Cell Reports Medicine最新文献

Despite the beneficial impact of bariatric surgery on obesity and metabolic disease, continued post-surgical obesity and weight recurrence are common, but may be impacted by diet. While guidelines recommend a post-operative high-protein diet to preserve lean mass, emerging evidence suggests that humans and mice are metabolically healthier on low-protein diets. Here, we assess the effect of varying dietary protein on post-surgical metabolism in a mouse model of sleeve gastrectomy. We find that a low-protein diet optimally drives post-surgical weight loss, boosts energy expenditure, and improves blood glucose regulation, likely, in part, due to the induction of FGF21. Through multi-omics, we identified clusters of differentially expressed genes and metabolites that correlate with these phenotypes and find that diet heavily influences the liver's molecular response to sleeve gastrectomy. These results suggest that current post-surgical high-protein diets may limit the short- and long-term benefits of surgery and warrant human clinical trials.

Tegileridine is a biased μ-opioid receptor agonist that selectively activates the G protein pathway, designed to provide analgesia with fewer opioid-related adverse effects. In this phase 3 study, 526 patients with postoperative pain after abdominal surgery were randomized and received placebo, tegileridine at 0.75 mg, tegileridine at 1.0 mg, or morphine. For the primary outcome of effective analgesia, the mean (SD) summed pain intensity difference at rest over the first 24 h from time 0 (SPID₂₄) scores are -61.15 (28.25) and -68.98 (30.33) for the 0.5 and 0.75 mg doses of tegileridine, respectively, compared with -49.63 (29.35) in the placebo group (all p < 0.001) and -71.16 (34.76) in the morphine group. The total pain relief scores (mean [SD]) at 24 h were 58.76 (21.79) with 0.75 mg tegileridine and 61.95 (18.94) with 1.0 mg tegileridine, compared with 47.56 (21.00) with placebo and 59.09 (19.34) with morphine. In summary, tegileridine provides effective analgesia that was significantly superior to placebo and comparable to morphine.

Selective autophagy of mitochondria is known to promote cancer cell survival and progression, including in triple-negative breast cancer (TNBC). Here, we apply an integrated multi-omics approach together with functional experimental analyses to investigate metabolic adaptations that support mitochondrial quality control in TNBC. We detail a mitochondrial quality control mechanism, complementary to mitophagy, that is enabled by a program of heightened extracellular sphingomyelin salvaging in TNBC coupled with extracellular vesicle-mediated intracellular clearance of mitochondrial damage. Targeting of this onco-metabolic pathway via repurposing of eliglustat, a selective small molecule inhibitor of glucosylceramide synthase, results in ceramide-mediated compensatory mitophagy and cancer cell death in vitro and attenuates tumor growth and prolongs overall survival at clinically achievable doses in orthotopic syngeneic mouse models of TNBC as well as in human cell line-derived xenograft models. Our study defines an unexplored mechanism of aberrant sphingolipid metabolism that underlies an actionable metabolic vulnerability for anti-cancer treatment.

Glucagon-like peptide-1 (GLP-1) medicines are used for the treatment of type 2 diabetes (T2D) and obesity and reduce rates of cardiovascular disease, including stroke, in people with T2D. Substantial evidence from real-world data and clinical trials highlights the therapeutic potential of GLP-1 medicines for the treatment of neurodegenerative disorders such as Parkinson's and Alzheimer's diseases. Similarly, there is growing evidence for the potential utility of using GLP-1 medicines to reduce rates of smoking, or use of alcohol, tobacco, cannabis, or cocaine in individuals with substance use disorders. More limited clinical data suggest utility for GLP-1 medicines in patients with migraine or intracranial hypertension. The available data suggest that the use of GLP-1 medicines exhibits an acceptable safety profile in most individuals with neuropsychiatric disorders. Here, we review recent clinical evidence and ongoing trials exploring the efficacy and safety of GLP-1 medicines across a broad range of neurological conditions.

The objective of the current study was to determine the optimal individualized surveillance strategies for human papillomavirus (HPV)-positive oropharyngeal cancer. A consecutive cohort of 1,146 HPV-positive oropharyngeal cancer patients treated by intensity-modulated radiation therapy between 2013 and 2023 was included. The primary endpoint was the power of detecting recurrence at each follow-up. Three distinct, stage-specific follow-up strategies were developed based on the monthly probability of disease recurrence. Over a 5-year post-treatment period, the total number of follow-up visits was 10, 11, and 12 for Stages I, II, and III, respectively. These risk-adapted follow-up strategies demonstrated superior detection efficiency when compared to existing clinical guidelines. Furthermore, validation using Markov decision-analytic models confirmed that the risk-based strategies were both more effective and cost-efficient than the guideline-recommended strategy. Compared to the surveillance strategies recommended by the clinical guidelines, our proposed approach for HPV-positive oropharyngeal cancer demonstrated greater cost-effectiveness.

Glioblastomas (GBMs) are incurable brain tumors. Zika virus (ZIKV) has specificity in killing GBM stem cells, which drive treatment resistance. In mouse models of GBM, ZIKV also generates an anti-tumor inflammatory response and prolongs survival. To support clinical development and address safety concerns for intra-tumoral treatment, we assessed the effects of injection of an immune-sensitized ZIKV (Δ10 3'-UTR ZIKV), which cannot be transmitted by mosquitos, into non-tumor-bearing rhesus macaque brains. After injection, the primates showed no clinical signs of illness. Histologically, as expected, ZIKV infection elicited mild inflammation, which resolved within 2 weeks. No infectious virus was detected in the brain or any organs at 14 dpi. These findings, along with our preclinical observations, support the development of immune-sensitized ZIKV as a treatment for GBM.

Cell-free DNA (cfDNA) is an emerging technology to predict and monitor response to cancer treatment, including immune checkpoint blockade (ICB). However, data on cfDNA dynamics during ICB in metastatic triple negative breast cancer (mTNBC) are limited. While most applications of cfDNA involve assays that focus on mutation detection, mTNBC and multiple other cancer types are driven by copy-number alterations (CNAs). We evaluate cfDNA-based copy-number profile abnormality (CPA) score as a potential biomarker for monitoring ICB response in mTNBC, analyzing data from 87 patients enrolled in stage 1 and stage 2 of the TONIC trial. We find significant concordance between cfDNA-based and tissue-based CNA profiles. Additionally, responders show a decrease in CPA scores upon ICB at week 6 (three cycles of nivolumab). These findings underscore the potential of cfDNA-based CNA dynamics as a non-invasive biomarker for ICB early response assessment in patients with mTNBC. The TONIC trial is registered at ClinicalTrial.gov (NCT02499367).

Adoptive cell therapy (ACT) targeting tumor-specific antigens holds promise for solid tumors, but limited neoantigen presentation remains a key barrier to efficacy. Here, we identify and characterize a T cell receptor (TCR), T104, for the KRAS.G12V mutation, a prevalent neoantigen in colorectal, lung, and pancreatic cancers. TCR-T104 selectively recognizes and kills KRAS.G12V-expressing tumor cells. Combining T cell therapy with lymphodepleting chemotherapy significantly enhances tumor cell killing, particularly by TCR-T cells, tumor-infiltrating lymphocytes (TILs), and T cell engager antibodies across multiple cancer types and target antigens. Mechanistically, chemotherapy upregulates immunoproteasome activity and human leukocyte antigen (HLA)-I surface expression. HLA-immunopeptidome analyses reveal that chemotherapy remodels the antigenic landscape across tumor cell lines and in vivo models, increasing peptide abundance and hydrophobicity while altering proteasomal cleavage preferences. These findings establish a synergistic role for chemotherapy in enhancing neoantigen presentation and T cell-mediated tumor recognition and suggest that fine-tuning these regimens could improve ACT efficacy, particularly in tumors with low-abundance neoantigens.

Medulloblastoma (MB), particularly Group_3 (G3-MB), remains the most aggressive subgroup due to strong stemness and therapeutic resistance. Through genome-wide DNA methylation and transcriptomic analysis of human MB samples, we identify enhancer hypomethylation as a key feature sustaining G3-MB stemness and tumor progression. Notably, hypomethylation of the Otx2 super-enhancer (SE) is a prognostic marker and potential therapeutic target for G3-MB patients. We demonstrate that disrupting Otx2 SE activity effectively reduces tumor growth in vivo, highlighting its critical role in G3-MB maintenance. TET3, recruited by OTX2, demethylates the Otx2 SE, promoting chromatin opening and sustaining tumor proliferation and stemness. To translate these findings into therapy, we develop a liposomal nanoparticle (LNP)-based delivery system for siTET3 or a cytosine-based inhibitor of TET3, achieving significant tumor-suppressive effect in a patient-derived orthotopic xenograft model of G3-MB. Our study provides the targeted approach for Otx2-driven G3-MB and introduces LNP-based epigenetic therapy as a promising low-toxicity strategy.

AI-enhanced pathology evaluation systems hold significant potential to improve cancer diagnosis but frequently exhibit biases against underrepresented populations due to limited diversity in training data. Here, we present the Fairness-aware Artificial Intelligence Review for Pathology (FAIR-Path), a framework that leverages contrastive learning and weakly supervised machine learning to mitigate bias in AI-based pathology evaluation. In a pan-cancer AI fairness analysis spanning 20 cancer types, we identify significant performance disparities in 29.3% of diagnostic tasks across demographic groups defined by self-reported race, gender, and age. FAIR-Path effectively mitigates 88.5% of these disparities, with external validation showing a 91.1% reduction in performance gaps across 15 independent cohorts. We find that variations in somatic mutation prevalence among populations contribute to these performance disparities. FAIR-Path represents a promising step toward addressing fairness challenges in AI-powered pathology diagnoses and provides a robust framework for mitigating bias in medical AI applications.