Cancer Biology & Medicine最新文献

Objective: The role of intraoperative radiation therapy (IORT) in the management of resectable pancreatic cancer (RPC) remains unclear. To date, the application of IORT using a low-energy X-ray source has not been extensively investigated. Therefore, this study was conducted to evaluate the safety and efficacy of IORT using a 50 kV X-ray source in treating RPC.

Methods: Patients with RPC who underwent radical pancreatectomy and IORT were enrolled. The primary endpoint was time to treatment failure (TTF) survival, whereas the secondary endpoints were safety and overall survival (OS).

Results: By November 2023, 35 patients with RPC were treated according to the study protocol. The median TTF was 11.67 months, whereas the median OS for the cohort was 22.2 months. The local recurrence rate was 20%. The most common postoperative complication was pancreatic fistula. The incidence of delayed gastric emptying was 20%. Within 30 days after surgery, one patient experienced abdominal pain, another experienced vomiting, and one died because of abdominal infection and a grade C pancreatic fistula. Carcinoembryonic antigen (CEA) and D-dimer levels significantly correlated with TTF and OS in multivariate analyses. The carbohydrate antigen 19-9 (CA19-9) level was another prognostic factor significantly associated with OS. Patients with low D-dimer and normal CA19-9 levels showed prolonged OS with an IORT dose ≤ 15 Gy.

Conclusions: This study supports use of IORT with a 50 kV X-ray source in treating RPC. IORT using a low-energy X-ray source was well-tolerated and feasible. Additionally, D-dimer, CEA, and CA19-9 levels may help identify patient profiles potentially benefitting from IORT.

Artificial intelligence (AI) is significantly advancing precision medicine, particularly in the fields of immunogenomics, radiomics, and pathomics. In immunogenomics, AI can process vast amounts of genomic and multi-omic data to identify biomarkers associated with immunotherapy responses and disease prognosis, thus providing strong support for personalized treatments. In radiomics, AI can analyze high-dimensional features from computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) images to discover imaging biomarkers associated with tumor heterogeneity, treatment response, and disease progression, thereby enabling non-invasive, real-time assessments for personalized therapy. Pathomics leverages AI for deep analysis of digital pathology images, and can uncover subtle changes in tissue microenvironments, cellular characteristics, and morphological features, and offer unique insights into immunotherapy response prediction and biomarker discovery. These AI-driven technologies not only enhance the speed, accuracy, and robustness of biomarker discovery but also significantly improve the precision, personalization, and effectiveness of clinical treatments, and are driving a shift from empirical to precision medicine. Despite challenges such as data quality, model interpretability, integration of multi-modal data, and privacy protection, the ongoing advancements in AI, coupled with interdisciplinary collaboration, are poised to further enhance AI's roles in biomarker discovery and immunotherapy response prediction. These improvements are expected to lead to more accurate, personalized treatment strategies and ultimately better patient outcomes, marking a significant step forward in the evolution of precision medicine.

Pulmonary metastasis is a life-threatening complication for patients with hepatocellular carcinoma (HCC) undergoing liver transplantation (LT). In addition to the common mechanisms underlying tumor metastasis, another inevitable factor is that the application of immunosuppressive agents, including calcineurin inhibitors (CNIs) and rapamycin inhibitors (mTORis), after transplantation could influence tumor recurrence and metastasis. In recent years, several studies have reported that mTORis, unlike CNIs, have the capacity to modulate the tumorigenic landscape post-liver transplantation by targeting metastasis-initiating cells and reshaping the pulmonary microenvironment. Therefore, we focused on the effects of immunosuppressive agents on the lung metastatic microenvironment and how mTORis impact tumor growth in distant organs. This revelation has provided profound insights into transplant oncology, leading to a renewed understanding of the use of immunosuppressants after LT for HCC.

Prostate cancer is a leading cause of cancer-related death in men worldwide. Luteinizing hormone-releasing hormone receptor (LHRH-R) agonists and antagonists are known to achieve castration-level testosterone suppression; however, long-term data comparing the survival benefits of these therapies are insufficient to inform treatment decisions. Furthermore, the advent of next-generation hormonal agents (NHAs), such as abiraterone and enzalutamide, have shifted the paradigm of managing prostate cancer. Although LHRH-R agonists and antagonists remain the cornerstone treatment across various stages of prostate cancer, they are increasingly administered with NHAs, because the combination treatment confers a survival advantage. Nevertheless, the differences in efficacy and safety profiles among various combinations of LHRH-R agonists and antagonists and NHAs remain unclear. Hence, this narrative review is aimed at providing a comprehensive overview of the long-term outcomes of various LHRH-R agonists and antagonists. Key data from major clinical studies are summarized, categorized by disease stage. LHRH-R agonists and antagonists, particularly goserelin, have demonstrated long-term survival benefits in patients with localized and locally advanced prostate cancer. The clinical outcomes of different LHRH-R agonists and antagonists in combination with NHAs have also been evaluated. Among the various combinations, goserelin plus abiraterone appears to have a manageable safety profile with relatively low rates of hot flushes and fatigue. Overall, long-term survival data and safety profiles should be considered in selecting optimal combination therapies for prostate cancer treatment.