Predicting the financial strain of cancer

Hollings researchers develop machine learning tool to identify patients at risk for treatment-related financial stress

Medical University of South Carolina

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Dr. Haluk Damgacioglu uses machine learning to identify trends in cancer risk and cancer care. Photo by Clif Rhodes

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Credit: Medical University of South Carolina

Researchers at MUSC Hollings Cancer Center have developed a machine learning tool to identify cancer patients who may be at high risk for financial toxicity – the financial stress and hardship that can accompany a cancer diagnosis and treatment. The study brought together several investigators from the Hollings Cancer Prevention and Control Research Program, reflecting the project’s focus on cancer outcomes, survivorship and care delivery.

The study, published in JNCI Cancer Spectrum, describes a personalized risk prediction model that uses patient information to estimate the likelihood that someone will struggle with cancer-related financial burdens, such as medical debt, unpaid bills or anxiety about treatment costs.

“Cancer treatment is unfortunately expensive, and financial toxicity is a complex problem,” said lead author Haluk Damgacioglu, Ph.D. “There are transportation costs, lodging costs, lost income, medical bills and out-of-pocket expenses. In some cases, financial stress may even lead patients to delay or discontinue treatment. We aimed to identify people at risk earlier, before these challenges escalate.”

Connecting patients with support sooner

Nearly a quarter of people with cancer in the U.S. experience financial toxicity, which includes both financial hardship and psychological stress.

“There’s the material side, like debt or unpaid bills, but there’s also the psychological side,” Damgacioglu explained. “Even worrying about how you’ll pay for treatment can become a major source of stress.”

Many studies have examined who is most likely to experience financial hardship during cancer care, but ways to predict a patient’s risk have remained limited. Earlier identification could help to connect at-risk patients with counseling and other services before financial strain affects care decisions, treatment adherence or quality of life.

Hollings offers a wide range of patient services for patients and their families. That includes financial counseling staffed by counselors who specialize in cancer care.

“At Hollings, we have financial navigation and counseling resources,” Damgacioglu said. “The first step is identifying the patients who may need additional support so we can connect them with those resources sooner.”

Building a tool to predict financial risk

To address that gap, the research team analyzed national survey data from almost 800 cancer patients who were undergoing or had completed cancer treatment within the past year. Patients were classified as experiencing financial toxicity if they responded “yes” to at least one of several material hardship or psychological stress questions, such as borrowing money, being unable to pay medical bills, going into debt, filing for bankruptcy or worrying about future medical costs related to cancer care.

The researchers tested six machine learning models, using patients’ demographic, clinical and financial information to predict who would experience financial toxicity. They then fine-tuned the models to maximize sensitivity, prioritizing the identification of as many at-risk patients as possible.

“We didn’t want to miss anyone who may experience financial toxicity,” Damgacioglu said. “That was one of the most important goals of the study.”

The best-performing model identified patients at risk for financial toxicity with 84% sensitivity and 75% specificity, balancing the ability to detect patients who need support with minimizing false alarms. The model identified most patients likely to experience financial toxicity without unnecessarily flagging many false positives.

The study also used interpretable machine learning methods to identify the factors most strongly linked to financial risk. Among the strongest predictors were:

• Younger age.
• Lower income.
• Poorer overall health.
• Active cancer treatment.
• Higher out-of-pocket medical expenses.

To translate the research into clinical care, the team developed a publicly available web-based risk calculator that estimates how likely a patient is to experience financial toxicity and categorizes it as low, moderate or high. The researchers envision the tool as eventually helping to connect patients with financial and other support services earlier in care. They will now focus on improving the model, testing it in real-world clinical settings and exploring how financial stress affects patients’ long-term health outcomes.

“Financial toxicity is another side effect of cancer,” Damgacioglu said. “If we can identify risk early, there may be opportunities to help patients before that stress becomes overwhelming.”

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Journal

JNCI Cancer Spectrum

DOI

10.1093/jncics/pkag049 

Method of Research

Computational simulation/modeling

Subject of Research

People

Article Title

Personalized risk prediction of financial toxicity in patients with cancer: An interpretable machine learning study

 

T cells may be key to stopping measles virus—and its deadly relatives

LJI researchers discover that 'cross-reactive' T cells can recognize measles and the highly lethal Nipah virus

La Jolla Institute for Immunology

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LJI Professor Alessandro Sette, Dr.Biol.Sci.

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Credit: La Jolla Institute for Immunology

Highlights:

• Measles cases are rising, and many are concerned about a closely related virus called Nipah virus.

• Scientists are eager to develop vaccines or therapies to fight these viruses and their relatives across the paramyxovirus family.

• In a new study, scientists from La Jolla Institute for Immunology (LJI) show exactly how "cross-reactive" T cells can recognize many species of paramyxovirus at once.

• These findings may guide the development of new vaccines and therapies that stop measles, Nipah, and other paramyxovirus infections before they turn deadly.

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LA JOLLA, CA—T cells are some of the immune system's most important warriors. They can stop tumor growth and even fight off severe infections. Now scientists at La Jolla Institute for Immunology (LJI) have discovered how T cells target paramyxoviruses, a viral family that includes measles virus and Nipah virus. 

Paramyxoviruses are pathogens of pandemic concern. Measles virus is highly infectious, and Nipah virus has a high mortality rate. The new study shows how we might harness T cells to save lives.

Instead of vaccinating against one virus at a time, the researchers found that activating "cross-reactive" T cells may protect against the wider paramyxovirus family. This broad protection is essential when you don't know which virus will strike next.

"No one knows which particular viral species or strain of a virus might be responsible for an outbreak, as we've seen in the recent cases of Andes hantavirus," says study leader LJI Professor Alessandro Sette, Dr.Biol.Sci.

"Activating T cells can be your first line of defense when you don't know what's going to be thrown at you," adds study co-leader LJI Research Assistant Professor Alba Grifoni, Ph.D.

The new Cell Reports Medicine study was supported by the National Institutes of Health's National Institute of Allergy and Infectious Diseases (NIAID) and the Coalition for Epidemic Preparedness Innovations (CEPI).

T cells are key to fighting emerging diseases

T cells are part of the adaptive immune system, which means each T cell adapts and learns to target a specific threat. A T cell might respond to influenza virus infection but not malaria parasite infection, for example. T cells are specialists.

How do our T cells do it? Each T cell looks for a specific small molecular site that marks friend from foe. Scientists call these sites "epitopes." In general, T cell epitopes on one pathogen look very different from T cell epitopes on another pathogen.

But viruses aren't as sneaky as they seem. Even as viruses evolve, some "conserved" features remain unchanged within viral families.

That's where immunologists come in. LJI scientists have shown that some T cells can "cross-react" to different viruses, as long as the viruses share similar epitopes. 

In a series of landmark studies during the COVID-19 pandemic, Sette, Grifoni, LJI Assistant Professor Daniela Weiskopf, Ph.D., and Professor and Chief Scientific Officer Shane Crotty, Ph.D., showed that cross-reactive T cells can recognize the family resemblance between different coronaviruses. A person who had previously contracted a common cold coronavirus may already have T cells primed to recognize SARS-CoV-2, the coronavirus that causes COVID-19.

More recently, Sette and Grifoni demonstrated that cross-reactive T cells may offer broad protection against the deadly Lassa virus and the wider viral family of arenaviruses. [Read: We can help the body fight entire viral families] Their findings suggest that future vaccines and therapies could activate these cross-reactive T cells to protect against many dangerous viruses at once.

Each study makes it clear: cross-reactive T cells are key to stopping emerging viruses.

Why paramyxoviruses are a problem

Doctors and scientists in the United States have their eyes on one virus in particular: measles virus. Falling vaccination rates have led to a surge in measles cases in recent years. In 2026 alone, the United States has had 2,033 confirmed measles cases. Already, we are on track to surpass the total U.S. measles cases in 2025.

Measles is a threat worldwide. People in Southeast Asia also have to keep watch for a related threat: Nipah virus. Nipah virus is a paramyxovirus that is spread by bats. Cases are rare, but they turn deadly, fast. Nipah virus has a fatality rate of between 40 percent and 75 percent, which is much higher than measles. "Outbreaks are becoming more and more frequent, especially in the Malaysian region," says Grifoni. 

The new LJI study suggests cross-reactive T cells may be just the weapons we need to combat the dangerous paramyxovirus family.

The scientists worked with LJI's John and Susan Major Center for Clinical Investigation to collect and analyze T cells from the blood of 31 study participants. These study participants had received their MMR vaccines, which protect against severe infection from the measles and mumps viruses (both are paramyxoviruses) and the rubella virus. As a result, the blood samples contained T cells that were ready to fight measles infection. 

First, the researchers studied exactly how these T cells recognized their enemy. When the T cells spotted measles, what did they see?

LJI Postdoctoral Fellow Alison Tarke, Ph.D., and LJI Senior Staff Scientist Ricardo Da Silva Antunes, Ph.D., spearheaded experiments to map T cell epitopes on measles virus. 

These findings were important on their own. "Even though measles has been studied for quite some time, and there is a vaccine for measles, there was not a lot known about the specific T-cell response elicited by the measles vaccine," says Sette.

T cells take aim at Nipah virus

Alison Tarke  and the LJI team then tested how these same T cells reacted to Nipah virus. From blood tests, the scientists knew that the study participants had never been infected with Nipah virus. Their T cells hadn't had a chance to "adapt" or learn to target epitopes on Nipah virus.

And yet—the researchers found that some measles-fighting T cells could also recognize Nipah virus. These T cells had the ability to cross-react between the two related viruses. The two paramyxoviruses had "conserved" epitopes in common. 

"Focusing immune responses on these conserved regions could have a broad, protective capacity for the whole viral family," says Sette.

The new study is actually the first to map T cell epitopes on Nipah virus. The researchers were also able to zero in on a specific epitope shared between measles and Nipah viruses: a region of the viral fusion or "F" protein. A large number of cross-reactive T cells targeted this relatively small, conserved viral structure. 

"It appears that if someone is vaccinated against measles, their T cells will have some degree of cross-reactivity to Nipah," says Sette. "That raises the possibility that during a Nipah outbreak, one could perhaps vaccinate people with a measles vaccine, and this cross-reactivity could potentially offer some benefit."

Additional authors of the study, "Comprehensive mapping of human CD4+ T cell epitopes for Nipah and measles as prototype Paramyxoviruses," include Mariah Macias, Claudia Francisco Morales, Tanner Michaelis, Leila Siddiqui, Esther Dawen Yu, Raphael Trevizani, Abril Zuniga, Christian Zmasek, Elizabeth Phillips, Simon Mallal, Brandon Lin, Jesus O. Estevez, Jonathan R. Erlich, Nicole V. Johnson, Jason S. McLellan, April Frazier, and Gene S. Tan.

This study was supported by the National Institute of Allergy and Infectious Diseases, of the National Institutes of Health, under award number 75N93024C00056; and by CEPI through the CEPI Immunogen Design for Disease X program.

Learn more:

• LJI Center for Vaccine Innovation
• CEPI article: The Paramyxoviruses

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Journal

Cell Reports Medicine

DOI

10.1016/j.xcrm.2026.102838 

Method of Research

Experimental study

Subject of Research

Cells

Article Title

Comprehensive mapping of human CD4+ T cell epitopes for Nipah and measles as prototype Paramyxoviruses

Article Publication Date

2-Jun-2026

COI Statement

A.S. is a consultant for Darwin Health, Desna Therapeutics, EmerVax, Geovax, Gilead Sciences, Guggenheim Securities, Link Campus University and RiverVest Venture Partners. LJI has filed for patent protection for various aspects of T cell epitope and vaccine design work.

 

Flatworms reveal explosive new type of immune cell

Stanford University

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In Brief:

• Stanford scientists discovered a new type of cytotoxic cell called “ruptoblasts” in experiments with planarian flatworms.

• Unlike common blood-derived immune cells, ruptoblasts are specialized gland cells that undergo an explosive cell death called "ruptosis” when triggered by a specific hormone.

• A single ruptoblast can kill dozens of target cells within minutes through an explosion of toxic agents that quickly dissipate.

• Ruptosis is the most explosive form of cell death known to date, making it distinct from all previously described cell death pathways.

Stanford scientists have discovered a new type of immune cell that kills surrounding cells via explosion – a cellular detonation so fast and complete that the cell vanishes within minutes, leaving no trace behind. This discovery comes from an unlikely source: planarian flatworms. These aquatic, slithering pancake versions of worms are famous for their ability to survive dismemberment and grow whole new organisms from the sliced-up segments of their formerly unified body. Understanding how these flatworms’ immune systems have managed to endure for hundreds of millions of years could hold important insights for modern medicine.

In a new study published June 2 in Cell, the team describes the discovery and names these new cells “ruptoblasts” for their explosive response to a certain hormone.

“We never expected that a cell could just explode like a bomb and kill the cells surrounding it,” said senior author Bo Wang, associate professor of bioengineering in the schools of Engineering and Medicine.

Flatworm inflammation

Chew Chai, a postdoctoral researcher in the Wang lab, first observed these cells while investigating the long-standing mystery in flatworm biology of whether or not they can tell the difference between their own tissues and those of another individual. To find out, she longitudinally sliced the flatworms and fused them together with a separate worm. Although adept at regrowing their own tissues, Chai noted that these “Frankenstein” worms rejected halves of other worms, similar to how a human body may reject an organ transplant from a donor. 

Unlike humans, however, a different cellular defense mechanism sprang into action. 

“It’s this huge inflammatory response. Like there’s a fire and an alarm goes off, and the cells just blow up,” said Chai, who is lead author of the paper.

Through previous studies of flatworms’ regeneration abilities, scientists know that levels of the hormone activin play a key role in their survival. High levels of activin are known to reduce a flatworm’s ability to regrow its body, while low levels inhibit their ability to reproduce with other worms. When Chai noticed the worms rejecting the tissues of another worm, she also observed a spike in activin levels and subsequent chronic inflammation. The flatworms did not immediately perish from this inflammation, but died within a few days. Chai also observed that injecting otherwise healthy, nonfused flatworms with activin triggered a similar level of inflammation.

Looking into this response on the cellular level required Chai to use live-cell microscopy and flow cytometry – a laser-based analysis technique. She stained cells with different fluorescent dyes, then sorted individual cells to isolate those that responded to activin exposure. A subset of these cells burst open and spewed contents that killed surrounding cells, then vanished within five minutes of the explosion. Chai and Wang call this response “ruptosis.”

The swift and complete self-destruction of these ruptoblast cells is one aspect that makes them so unique from other forms of cell death.

“Some mammalian cells and bacteria may also do an explosive sort of cell death, but the timescale is really long. They are exploding, but it’s more like pores that slowly leak things out over the course of several hours,” said Chai. “Ruptosis happens within seconds to minutes.” 

An ancient evolutionary solution

In a matchup against E. coli bacteria, human kidney cells, and mouse blood cells, ruptoblasts destroyed all three. Yet the authors noted that cell fatalities were limited to the immediate area of the explosion and did not trigger any sort of chain reaction or lingering toxicity. This localized effect, Wang says, holds promise for targeted treatments of bacterial infections or tumors. 

Another characteristic that sets ruptoblasts apart from other immune cells, like T-cells or neutrophils, is the fact that they are glandular cells rather than hematopoietic cells, or blood cells produced in the bone marrow. The ruptoblasts seem to figure out a way to amplify their secretion machinery to suddenly and violently release cytotoxic substances in response to activin. A sharp increase in calcium from the endoplasmic reticulum within the ruptoblast helps facilitate the ruptosis.

In searching for these cells in other organisms, Chai discovered that they only appear in basal bilaterians like the flatworms, which points to these cells having early evolutionary origins. Chai wonders if the reason these cells were filtered out of modern vertebrate immune systems is because vertebrates lack the ability to repair other cells after ruptosis occurs, unlike flatworms that are rich in stem cells. 

“It demonstrates there’s lots of different immune mechanisms out there. There’s all these animals that live in an environment where there’s lots of bacteria, lots of viruses, and we know so little about their immune mechanisms,” said Wang. 

These findings point out how much value strange, seemingly simplistic creatures like flatworms can add to the study of immune responses. Looking outside of traditional model organisms, Wang said, can inspire new strategies and innovative solutions for some of the most difficult medical problems. 

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Acknowledgements

Additional Stanford co-authors include postdoctoral scholar Souradeep Sarkar; former Undergraduate Visiting Research Program scholar Lihan Zhong; Dania Nanes Sarfati, PhD ’24; Christine Jacobs-Wagner, the Dennis Cunningham Professor and professor of biology in the School of Humanities and Sciences and of microbiology and immunology in the School of Medicine; and Hawa Racine Thiam, assistant professor of bioengineering in the schools of Engineering and Medicine and of microbiology and immunology in the School of Medicine. Additional co-authors, including co-senior author Benyamin Rosental, are from Ben Gurion University of the Negev.

Jacobs-Wagner is also a member of Stanford Bio-X and an institute scholar at Sarafan ChEM-H. Thiam is also a member of Bio-X and the Maternal & Child Health Research Institute (MCHRI), and an institute scholar at Sarafan ChEM-H. Wang is also a member of Bio-X and the Wu Tsai Neurosciences Institute.

This research was funded by a National Science Foundation Graduate Research Fellowship, a Stanford Graduate Fellowship, a Stanford DARE fellowship, a Human Frontier Science Program grant, a National Institutes of Health grant, and the European Research Council.

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Journal

Cell

DOI

10.1016/j.cell.2026.05.008 

Article Title

Explosive cytotoxicity of ruptoblasts bridges hormone surveillance and immune defense

Article Publication Date

2-Jun-2026

6G networks will improve network utilization

Applications in Digital Medicine

Technical University of Munich (TUM)

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To date, computing power in hospitals is not always available exactly where it is needed. However, delays or interruptions in data transmission can have serious consequences for applications such as teleoperation.

Provide computing power where it's needed

A research team has developed an approach that will allow future 6G networks to distribute medical applications more flexibly across the network. The central question is where individual applications are best executed: as close as possible to the patient, directly within the hospital, at a nearby network node, or in a remote data center.

The closer the processing takes place to the patient, the better delays can be reduced, and high demands on data transmission and computing power can be met. At the same time, the network would be overloaded if all applications were processed directly there. Therefore, it is important to dynamically shift technology to where it provides the greatest benefit in each situation.

“For medical applications, it’s not enough to simply transfer data from A to B as quickly as possible,” says Wolfgang Kellerer, professor of communication networks (bitte Link hinterlegen:  at the TUM School of Computation, Information, and Technology and a member of TUM-MIRMI. “In the future, decisions will have to be made within the networks about where computing power is needed, which applications take priority, and when functions need to be shifted within the network. Especially in medicine, this flexibility can play a crucial role in ensuring that digital services are reliably available.”

Up to 40 percent more applications running simultaneously

The method is based on solving an optimization problem. The system assesses which applications are active, what their requirements are, and what network and computing resources are available. “From this, we can determine where the respective processes should be executed within the network,” adds Wolfgang Kellerer.

Simulations show that this approach allows up to 40 percent more medical applications to be run simultaneously—even when network capacity and computing power are limited. Future 6G networks could thus provide an important technical foundation for reliable, flexible, and more digitally supported medical care.

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Article Title

In-Network Processing of Medical Applications in Emerging 6G Networks Considering Migration

 

The discovery that didn't count

A new measure of scientific disruption provides an innovative way to identify and credit research breakthroughs

University of Virginia School of Data Science

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UVA data science doctoral candidate Munjung Kim

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Credit: University of Virginia School of Data Science

Across the globe, scientists conduct research and publish papers reporting their findings. With this deluge of information, how do we measure a research paper’s impact? The most prominent method used to be to count how many times a published paper was cited. But this system measured popularity instead of impact.

In 2017, the Consolidation-Disruption (CD) Index was created to address this issue. Its goal was to identify when a paper was disruptive — meaning the contribution that it represented was so distinct that it qualified as a breakthrough, causing the new study to eclipse previous research on the topic.

But the CD Index has a blind spot. If two research teams make simultaneous discoveries and one team cites the other, the index will rank one paper at the top, giving it breakthrough status, and the other at the bottom. Sometimes, both papers end up at the bottom. Research shows that small changes in citation patterns renders the score unstable.

Munjung Kim, a Ph.D. data science candidate at the University of Virginia, is working with YY Ahn of the University of Virginia and Sadamori Kojaku of Binghamton University to address this issue.

Their paper, “Uncovering simultaneous breakthroughs with a robust measure of disruptiveness,” which was recently published in Science Advances, proposes a different citation ranking system called EDM (Embedding Disruptiveness Measure).

“EDM uses techniques from neural language models to give each paper two representations, one for what came before it and one for what came after,” Kim explained. “Nobel-level work tends to score significantly higher under EDM, it's stable against small citation changes, and it can identify simultaneous discoveries the older method couldn't.”

Kim hopes that EDM will be applied to large-scale studies similar to the ones the original disruption index inspired. “These include research on how breakthroughs happen, what kinds of teams produce them, and how funding relates to disruptiveness,” Kim said. “A more robust measure makes that work a little more reliable, especially when simultaneous discovery is common, which the sociologist Robert K. Merton argued is actually the rule, rather than the exception in science.”

Kim initially became fascinated by the idea behind the disruption index; the notion that a disruptive paper is so notable that it makes all that came before it obsolete. Around the same time, she was studying graph embeddings, a machine learning approach that turns networks into vectors in a geometric space.

“It struck me that this kind of tool might let us improve the disruption index, which had relied only on counting immediate citation relationships,” she said. “The breakthrough came in a conversation with Sadamori Kojaku, my collaborator on this paper and an assistant professor at Binghamton University.”

Kojaku suggested using directional embeddings — learning two separate representations per paper, one for what it cites and one for what cites it. Once they built the model that way, something interesting happened. “When we looked at the papers where our measure disagreed most strongly with the CD index, they turned out to be famous cases of simultaneous discovery. That's how we realized the original index had a systematic blind spot, and that's the issue our paper ends up fixing.”

Since the paper’s publication, Kim was invited to present at an NAS workshop panel, and her work appeared in Physics Magazine, an online publication of the American Physical Society.

Kim said that being part of the highly collaborative, interdisciplinary research ecosystem at UVA has elevated the impact and relevance of her work: “The School of Data Science has given me a great environment for sharing this work and putting it in front of people with diverse backgrounds.”

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DOI

10.1126/sciadv.adx3420 

Method of Research

Data/statistical analysis

Subject of Research

Not applicable

Article Title

The Discovery That Didn't Count: A New Measure of Scientific Disruption Provides an Innovative Way to Identify and Credit Research Breakthroughs

Article Publication Date

2-Jun-2026