Radically different
Nature Medicine, Published online: 13 July 2026; doi:10.1038/s41591-026-04518-z
In a phase 1 trial, treatment with the antibody−drug conjugate DYP688, targeting PMEL and delivering a Gq/11 inhibitor, in patients with GNAQ/GNA11-mutant metastatic uveal melanoma or other GNAQ/GNA11-mutant melanomas, showed acceptable safety and encouraging preliminary efficacy, with supporting biomarker data for proof of mechanism.
Scientists have found that damaged myelin sheaths lead to abnormal rhythms in brainwave activity during sleep. These findings could have major implications for the development of biomarkers and therapies for neurodegenerative diseases that affect myelin, such as multiple sclerosis (MS) or Alzheimer’s disease.
“The myelin sheath helps electrical signals travel efficiently through brain circuits,” said Mohit Dubey, PhD, senior scientist at the Netherlands Institute for Neuroscience, who presented the study at the Federation of European Neuroscience Societies (FENS) Forum 2026. “We wanted to understand whether myelin damage could also affect how brain circuits behave during sleep. By studying this link, we hope to better understand what causes sleep disturbances in neurological disease and whether sleep-related brain signals could serve as biomarkers for diseases that are yet to show clinical symptoms, as well as showing disease progression.”
Although sleep is known to play a key role in brain health, its study has often been overlooked in the context of neurodegenerative diseases. In conditions like Alzheimer’s, sleep disruptions are known to contribute to fatigue and cognitive decline, especially during the REM sleep phase, which is critical to preserve healthy cognition and memory.
“Sleep disturbances are extremely common in neurological diseases such as multiple sclerosis and Alzheimer’s disease, but the biological reasons for these problems remain poorly understood,” said Dubey. “Understanding the biological link between sleep and brain circuit dysfunction could help guide future strategies for improving sleep and brain health in these conditions.”
Dubey’s team had previously shown that loss of myelin sheaths cause abnormal spikes of brainwave activity in the brain during sleep that seemed to resemble those observed in epilepsy and Alzheimer’s. In the current study, the researchers compared electroencephalogram (EEG) recordings of MS patients during sleep with mouse models of damaged myelin and Alzheimer’s. In both humans and mice, myelin loss resulted in similar abnormal bursts of brain activity during non-REM sleep phases and slower brainwave rhythms during REM sleep.
“REM is a stage of sleep associated with dreaming and replay of daytime experiences. In this state the brain produces rhythmic electrical patterns called oscillations that help coordinate communication between neurons,” said Dubey. “Our findings show that these rhythms become disrupted and slower when myelin degenerates, and that the electrical spikes seen during sleep are closely linked to the stability of brain circuits affected by neurodegenerative diseases such as MS and Alzheimer’s.”
While there are no treatments that can repair damaged myelin, some MS drugs are able to slow down the immune system’s attack on myelin sheath to reduce or halt disease progression. Further research to determine how exactly myelin damage alters brainwave patterns during sleep could aid the early detection of myelin degeneration and inform the design of therapeutic approaches that target myelination.
“This opens new research directions exploring how sleep rhythms depend upon the myelination status of the brain circuits,” said Dubey. “Sleep recordings may provide a non-invasive way to detect early changes in brain circuit myelination in neurological disease. This could eventually help clinicians monitor disease progression, and we want to investigate whether sleep recordings could be used as biomarkers to detect early changes in brain circuit function.”
The post Sleep Brainwaves Could Reveal Early Signs of Alzheimer’s and Multiple Sclerosis appeared first on Inside Precision Medicine.
Nature Medicine, Published online: 09 July 2026; doi:10.1038/s41591-026-04490-8
The 2024 revision of the McDonald diagnostic criteria is an important step toward earlier and more inclusive diagnosis of multiple sclerosis. Realizing its full potential will require continued refinement of biomarkers, disease stratification and clinical trial approaches.
COMPASS, a novel AI model that combines transcriptomic data with tumor-immune information, predicts immunotherapy response more accurately than existing biomarkers across different cancer types, shows data published in Nature Medicine.
The model also offers insight into why a patient may or may not respond to treatment, giving clinicians a better understanding of why responses might not be as expected.
To date, predicting response to immune checkpoint inhibitors (ICIs) has largely relied on tumor mutational burden (TMB) and PD-L1 immunohistochemistry. In addition, researchers have developed transcriptomic signatures that capture T cell dysfunction or score immune checkpoint activity, while network based and machine learning approaches have tried to incorporate gene interaction data, but each of these approaches has limitations because people undergoing ICI treatment often respond in unexpected ways.
“In our benchmarking against 22 of these methods, previous approaches showed inconsistent performance once tested across different cancer types and treatments, which is the core problem COMPASS addresses,” said study senior author Marinka Zitnik, PhD, associate professor of biomedical informatics in the Blavatnik Institute at Harvard Medical School.
She told Inside Precision Medicine that “most AI models for predicting immunotherapy response are validated within a single cancer type or a single drug class, which limits how much they generalize once you move to a new tumor type, a new therapy, or a different hospital’s data. This is significant because it means we can have a model that predicts response in one population of cancer patients, but fails when applied to a different population, a different tumor type, or a different treatment regimen, which is exactly the kind of gap that keeps promising biomarkers from ever reaching routine clinical use.”
Zitnik explained that “COMPASS is different because it is pretrained on transcriptomic data from over 10,000 tumors spanning 33 cancer types before it ever sees a clinical outcome, so it learns broad, biologically grounded patterns of tumor immune biology.”
The model uses a “concept bottleneck architecture” which means that rather than going straight from gene expression data to a prediction, it forces the information through an intermediate layer, or bottleneck, before making the final prediction. In this case, the bottleneck is biologically defined immune concepts, like T cell exhaustion, macrophage activity, or transforming growth factor (TGF)-β signaling. This means clinicians and researchers can see which biological programs are driving a given patient’s predicted response or resistance.
Zitnik and team tested the performance of COMPASS in 1133 patients from 16 clinical cohorts spanning seven cancers and six ICI regimens. The found that, compared with the second-best performing biomarkers of the 22 tested (these varied by cohort), COMPASS improved accuracy by 8.5 percentage points and area under the precision-recall curve by 15.7 percentage points, on average.
“In a setting where response rates to immunotherapy are already low, a meaningful jump in prediction accuracy translates directly into better patient selection,” said Zitnik. “The improvements we saw mean fewer patients would be steered toward a treatment unlikely to help them, and more responders would be correctly identified for therapies that could extend their lives. This is important in cancer where checkpoint inhibitors carry toxicity risks and cost, so gains in identifying who is likely to benefit can meaningfully change treatment decisions and clinical trial design at scale.”
In survival analyses, the team found that individuals with a COMPASS response probability (PR) of 0.5 or higher had a 1-year overall survival rate of 86% compared with 40% for those with a PR below 0.5, yielding a hazard ratio for survival of 4.7.
Performance varied by cancer type and cohort size, but the researchers note that COMPASS was still able to accurately predict response for cancer types it had never seen.
“When we excluded lung adenocarcinoma entirely from training and tested only on that held out cohort, COMPASS still achieved 76.5% accuracy,” said Zitnik. “We saw similarly strong cross cancer generalization for urothelial carcinoma, melanoma, and other tumor types.”
Furthermore, COMPASS achieved 85.3% accuracy for predicting a response to combination therapies (ipilimumab plus pembrolizumab) when trained only on monotherapy cohorts.
In addition to predicting treatment response, the tool also generates a personalized response map for each patient that traces exactly which genes and immune programs are shaping their prediction.
“This explainable AI feature means a clinician is not just given a predicted ‘yes’ or ‘no’, they can see whether a patient’s tumor looks inflamed but is failing due to TGF-β driven suppression or whether it looks immune desert but still shows residual cytotoxic activity that could respond to treatment,” said Zitnik.
“This kind of mechanistic insight could help with patient stratification in clinical trials, indication selection when a drug is being tested in a new cancer type and generating hypotheses about resistance mechanisms that could point toward combination therapies.”
The investigators are now planning to move from using bulk RNA sequencing data for training to single cell and spatial transcriptomic data. They are also exploring how to connect COMPASS with AI agent systems, to help translate the mechanistic insights into more autonomous research workflows.
The post Predicting Cancer Immunotherapy Response Better with COMPASS AI Model appeared first on Inside Precision Medicine.
Antidepression treatment based on a person’s individual biomarkers could help determine which of the world’s most popular medications to use, a clinical trial suggests.
The SMART Trial to Predict Anhedonia Response to Antidepressant Treatment results suggest that behavioral, brain, and clinical data could together determine the optimal antidepressant to choose before a person starts treatment.
There were no significant primary endpoint differences in depression outcomes between participants who received bupropion or sertraline based on biomarkers identified in the prior Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) study.
But people negative for biomarkers with both drugs had significantly worse depression symptom trajectories than those who had at least one positive biomarker, regardless of the drug they received.
Response rates among participants with both biomarkers were almost double that of those without any biomarkers, with those who had at least one biomarker having an intermediate response.
“Our results suggest that we could boost response rate by using two sets of biomarkers previously identified in the EMBARC study, making an important contribution to advancing the goals of precision psychiatry,” reported Peter Zhukovsky, PhD, from Harvard Medical School, and co-workers in Nature Mental Health.
“Ultimately, we strongly hope these advances will enable personalized treatment guidance to accelerate and boost antidepressant benefits.”
Treatment for depression is often still trial and error, with symptoms improving in only half the people taking an antidepressant. This could be due to treatments not being chosen based on people’s individual characteristics.
Finding markers that predict response to different antidepressants could therefore provide patients and clinicians with valuable information to guide treatment choice.
The trial was among the first to investigate how treatment could be guided using clinical information such as responses to questionnaires, behavioral information such as performance in computerized tasks, and brain data such as magnetic resonance imaging (MRI) scans.
It was carried out as part of the Wellcome Leap Multi-Channel Psych Program effort to double the number of people who respond to the first treatment they try for depression via the integration of multimodal biomarkers.
Firstly, the researchers investigated biomarker models that predicted response to the selective serotonin reuptake inhibitor (SSRI) sertraline or the norepinephrine-dopamine reuptake inhibitor bupropion using the EMBARC study.
The treatment-assignment algorithm that was developed generated two marker-based indications for each patient—one for bupropion and sertraline—with the predictive model achieving a cross-validated area under the curve of 0.86 and 0.66, respectively.
The team then examined whether antidepressant response could be boosted using their created biomarker combination of a functional MRI imaging marker, reward learning and sensitivity, cognitive control, the clinical variables of depression severity and neuroticism, and the demographic variable of employment status.
After analyzing these biomarkers among participants, who had major depressive disorder, the group was randomly assigned to receive a full 8-week course of an SSRI or non-SSRI.
The primary outcome was the change in depression severity from pretreatment baseline to eight weeks after the start of treatment, with no significant differences in treatment outcomes for those assigned a drug consistent versus inconsistent with their biomarkers.
This, the researchers say was possibly due to the limited power to detect moderate effects.
However, significant differences emerged in symptom reduction trajectories for those with positive markers for both medications, with a response rate of 71.4% compared with 65.4% for those with a positive biomarker for either drug and 42.9% for those with two negative markers.
The authors concluded: “We found that, relative to patients with two negative markers, those with one or two markers were characterized by significantly larger reduction in depressive symptoms, showing that biomarker-guided treatment selection can boost efficacy for two of the most widely prescribed antidepressants around the world.”
The post Biomarkers Could Help in Antidepressant Choice appeared first on Inside Precision Medicine.