Drug delivery systems often face two critical challenges: poor solubility and inefficient delivery within the body. Many drugs do not dissolve well, making it difficult for them to reach their intended targets. Furthermore, current delivery systems waste a significant portion of the drug during preparation — only 5-10% of the drug is successfully loaded, leading to less effective treatments.

Peptide Helpers

The research team has developed a novel solution by designing peptides — short strings of amino acids — to bind with specific drugs and create therapeutic nanoparticles. These nanoparticles are primarily composed of the drug, with a thin peptide coating that improves solubility, enhances stability in the body, and optimizes delivery to targeted areas. Remarkably, this approach achieves drug loadings of up to 98%, a dramatic improvement over traditional methods.

By using a combination of computer models and laboratory tests, new drug/peptide nanoparticles where identified. They subsequently demonstrated remarkable results in leukemia models. The nanoparticles were more effective at shrinking tumors compared to the drugs alone. Additionally, their high efficiency allows for lower doses of drugs, potentially reducing the side effects.

“Peptides, which are designed molecules made from the same building blocks as the proteins in our body, are extremely versatile,” said Co-Principal Investigator Rein Ulijn, director of the Nanoscience Initiative at CUNY ASRC and a chemistry professor at Hunter College. “We thought they could be useful in solving two big problems seen in many drugs: poor solubility and inefficient delivery. By designing a peptide that binds the drug while enhancing its solubility, we were able to create nanoparticles with very high loading.”

Customizable Technology

This innovation holds significant potential because peptides can be customized to enhance the effectiveness of various drugs. Given the vast range of possible interactions in peptide design, it may be feasible to tailor peptides for specific drugs, extending their applicability beyond cancer treatments.

“This breakthrough enables the development of better precision medicines,” said Co-Principal Investigator Daniel Heller,head of the Cancer Nanomedicine Laboratory at Memorial Sloan Kettering Cancer Center’s Molecular Pharmacology Program. “Using specially designed peptides, we can build nanomedicines that make existing drugs more effective and less toxic and even enable the development of drugs that might not be able to work without these nanoparticles.”

Naxhije “Gia” Berisha, a former CUNY Graduate Center Ph.D. student who performed much of the experimental work, highlighted the potential of the peptide approach: “We used experimental testing to identify promising peptides and computational modeling to analyze their interactions with therapeutic molecules,” she said “It’s incredible to see how simple variations in peptide sequence could match specific drugs. This suggests there may be a peptide match for every drug, potentially revolutionizing the way medicines are delivered.”

Looking Ahead

The research team is now adopting lab automation methods to further refine and accelerate the peptide-drug matching process. Their next steps include verifying the approach’s potential in a wider range of diseases. If successful, this innovation could lead to more effective treatments, reduced side effects, and significant cost savings in drug development.

In a new paper published in Advanced Materials, a team led by Professor Tobin Filleter describes how they made nanomaterials with properties that offer a conflicting combination of exceptional strength, light weight and customizability. The approach could benefit a wide range of industries, from automotive to aerospace.

“Nano-architected materials combine high performance shapes, like making a bridge out of triangles, at nanoscale sizes, which takes advantage of the ‘smaller is stronger’ effect, to achieve some of the highest strength-to-weight and stiffness-to-weight ratios, of any material,” says Peter Serles, the first author of the new paper.

“However, the standard lattice shapes and geometries used tend to have sharp intersections and corners, which leads to the problem of stress concentrations. This results in early local failure and breakage of the materials, limiting their overall potential.

“As I thought about this challenge, I realized that it is a perfect problem for machine learning to tackle.”

Nano-architected materials are made of tiny building blocks or repeating units measuring a few hundred nanometres in size — it would take more than 100 of them patterned in a row to reach the thickness of a human hair. These building blocks, which in this case are composed of carbon, are arranged in complex 3D structures called nanolattices.

To design their improved materials, Serles and Filleter worked with Professor Seunghwa Ryu and PhD student Jinwook Yeo at the Korea Advanced Institute of Science & Technology (KAIST) in Daejeon, South Korea. This partnership was initiated through the University of Toronto’s International Doctoral Clusters program, which supports doctoral training through research engagement with international collaborators.

The KAIST team employed the multi-objective Bayesian optimization machine learning algorithm. This algorithm learned from simulated geometries to predict the best possible geometries for enhancing stress distribution and improving the strength-to-weight ratio of nano-architected designs.

Serles then used a two-photon polymerization 3D printer housed in the Centre for Research and Application in Fluidic Technologies (CRAFT) to create prototypes for experimental validation. This additive manufacturing technology enables 3D printing at the micro and nano scale, creating optimized carbon nanolattices.

These optimized nanolattices more than doubled the strength of existing designs, withstanding a stress of 2.03 megapascals for every cubic metre per kilogram of its density, which is about five times higher than titanium.

“This is the first time machine learning has been applied to optimize nano-architected materials, and we were shocked by the improvements,” says Serles. “It didn’t just replicate successful geometries from the training data; it learned from what changes to the shapes worked and what didn’t, enabling it to predict entirely new lattice geometries.

“Machine learning is normally very data intensive, and it’s difficult to generate a lot of data when you’re using high-quality data from finite element analysis. But the multi-objective Bayesian optimization algorithm only needed 400 data points, whereas other algorithms might need 20,000 or more.?So, we were able to work with a much smaller but an extremely high-quality data set.”

“We hope that these new material designs will eventually lead to ultra-light weight components in aerospace applications, such as planes, helicopters and spacecraft that can reduce fuel demands during flight while maintaining safety and performance,” says Filleter. “This can ultimately help reduce the high carbon footprint of flying.”

“For example, if you were to replace components made of titanium on a plane with this material, you would be looking at fuel savings of 80 litres per year for every kilogram of material you replace,” adds Serles.

Other contributors to the project include University of Toronto professors Yu Zou, Chandra Veer Singh, Jane Howe and Charles Jia, as well as international collaborators from Karlsruhe Institute of Technology (KIT) in Germany, Massachusetts Institute of Technology (MIT) and Rice University in the United States.

“This was a multi-faceted project that brought together various elements from material science, machine learning, chemistry and mechanics to help us understand how to improve and implement this technology,” says Serles, who is now a Schmidt Science Fellow at the California Institute of Technology (Caltech).

“Our next steps will focus on further improving the scale up of these material designs to enable cost effective macroscale components,” adds Filleter.

“In addition, we will continue to explore new designs that push the material architectures to even lower density while maintaining high strength and stiffness.”

North Carolina State University researchers firmly point the finger at the South American Andes Mountains as the place where the Irish potato famine pathogen, Phtytophthora infestans, originated.

In a wide-ranging study of the genetic material found in P. infestans and other members of the Phytophthora species, the NC State researchers provide more evidence that P. infestans spread from South America to North America before wreaking havoc in Ireland in the 1840s. The pathogen still causes late-blight disease on potato and tomato plants around the world.

Much of the study’s evidence compares whole genomes of P. infestans with those of close relative pathogens — Phytophthora andina and Phytophthora betacei — which are only found in South America. The results show that these three species are very similar.

“It’s one of the largest whole-genome studies of not only P. infestans, but also the sister lineages,” said Jean Ristaino, William Neal Reynolds Distinguished Professor of Plant Pathology at North Carolina State University and corresponding author of a paper in PLOS One that describes the study. “By sequencing these genomes and accounting for evolutionary relationships and migration patterns, we show that the whole Andean region is a hot spot for speciation, or where a species splits into two or more distinct species.”

In recent decades, scientists have been split in their theories about the point of origin for P. infestans, with some hypothesizing a Mexico origin rather than a South American origin. Yet, the paper shows distinct differences between P. infestans and the two Mexican pathogen species, P. mirabilis and P. ipomoea.

“A lot of the search for resistance to this disease has focused on a wild potato species in Mexico — Solanum demissum — which was used to breed resistant potato lines that were used for the past 100 years,” Ristaino said.

“It points out the importance of looking at the center of origin where a host and pathogen have evolved together over thousands of years,” she said. “Climate change is bringing more drought to higher Andean elevations, so we could be losing some of these potatoes before we learn if they could provide resistance to late-blight disease.” Ristaino added that more research is needed to examine wild potato species from the Andes to learn more about host resistance to P. infestans.

“Our data show that there have been more migrations of the pathogen into and out of South America, and the migrations into and out of Mexico are small in comparison,” said Allison Coomber, a former NC State graduate student researcher and lead author of the paper. “We did find there was gene flow from the Andes to Mexico, and also in reverse, because there’s a big Mexican potato breeding program and potatoes have gone into the Andean region in more recent times. But in historic times it was the other way around.”

“Historic P. infestans — the samples collected from 1845-1889 — were the first to diverge from all other P. infestans populations, with modern South American and Mexican populations both showing shared ancestry derived from historic P. infestans,” Ristaino said. “Modern global trade appears to contribute to mixing together the pathogen populations in South America and Mexico.”

Amanda C. Saville, a research and laboratory specialist in Ristaino’s lab, and Ignazio Carbone, a professor of plant pathology at NC State, also co-authored the paper, along with Michael Martin and Vanessa Bieker from the Norwegian University of Science and Technology. Funding was provided by a National Science Foundation National Research Training Grant (award number 1828820), and by two USDA APHIS Plant Protection Act 7721 grants: AP21PPQ&ST000020 and AP21PPQ&ST000062.

Visual information has long been proven to affect balance—for example, strobe lights and swirling images can cause instability—but a new study published in PLOS ONE shows that sounds can also be a disruptive factor for those who have vestibular hypofunction, a vestibular system disorder resulting in impaired balance.

“People with vestibular hypofunction have difficulty in places like busy streets or train stations where the overwhelming visual information may cause them to lose balance or be anxious or dizzy,” says lead author Anat Lubetzky, associate professor of physical therapy at NYU Steinhardt School of Culture, Education, and Human Development. “Sounds are not typically considered during physical therapy, making our findings particularly relevant for future interventions.”

The researchers conducted an experiment with 69 participants divided into two groups: healthy controls and individuals with unilateral vestibular hypofunction (affecting one ear).

Participants wore a virtual reality headset that simulated the experience of being in a New York City subway. As they experienced the sights and sounds of the “subway,” they stood on a platform that measured their body movement (known as sway), while the headset recorded their head movement, two indicators of balance. Participants were provided with different subway scenarios: static or moving visuals paired with silence, white noise, or recorded subway sounds.

The results revealed that for the group with vestibular hypofunction, the moving visuals accompanied by audio (either white noise or subway sounds) resulted in the greatest amount of sway. This sway was evident by the body’s forward and backward movements, as well as head movements left to right, and head tilts upward and downward. Audio conditions did not affect the balance of the healthy individuals.

“What we’ve learned is that sound should be included as part of both the assessment of balance and intervention programs,” says Lubetzky.  “Because balance training is known to be task-specific, ideally, these should be real sounds related to patients’ typical environments and combined with salient and increasingly challenging visual cues. Portable virtual headsets are a promising tool for both assessing and treating balance problems.”

Funding for this study was provided by a grant from the National Institute on Deafness and Other Communication Disorders (R21DC018101), resources from the Icahn School of Medicine at Mount Sinai, and a grant from the National Center for Advancing Translational Science (UL1TR004419).

AML, which affects four out of 100,000 patients in the U.S. every year, according to the National Cancer Institute, is a type of cancer that first attacks the bone marrow before moving to infect the blood. The current treatment plan includes targeted chemotherapy followed by a stem cell transplant. Unfortunately, up to 40% of these patients relapse after transplant and have a median survival of six months. At that stage, the only hope for remission is through immunotherapy.

Led by Elham Azizi, associate professor of biomedical engineering at Columbia Engineering, the research explores how coordinated immune networks in leukemia bone marrow microenvironments influence responses to cellular therapy, raising the question: why do some patients benefit from immunotherapy while others do not? The current treatment for relapsed AML, donor lymphocyte infusion (DLI) — a therapy involving donor immune cells — has a 5-year survival rate of only 24%, according to research conducted by Pfizer.

This new study finds that a unique population of T cells found in patients who are responding to DLI might be the key. These cells fight leukemia by boosting the immune response. Additionally, the study shows that patients with a healthier, more active and diverse immune environment in the bone marrow are better able to support these cells and their cancer-fighting abilities.

Utilizing the team’s proprietary computational DIISCO approach, the researchers discovered key interactions between the unique T cell population and other immune cells may lead to patient remission. They also traced these T cells back to the donor product. However, it was discovered that the donor’s immune cell composition has little to no effect on the patient’s success. In fact, the success of this treatment is determined by the patient’s immune environment. DIISCO is a machine learning method used to analyze how cell interactions change over time with a focus on cancer and immune cells profiled in clinical specimens.

The study’s findings can lead to new intervention options such as improving the immune environment before starting the standard DLI treatment and exploring combinations of immunotherapies. This will help patients who don’t typically respond well to find a personalized option that works for them.

“This research exemplifies the power of combining computational and experimental methods through close collaboration to answer complex biological questions and uncover unexpected insights,” said Azizi, who is a member of the Irving Institute for Cancer Dynamics, the Herbert Irving Comprehensive Cancer Center, and Columbia’s Data Science Institute. “Our findings not only shed light on mechanisms underlying successful immunotherapy response in leukemia, but also provide a roadmap for developing effective treatments guided by innovative machine learning tools.”

“Seeing our findings validated through functional experiments is incredibly exciting and offers real hope for improving cancer immunotherapy,” said Cameron Park, a PhD student in the Azizi lab, who co-led this study with Katie Maurer at the Catherine Wu Lab at Dana Farber-Cancer Institute. Park was also a co-developer of the DIISCO algorithm.

In this particular research’s future, the team plans to explore interventions that enhance the effectiveness of DLI while focusing on modulating the tumor microenvironment. Although exciting, much more work has to be done before the team can head to clinical trials with the hope to improve outcomes for patients with relapsed AML.

Sepsis is one of the most frequent causes of death worldwide, according to the World Health Organization (WHO), killing 11 million people each year. It’s characterized by runaway inflammation, usually sparked by an infection. It can lead to shock, multiple organ failure, and death if treatment is not rapid enough or effective.

But recent research has shown that it isn’t actually the infection that causes the spiraling inflammation: it’s the cells caught up in it. Even if those cells aren’t infected, they act as if they are, and die. As they die, they send out messages to other cells. Those messages somehow cause the recipient cells to die. If scientists understood what caused this deadly message chain, they might be able to stop it. And that could help heal sepsis.

The deadly message mystery may now be solved. It appears that the “messages” are a byproduct of the cells trying to stay alive, UConn School of Medicine researchers report in Cell.

The process starts with cells that really are infected. To prevent the infection from spreading, those cells destroy themselves by sending a protein called gasdermin-D to their surface. Several gasdermin-D proteins will link together to create a round pore on the cell, like a hole punched in a balloon. The cell’s contents leak out, the cell collapses, and dies.

But the collapse isn’t inevitable. Sometimes cells can act quickly and eject the section of their surface membrane with the gasdermin-D pore. The cell then zips the membrane closed and survives. The ejected membrane forms a little bubble, called a vesicle, that just happens to carry the deadly gasdermin-D pore. The vesicle floats around, and when it encounters a cell nearby, that deadly gasdermin-D pore punches into the healthy nearby cell’s membrane and causes that cell to spill and die.

“When a dying cell releases these vesicles, they can transplant these pores to a neighboring cell’s surface, which leads to the neighboring cell’s death,” says Vijay Rathinam, an immunologist in the UConn School of Medicine. In other words, the deadly messages are a side effect of cells just trying to save themselves. A group of dying cells can release enough gasdermin-D vesicles to kill a considerable number of nearby cells. That spreading message of death fuels the spiraling inflammation of sepsis.

Rathinam and his colleagues are now looking for a way to damp down the deadly gasdermin-D vesicles. If successful, it could lead to a treatment for inflammatory diseases like sepsis.

This study led by Skylar Wright, an MD/PhD student in the Rathinam lab, was done in collaboration with the laboratories of Drs. Jianbin Ruan, Beiyan Zhou, Sivapriya Kailasan Vanaja of UConn Health and Dr. Katia Cosentino of University of Osnabrück, Germany. This project was funded by grants from the National Institutes of Health to Dr. Rathinam.

To address this challenge, a team led by researchers at the National Synchrotron Light Source II (NSLS-II) — a U.S. Department of Energy (DOE) Office of Science user facility at DOE’s Brookhaven National Laboratory — used complementary X-ray techniques at two different beamlines to gain new insights into these materials. Their results were published in Physical Review Letters.

New discoveries in a long history

Superconductivity was first discovered in mercury more than 100 years ago. Superconducting materials allow current to flow through them with no resistance and therefore have no power loss. As these materials enter a superconducting state, the persistent electric current allows them to expel a magnetic field and levitate over magnetic materials as well.

Initially, superconducting properties seemed to only appear at extremely low temperatures — -415 degrees Fahrenheit. In the mid-1980s, however, researchers found that copper-based oxide materials, or “cuprates,” can display these properties at -297.7 degrees Fahrenheit. This spearheaded research in “high-temperature” superconductivity and the search for other cuprate-like high-temperature superconductors. If researchers can find a way to engineer materials to superconduct at higher, more practical temperatures, they might one day contribute to eliminating energy losses in the power grid and paving the way for other novel technologies like maglev trains, more efficient MRI machines, and high-capacity energy storage for electric vehicles.

More recently, nickel-based materials have attracted attention as a new family of high temperature superconductors analogous to cuprates. Neodymium nickelate becomes particularly interesting when strontium is added to its structure. This compound is known as an “infinite layer nickelate,” since nickel atoms are arranged in a two-dimensional square lattice that repeats indefinitely in two dimensions, earning the moniker “infinite.”

Superconductivity in nickelates has, so far, only been observed in very thin films. This raises questions about whether the superconducting properties depend on interactions at the interfaces between the nickelate material and its substrate or capping layer. Early studies provided conflicting results on the properties of these materials.

“This system is sensitive to water and oxygen,” explained Jonathan (Johnny) Pelliciari, a beamline scientist at NSLS-II’s Soft Inelastic X-ray Scattering (SIX) beamline, “so past studies used a very thin protective capping layer and attributed electronic orders to the lack of a thick surface layer. Given how sensitive these systems are, small changes or defects can also affect the material’s properties. We wanted to see how much of a role this capping layer was playing and what signals may be spurious.”

To answer this question, the team employed two beamlines at NSLS-II to investigate high quality nickelate thin film samples with and without a capping layer of strontium titanate to see if the layer has an effect on magnetic and electronic properties. Magnetic properties are critical because they relate to the material’s intrinsic electronic structure, which is directly linked to its superconductivity.

Complementary techniques complete the picture

Resonant Elastic X-Ray Scattering (REXS), performed at the Coherent Soft X-ray Scattering (CSX) beamline at NSLS-II, offers researchers a detailed view of a material’s structural properties. This part of the experiment revealed the atomic and electronic structure of the infinite-layer nickelate thin films. Resonant Inelastic X-ray Scattering (RIXS), performed at the SIX beamline, then measured how X-rays lose energy as they scatter off the films. By analyzing the density, motion, and interactions of electrons and spins, researchers gained valuable insight into processes related to electronic and magnetic properties in the material.

Combining these perspectives gave a complete picture of how the material behaved, especially any changes introduced by capping. The group found that the material’s magnetic fluctuations, or “spin excitations,” are present whether or not the capping layer is applied, showing that magnetism is an inherent quality of these nickelates. In capped samples, these magnetic properties are only slightly stronger because of interfacial effects, which might be due to slight structural adjustments at the interface where the capped layer meets the nickelate, crystal defects, or lattice disorder. The data also confirmed that spin excitations in these materials are stable in the superconducting phase, similar to what is seen in cuprates.

“RIXS is very sensitive to magnetism,” said Shiyu Fan, a postdoctoral researcher at SIX and lead author of this study. “Perhaps the most important finding of this research is the evolution of the spin wave in the presence or absence of the capping layer, which points to the magnetic and superconducting properties being intrinsic to the infinite layer nickelate material.”

“The similarity between copper oxide planes in superconducting cuprates and nickel oxide planes in nickelates have had scientists searching for superconductivity in nickelates for 25 years,” said Claudio Mazzoli, lead beamline scientist at CSX. “Now that it has finally been found, we need to understand the differences and commonalities in these two cases and the physics behind them to gain control of this fascinating phenomenon for technological applications.”

The research and the facilities used were funded by the DOE Office of Science.

In order to find out which of a million different molecules would conduct positive charges and make perovskite solar cells particularly efficient, one would need to synthesize and test all of them — or do as the researchers headed by Tenure-track Professor Pascal Friederich, who specializes in the applications of AI in materials science at KIT’s Institute of Nanotechnology, and Professor Christoph Brabec from the Helmholtz Institute Erlangen-Nürnberg (HI ERN). “With only 150 targeted experiments, we were able to achieve a breakthrough that would otherwise have required hundreds of thousands of tests. The workflow we have developed will open up new ways to quickly and economically discover high-performance materials for a wide range of applications,” Brabec said. With one of the discovered materials, they increased the efficiency of a reference solar cell by approximately two percentage points to 26.2 percent. “Our success shows that enormous amounts of time and resources can be saved by applying skillful strategies for the discovery of new energy materials,” Friedrich said.

The starting point at HI ERN was a database with structural formulae for approximately one million virtual molecules that could be synthesized from commercially available substances. From these virtual molecules, 13,000 were selected at random. The KIT researchers used established quantum mechanical methods to determine their energy levels, polarity, geometry and other properties.

Training AI with Data from Just 101 Molecules

From the 13,000 molecules, the scientists chose 101 with the greatest differences in their properties, synthesized them with robotic systems at HI ERN, used them to produce otherwise identical solar cells, and then measured the efficiency of the solar cells. “Being able to produce truly comparable samples thanks to our highly automated synthesis platform, and thus being able to determine reliable efficiency values, was crucial to our strategy’s success,” said Brabec, who headed the work at HI ERN.

The researchers at KIT used the achieved efficiencies and the properties of the associated molecules to train an AI model, which suggested 48 other molecules to synthesize. Its suggestions were based on two criteria: high expected efficiency and unforeseeable properties. “When the machine learning model is uncertain about the predicted efficiency, it’s worthwhile to synthesize the molecule and take a closer look at it,” Friederich said, explaining the second criterion. “It might surprise us with a high efficiency level.”

Using the molecules suggested by the AI, it was indeed possible to build solar cells with above-average efficiency, including some exceeding the capabilities of the most advanced materials currently used. “We can’t be sure we’ve really found the best one of a million molecules, but we’re certainly close to the optimum,” Friederich said.

AI Versus Chemical Intuition

Since the researchers used an AI that indicates which of the virtual molecules’ properties its suggestions were based on, they were able to gain some insight into the molecules it suggested. For example, they determined that the AI-suggestions are based in part on the presence of certain chemical groups, such as amines, that chemists had previously neglected.

Brabec and Friederich believe that their strategy holds promise for other applications in materials science or can be extended to the optimization of entire components.

The findings, which are the result of research conducted in collaboration with scientists from FAU Erlangen-Nürnberg, South Korea’s Ulsan National Institute of Science, and China’s Xiamen University and University of Electronic Science and Technology, were published recently in the journal Science.

In recent years, immunotherapies have had significant success in the treatment and cure of a wide range of cancers. However, for some patients, these agents are still not sufficiently effective. As part of a preclinical study, Maria Sibilia, Head of the Center for Cancer Research at the Medical University of Vienna, therefore investigated the effects of a combination immunotherapy consisting of systemic administration of the tissue hormone interferon (IFN)-I and local imiquimod therapy. Imiquimod is an active substance that activates the innate receptors TLR7/8 and used to treat basal cell carcinomas. The researchers employed various preclinical mouse tumor models of melanoma and breast cancer. What both tumors have in common is that they are accessible to local therapy and often form distant metastases.

Effective for local tumors and distant metastases

Immunotherapies use the body’s own immune system to fight cancer cells. Plasmacytoid dendritic cells (pDCs), which are activated by Imiquimod via TLR7/8, play an important role in this process. The study showed that oral imiquimod stimulates pDCs to produce the tissue hormone IFN-I. This sensitized other dendritic cells and macrophages in the tumor environment to topical imiquimod therapy, which inhibited the formation of new blood vessels via the cytokine IL12 leading to the death of tumor cells. The combination immunotherapy not only had an effect on the treated tumors, but also on distant metastases. It reduced the formation of new metastases thus preventing tumor relapses and increasing the sensitivity of melanomas to checkpoint inhibitors.

“These findings illustrate that the combination of systemic treatment with imiquimod or IFN-I and topical therapy with imiquimod has the potential to expand treatment options for patients and improve therapy outcomes in locally accessible tumors such as melanoma or breast cancer,” emphasizes Maria Sibilia. “Topical treatment of the primary tumor with imiquimod is essential for this combination therapy with systemic IFN-I to be effective at the treated site and also to clear distant metastases,” adds Philipp Novoszel, MedUni Vienna, one of the first authors of the study.

The results suggest that this therapeutic strategy has the potential to improve treatment outcomes in superficial and thus locally accessible tumors such as melanoma and breast cancer — on the one hand through therapy-associated cancer cell death at the locally treated tumors, but also through the induction of a T cell-induced anti-tumor immune response at distant metastases, which is further enhanced by checkpoint inhibitors.

“Our aim is to continue developing immunotherapeutic strategies in order to improve the long-term prospects for patients who are not yet responding well to these agents,” says Maria Sibilia, who is also Deputy Head of the Comprehensive Cancer Center of MedUni Vienna and University Hospital Vienna.

“As systemic interferon is a well-known cancer therapy and dendritic cells are activated in a similar way to our preclinical models, we believe that the new combination therapy can show an effect in patients,” adds Martina Sanlorenzo, dermato-oncologist at MedUni Vienna and co-first author of the study.