Latest ArticlesArtificial 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.
Lung cancer is the most common cause of cancer-related deaths worldwide. Platinum-based chemotherapy is one of the main treatment options for patients with non-small cell lung cancer (NSCLC) but the effectiveness of chemotherapy is encumbered by drug resistance. Therefore, understanding the molecular mechanisms underlying chemotherapy resistance is crucial in improving treatment outcomes and prognosis.
The cell viability assay and apoptosis were used to analyze chemoresistance. Western blot analysis and wound healing testing were used to evaluate the epithelial-to-mesenchymal transition (EMT). Immunoprecipitation was used for analysis of protein modification. Promoter activity was determined using the luciferase reporter assay. Immunofluorescence staining was used to determine reactive oxygen species levels. The expression patterns of EMT markers and carnitine palmitoyltransferase 1C (CPT1C) were determined by Western blot analysis.
CPT1C, which was shown to be highly expressed in lung cancer, is associated with cisplatin resistance in NSCLC cells. CPT1C depletion increased NSCLC cell sensitivity to cisplatin, while overexpression of CPT1C increased NSCLC cell resistance to cisplatin. Induction of EMT mediated CPT1C-induced cisplatin resistance. Ectopic expression of Snail reversed the increase in cisplatin sensitivity triggered by CPT1C knockdown. Moreover, CPT1C was shown to be regulated at the post-translational level and an E3-ubiquitin ligase, NEDD4L, was shown to be a major regulator of CPT1C stability and activity.
These data provide evidence for the first time that the lipid metabolism enzyme, CPT1C, mediates resistance to chemotherapy. Therefore, the use of combination therapy with a CPT1C inhibitor may be a promising new avenue in lung cancer treatment.
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.
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).
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.
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.
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