Citations

The bone marrow immune microenvironment (BMME) is a critical factor in multiple myeloma (MM) pathogenesis. Despite higher incidence of MM among self-identified African Americans, the relationship between African genetic similarity and the BMME remains unclear. We present the largest analysis to date assessing the impact of Ancestry on the BMME in myeloma (N=320, including 64 with ≥50% African genetic similarity, AFR-High) leveraging our recently generated Immune Atlas. AFR-High was associated with enrichment of late T-effector cells, driven by rare cytotoxic CD4⁺ T cells, suggesting a distinct adaptive BMME. AFR-High exhibited expansion of late-differentiated adaptive NK cells with high cytotoxic GNLY and GZMH expression. AFR-High depicted reduced CD14⁺ monocytes, increased CD16⁺ non-classical monocytes, and expansion of macrophage-like monocyte populations with enrichment of metabolic adaptation, efferocytosis and phagocytic clearance programs, and immunoregulatory pathways. These findings reveal a shift from an innate inflammatory immune landscape toward adaptive cytotoxic and immunoregulatory remodeling associated with African genetic similarity in MM.

Abstract Purpose: Inhibitors of the Ataxia telangiectasia and Rad3-related (ATR) protein kinase, a critical component of the DNA damage repair response, have synergistic anti-cancer activity with platinum compounds in preclinical models. We therefore conducted a Phase 2 study of the ATR inhibitor berzosertib+carboplatin vs. docetaxel+carboplatin in metastatic castration-resistant prostate cancer. Patients and Methods: Patients previously treated with at least one androgen receptor pathway inhibitor and taxane underwent mandatory biopsy and were randomized 1:1 to receive Arm A (docetaxel 60 mg/m2+carboplatin AUC4 day 1; or carboplatin AUC5 if not a docetaxel candidate) or Arm B (berzosertib 90 mg/m2 days 2,9+carboplatin AUC5 day 1) every 21 days. The primary endpoint was overall response rate (ORR; ≥50% PSA decline or radiographic response). Results: Of 73 randomized patients, 65 received protocol treatment: 34 on Arm A (26 docetaxel+carboplatin; 8 carboplatin alone) and 31 on Arm B. Thirteen patients (38%) in Arm A and 20 (65%) in Arm B had ≥Grade 3 treatment-related adverse events (TrAEs). ORR was 15% in Arm A (5/34; 5/26[19%] with docetaxel+carboplatin) and 0% in Arm B (0/31). At interim analysis after 65 of planned 130 patients were treated, enrollment was halted due to futility. Homologous recombination repair deficiency status from tissue and blood did not correlate with clinical outcomes, but ATM-deficiency or high levels of phoshpo-KAP1 detected in tissue was associated with clinical benefit across both arms of the trial. Conclusions: Berzosertib+carboplatin demonstrated lower ORR and more frequent ≥grade 3 TrAEs compared to docetaxel+carboplatin in this heavily pre-treated, biomarker-unselected population. NCT03517969

Background: Several breast cancer (BC) risk prediction models have been developed to provide personal risk assessments. Though individually validated, their performance has not been systematically evaluated across a wide range of populations or ages. Methods: We harmonized individual-level baseline questionnaire data and incident BC diagnoses from 21 cohorts from North America, Europe, and Australia participating in the Breast Cancer Risk Prediction Project. Five-year absolute risk of invasive BC was estimated for five established risk prediction models using classical risk factors only. Discrimination was evaluated by area under the curve (AUC). Calibration was assessed using average and risk-decile specific expected to observed (E/O) ratios. Performance metrics were meta-analyzed across cohorts and models. Metaregression tested associations between cohort characteristics and performance metrics. Results: This analysis included 1,595,977 women aged 20-75 years, enrolled in studies between 1976-2015, with 19,062 (1.2%) invasive BC cases ascertained within 5 years from exposure assessment. Age-adjusted AUCs were similar across models and cohorts (pooled AUCs by model: 0.57-0.58), while E/O ratios varied substantially (pooled E/O ratios by model: 0.83-1.25). Overestimation was common among predicted high-risk individuals (>3%). No appreciable differences in model performance by cohort age, birth year, race, and variable missingness emerged. Calibration improved after assigning race-specific incidence rates. Conclusion: Existing BC risk prediction models provided similar risk discrimination across multiple cohorts, although there was overestimation of risk for high-risk individuals. Performance variation across cohorts was not driven by specific characteristics, which supports development of a unified risk model for diverse populations that leverages appropriate incidence rates.

PURPOSE EA1181 (ClinicalTrials.gov identifier: NCT04266249 ) is a single-arm trial evaluating neoadjuvant taxane, trastuzumab, and pertuzumab (THP) in patients with clinical stage II/IIIa human epidermal growth factor receptor 2 (HER2)–positive breast cancer. This report focuses on the secondary end points of pathologic complete response (pCR) rates and associated factors. The primary end point—3-year recurrence-free survival among patients achieving a pCR (ypT0/Tis, ypN0)—will be reported when data mature. METHODS Patients received four cycles of trastuzumab and pertuzumab (HP) with either once per week paclitaxel (12 weeks) or docetaxel (every 3 weeks for four cycles), followed by surgery. Clinicopathologic characteristics were assessed in all patients. The HER2DX pCR score was determined using diagnostic biopsy samples in a representative subset. Logistic regression models identified factors associated with pCR. RESULTS A total of 2,175 patients were enrolled (781 HER2+/estrogen receptor‑negative [ER–]; 1,394 HER2+/ER+). Of 2,141 patients who initiated THP, the overall pCR rate was 43.8%: 63.7% in HER2+/ER– and 32.4% in HER2+/ER+ tumors. Higher pCR rates were observed in tumors that were ER-negative or low ER expressing, progesterone receptor-negative or low expressing, HER2 immunohistochemistry (IHC) 3+, and in patients treated with once per week paclitaxel. T3 disease was associated with a lower pCR rate in HER2+/ER– tumors; otherwise, tumor (T) and nodal (N) stage did not significantly affect pCR. Among 569 patients assessed for HER2DX pCR scores, a high score was independently associated with higher pCR rates, after adjustment for clinicopathologic variables. CONCLUSION Neoadjuvant THP achieved pCR in nearly two-thirds of patients with HER2+/ER– and one third with HER2+/ER+ breast cancer. Low hormone receptor expression, HER2 IHC 3+ status, once per week paclitaxel use, and a high HER2DX pCR score were independent predictors of pCR. These findings should help optimize risk stratification and treatment personalization for patients with HER2-positive breast cancer.

Background New antiamyloid therapies (AAT) for treating patients with early Alzheimer disease (AD) have become available in the past 3 years. Although AAT can be beneficial in slowing progression of AD, they also come with potentially serious side effects, particularly for patients requiring anticoagulation or thrombolysis for cardiovascular conditions. Given that most patients with AD are older and likely to have cardiovascular risk factors, there is urgent need for additional guidance for clinicians about potential risks and benefits associated with AAT for patients with cardiovascular comorbidities, best practices for management of these patients, and special considerations with respect to emergency evaluation and treatment of patients on AAT who develop acute neurological symptoms. Methods The American Heart Association convened 75 multidisciplinary experts for the Amyloid‐Related Imaging Abnormalities Summit: Cardiovascular and Stroke Considerations for β‐Amyloid Immunotherapy in AD in July 2025. These experts were divided into 8 work groups to evaluate the current evidence related to AAT usage in patients with cardiovascular risk factors and conditions and develop consensus‐based guidance for clinicians. Results This statement outlines the outcomes of the Summit, including considerations for prescribers, primary care professionals, emergency personnel, and health care system stakeholders. These considerations are intended to improve identification of patients as appropriate candidates for AAT, management of patients with co‐occurring AD and cardiovascular conditions, and their emergency evaluation and treatment. The statement also includes guidance for shared decision‐making, implementation of system changes for improving dementia care, and recommendations for future research and data collection to add to the body of evidence for treating patients with concomitant AD and cardiovascular conditions. Conclusions Building on lessons learned from the establishment of stroke systems of care, the consensus‐based considerations outlined in this document can serve as a foundation for AD systems of care aimed at ensuring optimal use of these ground‐breaking therapies.

BACKGROUND AND OBJECTIVES: Medical management options for nontraumatic subdural hematoma (SDH) remain limited, with only atorvastatin demonstrating benefit in a single phase 2 randomized clinical trial and mixed evidence for dexamethasone. Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) may benefit SDH outcomes. The objective of this study was to evaluate the association between GLP-1 RA use and rescue surgery rates and mortality in patients with medically managed nontraumatic SDH. METHODS: We conducted a retrospective cohort study using the TriNetX database from 2016 to 2025, including adults with SDH who were managed conservatively without surgical evacuation or middle meningeal artery embolization. Patients prescribed GLP-1 RAs were compared with nonusers. After propensity matching, rescue surgical evacuation, all-cause mortality at 1 year, and GLP-1 RA–associated adverse events were analyzed. RESULTS: A total of 134 901 patients with conservatively managed nontraumatic SDH were identified, of whom 1470 (1.1%) were GLP-1 RA users. After propensity score matching, 2922 patients remained, with 1461 in each group. At 1 year, GLP-1 RA users experienced significantly lower rates of rescue surgery (2.23% vs 3.51%; P = .046), culminating in an overall 38% reduction in relative hazard. All-cause mortality was also significantly lower for GLP-1 RA users at 1 year (16.1% vs 23.7%, P < .001), reflecting a 33% reduction in hazards. GLP-1 RA users did not experience different rates of adverse events, such as pancreatitis, gallbladder disease, or ileus/bowel obstruction (all P > .05). CONCLUSION: Among patients with medically managed nontraumatic SDH, GLP-1 RA use was associated with significantly lower rates of rescue surgery and all-cause mortality without significantly increased risk of common GLP-1 RA–related complications.

Summary Background Body composition is recognized as a major determinant of health outcomes, but its multidimensional nature makes clinical adoption challenging. We sought to develop and validate a body composition index (BCI) for all-cause mortality risk assessment, integrating variables of six body composition tissues. Methods We analyzed 28509 consecutive patients undergoing myocardial perfusion imaging with routine low-dose chest CT attenuation correction (CTAC) scans acquired during myocardial perfusion imaging (MPI) at 12 centers across four countries. An artificial intelligence-based BCI was developed in a cohort of 15037 patients’ CTACs by integrating the CT-derived metrics of bone, skeletal muscle, and four adipose tissue compartments, coronary artery calcium score, and basic demographic variables (age, sex, BMI). The performance of BCI for mortality prediction was validated in an internal cohort of 6444 patients and an external cohort of 7028 patients by prognosis, calibration, net benefit, and explainability. Model-based simulation of tissue metrics modification was performed to evaluate estimated mortality risk reduction. Findings During a median of 3.5 (IQR [1.9, 5.1]) years, 4697 (16%) patients died. In the external testing cohort, the BCI demonstrated excellent discrimination for mortality (area under receiver operating characteristic curve 0.78 (95% CI [0.76, 0.79]) and Harrell’s concordance index 0.75 [0.73, 0.76]), calibration, and net benefit overall and across pre-specified subgroups stratified by patient characteristics and imaging protocols. Visceral adipose tissue attenuation was the most influential body composition measure, followed by skeletal muscle volume. Simulated improvement in body composition was associated with significant mortality risk reduction. Interpretation An index combining six body composition measures obtained opportunistically from routine chest CT provides robust mortality risk stratification. By converting complex body composition information into a single interpretable score, the BCI can facilitate clinical implementation of opportunistic CT biomarkers and guide individualized preventive strategies. Funding National Institute of Health Research in context Evidence before this study We searched PubMed and Google Scholar on July 7, 2026 for English language studies using the terms (“mortality prediction” OR “death prediction”) AND (“integrated body composition analysis” OR “body composition metrics integration”) AND (“predictive model” OR “model development”), and identified 41 publications describing predictive models incorporating body composition metrics. Recent advances in artificial intelligence (AI) have enabled increasingly granular quantification of body composition from CT, with multiple tissues and tissue-specific metrics showing independent prognostic value for all-cause mortality (ACM) prediction. However, reported associations of body composition metrics with mortality risk were highly inconsistent or even contradictory across studies. This heterogeneity likely reflected the differences in study design or the inherent complexity of body composition analysis, such as the interaction among variables derived from multiple tissues. Thus, it is difficult to interpret body composition results reliably in clinical practice. We hypothesized that integrating multiple body composition metrics could achieve comprehensive and reliable prediction of mortality across patient populations. Added value of this study In this large international, multicenter longitudinal cohort study of 28509 consecutive patients with suspected or known coronary artery disease (CAD) undergoing myocardial perfusion imaging across 12 sites in four countries, we developed and externally validated an artificial intelligence-based body composition index (BCI) for all-cause mortality prediction. The BCI integrated chest CT-derived metrics of six body composition tissue compartments – bone, skeletal muscle (SM), subcutaneous adipose tissue (SAT), intramuscular adipose tissue (IMAT), visceral adipose tissue (VAT), and epicardial adipose tissue (EAT), together with demographics (age, sex, BMI) and CT-derived coronary artery calcium (CAC) score, all derived automatically from routine low-dose attenuation correction CT scans without additional imaging or laboratory tests. In an external validation cohort of 7028 patients, the BCI achieved robust discrimination for mortality prediction, substantially outperforming a baseline model using demographics and calcium score alone, and exceeded the discriminative performance of prior body composition models. Performance was consistent across subgroups defined by sex, age, body mass index, cardiometabolic risk factors, imaging modality, and acquisition protocol, supporting generalizability across clinical settings. The BCI demonstrated excellent calibration and net clinical benefit. Explainability analysis identified visceral adipose tissue attenuation and skeletal muscle volume as the dominant contributors to mortality risk, offering tissue-specific therapeutic targets that complement generic weight-based metrics. Model-based simulations suggested that improvements in these specific compartments could reduce estimated mortality risk more effectively than equivalent changes in BMI alone, pointing toward precision therapeutic targets beyond weight reduction. Our study improved over previous literatures on body composition-based predictive models in one or more of the following ways: (1) we considered six types of body composition tissues while existing studies considered only three or less tissues, thus significantly higher granularity and improved ability to capture refined tissue interactions; (2) our model was rigorously validated in external sites, showing cross-site generalizability while most previous work lacked external validation; (3) our model achieved significant prediction performance improvement over previous body composition predictive models, offering better clinical outcomes prediction and facilitating the suitability for widespread clinical adoption; (4) our study was conducted with a large multi-center cohort across multiple countries, achieving statistical power and demonstrating reliability towards patients characteristics and imaging acquisition protocols while previous studies involved small single-center cohorts; (5) our model required only CT scans and basic demographics which were widely available in clinical routine while the existing models required dedicated laboratory tests or specialized imaging modalities, enhancing the applicability in routine clinical practice. Implications of all the available evidence BCI offers an explainable and clinically deployable tool for improved prediction of mortality risk from existing chest CT scans. Its use could inform updates to existing risk models, guide precision preventive strategies, and support development of therapeutics targeting specific body composition phenotypes.

TRAIL is a TNF family ligand that trimerizes TRAIL-R1 (DR4) or TRAIL-R2 (DR5) to induce apoptosis, necroptosis, and/or NF-κB activation in receptor-bearing cells. We previously identified TRAILshort as a splice variant of TRAIL that lacks cysteine 230, cannot trimerize, and acts as a dominant-negative ligand that blocks TRAIL-mediated apoptosis. TRAILshort is expressed on cell surfaces and within extracellular vesicles, enabling it to confer TRAIL resistance to both producing and bystander cells. In this study, we showed that elevated TRAILshort levels were associated with chronic viral infections, cancer, and autoimmune diseases, suggesting a link to impaired immune regulation. Using unbiased phosphoproteomics and mechanistic studies, we demonstrated that TRAILshort binding to DR5 recruited and activated the phosphatase Src homology region 2 domain–containing phosphatase 1 (SHP-1), leading to zeta-chain-associated protein kinase 70 (ZAP-70) dephosphorylation, disruption of ZAP-70–CD3ζ interactions, and impaired T cell receptor signaling, thereby reducing T cell activation, proliferation, and cytokine production in response to antigen or CD3/CD28 ligation. Genetic or pharmacologic SHP-1 inhibition reverses these effects. In humanized mouse models, TRAILshort promoted the persistence of transformed mouse embryonic fibroblasts (MEFs) and L428 and antagonized CD19-directed CAR T cell activity, revealing TRAILshort as an immunomodulator of T cell function with therapeutic implications, including blocking TRAILshort to restore T cell immunity or delivering TRAILshort to enforce tolerance. TRAILshort, a variant of TRAIL, suppresses T cell function and immune responses in cancer and infectious diseases, highlighting its potential as a therapeutic target.