Cancer Biotherapy and Radiopharmaceuticals最新文献

Background: Gastric cancer (GC) is among the most common malignancies worldwide and represents a major cause of cancer-related mortality. The incidence of GC varies significantly across regions due to genetic predisposition, environmental exposure, dietary habits, and infectious agents, particularly Helicobacter pylori. Despite its high prevalence, GC is often diagnosed at an advanced stage because of nonspecific or absent early clinical symptoms. Objective: The objective of this review is to provide a comprehensive overview of the epidemiology, etiology, risk factors, molecular mechanisms, diagnostic biomarkers, and emerging therapeutic strategies for gastric cancer. Methods: Relevant literature was reviewed to analyze the role of key molecular signaling pathways involved in gastric carcinogenesis, progression, metastasis, and therapeutic response. Current diagnostic tools, biomarkers, and treatment approaches-including conventional therapies, alternative systems of medicine, nanotechnology-based interventions, and ongoing clinical research-were evaluated. Results: Recent molecular classifications have highlighted the critical involvement of signaling pathways such as EGFR/HER2, p53, PI3K, and related pathways in GC development and progression. The identification of biomarkers, including circulating tumor cells and ribonucleic acids, has improved diagnostic accuracy. Advances in targeted therapy and nanotechnology-based drug delivery systems have demonstrated potential in enhancing treatment efficacy and reducing adverse effects. Conclusion: Gastric cancer remains a serious global health concern with limited survival outcomes despite advances in treatment. Improved understanding of molecular signaling pathways and biomarker-based diagnostics offers promising opportunities for early detection and targeted therapy. Continued research into innovative preventive and therapeutic approaches is essential for patients with gastric cancer.

Postphenomenology examines the cultural dimension of human-technology relations whereby innovations, such as artificial intelligence (AI), re/shape our behavior and relation to reality. Generative AI is amoral and uncaring, but it is mediating evolutionary changes in human cognitive function, consciousness, and behavior. The role of phronesis, in the preservation of human values in the face of ChatGPT challenges, is explored here through the lens of theranostic nuclear oncology practice. Phronesis involves moral grounding, epistemic humility, and the integration of cognitive, affective, and contextual social expertise. Empathic, efficient care of the individual patient requires judicious symbiosis between the formidable epistemic capabilities of large language models, particularly in radiogenomics, radiomolecular biology, and tumor radiation dosimetry, and compassionate, responsible, accountable personal care by the doctor. Being cognizant of the strengths and limitations of AI, and the critical role of phronesis in personalized patient care, the physician can ensure optimal theranostic clinical oncology outcomes of human-AI collaboration.

The development of resistance to epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) remains a huge challenge in treating EGFR-mutant non-small-cell lung cancer (NSCLC). Recent improvements in ultrasound-based cancer biotherapies have necessitated the discovery of responsive molecular targets that can improve therapeutic accuracy. This study looks at the diagnostic usefulness and functional importance of spondin-1 (SPON1) and spondin-2 (SPON2) in EGFR-mutated NSCLC, with a focus on their possible response to ultrasound-enhanced therapies. Plasma levels of SPON1 and SPON2 were considerably higher in EGFR-mutant NSCLC patients than in healthy controls. Receiver operating characteristic curve analysis demonstrated that both proteins had good sensitivity and specificity. SPON1 and SPON2 expression were linked with aggressive clinical characteristics such as tumor size (≥5 cm), advanced TNM stage (III-IV), and lymph node involvement. Importantly, both markers were significantly elevated in gefitinib-resistant, EGFR-mutant NSCLC cells. Functional investigations revealed that suppressing SPON1 and SPON2 reversed resistance by inhibiting proliferation and invasion while increasing apoptosis. In contrast, overexpression conferred resistance to gefitinib in parental cells. Given their dual roles in diagnosis and resistance, SPON1 and SPON2 are intriguing ultrasound-responsive biomarkers in EGFR-mutant NSCLC. These findings provide the groundwork for future incorporation of ultrasound-mediated delivery methods or sonodynamic treatments targeting SPON1/SPON2, opening up new possibilities for overcoming EGFR-TKI resistance and improving therapeutic effectiveness in resistant NSCLC.

Aim: This study aims to evaluate the utility of ⁶⁸Ga-FAPI PET/CT in combination with MAMMI PET for assessing pathological complete response (pCR) to neoadjuvant chemotherapy (NACT) in patients with breast cancer. Materials and Methods: This study retrospectively reviewed patients with breast cancer who underwent NACT and had pre/post-therapy imaging with ¹⁸F-FDG PET/CT, ⁶⁸Ga-FAPI PET/CT, and ⁶⁸Ga-FAPI MAMMI PET. Radiological and histopathological findings before and after treatment were documented. Quantitative PET parameters were calculated, and the post-therapy diagnostic performance of PET imaging was assessed using ROC analysis. Threshold values for detecting residual tumor were calculated, and univariate and multivariate analyses were performed for the breast and axilla. Results: Twenty female patients were included. In visual assessment of residual tumor detection in the breast, the sensitivity, specificity, and accuracy were as follows: 73%, 70%, and 71% for ¹⁸F-FDG PET/CT; 73%, 80%, and 76% for ⁶⁸Ga-FAPI PET/CT; and 64%, 70%, and 67% for ⁶⁸Ga-FAPI MAMMI PET (all lesions); for detecting residual disease in the axilla, the metrics were: 33%, 91%, and 71% for ¹⁸F-FDG PET/CT; 50%, 100%, and 82% for ⁶⁸Ga-FAPI PET/CT; and 50%, 70%, and 63% for MRI. In quantitative analysis, post-therapy ⁶⁸Ga-FAPI MAMMI PET tumor background rate (TBR) SUV_max was the only significant parameter in multivariate analysis, demonstrating 91% sensitivity, 80% specificity, and 86% accuracy at a threshold value of 1.35 for detecting residual tumor (p = 0.002; AUC: 0.900; 95% CI: 0.765-1.000). Conclusions: The diagnostic performance of quantitative parameters derived from ⁶⁸Ga-FAPI PET/CT combined with MAMMI PET was superior to current diagnostic methods for determining pCR in the breast; however, the sensitivity in the axilla remains limited. Further research in larger patient groups should be conducted.

Background: Ovarian cancer (OC) often goes undetected until advanced stages due to mild early symptoms. Methods: This research proposes a novel methodology for assessing OC severity through histopathological image analysis, utilizing Rank-Based Leaf in Wind Optimization and Alpha Piecewise Linear Fuzzy techniques. It enhances tissue image quality through normalization and Contrast Limited Adaptive Histogram Equalization, employs ResNet 50 with Inception v4 for feature extraction, and uses a ranking layer to prioritize key features. Results: The model achieved 99.25% accuracy and 97.98% precision, effectively classifying tumor severity levels under diagnostic uncertainty. Conclusion: This robust approach enhances diagnostic accuracy, supports early detection, and improves treatment planning. Future work will explore cross-validation, model pruning, and real-time integration for clinical applications.

Recently, exosomes, or "natural nanoparticles," have been considered as potential drug delivery methods. Due to exosome carriers' natural properties, exosome-mediated drug delivery systems (DDSs) are efficient cancer treatments. Exosomes, small membrane vesicles from many cell types, can transfer phytoconstituents, proteins, nucleic acids, and small molecule medicines across biological boundaries. Recent DDS advances have improved this potential using plant-derived exosomes (PDEs), which are biocompatible and low toxic. PDEs have anticancer effects, especially in the context of conventional treatment resistance, untargeted toxicity, and response variability. This review fills a gap by discussing the latest findings and offering new perspectives on exosome drug delivery in cancer. The study summarizes isolation and loading approaches such as ultracentrifugation and immunological isolation and the characterization parameters for the formulation of exosomes. The exosome-based DDSs are discussed in depth, along with the emphasis on PDEs. The article highlights emerging trends and challenges, including molecular targets and ongoing clinical trials, during the past decade that are critically relevant to the current scenario. Nanotechnology and personalized medicine could improve and lower the cost of exosome-mediated cancer treatment. While the preclinical data have been encouraging, clinical applications of exosome-based therapies are continuing to evolve in its early stages, and some of the problems include scalability, purification, and regulatory compliance. [Figure: see text].

Alkylating agents, characterized by their ability to bind to and modify DNA, have shown promising impacts on breast cancer patients in clinical trials across various stages and phases. This review, utilizing data from the National Library of Medicine's clinicaltrials.gov, investigates the efficacy of these drugs in breast cancer treatment. The report focuses on cyclophosphamide, an alkylating agent that prevents cancer cell DNA replication, and its synergistic effects when combined with other medications such as docetaxel, a taxane that suppresses cell division. Results indicate that these combination therapies may enhance treatment efficacy and improve outcomes. This survey highlights the widespread use of alkylating agents in clinical studies for breast cancer, a disease affecting over a million people annually in India alone. Commonly used alkylating drugs for breast cancer treatment include carmustine, chlorambucil, and cyclophosphamide. These agents have shown effectiveness in treating metastatic breast cancer and reducing the risk of recurrence, underscoring their significant role in breast cancer therapy.

Creation of a virtual avatar of a patient with cancer has the potential to transform theranostics into a truly individualized precision treatment of specific cancers, which express targetable receptors. Each patient is unique. Their cancer molecular biology has its own inherent relationship to their genomic phenotype and the metabolomic and immunological milieu of their tumor. This singularity can be captured and simulated through generation of an avatar, incarnated by means of artificial intelligence collection, collation and analysis of personal radio-genomics, tumor pathology, and molecular biology data, in the form of a digital twin. The capacity to replicate these idiosyncratic individual interactions within a digital twin construct of such a virtual avatar allows contemplation of ex vivo prototypical design and testing of N-of-1 theranostic strategies in real time. Continuing follow-up and analysis of evolving data confers the opportunity to adapt treatments to predict tumor response of the cancer in the avatar in order to optimize clinical outcomes in the actual patient.

Background: Neoadjuvant immunotherapy has become a standard treatment for locally advanced esophageal squamous cell carcinoma (ESCC), but predictive biomarkers for treatment efficacy remain limited. This study investigates the role of serum interleukin-6 (IL-6) levels as a prognostic biomarker in patients receiving neoadjuvant immunotherapy for ESCC. Methods: A retrospective cohort study was conducted in 47 patients with locally advanced ESCC who underwent neoadjuvant immunochemotherapy followed by esophagectomy. Pretreatment serum levels of IL-6 and the combined positive score were analyzed. Pathological responses were evaluated using the College of American Pathologists Tumor Regression Grade system, and survival outcomes were assessed by Kaplan-Meier analysis. IL-6 knockout mice models were used to validate the impact of IL-6 on anti-PD-1 therapy efficacy. Results: Lower pretreatment serum IL-6 levels were significantly associated with better pathological response compared with higher IL-6 levels. Elevated IL-6 levels (>61.495 pg/mL) were identified as an independent risk factor for poorer disease-free survival and overall survival. IL-6 deficiency enhanced the efficacy of anti-PD-1 therapy in mice, reducing tumor burden compared with wild-type controls. Conversely, exogenous IL-6 administration attenuated anti-PD-1 effects. Mechanistically, lower serum IL-6 levels increased CD8⁺ T cell activation and decreased the regulatory T cell proportion during immunotherapy. Conclusions: Low serum IL-6 levels enhance the efficacy of neoadjuvant immunotherapy in locally advanced ESCC.

Background: Breast disease, particularly breast cancer, ranks among the most prevalent malignancies affecting women globally. Accurate clinicopathological diagnosis is critical for early detection and prognostication of breast cancer. This study aimed to establish an ultrasensitive diagnostic model utilizing machine learning to assist in breast cancer pathology. Methods: By integrating bioinformatics, we identified four targets-DPP3, KIF4A, TK1, and UBE2C-with significantly higher expression levels in breast cancer tissues compared to adjacent normal tissues, supported by corresponding immunohistochemical staining images obtained from the HPA database. Using machine learning, we developed a pathological image recognition algorithm for breast cancer. Results: Our findings revealed that the diagnostic accuracy for DPP3 and KIF4A was significantly superior, achieving 93% and 92%, respectively, while TK1 and UBE2C attained accuracies of only 76% and 62%. However, the combined diagnostic efficacy of TK1 and UBE2C increased to 99%. Conclusion: This study highlights the potential of machine learning algorithms in the classification and diagnosis of breast cancer pathology images, emphasizing the importance of integrating bioinformatics with machine learning to enhance early diagnosis and facilitate personalized treatment strategies for breast cancer.