Cancer Biotherapy and Radiopharmaceuticals最新文献

Introduction: Integrating radiopharmaceuticals in anesthesia and orthopedic oncology has revolutionized cancer biotherapy and targeted therapy. This multidisciplinary approach leverages molecular imaging, radioisotopes, and precision medicine to enhance perioperative pain management and improve therapeutic efficacy. Methods: Radiopharmaceutical-based anesthetic techniques (R-ATs) have emerged to facilitate intraoperative monitoring and postsurgical pain control, ensuring better patient outcomes in orthopedic oncology procedures. This article explores combining radiopharmaceuticals with orthopedic cancer management, emphasizing novel theranostic agents, α- and β^--emitting radionuclides, in treating metastatic bone disease. Innovations in peptide receptor radionuclide therapy (PRRT) and radiolabeled bisphosphonates have provided a significant leap forward in mitigating skeletal-related events and improving survival rates. Results: This article discusses radiopharmaceutical-guided anesthesia's role in enhancing intraoperative imaging precision and personalizing analgesic regimens for patients with cancer undergoing orthopedic interventions. The article aligns with recent developments in molecular medicine by addressing the translational impact of radiopharmaceuticals on cancer treatment paradigms. In targeted therapy, R-AT attained an effectiveness of up to 96.25%, while PRRT reached 97.45%. Conclusions: It highlights integrating artificial intelligence and molecular imaging in real-time surgical decision-making, redefining personalized oncology care. The synergistic use of radiopharmaceuticals in anesthesia and orthopedic oncology holds immense promise in precision-driven therapeutic strategies for cancer biotherapy.

Introduction: Pancreatic cancer remains one of the most challenging malignancies to treat, characterized by limited therapeutic options and persistently poor survival rates. Conventional radiotherapy presents several limitations, including nonspecific tumor targeting, elevated toxicity to adjacent healthy tissues, and intrinsic radioresistant pancreatic tumors, necessitating innovative treatment strategies. In comparison to previous studies, which reported a median survival rate of 12 months for patients undergoing conventional radiotherapy, the results of this study demonstrate a significant improvement, with a median survival increase to 18 months using a novel targeted approach. Additionally, our findings show a 30% reduction in off-target tissue toxicity, compared with the 45% toxicity seen with traditional methods. Methods: Nanoparticle-enhanced radiotherapy (NERT) introduces a novel therapeutic approach using biocompatible nanoparticles functionalized with tumor-specific ligands. These nanoparticles serve as radiosensitizers, selectively increasing the local radiation dose within the tumor microenvironment while minimizing exposure normal tissues. This targeted delivery mechanism leverages precision nanotechnology to enhance the therapeutic index. Results: Preclinical studies have shown NERT significantly improves treatment outcomes in pancreatic cancer. The method achieves 97.4% increase in treatment efficiency, 45.2% reduction in toxicity, 96.3% enhancement in patient outcomes, 40.3% decrease in systemic side-effects, and 98.6% improvement in tumor targeting when compared with conventional radiotherapy. Conclusions: These findings underscore the transformative potential of NERT in addressing key limitations of traditional pancreatic cancer treatments. By integrating precision targeting with advanced nanotechnology, NERT enhances the efficacy radiotherapy while mitigating adverse effects, thereby improving patient outcomes. This innovative modality holds promise for redefining clinical protocols and elevating standards of care in oncology. The proposed method achieves the treatment efficiency by 97.4%, toxicity by 45.2%, patient outcome by 96.3%, systematic side-effect by 40.3%, and tumor targeting by 98.6%.

Objective: This study identifies shared genetic factors linking Hashimoto's thyroiditis (HT) and thyroid cancer (TC) using an integrated multiomics approach. Methods: We combined Mendelian randomization (MR) analysis using FinnGen genome-wide association study data, single-cell RNA sequencing of 76,243 thyroid cells, and machine learning classification models to identify causal genes and their expression patterns across disease states. Results: MR analysis identified 10 genes with consistent directional effects across both diseases. Peroxiredoxin 2 (PRDX2) emerged as the strongest protective factor (HT: odds ratio [OR] = 0.54, 95% confidence interval [CI]: 0.31-0.94; TC: OR = 0.68, 95% CI: 0.50-0.91). Single-cell analysis revealed progressively decreased PRDX2 expression from normal thyroid to papillary to anaplastic TC. Machine learning confirmed PRDX2 as the most discriminative gene for disease classification. PRDX2 expression negatively correlated with inflammatory TNF-TNFRSF1A signaling and was associated with improved survival in patients with TC (hazard ratios = 0.33, 95% CI: 0.11-0.96, p = 0.043). Conclusions: PRDX2 functions as a key protective factor in both HT and TC pathogenesis, likely through modulation of oxidative stress and inflammatory signaling. These findings provide mechanistic insights into the HT-TC relationship and highlight PRDX2 as a promising therapeutic target for thyroid diseases.

Treatment resistance prevents patients with preoperative chemoradiotherapy or targeted radiolabeled immunotherapy from achieving a good result, which remains a major challenge in the prostate cancer (PCa) area. A novel integrative framework combining a machine learning workflow with proteogenomic profiling was used to identify predictive ultrasound biomarkers and classify patient response to radiolabeled immunotherapy in high-risk PCa patients who are treatment resistant. The deep stacked autoencoder (DSAE) model, combined with Extreme Gradient Boosting, was designed for feature refinement and classification. The Cancer Genome Atlas and an independent radiotherapy-treated cohort have been utilized to collect multiomics data through their respective applications. In addition to genetic mutations (whole-exome sequencing), these data contained proteomic (mass spectrometry) and transcriptomic (RNA sequencing) data. Maintaining biological variety across omics layers while reducing the dimensionality of the data requires the use of the DSAE architecture. Resistance phenotypes show a notable relationship with proteogenomic profiles, including DNA repair pathways (Breast Cancer gene 2 [BRCA2], ataxia-telangiectasia mutated [ATM]), androgen receptor (AR) signaling regulators, and metabolic enzymes (ATP citrate lyase [ACLY], isocitrate dehydrogenase 1 [IDH1]). A specific panel of ultrasound biomarkers has been confirmed in a state deemed preclinical using patient-derived xenografts. To support clinical translation, real-time phenotypic features from ultrasound imaging (e.g., perfusion, stiffness) were also considered, providing complementary insights into the tumor microenvironment and treatment responsiveness. This approach provides an integrated platform that offers a clinically actionable foundation for the development of radiolabeled immunotherapy drugs before surgical operations.

Background: Colon cancer is a heterogeneous disease, and rare subtypes like triphasic colon cancer are difficult to detect with standard methods. Artificial intelligence (AI)-driven ultrasound combined with genomic analysis offers a promising approach to improve subtype identification and uncover molecular mechanisms. Methods: The authors used an AI-driven ultrasound model to identify rare triphasic colon cancer, characterized by a mix of epithelial, mesenchymal, and proliferative components. The molecular features were validated using immunohistochemistry, targeting classical epithelial markers, mesenchymal markers, and proliferation indices. Subsequently, ultrasound genomic techniques were applied to map transcriptomic alterations in conventional colon cancer onto ultrasound images. Differentially expressed genes were identified using the edgeR package. Pearson correlation analysis was performed to assess the relationship between imaging features and molecular markers. Results: The AI-driven ultrasound model successfully identified rare triphasic features in colon cancer. These imaging features showed significant correlation with immunohistochemical expression of epithelial markers, mesenchymal markers, and proliferation index. Moreover, ultrasound genomic techniques revealed that multiple oncogenic transcripts could be spatially mapped to distinct patterns within the ultrasound images of conventional colon cancer and were involved in classical cancer-related pathway. Conclusions: AI-enhanced ultrasound imaging enables noninvasive identification of rare triphasic colon cancer and reveals functional molecular signatures in general colon cancer. This integrative approach may support future precision diagnostics and image-guided therapies.

Introduction: Melanoma, with its aggressive behavior and high metastatic potential, presents significant clinical challenges. The melanocortin-1 receptor (MC1R) is a promising target for diagnosis and therapy due to its overexpression in metastatic melanoma. Methods: This study compares the theranostic potential of DOTARe-CCMSH, labeled with ⁶⁸Ga and ¹⁷⁷Lu, in subcutaneous and intradermal murine melanoma models over an extended period. Radiolabeling achieved high molar activities for both isotopes, enabling precise imaging and therapeutic applications. Results: PET imaging with [⁶⁸Ga]Ga-DOTA-Re-CCMSH showed specific tumor accumulation, with a mean uptake of 2.25 ± 0.2% ID/g at 2 hours post-injection, enhanced by gelofusine pre-administration. SPECT imaging with [¹⁷⁷Lu]LuDOTA-Re-CCMSH revealed significant and sustained tumor uptake in both models, with mean values of 21.9 ± 7.98 for subcutaneous and 19.8 ± 5.36 for intradermal tumors at 4 hours post-injection, extending up to 24 hours. This study tracked the therapeutic radiotracer uptake for up to 7 days post-injection, showing continued retention and tumor specificity, especially in the tumor-to muscle ratio, which reached 172 at 24 hours. Discussion and Conclusions: Comparative biodistribution analyses highlighted differences between subcutaneous and intradermal models, including distinct peritumoral edema arrangements. These findings emphasize the value of long-term theranostic studies in understanding tumor behavior and the efficacy of radiolabeled peptides in melanoma treatment, advancing personalized oncology approaches.

Objectives: To prepare a novel ⁶⁸Ga-labeled pH (low) insertion peptide-like peptide, YJL-11, and study its ability to be used as a probe for the diagnosis of triple-negative breast cancer (TNBC) via in vivo imaging of tumor-bearing nude mice. Methods: Circular dichroism (CD) analysis of YJL-11 was performed to assess its secondary structure. YJL-11 was labeled with ⁶⁸Ga, and the in vivo biodistribution of ⁶⁸Ga-YJL-11 in MDA-MB-231 xenograft mice was evaluated. This probe was then applied for small animal positron emission tomography (PET) imaging of tumor-bearing nude mice. Results: CD analysis of YJL-11 confirmed a typical pH-dependent transition in its secondary structure. The radiochemical yield of ⁶⁸Ga-YJL-11 was 75.5 ± 0.25%, and the radiochemical purity was 95.75 ± 0.15%. Biodistribution studies showed that the tumor uptake of ⁶⁸Ga-YJL-11 was significantly higher than in the control group, 1 and 2 h after injection. Small animal PET imaging results were consistent with the biodistribution data, showing clear images of the tumors and livers 1 and 2 h after injection of ⁶⁸Ga-YJL-11, whereas tumors were not detected in the control group. Conclusion: ⁶⁸Ga-YJL-11 was prepared with high radiochemical yield and can target TNBC tissues, indicating that it has great potential in the diagnosis of TNBC.

Background: Nanobodies (Nbs), derived from Camelidae heavy-chain antibodies, are single-domain fragments (15 kDa) with high antigen-binding specificity, enhanced tissue penetration, and low immunogenicity. These attributes address limitations of conventional antibodies, positioning Nbs as pivotal tools for targeted molecular imaging in diagnostics and therapeutics. Methods: Nbs are screened through phage/mRNA display or single B-cell sequencing, expressed in prokaryotic or yeast systems, and humanized via CDR grafting. Functional probes are engineered by conjugating Nbs with radionuclides (⁶⁸Ga, ^99mTc) or fluorophores (IRDye 800CW) for compatibility with PET, SPECT, NIRF, and ultrasound modalities. Results: Clinical trials validated Nb efficacy: ⁶⁸Ga-HER2-Nb PET/CT achieved tumor-specific uptake in HER2+ cancers (NCT04467515), while ^99mTc-PD-L1-Nb enabled quantitative SPECT-guided immunotherapy in NSCLC. NIRF-Nb conjugates (e.g., 11A4-800CW) enhanced intraoperative tumor delineation in murine models. Dual-targeted ultrasound microbubbles demonstrated multi-biomarker imaging via acoustic pressure modulation. Conclusion: Nbs advance biological imaging through superior resolution and rapid pharmacokinetics. Challenges persist in optimizing probe stability, minimizing immunogenicity, and scaling production. Future priorities include integrating multi-modal platforms, expanding applications to neurodegenerative disorders, and refining personalized diagnostic paradigms, underscoring their transformative potential in precision medicine.