Cancer Biotherapy and Radiopharmaceuticals最新文献

In cancer immunotherapy, the stimulator of interferon genes (STING) pathway regulation has become a promising new approach, offering potential solutions to overcome limitations of current treatments. Recent advances have revealed intricate mechanisms of STING activation and regulation, leading to the development of novel small-molecule agonists with improved properties. Preclinical studies have shown that STING agonists can convert "cold" tumors to "hot" ones, enhancing immune cell infiltration and overcoming resistance to checkpoint inhibitors. Combination strategies, particularly with existing immunotherapies and conventional treatments, have demonstrated synergistic effects. Early clinical trials evaluating STING agonists, both as monotherapies and in combination with checkpoint inhibitors, have yielded promising results. More specific methods have been made possible by biomarker investigations, which have revealed light on mechanisms of action and possible response predictors. Indirect STING activation through ENPP1 inhibition has emerged as a novel strategy, offering more controlled antitumor immunity enhancement while minimizing systemic toxicity. Innovative delivery systems, including nanoparticles and exosome-based therapies, improve STING modulators' therapeutic index. While challenges remain, including precise regulation of STING activation and managing immune-related adverse events, rapid progress suggests that STING-targeted therapies could become cornerstone treatments. By harnessing innate immunity and enhancing its interplay with adaptive responses, STING modulators offer a potentially more accessible, cost-effective, and broadly applicable approach to cancer immunotherapy, addressing many current treatment limitations.

Background: Hepatocellular carcinoma (HCC) is still the largest cause of cancer-related death globally, with alcohol-related HCC (AR-HCC) being a particularly difficult subtype with poor clinical results. Noninvasive biomarkers, such as the fibrosis-4 (FIB-4) index, may provide significant prognostic information that might aid in guiding new interventional techniques, such as ultrasound-based treatments. Methods: The authors did a retrospective cohort analysis on male AR-HCC patients diagnosed between January 2008 and December 2018. Clinical data and survival outcomes were obtained from electronic medical records, with HCC diagnosed as the baseline. The major endpoint was 12-month mortality. Multivariate logistic regression and limited cubic spline analysis were used to assess the relationship between FIB-4 and mortality risk. Results: Among 786 AR-HCC patients (mean age 57 years), 90.1% reported a history of alcohol usage for more than 10 years. The Barcelona Clinic Liver Cancer staging showed 42.8% in stage 0/A, 45.9% in stage B/C, and 11.3% in stage D. Deceased individuals had substantially higher FIB-4 levels (p < 0.05). Logistic regression demonstrated that higher FIB-4 was independently related with increased mortality, and spline analysis revealed a linear risk increase with a threshold of 5.61. Conclusion: Elevated FIB-4 (≥5.61) predicts worse mortality in AR-HCC, indicating its potential relevance in stratifying patients for ultrasound-based cancer therapy. The level of fibrosis may impact both therapeutic response and procedural risk. Routine incorporation of FIB-4 into clinical processes may aid precision decision-making in choosing AR-HCC patients most likely to benefit from ultrasound-guided or ultrasound-enhanced biotherapeutic. Keywords: alcohol, hepatocellular carcinoma, FIB-4, multivariate logistic regression analyses, mortality.

β-glucans are structurally diverse polysaccharides from fungi, yeasts, bacteria, and cereals, exhibiting variable branching and molecular weights that shape their biological activity. Emerging preclinical and clinical evidence highlights their ability to modulate innate and adaptive immunity, exerting direct and adjunct antitumor effects via dectin-1, toll-like receptors, and complement receptor 3. Although well known as nutraceuticals, their integration into advanced cancer biotherapeutics, such as monoclonal antibody regimens, cytokine modulation, and nanoparticle delivery, remains in early translation. This review examines the molecular basis of β-glucan-induced immunostimulation, emphasizing how linkage type, branching frequency, triple-helical structure, and source influence receptor engagement and downstream immune responses. Emerging evidence is presented on β-glucan formulation engineering, including β-glucan-coated polymeric nanoparticles and micelles, β-glucan-complexed lipid nanoparticles for nucleic acid delivery, polymersomes with splenic/myeloid avidity, and β-glucan-stabilized nanosuspensions, several of which show enhanced lymphatic targeting, improved drug bioavailability, or reduced tumor growth in preclinical cancer models. Clinical translation is analyzed with attention to dosing protocols, administration routes (oral, intravenous, topical), and the impact of β-glucan adjuvancy in therapeutic antibodies, immunotoxins, and vascular disrupting agents. The review further addresses essential safety and toxicology data, regulatory compliance challenges, and the imperative for rigorous physicochemical standardization to ensure clinical reproducibility and patient safety. β-glucans have emerged as multifunctional immunomodulators and drug delivery enhancers, driving progress toward personalized cancer immunotherapy and innovative combinatorial regimens. Continued interdisciplinary research and harmonization of extraction, characterization, and delivery protocols are paramount for success in precision oncology.

Introduction: Epithelial cell adhesion molecule (EpCAM) is overexpressed in a wide range of epithelial malignancies, and thus is a potential target for antibody-based radiotherapy. This work describes the synthesis, labeling, and biological evaluation of an alpha-emitting radioconjugate, [²²⁵Ac]Ac-Macropa-PEG₄-HEA125, as a targeted alpha therapy candidate for EpCAM-positive tumors. Materials and Methods: The murine anti-EpCAM monoclonal antibody HEA125 was site-specifically conjugated to the chelator Macropa using a PEG₄-maleimide linker. The structural integrity and chelator-to-antibody (C/A) ratio of the conjugate were confirmed by SDS-PAGE and LC-MS. Radiolabeling with ²²⁵Ac was performed under mild conditions, and radiochemical purity was assessed using iTLC and radio-HPLC. In vitro studies included stability testing, immunoreactivity, and cytotoxicity assays using MCF-7 (EpCAM⁺) and CHO-K1 (EpCAM^-) cell lines. In vivo biodistribution and therapeutic efficacy were evaluated in MCF-7 xenograft-bearing female athymic nude mice (BALB/c nu/nu). Results: Conjugation with HEA125 resulted in a C/A ratio of 4.2 ± 0.3, and SDS-PAGE proved integrity of antibodies to be preserved. Purity of radiolabeling was >98%, and >94% stability was retained for more than 120 h both in PBS and serum. Immunoreactive fraction was 86.2 ± 2.4%, and cytotoxicity assays showed, dose-dependent MCF-7 cell killing with minimal impact on EpCAM-negative controls. In vivo, [²²⁵Ac]Ac-Macropa-PEG₄-HEA125, exhibited significant tumor uptake (15.7 ± 2.3 %ID/g at 24 h), maintained retention (12.1 ± 1.9 %ID/g at 72 h), and minimal off-target accumulation. Therapeutic injection resulted in extensive tumor growth inhibition and long-term survival, with 60% of the mice surviving past day 30 with little overt toxicity. Conclusions: [²²⁵Ac]Ac-Macropa-PEG₄-HEA125, establishes high radiochemical purity, in vitro stability, EpCAM specificity, and strong antitumor activity in preclinical models. These results warrant its advancement as a promising targeted alpha therapy candidate for EpCAM-expressing carcinomas.

This review assesses the promise of nanoparticles containing melatonin and lactoferrin (ML-Lf-NPs) in treating cancer, concentrating on their capacity to improve drug delivery, pinpoint tumors, and optimize therapeutic efficacy. A thorough examination of recent progress in nanoparticle-oriented drug delivery systems was performed, highlighting the physicochemical characteristics, mechanisms of action, and biological interactions of ML-Lf-NPs. Melatonin nanoparticles demonstrate antioxidant and anti-inflammatory characteristics that enhance tumor targeting and therapeutic results. Lactoferrin nanoparticles show potential anticancer effects by improving cellular absorption and enabling targeted drug release at tumors. Both systems demonstrate considerable promise for enhancing drug bioavailability and minimizing side effects. ML-Lf-NPs signify creative strategies for cancer treatment. Their distinct characteristics allow for precise delivery and improved therapeutic effectiveness, opening doors for future clinical uses in cancer treatment. [Figure: see text].

Objectives: Nectin-4 has been successfully used as a target for tumor therapy. Although several bicyclic peptides and antibodies, Nectin-4 positron emission tomography (PET) probes, have been reported for tumor imaging and expression detection, their production costs or pharmacokinetics still need further improvement. This study developed a novel linear peptide PET probe for rapid examination of Nectin-4-related tumors. Methods: [⁶⁸Ga]Ga-NOTA-SP was prepared by a one-step chelation reaction, and its quality control was carried out by using radio-high-performance liquid chromatography and thin-layer chromatography. Molecular docking was used to predict the predominant binding of NOTA-SP to Nectin-4. Cell experiments using SW780 cells and PET/computed tomography (CT) imaging, using the SW780 tumor model, were performed to assess the specific binding and targeting ability of [⁶⁸Ga]Ga-NOTA-SP to Nectin-4. Normal BALB/c mice were used to investigate the plasma concentration-time curves. Results: Under optimal labeling conditions, the labeling efficiency of [⁶⁸Ga]Ga-NOTA-SP can reach above 95%, with a molar-specific activity of 2.45 MBq/nmol and high in vitro stability. The high specificity of [⁶⁸Ga]Ga-NOTA-SP to Nectin-4 is demonstrated by molecular docking and cell uptake experiment, showing a binding energy of -5.4 kcal/mol and K_d value of 2.483 nM, which was further confirmed by PET-CT imaging. Conclusions: [⁶⁸Ga]Ga-NOTA-SP using a linear peptide as a vector shows favorable pharmacokinetics and specific targeting ability to Nectin-4, enabling rapid tumor mouse model imaging. It would be a promising PET/CT imaging probe for optimizing Nectin-4-related tumor diagnoses and therapy.

Background: Liver hepatocellular carcinoma (LIHC) is a very aggressive kind of cancer that has a dramatic impact on the quality of life and mean survival of the patient. Consequently, a specific requirement emerges to predict the prognosis of individual patients as well as to guide the individualized therapeutic strategy in clinic. Telomere- related genes (TRGs) have recently been unraveled as key players in tumor biology and a constituent of the tumor immune microenvironment. Thus, the authors constructed a risk prediction model rooted in TRGs for the purpose of improving the predictive value of prognosis in LIHC patients. Methods: The data in different datasets such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus were collected in TCGA-LIHC as well as GSE116174 and GSE14520. The differential expression analysis was performed to identify telomere location-related differential expression genes (TRGs), and the gene ontology (GO) and KEGG enrichment analyses were performed to investigate the function of TRGs in bioprocess, metabolism, and signaling pathways. Prognostic risk prediction model correlated with outcome was constructed by the LASSO Cox regression model and the key genes associated with the prognosis of LIHC. The predictive capacity of the risk signature based on TRG was further confirmed in two external cohorts. The predictive ability of risk model was assessed, and a series of clinical factors associated with the prognosis of liver cancer were determined. Univariate and multivariate analyses were used to identify independent prognostic factors of LIHC. Results: The authors discovered a set of TRG-associated DGEs with telomere states compared between LIHC and normal. Functional enrichment analysis of these DGEs indicated that they might participate in fundamental biological processes, such as genome maintenance and replication as well as multiple metabolic and signaling pathways. A risk prediction model and signature genes associated with patient prognosis were established by the LASSO Cox regression analysis for LIHC. The prognostic accuracy of the TRG-based risk model was also verified in two independent datasets. Furthermore, the prediction accuracy of the model was analyzed, and clinical indicators associated with the prognosis of liver cancer patients were enumerated. Univariate and multivariate analyses were conducted to investigate the association of clinical variables and prognosis in patients with LIHC. Conclusions: In conclusion, the authors validate that diagnostic, therapeutic, and prognostic accuracy would be enhanced through the study of gene expression data, construction of risk prediction models, and identification of risk-associated clinical factors of LIHC patients. The findings provide new biomarkers and risk prediction models for clinicians to better estimate the risk of patients for the purpose of treatment decisions.

Background: Colorectal cancer (CRC) is a significant cause of cancer-related mortality worldwide. While many health examination indicators might be associated with CRC, their causal relationships remain unclear. The authors analyzed their causal relationship in European and East Asian populations. Methods: The authors collected the genome-wide association data for 33 clinical indicators and CRC in European and East Asian populations from the IEU OpenGWAS project and Riken's Japanese Genetic Association Database. These indicators include 13 hematological indicators, 7 liver function indicators, 2 kidney function indicators, 5 lipid metabolism indicators, 2 glucose metabolism indicators, 1 electrolyte indicator, and 3 comorbidity indicators. The authors performed univariate (UV) and multivariate (MV) Mendelian randomization (MR) analyses on the European and East Asian populations and followed a meta-analysis. Results: UVMR analysis identified 11 indicators (white blood cell count [WBC], mean corpuscular hemoglobin [MCH], mean corpuscular hemoglobin concentration, mean corpuscular volume [MCV], platelet count [Plt], C-reactive protein [CRP], total protein [TP], aspartate aminotransferase [AST], total cholesterol [TC], low-density lipoprotein cholesterol, and apolipoprotein B) with significant causal relationships (p < 0.05). Notably, AST, TC, glycated hemoglobin, and serum creatinine showed inverted causal relationships in different populations. After MV adjustment for TC and TP, MCH (odds ratio [OR]_EU = 1.0012, 1.0000 to 1.0024; OR_meta = 1.0012, 1.0001 to 1.0024), Plt (OR_EU = 0.9986, 0.9974 to 0.9998; OR_meta = 0.9986, 0.9974 to 0.9998), and CRP (OR_EU = 0.9981, 0.9965 to 0.9998; OR_meta = 0.9981, 0.9965 to 0.9998) were independent influencing indicators in European and Eurasian populations, whereas WBC (OR_EAS = 0.8316, 0.7005 to 0.9871), MCH (OR_EAS = 1.2430, 1.1132 to 1.3879), and MCV (OR_EAS = 1.0012, 1.0001 to 1.0024) were independent influencing indicators in the East Asian population. Conclusion: The causal relationship between MCH, TP, and Plt and CRC has been discovered for the first time. Furthermore, TC and CRP were also independent influencing indicators. These findings offer beneficial referential value for the enhancement of preliminary screening protocols for CRC.

Background: The molecular mechanisms driving hepatocellular carcinoma (HCC) predicting chemotherapy sensitivity remain unclear; therefore, identification of these key biomarkers is essential for early diagnosis and treatment of HCC. Method: We collected and processed Computed Tomography (CT) and clinical data from 116 patients with autoimmune hepatitis (AIH) and HCC who came to our hospital's Liver Cancer Center. We then identified and extracted important characteristic features of significant patient images and correlated them with mitochondria-related genes using machine learning techniques such as multihead attention networks, lasso regression, principal component analysis (PCA), and support vector machines (SVM). These genes were integrated into radiomics signature models to explore their role in disease progression. We further correlated these results with clinical variables to screen for driver genes and evaluate the predict ability of chemotherapy sensitive of key genes in liver cancer (LC) patients. Finally, qPCR was used to validate the expression of this gene in patient samples. Results: Our study utilized attention networks to identify disease regions in medical images with 97% accuracy and an AUC of 94%. We extracted 942 imaging features, identifying five key features through lasso regression that accurately differentiate AIH from HCC. Transcriptome analysis revealed 132 upregulated and 101 downregulated genes in AIH, with 45 significant genes identified by XGBOOST. In HCC analysis, PCA and random forest highlighted 11 key features. Among mitochondrial genes, SLC25A42 correlated positively with normal tissue imaging features but negatively with cancerous tissues and was identified as a driver gene. Low expression of SLC25A42 was associated with chemotherapy sensitive in HCC patients. Conclusions: In conclusion, machine learning modeling combined with genomic profiling provides a promising approach to identify the driver gene SLC25A42 in LC, which may help improve diagnostic accuracy and chemotherapy sensitivity for this disease.

Background: Colorectal cancer (CRC) is a leading cause of cancer mortality globally. The molecular mechanisms of CRC and the accumulating immune cell infiltration in the tumor microenvironment (TME) are essential for enhancing the treatment strategy and evaluation of the prognosis. In this study, the authors applied machine learning techniques to single-cell RNA sequencing data to investigate the gene expression characteristics of immune cells in CRC and their association with immune cell infiltration. Methods: Differentially expressed genes (DEGs) in CRC were identified by machine learning methods, including clustering analysis, survival analysis, and gene enrichment analysis, and prognostic models were constructed. CIBERSORT and ESTIMATE algorithms were used to evaluate the abundance of infiltrating immune cells and UMAP and t-SNE techniques were used for dimensionality reduction and visualization of the data. Results: Specific gene expression patterns are closely related to immune cell infiltration in CRC patients. Clustering analysis demonstrated two unique subgroups in the CRC samples, characterized by significant differences in survival outcomes (p = 0.049). These DEGs are enriched in various biological processes, according to gene enrichment analysis. The prognostic models of the receiver operating characteristic curves had good predictive accuracy, with area under the curve values. Single-cell data analysis also showed the intricate associations of immune cells with tumor cells in the TME. Conclusions: This study reveals the complex relationship between gene expression and immune infiltration in CRC using machine learning techniques, and establishes prognostic models with potential value in the clinic. These findings reveal the new potential biomarkers for CRC desensitization and immunotherapy.