Radically different
Nature Medicine, Published online: 25 June 2026; doi:10.1038/s41591-026-04471-x
In the ongoing phase 2 GMMG-HD10/DSMM-XX (MajesTEC-5) trial in patients with transplant-eligible, newly diagnosed multiple myeloma, induction with the BCMA×CD3 bispecific engager teclistamab in combination with daratumumab plus lenalidomide, with or without bortezomib, had a similar toxicity profile to other bispecific regimens with an encouraging and deep response rate.
Get your daily dose of health and medicine every weekday with STAT’s free newsletter Morning Rounds. Sign up here.
Good morning and happy Thursday. Let’s get right to it.
Two senators are renewing their push to greatly expand access to methadone treatment for opioid addiction.
On Thursday, Sens. Ed Markey (D-Mass.) and Rand Paul (R-Ky.) are introducing an updated version of legislation that would allow doctors who hold board certifications in addiction medicine to prescribe methadone directly to patients for pickup at a pharmacy.
Nature Medicine, Published online: 24 June 2026; doi:10.1038/s41591-026-04508-1
Retraction Note: Time-of-day immunochemotherapy in non-small cell lung cancer: a randomized phase 3 trial
Nature Medicine, Published online: 24 June 2026; doi:10.1038/s41591-026-04532-1
Publisher Correction: Oral small molecule GLP-1 receptor agonist aleniglipron in people with overweight or obesity: a randomized, double-blind, placebo-controlled phase 2b trial
Cholesterol-related heart disease remains the leading cause of death worldwide, and while doctors have more tools than ever to treat it, many patients still can’t achieve safe cholesterol levels or can’t tolerate the side effects of available medications. Researchers at the University of California (UC), San Diego, School of Medicine have now uncovered a hidden biological pathway, dependent on a protein known as Ral, which explains why high-cholesterol diets steadily chip away at our body’s ability to clear harmful low-density lipoprotein (LDL) cholesterol from the blood. The team‘s preclinical study, including tests in mice, also identified a drug candidate already proven safe in humans that could potentially target the pathway.
“We’ve known for a long time that a high-cholesterol diet reduces the liver’s ability to clear cholesterol from the blood, but we didn’t fully understand why,” said Alan Saltiel, PhD, professor of medicine at UC San Diego School of Medicine and director of the UC San Diego/UCLA Diabetes Research Center. “This new discovery explains a critical piece of that puzzle.” Saltiel is senior author of the researchers’ published paper in Nature, titled “Dietary cholesterol activates a Ral-dependent pathway driving LDLR turnover,” in which they concluded, “Together, our findings reveal a Ral-dependent signalling pathway as a key regulator of LDLR turnover and cholesterol homeostasis.”
Disruptions in cholesterol homeostasis are closely linked to an increased risk of atherosclerosis and cardiovascular disease (CVD), the authors wrote. “Elevated low-density lipoprotein cholesterol (LDL-C) significantly contributes to CVD by promoting the formation of atherosclerotic plaques in arteries.”
The liver is the main organ involved in removing cholesterol from the blood so it can be broken down and used elsewhere. This is done through LDL receptors (LDLRs), which sit on the surface of liver cells and act like docking stations, grabbing LDL cholesterol from the bloodstream and pulling it inside the cell for processing. “LDLRs have a crucial role in the uptake of LDL-C from the circulation by hepatocytes,” the investigators continued. The more LDL receptors on liver cells, the more cholesterol gets cleared from the blood, which is why most cholesterol-lowering drugs, such as statins or PCSK9 inhibitors, work by preserving or increasing the number of these receptors. However, the team noted, such treatments have their limitations. “The molecular switches that coordinate LDLR trafficking and turnover in response to nutritional cues, including high dietary cholesterol, remain poorly defined.”
The new research, carried out in mice and in human cells, reveals a previously unknown mechanism that quietly works against the cholesterol removal process, slowly reducing the number of LDL receptors and contributing to high blood cholesterol. The team found that this process begins when a protein called Ral—which Saltiel has previously studied in fat cells—is activated by high dietary cholesterol. “We describe here a previously unrecognized role for Ral signaling in orchestrating LDLR cellular trafficking and lysosomal routing in hepatocytes under chronic cholesterol stress,” the team stated.
Their studies showed that the more Ral is activated, the fewer LDL receptors remain available to clear cholesterol from the blood. This depletion process ultimately relies on a lysosomal protease enzyme called cathepsin A (CTSA). They further explained, “Ral engages the endocytic RalBP1–REPS1 complex to promote LDLR internalization and lysosomal routing, where LDLR is degraded by the lysosomal protease cathepsin A (CTSA).”
The researchers also found that blocking CTSA with a selective small molecule inhibitor (SAR164653) was enough to stabilize LDL receptors and dramatically lower circulating LDL cholesterol in mice. “Pharmacological inhibition of CTSA activity increases hepatic LDLR function and improves cholesterol clearance, offering a potential new therapeutic strategy for hypercholesterolaemia and cardiovascular disease,” they stated.
“There’s still a real need for new cholesterol-lowering options, since some people can’t get to safe levels even with the drugs we have now,” said Saltiel. “This new pathway we discovered is completely separate from anything that existing drugs target, so it gives us a new opportunity to fill that gap.”
After a fundamental biological breakthrough, it typically takes significant additional research to find drugs that target it. However, in this case, a CTSA inhibitor has already been through the early stages of drug development, with the initial goal of treating heart failure. While it was eventually shelved for strategic reasons, the drug had previously advanced to a Phase I clinical trial, where it was successfully tested for safety.
This discovery suggests that the investigational drug is already ready for testing in a Phase II trial for high cholesterol. “Luckily, there’s an experimental drug sitting on the shelf that’s already been shown to be safe in humans,” said Saltiel. “We hope to test whether this might be effective by conducting a clinical trial, which could potentially bring a new treatment option to patients much sooner than would have been expected.”
The post Drug Targets LDL Receptor Pathway to Control Cholesterol appeared first on GEN – Genetic Engineering and Biotechnology News.
The pharmaceutical giants behind the monumentally successful weight loss drugs Wegovy and Mounjaro have been teasing an expansion into other aesthetic fields like hair loss or skin care.
Now, one of them is making a move, investing in a small startup developing a medication to spur hair growth, and potentially also treat endometriosis.
On Wednesday, Absci announced that it raised $100 million from a group led by Eli Lilly. Lilly brought the lion’s share of the funding, handing over $40 million in exchange for equity in Absci, which is publicly traded on the Nasdaq.
A new AI framework can classify hundreds of genetic variants as accurately as a human expert in a fraction of the time, research suggests.
Combining AI-assisted CURAtor (AI-CURA) with the latest large language models (LLMs) could streamline the diagnosis of rare genetic diseases.
The workflow system, described in Science Translational Medicine, performed as well as clinical experts in classifying 150 variants, while adhering to complex expert guidelines.
It was also able to categorize 150 variants with conflicting classifications.
“This study pushes the boundaries of fully automated variant interpretation,” commented senior journal editor Catherine Charneski, PhD, from the University of Bath.
Whole genome sequencing (WGM) has proven pivotal in ending the prolonged diagnostic odyssey of many patients with rare genetic disorders.
To manage the huge number of variants identified through WGS, attempts have been made by expert associations and working groups to establish guidelines and recommendations.
These now form a widely adopted classification system that categorizes variant-associated evidence into distinct rule-based categories.
But while some rules can be readily automated, most evidence still needs manual interpretation of the literature. This requires variant curators to possess broad knowledge across various aspects of molecular biology and genetics, as well as a deep understanding of expert recommendations to accurately interpret and score variants.
Wei Ma, PhD, and colleagues from the Hong Kong Genome Project (HKGP) therefore developed AI-CURA, a fully automated framework for variant classification that integrates LLMs to handle both literature-independent and literature-dependent evidence.
The tool integrates the assessment of evidence for non–literature-based criteria, which can be automated using standard bioinformatic tools, with a separate LLM-supported assessment of literature-based evidence.
Two state-of-the-art LLMs—DeepSeek-R1 and o3-mini-high—were tested for their ability to summarize literature-derived evidence relevant to variant classification.
The team found that the open-source DeepSeek-R1 outperformed o3-minihigh and had high sensitivity and 100% specificity in interpreting rules from the American College of Medical Genetics and Genomics (ACMG) that require understanding literature-based evidence.
They then tested it using 150 variants curated by ClinGen experts, with 150 expert-curated variants and 150 variants with conflicting classifications from the Clinical Genome Resource.
The open-source LLM DeepSeek-R1 showed high concordance with ClinGen experts in establishing a final diagnosis.
“In this study, DeepSeek-R1 demonstrated high accuracy (89.3 to 100%) in determining the application of seven literature-dependent ACMG rules,” the authors reported.
They added: “Our use of LLMs substantially streamlined the variant analysis and interpretation process. LLMs can finish summarizing the literature evidence in minutes.
“In comparison, curators in the HKGP typically spend around four hours per patient on WGS curation, with most of this time dedicated to reviewing literature.”
The post AI-CURA Automates Genetic Variant Classification appeared first on Inside Precision Medicine.
Researchers in China have developed a biosensor chip that uses light to detect extremely low concentrations of biomarkers of traumatic brain injury (TBI) at concentrations as low as femtograms per milliliter. The technology could one day be used to make faster diagnoses after a head injury, helping doctors choose the best treatment course and providing early warning of complications.
“Although several biomarkers have been validated as indicators of TBI, current methods for measuring them are time-consuming and require multiple complex laboratory steps,” said Guangyuan Li, PhD, professor at the Beijing Institute of Technology. “To address this challenge, we developed metasurface biosensors that are exceptionally sensitive, allowing them to produce clear, reliable optical signals even when only tiny amounts of a biomarker are present.”
The biosensor achieves its sensitivity thanks to metasurfaces—ultra-thin materials with microscopic patterns etched on them, which enable the device to manipulate light very precisely. For this study, Li and colleagues coated a gold metasurface with antibodies that specifically target TBI biomarkers. When the target molecules bind to the antibodies on the metasurface, the light wavelengths it reflects change slightly, indicating the presence of the biomarker even at extremely low concentrations.
To test this approach, the researchers built two separate sensors targeting two key biomarkers of TBI: the glial fibrillary acidic protein (GFAP) and S100 calcium-binding protein β (S100β). Results showed that the sensor could accurately detect subtle wavelength shifts depending on the biomarker concentration, with a sensitivity as low as under a femtogram per milliliter. This response was highly sensitive to the target biomarker, even when other biomarkers were present in the sample.
In recent years, light-based sensors have been increasingly gaining traction as diagnostic tools thanks to their potential to make biomarker detection much more precise compared to conventional methods, with promising applications currently being explored in early cancer diagnosis and real-time monitoring of diabetes.
However, more work will be needed before this technology can be routinely used in a clinical setting. With further development, the platform could be adapted to create metasurface sensors capable of detecting multiple biomarkers simultaneously to offer a more complete picture of a patient’s state in a short period of time. Going forward, Li and colleagues plan to continue working to reduce the costs of manufacturing the sensor, adapting fluid handling and packaging for clinical use, and ultimately validating the technology in clinical trials to assess its performance in a real-world setting.
“If developed into a point‑of‑care format, this technology could help provide faster and accurate answers after brain injury—perhaps using just a finger prick,” said Yunhui Liu, PhD, associate professor at the Shenzhen Institutes of Advanced Technology. “This could potentially reduce unnecessary CT scans for low‑risk cases while flagging higher‑risk patients earlier. It could also enable more accessible biomarker detection in ambulances, rural clinics, sports settings or emergency departments where time matters.”
The post Light Sensor Detects Ultra-Low Levels of Traumatic Brain Injury Biomarkers appeared first on Inside Precision Medicine.
A study by researchers at the Icahn School of Medicine at Mount Sinai has found that social determinants of health—including environmental conditions, health behaviors, access to resources, and social well-being—can contribute as much as or more than genetic risk in predicting several common diseases. The research, published in The American Journal of Human Genetics, showed that incorporating social, behavioral, and environmental information into disease-risk models improved prediction when incorporated with genetic information for conditions including asthma, chronic kidney disease, coronary heart disease, high cholesterol, breast cancer, and prostate cancer.
“Genes are an important part of the equation, but they do not determine destiny,” said senior author Samira Asgari, PhD, an assistant professor of genetics and genomic sciences at Mount Sinai. “We found that the circumstances of people’s lives—their environments, behaviors, and social experiences—can contribute as much as genetics to predicting disease risk. To truly understand health, we have to look at the whole person, not just their DNA.”
According to the researchers, complex diseases arise through the interaction of genetic predisposition with environmental, behavioral, and social influences, yet these factors are often studied in isolation. Existing genetic models often rely on polygenic risk scores, while epidemiological approaches focus on lifestyle, environmental exposures, or social factors independently. The researchers sought to bridge that gap by integrating both types of data into a single risk prediction framework.
To conduct the study, the team analyzed data from 413,457 participants in the All of Us Research Program, a nationwide research effort in the U.S. supported by the National Institutes of Health. The team combined genetic information, electronic health records, and survey responses, with more than 100 environmental, behavioral, and social variables were evaluated, to create a broad picture of the different factors that may influence health.
Rather than selecting a limited number of known social risk factors in advance for their survey, the researchers used a statistical technique called multiple correspondence analysis, or MCA. The approach converted more than 100 categorical social, environmental, and behavioral measures into low-dimensional representations that helped identify patterns of non-genetic risk.
The choice to use MCA distinguished the study from many previous approaches. Past methods often have depended on selecting a small set of established risk factors or using statistical procedures that prioritize only the strongest predictors. By contrast, MCA identifies patterns across many correlated variables simultaneously, allowing researchers to examine broader social and environmental variables without assuming beforehand which factors have the most influence on health.
The analysis found known contributors to disease risk such as economic status and smoking, but also identified factors that receive less attention in published studies, including loneliness and spirituality. First author Abhijith Biji, a PhD candidate at Icahn School of Medicine, said the data showing associations involving loneliness was particularly notable.
“Some risk factors, such as smoking, have been studied extensively for decades,” Biji said. “What is especially intriguing is that we also observed associations involving factors like loneliness. Understanding how these experiences may become biologically embedded could open new avenues for research and ultimately improve our understanding of disease.”
When the researchers incorporated the MCA-derived measures into prediction models alongside demographic information and polygenic risk scores, predictive performance improved across all six diseases studied. For four of the six diseases, the gains from the MCA-based measures exceeded those attributable to polygenic risk scores.
The findings also suggested that genetic and non-genetic influences generally act independently rather than modifying one another. The researchers found little evidence for broad gene-environment interactions. Instead, inherited genetic risk and social, behavioral, and environmental context appeared to contribute additively to disease risk.
“This additive relationship suggests that interventions targeting social and behavioral factors can reduce disease risk regardless of genetic background, offering hope for broadly applicable public-health strategies,” the researchers wrote.
The researchers noted that the study does not establish causation. Because many survey responses were collected at a single point in time and some exposures may have occurred after disease onset, the findings should be considered as contributions to disease-risk prediction rather than proof that specific factors cause disease.
Building on this work, the team next will seek to integrate social determinants of health with additional biological measures and look mechanisms that may directly connect social experiences to disease. The investigators will also bring in longitudinal data, harmonize survey instruments across cohorts, and integrating other data types to better understand how environmental, behavioral, and social factors influence disease development and interact with biological processes.
“Our goal is to build a more complete understanding of health and disease,” Asgari said. “By combining genetics with social and environmental context, we can move toward risk models that better reflect the realities of people’s lives and help advance more personalized approaches to health.”
The post Social Determinants of Health Improve Disease Risk Prediction Beyond Genetics Alone appeared first on Inside Precision Medicine.