Citations

Aneuploidy is a hallmark of cancer often associated with inferior prognosis. Copy number gains of chromosome 8 (chr8) are recurrent in multiple cancers, including breast, prostate, colorectal cancers, and sarcomas such as malignant peripheral nerve sheath tumors (MPNSTs). MPNSTs are aggressive, hard-to-treat sarcomas frequently linked to the Neurofibromatosis type 1 (NF1) cancer predisposition syndrome. To investigate the role of chr8 gain in MPNST pathogenesis, we performed a CRISPR knockout screen and identified 58 essential genes on chr8, including PTK2, which encodes focal adhesion kinase (FAK). We evaluated FAK as a therapeutic target and tested small-molecule FAK inhibitors (FAKi) alone or combined with RAF/MEK inhibitors (RAF/MEKi), a class of agents relevant to NF1-deficient tumors with ERK pathway hyperactivation. Both pharmacological and genetic inhibition of FAK reduced MPNST cell proliferation in vitro and tumor growth in vivo. Combined FAKi and RAF/MEKi treatment further suppressed phosphorylation of FAK, STAT3, and AKT while increasing cleaved caspase-3 and PARP-1, indicating enhanced apoptosis. In MPNST patient-derived xenograft (PDX) models, combination therapy significantly reduced tumor growth, showing superior efficacy, particularly in chr8 gain MPNST-PDX. These results support FAK/RAF/MEK co-targeting as a promising therapeutic strategy for chr8 gain MPNST and related tumors.

Photodynamic priming (PDP), a fallout of photodynamic therapy, transiently modulates the tumor microenvironment (TME), enhances therapeutic susceptibility, and promotes immunogenic cell death through the release of damage-associated molecular patterns (DAMPs). Pancreatic ductal adenocarcinoma (PDAC) remains non-responsive to current therapies with a desmoplastic and immunosuppressive TME that limits drug delivery and blunts responses to immune checkpoint blockade. We investigated whether PDP could enhance anti-PD1 therapy responses in PDAC using patient-derived organoids (PDOs). PDOs were treated with Visudyne and red light (25, 75, and 100 J/cm²), followed by assessment of cytotoxicity, DAMP expression (HSP60, calreticulin, HMGB1), and transcriptomic changes. Monocyte-derived dendritic cells (mDCs) from healthy donors were cocultured with PDP-treated (25 J/cm 2 ) PDOs, then with matched naïve T cells. mDC and T cell activation markers were analyzed. Pembrolizumab (anti-PD1) was added to PDO-mDC-T cell cocultures to evaluate combined effects on PDO viability and T cell activation. PDP induced dose-dependent cytotoxicity and upregulated DAMPs. Gene profiling in PDOs showed increased IFN-γ , TNF-α , and CXCL12 , with reduced PD-L1 , TGF-β1 , and FOXP1 expression. mDCs exposed to PDP-treated PDOs upregulated CD40, CD86, and MHC-II, driving activation of CD4 + and CD8 + T cells, evidenced by elevated PD1 expression. Addition of pembrolizumab further decreased PDO viability and amplified effector cytokines ( IFN-γ , TNF-α , FASLG , granzyme B, IL-2 , IL-21 ). PDP was associated with modulation of the PDAC TME toward a more immunogenic phenotype by enhancing tumor immunogenicity, activating dendritic cells and T cells, and potentiating PD1 blockade. These findings provide mechanistic support for further preclinical and clinical evaluation of PDP combined with checkpoint inhibition in PDAC.

Abstract The liver is organized into tightly regulated zones with distinct metabolic functions but zonation erodes with age. Cellular senescence contributes to aging and liver diseases, however, its impact on aging biology is ill-defined. As part of The Cellular Senescence Network Consortium, we used multiple spatial transcriptomics approaches (GeoMx, Visium, CosMx) with snRNA-seq to profile senescence signatures, zonation markers, and metabolic pathways in livers from wild-type (WT) mice of multiple ages. We observed a loss of canonical zone signatures in aged mouse livers characterized by “expansion” of midlobular (zone 2) marker gene expression, accompanied by diminished expression of zone 3 marker genes by middle-age (18 months), indicative of loss of cell identity. Multiple analytic approaches identified distinct age-, zone- and sex-specific senescence signatures, which were significantly associated with zonation markers changes. This was recapitulated in Ercc1 mutant models of accelerated senescence, supporting a causal role of senescent cells in liver aging. A “no-zone” hepatocyte-like cluster expanded with age and with the strongest Senescence-Associated Secretory Phenotype (SASP) profile. Gene expression profiles from senescent hepatocytes implicate decreased WNT signaling and increased BMP as contributing to age-related loss of zonation. Together, these data elucidate the role of senescent cells in driving aging biology in non-diseased liver through disruption of cell:cell signaling and the loss of metabolic and cell identity gene expression necessary for hepatocyte function.

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.

Abstract Introduction Clozapine-induced constipation may occur in at least 60% of patients, with first-line options typically involving polyethylene glycol or sennosides. However, there may be adherence barriers to an over-the-counter bowel regimen due to the dosage form, lack of insurance coverage, or lack of benefit. One possible solution to address these adherence barriers may include prescribing a secretagogue laxative. However, there is limited literature supporting the use of secretagogue laxatives in clozapine-associated constipation. A case series is presented in which adherence barriers impeded the use of over-the-counter laxatives, and a secretagogue laxative was implemented to manage clozapine-induced constipation. Case Report In 4 cases, lubiprostone or linaclotide was initiated, either as monotherapy or in combination with over-the-counter laxatives. Reasons for secretagogue laxative initiation included: (1) reducing a complex over-the-counter laxative regimen, (2) a lack of insurance coverage of over-the-counter laxatives, (3) dissatisfaction with polyethylene glycol’s palatability, and (4) cognitive impairment leading to decreased adherence to polyethylene glycol. Discussion It is important that clozapine-induced constipation is managed adequately to prevent severe consequences of gastrointestinal hypomotility. In addition to timely initiation and titration of constipation pharmacotherapy, adherence should be assessed. When adherence barriers to the use of over-the-counter laxatives are present, the use of a secretagogue laxative may be an option, recognizing there is limited evidence to guide this decision. Conclusion In these cases, adherence barriers to over-the-counter laxatives were identified. A secretagogue laxative was implemented, well-tolerated, and able to be continued for the duration of follow-up.