyeon Jeong, a PhD student in the Personal Robots group; Brianna O’Connell, Duncan Smith-Freedman, and Peter Weinstock, all of Boston Children’s Hospital; and Matthew Goodwin and James Heathers, both of Northeastern University.
Boosting mood
First prototyped in 2006, Huggable is a plush teddy bear with a screen depicting animated eyes. While the eventual goal is to make the robot fully autonomous, it is currently operated remotely by a specialist in the hall outside a child’s room. Through custom software, a specialist can control the robot’s facial expressions and body actions, and direct its gaze. The specialists could also talk through a speaker — with their voice automatically shifted to a higher pitch to sound more childlike — and monitor the participants via camera feed. The tablet-based avatar of the bear had identical gestures and was also remotely operated.
During the interventions involving Huggable — involving kids ages 3 to 10 years — a specialist would sing nursery rhymes to younger children through robot and move the arms during the song. Older kids would play the I Spy game, where they have to guess an object in the room described by the specialist through Huggable.
Through self-reports and questionnaires, the researchers recorded how much the patients and families liked interacting with Huggable. Additional questionnaires assessed patient’s positive moods, as well as anxiety and perceived pain levels. The researchers also used cameras mounted in the child’s room to capture and analyze speech patterns, characterizing them as joyful or sad, using software.
A greater percentage of children and their parents reported that the children enjoyed playing with Huggable more than with the avatar or traditional teddy bear. Speech analysis backed up that result, detecting significantly more joyful expressions among the children during robotic interventions. Additionally, parents noted lower levels of perceived pain among their children.
The researchers noted that 93 percent of patients completed the Huggable-based interventions, and found few barriers to practical implementation, as determined by comments from the specialists.
A previous paper based on the same study found that the robot also seemed to facilitate greater family involvement in the interventions, compared to the other two methods, which improved the intervention overall. “Those are findings we didn’t necessarily expect in the beginning,” says Jeong, also a co-author on the previous paper. “We didn’t tell family to join any of the play sessions — it just happened naturally. When the robot came in, the child and robot and parents all interacted more, playing games or in introducing the robot.”
An automated, take-home bot
The study also generated valuable insights for developing a fully autonomous Huggable robot, which is the researchers’ ultimate goal. They were able to determine which physical gestures are used most and least often, and which features specialists may want for future iterations. Huggable, for instance, could introduce doctors before they enter a child’s room or learn a child’s interests and share that information with specialists. The researchers may also equip the robot with computer vision, so it can detect certain objects in a room to talk about those with children.
“In these early studies, we capture data … to wrap our heads around an authentic use-case scenario where, if the bear was automated, what does it need to do to provide high-quality standard of care,” Breazeal says.
In the future, that automated robot could be used to improve continuity of care. A child would take home a robot after a hospital visit to further support engagement, adherence to care regimens, and monitoring well-being.
“We want to continue thinking about how robots can become part of the whole clinical team and help everyone,” Jeong says. “When the robot goes home, we want to see the robot monitor a child’s progress. … If there’s something clinicians need to know earlier, the robot can let the clinicians know, so [they’re not] surprised at the next appointment that the child hasn’t been doing well.”
Next, the researchers are hoping to zero in on which specific patient populations may benefit the most from the Huggable interventions. “We want to find the sweet spot for the children who need this type of of extra support,” Logan says. | | 8:00a |
Translating proteins into music, and back Want to create a brand new type of protein that might have useful properties? No problem. Just hum a few bars.
In a surprising marriage of science and art, researchers at MIT have developed a system for converting the molecular structures of proteins, the basic building blocks of all living beings, into audible sound that resembles musical passages. Then, reversing the process, they can introduce some variations into the music and convert it back into new proteins never before seen in nature.
Although it’s not quite as simple as humming a new protein into existence, the new system comes close. It provides a systematic way of translating a protein’s sequence of amino acids into a musical sequence, using the physical properties of the molecules to determine the sounds. Although the sounds are transposed in order to bring them within the audible range for humans, the tones and their relationships are based on the actual vibrational frequencies of each amino acid molecule itself, computed using theories from quantum chemistry.
The system was developed by Markus Buehler, the McAfee Professor of Engineering and head of the Department of Civil and Environmental Engineering at MIT, along with postdoc Chi Hua Yu and two others. As described today in the journal ACS Nano, the system translates the 20 types of amino acids, the building blocks that join together in chains to form all proteins, into a 20-tone scale. Any protein’s long sequence of amino acids then becomes a sequence of notes.
While such a scale sounds unfamiliar to people accustomed to Western musical traditions, listeners can readily recognize the relationships and differences after familiarizing themselves with the sounds. Buehler says that after listening to the resulting melodies, he is now able to distinguish certain amino acid sequences that correspond to proteins with specific structural functions. “That’s a beta sheet,” he might say, or “that’s an alpha helix.”
Learning the language of proteins
The whole concept, Buehler explains, is to get a better handle on understanding proteins and their vast array of variations. Proteins make up the structural material of skin, bone, and muscle, but are also enzymes, signaling chemicals, molecular switches, and a host of other functional materials that make up the machinery of all living things. But their structures, including the way they fold themselves into the shapes that often determine their functions, are exceedingly complicated. “They have their own language, and we don’t know how it works,” he says. “We don’t know what makes a silk protein a silk protein or what patterns reflect the functions found in an enzyme. We don’t know the code.”
By translating that language into a different form that humans are particularly well-attuned to, and that allows different aspects of the information to be encoded in different dimensions — pitch, volume, and duration — Buehler and his team hope to glean new insights into the relationships and differences between different families of proteins and their variations, and use this as a way of exploring the many possible tweaks and modifications of their structure and function. As with music, the structure of proteins is hierarchical, with different levels of structure at different scales of length or time.
The new method translates an amino acid sequence of proteins into this sequence of percussive and rhythmic sounds. Courtesy of Markus Buehler.
The team then used an artificial intelligence system to study the catalog of melodies produced by a wide variety of different proteins. They had the AI system introduce slight changes in the musical sequence or create completely new sequences, and then translated the sounds back into proteins that correspond to the modified or newly designed versions. With this process they were able to create variations of existing proteins — for example of one found in spider silk, one of nature’s strongest materials — thus making new proteins unlike any produced by evolution.
The percussive, rhythmic, and musical sounds heard here are generated entirely from amino acid sequences. Courtesy of Markus Buehler.
Although the researchers themselves may not know the underlying rules, “the AI has learned the language of how proteins are designed,” and it can encode it to create variations of existing versions, or completely new protein designs, Buehler says. Given that there are “trillions and trillions” of potential combinations, he says, when it comes to creating new proteins “you wouldn’t be able to do it from scratch, but that’s what the AI can do.”
“Composing” new proteins
By using such a system, he says training the AI system with a set of data for a particular class of proteins might take a few days, but it can then produce a design for a new variant within microseconds. “No other method comes close,” he says. “The shortcoming is the model doesn’t tell us what’s really going on inside. We just know it works.”
This way of encoding structure into music does reflect a deeper reality. “When you look at a molecule in a textbook, it’s static,” Buehler says. “But it’s not static at all. It’s moving and vibrating. Every bit of matter is a set of vibrations. And we can use this concept as a way of describing matter.”
The method does not yet allow for any kind of directed modifications — any changes in properties such as mechanical strength, elasticity, or chemical reactivity will be essentially random. “You still need to do the experiment,” he says. When a new protein variant is produced, “there’s no way to predict what it will do.”
The team also created musical compositions developed from the sounds of amino acids, which define this new 20-tone musical scale. The art pieces they constructed consist entirely of the sounds generated from amino acids. “There are no synthetic or natural instruments used, showing how this new source of sounds can be utilized as a creative platform,” Buehler says. Musical motifs derived from both naturally existing proteins and AI-generated proteins are used throughout the examples, and all the sounds, including some that resemble bass or snare drums, are also generated from the sounds of amino acids.
The researchers have created a free Android smartphone app, called Amino Acid Synthesizer, to play the sounds of amino acids and record protein sequences as musical compositions.
“Markus Buehler has been gifted with a most creative soul, and his explorations into the inner workings of biomolecules are advancing our understanding of the mechanical response of biological materials in a most significant manner,” says Marc Meyers, a professor of materials science at the University of California at San Diego, who was not involved in this work.
Meyers adds, “The focusing of this imagination to music is a novel and intriguing direction. This is experimental music at its best. The rhythms of life, including the pulsations of our heart, were the initial sources of repetitive sounds that engendered the marvelous world of music. Markus has descended into the nanospace to extract the rythms of the amino acids, the building blocks of life.”
“Protein sequences are complex, as are comparisons between protein sequences,” says Anthony Weiss, a professor of biochemistry and molecular biotechnology at the University of Sydney, Australia, who also was not connected to this work. The MIT team “provides an impressive, entertaining and unusual approach to accessing and interpreting this complexity. ... The approach benefits from our innate ability to hear complex musical patterns. Through harmony and discord, we now have an entertaining and useful tool to compare and contrast amino acid sequences.”
The team also included research scientist Zhao Qin and Francisco Martin-Martinez at MIT. The work was supported by the U.S. Office of Naval Research and the National Institutes of Health. | | 9:52a |
New AI programming language goes beyond deep learning A team of MIT researchers is making it easier for novices to get their feet wet with artificial intelligence, while also helping experts advance the field.
In a paper presented at the Programming Language Design and Implementation conference this week, the researchers describe a novel probabilistic-programming system named “Gen.” Users write models and algorithms from multiple fields where AI techniques are applied — such as computer vision, robotics, and statistics — without having to deal with equations or manually write high-performance code. Gen also lets expert researchers write sophisticated models and inference algorithms — used for prediction tasks — that were previously infeasible.
In their paper, for instance, the researchers demonstrate that a short Gen program can infer 3-D body poses, a difficult computer-vision inference task that has applications in autonomous systems, human-machine interactions, and augmented reality. Behind the scenes, this program includes components that perform graphics rendering, deep-learning, and types of probability simulations. The combination of these diverse techniques leads to better accuracy and speed on this task than earlier systems developed by some of the researchers.
Due to its simplicity — and, in some use cases, automation — the researchers say Gen can be used easily by anyone, from novices to experts. “One motivation of this work is to make automated AI more accessible to people with less expertise in computer science or math,” says first author Marco Cusumano-Towner, a PhD student in the Department of Electrical Engineering and Computer Science. “We also want to increase productivity, which means making it easier for experts to rapidly iterate and prototype their AI systems.”
The researchers also demonstrated Gen’s ability to simplify data analytics by using another Gen program that automatically generates sophisticated statistical models typically used by experts to analyze, interpret, and predict underlying patterns in data. That builds on the researchers’ previous work that let users write a few lines of code to uncover insights into financial trends, air travel, voting patterns, and the spread of disease, among other trends. This is different from earlier systems, which required a lot of hand coding for accurate predictions.
“Gen is the first system that’s flexible, automated, and efficient enough to cover those very different types of examples in computer vision and data science and give state of-the-art performance,” says Vikash K. Mansinghka ’05, MEng ’09, PhD ’09, a researcher in the Department of Brain and Cognitive Sciences who runs the Probabilistic Computing Project.
Joining Cusumano-Towner and Mansinghka on the paper are Feras Saad and Alexander K. Lew, both CSAIL graduate students and members of the Probabilistic Computing Project.
Best of all worlds
In 2015, Google released TensorFlow, an open-source library of application programming interfaces (APIs) that helps beginners and experts automatically generate machine-learning systems without doing much math. Now widely used, the platform is helping democratize some aspects of AI. But, although it’s automated and efficient, it’s narrowly focused on deep-learning models which are both costly and limited compared to the broader promise of AI in general.
But there are plenty of other AI techniques available today, such as statistical and probabilistic models, and simulation engines. Some other probabilistic programming systems are flexible enough to cover several kinds of AI techniques, but they run inefficiently.
The researchers sought to combine the best of all worlds — automation, flexibility, and speed — into one. “If we do that, maybe we can help democratize this much broader collection of modeling and inference algorithms, like TensorFlow did for deep learning,” Mansinghka says.
In probabilistic AI, inference algorithms perform operations on data and continuously readjust probabilities based on new data to make predictions. Doing so eventually produces a model that describes how to make predictions on new data.
Building off concepts used in their earlier probabilistic-programming system, Church, the researchers incorporate several custom modeling languages into Julia, a general-purpose programming language that was also developed at MIT. Each modeling language is optimized for a different type of AI modeling approach, making it more all-purpose. Gen also provides high-level infrastructure for inference tasks, using diverse approaches such as optimization, variational inference, certain probabilistic methods, and deep learning. On top of that, the researchers added some tweaks to make the implementations run efficiently.
Beyond the lab
External users are already finding ways to leverage Gen for their AI research. For example, Intel is collaborating with MIT to use Gen for 3-D pose estimation from its depth-sense cameras used in robotics and augmented-reality systems. MIT Lincoln Laboratory is also collaborating on applications for Gen in aerial robotics for humanitarian relief and disaster response.
Gen is beginning to be used on ambitious AI projects under the MIT Quest for Intelligence. For example, Gen is central to an MIT-IBM Watson AI Lab project, along with the U.S. Department of Defense’s Defense Advanced Research Projects Agency’s ongoing Machine Common Sense project, which aims to model human common sense at the level of an 18-month-old child. Mansinghka is one of the principal investigators on this project.
“With Gen, for the first time, it is easy for a researcher to integrate a bunch of different AI techniques. It’s going to be interesting to see what people discover is possible now,” Mansinghka says.
Zoubin Ghahramani, chief scientist and vice president of AI at Uber and a professor at Cambridge University, who was not involved in the research, says, "Probabilistic programming is one of most promising areas at the frontier of AI since the advent of deep learning. Gen represents a significant advance in this field and will contribute to scalable and practical implementations of AI systems based on probabilistic reasoning.”
Peter Norvig, director of research at Google, who also was not involved in this research, praised the work as well. “[Gen] allows a problem-solver to use probabilistic programming, and thus have a more principled approach to the problem, but not be limited by the choices made by the designers of the probabilistic programming system,” he says. “General-purpose programming languages … have been successful because they … make the task easier for a programmer, but also make it possible for a programmer to create something brand new to efficiently solve a new problem. Gen does the same for probabilistic programming.”
Gen’s source code is publicly available and is being presented at upcoming open-source developer conferences, including Strange Loop and JuliaCon. The work is supported, in part, by DARPA. | | 1:59p |
Confining cell-killing treatments to tumors Cytokines, small proteins released by immune cells to communicate with each other, have for some time been investigated as a potential cancer treatment.
However, despite their known potency and potential for use alongside other immunotherapies, cytokines have yet to be successfully developed into an effective cancer therapy.
That is because the proteins are highly toxic to both healthy tissue and tumors alike, making them unsuitable for use in treatments administered to the entire body.
Injecting the cytokine treatment directly into the tumor itself could provide a method of confining its benefits to the tumor and sparing healthy tissue, but previous attempts to do this have resulted in the proteins leaking out of the cancerous tissue and into the body’s circulation within minutes.
Now researchers at the Koch Institute for Integrative Cancer Research at MIT have developed a technique to prevent cytokines escaping once they have been injected into the tumor, by adding a Velcro-like protein that attaches itself to the tissue.
In this way the researchers, led by Dane Wittrup, the Carbon P. Dubbs Professor in Chemical Engineering and Biological Engineering and a member of the Koch Institute, hope to limit the harm caused to healthy tissue, while prolonging the treatment’s ability to attack the tumor.
To develop their technique, which they describe in a paper published today in the journal Science Translational Medicine, the researchers first investigated the different proteins found in tumors, to find one that could be used as a target for the cytokine treatment. They chose collagen, which is expressed abundantly in solid tumors.
They then undertook an extensive literature search to find proteins that bind effectively to collagen. They discovered a collagen-binding protein called lumican, which they then attached to the cytokines.
“When we inject (a collagen-anchoring cytokine treatment) intratumorally, we don’t have to worry about collagen found elsewhere in the body; we just have to make sure we have a protein that binds to collagen very tightly,” says lead author Noor Momin, a graduate student in the Wittrup Lab at MIT.
To test the treatment, the researchers used two cytokines known to stimulate and expand immune cell responses. The cytokines, interleukin-2 (IL-2) and interleukin-12 (IL-12), are also known to combine well with other immunotherapies.
Although IL-2 already has FDA approval, its severe side-effects have so far prevented its clinical use. Meanwhile IL-12 therapies have not yet reached phase 3 clinical trials due to their severe toxicity.
The researchers tested the treatment by injecting the two different cytokines into tumors in mice. To make the test more challenging, they chose a type of melanoma that contains relatively low amounts of collagen, compared to other tumor types.
They then compared the effects of administering the cytokines alone and of injecting cytokines attached to the collagen-binding lumican.
“In addition, all of the cytokine therapies were given alongside a form of systemic therapy, such as a tumor-targeting antibody, a vaccine, a checkpoint blockade, or chimeric antigen receptor (CAR)-T cell therapy, as we wanted to show the potential of combining cytokines with many different immunotherapy modalities,” Momin says.
They found that when any of the treatments were administered individually, the mice did not survive. Combining the treatments improved survival rates slightly, but when the cytokine was administered with the lumican to bind to the collagen, the researchers found that over 90 percent of the mice survived with some combinations.
“So we were able to show that these combinations are synergistic, they work really well together, and that cytokines attached to lumican really helped reap the full benefits of the combination,” Momin says.
What’s more, attaching the lumican eliminated the problem of toxicity associated with cytokine treatments alone.
The paper attempts to address a major obstacle in the oncology field, that of how to target potent therapeutics to the tumor microenvironment to enable their local action, according to Shannon Turley, a staff scientist and specialist in cancer immunology at Genentech, who was not involved in the research.
“This is important because many of the most promising cancer drugs can have unwanted side effects in tissues beyond the tumor,” Turley says. “The team’s approach relies on two principles that together make for a novel approach: injection of the drug directly into the tumor site, and engineering of the drug to contain a ‘Velcro’ that attaches the drug to the tumor to keep it from leaking into circulation and acting all over the body.”
The researchers now plan to carry out further work to improve the technique, and to explore other treatments that could benefit from being combined with collagen-binding lumican, Momin says.
Ultimately, they hope the work will encourage other researchers to consider the use of collagen binding for cancer treatments, Momin says.
“We’re hoping the paper seeds the idea that collagen anchoring could be really advantageous for a lot of different therapies across all solid tumors.” |
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