A personalized mRNA vaccine has exhibited potential in the treatment of pancreatic cancer

Source：Holistic Integrative Oncology

The purpose of this study is to describe worldwide gene therapy clinical trials aimed at treating ophthalmic disorders. Information regarding all worldwide clinical trials was collected through 15 different sources, including ClinicalTrials.gov. There were 159 gene therapy clinical trials on ophthalmic diseases up until 2022. Phase 1/2 trials had the highest frequency (50—32%), followed by phase 2 (33—21%); 107 trials (67%) were conducted in a single country, and 50 trials (31%) were multinational. Overall, the USA was the site of 113 (71%) single or multinational trials. Of the trials, 153 (96%) targeted retina and optic nerve disorders, 3 (2%) glaucoma, 2 (1%) uveitis, and 1 (1%) cornea; 104 trials (65%) employed gene augmentation using viral vectors, and the remaining employed other methods such as inhibitory RNA (18—11%) and cell-based gene therapy using encapsulated cell technology (18—11%). For gene augmentation trials, adeno-associated virus was used for transgene delivery in 87% of cases. The most common conditions targeted by gene augmentation included inherited retinal (74%) and age-related macular degeneration (wet, 14%; dry, 7%). Overall, a large number of gene therapy clinical trials have been conducted in the eye, and so far, one has led to regulatory approval.

Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.