Molecular Cancer Therapeutics

Background: Predicting whether a treatment will demonstrate meaningful clinical benefit before committing to a large-scale trial remains a major unmet need in oncology. Patient-derived organoids (PDOs) recapitulate individual tumor drug sensitivity, but have not been used to forecast population-level trial outcomes. We developed SCOPE (Screening-to-Clinical Outcome Prediction Engine), a platform that integrates PDO drug screening with clinical prognostic modeling to predict arm-level median progression-free survival (mPFS) and objective response rate (ORR) without access to any trial outcome data. Patients and methods: SCOPE was trained on 54 treatment lines from patients with metastatic colorectal cancer (mCRC, n=15) and metastatic pancreatic ductal adenocarcinoma (mPDAC, n=39) with matched clinical data and PDO drug screening across 9 compounds. A Clinical Score module captures baseline prognosis; a Drug Screen Score module quantifies treatment-specific organoid sensitivity. To predict trial outcomes, synthetic patient profiles are generated from published eligibility criteria and matched to a biobank of 81 PDO lines. Predictions were externally validated against 32 arms from 23 published trials, treatment ranking was assessed across 8 head-to-head comparisons, and prospective applicability was tested for daraxonrasib (RMC-6236), a novel pan-RAS inhibitor in mPDAC. Results: Predicted mPFS strongly agreed with published outcomes (R2=0.85, MAE=0.82 months; Pearson r=0.92, P<0.001), approaching the empirical concordance between two independently measured clinical endpoints (ORR vs. mPFS, R2=0.87). ORR prediction was similarly robust (R2=0.71, MAE=7.3 percentage points). Integrating organoid and clinical data significantly outperformed either alone (P=0.001). SCOPE correctly identified the superior arm in 7 of 8 head-to-head comparisons (88%, P<0.05). Applied to daraxonrasib prior to phase 3 data availability, the platform predicted superiority over standard chemotherapy in KRAS-mutant mPDAC, consistent with emerging clinical data. Conclusion: By combining functional organoid drug screening with clinical modeling, SCOPE generates calibrated efficacy predictions for both established regimens and novel agents without prior clinical data. This approach could support clinical trial design, treatment arm selection, and go/no-go decisions, offering a new tool to improve the efficiency of gastrointestinal cancer drug development.

Background: Pharmacogenomic testing (PGx) can optimise drug efficacy and minimise toxicity, but the extent of prescriber adherence to PGx recommendations remains unclear. We aimed to quantify clinician adherence to international genotype-guided prescribing recommendations in a cohort of paediatric oncology patients. Methods: We reviewed files of children enrolled in the MARVEL-PIC (NCT05667766) randomised control trial, who had PGx recommendations available. Patients were included if 12 weeks had passed since their PGx report was released to clinicians. Prescribing events were identified for actionable PGx recommendations, and classified as "explicitly followed", "inadvertently followed", or "not followed". Adherence was assessed by patient, drug, and recommendation. Results: 2,063 PGx recommendations were available for 216 patients. 64 (3.1%) recommendations were actionable for 44 patients and 10 drugs within the 12-week study period. Recommendations were explicitly followed in 57/288 (19.8%) of prescribing events, inadvertently followed in 145 (50.3%), and not followed in 86 (29.9%). Mercaptopurine demonstrated the highest rate of explicit adherence (87.5%). No significant associations were observed between adherence and age group, cancer type, drug type, or strength of recommendation. Conclusion: Adherence to pharmacogenomic recommendations was very low, highlighting the need to understand barriers to PGx implementation, and consideration of clinical decision supports to facilitate adherence.

Background: Sezary syndrome (SS) is an aggressive leukemic variant of cutaneous T-cell lymphoma (CTCL) with distinct clinical and biological features compared to rarer entities such as primary cutaneous CD8+ aggressive epidermotropic cytotoxic T-cell lymphoma (PCAECTCL). Although recurrent genomic alterations in CTCL have been described, comparative analyses at the pathway level across biologically divergent subtypes remain limited. Here, we leveraged a conversational artificial intelligence (AI) platform for precision oncology to enable rapid, integrative, and hypothesis-driven interrogation of publicly available genomic datasets. Methods: We conducted a secondary analysis of somatic mutation and clinical data from the Columbia University CTCL cohort accessed via cBioPortal. Cases were stratified into SS (n=26) and PCAECTCL (n=13). High-confidence coding variants were curated and mapped to biologically relevant signaling pathways and functional gene categories implicated in CTCL pathogenesis. Pathway-level mutation frequencies were compared using Chi-square or Fisher's exact tests, with effect sizes quantified as odds ratios. Tumor mutational burden (TMB) was compared using the Wilcoxon rank-sum test. Subtype-specific co-mutation patterns were evaluated using pairwise association analyses and visualized through oncoplots and network heatmaps. Conversational AI agents, AI-HOPE, were used to iteratively refine cohort definitions, prioritize pathway-level signals, and contextualize findings. Results: TMB was comparable between SS and PCAECTCL (p = 0.96), indicating no significant difference in global mutational load. In contrast, pathway-centric analyses revealed marked qualitative differences. SS demonstrated enrichment of alterations in epigenetic regulators, tumor suppressor and cell-cycle control pathways, NFAT signaling, and DNA damage response mechanisms, consistent with transcriptional dysregulation and immune modulation. PCAECTCL exhibited relatively higher frequencies of alterations involving epigenetic regulators and MAPK pathway signaling, suggesting distinct oncogenic dependencies. Co-mutation analysis revealed a more constrained and focused interaction landscape in SS, whereas PCAECTCL displayed broader and more heterogeneous co-mutation networks, indicative of divergent evolutionary trajectories. Notably, ERBB2 mutations were significantly enriched between subtypes (p = 0.031), highlighting a potential subtype-specific therapeutic vulnerability. Conclusions: This study demonstrates that SS is distinguished from PCAECTCL not by increased mutational burden but by distinct pathway-level architectures, particularly involving epigenetic regulation, immune signaling, and transcriptional control. These findings generate biologically grounded, testable hypotheses for subtype-specific therapeutic targeting and underscore the value of conversational AI as a scalable framework for accelerating discovery in translational cancer genomics.

Many genetically validated targets in cancer, including the transcription factor {beta}-catenin ({beta}-cat), have historically been viewed as undruggable. Cell-based phenotypic screening of chemical compounds can reveal new biological and pharmacological principles. Natural products are powerful probes because of their superior structural diversity, drug-like properties, and biological activities as compared to unoptimized synthetic compounds. We screened 326,304 natural product mixtures (40,744 extracts and 285,560 fractions derived from them) using mammalian cells expressing an oncogenic version of {beta}-cat fused to a suicide protein. Multiple fractions degraded the {beta}-cat fusion protein or drove it into a compartment where both fusion partners were apparently inactive. The active natural product from one of the latter specifically activates novel, but not classical, protein kinase Cs (PKCs) and thereby relocates {beta}-cat to juxtamembrane vacuolar structures. These findings suggest a path for inactivating oncogenic {beta}-cat and underscore the power of screening natural product collections with robust phenotypic assays.

BackgroundTreatment optimization in HPV-associated oropharyngeal cancer (OPSCC) remains challenging, as recent de-escalation trials have shown limited success. Current patient selection strategies based on smoking history and TNM classification are insufficient, highlighting the need for robust, standardized prognostic biomarkers. We report the first validation of the Immunoscore (IS) for prognostic stratification in HPV-associated OPSCC. Patients and methodsWe analyzed 191 HPV-associated (p16+ and HPV DNA/RNA+) OPSCC patients from an international multicenter cohort (2015-2024), comprising a French monocentric retrospective training cohort (N = 48) and three validation cohorts: French monocentric retrospective (N = 48), French multicenter prospective (N = 50), and US multicenter retrospective (N = 45). IS is a standardized digital pathology assay quantifying CD3lJ and CD8lJ densities in tumor cores and invasive margins, with cut-offs defined in the training cohort and validated across cohorts. Associations with disease-free survival (DFS), time to recurrence (TTR) and overall survival (OS) were assessed, alongside 3RNA-seq and sequential immunofluorescence profiling of immune composition. ResultsMedian age 65; 80% male; 74% smokers; 66% T1-2; 82% N0-1 (AJCC8th). IS-High patients demonstrated superior 3-year DFS in the training and validation cohorts 1-3 (all log-rank P < 0.05). Multivariable analysis identified IS-Low as the strongest independent risk factor for DFS (HR 9.03; 95% CI: 4.02-20.31; P < 0.001). The model combining IS with clinical factors showed higher predictive accuracy for DFS (C-index 0.82) than clinical variables alone (0.7; P < 0.0001). Similar findings were observed for TTR and OS. IS-High tumors showed markedly higher enrichment of lymphoid and myeloid immune cell populations, contrasting with immune-poor signatures in IS-Low tumors. ConclusionsIS is a robust biomarker that outperforms standard clinical variables in both prognostic and predictive accuracy. The enriched cytotoxic immune infiltrate in IS-High tumors explains favorable outcomes and supports their suitability for treatment de-escalation. Prospective validation is warranted.

Background and aims: Pancreatic ductal adenocarcinoma (PDAC) surveillance is limited to individuals with familial or genetic risk although most future cases arise outside these groups. In a retrospective study, PRISM, an electronic health record (EHR)-based PDAC risk model, identified individuals in the general population at elevated near-term risk of PDAC. We aimed to prospectively evaluate whether PRISM can identify high-risk individuals beyond current surveillance groups across U.S. health systems. Methods: We performed a prospective multicenter cohort study after deployment of PRISM in April 2023 across 44 U.S. health care organizations. Eligible adults aged [&ge;]40 years without prior PDAC received a single baseline risk score and were assigned to prespecified risk tiers. Patients were followed for incident PDAC for 30 months. We estimated tier-specific 30-month cumulative incidence (positive predictive value, PPV), number needed to screen (NNS), standardized incidence ratios (SIRs), and time from deployment and first high-risk flag to diagnosis. Results: Among 6,282,123 adults assigned a PRISM score, 5,058,067 had follow-up; 3,609 developed PDAC. The highest-risk tier had 30-fold higher PDAC incidence than the study population. At the SIR 5 threshold, 30-month cumulative incidence was 0.35% (NNS, 284.2); at SIR 16, 1.14% (NNS, 87.4); and at SIR 30, 2.19% (NNS, 45.7). Median time from deployment to PDAC diagnosis was 9.5 months, and median time from first high-risk flag to diagnosis at SIR 5 was 3.5 years. Shapley additive explanations (SHAP) analyses supported patient- and tier-level interpretability. Conclusions: Prospective deployment of PRISM across multiple U.S. health care organizations identified individuals at elevated near-term risk for PDAC, with substantial risk enrichment and lead time before diagnosis. These findings support the real-world scalability and generalizability of EHRbased risk stratification for risk-adapted early detection. ClinicalTrials.gov identifier NCT05973331

BackgroundDespite extensive characterization of key oncogenic drivers, pancreatic ductal adenocarcinoma (PDAC) continues to exhibit profound molecular heterogeneity and inconsistent responses to standard therapies, including gemcitabine. The role of pathway-level alterations, particularly in the context of age at onset and therapeutic exposure, remains insufficiently defined. MethodsIn this study, we leveraged a conversational artificial intelligence framework (AI-HOPE-TP53 and AI-HOPE-PI3K) to enable precision oncology, driven interrogation of clinical and genomic data from 184 PDAC tumors, stratified by age at diagnosis and gemcitabine exposure. Using AI-enabled cohort construction and pathway-centric analyses, we evaluated alterations in TP53 and PI3K signaling networks, with findings validated through conventional statistical methods. ResultsTP53 pathway analysis revealed a significantly higher frequency of TP53 mutations in early-onset compared to late-onset PDAC among gemcitabine-treated patients (86.7% vs. 57.1%, p = 0.04), with a similar trend observed between treated and untreated early-onset cases (86.7% vs. 40%, p = 0.07). Notably, in late-onset PDAC patients not treated with gemcitabine, absence of TP53 pathway alterations was associated with improved overall survival (p = 0.011). Complementary analyses of the PI3K pathway demonstrated a higher prevalence of pathway alterations in late-onset gemcitabine-treated tumors compared to untreated counterparts (13.2% vs. 2.7%, p = 0.02). Importantly, among late-onset patients not receiving gemcitabine, those without PI3K pathway alterations exhibited significantly improved overall survival (p < 0.0001). ConclusionTogether, these findings identify distinct TP53 and PI3K pathway dependencies that are modulated by both age-of-onset and treatment exposure in PDAC. This work highlights the utility of conversational artificial intelligence in enabling rapid, integrative, and hypothesis-generating analyses within a precision oncology framework, supporting the identification of clinically relevant molecular stratification strategies for this aggressive disease.

Background. Pancreatic ductal adenocarcinoma (PDAC) has a five-year survival rate of approximately 12%, largely because it is typically diagnosed at an advanced stage. CT-based computational methods for early detection exist but rely on black-box deep learning or large texture feature sets without tissue-specific interpretability. Methods. We developed Virtual Spectral Decomposition (VSD), which applies six parameterized sigmoid functions S(HU) = 1/(1+exp(-alpha x (HU - mu))) to standard portal-venous CT, decomposing each pixel into tissue-specific response channels for fat (mu=-60), fluid (mu=10), parenchyma (mu=45), stroma (mu=75), vascular (mu=130), and calcification (mu=250). Dendritic Binary Gating identifies structural content per channel using morphological filtering, enabling co-firing analysis and lone firer identification. A 25-feature signature was extracted per patient. Three independent datasets were analyzed: NIH Pancreas-CT (n=78 healthy), Medical Segmentation Decathlon Task07 (n=281 PDAC, paired tumor/adjacent tissue), and CPTAC-PDA from The Cancer Imaging Archive (n=82, multi-institutional, with DICOM time point tags). The same six sigmoid parameters were used across all datasets without retraining. Results. VSD achieved AUC 0.943 for field effect detection (healthy vs cancer-adjacent parenchyma) and AUC 0.931 for patient-stratified tumor specification on MSD. On CPTAC-PDA, VSD achieved AUC 0.961 (6 features) and 0.979 (25 features) for distinguishing healthy from cancer-bearing pancreas on scans obtained prior to pathological diagnosis. All significant features replicated across datasets in the same direction: z_fat (d=-2.10, p=3.5e-27), z_fluid (d=-2.76, p=2.4e-38), fire_fat (d=+2.18, p=1.2e-28). Critically, VSD severity did not correlate with days-from-diagnosis (r=-0.008, p=0.944) across a range of day -1394 to day +249. Patient C3N-01375, scanned 3.8 years before pathological diagnosis, had VSD severity 1.87, well above the healthy mean of 0.94 +/- 0.33. The tissue transformation signature was temporally stable, indicating an early, persistent tissue state rather than a progressively worsening process. Conclusions. VSD with Dendritic Binary Gating detects a stable pancreatic tissue composition signature on standard CT that is present years before clinical diagnosis, validated across three independent datasets without parameter adjustment. The six sigmoid channels map to biologically meaningful tissue components through a fully transparent interpretability chain. The temporal stability of the signal implies a detection window of 3-7 years, consistent with known PanIN-3 microenvironment transformation timelines. VSD functions as a single-scan screening tool applicable to any abdominal CT performed during the pre-clinical window.

Background: Stomach adenocarcinoma is driven by heterogeneity, limiting therapeutic success. Although ROS acts as a continuous redox rheostat for tumor evolution, it is categorized based on binary models that are masked by tumor-microenvironment (TME) confounders. Here, we have defined a continuous, TME-independent ROS axis to help identify intrinsic vulnerabilities and improve patient stratification. Methods: Non-negative matrix factorization (NMF) defined a ROS-Axis in TCGA-STAD which was validated in ACRG Cohort. Multivariate regression model isolated intrinsic signatures via residual ROS scores by adjusting for TME confounders. Survival was assessed using Cox hazard models. Drug sensitivities were mapped using GDSC2/ElasticNet modeling with cross-cohort replication. Results: Our results define a reproducible ROS gradient, driven by effectors like NQO1 and SOD1, characterizing ROS-high tumors as proliferative, epithelial and immune -cold. High residual ROS score was associated with an improved prognosis, regardless of TNM stage and age. Pharmacogenomic mapping revealed an overlapping sensitivity to mTOR inhibitors in ROS-high gastric cancer tumors which persisted after TME confounder adjustment. Conclusion: The continuous ROS axis provides a functional readout of metabolic dependency that refines traditional anatomical staging. By identifying mTOR dependent cold tumors, our framework offers a precision strategy for immunotherapy-resistant patients like those affected by microsatellite-stable gastric cancer.

BackgroundMulticancer early detection tests could be used for cancer screening, but may lead to harms, including false positive results and overdiagnosis of indolent tumours that would not have become clinically evident during that persons lifetime. We assessed the potential for these screening harms in the context of future population-based screening with a multicancer early detection test. MethodsWe used a microsimulation model to assess potential population-level impacts of screening at ages 50-75 years with a multicancer early detection test in Canada. We assumed high test specificity (97-99.1%) and test sensitivity increasing with cancer stage. The model includes latent indolent cancers that would not be diagnosed within that persons lifetime but can be overdiagnosed through screen-detection. We calculated the yearly and cumulative lifetime probabilities of screening overdiagnosis and false positive test results, assuming a range of preclinical screen-detectable periods (2-5 years). ResultsAn estimated 2.1-6.0% of all yearly screen-detected cancers with a multicancer screening test were predicted to be overdiagnoses across scenarios. The proportion of overdiagnosis varied by site, and strongly increased with age, going from 1% at age 50 to over 10% of screen-detected cancers by age 75. The test positive predictive value ranged from 15.9%-77.6%, meaning that there could be 0.3-5.3 false positives with no underlying cancer for every true cancer case detected by the test. ConclusionPopulation-level multicancer screening with a multicancer early detection test would likely not lead to substantial screen-related overdiagnosis. Healthcare systems should consider how screening false positives may increase their diagnostic service caseload.

Circulating tumor cells (CTCs), and especially CTC-clusters, are linked to poor prognosis and may reveal mechanisms of metastasis and treatment resistance. Therefore, developing unbiased methods for the functional characterization of CTCs in liquid biopsies is an urgent need. Here, we present an evaluation of multiplex imaging mass cytometry (IMC) to analyze CTCs in mice with human xenograft tumors. In a single-step process, IMC uses metal-labeled antibodies to simultaneously detect a large number of proteins/modifications within minimally manipulated small volumes of blood from the tail vein or heart. We used breast cancer cell lines and a patient-derived xenograft (PDX) to assess antibodies for cross-species interpretation. Along with manual verification, HALO-AI-based cell segmentation was used to identify CTCs and quantify markers. Despite some limitations regarding human-specificity, this technology can be used to investigate the effect of genetic and pharmacological interventions on the properties of single and cluster CTCs in tumor-bearing mice.

Children with acute lymphoblastic leukemia (ALL) are treated with multiagent chemotherapy that causes profound changes to the immune system. There are limited data on how disease and therapy impact antigen-specific immune memory, leading to inconsistent guidelines on best practices for revaccination of this population. Here, to inform vaccine guidance, we investigated whether immunity derived from routine childhood measles and varicella zoster virus (VZV) vaccines is maintained during and after therapy for childhood ALL. We report that antibodies against measles and VZV were significantly reduced in children with ALL (n=45) compared to healthy controls (n=13), particularly in older children in whom a longer time had passed since their most recent vaccine dose. However, the avidity of the measles and VZV-specific antibodies was indistinguishable between groups. Despite changes to the composition of the T cell compartment, both overall and antigen-specific T cell function were preserved in children with ALL. These data provide compelling evidence for revaccination of children following ALL treatment. Intact T cell responses suggest that post-treatment revaccination would be effective.

ImportanceBlood neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) are entering clinical use in neurology as markers of neuroaxonal and astrocytic injury, but their utility in psychiatry is unclear. ObjectiveTo determine whether psychiatric diagnoses are associated with altered plasma NfL and GFAP levels. Design, Setting, and ParticipantsThis population-based study examined plasma NfL and GFAP among 47,495 participants from the UK Biobank (54.0% female; 93.5% White; mean [SD] age 56.8 [8.2] years) who provided blood samples and sociodemographic and clinical data between 2006 and 2010. Normative modeling was applied to assess associations between 7 lifetime psychiatric diagnostic categories and deviations from expected NfL and GFAP levels, while accounting for neurological diagnoses, cardiometabolic burden, and substance use. Data were analyzed between July 2025 and March 2026. Main Outcomes and MeasuresDeviations in plasma NfL and GFAP levels from normative predictions. ResultsRelative to the reference population, plasma NfL levels were higher among individuals with bipolar disorder (d=0.20; 95% CI, 0.03-0.37; p=0.03), recurrent depressive disorder (d=0.23; 95% CI, 0.07-0.38; p=0.009), and depressive episodes (d=0.06; 95% CI, 0.02-0.10; p=0.01), lower among individuals with anxiety disorders (d=-0.07; 95% CI, -0.12 to -0.02; p=0.008), but did not differ in schizophrenia spectrum, stress-related, or other psychiatric disorders. Plasma GFAP levels were not elevated in any psychiatric disorders. Variability in NfL levels was greater among individuals with schizophrenia spectrum disorders (variance ratio [VR]=1.30; p=0.005), depressive episodes (VR=1.06; p=0.006), and anxiety disorders (VR=1.08; p=0.005). Variability in GFAP levels was increased only in anxiety disorders (VR=1.08; p=0.01). Plasma NfL levels exceeding percentile-based normative thresholds were more common among individuals with schizophrenia spectrum disorders, bipolar disorder, recurrent depressive disorder, and depressive episodes. Neurological diagnoses, cardiometabolic burden, and substance use were associated with plasma NfL and GFAP levels. Conclusions and RelevanceThis study provides population-level evidence of plasma NfL elevation in bipolar and depressive disorders and increased variability in schizophrenia spectrum, bipolar and depressive disorders, supporting its potential as a biomarker in psychiatry and informing its ongoing neurological applications. Plasma GFAP levels, in contrast, were largely unaltered across psychiatric disorders. Key PointsO_ST_ABSQuestionC_ST_ABSAre plasma neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) levels altered in psychiatric disorders? FindingsIn this cohort study including 47,495 individuals, normative modeling revealed that plasma NfL levels were elevated in bipolar and depressive disorders, whereas plasma GFAP levels were not elevated in any psychiatric disorder. Plasma NfL levels also showed higher variability in schizophrenia spectrum, bipolar, and depressive disorders. MeaningPlasma NfL shows distinct alterations in schizophrenia spectrum and affective disorders, supporting its further investigation as a biomarker in clinical psychiatry and highlighting the need to consider psychiatric comorbidity in neurological applications.

Background During Nigeria's largest recorded diphtheria outbreak, hospital capacity in Kano State was rapidly overwhelmed. Medecins Sans Frontieres introduced home-based care (HBC) for patients with mild disease to prioritise facility-based care for severe cases. We assessed whether HBC was non-inferior to facility-based treatment in terms of mortality, sequelae, and household transmission. Methods We conducted a retrospective matched cohort study. Mild diphtheria cases treated between January 2023 and May 2024 were matched 1:1 by treatment modality (HBC or diphtheria treatment centre [DTC]) on sex, age group, vaccination status, and residence. Conditional logistic regression estimated the association between treatment modality and mortality, with robustness assessed through propensity score weighting, sensitivity analyses, and E-value computation. Findings Of 990 sampled patients, 678 (367 HBC, 311 DTC) were enrolled (68.5%). After adjustment, treatment modality was not independently associated with mortality (HBC vs. DTC: aOR 0.40, 95% CI 0.13-1.30), with similar estimates across sensitivity analyses (E-value 4.40). Clinical complications were the strongest predictor of death (aOR 23.1, 95% CI 1.73-307). Vaccination was protective (aOR 0.28, 95% CI 0.08-0.94) and treatment delay of four or more days increased mortality (aOR 4.15, 95% CI 1.23-14.0). HBC was not associated with increased household transmission or long-term sequelae. Interpretation Vaccination and early treatment, rather than care setting, were the main determinants of survival. When supported by clinical triage and structured follow-up, decentralised care can be used to manage mild cases during diphtheria epidemics in settings with constrained hospital capacity.

Single photon emission computed tomography (SPECT) is a highly specialized imaging modality that enables measurement of regional cerebral perfusion and, in particular, resting cerebral blood flow (rCBF). Recent technological advances have improved SPECT quantification and reliability, making it increasingly useful for studying rCBF abnormalities and perfusion network alterations in psychiatric and neurological disorders. To characterize large scale functional organization in SPECT data, data driven decomposition methods such as independent component analysis (ICA) have been used to extract covarying perfusion patterns that map onto interpretable brain networks. Blind ICA provides a data driven approach to estimate these networks without strong prior assumptions. More recently, a hybrid approach that leverages spatial priors to guide a spatially constrained ICA (sc ICA) have been used to fully automate the ICA analysis while also providing participant-specific network estimates. While this has been reliably demonstrated in fMRI with the NeuroMark template, there is currently no comparable SPECT template. A SPECT template would enable automatic estimation of functional SPECT networks with participant-specific expressions that correspond across participants and studies. The current study introduces a new replicable NeuroMark SPECT template for estimating canonical perfusion covariance patterns (networks). We first identify replicable SPECT networks using blind ICA applied to two large sample SPECT datasets. We then demonstrate the use of the resulting template by applying sc-ICA to an independent schizophrenia dataset. In sum, this work presents and shares the first NeuroMark SPECT template and demonstrating its utility in an independent cohort, providing a scalable and robust framework for network-based analyses.

Background: Gamma oscillation dysfunction has been implicated in neuropsychiatric disorders. Restoring gamma oscillations via brain stimulation represents an emerging therapeutic approach. However, the strength of its clinical effects and treatment moderators remain unclear. Method: We conducted a systematic review and meta-analysis to examine the clinical effects of gamma neuromodulation in neuropsychiatric disorders. A literature search for controlled trials using gamma stimulation was performed across five databases up until April 2025. Effect sizes were calculated using Hedge's g. Separate analyses using the random-effects model examined the clinical effects in schizophrenia (SZ), major depressive disorder (MDD), bipolar disorder, and autism spectrum disorder. For SZ and MDD, subgroup analyses evaluated the effects of stimulation modality, stimulation frequency, treatment duration, and pulses per session. Result: Fifty-six studies met the inclusion criteria (NSZ = 943, NMDD = 916, NBD = 175, NASD = 232). In SZ, gamma stimulation was associated with improvements in positive (k = 10, g = -0.60, p < 0.001), negative (k = 12, g = -0.37, p = 0.03), depressive (k = 8, g = -0.39, p < 0.001), anxious symptoms (k = 5, g = -0.59, p < 0.001), and overall cognitive function (k = 7, g = 0.55, p < 0.001). Stimulation frequency and treatment duration moderated therapeutic effects. In MDD, reductions in depressive symptoms were observed (k = 23, g = -0.34, p = 0.007). Conclusion: Gamma neuromodulation showed moderate therapeutic benefits in SZ and MDD. Substantial heterogeneity likely reflects protocol differences, highlighting the need for well-powered future trials.

BackgroundEarly-life adversity (ELA) is a risk factor for enduring pain in youth and is associated with alterations in brain morphology and function. However, it remains unclear whether ELA-related neurobiological changes contribute to the development of enduring pain in early adolescence. MethodsUsing data from the Adolescent Brain Cognitive Development (ABCD) Study, we examined multimodal magnetic resonance imaging (MRI) markers in children assessed at baseline (ages 9-11 years) and at 2-year follow-up (ages 11-13 years). ELA exposure was defined at baseline to maximise temporal separation between early adversity and later enduring pain. Participants with enduring pain at follow-up (n = 322) were compared to matched pain-free controls (n = 644). Structural MRI, diffusion MRI (fractional anisotropy, mean diffusivity), and resting-state functional connectivity data were analysed. Linear models tested main effects of enduring pain, ELA, and their interaction on brain metrics, controlling for relevant covariates. ResultsELA exposure was associated with smaller caudate and nucleus accumbens volumes, and reduced surface area of the left rostral middle frontal gyrus. No significant effects of enduring pain or ELA-by-enduring pain interaction were observed across grey matter, white matter, or functional connectivity measures. ConclusionsELA was associated with alterations in fronto-striatal regions in late childhood, but these changes were not linked to enduring pain in early adolescence. These findings suggest that ELA-related neurobiological alterations may represent early markers of vulnerability rather than concurrent correlates of enduring pain. Longitudinal follow-up is needed to determine whether these alterations contribute to later chronic pain risk.

BackgroundNegative symptoms are core features of schizophrenia that relate strongly to functional impairment, yet interventions targeting these symptoms remain largely ineffective. Emerging theoretical work highlights how environmental factors may shape and maintain negative symptoms. Although racial disparities in schizophrenia diagnosis among Black Americans are well documented and linked to racial stress and psychosis, the impact of racial stress on negative symptoms has not been examined. This study provides an initial test of a novel theory proposing that racial stress - here measured by racial discrimination - influences negative symptom severity through exacerbation of negative cognitions about the self, particularly defeatist performance beliefs (DPB). Study DesignParticipants diagnosed with schizophrenia-spectrum disorder (SSD) (N = 208; 80 Black, 128 White) completed the Positive and Negative Syndrome Scale (PANSS), the Defeatist Beliefs Scale, and self-report measures of subjective racial and ethnic discrimination (Racial and Ethnic Minority Scale and General Ethnic Discrimination Scale). Relationships among variables were tested using linear regression and mediation analysis. Study ResultsBlack participants exhibited significantly greater total and experiential negative symptoms than White participants with no group difference in DPB. Racial discrimination explained 46% of the relationship between race and negative symptoms. Among Black participants, higher DPB were associated with greater negative symptom severity. Discrimination was positively related to both DPB and negative symptoms. DPB partially mediated the relationship between discrimination and negative symptoms. ConclusionsFindings suggest that racial stress contributes to negative symptom severity via defeatist beliefs among Black individuals, highlighting potential targets for culturally informed interventions.

Background Higher levels of sedentary behaviour, such as leisure screen time (LST), and lower levels of physical activity are associated with diseases across multiple body systems which contribute to a large global health burden. Whether these associations are causal is unclear. The primary aim of this study is to investigate the causal effects of higher LST (given greater power) and, secondarily, lower moderate-to-vigorous intensity physical activity (MVPA), on a wide range of diseases in a hypothesis-free approach. Methods A two-sample Mendelian randomisation phenome-wide association study was conducted for the main analyses. Genetic single nucleotide polymorphisms (SNPs) were first selected as exposure genetic instruments for LST (hours of television watched per day; 117 SNPs) and MVPA (higher vs. lower; 18 SNPs) based on the genome-wide significant threshold (p < 5*10-8) from the largest relevant genome-wide association study (GWAS). For disease outcomes, we used summary results from FinnGen GWAS, including 1,719 diseases defined by hospital discharge International Classification of Diseases (ICD) codes in 453,733 European participants. For the main analyses, we used the inverse-variance weighting method with a Bonferroni corrected p-value of p [&le;] 3.47*10-4. Sensitivity analyses included Steiger filtering, MR-Egger and weighted median analyses, and data from UK Biobank were used to explore replication. Findings Genetically predicted higher LST was associated with increased risk of 87 (5.1% of the 1,719) diseases. Most of these diseases were in musculoskeletal and connective tissue (n=37), genitourinary (n=12) and respiratory (n=8) systems. Genetic liability to lower MVPA was associated with six diseases: three in musculoskeletal and connective tissue and genitourinary systems (with greater risk of these diseases also identified with higher LST), and three in respiratory and genitourinary systems. Sensitivity analyses largely supported the main analyses. Results replicated in UK Biobank, where data available. Conclusions Higher levels of sedentary behaviour, and lower levels of physical activity, causally increase the risk of diseases across multiple body systems, making them promising targets for reducing multimorbidity.

Objectives: Longer lifespans lead to longer time on retirement, despite the efforts to raise the retirement age. Therefore, it is important to study how the retirement years can be spent without diseases. This study examined socioeconomic and sociodemographic differences in healthy years spent on retirement. Methods: We followed a cohort of retired Finnish municipal employees (N=4231, average follow-up 15.4 years) on national administrative registers for major chronic diseases: cancer, coronary heart disease, cerebrovascular disease, diabetes, asthma or chronic obstructive pulmonary disease, dementia, mental disorders, and alcohol-related disorders. Median healthy years on retirement and age at first occurrence of illness (ICD-10 and ATC-based) in each combination of sex, occupational class, and age of retirement were predicted using Royston-Parmar models. Prevalence rates for each diagnostic group were calculated. Results: Most healthy years on retirement were spent by women having worked in semi-professional jobs who retired at age 60-62 (median predicted healthy years 11.6, 95% CI 10.4-12.7). The least healthy years on retirement were spent by men having worked in routine non-manual jobs who retired after age 62 (median predicted healthy years 6.5, 95% CI 4.4-9.5). Diabetes was slightly more common among lower occupational class women, and dementia among manual working women having retired at age 60-62. Discussion: Healthy years on retirement are not enjoyed equally by women and men and those who retire early or later. Policies aiming to increase the retirement age should consider the effects of these gaps on retirees and the equitability of those effects.