Citations

Abstract Background Fibroblast growth factor-inducible 14 (Fn14) belongs to the TNFR superfamily. Fn14 overexpression can drive receptor-autonomous signaling, increase both cell invasion and tumor-associated macrophages/microglia (TAMMs) recruitment, and correlates with reduced survival in glioblastoma (GBM) patients and rat gliomas. While prior studies report Fn14 expression in non-tumor cells within the GBM tumor microenvironment (TME), their relative contributions to glioma pathobiology remain unclear. Methods Using tumor-host pairings of Fn14-positive and -knockout (-KO) cells and mice, we examined the role of Fn14 in glioma biology. Mouse glioma and human GBM datasets were analyzed at the cellular, protein, and transcriptomic levels to assess Fn14-associated changes in the glioma TME and survival outcomes. Results Fn14 was found to be highly expressed in tumor cells and TAMMs in human GBM and 2 well-characterized murine glioma models. Fn14 KO in both tumor and host cells increased overall survival. Notably, this survival benefit was greater in the glioma model characterized by a more immunologically activated TME. Immunophenotyping revealed that Fn14 loss reshapes the tumor-immune landscape, reducing the presence of immunosuppressive macrophages and exhausted T-cells, suggesting that Fn14 modulates both innate and adaptive immune responses. These findings were supported by analyses of human GBM datasets, where high Fn14 expression correlated with immunosuppressive shifts and poor patient responses to immune checkpoint inhibitor therapy. Conclusions This study provides the first description of the contributions of both tumor- and host-derived Fn14 expression to tumor immunity and survival and identifies Fn14 as an important mediator of innate and adaptive immune responses in gliomas.

Background and aims: Aberrations in telomere length can have important implications in cancer. Using a cohort of 1007 patients, we investigated whether leukocyte telomere length (LTL) in patients with colorectal cancer (CRC) is associated with survival. We also investigated whether some telomere maintenance genes are associated with survival in these patients. Methods: The Biobank for Gastrointestinal Health Research (BGHR), an ongoing project involving collection of biospecimens at the Mayo Clinic, was utilized to obtain data from patients diagnosed with stage II or III CRC. Blood samples were collected prior to chemotherapy/radiation and DNA was extracted for measuring median LTL. The main outcome measures were overall survival (OS) and disease-free survival (DFS) by disease stage. Results: A significant inverse relationship was observed with patient age and LTL (spearman correlation coefficient (r) = −0.48, 95%; p = 1.13 × 10−58). Females had significantly longer LTL than males (p = 3.97 × 10−5). The rs1317082 SNP in the TERC gene was significantly associated with both OS and DFS in combined stage II and stage III patients (p = 0.017 and p = 0.023, respectively). A statistically significant association of the OBFC1 SNP (rs9419958) was observed for OS for the combined stage II and stage III patients (p = 0.016). Importantly, LTL was significantly associated with both OS and DFS (p = 0.008 and 0.044 respectively) in combined stage II and stage III patients. Conclusions: Our results show that LTL is predictive of OS and DFS for stage II and III CRC patients, particularly over a longer follow-up, extending beyond five years after a diagnosis of CRC, and certain SNPs in genes involved in telomere maintenance are significantly associated with patient outcomes, independent of telomere length.

ABSTRACT Objective Identifying risk factors enables stratification of patients’ susceptibility to inflammatory arthritis immune-related adverse events (IA-irAE). This retrospective study examines whether preexisting osteoarthritis (OA) increases the likelihood of de novo IA in patients treated with immune checkpoint inhibitors (ICIs). Methods The prevalence of OA among ICI–treated patients who developed IA-irAE, those who developed other types of irAEs but not IA (non-IA irAE), and those who did not develop any irAEs (non-irAE) were compared. Electronic medical records were reviewed to extract demographic, clinical and laboratory data. Group comparisons and logistic regression analyses were performed. Results 181 de novo IA-irAE patients, 140 non-IA irAE patients and 170 non-irAE patients were included. The prevalence of OA was significantly higher in the IA-irAE group (69%) than the non-IA irAE group (48%) and the non-irAE group (48%) (both p < 0.001). The IA-irAE group demonstrated a higher frequency of multisite OA, with predominant hand involvement (62%) than the non-IA irAE with OA group (13%) and the non-irAE with OA group (13%) (both p < 0.001). A family history of autoimmune disease (AID) (OR 2.03, 95% CI 1.02–4.05), preexisting OA (OR 2.88, 95% CI 1.85–4.52) and melanoma (OR 2.63, 95% CI 1.56–4.47) were identified as risk factors for the development of IA-irAE. Conclusions OA was more prevalent among ICI-treated patients developing IA-irAE than those who did not. Hand OA was the most common OA pattern in IA-irAE patients. Preexisting OA, melanoma and a family history of AID were risk factors for IA-irAE.

Abstract Primary liver cancers, including hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), arise from the neoplastic transformation of hepatocytes and cholangiocytes, respectively. Loss or downregulation of PTEN, a tumor suppressor negatively regulating the PI3K/AKT pathway, is frequently observed in CCA and HCC. Notably, PTEN mutations are observed at nearly twice the frequency in combined CCA-HCC tumors than either HCC or CCA alone. Using lineage-specific liver-targeted PTEN-deficient mouse models, we demonstrate that PTEN loss drives cellular dedifferentiation and tumorigenesis, a process that is critically dependent on AKT2. Mechanistically, PTEN deficiency induces activation of NOTCH and upregulation of transcriptional factor SOX9, which plays a central role in tumor cell transformation. In parallel, PTEN loss increases SMAD4 expression and sensitizes the tumor cells to TGFβ signaling, with TGFβ treatment repressing SOX9 expression in tumor cells lacking PTEN. Together, our study defined a critical role for PTEN-AKT2 signaling in maintaining liver epithelial lineage fidelity and revealed how its disruption promotes the conversion of mature hepatocytes or cholangiocytes into liver cancer stem-like cells (LCSCs). Furthermore, we identify a PTEN-dependent crosstalk between NOTCH and TGFβ pathways that governs liver tumor development. Together, this work provides mechanistic insight into lineage plasticity in liver cancer with implications for pathway-directed therapy.

Complex bidirectional interactions between the nervous and cardiovascular systems are integrated through the heart–brain axis, a physiological pathway that governs both neural regulation of cardiovascular function and cardiovascular influences on brain health. Emerging evidence indicates that even in the absence of overt disease, subclinical changes in the heart, aortic arch, and extracranial arteries contribute to brain injury and cognitive vulnerability. This review synthesizes current knowledge on how subclinical heart–brain axis dysfunction affects brain structure and function, highlighting the roles of central and autonomic neural pathways as well as hormonal signaling in driving neurological decline. Key biomarkers linked to silent brain injury, cognitive decline, and dementia are discussed, emphasizing their potential for early risk stratification and as targets for preventive interventions. We also outline mechanistic pathways connecting subclinical heart–brain axis dysfunction to adverse brain outcomes, identify major gaps in current evidence, and propose priorities for future research and clinical trials aimed at early detection and risk reduction to preserve brain health.

Abstract Deep learning models enable the prediction of clinical endpoints from whole-slide images (WSIs), but many such models function as “black boxes”, lacking transparency about whether and which histomorphological patterns drive their predictions, hindering interpretability and clinical adoption. Here we propose a human-in-the-loop explanation framework, MorphoXAI, which provides both local and global interpretability for deep learning models by incorporating human-expert interpretations. At the global level, it reveals the histomorphological patterns on which the model consistently relies to distinguish between classes of WSIs, as well as the patterns associated with confusion between classes. At the local level, it indicates which of these patterns are used in the prediction of an individual WSI and which regions within the slide correspond to such patterns. We validated our method on a deep learning model trained for ovarian tumor histologic subtype prediction. The results show that our framework generates explanations that accurately reflect the histomorphology underlying the model’s predictions at both global and local levels. For interpretability and clinical utility in diagnostic contexts, human evaluation results showed that our explanations were easy to interpret, rich in diagnostic features, and directly helpful for diagnostic decision-making, thereby enhancing pathologist-AI collaboration. Our work highlights that unifying global and local explanations and grounding them in expert-interpreted morphology enhances the interpretability and verifiability of deep learning models, thereby facilitating the transparent deployment of such models in clinical practice.

Objective: This study aims to develop a contrast-free, high-sensitivity ultrasound method, denoted as Radon transform-based flow measurement (R-Flow), for in vivo mapping of microvascular flow vectors and for establishing R-Flow-derived vector-field metrics to noninvasively quantify microcirculatory patterns in liver cirrhosis. Impact Statement: R-Flow enables robust, contrast-free imaging of microvascular dynamics and demonstrates translational feasibility in the human liver. Its direction-aware indices offer pilot in vivo quantification of flow redistribution and remodeling, providing unique insights into hepatic flow dynamics. Introduction: Microvascular dysfunction is a hallmark of many diseases, yet noninvasive visualization and quantitative assessment of abnormal microcirculation remain limited. Methods: R-Flow leverages Radon transform to decode red blood cell dynamics from the spatiotemporal domain of ultrasound flow signals, reconstructing velocity vectors at microvascular scale. From these vector maps, unique direction-aware indices are further derived to characterize flow distribution and heterogeneity. Results: Validated across simulations, phantoms, and in vivo studies, R-Flow provides robust velocity estimation across a wide range (1 to 60 mm/s). Notably, it enables high-sensitivity microvascular flow vector mapping of human liver, showing strong agreement with references ( r > 0.9). In a rat model, these direction-aware indices revealed a shift from healthy multipath perfusion in control livers to directionally biased vascular pattern in cirrhotic livers, demonstrating significant correlation with pathological indicators. Conclusion: R-Flow enables noninvasive, contrast-free mapping of microvascular blood flow velocity and offers a promising approach for high-resolution assessment of microvascular flow characteristics.

Introduction: Optimizing patient selection and time-to-intervention will be crucial to the success of future clinical trials seeking to implement therapies and improve intracerebral hemorrhage (ICH) patient outcomes. Automated and accurate ICH and perihematomal edema (PHE) volumetry carries significant potential to strengthen trials targeting ICH and PHE expansion. Aims: The Quantification of Hematoma and Perihematomal Edema Volumes in Intracerebral Hemorrhage (QUANTUM) trial was designed with aims of (1) validating a fully automated artificial intelligence (AI) method for supratentorial, spontaneous ICH and PHE volumetry against the manual and semiautomated quantification methods and (2) establishing a model framework for the translation of AI technology through conduct of a clinical trial under the equivalence hypothesis paradigm. Methods: This trial was a prospective, randomized, clinical study with blinded outcome assessment. Six independent raters were randomized in a 1:1 ratio to ICH and PHE volumetry from non-contrast CT scans in the Virtual International Stroke Trials Archive with either the semiautomated or manual quantification method. The primary outcomes were equivalence between (1)ICH volume measurements on admission (<24h from symptom onset) for the manual versus the fully automated or the sem-automated quantification methods, and (2)PHE volume measurements on day 3 (72±12h from symptom onset) for the manual versus the fully automated or the semiautomated quantification methods. The required sample size was 126 ICH and 126 PHE measurements, based upon a significance level of 0.025 to account for the two equivalence tests. Results: The geometric mean relative discrepancies between the fully automated and the manual ICH and PHE volume measurements did not cross the pre-specified 10% equivalence margin. The geometric mean relative discrepancies between the semiautomated and the manual ICH and PHE volume measurements did cross the pre-specified 10% equivalence margin. The fully automated method (mean 10.0±2.7s/scan) was significantly faster than the manual (mean 189±199 s/scan; P <0.001) and semiautomated (mean 79±68s/scan; P <0.001) methods. Conclusions: ICH and PHE volumes quantified by AI could replace clinician measurements within a 10% error margin and with substantially greater efficiency. The semiautomated method could not replace manual measurements. This trial demonstrated feasibility of the equivalence hypothesis framework for AI validation studies.

Background: Although the embolic stroke of undetermined source (ESUS) construct was proposed as a unifying concept for embolic-appearing strokes without an identified cause, it includes patients with diverse and often unrecognized underlying mechanisms. This study investigates the differential rate of recurrent stroke according to the completeness of neurodiagnostic testing in a diverse multicenter cohort. Methods: This was a retrospective analysis of a multicenter observational cohort study (n=27 sites). Patients with ESUS were categorized into three groups: (1) established mechanism after further work up (ESUS-E), (2) patients who underwent a comprehensive diagnostic workup without an identified etiology (ESUS 2.0), and (3) no advanced workup (ESUS-NAW). The primary outcome of recurrent ischemic stroke was assessed by comparing ESUS 2.0 to both ESUS-E and ESUS-NAW groups using adjusted Cox proportional hazards models. Results: Of the 2,281 patients included, 694 (30%) were classified as ESUS-E, 344 (15%) as ESUS 2.0, and 1,443 (55%) as ESUS-NAW. The ESUS 2.0 group had a significantly lower annualized recurrence rate (1.98 per 100 person-years) compared to both the ESUS-NAW group (7.7 per 100 person-years; aHR 0.27, 95% CI: 0.11–0.62; p=0.002) and the ESUS-E group (6.10 per 100 person-years; aHR = 0.35; 95% CI 0.15–0.85, p=0.020). Within the ESUS-E group, cancer (aHR 5.33; 95% CI: 2.09–13.63; p < 0.001) and non-stenosing atherosclerotic disease or arch atheroma (aHR 3.40; 95% CI: 1.18–9.83; p = 0.023) were associated with significantly increased risk of recurrence when compared to ESUS 2.0. Conclusion: These findings demonstrate a lower risk of recurrent stroke in patients classified as ESUS 2.0, diverging from the 5–6% annual recurrence rates observed in prior ESUS trials. These findings suggest that patients who receive comprehensive diagnostic evaluation without identification of a specific etiology have a lower risk of recurrent stroke, an important consideration for the design of future ESUS studies.