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New PRS Tool Identifies Inherited Risk for Eight Cardiovascular Conditions

Researchers at the Mass General Brigham Heart and Vascular Institute and collaborators have developed and validated a new integrated polygenic risk score (PRS) that estimates inherited risk across eight cardiovascular conditions using a single genetic test. The tool is a combination of a handful of genetic risk models collected into a comprehensive risk tool and is designed to improve identification of individuals at elevated risk for coronary artery disease (CAD), atrial fibrillation, type 2 diabetes, venous thromboembolism, thoracic aortic aneurysm, extreme hypertension, severe hypercholesterolemia, and elevated lipoprotein(a).

The validation study, published in the Journal of the American College of Cardiology, showed that individuals with high genetic risk scores had significantly higher odds of developing disease compared with those at average risk, including a 3.7-fold higher odds for CAD and a 4.1-fold higher odds for severe hypercholesterolemia.

“Interpreting DNA risk is new for the public as well as clinicians,” said co-senior author Pradeep Natarajan, MD, director of preventive cardiology at Mass General Brigham Heart and Vascular Institute. “It was very important to us to provide a clear genetic risk report that would be accessible and patient-friendly.”

The researchers developed the integrated PRS tool using PRSmix, an elastic-net approach that combines previously published polygenic risk scores from the Polygenic Score Catalog. The model was trained using genotype and clinical data from 245,394 participants in the NIH All of Us Research Program and validated using data from 53,306 people from the Mass General Brigham Biobank. In the validation cohort, the integrated scores demonstrated strong discrimination across all eight conditions, with individuals in the top 10% of genetic risk showing increased odds of disease, including CAD (odds ratio 3.7), type 2 diabetes (3.1), atrial fibrillation (3.0), venous thromboembolism (1.9), hypertension (2.1), and lipoprotein(a) (41.0).

Current cardiovascular risk assessments typically rely on age, sex, blood pressure, cholesterol, and other clinical factors. The Mass General team noted that these methods may miss individuals with substantial inherited risk who do not yet show clinical symptoms. By comparison, the new PRS tool gives a single genetic assessment that can be applied early in life and across multiple disease pathways simultaneously.

“Although PRS have typically been evaluated one condition at a time, a single genotyping assay enables calculation of PRS for any heritable trait without significant additional cost, creating an opportunity to assess inherited risk across multiple cardiovascular conditions simultaneously,” the researchers wrote noting the value of this new method.

In this study, the integrated PRS improved risk classification when incorporated into clinical prediction models, including better stratification of individuals near clinical decision thresholds for cardiovascular disease. The system also generated standardized risk categories—high, average, or low—for each condition and presented results in clinician-facing and patient-facing formats that can be integrated into electronic health records.

Clinically, the new PRS is could be use to identify people whose inherited risk could provide the opportunity for preventive interventions such as increased monitoring, lifestyle modification, or preventive therapies, even when conventional risk factors appear normal. For CAD specifically, the findings suggest that individuals with high polygenic risk may have risk levels comparable to other established high-risk groups, despite modest or normal cholesterol levels.

The researchers also evaluated how the tool could work across diverse populations and clinical settings. They found that while the PRS performed consistently across ancestry groups, predictive strength was reduced in individuals with greater genetic variation from people of European ancestry, exposing the inherent limitations in current genomic reference datasets. Despite this, the integrated framework was designed to allow updating as new data become available.

The PRS is currently available through Mass General Brigham Laboratory for Molecular Medicine and Broad Clinical Labs. The researchers said that next steps to further refine the tool include broader prospective validation across diverse populations, evaluation its cost-effectiveness, and research to determine how genetic risk information should influence clinical decision-making.

 

The post New PRS Tool Identifies Inherited Risk for Eight Cardiovascular Conditions appeared first on Inside Precision Medicine.

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Who Gets a Second Cancer—and When? Large Study Reveals Complex Patterns

As cancer survival rates improve, a new challenge is coming into sharper focus: what happens after the first cancer is treated. A large population-based study drawing on decades of U.S. registry data offers one of the most comprehensive looks yet at the risk of subsequent primary cancers (SPCs)—and reveals a complex, evolving landscape shaped by age, sex, and generational exposure. The work was published in PLOS Medicine.

Using data from more than 3.3 million individuals diagnosed with a first primary cancer between 1975 and 2019, investigators from the Virginia Commonwealth University (VCU) School of Medicine analyzed nearly 30 million person-years of follow-up, identifying more than 510,000 second cancers. Their findings, based on Surveillance, Epidemiology, and End Results (SEER) registries, show that SPC risk is not static but varies significantly depending on when patients were born, how old they were at diagnosis, and the type of cancer they initially had.

“We have follow-up guidelines after treatment for the primary cancer, but we don’t really know what risk profile these patients fall into for another cancer,” said Susan Hong, MD, who co-directs a cancer survivorship outcomes research program at VCU and co-directed the study. “They’re not average-risk individuals—but they’re not necessarily at extremely high risk across the board either. That’s where it becomes very nuanced and complex.”

Age and sex drive risk—but not uniformly

The analysis confirmed that SPC incidence increases with age at first cancer diagnosis, rising substantially in both men and women, though more steeply in males. Among women, rates climbed from 915 per 100,000 person-years at ages 35–39 to 1,980 at ages 75–79; in men, the increase was from 1,228 to 2,945.

But these patterns were not consistent across all cancer types. For breast cancer survivors, the risk of developing a second cancer remained relatively stable regardless of age at diagnosis—a finding that surprised the investigators.

“I was kind of surprised that the risk of subsequent cancer didn’t vary by age among breast cancer survivors,” said Hui Cheng, PhD, the study’s lead analyst. “I thought older patients would have higher risk, but that wasn’t necessarily the case.”

By contrast, survivors of lung and bladder cancers and melanoma showed a clear age-related increase in SPC risk, suggesting that surveillance strategies may need to differ significantly by index cancer type.

Cohort effects point to environmental and behavioral drivers

One of the study’s most striking findings emerged from its age–period–cohort modeling: SPC risk peaked among individuals born between 1935 and 1945, then declined in more recent birth cohorts—with notable exceptions.

Researchers observed rising risks among female lung cancer survivors and male bladder cancer survivors, even as overall SPC incidence declined in more recent decades. The cohort-specific patterns hint at underlying environmental or behavioral exposures that vary across generations.

“We observed higher risk in cohorts born in the ’40s and ’50s,” Cheng said. “If we think back, those individuals were young adults during peak tobacco use, which may be a contributing factor. But we don’t have individual-level smoking data, so we can’t confirm that directly.”

This limitation underscores a key challenge of large registry-based studies: while they offer statistical power and long-term follow-up, they often lack granular data on treatment exposures, genetics, and lifestyle factors.

Treatment advances and unintended consequences

The findings also reflect the dual-edged nature of cancer treatment progress. As therapies improve and patients live longer, the window for developing late effects—including second malignancies—widens.

“We’re doing so well treating primary cancers, and people are living longer,” Hong said. “But we also need to think about what risk profile these patients fall into over time.”

Radiation therapy and certain chemotherapies are known contributors to secondary malignancies, with risks often emerging 10 to 15 years after exposure. Yet without detailed treatment data, the current analysis cannot disentangle these effects.

Instead, the study serves as what investigators describe as a “hypothesis-generating” effort—mapping broad patterns that can guide more targeted research.

Toward risk-stratified survivorship care

“We’re trying to figure out how to risk-stratify patients and pay attention to their long-term health care needs without overreacting or underreacting,” Hong said.

In practice, that could mean more intensive surveillance for older survivors of certain cancers, while maintaining consistent monitoring across age groups for others, such as breast cancer.

The findings also highlight gaps in the current survivorship infrastructure—particularly for adult-onset cancers. While pediatric oncology has long benefited from coordinated long-term follow-up systems, similar frameworks are less developed for adult survivors.

“There’s been a long-standing effort to follow childhood cancer survivors, but we don’t have that kind of system in place for adults,” Hong noted. “Now that we’re seeing more cancers in younger adults, we need to think about how to address decades of survivorship.”

A starting point for deeper investigation

Ultimately, the study raises as many questions as it answers. Why do certain cohorts carry higher risks? How do treatment regimens, genetics, and lifestyle interact to drive SPC development? And which risk factors are modifiable?

The research team is already looking ahead to studies that can incorporate more detailed patient-level data, with a focus on identifying actionable prevention strategies.

“We’re very interested in understanding what factors are associated with better or worse outcomes—especially modifiable risk factors,” Cheng said.

The post Who Gets a Second Cancer—and When? Large Study Reveals Complex Patterns appeared first on Inside Precision Medicine.

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Supply Chain Digital Twins: An Evolution, Not a Breakthrough

Digital twins help optimize drug production processes by modeling the thousands of interactions that cells, raw materials, and reagents undergo in culture. And new analysis suggests they could do the same thing for supply chains.

Researchers at the U.S. National Institute of Standards and Technology (NIST) and EMD Millipore put forward the idea, arguing that twins could make drug distribution, which is also characterized by thousands of interactions, more resilient and efficient.

Lead author Perawit Charoenwut, a logistics researcher at NIST’s systems integration division, tells GEN, “A digital twin could be extremely helpful in all phases of the biopharmaceutical supply chain. Starting from demand planning triggered by global events such as pandemics, regional disease outbreaks, aging demographics, etc., through to being able to provide visibility on capacity requirements and limitations.”

In silico models could also provide solutions to disruption by identifying alternative supply options, such as distribution centers or regional inventories, in less time, Charoenwut says.

“Digital twins could also be helpful in evaluating different suppliers by running simulations on their potential performance, based on different demand scenarios versus their individual capacities and capabilities,” he continues.

Standards

In theory, digital twins are a good option for supply chain modeling and management. In practice, however, firms interested in the approach will need to overcome some technical challenges.

For example, one major hurdle is the lack of data standardization, according to study co-author Boonserm Kulvatunyou, PhD, a computer engineer at NIST. “Supply chain digital twins require data from across organizations and third-party sources,” he tells GEN. “The lack of industry standards creates challenges in obtaining all the necessary data.”

With this in mind, the NIST’s Industrial Ontology Foundry (IOF) is working with the National Innovation Institute for Manufacturing Biopharmaceuticals (NIIMBL) to develop open-source ontology and schema standards for connecting data.

Kulvatunyou says, “The aim is to provide a semantic foundation for connecting data and knowledge across the manufacturing and supply chain operations.

“Further work is being conducted to cover broader materials, processes, and quality data,” he says. “We would like to invite industry and academia to join this effort and benefit from these new standards.”

Industry interest

Biopharma firms interested in digital supply chains will also need to establish a solid data infrastructure, according to Charoenwut, who says companies should start small and pace themselves.

“We think that biopharma companies do believe that digital twins could make a significant difference in their supply chain efficiency and resiliency. Many of them are probably building prototypes and proofs-of-concept to demonstrate the value and potential benefits, but then soon realize the digital data foundation gaps that need to be addressed in parallel in order to fully adopt this technology.

“As digital twins can vary in detail and complexity, companies should strategize digital twin adoption by starting with lower-complexity cases based on available digital data and progressively moving up the scale to gain greater precision and new capabilities. In other words, the implementation of digital twins should be viewed as an evolution rather than a breakthrough,” he says.

The post Supply Chain Digital Twins: An Evolution, Not a Breakthrough appeared first on GEN – Genetic Engineering and Biotechnology News.

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Infant formula largely safe from heavy metals, FDA finds

New contamination testing results from the Food and Drug Administration confirm the safety of infant formula in the U.S., the agency said Wednesday. 

The FDA tested 312 samples from 16 infant formula brands for contaminants like heavy metals, pesticides, and the “forever chemicals” known as per- and polyfluoroalkyl substances, or PFAS. The vast majority had undetectable or very low levels of contaminants, the agency said, with levels of lead, mercury, cadmium, and arsenic coming in below federal requirements for drinking water across all samples. 

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Single-Cell Survival Modeling Tool Offers New Precision in Cancer Prognosis

Oregon Health & Science University (OHSU) researchers have developed a first of its kind tool, scSurvival, that directly links information from individual tumor cells to patient survival outcomes, allowing clinicians to understand which specific cells are driving disease progression rather than treating them all the same.

“Traditional survival models in cancer rely on bulk data, which average signals across millions of cells and obscure important heterogeneity,” explained senior author Zheng Xia, PhD, associate professor of biomedical engineering in the OHSU School of Medicine and a member of the OHSU Knight Cancer Institute. “Tumors are highly complex ecosystems where different cell subpopulations can have very different and sometimes opposing effects on patient outcomes.”

He told Inside Precision Medicine that “scSurvival is designed to directly model survival using single-cell data, preserving this heterogeneity. Instead of treating a tumor as a single entity, it treats it as a collection of individual cells and learns which specific subpopulations are most associated with survival outcomes. This enables both more accurate prediction and deeper biological insight.”

The tool was designed using a statistical method known as an attention-based multiple-instance Cox regression framework, which constructs survival prediction models from single-cell cancer cohort data while simultaneously identifying cell subpopulations that are strongly associated with patient risk.

“The attention mechanism preserves cellular heterogeneity within each patient, allowing [cell] subpopulations with higher attention scores to be more closely linked to survival probability,” the researchers explain in Cancer Discovery. “The resulting outputs of scSurvival are the attention-adjusted hazard score for each cell along with patient-level risk scores.”

Xia and team tested the performance of scSurvival in two cohorts that included 32 patients with melanoma and 124 patients with liver cancer. Together, the cohorts provided single cell RNA sequencing data for more than 1.1 million individual cells.

They found that key immune cell types were enriched for higher- or lower-hazard cells. For example, monocytes/macrophages were enriched for high-risk subpopulations in both the melanoma and liver cancer cohort, but B cells were enriched for low-risk subpopulations in the melanoma cohort and high-risk subpopulations in the liver cancer cohort.

In both groups, the tool accurately predicted patient outcomes, with cells taken from melanoma patients who did not respond to immunotherapy having significantly higher hazard scores than those taken from responders.

Xia noted that the information scSurvival provides has several translational applications. “Differential gene expression between high- and low-risk cells can be used to develop prognostic biomarkers,” he said. “Pathways enriched in high-risk populations may reveal actionable therapeutic targets, while the abundance of specific cell types can support patient stratification for treatment selection. Importantly, these insights are derived at single-cell resolution, providing greater biological precision than bulk approaches.”

At present, scSurvival is primarily a research tool but Xia believes that longer term, it has potential clinical relevance. “For example, signatures derived from survival-associated cell populations could be translated into more practical assays (e.g., bulk RNA or targeted panels) for patient stratification,” he suggested. “However, direct clinical deployment would require further validation, simplification, and standardization.”

According to Xia, one of the biggest challenges to widespread adoption of the tool is the limited availability of large, well-annotated single-cell datasets with matched survival data, as single-cell sequencing is not yet routine in clinical workflows. But as more clinical trials adopt single-cell sequencing, he expects scSurvival to see broader use in resolving disease at cellular resolution.

The investigators now plan to extend the framework to incorporate spatial transcriptomics, which will allow them to account for how cells are organized within the tumor microenvironment. “We also aim to improve the model’s robustness across datasets and sequencing platforms, and to enhance its biological interpretability. Ultimately, we hope to translate the survival-associated signatures identified by scSurvival into clinically practical tests,” Xia said.

The study findings were also presented at the American Association for Cancer Research Annual meeting 2026 and the open-source scSurvival program and its tutorials are freely available at GitHub, Zenodo and Code Ocean.

The post Single-Cell Survival Modeling Tool Offers New Precision in Cancer Prognosis appeared first on Inside Precision Medicine.

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STAT+: Pharmalittle: We’re reading about the FDA speeding up trials, a Supreme Court hearing on ‘skinny labels,’ and more

Top of the morning to you. The middle of the week is upon us and, since you made it this far, why not forge ahead? After all, there is always light at the end of the proverbial tunnel. You never know what you may accomplish. So please join us as we celebrate this notion with a cup or three of delicious stimulation. Our choice today is chocolate raspberry. Meanwhile, we have assembled the latest menu of tidbits to help you along. So please dig in. Have a smashing day, and please feel free to forward any secrets you come across. Our “in basket” is always open. …

The U.S. Food and Drug Administration announced efforts to make clinical trials more efficient, starting by reviewing data in real time from trials conducted by AstraZeneca and Amgen, STAT writes. The agency also asked the public to weigh in on a potential pilot program to work with companies that use AI to enhance safety monitoring and medication dose selections, identify safety signals, and improve patient recruitment in clinical trials. The trials will rely on a real-time data platform built by Paradigm Health, and the goal is to cut down on the time regulators and companies spend sending data back and forth. FDA Commissioner Marty Makary said at a press conference that agency reviewers will be able to view safety signals and clinical endpoints via Paradigm’s platform.  

Pfizer settled ‌patent disputes with three generic drugmakers over its blockbuster heart drug Vyndamax, effectively extending its patent protection until 2031 and delaying cheaper ​copies from entering the market, Reuters says. The deals resolve ​patent infringement lawsuits against Dexcel Pharma, Hikma Pharmaceuticals, ⁠and Cipla in Delaware federal court over Pfizer’s ​oral drug Vyndamax. A trial over the patent had started ​this week. Pfizer sold nearly $6.4 billion of Vyndamax and related drugs, which treat a serious heart condition called transthyretin amyloid cardiomyopathy, in 2025. The settlements extend ​U.S. patent protection for Vyndamax until June 1, ​2031, subject to other pending litigation. The company had previously expected ‌a ⁠sharp drop in U.S. revenue for the drug in 2029 but now expects sales to hold relatively steady from 2028 through mid-2031.

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New report shows some health equity wins. Experts fear they will be short-lived

A report released Wednesday highlights successes in reducing health disparities in U.S. states. Since the last iteration of the analysis by the Commonwealth Fund, two states expanded eligibility for Medicaid, many states extended postpartum coverage for mothers, and enrollment in Affordable Care Act marketplace plans increased at an unprecedented clip. 

But given the report covered the years 2022 to 2024, many equity researchers fear the gains may be short-lived. Outside experts who reviewed the report predict that policies from the second Trump administration, including changes to insurance coverage and vaccine policies and cuts to programs promoting diversity, equity, and inclusion, will exacerbate inequities.

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STAT+: Health system CEOs get off easy at Congressional hearing on affordability 

The four health system CEOs summoned before a Congressional committee Tuesday likely breathed sighs of relief early in the hearing, when it became clear they had friends in the audience. 

Instead, committee members largely blamed the other party’s health care policies for driving U.S. health care prices to levels inaccessible to many Americans.

The hearing was part of the House Ways and Means Committee’s effort to understand the root causes of rising health care costs in the U.S. It comes three months after the committee heard from the CEOs of the country’s largest health insurers, who largely deflected blame onto hospitals and drugmakers. 

In attendance were the CEOs of some of the country’s largest health systems: HCA Healthcare, a for-profit system of 190 hospitals, and CommonSpirit Health, a nonprofit system of 158 hospitals. The CEOs of New York-Presbyterian and North Carolina’s ECU Health were also there.

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